model_random_forest.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: sklearn in c:\\users\\bever\\anaconda3\\envs\\pythondata\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied, skipping upgrade: scikit-learn in c:\\users\\bever\\anaconda3\\envs\\pythondata\\lib\\site-packages (from sklearn) (0.23.1)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in c:\\users\\bever\\anaconda3\\envs\\pythondata\\lib\\site-packages (from scikit-learn->sklearn) (0.16.0)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in c:\\users\\bever\\anaconda3\\envs\\pythondata\\lib\\site-packages (from scikit-learn->sklearn) (1.17.0)\n",
      "Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in c:\\users\\bever\\anaconda3\\envs\\pythondata\\lib\\site-packages (from scikit-learn->sklearn) (2.1.0)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.19.1 in c:\\users\\bever\\anaconda3\\envs\\pythondata\\lib\\site-packages (from scikit-learn->sklearn) (1.5.0)\n"
     ]
    }
   ],
   "source": [
    "# Update sklearn to prevent version mismatches\n",
    "!pip install sklearn --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: joblib in c:\\users\\bever\\anaconda3\\envs\\pythondata\\lib\\site-packages (0.16.0)\n"
     ]
    }
   ],
   "source": [
    "# install joblib. This will be used to save your model. \n",
    "# Restart your kernel after installing \n",
    "!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import joblib\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "https://www.kaggle.com/uciml/mushroom-classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Website with further information about the data\n",
    "https://archive.ics.uci.edu/ml/datasets/Mushroom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context\n",
    "Although this dataset was originally contributed to the UCI Machine Learning repository nearly 30 years ago, mushroom hunting (otherwise known as \"shrooming\") is enjoying new peaks in popularity. Learn which features spell certain death and which are most palatable in this dataset of mushroom characteristics. And how certain can your model be?\n",
    "\n",
    "## Content\n",
    "This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like \"leaflets three, let it be'' for Poisonous Oak and Ivy.\n",
    "\n",
    "Time period: Donated to UCI ML 27 April 1987"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
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       "  </thead>\n",
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       "      <td>o</td>\n",
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       "      <td>n</td>\n",
       "      <td>m</td>\n",
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       "      <td>p</td>\n",
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       "      <td>t</td>\n",
       "      <td>p</td>\n",
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       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
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       "      <td>p</td>\n",
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       "      <td>s</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
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       "      <td>...</td>\n",
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       "      <td>a</td>\n",
       "      <td>g</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 23 columns</p>\n",
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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]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(\"data\", \"mushrooms.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>n</td>\n",
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      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "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]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Since There are no null values we don't have anything we need to get rid of due to null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Attribute Information:\n",
    "\n",
    "1. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s\n",
    "2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s\n",
    "3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y\n",
    "4. bruises?: bruises=t,no=f\n",
    "5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s\n",
    "6. gill-attachment: attached=a,descending=d,free=f,notched=n\n",
    "7. gill-spacing: close=c,crowded=w,distant=d\n",
    "8. gill-size: broad=b,narrow=n\n",
    "9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y\n",
    "10. stalk-shape: enlarging=e,tapering=t\n",
    "11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?\n",
    "12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
    "15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
    "16. veil-type: partial=p,universal=u\n",
    "17. veil-color: brown=n,orange=o,white=w,yellow=y\n",
    "18. ring-number: none=n,one=o,two=t\n",
    "19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z\n",
    "20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y\n",
    "21. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y\n",
    "22. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target Data\n",
    "class:edible=e, poisonous=p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mushroom Diagram\n",
    "![Mushroom Diagram](images/structure-mushroom.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mushroom Parts\n",
    "Cap: The cap is the top of the mushroom (and often looks sort of like a small umbrella). Mushroom caps can come in a variety of colors but most often are brown, white, or yellow.\n",
    "\n",
    "Gills, Pores, or Teeth: These structures appear under the mushroom's cap. They look similar to a fish's gills.\n",
    "\n",
    "Ring: The ring (sometimes called the annulus) is the remaining structure of the partial veil after the gills have pushed through.\n",
    "\n",
    "Stem or Stipe: The stem is the tall structure that holds the cap high above the ground.\n",
    "\n",
    "Volva: The volva is the protective veil that remains after the mushroom sprouted up from the ground. As the fungus grows, it breaks through the volva.\n",
    "\n",
    "Spores: Microscopic seeds acting as reproductive agents; they are usually released into the air and fall on a substrate to produce a new mushroom.\n",
    "\n",
    "(resource:http://www.visualdictionaryonline.com/plants-gardening/plants/mushroom/structure-mushroom.php)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change class\n",
    "class:edible=e, poisonous=p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       p\n",
       "1       e\n",
       "2       e\n",
       "3       p\n",
       "4       e\n",
       "       ..\n",
       "8119    e\n",
       "8120    e\n",
       "8121    e\n",
       "8122    p\n",
       "8123    e\n",
       "Name: class, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       poisonous\n",
       "1          edible\n",
       "2          edible\n",
       "3       poisonous\n",
       "4          edible\n",
       "          ...    \n",
       "8119       edible\n",
       "8120       edible\n",
       "8121       edible\n",
       "8122    poisonous\n",
       "8123       edible\n",
       "Name: class, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'] = df['class'].replace(['e','p'],['edible','poisonous'])\n",
    "df['class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change cap-shape\n",
    "cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       x\n",
       "1       x\n",
       "2       b\n",
       "3       x\n",
       "4       x\n",
       "       ..\n",
       "8119    k\n",
       "8120    x\n",
       "8121    f\n",
       "8122    k\n",
       "8123    x\n",
       "Name: cap-shape, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cap-shape']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        convex\n",
       "1        convex\n",
       "2          bell\n",
       "3        convex\n",
       "4        convex\n",
       "         ...   \n",
       "8119    knobbed\n",
       "8120     convex\n",
       "8121       flat\n",
       "8122    knobbed\n",
       "8123     convex\n",
       "Name: cap-shape, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['cap-shape'] = df['cap-shape'].replace(['b','c','x','f','k','s'],['bell','conical','convex','flat', 'knobbed', 'sunken' ])\n",
    "df['cap-shape']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mushroom cap shape\n",
    "![Mushroom cap shape](images/mushroom-cap-shapes.jpg)\n",
    "\n",
    "\n",
    "(resource:https://midwestmycology.org/mushroom-cap-shapes/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change cap-surface\n",
    "cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       s\n",
       "1       s\n",
       "2       s\n",
       "3       y\n",
       "4       s\n",
       "       ..\n",
       "8119    s\n",
       "8120    s\n",
       "8121    s\n",
       "8122    y\n",
       "8123    s\n",
       "Name: cap-surface, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cap-surface']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       smooth\n",
       "1       smooth\n",
       "2       smooth\n",
       "3        scaly\n",
       "4       smooth\n",
       "         ...  \n",
       "8119    smooth\n",
       "8120    smooth\n",
       "8121    smooth\n",
       "8122     scaly\n",
       "8123    smooth\n",
       "Name: cap-surface, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['cap-surface'] = df['cap-surface'].replace(['f','g','y','s'],['fibrous','grooves','scaly','smooth'])\n",
    "df['cap-surface']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Mushroom cap surface\n",
    "![Mushroom cap surface](images/mushroom-cap-surface.jpg)\n",
    "\n",
    "\n",
    "(resource:https://datascienceplus.com/mushrooms-classification-part-1/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change cap-color\n",
    "cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       n\n",
       "1       y\n",
       "2       w\n",
       "3       w\n",
       "4       g\n",
       "       ..\n",
       "8119    n\n",
       "8120    n\n",
       "8121    n\n",
       "8122    n\n",
       "8123    n\n",
       "Name: cap-color, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cap-color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        brown\n",
       "1       yellow\n",
       "2        white\n",
       "3        white\n",
       "4         gray\n",
       "         ...  \n",
       "8119     brown\n",
       "8120     brown\n",
       "8121     brown\n",
       "8122     brown\n",
       "8123     brown\n",
       "Name: cap-color, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cap-color'] = df['cap-color'].replace(['n','b','c','g','r','p','u','e','w','y'],['brown','buff','cinnamon','gray','green', 'pink','purple','red','white','yellow'])\n",
    "df['cap-color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change bruises\n",
    "bruises: bruises=t,no=f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       t\n",
       "1       t\n",
       "2       t\n",
       "3       t\n",
       "4       f\n",
       "       ..\n",
       "8119    f\n",
       "8120    f\n",
       "8121    f\n",
       "8122    f\n",
       "8123    f\n",
       "Name: bruises, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bruises']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       bruises\n",
       "1       bruises\n",
       "2       bruises\n",
       "3       bruises\n",
       "4            no\n",
       "         ...   \n",
       "8119         no\n",
       "8120         no\n",
       "8121         no\n",
       "8122         no\n",
       "8123         no\n",
       "Name: bruises, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bruises'] = df['bruises'].replace(['t','f'],['bruises','no'])\n",
    "df['bruises']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change gill-attachment\n",
    "gill-attachment: attached=a,descending=d,free=f,notched=n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       f\n",
       "1       f\n",
       "2       f\n",
       "3       f\n",
       "4       f\n",
       "       ..\n",
       "8119    a\n",
       "8120    a\n",
       "8121    a\n",
       "8122    f\n",
       "8123    a\n",
       "Name: gill-attachment, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gill-attachment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           free\n",
       "1           free\n",
       "2           free\n",
       "3           free\n",
       "4           free\n",
       "          ...   \n",
       "8119    attached\n",
       "8120    attached\n",
       "8121    attached\n",
       "8122        free\n",
       "8123    attached\n",
       "Name: gill-attachment, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gill-attachment'] = df['gill-attachment'].replace(['a','d','f','n'],['attached','descending','free','notched'])\n",
    "df['gill-attachment']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change order\n",
    "odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       p\n",
       "1       a\n",
       "2       l\n",
       "3       p\n",
       "4       n\n",
       "       ..\n",
       "8119    n\n",
       "8120    n\n",
       "8121    n\n",
       "8122    y\n",
       "8123    n\n",
       "Name: odor, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['odor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       pungent\n",
       "1        almond\n",
       "2         anise\n",
       "3       pungent\n",
       "4          none\n",
       "         ...   \n",
       "8119       none\n",
       "8120       none\n",
       "8121       none\n",
       "8122      fishy\n",
       "8123       none\n",
       "Name: odor, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['odor'] = df['odor'].replace(['a','l','c','y','f','m','n','p','s'],['almond','anise','creosote','fishy','foul','musty','none','pungent','spicy'])\n",
    "df['odor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change gill-spacing\n",
    "gill-spacing: close=c,crowded=w,distant=d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       c\n",
       "1       c\n",
       "2       c\n",
       "3       c\n",
       "4       w\n",
       "       ..\n",
       "8119    c\n",
       "8120    c\n",
       "8121    c\n",
       "8122    c\n",
       "8123    c\n",
       "Name: gill-spacing, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gill-spacing']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         close\n",
       "1         close\n",
       "2         close\n",
       "3         close\n",
       "4       crowded\n",
       "         ...   \n",
       "8119      close\n",
       "8120      close\n",
       "8121      close\n",
       "8122      close\n",
       "8123      close\n",
       "Name: gill-spacing, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gill-spacing'] = df['gill-spacing'].replace(['c','w','d'],['close','crowded','distant'])\n",
    "df['gill-spacing']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change gill-size\n",
    "gill-size: broad=b,narrow=n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       n\n",
       "1       b\n",
       "2       b\n",
       "3       n\n",
       "4       b\n",
       "       ..\n",
       "8119    b\n",
       "8120    b\n",
       "8121    b\n",
       "8122    n\n",
       "8123    b\n",
       "Name: gill-size, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gill-size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       narrow\n",
       "1        broad\n",
       "2        broad\n",
       "3       narrow\n",
       "4        broad\n",
       "         ...  \n",
       "8119     broad\n",
       "8120     broad\n",
       "8121     broad\n",
       "8122    narrow\n",
       "8123     broad\n",
       "Name: gill-size, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gill-size'] = df['gill-size'].replace(['b','n'],['broad','narrow'])\n",
    "df['gill-size']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change gill-color\n",
    "gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       k\n",
       "1       k\n",
       "2       n\n",
       "3       n\n",
       "4       k\n",
       "       ..\n",
       "8119    y\n",
       "8120    y\n",
       "8121    n\n",
       "8122    b\n",
       "8123    y\n",
       "Name: gill-color, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gill-color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        black\n",
       "1        black\n",
       "2        brown\n",
       "3        brown\n",
       "4        black\n",
       "         ...  \n",
       "8119    yellow\n",
       "8120    yellow\n",
       "8121     brown\n",
       "8122      buff\n",
       "8123    yellow\n",
       "Name: gill-color, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gill-color'] = df['gill-color'].replace(['k','n','b','h','g','r','o','p','u','e','w','y'],['black','brown','buff','chocolate','gray', 'green','orange','pink','purple','red', 'white','yellow'])\n",
    "df['gill-color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change stalk-shape\n",
    "stalk-shape: enlarging=e,tapering=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       e\n",
       "1       e\n",
       "2       e\n",
       "3       e\n",
       "4       t\n",
       "       ..\n",
       "8119    e\n",
       "8120    e\n",
       "8121    e\n",
       "8122    t\n",
       "8123    e\n",
       "Name: stalk-shape, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-shape']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       enlarging\n",
       "1       enlarging\n",
       "2       enlarging\n",
       "3       enlarging\n",
       "4        tapering\n",
       "          ...    \n",
       "8119    enlarging\n",
       "8120    enlarging\n",
       "8121    enlarging\n",
       "8122     tapering\n",
       "8123    enlarging\n",
       "Name: stalk-shape, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-shape'] = df['stalk-shape'].replace(['e','t'],['enlarging','tapering'])\n",
    "df['stalk-shape']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change stalk-root\n",
    "stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       e\n",
       "1       c\n",
       "2       c\n",
       "3       e\n",
       "4       e\n",
       "       ..\n",
       "8119    ?\n",
       "8120    ?\n",
       "8121    ?\n",
       "8122    ?\n",
       "8123    ?\n",
       "Name: stalk-root, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-root']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         equal\n",
       "1          club\n",
       "2          club\n",
       "3         equal\n",
       "4         equal\n",
       "         ...   \n",
       "8119    missing\n",
       "8120    missing\n",
       "8121    missing\n",
       "8122    missing\n",
       "8123    missing\n",
       "Name: stalk-root, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-root'] = df['stalk-root'].replace(['b','c','u','e','z','r','?'],['bulbous','club','cup','equal', 'rhizomorphs','rooted','missing'])\n",
    "df['stalk-root']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change stalk-surface-above-ring\n",
    "stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       s\n",
       "1       s\n",
       "2       s\n",
       "3       s\n",
       "4       s\n",
       "       ..\n",
       "8119    s\n",
       "8120    s\n",
       "8121    s\n",
       "8122    s\n",
       "8123    s\n",
       "Name: stalk-surface-above-ring, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-surface-above-ring']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       smooth\n",
       "1       smooth\n",
       "2       smooth\n",
       "3       smooth\n",
       "4       smooth\n",
       "         ...  \n",
       "8119    smooth\n",
       "8120    smooth\n",
       "8121    smooth\n",
       "8122    smooth\n",
       "8123    smooth\n",
       "Name: stalk-surface-above-ring, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-surface-above-ring'] = df['stalk-surface-above-ring'].replace(['f','y','k','s'],['fibrous','scaly','silky','smooth'])\n",
    "df['stalk-surface-above-ring']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change stalk-surface-below-ring\n",
    "stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       s\n",
       "1       s\n",
       "2       s\n",
       "3       s\n",
       "4       s\n",
       "       ..\n",
       "8119    s\n",
       "8120    s\n",
       "8121    s\n",
       "8122    k\n",
       "8123    s\n",
       "Name: stalk-surface-below-ring, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-surface-below-ring']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       smooth\n",
       "1       smooth\n",
       "2       smooth\n",
       "3       smooth\n",
       "4       smooth\n",
       "         ...  \n",
       "8119    smooth\n",
       "8120    smooth\n",
       "8121    smooth\n",
       "8122     silky\n",
       "8123    smooth\n",
       "Name: stalk-surface-below-ring, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "df['stalk-surface-below-ring'] = df['stalk-surface-below-ring'].replace(['f','y','k','s'],['fibrous','scaly','silky','smooth'])\n",
    "df['stalk-surface-below-ring']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change stalk-color-above-ring\n",
    "# stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       w\n",
       "1       w\n",
       "2       w\n",
       "3       w\n",
       "4       w\n",
       "       ..\n",
       "8119    o\n",
       "8120    o\n",
       "8121    o\n",
       "8122    w\n",
       "8123    o\n",
       "Name: stalk-color-above-ring, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-color-above-ring']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        white\n",
       "1        white\n",
       "2        white\n",
       "3        white\n",
       "4        white\n",
       "         ...  \n",
       "8119    orange\n",
       "8120    orange\n",
       "8121    orange\n",
       "8122     white\n",
       "8123    orange\n",
       "Name: stalk-color-above-ring, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-color-above-ring'] = df['stalk-color-above-ring'].replace(['n','b','c','g','o','p','e','w','y'],['brown','buff','cinnamon','gray','orange','pink','red','white','yellow'])\n",
    "df['stalk-color-above-ring']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change stalk-color-below-ring\n",
    "stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       w\n",
       "1       w\n",
       "2       w\n",
       "3       w\n",
       "4       w\n",
       "       ..\n",
       "8119    o\n",
       "8120    o\n",
       "8121    o\n",
       "8122    w\n",
       "8123    o\n",
       "Name: stalk-color-below-ring, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-color-below-ring']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        white\n",
       "1        white\n",
       "2        white\n",
       "3        white\n",
       "4        white\n",
       "         ...  \n",
       "8119    orange\n",
       "8120    orange\n",
       "8121    orange\n",
       "8122     white\n",
       "8123    orange\n",
       "Name: stalk-color-below-ring, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stalk-color-below-ring'] = df['stalk-color-below-ring'].replace(['n','b','c','g','o','p','e','w','y'],['brown','buff','cinnamon','gray','orange','pink','red','white','yellow'])\n",
    "df['stalk-color-below-ring']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change Veil-type\n",
    "veil-type: partial=p,universal=u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       p\n",
       "1       p\n",
       "2       p\n",
       "3       p\n",
       "4       p\n",
       "       ..\n",
       "8119    p\n",
       "8120    p\n",
       "8121    p\n",
       "8122    p\n",
       "8123    p\n",
       "Name: veil-type, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['veil-type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       partial\n",
       "1       partial\n",
       "2       partial\n",
       "3       partial\n",
       "4       partial\n",
       "         ...   \n",
       "8119    partial\n",
       "8120    partial\n",
       "8121    partial\n",
       "8122    partial\n",
       "8123    partial\n",
       "Name: veil-type, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['veil-type'] = df['veil-type'].replace(['p','u'],['partial','universal'])\n",
    "df['veil-type']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change veil-color\n",
    "veil-color: brown=n,orange=o,white=w,yellow=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       w\n",
       "1       w\n",
       "2       w\n",
       "3       w\n",
       "4       w\n",
       "       ..\n",
       "8119    o\n",
       "8120    n\n",
       "8121    o\n",
       "8122    w\n",
       "8123    o\n",
       "Name: veil-color, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['veil-color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        white\n",
       "1        white\n",
       "2        white\n",
       "3        white\n",
       "4        white\n",
       "         ...  \n",
       "8119    orange\n",
       "8120         n\n",
       "8121    orange\n",
       "8122     white\n",
       "8123    orange\n",
       "Name: veil-color, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['veil-color'] = df['veil-color'].replace(['b','o','w','y'],['brown','orange','white','yellow'])\n",
    "df['veil-color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change ring-number\n",
    "ring-number: none=n,one=o,two=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       o\n",
       "1       o\n",
       "2       o\n",
       "3       o\n",
       "4       o\n",
       "       ..\n",
       "8119    o\n",
       "8120    o\n",
       "8121    o\n",
       "8122    o\n",
       "8123    o\n",
       "Name: ring-number, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ring-number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       one\n",
       "1       one\n",
       "2       one\n",
       "3       one\n",
       "4       one\n",
       "       ... \n",
       "8119    one\n",
       "8120    one\n",
       "8121    one\n",
       "8122    one\n",
       "8123    one\n",
       "Name: ring-number, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ring-number'] = df['ring-number'].replace(['n','o','t'],['none','one','two'])\n",
    "df['ring-number']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change ring-type\n",
    "ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       p\n",
       "1       p\n",
       "2       p\n",
       "3       p\n",
       "4       e\n",
       "       ..\n",
       "8119    p\n",
       "8120    p\n",
       "8121    p\n",
       "8122    e\n",
       "8123    p\n",
       "Name: ring-type, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ring-type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          pendant\n",
       "1          pendant\n",
       "2          pendant\n",
       "3          pendant\n",
       "4       evanescent\n",
       "           ...    \n",
       "8119       pendant\n",
       "8120       pendant\n",
       "8121       pendant\n",
       "8122    evanescent\n",
       "8123       pendant\n",
       "Name: ring-type, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ring-type'] = df['ring-type'].replace(['c','e','f','l','n','p','s','z'],['cobwebby','evanescent','flaring','large','none','pendant','sheathing','zone'])\n",
    "df['ring-type']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change spore-print-color\n",
    "spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       k\n",
       "1       n\n",
       "2       n\n",
       "3       k\n",
       "4       n\n",
       "       ..\n",
       "8119    b\n",
       "8120    b\n",
       "8121    b\n",
       "8122    w\n",
       "8123    o\n",
       "Name: spore-print-color, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['spore-print-color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        black\n",
       "1        brown\n",
       "2        brown\n",
       "3        black\n",
       "4        brown\n",
       "         ...  \n",
       "8119      buff\n",
       "8120      buff\n",
       "8121      buff\n",
       "8122     white\n",
       "8123    orange\n",
       "Name: spore-print-color, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['spore-print-color'] = df['spore-print-color'].replace(['k','n','b','h','r','o','u','w','y'],['black','brown','buff','chocolate','green','orange','purple','white','yellow'])\n",
    "df['spore-print-color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change population\n",
    "population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       s\n",
       "1       n\n",
       "2       n\n",
       "3       s\n",
       "4       a\n",
       "       ..\n",
       "8119    c\n",
       "8120    v\n",
       "8121    c\n",
       "8122    v\n",
       "8123    c\n",
       "Name: population, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       scattered\n",
       "1        numerous\n",
       "2        numerous\n",
       "3       scattered\n",
       "4        abundant\n",
       "          ...    \n",
       "8119    clustered\n",
       "8120      several\n",
       "8121    clustered\n",
       "8122      several\n",
       "8123    clustered\n",
       "Name: population, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['population'] = df['population'].replace(['a','c','n','s','v','y'],['abundant','clustered','numerous','scattered','several','solitary'])\n",
    "df['population']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change habitat\n",
    "habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       u\n",
       "1       g\n",
       "2       m\n",
       "3       u\n",
       "4       g\n",
       "       ..\n",
       "8119    l\n",
       "8120    l\n",
       "8121    l\n",
       "8122    l\n",
       "8123    l\n",
       "Name: habitat, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['habitat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         urban\n",
       "1       grasses\n",
       "2       meadows\n",
       "3         urban\n",
       "4       grasses\n",
       "         ...   \n",
       "8119     leaves\n",
       "8120     leaves\n",
       "8121     leaves\n",
       "8122     leaves\n",
       "8123     leaves\n",
       "Name: habitat, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['habitat'] = df['habitat'].replace(['g','l','m','p','u','w','d'],['grasses','leaves','meadows','paths','urban','waste','woods'])\n",
    "df['habitat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>poisonous</td>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>brown</td>\n",
       "      <td>bruises</td>\n",
       "      <td>pungent</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>narrow</td>\n",
       "      <td>black</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>black</td>\n",
       "      <td>scattered</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>edible</td>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>yellow</td>\n",
       "      <td>bruises</td>\n",
       "      <td>almond</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>broad</td>\n",
       "      <td>black</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>brown</td>\n",
       "      <td>numerous</td>\n",
       "      <td>grasses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>edible</td>\n",
       "      <td>bell</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>bruises</td>\n",
       "      <td>anise</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>broad</td>\n",
       "      <td>brown</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>brown</td>\n",
       "      <td>numerous</td>\n",
       "      <td>meadows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>poisonous</td>\n",
       "      <td>convex</td>\n",
       "      <td>scaly</td>\n",
       "      <td>white</td>\n",
       "      <td>bruises</td>\n",
       "      <td>pungent</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>narrow</td>\n",
       "      <td>brown</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>black</td>\n",
       "      <td>scattered</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>edible</td>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>gray</td>\n",
       "      <td>no</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>crowded</td>\n",
       "      <td>broad</td>\n",
       "      <td>black</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>evanescent</td>\n",
       "      <td>brown</td>\n",
       "      <td>abundant</td>\n",
       "      <td>grasses</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  \\\n",
       "0  poisonous    convex      smooth     brown  bruises  pungent   \n",
       "1     edible    convex      smooth    yellow  bruises   almond   \n",
       "2     edible      bell      smooth     white  bruises    anise   \n",
       "3  poisonous    convex       scaly     white  bruises  pungent   \n",
       "4     edible    convex      smooth      gray       no     none   \n",
       "\n",
       "  gill-attachment gill-spacing gill-size gill-color  ...  \\\n",
       "0            free        close    narrow      black  ...   \n",
       "1            free        close     broad      black  ...   \n",
       "2            free        close     broad      brown  ...   \n",
       "3            free        close    narrow      brown  ...   \n",
       "4            free      crowded     broad      black  ...   \n",
       "\n",
       "  stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring  \\\n",
       "0                   smooth                  white                  white   \n",
       "1                   smooth                  white                  white   \n",
       "2                   smooth                  white                  white   \n",
       "3                   smooth                  white                  white   \n",
       "4                   smooth                  white                  white   \n",
       "\n",
       "  veil-type veil-color ring-number   ring-type spore-print-color population  \\\n",
       "0   partial      white         one     pendant             black  scattered   \n",
       "1   partial      white         one     pendant             brown   numerous   \n",
       "2   partial      white         one     pendant             brown   numerous   \n",
       "3   partial      white         one     pendant             black  scattered   \n",
       "4   partial      white         one  evanescent             brown   abundant   \n",
       "\n",
       "   habitat  \n",
       "0    urban  \n",
       "1  grasses  \n",
       "2  meadows  \n",
       "3    urban  \n",
       "4  grasses  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8124</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",
       "      <td>...</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",
       "      <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>edible</td>\n",
       "      <td>convex</td>\n",
       "      <td>scaly</td>\n",
       "      <td>brown</td>\n",
       "      <td>no</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>broad</td>\n",
       "      <td>buff</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>white</td>\n",
       "      <td>several</td>\n",
       "      <td>woods</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     edible    convex       scaly     brown      no  none            free   \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           close     broad       buff  ...                   smooth   \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                     white                  white   partial      white   \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            one   pendant             white    several   woods  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Features to determine the correct features to use for our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## poisonous vs edible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hhUWSW5LsTXJ/X+3MJLcneai9n9G37foku5LsTHJpX/3CJNvbthvbE/MkSSM0zCOL9cCKKbXrgC1VtRjY0tZJcj6wCrig9bkpybzW52ZgDb1HrS4esE9J0pANLSyq6k7gW1PKK4ENbXkDcFlf/daq2ldVDwO7gOXtmd3zq+quqipgY18fSdKIjPqaxTlVtQegvZ/d6guAx/ra7W61BW15al2SNELHygXuQdchapr64J0ka5JMJJmYnJw8aoOTpLlu1GHxRDu1RHvf2+q7gYV97caBx1t9fEB9oKpaV1XLqmrZ2NjYUR24JM1low6LzcDqtrwauK2vvirJqUnOo3che2s7VfVUkovaLKir+vpIkkbk5GHtOMlHgJcAZyXZDbwVeDuwKcnrgUeBywGqakeSTcADwH7gmqo60HZ1Nb2ZVacDn24vSdIIDS0squqKw2y65DDt1wJrB9QngCVHcWiSpB/SsXKBW5J0DDMsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUaVbCIskjSbYn2ZZkotXOTHJ7kofa+xl97a9PsivJziSXzsaYJWkum80ji1+oqqVVtaytXwdsqarFwJa2TpLzgVXABcAK4KYk82ZjwJI0Vx1Lp6FWAhva8gbgsr76rVW1r6oeBnYBy2dhfJI0Z81WWBTwmST3JFnTaudU1R6A9n52qy8AHuvru7vVDpFkTZKJJBOTk5NDGrokzT0nz9LnXlxVjyc5G7g9ydemaZsBtRrUsKrWAesAli1bNrCNJOmHNytHFlX1eHvfC3yC3mmlJ5KcC9De97bmu4GFfd3HgcdHN1pJ0sjDIsmPJXnWwWXgnwL3A5uB1a3ZauC2trwZWJXk1CTnAYuBraMdtSTNbbNxGuoc4BNJDn7+n1bV/0zyZWBTktcDjwKXA1TVjiSbgAeA/cA1VXVgFsYtSXPWyMOiqr4OvGhA/ZvAJYfpsxZYO+ShSZIO41iaOitJOkYZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6nTchEWSFUl2JtmV5LrZHo8kzSXHRVgkmQe8F/hnwPnAFUnOn91RSdLccVyEBbAc2FVVX6+q/wvcCqyc5TFJ0pwx8mdw/4gWAI/1re8G/uHURknWAGva6veS7BzB2OaCs4AnZ3sQx4K8Y/VsD0GH8u/zoLfmaOzlJwYVj5ewGPRfoA4pVK0D1g1/OHNLkomqWjbb45AG8e9zNI6X01C7gYV96+PA47M0Fkmac46XsPgysDjJeUmeAawCNs/ymCRpzjguTkNV1f4k1wJ/BswDbqmqHbM8rLnEU3s6lvn3OQKpOuTUvyRJP+B4OQ0lSZpFhoUkqZNhMQcleb+/gNfxJskvJ/nDtvzGJFe15TuSHDJ1tr+9jtxxcYFbR1dV/evZHoN0JKrqj2Z7DHONRxYngCSLknwtyYYk9yX5aJJnJrkkyVeTbE9yS5JTW/s7kixLMi/J+iT3tza/3rYvTfKltq9PJDmjr99/TrI1yV8k+UetflqSD7Z9fDXJL7T6D/zLLsknk7zkcJ+ruS3Jle1va1uSP25/J69rf2ufAy7ua3tDkn/f1/3KJF9sf1PLB+x7LMnHkny5vS6e2kbTMyxOHD8FrKuqFwLfBf4dsB54dVX9DL2jyKun9FkKLKiqJa3NB1t9I/CbbV/bgbf29Tm5qpYDb+6rXwPQ9nEFsCHJadOM9XCfqzkqyU8DrwYurqqlwAHgSuBt9ELin9C7iejh/FhV/Rzwb4BbBmx/N/CuqvoHwL8C3n8Uhz8nGBYnjseq6gtt+U+AS4CHq+ovWm0D8I+n9Pk68JNJ3pNkBfDdJH8HeHZVfe4w/T7e3u8BFrXlnwc+BFBVXwP+EnjBNGM95HNn/jV1groEuBD4cpJtbf3XgTuqarLdQPS/TtP/IwBVdScwP8mzp2x/GfCHbd+bW5tnHe0vcSIzLE4cP/QPZqrq28CLgDvoHR3M5F9b+9r7AZ6+5nW4u5ft5wf/xk47gs/ViS3Ahqpa2l4/BdzAzP+up7abun4S8LN9+19QVU8d2ZDnFsPixPG8JD/blq8APgssSvL3Wu21wOf6OyQ5Czipqj4G/Dbw96vqr4FvH7weMajfAHcCr2n7fAHwPGAn8AiwNMlJSRbSu9X8wM/90b6yTiBbgF9KcjZAkjOBrwIvSfKcJKcAl0/T/9Wt388Df93+jvt9Brj24EqSpUdz8HOBs6FOHA8Cq5P8MfAQ8CbgS8B/S3IyvftrTZ1BsgD4YJKD/2i4vr2vBv4oyTPpnTJ6Xcdn39Tab6d3NPHLVbUvyReAh+ld97gf+ErH52qOqqoHkvwW8Jn2d/G39I46bwDuAvbQ+/uZd5hdfDvJF4H5wK8M2P5rwHuT3Efv/3t3Am88ql/iBOftPk4ASRYBn6yqJbM8FEknKE9DSZI6eWQhSerkkYUkqZNhIUnqZFhIkjoZFtIQDLh3kXRcMywkSZ0MC+koSHJVu0vvvUk+NGXbr7Y7nd7b7nz6zFa/vN0l9d4kd7baBX13Xr0vyeLZ+D7SVE6dlY5Qkgvo3WDx4qp6st2q4teA71XVO5I8p6q+2dr+J+CJqnpP+8X7iqr6RpJnV9V3krwH+FJVfTjJM4B5VfU3s/XdpIM8spCO3EuBj1bVkwBV9a0p25ck+d8tHF4DXNDqXwDWJ/lVnr6NxV3AW5L8JvATBoWOFYaFdOTC9HdHXQ9c257d8TaevvvuG4HfAhYC29oRyJ8CrwD+BvizJC8d5sClmTIspCO3BXhVkufA/79jar9nAXvanVNfc7CY5PlVdXdV/Q7wJLAwyU8CX6+qG+k9d+GFI/kGUgfvOisdoarakWQt8LkkB+jdWvuRvia/DdxN76FQ2+mFB8DvtwvYoRc49wLX0XtE6N8CfwX87ki+hNTBC9ySpE6ehpIkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVKn/wc64skRfffJsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['class']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sns.countplot(x='class', hue='cap-shape',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>brown</td>\n",
       "      <td>bruises</td>\n",
       "      <td>pungent</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>narrow</td>\n",
       "      <td>black</td>\n",
       "      <td>enlarging</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>black</td>\n",
       "      <td>scattered</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>yellow</td>\n",
       "      <td>bruises</td>\n",
       "      <td>almond</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>broad</td>\n",
       "      <td>black</td>\n",
       "      <td>enlarging</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>brown</td>\n",
       "      <td>numerous</td>\n",
       "      <td>grasses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bell</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>bruises</td>\n",
       "      <td>anise</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>broad</td>\n",
       "      <td>brown</td>\n",
       "      <td>enlarging</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>brown</td>\n",
       "      <td>numerous</td>\n",
       "      <td>meadows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>convex</td>\n",
       "      <td>scaly</td>\n",
       "      <td>white</td>\n",
       "      <td>bruises</td>\n",
       "      <td>pungent</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>narrow</td>\n",
       "      <td>brown</td>\n",
       "      <td>enlarging</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>black</td>\n",
       "      <td>scattered</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>gray</td>\n",
       "      <td>no</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>crowded</td>\n",
       "      <td>broad</td>\n",
       "      <td>black</td>\n",
       "      <td>tapering</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>evanescent</td>\n",
       "      <td>brown</td>\n",
       "      <td>abundant</td>\n",
       "      <td>grasses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  cap-shape cap-surface cap-color  bruises     odor gill-attachment  \\\n",
       "0    convex      smooth     brown  bruises  pungent            free   \n",
       "1    convex      smooth    yellow  bruises   almond            free   \n",
       "2      bell      smooth     white  bruises    anise            free   \n",
       "3    convex       scaly     white  bruises  pungent            free   \n",
       "4    convex      smooth      gray       no     none            free   \n",
       "\n",
       "  gill-spacing gill-size gill-color stalk-shape  ... stalk-surface-below-ring  \\\n",
       "0        close    narrow      black   enlarging  ...                   smooth   \n",
       "1        close     broad      black   enlarging  ...                   smooth   \n",
       "2        close     broad      brown   enlarging  ...                   smooth   \n",
       "3        close    narrow      brown   enlarging  ...                   smooth   \n",
       "4      crowded     broad      black    tapering  ...                   smooth   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                  white                  white   partial      white   \n",
       "1                  white                  white   partial      white   \n",
       "2                  white                  white   partial      white   \n",
       "3                  white                  white   partial      white   \n",
       "4                  white                  white   partial      white   \n",
       "\n",
       "  ring-number   ring-type spore-print-color population  habitat  \n",
       "0         one     pendant             black  scattered    urban  \n",
       "1         one     pendant             brown   numerous  grasses  \n",
       "2         one     pendant             brown   numerous  meadows  \n",
       "3         one     pendant             black  scattered    urban  \n",
       "4         one  evanescent             brown   abundant  grasses  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use all features to evaluate which features to use.\n",
    "selected_features = df.drop(\"class\", axis=1)\n",
    "selected_features_names = selected_features.columns\n",
    "selected_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x49izZw+pqanceuutXHPNNQet06dPH/r06VNg/6VLl9K2bVuaN29eGuGKFJmShUgEDR8+nJUrV7J3716GDBlCYmJikfvee++9PPbYY3lnRImUJUoWIhH0/PPPF7vvHXfcwR133BHBaEQiRwe4RUQkLCULEREJS8lCRETCUrIQEZGwdIBbKqQuj3SJ6HgLblxQ7L7Dhg3jlltuoWXLlkdcZ+jQoVx44YX079+/2J8jUpKULERK2FNPPRXtEESOmqahRCJk/fr1NG/enCFDhtCmTRv69+/Pnj17SEtLy6tAW7t2bcaMGUPbtm3p1KkTW7duPWycO++8k6FDh7J///7S3gSRI1KyEImgNWvWMHz4cJYtW8Zxxx3Ho48+etDyH3/8kU6dOrF06VJSU1N58sknD1r+pz/9iW3btjFlyhSqVNF/Tyk79G0UiaC4uDi6dAkdLxk0aBDz588/aHn16tW58MILAUhKSmL9+vV5y+655x527drFE088gZmVWswiRaFkIRJBh/6SP/R9tWrV8tpiYmIOus1qhw4dyMzM5Ntvvy35QEV+JSULkQjasGED6enpALzwwgt07dq1yH179uzJHXfcwQUXXEB2dnZJhShSLDobSiqkoznV9Wi0aNGCadOmce2119K0aVNGjhzJm2++WeT+AwYMIDs7m969e/POO+/k3XVPJNqssLt7lWfJycme/x7IUvatWrWKFi1aRDuMYlu/fj0XXnghn3/+eYl/1sqNO4rdt2Vc6dzXu7z/PCsrM8t09+RD20tsGsrM4sxstpmtMrMVZnZT0H68mf3TzNYGz3Xz9RltZl+a2RozOzdfe5KZLQ+WTTQd/RMRKVUlecwiB7jV3VsAnYDrzawlcAcwy92bArOC9wTLLgESgJ7Ao2Z24L6SjwHDgabBo2cJxi1SLPHx8aWyVyESDSWWLNx9i7svDl5nA6uARkAf4MCNh6cBfYPXfYAX3f1nd18HfAl0NLOGwHHunu6hObNn8vUREZFSUCpnQ5lZPNAe+BQ40d23QCihACcEqzUCNubrtiloaxS8PrS9oM8ZbmYZZpaxffv2SG6CiEilVuLJwsxqA68AN7v7D4WtWkCbF9J+eKP7JHdPdvfkBg0a/PpgRUSkQCWaLMysGqFEMd3dXw2atwZTSwTP24L2TUBcvu6Ngc1Be+MC2kVEpJSU2HUWwRlLTwOr3P2BfIveAIYA9wbP/8jX/ryZPQCcTOhA9mfunmtm2WbWidA01mDgkZKKWyqGj1O7RXS8bnM/DrtOaZ46K1LaSvKivC7AFcByM8sK2v6LUJKYYWZXAxuAAQDuvsLMZgArCZ1Jdb275wb9RgJTgWOAd4OHSLmTm5tLTExM+BVFypiSPBtqvrubu7dx93bB4x133+nu3d29afD8bb4+4939d+7ezN3fzdee4e6tgmU3eEW9klDKvZycnMNKlMfHx3P33XfTtWtXXn75ZV544QVat25Nq1atuP322wGYMWMGt9xyCwAPP/wwp556KgD/+te/8kqGxMfHM3bsWBITE+l7Tipffbk2OhsplZJqQ4lE0JFKlNesWZP58+eTmprK7bffzkcffURWVhaLFi3i9ddfJzU1lXnz5gEwb9486tWrx9dff838+fNJSUnJG79+/fosXryYgVcMZeqkv0dlG6VyUrIQiaAjlSgfOHAgAIsWLSItLY0GDRpQtWpVLr/8cubOnctJJ53E7t27yc7OZuPGjVx22WXMnTuXefPmHZQsLrroIgASWrfl640bESktShYiEXSkEuWxsbEAFDaD2rlzZ6ZMmUKzZs1ISUlh3rx5pKen5yUfgBo1agBQpUoMubk5RxpKJOKULEQiKFyJ8tNPP52PP/6YHTt2kJubywsvvEC3bqEzt1JTU5kwYQKpqam0b9+e2bNnU6NGDerUqVPq2yFyKJUolwqpKKe6loSCSpQ/8sgvZ3o3bNiQv/zlL5x55pm4O+effz59+vQBICUlhY0bN5KamkpMTAxxcXE0b948KtshciiVKJcyQyWti04lyqWklHqJchERqTiULEREJCwlCxERCUvJQkREwlKyEBGRsJQsREQkLF1nIRXS3259M6Lj3fDXXmHX+bUlyn/++WcuuOACduzYwejRozn55JMZMWIE1apVIz09nWOOOeZowxaJGCULkShZsmQJ+/btIysrVMF/xIgRjBo1iiuvvDLKkYkcTtNQIhF0pBLlO3aELqLLyMggLS2Nbdu2MWjQILKysmjXrh1PPPEEM2bM4O677+byyy+P8laIHE7JQiSCjlSi/FAnnHACTz31FCkpKWRlZXHttdfSu3dv7r//fqZPn17KUYuEp2moI0i67Zli9828f3AEI5Hy5NAS5RMnToxyRCKRoT0LkQgqqER51apV2b9/PwB79+6NRlgiR03JQiSCCipRHh8fT2ZmJgCvvPJKNMMTKTZNQ0mFVJRTXUtCQSXKO3bsyNVXX83//u//cvrpp0clLpGjpWQhEiHx8fGsXLnysPaUlBS++OKLw9rT0tJIS0vLez916tQSjE7k6GgaSkREwlKyEBGRsJQsREQkLCULEREJq0jJwsxmFaVNREQqpkLPhjKzmkAtoL6Z1QUOXHF0HHByCccmIiJlRLhTZ68FbiaUGDL5JVn8APy9BOMSOSrjB/WP6HhjnpsZ0fEOGDduHLVr12bUqFElMr5IpBSaLNz9YeBhM7vR3R8ppZhEyj13x92pUkWHBaViKNI32d0fMbMzzOwyMxt84FFYHzObbGbbzOzzfG3jzOxrM8sKHufnWzbazL40szVmdm6+9iQzWx4sm2iHFt8RKSPWr19PixYtuO6660hMTOSee+6hQ4cOtGnThrFjx+atN378eJo1a8bZZ5/NmjVrohixSNEV6QpuM3sW+B2QBeQGzQ4UVpp1KvC3AtZ50N0nHDJ+S+ASIIHQlNeHZnaau+cCjwHDgYXAO0BP4N2ixC1S2tasWcOUKVPo27cvM2fO5LPPPsPd6d27N3PnziU2NpYXX3yRJUuWkJOTQ2JiIklJSdEOWySsopb7SAZaursXdWB3n2tm8UVcvQ/worv/DKwzsy+Bjma2HjjO3dMBzOwZoC9KFlJGNWnShE6dOjFq1Cg++OAD2rdvD8Du3btZu3Yt2dnZ9OvXj1q1agHQu3fvaIYrUmRFnVD9HDgpQp95g5ktC6ap6gZtjYCN+dbZFLQ1Cl4f2l4gMxtuZhlmlrF9+/YIhStSdLGxsUDomMXo0aPJysoiKyuLL7/8kquvvho4vIy5SHlQ1GRRH1hpZu+b2RsHHsX4vMcITWe1A7YAfw3aC/rf44W0F8jdJ7l7srsnN2jQoBjhiUTGueeey+TJk9m9ezcAX3/9Ndu2bSM1NZXXXnuNn376iezsbN58880oRypSNEWdhhoXiQ9z960HXpvZk8BbwdtNQFy+VRsDm4P2xgW0ixSqpE51LaoePXqwatUqOnfuDEDt2rV57rnnSExMZODAgbRr144mTZqQkpIS1ThFiqpIycLdP47Eh5lZQ3ffErztR2h6C+AN4Hkze4DQAe6mwGfunmtm2WbWCfgUGAzoFF4pk+Lj4/n887yT/7jpppu46aabDltvzJgxjBkzpjRDEzlqRT0bKptfpn+qA9WAH939uEL6vACkEbr6exMwFkgzs3bBWOsJXfSHu68wsxnASiAHuD44EwpgJKEzq44hdGBbB7dFREpZUfcsjs3/3sz6Ah3D9Lm0gOanC1l/PDC+gPYMoFVR4hQRkZJRrMtL3f114KwIxyIiImVUUaehLsr3tgqh6y6KfM2FiIiUb0U9G6pXvtc5hI439Il4NCIiUiYV9ZjFlSUdiIiIlF1FnYZqTOiU1S6Epp/mAze5+6ZCO4pEyarxH0V0vBZjdIhOKreiHuCeQuhaiJMJldt4M2gTkSJyd/bv3x/tMESKpajJooG7T3H3nOAxFVA9DZFDPPDAA7Rq1YpWrVrx0EMPHVa2fOPGjYwcOZLk5GQSEhIOKl0eHx/P2LFjSUxMpHXr1qxevRqA7du3c84555CYmMi1115LkyZN+O7bnQC8+erLDOzVg4t6pjHujlvJzc0tMC6Ro1XUZLHDzAaZWUzwGATsLMnARMqbzMxMpkyZwqeffsrChQt58skn+e6771izZg2DBw9myZIlNGnShPHjx5ORkcGyZcv4+OOPWbZsWd4Y9evXZ/HixYwcOZIJE0KV/O+66y7OOussFi9eTL9+/diwYQMA/1r7Be+++TrPvfo2r743hyoxMbz1WnTLnEjFVdSzoa4idG+KBwkds/gE0EFvkXzmz59Pv3798irPXnTRRcybNy+vbPkBM2bMYNKkSeTk5LBlyxZWrlxJmzZt8voAJCUl8eqrr+aN+9prrwHQs2dP6tYNFWteuGAuK5cvZWCvcwD4ee9e6tWrXzobK5VOUZPFPcAQd/8OwMyOByYQSiISQV0e6VLsvgtuXBDBSOTXOtLtXg4kD4B169YxYcIEFi1aRN26dRk6dCh79+7NW16jRg0AYmJiyMnJKXRc3OnTfyB/vOPOCG2ByJEVdRqqzYFEAeDu3wLtSyYkkfIpNTWV119/nT179vDjjz/y2muvHVZV9ocffiA2NpY6deqwdetW3n03fKmzrl27MmPGDAA++OADvvsu9F/x9C6pfPDOm+zcEbp3y65d37F508YjjiNyNIq6Z1HFzOoesmdR1L4ipS4ap7omJiYydOhQOnYMlU0bNmxY3pTRAW3btqV9+/YkJCRw6qmn0qVL+D3JsWPHcumll/LSSy/RrVs3GjZsSGxsbeoeX48/jBrNNYMG4PudqlWr8t9//j9ObhwXdkyRX6uov/D/CnxiZjMJHbO4mAKK/olUdrfccgu33HLLQW35y5YDTJ06tcC+69evz3udnJzMnDlzAKhTpw7vv/8+VatWJT09ndmzZ1M9mK46r3c/zuvdL2LxixxJUa/gfsbMMggVDzTgIndfWaKRiQgAGzZs4OKLL2b//v1Ur16dJ598MtohSSVU5KmkIDkoQYiUsqZNm7JkyZKD2lZu3BGlaKSyKlaJchERqVyULEREJCwlCxERCUvJQkREwtK1ElIhjRs3rkyM9/jjj1OrVi0GDx4c0XhESpuShUgJGjFiRLRDEIkITUOJRNAzzzxDmzZtaNu2LVdccQXjxo3Lqx6blpbG7bffTseOHTnttNOYN28eELpI76KLLqJnz540bdqUP/3pT3njHamc+TlnJPLQ//2Zy/qex8UXnM3K5Uu5ZtAAenbtwEvPTgVCNaUmjB9Hn7NT6HtOKu++ESpGOGfOHNLS0ujfvz/Nmzfn8ssvP3L9KZGA9ixEImTFihWMHz+eBQsWUL9+fb799lsmTpx40Do5OTl89tlnvPPOO9x11118+OGHAGRlZbFkyRJq1KhBs2bNuPHGG4mLi2P8+PEcf/zx5Obm0r17d5YtW5ZXofakkxvx/Ovvcu9d/82YW//Ac6++zc8/76XP2SkMvGIo/3z3LVav+JxX35/Dd9/uZGCvHiSf3hmAJUuWsGLFCk4++WS6dOnCggUL6Nq1a+n+g0m5oj0LkQj56KOP6N+/P/Xrh8qEH3/88Yetk78Eef7yHt27d6dOnTrUrFmTli1b8u9//xsIlTNPTEykffv2rFixgpUrf7ku9sxzegJwWvMWtG6XSGzt2hxfrz7Va9Tgh++/Z/GiTzm/Tz9iYmKo3+AEOpx+BsuXZgHQsWNHGjduTJUqVWjXrt1BsYgURHsWIhHi7phZoesUVII8f3v+ZeHKmVevXh0Aq1KF6jWq57VXqVKF3NycQqeWCvo8kcJoz0IkQrp3786MGTPYuTN0E8lvv/32qMYrTjnz/JJP78y7b75Obm4u3+7cQcZn6bRupzsLSPFoz0IqpEifOlsUCQkJjBkzhm7duhETE0P79u2Jj48v9njFKWee39k9L2Dp4gwuOjcNM+PW0f9DgxNOZFv29mLHJJWXVdSzIJKTkz0jI6PY/ZNue6bYfTPvL/459ZX5TnmrVq2iRYsW0Q6jXDiaQoIt40rn1qv6eZZPZpbp7smHtpfYNJSZTTazbWb2eb62483sn2a2Nnium2/ZaDP70szWmNm5+dqTzGx5sBG+/CkAAA0ISURBVGyihZsUFhGRiCvJYxZTgZ6HtN0BzHL3psCs4D1m1hK4BEgI+jxqZjFBn8eA4UDT4HHomCIiUsJKLFm4+1zg0CN8fYBpwetpQN987S+6+8/uvg74EuhoZg2B49w93UPzZc/k6yMiIqWktM+GOtHdtwAEzycE7Y2A/Hea3xS0NQpeH9ouIiKlqKycOlvQcQgvpL3gQcyGm1mGmWVs364zPkREIqW0k8XWYGqJ4Hlb0L4JiMu3XmNgc9DeuID2Arn7JHdPdvfkBg0aRDRwEZHKrLSvs3gDGALcGzz/I1/782b2AHAyoQPZn7l7rpllm1kn4FNgMPBIKccs5dCMlztGdLyLB3wW0fEAateuze7du1m/fj0XXnghn3/+efhOIlFSYsnCzF4A0oD6ZrYJGEsoScwws6uBDcAAAHdfYWYzgJVADnC9u+cGQ40kdGbVMcC7wUNEREpRSZ4Ndam7N3T3au7e2N2fdved7t7d3ZsGz9/mW3+8u//O3Zu5+7v52jPcvVWw7AavqFcRSrl355138vDDD+e9HzNmDBMnTuT++++nQ4cOtGnT5qAy4wXZu3cvV155Ja1bt6Z9+/bMnj0bgPPPP59ly5YB0L59ex59KFT2fOKEvzDzhWdLaItEflFWDnCLlHtXX30106aFzgzfv38/L774IieeeCJr167ls88+Iysri8zMTObOnXvEMf7+978DsHz5cl544QWGDBnC3r17SU1NZd68efzwww9UrVqVJRmfArB40ackdexc8hsnlZ6ShUiExMfHU69ePZYsWcIHH3xA+/btWbRoUd7rxMREVq9ezdq1a484xvz587niiisAaN68OU2aNOGLL74gJSWFuXPnMn/+fC644AL2/PgjP/20h82bNnLK735fWpsolZgKCYpE0LBhw5g6dSrffPMNV111FbNmzWL06NFce+21Rep/pFnWDh06kJGRwamnnso555zDF+s3MvP5Z2nZqk0kwxc5Iu1ZiERQv379eO+991i0aBHnnnsu5557LpMnT2b37t0AfP3112zbtu2I/VNTU5k+fToAX3zxBRs2bKBZs2ZUr16duLg4ZsyYQadOnUjq0Impkx4lqWOnUtkuEe1ZSIVUEqe6FkX16tU588wz+c1vfkNMTAw9evRg1apVdO4cOq5Qu3ZtnnvuOU444YQC+1933XWMGDGC1q1bU7VqVaZOnZp3o6KUlBRmzZpFrVq1SOrYmW+2bFaykFKjZCESQfv372fhwoW8/PLLeW033XQTN91002HrHtjbiI+Pz7vGombNmkydOrXAse+55x7uueceAE446SRWbFCVAik9moYSiZCVK1fy+9//nu7du9O0adNohyMSUdqzEImQli1b8tVXX0U7DJESoT0LKVN0zWXFoJ9jxaNkIWVGzZo12blzp37RlHPuzs6dO6lZs2a0Q5EI0jSUlBmNGzdm06ZNqLx8eN98t7vYfW13yf/71qxZk8aNG4dfUcoNJQspM6pVq8Ypp5wS7TDKhUG3PVPsvpn3D45gJFJZaBpKRETCUrIQEZGwlCxERCQsJQsREQlLyUJERMJSshARkbCULEREJCwlCxERCUvJQkREwlKyEBGRsJQsREQkLCULEREJS8lCRETCUrIQEZGwVKJcpJLZcHfrYve9tO5xxeq34MYFxf5MKRu0ZyEiImEpWYiISFhRSRZmtt7MlptZlpllBG3Hm9k/zWxt8Fw33/qjzexLM1tjZudGI2YRkcosmnsWZ7p7O3dPDt7fAcxy96bArOA9ZtYSuARIAHoCj5pZTDQCFhGprMrSNFQfYFrwehrQN1/7i+7+s7uvA74EOkYhPhGRSitaycKBD8ws08yGB20nuvsWgOD5hKC9EbAxX99NQdthzGy4mWWYWcb27dtLKHQRkconWqfOdnH3zWZ2AvBPM1tdyLpWQJsXtKK7TwImASQnJxe4joiI/HpR2bNw983B8zbgNULTSlvNrCFA8LwtWH0TEJeve2Ngc+lFKyIipZ4szCzWzI498BroAXwOvAEMCVYbAvwjeP0GcImZ1TCzU4CmwGelG7WISOUWjWmoE4HXzOzA5z/v7u+Z2SJghpldDWwABgC4+wozmwGsBHKA6909Nwpxi4hUWqWeLNz9K6BtAe07ge5H6DMeGF/CoZV7H6d2K3bf5R1GFbvvDX/tVey+IlI+lKVTZ0VEpIxSshARkbCULEREJCwlCxERCUvJQkREwlKyEBGRsJQsREQkLCULEREJS8lCRETCUrIQEZGwlCxERCQsJQsREQlLyUJERMJSshARkbCULEREJCwlCxERCUvJQkREworGbVVFRMq8VeM/KnbfFmPOimAkZYP2LEREJCwlCxERCUvJQkREwlKyEBGRsJQsREQkLCULEREJS8lCRETC0nUWIlJhjR/Uv9h9L2pxXQQjKf+0ZyEiImEpWYiISFhKFiIiEla5OWZhZj2Bh4EY4Cl3vzfKIUmUjRs3rlT7iVRm5WLPwsxigL8D5wEtgUvNrGV0oxIRqTzKy55FR+BLd/8KwMxeBPoAK6MalZRLM17uWOy+Fw/4LIKRVB4fp3Yrdt9ucz+OYCRSXObu0Y4hLDPrD/R092HB+yuA0939hkPWGw4MD942A9aUaqAVV31gR7SDEDkCfT8jq4m7Nzi0sbzsWVgBbYdlOXefBEwq+XAqFzPLcPfkaMchUhB9P0tHuThmAWwC4vK9bwxsjlIsIiKVTnlJFouApmZ2iplVBy4B3ohyTCIilUa5mIZy9xwzuwF4n9Cps5PdfUWUw6pMNLUnZZm+n6WgXBzgFhGR6Cov01AiIhJFShYiIhKWkkUlZGZP6Qp4KW/MbKiZ/S14PcLMBgev55jZYafO5l9fjl65OMAtkXXg4kaR8srdH492DJWN9iwqADOLN7PVZjbNzJaZ2Uwzq2Vm3c1siZktN7PJZlYjWH+OmSWbWYyZTTWzz4N1/hgsb2dmC4OxXjOzuvn6/Z+ZfWZmX5hZStBe08ymBGMsMbMzg/aD/rIzs7fMLO1InyuVm5kNCr5bWWb2RPA9uTL4rn0MdMm37jgzG5Wv+yAz+yT4Th1Wz8XMGpjZK2a2KHh0OXQdKZySRcXRDJjk7m2AH4BbgKnAQHdvTWgvcuQhfdoBjdy9VbDOlKD9GeD2YKzlwNh8faq6e0fg5nzt1wMEY1wKTDOzmoXEeqTPlUrKzFoAA4Eu7t4OyAUGAXcRShLnECoieiSx7n4GcB0wuYDlDwMPunsH4P8BT0Uw/EpByaLi2OjuC4LXzwHdgXXu/kXQNg1IPaTPV8CpZvZIUAL+BzOrA/zG3T8+Qr9Xg+dMID543RV4FsDdVwP/Bk4rJNbDPrfomykVVHcgCVhkZlnB+z8Cc9x9u7v/B3ipkP4vALj7XOA4M/vNIcvPBv4WjP1GsM6xkd6IikzJouL41RfMuPt3QFtgDqG9g6L8tfVz8JzLL8e8CqrdBZDDwd+xmkfxuVKxGTDN3dsFj2bAOIr+vT50vUPfVwE65xu/kbtnH13IlYuSRcXxWzPrHLy+FPgQiDez3wdtVwAH1Xo2s/pAFXd/BbgTSHT374HvDhyPKKhfAeYClwdjngb8llDF3/VAOzOrYmZxhErNF/i5xdtkqUBmAf3N7AQAMzseWAKkmVk9M6sGDCik/8CgX1fg++B7nN8HQF6VajNrF8ngKwOdDVVxrAKGmNkTwFrgJmAh8LKZVSVUX+vQM0gaAVPM7MAfDaOD5yHA42ZWi9CU0ZVhPvvRYP3lhPYmhrr7z2a2AFhH6LjH58DiMJ8rlZS7rzSz/wY+CL4X+wjtdY4D0oEthL4/MUcY4jsz+wQ4DriqgOV/AP5uZssI/d6bC4yI6EZUcCr3UQGYWTzwlru3inIoIlJBaRpKRETC0p6FiIiEpT0LEREJS8lCRETCUrIQEZGwlCxESkABtYtEyjUlCxERCUvJQiQCzGxwUKV3qZk9e8iya4JKp0uDyqe1gvYBQZXUpWY2N2hLyFd5dZmZNY3G9ogcSqfOihwlM0sgVGCxi7vvCEpV/AHY7e4TzKyeu+8M1v0zsNXdHwmueO/p7l+b2W/cfZeZPQIsdPfpZlYdiHH3n6K1bSIHaM9C5OidBcx09x0A7v7tIctbmdm8IDlcDiQE7QuAqWZ2Db+UsUgH/svMbgeaKFFIWaFkIXL0jMKro04Fbgju3XEXv1TfHQH8NxAHZAV7IM8DvYGfgPfN7KySDFykqJQsRI7eLOBiM6sHeRVT8zsW2BJUTr38QKOZ/c7dP3X3/wF2AHFmdirwlbtPJHTfhTalsgUiYajqrMhRcvcVZjYe+NjMcgmV1l6fb5U7gU8J3RRqOaHkAXB/cADbCCWcpcAdhG4Rug/4Bri7VDZCJAwd4BYRkbA0DSUiImEpWYiISFhKFiIiEpaShYiIhKVkISIiYSlZiIhIWEoWIiIS1v8H0H3XGz5lTYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eeedx3nnnNVj+kOdiERHbgVJJhwGPSOpVR3Pl2kQd8Vz7mwpMBSgrK/M1DTNrttasWcO3vvUtduzYQdu2bZk2bVpe99cod0NFxN8lPUPmWsM6SUXJWUURsD5pVgN0zepWDLyVxItzxM3MWqwePXqwcOHCRttf3i5wS+qcnFEg6QDgK8CrwOPA6KTZaOCxZPlxYKSkdpK6k7mQPT8ZstokaZAkAaOy+piZWSPI55lFETA9uW7xGWBmRPxO0jxgpqTLgDXAuQARsUzSTGA5sA0YnwxjAVzBx7fOPoEvbpuZNap83g21BOiXI/4OMHQPfSYBk3LEK4G6rneYmVkeeboPMzNL5ek+zKzFeLb81Abd3qlznm3Q7QEcfPDBbN68merqas4888zdbrEtFJ9ZmJlZKhcLM7M8uvHGG/n1r3+96/MNN9zAlClTuPnmmznhhBPo06fPJ6Ykz2XLli1ccskl9O7dm379+vH0008D8LWvfW3X7LT9+vXjxz/+8a59/va3v23Q43CxMDPLo8suu4zp06cDsGPHDmbMmMFRRx3FqlWrmD9/PosWLaKqqoo5c+bscRu33norAEuXLuX+++9n9OjRbNmyhfLycp577jnef/99WrduzfPPZ6YzmTt37m7zT+0rFwszszwqKSnhiCOOYOHChTz55JP069ePBQsW7Fru378/r776KqtWrdrjNubOnctFF10EwJe+9CW6devGa6+9xuDBg5kzZw5z587l61//Ops3b+af//wn1dXVHHvssQ16HL7AbWaWZ5dffjkVFRW8/fbbXHrppcyePZsJEyYwduzYevXf03TkJ5xwApWVlRxzzDGcfvrpbNy4kWnTpjFgwICGTB/wmYWZWd6NGDGCP/zhDyxYsIBhw4YxbNgw7rzzTjZvzkzM/eabb+6aUjyX8vLyXU+7e+2111izZg3HHnssbdu2pWvXrsycOZNBgwYxePBgJk+e3OBDUOAzCzNrQfJxq2t9tG3bli9/+cscdthhtGrVijPOOIMVK1Zw4oknApnbZe+99949PtHuyiuvZNy4cfTu3ZvWrVtTUVGxa4bZwYMHM3v2bA488EAGDx5MTU1NXoqFmusD58rKyqKysvJT9x9w7d0NmM2nV3XzqEKnYE1QU/h+7g/fzRUrVnDccccVOg127NhB//79efDBB+nRo0eh09kl15+PpKqIKKvd1sNQZmZ5tHz5cr7whS8wdOjQJlUo9paHoczM8uj444/nr3/9a6HT2Gc+szCzZq25DrXvq739c3GxMLNmq3379rzzzjsuGLVEBO+88w7t27evdx8PQ5lZs1VcXExNTQ0bNmwodCpNTvv27SkuLk5vmHCxMLNmq02bNnTv3r3QaTQLHoYyM7NU+XwGd1dJT0taIWmZpO8l8ZskvSlpUfL6WlafCZJWS1opaVhWfICkpcm6KcmzuM3MrJHkcxhqG/CvEfGypA5AlaSnknW/jIjJ2Y0lHQ+MBHoCRwN/kvTF5DnctwNjgBeB3wPD8XO4zcwaTd7OLCJibUS8nCxvAlYAXerochYwIyK2RsTrwGpgoKQi4JCImBeZWxruBs7OV95mZra7RrlmIakE6Ae8lIS+I2mJpDsldUxiXYA3srrVJLEuyXLteK79jJFUKanSdz+YmTWcvBcLSQcDDwFXR8T7ZIaUPg+UAmuBn+9smqN71BHfPRgxNSLKIqKsc+fO+5y7mZll5LVYSGpDplDcFxEPA0TEuojYHhE7gGnAwKR5DdA1q3sx8FYSL84RNzOzRpLPu6EE/BewIiJ+kRUvymo2AnglWX4cGCmpnaTuQA9gfkSsBTZJGpRscxTwWL7yNjOz3eXzbqiTgYuApZIWJbF/A86XVEpmKKkaGAsQEcskzQSWk7mTanxyJxTAFUAFcACZu6B8J5SZWSPKW7GIiLnkvt7w+zr6TAIm5YhXAr0aLjszM9sb/gW3mZmlcrEwM7NULhZmZpaqXsVC0uz6xMzMrHmq8wK3pPbAgUCn5JfWOy9YH0Jm/iYzM2sB0u6GGgtcTaYwVPFxsXgfuDWPeZmZWRNSZ7GIiF8Dv5b03Yi4pZFyMjOzJqZev7OIiFsknQSUZPeJiLvzlJeZmTUh9SoWku4hM/nfImDnr6p3ThduZmbNXH1/wV0GHJ88T8LMzFqY+v7O4hXgs/lMxMzMmq76nll0ApZLmg9s3RmMiH/JS1ZmZtak1LdY3JTPJMzMrGmr791Qz+Y7ETMza7rqezfUJj5+lGlboA3wQUQckq/EzMys6ajvmUWH7M+Szubjx6GamVkz96lmnY2IR4EhDZyLmZk1UfWddfabWa9zJP2Uj4el9tSnq6SnJa2QtEzS95L44ZKekrQqee+Y1WeCpNWSVkoalhUfIGlpsm5K8ixuMzNrJPU9s/hG1msYsAk4K6XPNuBfI+I4YBAwXtLxwPXA7IjoAcxOPpOsGwn0BIYDt0lqlWzrdmAM0CN5Da9n3mZm1gDqe83ikr3dcESsBdYmy5skrQC6kCkypyXNpgPPANcl8RkRsRV4XdJqYKCkauCQiJgHIOlu4Gzgib3NyczMPp36DkMVS3pE0npJ6yQ9JKm4vjuRVAL0A14CjkoKyc6CcmTSrAvwRla3miTWJVmuHc+1nzGSKiVVbtiwob7pmZlZivoOQ90FPE7muRZdgP+bxFJJOhh4CLg6It6vq2mOWNQR3z0YMTUiyiKirHPnzvVJz8zM6qG+xaJzRNwVEduSVwWQ+q+xpDZkCsV9EfFwEl4nqShZXwSsT+I1QNes7sXAW0m8OEfczMwaSX2LxUZJF0pqlbwuBN6pq0Nyx9J/ASsi4hdZqx4HRifLo4HHsuIjJbWT1J3Mhez5yVDVJkmDkm2OyupjZmaNoL5zQ10K/CfwSzJDQC8AaRe9TwYuApZKWpTE/g34KTBT0mXAGuBcgIhYJmkmsJzMnVTjI2LnszOuACqAA8hc2PbFbTOzRlTfYvF/gNER8R5kfisBTCZTRHKKiLnkvt4AMHQPfSYBk3LEK4Fe9czVzMwaWH2HofrsLBQAEfEumbubzMysBahvsfhMrV9aH079z0rMzGw/V99/8H8OvCBpFplrFt8ix3CRmZk1T/X9BffdkirJTB4o4JsRsTyvmZmZWZNR76GkpDi4QJiZtUCfaopyMzNrWVwszMwslYuFmZmlcrEwM7NULhZmZpbKxcLMzFK5WJiZWSoXCzMzS+ViYWZmqVwszMwslYuFmZmlyluxkHSnpPWSXsmK3STpTUmLktfXstZNkLRa0kpJw7LiAyQtTdZNSR6tamZmjSifZxYVwPAc8V9GRGny+j2ApOOBkUDPpM9tklol7W8HxpB5JnePPWzTzMzyKG/FIiLmAO/Ws/lZwIyI2BoRrwOrgYGSioBDImJeRARwN3B2fjI2M7M9KcQ1i+9IWpIMU+18+l4X4I2sNjVJrEuyXDuek6QxkiolVW7YsKGh8zYza7Eau1jcDnweKAXWknkCH2QeqFRb1BHPKSKmRkRZRJR17tx5X3M1M7NEoxaLiFgXEdsjYgcwDRiYrKoBumY1LQbeSuLFOeJmZtaIGrVYJNcgdhoB7LxT6nFgpKR2krqTuZA9PyLWApskDUrughoFPNaYOZuZ2V48VnVvSbofOA3oJKkGmAicJqmUzFBSNTAWICKWSZpJ5rGt24DxEbE92dQVZO6sOgB4InmZmVkjyluxiIjzc4T/q472k4BJOeKVQK8GTM3MzPaSf8FtZmapXCzMzCyVi4WZmaVysTAzs1QuFmZmlsrFwszMUrlYmJlZKhcLMzNL5WJhZmapXCzMzCyVi4WZmaVysTAzs1QuFmZmlsrFwszMUrlYmJlZKhcLMzNL5WJhZmap8lYsJN0pab2kV7Jih0t6StKq5L1j1roJklZLWilpWFZ8gKSlybopybO4zcysEeXzzKICGF4rdj0wOyJ6ALOTz0g6HhgJ9Ez63CapVdLndmAM0CN51d6mmZnlWd6KRUTMAd6tFT4LmJ4sTwfOzorPiIitEfE6sBoYKKkIOCQi5kVEAHdn9TEzs0bS2NcsjoqItQDJ+5FJvAvwRla7miTWJVmuHc9J0hhJlZIqN2zY0KCJm5m1ZE3lAneu6xBRRzyniJgaEWURUda5c+cGS87MrKVr7GKxLhlaInlfn8RrgK5Z7YqBt5J4cY64mZk1osYuFo8Do5Pl0cBjWfGRktpJ6k7mQvb8ZKhqk6RByV1Qo7L6mJlZI2mdrw1Luh84DegkqQaYCPwUmCnpMmANcC5ARCyTNBNYDmwDxkfE9mRTV5C5s+oA4InkZWZmjShvxSIizt/DqqF7aD8JmJQjXgn0asDUzMxsLzWVC9xmZtaEuViYmVkqFwszM0vlYmFmZqlcLMzMLJWLhZmZpXKxMDOzVC4WZmaWysXCzMxSuViYmVkqFwszM0vlYmFmZqlcLMzMLJWLhZmZpXKxMDOzVC4WZmaWysXCzMxSFaRYSKqWtFTSIkmVSexwSU9JWpW8d8xqP0HSakkrJQ0rRM5mZi1ZIc8svhwRpRFRlny+HpgdET2A2clnJB0PjAR6AsOB2yS1KkTCZmYtVVMahjoLmJ4sTwfOzorPiIitEfE6sBoYWID8zMxarEIViwCelFQlaUwSOyoi1gIk70cm8S7AG1l9a5LYbiSNkVQpqXLDhg15St3MrOVpXaD9nhwRb0k6EnhK0qt1tFWOWORqGBFTgakAZWVlOduYmdneK8iZRUS8lbyvBx4hM6y0TlIRQPK+PmleA3TN6l4MvNV42ZqZWaMXC0kHSeqwcxk4A3gFeBwYnTQbDTyWLD8OjJTUTlJ3oAcwv3GzNjNr2QoxDHUU8Iiknfv/74j4g6QFwExJlwFrgHMBImKZpJnAcmAbMD4ithcgbzOzFqvRi0VE/BXomyP+DjB0D30mAZPynJqZme1BU7p11szMmigXCzMzS+ViYWZmqVwszMwslYuFmZmlcrEwM7NULhZmZpbKxcLMzFK5WJiZWSoXCzMzS+ViYWZmqVwszMwslYuFmZmlcrEwM7NULhZmZpaqUM/gNrP93Jof9y50CgCc3/GQQqfA8999vtAp5J3PLMzMLNV+UywkDZe0UtJqSdcXOh8zs5ZkvygWkloBtwJfBY4Hzpd0fGGzMjNrOfaXaxYDgdXJ87uRNAM4C1he0KysUT1bfmqhU+DUOc8WOgWzgthfikUX4I2szzXA/6jdSNIYYEzycbOklY2QW15p8uhCpwDQCdhY6CSaBKnQGTQZ3QqdwMcK/v3UVc3qe5Hzr3Z/KRa5/iZit0DEVGBq/tNpWSRVRkRZofMwy8Xfz8axX1yzIHMm0TXrczHwVoFyMTNrcfaXYrEA6CGpu6S2wEjg8QLnZGbWYuwXw1ARsU3Sd4A/Aq2AOyNiWYHTakk8tGdNmb+fjUARuw39m5mZfcL+MgxlZmYF5GJhZmapXCxaIEm/9S/gbX8j6WJJ/5ksj5M0Kll+RtJut85mt7d9t19c4LaGFRGXFzoHs30REXcUOoeWxmcWzYCkEkmvSpouaYmkWZIOlDRU0kJJSyXdKald0v4ZSWWSWkmqkPRK0ub7yfpSSS8m23pEUsesfv8hab6k1yQNTuLtJd2VbGOhpC8n8U/8z07S7ySdtqf9Wssm6cLku7VI0m+S78klyXftWeDkrLY3SfpBVvcLJb2QfKcG5th2Z0kPSVqQvE6u3cbq5mLRfBwLTI2IPsD7wDVABXBeRPQmcxZ5Ra0+pUCXiOiVtLkrid8NXJdsaykwMatP64gYCFydFR8PkGzjfGC6pPZ15Lqn/VoLJek44Dzg5IgoBbYDFwI/IlMkTiczieieHBQRJwFXAnfmWP9r4JcRcQLwP4HfNmD6LYKLRfPxRkTsfALLvcBQ4PWIeC2JTQfKa/X5K3CMpFskDQfel3QocFhEPLuHfg8n71VASbJ8CnAPQES8CvwN+GIdue623/ofpjVTQ4EBwAJJi5LP3weeiYgNEfH/gAfq6H8/QETMAQ6RdFit9V8B/jPZ9uNJmw4NfRDNmYtF87HXP5iJiPeAvsAzZM4O6vO/ra3J+3Y+vua1p1nUtvHJ71j7fdivNW8CpkdEafI6FriJ+n+va7er/fkzwIlZ292bebMAAAK3SURBVO8SEZv2LeWWxcWi+ficpBOT5fOBPwElkr6QxC4CPjG/tqROwGci4iHgRqB/RPwDeG/n9Yhc/XKYA1yQbPOLwOeAlUA1UCrpM5K6kplqPud+P90hWzMyGzhH0pEAkg4HFgKnSTpCUhvg3Dr6n5f0OwX4R/I9zvYk8J2dHySVNmTyLYHvhmo+VgCjJf0GWAV8D3gReFBSazLza9W+g6QLcJeknf9pmJC8jwbukHQgmSGjS1L2fVvSfimZs4mLI2KrpOeB18lc93gFeDllv9ZCRcRySf8beDL5XnxE5qzzJmAesJbM96fVHjbxnqQXgEOAS3Osvwq4VdISMv/uzQHGNehBNHOe7qMZkFQC/C4iehU4FTNrpjwMZWZmqXxmYWZmqXxmYWZmqVwszMwslYuFmZmlcrEwy4MccxeZ7ddcLMzMLJWLhVkDkDQqmaV3saR7aq37djLT6eJk5tMDk/i5ySypiyXNSWI9s2ZeXSKpRyGOx6w23zprto8k9SQzweLJEbExmariKmBzREyWdEREvJO0/QmwLiJuSX7xPjwi3pR0WET8XdItwIsRcZ+ktkCriPiwUMdmtpPPLMz23RBgVkRsBIiId2ut7yXpuaQ4XAD0TOLPAxWSvs3H01jMA/5N0nVANxcKaypcLMz2nah7dtQK4DvJszt+xMez744D/jfQFViUnIH8N/AvwIfAHyUNyWfiZvXlYmG272YD35J0BOyaMTVbB2BtMnPqBTuDkj4fES9FxA+BjUBXSccAf42IKWSeu9CnUY7ALIVnnTXbRxGxTNIk4FlJ28lMrV2d1eRG4CUyD4VaSqZ4ANycXMAWmYKzGLiezCNCPwLeBn7cKAdhlsIXuM3MLJWHoczMLJWLhZmZpXKxMDOzVC4WZmaWysXCzMxSuViYmVkqFwszM0v1/wEi6dy619UiUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MM89k9erVHDx4kLi4uIJtRVUyOtqse1paGm+99RbVq1fnwgsvZOjQoeTn5zN2bKhAxptvvsmiRYuYPXs2DzzwAOvWrTuhpKGRhYhICbv44ot57LHHCv6xPzRltHv3bho0aECVKlV47rnnyM8PreNJT0/n5ZdfJj8/n23btjF//nwAWrZsSVZWVkGp8ueee47zzz+/YJ9HH32ULl26UL9+fXbu3MnGjRtp1aoVBw8e5IsvvqBbt248+OCD7Nq1q+CxrMdLIwsRqbDevujZqPqVZNXZaPzhD3/g17/+NW3atMHdiY+P54033uCWW26hb9++TJs2jW7duhU8MOmqq65i3rx5JCYm0rx584KEEBcXx6RJk+jfvz95eXl06NCBESNGANCpUye2b99Oeno6EJp2OuOMMzAz8vLyuOaaa9i9ezfuzp133kndunVP6JxUolykkvv8/sQSO1bjP64psWOV1xLlJ5NyUaJcREROHkoWIiISkZKFiIhEpGQhIiIRKVmIiEhEShYiIhKR7rMQkQrro+E3lejxzl+0sESPdywmT55MRkYGjz/+eMxiOBqNLEREJCIlCxGRKD344IOMGzcOgDvvvLOg4N/cuXO55ppreOmll0hMTKR169bcfffdBfsV1z5p0qSCO7aXLv3xwUvTpk2jdevWtG3btuAO7VhTshARiVJ6ejqLFy8GICMjg5ycHA4cOMCSJUto1qwZd999N/PmzSMzM5MVK1Ywa9YsvvrqqyLbt23bxn333cfSpUt59913CyrQAtx///288847rF69mtmzZ8fqdA+jZCEiEqX27duzcuVK9uzZQ40aNejSpQsZGRksXryYunXr0rVrV+rXr0+1atUYPHgwixYtYsWKFUW2f/jhhwXtP/nJTxgwYEDB96SmpjJ06FCefvrpgmKDsaZkISISperVqxMfH8+kSZM499xzSUtLY/78+WzdupXGjRsXuc/R6u8VVZYcYPz48YwePZovvviCpKQkdu7cWSLxnwglCxGRY5Cens7YsWNJT08nLS2N8ePHk5SUROfOnVm4cCHffPMN+fn5vPTSS5x//vl06tSp2PYFCxawc+dODhw4wLRp0wq+Y+vWrXTq1In777+fevXq8cUXX8TwjENKbemsmU0EegE73L110PYQcDnwA7AVuM7dd5lZPLAB2BTsvszdRwT7tAcmAzWBfwN36DncIgKQPOGpqPqVZNXZtLQ0xowZQ5cuXahVqxZxcXGkpaXRoEED/vKXv9CtWzfcnUsvvZTevXsDFNs+atQounTpQoMGDUhOTi6Ycvrv//5vNm/ejLvTvXt32rZtW2LxH69SK1FuZulADjA1LFn0AOa5e56Z/Q3A3e8OksUbh/oVOs5y4A5gGaFkMc7d34r0/SpRHp2iykAfr1iuUZfjpxLllVe5KFHu7ouAbwu1zXH3vODjMqDR0Y5hZg2AOu7+QTCamApcWRrxiohI8WJ5zeJ6IHyE0MTMVpnZQjNLC9oaAtlhfbKDtiKZ2XAzyzCzjK+//rrkIxYRqaRikizM7HdAHvBC0LQNaOzu7YC7gBfNrA5Q1FKBYufN3H2Cu6e4e0r9+vVLOmwRkUqrzGtDmdkQQhe+ux+6UO3u+4H9wfuVZrYVaE5oJBE+VdUI+KpsIxYRkTIdWZhZT+Bu4Ap33xvWXt/MqgbvmwLNgE/cfRuwx8w6W2hB8rXAa2UZs4iIlO7S2ZeArkA9M8sG7gPuBWoA7wY3oxxaIpsO3G9meUA+MMLdD10cv5kfl86+xeHXOUREpAyUWrJw90FFND9bTN8ZwIxitmUARyypFRGZ8vTmKHtG12/kw5cffzDFmDx5Mj169OCss84CID4+noyMDOrVq1fi31WadAe3iEgpmjx5Ml99VfEvtSpZiIgcg6ysLFq2bMmQIUNo06YN/fr1Y+/evdx///106NCB1q1bM3z4cNyd6dOnk5GRweDBg0lKSmLfvn0APPbYYyQnJ5OYmMjGjRsBWLhwIUlJSSQlJdGuXTv27NkTy9M8gpKFiMgx2rRpE8OHD+fjjz+mTp06/POf/2TkyJGsWLGCtWvXsm/fPt544w369etHSkoKL7zwApmZmdSsWROAevXq8dFHH3HzzTczduxYAMaOHcsTTzxBZmYmixcvLuhbXihZiIgco7PPPpvU1FQArrnmGpYsWcL8+fPp1KkTiYmJzJs3j3Xr1hW7f58+fYBQyfOsrCwgVJb8rrvuYty4cezatYtq1crXU6+VLEREjlHh0uJmxi233ML06dNZs2YNw4YNIzc3t9j9a9SoAUDVqlXJywtVQLrnnnt45pln2LdvH507dy6YniovlCxERI7R559/zgcffACEHpl63nnnAaHppZycHKZPn17Qt3bt2lFdf9i6dSuJiYncfffdpKSklLtkUb7GOSIix2DIsGZR9SvpqrPnnHMOU6ZM4aabbqJZs2bcfPPNfPfddyQmJhIfH0+HDh0K+g4dOpQRI0ZQs2bNggRTlEcffZT58+dTtWpVEhISuOSSS0o05hOlZCEicoyqVKnC+PHjD2sbPXo0o0ePPqJv37596du3b8HnQ9coAFJSUliwYAEQWiFVnmkaSkREIlKyEBE5BvHx8axduzbWYZQ5JQsREYlIyUJERCKKKlmY2dxo2kRE5OR01NVQZhYHnEKozPhp/PjkujrAWaUcm4iIlBORls7eBPyaUGJYyY/J4nvgiVKMS0QkonGjf1+ix/vd89Mjdyohs2bNonnz5iQkJJTZd56Io05Dufs/3L0J8Bt3b+ruTYJXW3d/vIxiFBE56cyaNYv169fHOoyoRXVTnrs/ZmbnAvHh+7j71FKKS0Sk3HnwwQeJi4vj9ttv584772T16tXMmzePuXPnMmnSJGrXrs2KFSvYt28f/fr1409/+hMQqvs0e/ZsqlWrRo8ePejTpw+zZ89m4cKFjB49mhkzQs9+u/XWW/n666855ZRTePrpp2lZwneen4iokoWZPQf8Asgk9NhTAAeULESk0khPT+fhhx/m9ttvJyMjg/3793PgwAGWLFlCWloa/fv352c/+xn5+fl0796djz/+mEaNGjFz5kw2btyImbFr1y7q1q3LFVdcQa9evejXrx8A3bt3Z/z48TRr1owPP/yQW265hXnz5sX4jH8UbbmPFCDB3T3aA5vZRKAXsMPdWwdtPwNeITRCyQKudvfvgm33AjcQSka3u/s7QXt7fnwG97+BO44lDhGRktK+fXtWrlzJnj17qFGjBsnJyWRkZLB48WLGjRvHq6++yoQJE8jLy2Pbtm2sX7+ehIQE4uLiuPHGG7nsssvo1avXEcfNycnh/fffp3///gVt+/fvL8tTiyja+yzWAv/nGI89GehZqO0eYK67NwPmBp8xswRgINAq2OefZlY12OdJYDjQLHgVPqaISJmoXr068fHxTJo0iXPPPZe0tDTmz5/P1q1bqVmzJmPHjmXu3Ll8/PHHXHbZZeTm5lKtWjWWL19O3759mTVrFj17HvlP2MGDB6lbty6ZmZkFrw0bNsTgDIsXbbKoB6w3s3fMbPah19F2cPdFwLeFmnsDU4L3U4Arw9pfdvf97v4psAXoaGYNgDru/kEwmpgato+ISJlLT09n7NixpKenk5aWxvjx40lKSuL777+nVq1a/PSnP2X79u289dZbQGjUsHv3bi699FIeffRRMjMzgcNLl9epU4cmTZowbdo0ANyd1atXx+YEixHtNNSoEvq+M919G4C7bzOzM4L2hsCysH7ZQduB4H3hdimHHv+v10vsWCMfvrzEjiUnr9t/f2SV16KUZInytLQ0xowZQ5cuXahVqxZxcXGkpaXRtm1b2rVrR6tWrWjatGnBk/T27NlD7969yc3Nxd155JFHABg4cCDDhg1j3LhxTJ8+nRdeeIGbb76Z0aNHc+DAAQYOHEjbtm1LLO4TFe1qqIWlHIcV0eZHaS/6IGbDCU1Z0bhx45KJTEQkTPfu3Tlw4EDB5//85z8F7ydPnlzkPsuXLz+iLTU19Yils2+//XbJBFkKoi33scfMvg9euWaWb2bfH8f3bQ+mlgj+7gjas4Gzw/o1Ar4K2hsV0V4kd5/g7inunlK/fv3jCE9ERIoSVbJw99ruXid4xQF9geO5KW82MCR4PwR4Lax9oJnVMLMmhC5kLw+mrPaYWWcLPfT22rB9RESkjBxX1Vl3nwVccLQ+ZvYS8AHQwsyyzewG4K/ARWa2Gbgo+Iy7rwNeBdYDbwO3uvuh+zluBp4hdNF7K/DW8cQsIiLHL9qb8vqEfaxC6L6Lo97r4O6DitnUvZj+Y4AxRbRnAK2jiVNEREpHtKuhwpem5BG6oa53iUcjIiLlUrSroa4r7UBERKT8inYaqhHwGJBKaPppCaGyG9lH3VFEpBRlzyh2cWQh0fU753dHvRQLwKmnnkpOTk6U33vyiPYC9yRCK5bOInRT3OtBm4iIVALRJov67j7J3fOC12RANzKISKX20EMP0aFDB9q0acN9991X0H7llVfSvn17WrVqxYQJEwB48skn+Z//+Z+CPpMnT+a2224D4Pnnn6djx44kJSVx0003kZ+fT35+PkOHDqV169YkJiYW3PkdK9Emi2/M7Bozqxq8rgF2lmZgIiLl2Zw5c9i8eTPLly8nMzOTlStXsmjRIgAmTpzIypUrycjIYNy4cezcuZN+/frxr3/9q2D/V155hQEDBrBhwwZeeeUVli5dSmZmJlWrVuWFF14gMzOTL7/8krVr17JmzRquuy62l46jTRbXA1cD/w/YBvQDdNFbRCqtOXPmMGfOHNq1a0dycjIbN25k8+bNAIwbN462bdvSuXNnvvjiCzZv3kz9+vVp2rQpy5YtY+fOnWzatInU1FTmzp3LypUr6dChA0lJScydO5dPPvmEpk2b8sknn3Dbbbfx9ttvU6dOnZieb7RLZx8AhoQ9e+JnwFhCSUREpNJxd+69915uuummw9oXLFjAe++9xwcffMApp5xC165dyc3NBWDAgAG8+uqrtGzZkquuugozw90ZMmQIf/nLX474jtWrV/POO+/wxBNP8OqrrzJx4sQyObeiRDuyaHMoUQC4+7dAu9IJSUSk/Lv44ouZOHFiwcqoL7/8kh07drB7925OO+00TjnlFDZu3MiyZT8W1O7Tpw+zZs3ipZdeYsCAAUCoMOH06dPZsSNUKu/bb7/ls88+45tvvuHgwYP07duXBx54gI8++qjsTzJMtCOLKmZ2WqGRRbT7ioiUikZ9z4qqX0mWKD+kR48ebNiwgS5dugChJbXPP/88PXv2ZPz48bRp04YWLVrQuXPngn1OO+00EhISWL9+PR07dgQgISGB0aNH06NHDw4ePEj16tV54oknqFmzJtdddx0HDx4EKHLkUZai/Qf/YeB9M5tO6D6LqymiNIeIyMku/B6LO+64gzvuuOOIPocefFSUN95444i2AQMGFIw0wsV6NBEu2ju4p5pZBqHigQb0cff1EXYTEZGTRNRTSUFyUIIQEamEjqtEuYhITLjjftSC1xKlY/3fUclCRCqM/G3b2P3DD0oYJ8jd2blzJ3FxcVHvoxVNIlJh7H3hRbYP/r9806ABmEW9X5ySyxHi4uJo1KhR5I4BJQsRqTA8J4f/fWrCMe/XbtHCUoimctE0lIiIRKRkISIiEZV5sjCzFmaWGfb63sx+bWajzOzLsPZLw/a518y2mNkmM7u4rGMWEansyvyahbtvApIAzKwq8CUwk1AV20fcfWx4fzNLAAYCrQg9fOk9M2vu7vllGriISCUW62mo7sBWd//sKH16Ay+7+353/xTYAnQsk+hERASIfbIYCLwU9nmkmX1sZhPN7LSgrSHwRVif7KDtCGY23MwyzCzj66+/Lp2IRUQqoZgtnTWznwBXAPcGTU8Sem6GB38fJvS8jKIWUxe5aNrdJwATAFJSUsrVwurP708ssWM1/uOaEjuWiEg0YjmyuAT4yN23A7j7dnfPd/eDwNP8ONWUDZwdtl8j4KsyjVREpJKLZbIYRNgUlJk1CNt2FbA2eD8bGGhmNcysCdAMWF5mUYqISGymoczsFOAiIPx5hA+aWRKhKaasQ9vcfZ2ZvUqo4m0ecKtWQomIlK2YJG0HkyUAAApFSURBVAt33wucXqjtV0fpPwY9bElEJGZivRpKREQqACULERGJSMlCREQiUrIQEZGIlCxERCQiJQsREYlIyUJERCJSshARkYiULEREJCIlCxERiUjJQkREIlKyEBGRiGL28CORsrJhzLwSO9Y5v7ugxI4lUpFoZCEiIhEpWYiISERKFiIiEpGShYiIRKRkISIiESlZiIhIRDFJFmaWZWZrzCzTzDKCtp+Z2btmtjn4e1pY/3vNbIuZbTKzi2MRs4hIZRbLkUU3d09y95Tg8z3AXHdvBswNPmNmCcBAoBXQE/inmVWNRcAiIpVVeZqG6g1MCd5PAa4Ma3/Z3fe7+6fAFqBjDOITEam0YpUsHJhjZivNbHjQdqa7bwMI/p4RtDcEvgjbNztoO4KZDTezDDPL+Prrr0spdBGRyidW5T5S3f0rMzsDeNfMNh6lrxXR5kV1dPcJwASAlJSUIvuIiMixi8nIwt2/Cv7uAGYSmlbabmYNAIK/O4Lu2cDZYbs3Ar4qu2hFRKTMk4WZ1TKz2ofeAz2AtcBsYEjQbQjwWvB+NjDQzGqYWROgGbC8bKMWEancYjENdSYw08wOff+L7v62ma0AXjWzG4DPgf4A7r7OzF4F1gN5wK3unh+DuEVEKq0yTxbu/gnQtoj2nUD3YvYZA4wp5dBERKQY5WnprIiIlFNKFiIiEpGShYiIRKRkISIiESlZiIhIREoWIiISkZKFiIhEpGQhIiIRKVmIiEhEShYiIhKRkoWIiESkZCEiIhEpWYiISERKFiIiEpGShYiIRKRkISIiESlZiIhIREoWIiISUZknCzM728zmm9kGM1tnZncE7aPM7Eszywxel4btc6+ZbTGzTWZ2cVnHLCJS2ZX5M7iBPOC/3P0jM6sNrDSzd4Ntj7j72PDOZpYADARaAWcB75lZc3fPL80g2//31BI93szaJXo4EZEyVeYjC3ff5u4fBe/3ABuAhkfZpTfwsrvvd/dPgS1Ax9KPVEREDonFyKKAmcUD7YAPgVRgpJldC2QQGn18RyiRLAvbLZtikouZDQeGAzRu3LjU4haRoqU+llpix/pzbP95kkJi9v+GmZ0KzAB+7e7fm9mTwAOAB38fBq4HrIjdvahjuvsEYAJASkpKkX1ERMqTDWPmldixzvndBSV2rMJishrKzKoTShQvuPu/ANx9u7vnu/tB4Gl+nGrKBs4O270R8FVZxisiUtnFYjWUAc8CG9z972HtDcK6XQWsDd7PBgaaWQ0zawI0A5aXVbwiIhKbaahU4FfAGjPLDNp+CwwysyRCU0xZwE0A7r7OzF4F1hNaSXVraa+EEhGRw5V5snD3JRR9HeLfR9lnDDCm1IISEZGj0h3cIiISkdamiYgcgzHX9CvR4/U555YSPV5p0chCREQiUrIQEZGIlCxERCQiJQsREYlIyUJERCLSaigROek9/l+vxzqECk8jCxERiUjJQkREIlKyEBGRiHTNQqQCKsnH/uqRvxINjSxERCQiJQsREYlIyUJERCJSshARkYiULEREJCIlCxERiUhLZyug1MdSS+xYf9ZPQESiUGFGFmbW08w2mdkWM7sn1vGIiFQmFSJZmFlV4AngEiABGGRmCbGNSkSk8qgQyQLoCGxx90/c/QfgZaB3jGMSEak0zN1jHUNEZtYP6OnuNwaffwV0cveRhfoNB4YHH1sAm8o00JNXPeCbWAchUgz9PkvWz929fuHGinJ104poOyLLufsEYELph1O5mFmGu6fEOg6Rouj3WTYqyjRUNnB22OdGwFcxikVEpNKpKMliBdDMzJqY2U+AgcDsGMckIlJpVIhpKHfPM7ORwDtAVWCiu6+LcViViab2pDzT77MMVIgL3CIiElsVZRpKRERiSMlCREQiUrKohMzsGd0BLxWNmQ01s8eD9yPM7Nrg/QIzO2LpbHh/OXEV4gK3lKxDNzeKVFTuPj7WMVQ2GlmcBMws3sw2mtkUM/vYzKab2Slm1t3MVpnZGjObaGY1gv4LzCzFzKqa2WQzWxv0uTPYnmRmy4JjzTSz08L2+5uZLTez/5hZWtAeZ2aTgmOsMrNuQfth/2VnZm+YWdfivlcqNzO7JvhtZZrZU8Hv5Lrgt7YQSA3rO8rMfhO2+zVm9n7wm+pYxLHrm9kMM1sRvEqudHMloWRx8mgBTHD3NsD3wF3AZGCAuycSGkXeXGifJKChu7cO+kwK2qcCdwfHWgPcF7ZPNXfvCPw6rP1WgOAYg4ApZhZ3lFiL+16ppMzsHGAAkOruSUA+cA3wJ0JJ4iJCRUSLU8vdzwVuASYWsf0fwCPu3gHoCzxTguFXCkoWJ48v3H1p8P55oDvwqbv/J2ibAqQX2ucToKmZPWZmPYHvzeynQF13X1jMfv8K/q4E4oP35wHPAbj7RuAzoPlRYj3ie6M/TTlJdQfaAyvMLDP4fCewwN2/DgqIvnKU/V8CcPdFQB0zq1to+4XA48GxZwd9apf0SZzMlCxOHsd8w4y7fwe0BRYQGh1E819b+4O/+fx4zauo2l0AeRz+G4s7ge+Vk5sBU9w9KXi1AEYR/e+6cL/Cn6sAXcKO39Dd95xYyJWLksXJo7GZdQneDwLeA+LN7JdB26+AheE7mFk9oIq7zwD+ACS7+27gu0PXI4rarwiLgMHBMZsDjQlV/M0CksysipmdTajUfJHfe3ynLCeRuUA/MzsDwMx+BqwCuprZ6WZWHeh/lP0HBPudB+wOfsfh5gAFVarNLKkkg68MtBrq5LEBGGJmTwGbgTuAZcA0M6tGqL5W4RUkDYFJZnboPxruDf4OAcab2SmEpoyui/Dd/wz6ryE0mhjq7vvNbCnwKaHrHmuBjyJ8r1RS7r7ezH4PzAl+FwcIjTpHAR8A2wj9fqoWc4jvzOx9oA5wfRHbbweeMLOPCf27twgYUaIncZJTuY+TgJnFA2+4e+sYhyIiJylNQ4mISEQaWYiISEQaWYiISERKFiIiEpGShYiIRKRkIVIKiqhdJFKhKVmIiEhEShYiJcDMrg2q9K42s+cKbRsWVDpdHVQ+PSVo7x9USV1tZouCtlZhlVc/NrNmsTgfkcK0dFbkBJlZK0IFFlPd/ZugVMXtQI67jzWz0919Z9B3NLDd3R8L7njv6e5fmlldd99lZo8By9z9BTP7CVDV3ffF6txEDtHIQuTEXQBMd/dvANz920LbW5vZ4iA5DAZaBe1LgclmNowfy1h8APzWzO4Gfq5EIeWFkoXIiTOOXh11MjAyeHbHn/ix+u4I4PfA2UBmMAJ5EbgC2Ae8Y2YXlGbgItFSshA5cXOBq83sdCiomBquNrAtqJw6+FCjmf3C3T909z8C3wBnm1lT4BN3H0fouQttyuQMRCJQ1VmRE+Tu68xsDLDQzPIJldbOCuvyB+BDQg+FWkMoeQA8FFzANkIJZzVwD6FHhB4A/h9wf5mchEgEusAtIiIRaRpKREQiUrIQEZGIlCxERCQiJQsREYlIyUJERCJSshARkYiULEREJKL/D0YajCPrqlseAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a chart for all features showing poisonous vs edible\n",
    "for column in selected_features.columns:\n",
    "    sns.countplot(x='class', hue=column, data=df)\n",
    "    plt.savefig(os.path.join(\"images\", column))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform Categorical encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X=df.drop('class',axis=1) #Predictors\n",
    "y=df['class']\n",
    "\n",
    "Encoder_X = LabelEncoder() \n",
    "for col in X.columns:\n",
    "    X[col] = Encoder_X.fit_transform(X[col])\n",
    "Encoder_y=LabelEncoder()\n",
    "y = Encoder_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
       "0          2            3          0        0     7                1   \n",
       "1          2            3          9        0     0                1   \n",
       "2          0            3          8        0     1                1   \n",
       "3          2            2          8        0     7                1   \n",
       "4          2            3          3        1     6                1   \n",
       "\n",
       "   gill-spacing  gill-size  gill-color  stalk-shape  ...  \\\n",
       "0             0          1           0            0  ...   \n",
       "1             0          0           0            0  ...   \n",
       "2             0          0           1            0  ...   \n",
       "3             0          1           1            0  ...   \n",
       "4             1          0           0            1  ...   \n",
       "\n",
       "   stalk-surface-below-ring  stalk-color-above-ring  stalk-color-below-ring  \\\n",
       "0                         3                       7                       7   \n",
       "1                         3                       7                       7   \n",
       "2                         3                       7                       7   \n",
       "3                         3                       7                       7   \n",
       "4                         3                       7                       7   \n",
       "\n",
       "   veil-type  veil-color  ring-number  ring-type  spore-print-color  \\\n",
       "0          0           2            1          4                  0   \n",
       "1          0           2            1          4                  1   \n",
       "2          0           2            1          4                  1   \n",
       "3          0           2            1          4                  0   \n",
       "4          0           2            1          0                  1   \n",
       "\n",
       "   population  habitat  \n",
       "0           3        4  \n",
       "1           2        0  \n",
       "2           2        2  \n",
       "3           3        4  \n",
       "4           0        0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determine Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 0 - cap-shape, Score: 0.013813630712688475\n",
      "Feature 1 - cap-surface, Score: 0.04588107432071427\n",
      "Feature 2 - cap-color, Score: 0.0016209194226395245\n",
      "Feature 3 - bruises, Score: 0.3741956105210228\n",
      "Feature 4 - odor, Score: 0.007542916084986791\n",
      "Feature 5 - gill-attachment, Score: 0.3826431004392035\n",
      "Feature 6 - gill-spacing, Score: -0.566861915803495\n",
      "Feature 7 - gill-size, Score: 0.4583169822532783\n",
      "Feature 8 - gill-color, Score: -0.005481746518588057\n",
      "Feature 9 - stalk-shape, Score: -0.13325412471099418\n",
      "Feature 10 - stalk-root, Score: -0.15764500026017145\n",
      "Feature 11 - stalk-surface-above-ring, Score: -0.06816898101290109\n",
      "Feature 12 - stalk-surface-below-ring, Score: -0.04861136570900631\n",
      "Feature 13 - stalk-color-above-ring, Score: -0.01400231589202054\n",
      "Feature 14 - stalk-color-below-ring, Score: 0.012827343592604014\n",
      "Feature 15 - veil-type, Score: 8.326672684688674e-17\n",
      "Feature 16 - veil-color, Score: 0.10045253871622759\n",
      "Feature 17 - ring-number, Score: -0.15826775095248685\n",
      "Feature 18 - ring-type, Score: 0.004833807524544517\n",
      "Feature 19 - spore-print-color, Score: 0.05088934219890027\n",
      "Feature 20 - population, Score: -0.012688534579751012\n",
      "Feature 21 - habitat, Score: -0.030198412655210418\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://machinelearningmastery.com/calculate-feature-importance-with-python/\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# define the model\n",
    "model = LinearRegression()\n",
    "# fit the model\n",
    "model.fit(X, y)\n",
    "# get importance\n",
    "importance = model.coef_\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "    print(f'Feature {i} - {X.columns[i]}, Score: {v}')\n",
    "\n",
    "# plot feature importance\n",
    "fig, ax = plt.subplots(figsize = (12,4))\n",
    "plt.bar([x for x in range(len(importance))], importance)\n",
    "ax.set_xticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21])\n",
    "ax.set_xticklabels(X.columns, rotation=65)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Our Chosen Features with the higest numbers to use as our selected features\n",
    "* Feature 3 - bruises, Score: 0.3741956105210228\n",
    "* Feature 5 - gill-attachment, Score: 0.3826431004392035\n",
    "* Feature 6 - gill-spacing, Score: -0.566861915803495\n",
    "* Feature 7 - gill-size, Score: 0.4583169822532783\n",
    "* Feature 10 - stalk-root, Score: -0.15764500026017145\n",
    "* Feature 16 - veil-color, Score: 0.10045253871622759\n",
    "* Feature 17 - ring-number, Score: -0.15826775095248685"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>bruises</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>stalk-root</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bruises  gill-attachment  gill-spacing  gill-size  stalk-root  veil-color  \\\n",
       "0        0                1             0          1           2           2   \n",
       "1        0                1             0          0           1           2   \n",
       "2        0                1             0          0           1           2   \n",
       "3        0                1             0          1           2           2   \n",
       "4        1                1             1          0           2           2   \n",
       "\n",
       "   ring-number  \n",
       "0            1  \n",
       "1            1  \n",
       "2            1  \n",
       "3            1  \n",
       "4            1  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set features. This will also be used as your x values.\n",
    "selected_features = X[['bruises','gill-attachment','gill-spacing','gill-size','stalk-root','veil-color','ring-number']]\n",
    "\n",
    "selected_features_names = selected_features.columns\n",
    "selected_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bruises', 'gill-attachment', 'gill-spacing', 'gill-size', 'stalk-root',\n",
       "       'veil-color', 'ring-number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup Variables to use for our model\n",
    "target = y\n",
    "target_names = [\"edible\", \"poisonous\"]\n",
    "data = selected_features\n",
    "data_names = selected_features_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Train Test Split\n",
    "\n",
    "Use `class` for the y (target) values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      bruises  gill-attachment  gill-spacing  gill-size  stalk-root  \\\n",
       "3887        1                1             0          1           0   \n",
       "4119        1                1             0          0           0   \n",
       "1600        0                1             0          0           0   \n",
       "4988        1                1             0          0           0   \n",
       "6757        1                1             0          1           3   \n",
       "\n",
       "      veil-color  ring-number  \n",
       "3887           2            1  \n",
       "4119           2            1  \n",
       "1600           2            1  \n",
       "4988           2            1  \n",
       "6757           2            1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bruises</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>stalk-root</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3332</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6988</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      bruises  gill-attachment  gill-spacing  gill-size  stalk-root  \\\n",
       "1971        1                1             1          0           2   \n",
       "6654        1                1             0          1           3   \n",
       "5606        1                1             0          1           3   \n",
       "3332        0                1             0          0           0   \n",
       "6988        1                1             0          1           3   \n",
       "\n",
       "      veil-color  ring-number  \n",
       "1971           2            1  \n",
       "6654           2            1  \n",
       "5606           2            1  \n",
       "3332           2            1  \n",
       "6988           2            1  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling\n",
    "\n",
    "Scale the data using the MinMaxScaler and perform some feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale your data\n",
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.        , 0.        , ..., 0.        , 0.66666667,\n",
       "        0.5       ],\n",
       "       [1.        , 1.        , 0.        , ..., 0.        , 0.66666667,\n",
       "        0.5       ],\n",
       "       [0.        , 1.        , 0.        , ..., 0.        , 0.66666667,\n",
       "        0.5       ],\n",
       "       ...,\n",
       "       [0.        , 1.        , 0.        , ..., 1.        , 0.66666667,\n",
       "        0.5       ],\n",
       "       [1.        , 1.        , 0.        , ..., 0.75      , 0.66666667,\n",
       "        0.5       ],\n",
       "       [1.        , 1.        , 1.        , ..., 0.75      , 0.66666667,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.        , 1.        , ..., 0.5       , 0.66666667,\n",
       "        0.5       ],\n",
       "       [1.        , 1.        , 0.        , ..., 0.75      , 0.66666667,\n",
       "        0.5       ],\n",
       "       [1.        , 1.        , 0.        , ..., 0.75      , 0.66666667,\n",
       "        0.5       ],\n",
       "       ...,\n",
       "       [0.        , 1.        , 0.        , ..., 0.25      , 0.66666667,\n",
       "        0.5       ],\n",
       "       [1.        , 1.        , 1.        , ..., 0.75      , 0.66666667,\n",
       "        1.        ],\n",
       "       [1.        , 1.        , 0.        , ..., 0.75      , 0.66666667,\n",
       "        0.5       ]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.946824224519941"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf = rf.fit(X_train_scaled, y_train)\n",
    "rf.score(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.9481372066305597\n",
      "Testing Data Score: 0.946824224519941\n"
     ]
    }
   ],
   "source": [
    "# Output the training and testing score for the model\n",
    "print(f\"Training Data Score: {rf.score(X_train_scaled, y_train)}\")\n",
    "print(f\"Testing Data Score: {rf.score(X_test_scaled, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning\n",
    "\n",
    "Use `GridSearchCV` to tune the model's parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the GridSearch estimator along with a parameter object containing the values to adjust\n",
    "\n",
    "param_grid = {'n_estimators': [50, 100, 150, 200, 250],\n",
    "              'max_depth': [10, 20, 30, 40],\n",
    "             'max_features': ['auto', 'sqrt','log2']}\n",
    "grid = GridSearchCV(rf, param_grid, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=50 ................\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=50, score=0.950, total=   0.1s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=50 ................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=10, max_features=auto, n_estimators=50, score=0.949, total=   0.1s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=50 ................\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=50, score=0.947, total=   0.1s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=50 ................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=10, max_features=auto, n_estimators=50, score=0.956, total=   0.1s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=50 ................\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=50, score=0.939, total=   0.1s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=100 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=100, score=0.950, total=   0.3s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=100 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=100, score=0.949, total=   0.3s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=100 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=100, score=0.947, total=   0.3s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=100 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=100, score=0.956, total=   0.3s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=100 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=100, score=0.939, total=   0.3s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=150 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=150, score=0.950, total=   0.4s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=150 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=150, score=0.949, total=   0.4s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=150 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=150, score=0.947, total=   0.4s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=150 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=150, score=0.956, total=   0.4s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=150 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=150, score=0.939, total=   0.4s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=200 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=200, score=0.950, total=   0.5s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=200 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=200, score=0.949, total=   0.5s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=200 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=200, score=0.947, total=   0.6s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=200 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=200, score=0.956, total=   0.5s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=200 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=200, score=0.939, total=   0.5s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=250 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=250, score=0.950, total=   0.5s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=250 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=250, score=0.949, total=   0.6s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=250 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=250, score=0.947, total=   0.7s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=250 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=250, score=0.956, total=   0.7s\n",
      "[CV] max_depth=10, max_features=auto, n_estimators=250 ...............\n",
      "[CV]  max_depth=10, max_features=auto, n_estimators=250, score=0.939, total=   0.7s\n",
      "[CV] max_depth=10, max_features=sqrt, n_estimators=50 ................\n",
      "[CV]  max_depth=10, max_features=sqrt, n_estimators=50, score=0.950, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, n_estimators=50 ................\n",
      "[CV]  max_depth=10, max_features=sqrt, n_estimators=50, score=0.949, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, n_estimators=50 ................\n",
      "[CV]  max_depth=10, max_features=sqrt, n_estimators=50, score=0.947, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, n_estimators=50 ................\n",
      "[CV]  max_depth=10, max_features=sqrt, n_estimators=50, score=0.956, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, n_estimators=50 ................\n",
      "[CV]  max_depth=10, max_features=sqrt, n_estimators=50, score=0.939, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, n_estimators=100 ...............\n",
      "[CV]  max_depth=10, max_features=sqrt, n_estimators=100, score=0.950, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, n_estimators=100 ...............\n",
      "[CV]  max_depth=10, max_features=sqrt, n_estimators=100, score=0.949, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, n_estimators=100 ...............\n",
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      "[CV] max_depth=10, max_features=sqrt, n_estimators=100 ...............\n",
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      "[CV] max_depth=40, max_features=sqrt, n_estimators=50 ................\n",
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      "[CV] max_depth=40, max_features=sqrt, n_estimators=150 ...............\n",
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      "[CV] max_depth=40, max_features=sqrt, n_estimators=200 ...............\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=40, max_features=sqrt, n_estimators=200, score=0.956, total=   0.5s\n",
      "[CV] max_depth=40, max_features=sqrt, n_estimators=200 ...............\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=50 ................\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=50 ................\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=100 ...............\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=150 ...............\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=150 ...............\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=150 ...............\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=200 ...............\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=200 ...............\n",
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      "[CV] max_depth=40, max_features=log2, n_estimators=200 ...............\n",
      "[CV]  max_depth=40, max_features=log2, n_estimators=200, score=0.947, total=   0.5s\n",
      "[CV] max_depth=40, max_features=log2, n_estimators=200 ...............\n",
      "[CV]  max_depth=40, max_features=log2, n_estimators=200, score=0.956, total=   0.5s\n",
      "[CV] max_depth=40, max_features=log2, n_estimators=200 ...............\n",
      "[CV]  max_depth=40, max_features=log2, n_estimators=200, score=0.939, total=   0.5s\n",
      "[CV] max_depth=40, max_features=log2, n_estimators=250 ...............\n",
      "[CV]  max_depth=40, max_features=log2, n_estimators=250, score=0.950, total=   0.6s\n",
      "[CV] max_depth=40, max_features=log2, n_estimators=250 ...............\n",
      "[CV]  max_depth=40, max_features=log2, n_estimators=250, score=0.949, total=   0.6s\n",
      "[CV] max_depth=40, max_features=log2, n_estimators=250 ...............\n",
      "[CV]  max_depth=40, max_features=log2, n_estimators=250, score=0.947, total=   0.6s\n",
      "[CV] max_depth=40, max_features=log2, n_estimators=250 ...............\n",
      "[CV]  max_depth=40, max_features=log2, n_estimators=250, score=0.956, total=   0.6s\n",
      "[CV] max_depth=40, max_features=log2, n_estimators=250 ...............\n",
      "[CV]  max_depth=40, max_features=log2, n_estimators=250, score=0.939, total=   0.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed:  2.2min finished\n"
     ]
    }
   ],
   "source": [
    "# Train the model with GridSearch\n",
    "grid_result = grid.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 10, 'max_features': 'auto', 'n_estimators': 50}\n",
      "0.9481369827215772\n"
     ]
    }
   ],
   "source": [
    "# Output the grid search best params and best score\n",
    "print(grid.best_params_)\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.9481372066305597\n",
      "Testing Data Score: 0.946824224519941\n"
     ]
    }
   ],
   "source": [
    "# Output the grid training and test scores\n",
    "print(f\"Training Data Score: {grid.score(X_train_scaled, y_train)}\")\n",
    "print(f\"Testing Data Score: {grid.score(X_test_scaled, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=10, n_estimators=50)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Found in article https://medium.com/datadriveninvestor/an-introduction-to-grid-search-ff57adcc0998\n",
    "best_params = grid_result.best_params_\n",
    "best_rf = RandomForestClassifier(max_depth=best_params[\"max_depth\"], max_features=best_params[\"max_features\"], n_estimators=best_params[\"n_estimators\"])\n",
    "best_rf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Random Forest Training Data Score: 0.9481372066305597\n",
      "Best Random Forest Testing Data Score: 0.946824224519941\n"
     ]
    }
   ],
   "source": [
    "# Output the training and test scores for the best params model\n",
    "print(f\"Best Random Forest Training Data Score: {best_rf.score(X_train_scaled, y_train)}\")\n",
    "print(f\"Best Random Forest Testing Data Score: {best_rf.score(X_test_scaled, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      edible       0.93      0.97      0.95      1040\n",
      "   poisonous       0.97      0.92      0.94       991\n",
      "\n",
      "    accuracy                           0.95      2031\n",
      "   macro avg       0.95      0.95      0.95      2031\n",
      "weighted avg       0.95      0.95      0.95      2031\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Make predictions and calculate classification report\n",
    "predictions = best_rf.predict(X_test_scaled)\n",
    "print(classification_report(y_test, predictions,\n",
    "                            target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sDOPajItjHBU/J/TjPSgDNKv1dYdDbJMHtk/acDH8U3nh+Ceo1sfFMY5K5Zxob3e3Ur8tTEwE/ZgQ+VFQzWh93eEQ2+RJ+vwmqFT6s2a5Xa5JN1NT25f6cnGMo1I5J1x+1llOyYeC0hFS0zmxbZOnMuXdReu6sk9dbINJhoa2X6zz0D9+TjhPPhRUsyE1Ex6UTfKMjATVKUp0dbnnfVT2aR72WKJQzZvIev/4OaGXyUlobU30J/OhoGxLSrBRnvILuaXFvUrXlX2ahwU4Cnn0JqrNiay32SSVXngC5ENB6fBYbEqSMCGP69lOlUo2DwtwFCrHuL092PDOMpVt9h5Uc6SwTuRHQdnksehIkkhCYbrkRU1OQlvba6+9B7U9ebwnaOvW7YtE+znRHCnMoVAKSkSOFpF1IrJeRC6o8nmPiNwuIo+IyFoR+Yh+UZugvOx+HEwkSTQz0Lrviq8WcnS9GoP3oLYn4ftXrMTPieZIYQ41VFAi0gp8EzgGWAycIiKLKw77OPCEUmp/YAXwryIyTbOs6WFbmrluqllGrlubrsuvm2pj3Nqar/I/fk40h6Ue1MHAeqXUs0qpceB6YGXFMQqYJSICdAGvANmZ+bYlSYDe8Fs1eVyyNicmtg/vgd9vqKTaGHd3u5etGZZCYceCzC7NaRux0YMC9gA2lr3uK75Xzn8AbwFeAB4DzlVK7XAbv4icKSJrRGRNf39/TJEjUm2iRsW2JAkIFmRdm9zVPDqXrM1qIVO/GG1PFr3kegwNBTdrl+ONluawNEmi2upeab6/G3gY2B04APgPEdlhBVZKXaWUWqqUWjpnzpzIwsai2kSNio0KSucCXE0ely5m1xVsElTrI5fGOCp+Tugn4YcVQjgF1QfML3s9j8BTKucjwE0qYD3wR2CRHhGbREentrbqretWXlU6LjoVlOshPtflT4K89VHe2psElnpQDwD7iMjCYuLDB4HbKo55DjgSQER2Bd4MPKtT0NjYlpAAep7Sq9MadD3847r8SZC3Pspbe5MghSSJtkYHKKUmReQc4KdAK3C1UmqtiJxV/PxK4IvANSLyGEFI8DNKqZcMyh0eXRt7tt0TpDM8U8vadOViria/Lxa7PXnzKKq1t7MzeKyMJx4pJEk0VFAASqlVwKqK964s+/sF4F16RdOELq0/bdqON/7FobKqdFx07ovVitc/95ye85umVgq1f9zGawwNBTUWy8myR1FtTojYZ2i6hKVp5m6jS+vrsjZ11bPS7UFlLUnCsz1K7WgYuTTGUfFzQj+Wppm7jS6tr0tB2SYPVH8ImUvhH18lIR4ujXFU6s0J70XFY3h4Ry/cMPlQUDoWL13hENvkSev8Oqml9Eth2bxTa0F2aYyjUmtOdHYGj5fxRKfyuXEJkH0FZVuIzzZ50jq/Tmr1aZZDWFGoVYvSpTGOSr05kdU2Z5DsKyhdITWdHpRN8lQrE6Tz/ElQq0/9YhRQq39cGuOo1JsTWW2zSVIKi2ZfQen0WHRMbNvkqZW04VI181p9muUFOAp59CbqzYmsttkkY2MwY0biP5t9BWVbUoKN8rieYFCrDVlegKNQq3+6uoKN7yxSb054oyU6Ka0T+VBQNiUl2CiP6w+yGxmBmTN3fN97UAG1xjjL9wVVu+8L/JyIS0rrRPYVlI5isaA3SULHQOvKRqoXchRx52bXatlF3loOyGMafqEQ3Kxdifeq45HSHMq+gqo1UaNimwelK92znjyulwvyi1FAI+s3q15UNbzREg/vQVmObUkSuqjn0bmwwNdbXH04J6DenJsxI9gAzws+SSIe3oOyHNuSJEo0a/3W86BcWOBHR6vvP4EbCjYJ6s05F8Y4Klu3BjdpV8N7UPHwSRIGqDdRo2JbiA+ChbnZ6sz1Fi8XLmbXFWwS1OsjF8Y4Kn5O6MeH+Ayg8wmQM2bAli3Nn0dnPSsdHkI9190FD8R1+ZMgb32Ut/YmgQ/xGUCnt6IrKaFaVem46LAGXbc2XfcAkyBvIT4/J/TjPSgD2JaQoBsdF5vrSRL1xjiLi28c8uZRNJoTWWtvEngPygAmtH4zSQm603l1LC5Z9qA6OvKVoVYL70G9Rnt7UH/SEw3vQRlAt9ZvtjRMrarScdGxuDTyoGxfvOop2IQfDWAt9eadC2MclSyU77IN70EZQLfWb9ZjsU0eqJ+04UL4J+thXNO4MMZR8XNCP96DMoBuS6pZj8U2eaD+Q8hcCP80unBcKteUBi6McVQazYm2Nh/mi8r4OEyfnvjPZltB6bakmg2H2CZPmPPbbl036tMsV+wOQ6FQP9TpwhhHpdGc6O52u4RXjsi2gtLtljabAWSbPI2SNlywrhv1aRYX4Cg0KpbswhhHJcycyFqbM0q2FZRtHott8mzZUrtMELhRLLZR2DSLC3AUGvVPFhdrPyf0MjWl797NiGRbQdmWlGCjPPUu5NZW+/dvGin9vHtQYcJdWesfPyf0Uuup2wmQfQVlU1KCjfK4/rDCRm3Iu7XcqH+mTw82wLOEnxN6SXGdyLaCsi2kplueZi+0LKTjTk4GN1/WIoshrChkYYyjMjZW/35D70FFI8U5lG0FtWWL/htjbQrxTZvWXLpsGI9u2rTgRk9XyftilAUvOQ6NMhfzbLRExXtQBtFZTcA2Dwqaa1+Yau+uL/B5D+eEmXMtLcFGeF7I4r6bSbwH5Qi2JUk0SxgPyuYFfnIySOSoh+sKtlnCzDkXsjXD0ui+L/AeVFS8B2WAMBM1KrYlSUCwQE9OxpcnjAdl68UcxgO0WcEmQZg5Z/MYRyXM89byPieikmJtw+wqqJERfQ8GLNFsaEDnAxRLNHNXfBjX3WYPJKz8eV6MXB/jqOStvUngQ3wGMOGWtrU1F6vXXc0cmrMGXQ/xhfUA87wYhekjm8c4Kq5HBWzEh/gMkJf02mYuNteTJMKMcZYW3zjkzaMIOyey0t4k8B6UAWxLSDBFM4tLHjyovC9G3oPakbwXEI6K7R6UiBwtIutEZL2IXFDjmBUi8rCIrBWRX+kVMwamtH7cUv0mkjagucUlrAdl6+IVRsG6UK7JJI2KxYLdYxyVMHNCRP/TrbOMzR6UiLQC3wSOARYDp4jI4opjdgIuB45XSr0VOMmArNEwpfXjeixhFook5YHwGV62eiB5CeM2Q6GQr1R8Pyf0Y3kW38HAeqXUs0qpceB6YGXFMacCNymlngNQSr2oV8wYmOrUuB6LbfJAUINt2jRz5zdNXsK4prF5jKMSZU54LyocQ0P6M6JDEkZB7QFsLHvdV3yvnDcBO4vI3SLyoIh8uNqJRORMEVkjImv6+/vjSRwWU5ZU3HCIbfJEOb+t1nXYPm1vz15BVJ3YPMZRCTsnZs4MSqF5GqOU1Y/bqLZxUml6tAEHAscB7wY+LyJv2uFLSl2llFqqlFo6Z86cyMJGwrYQn23yhMVm6zpsn2ZpAY7C1q2NPWSwe4yjEmVOZKXNGSaMguoD5pe9nge8UOWYO5RSI0qpl4B7gP31iBgTUx5L3IvZNnmmphrvTYDdF3LYsGmWFuAohO0fm8c4Kn5OZIowCuoBYB8RWSgi04APArdVHHMr8Nci0iYiM4G/Av6gV9SI2Oax2CZP2KoWHR3B4wtsJKzSz9ICHIUo/ZMVDzOPbTZJo0eXGKat0QFKqUkROQf4KdAKXK2UWisiZxU/v1Ip9QcRuQN4FCgA31ZKPW5S8IbYlpTgqjwmUuN14UN89QnbP11d2SkW60N8ekk5EamhggJQSq0CVlW8d2XF668AX9EnWpOYTErYtCmePPPnNz4ujjxxQ46uZ8CFKQwK+Q3nhL0GWlqyk9EWdl6HvIF7YmKCvr4+xmyNIphmYgIuvBD+0FxArKOjg3nz5tFe7+GiVQiloJxkcNCu+44GBuCtb9UvT9y74qN4dCLB/TQpZfLUJYyH5z2o/BDmvi8Ibdj19fUxa9YsFixYgNgcTTDFyAj098OCBbFPoZTi5Zdfpq+vj4ULF0b6roUrjiampoKqD7qxLUki7kUTxYPq6gomqqt4D8pTSUgPamxsjNmzZ+dTOUH4ZKo6iAizZ8+O5YVmV0GZwrYkibhE8aBsXOCjhKS8B9WY6dPtTYYxQYTQeG6VE2hRUBC/D72CioptSRIlou4hRFm8bNxQ3rIFZswId6yNCjYJosw5G8c4KuPjwU3ZYcjhnLj77rt5z3veA8Btt93GpZdeWvPYV199lcsvvzyygrr44ov56le/2rSsJbKpoCYmwk/UqNiYlDBjRvS74qOEf2z0QKLKn7PFCHB/jKOSt/YWmYrxjLrjjz+eCy6oWvcbiK+gdJNNBWUynDZzJoyORv+eqWKxEO9icz3EF9UDzMhiFIkofWTjGEfF9ahAFTZs2MCiRYs47bTTWLJkCR/4wAcYHR1lwYIFXHLJJSxfvpwf/ehH3HnnnRx66KH09vZy0kknMVxMnLrjjjtYtGgRy5cv56abbtp23muuuYZzzjkHgE2bNnHCCSew//77s//++7N69WouuOACnnnmGQ54xzv49Be+AMBXvvIVDjroIJYsWcJFF1207Vxf+tKXePOb38w73/lO1q1bp7X92cziM7k5HDceHTa7KA6lxeX1rw//nSgenY0LfJQxzsLiG4e8eRRR54Qj7V23bh3f+c53WLZsGX//938feDYEqdv33nsvL730EieeeCI///nP6ezs5Mtf/jKXXXYZ559/Pv/wD//AL3/5S/bee29OPvnkquf/x3/8R97+9rdz8803MzU1xfDwMJdeeimPP/44D//kJ9DTw5133snTTz/N/fffj1KK448/nnvuuYfOzk6uv/56HnroISYnJ+nt7eXAAw/U1vZsKijbEhJM4z2o+mRh8Y2D96BqE7e9Rx8NL70U/Xu12GUXuOOOuofMnz+fZcuWAfChD32Ib3zjGwDbFM59993HE088se2Y8fFxDj30UJ588kkWLlzIPvvss+27V1111Q7n/+Uvf8n3v/99AFpbW+np6WHz5s3Bh8UQ35133smdd97J2972NgCGh4d5+umnGRoa4oQTTmDmzJlAEDrUSTYVVBLptUrZU2UhzsUW1YN6obL8YspEUbAzZsQLy7pO3pIkorR3+vR4Fe4bKBMTVGbAlV53dnYCwX1GRx11FNddd912xz388MPNZyAWFZRSigsvvJCPfvSj2338ta99zWiWo9+DikNnZ7T7gsJWlY5LnMUl6uJlmwcSxQixxZBImih11Gwc46hk9L6v5557jt/+9rcAXHfddSxfvny7zw855BB+85vfsH79egBGR0d56qmnWLRoEX/84x955plntn23GkceeSRXXHEFECRcDA4OMmvWLIaGhrYpqHe/+91cffXV2/a2nn/+eV588UUOP/xwbr75ZrZs2cLQ0BC333671rZnV0GZnKhR49e2yQPRHkJmY/gnb2HcuIRVzjaOcVQyOife8pa38L3vfY8lS5bwyiuvcPbZZ2/3+Zw5c7jmmms45ZRTWLJkCYcccghPPvkkHR0dXHXVVRx33HEsX76cvfbaq+r5v/71r3PXXXex3377ceCBB7J27Vpmz57NsmXL2Pe44/j0hRfyrne9i1NPPZVDDz2U/fbbjw984AMMDQ3R29vLySefzAEHHMD73/9+/vqv/1pr232ILw4lj2X33e2R55VXon0nykPIbLSuBwchYtkUq8KytmHjGEdlcBB22y388a2tqadRh6GlpYUrr9yu9CkbNmzY7vU73vEOHnjggR2+e/TRR/Pkk0/u8P7pp5/O6aefDsCuu+7KrbfeusMx1157LTzxBCxeDMC5557Lueeeu8Nxn/vc5/jc5z4XtjmRyK4HZdKSinox2yZPVGy0rqP2qevlmqJSKERTxjaOcVSizgmHMvnySjYVlGmPJerFbJs8UatO2LiBHjVsmoUFOAphK72XsHGMo5LBObFgwQIefzzdJxelSTYVlG0ei23yjI2FLxMEdlqaUZV+FhbgKMTpH9vGOCp5bLNJLHgES3YVlE0ei+vytLYGISObiKr087YYxQl3ua7A48wJ19tsEgv257KpoJJKknBZHteznaK2IQsLcBSizrmOjuB2CJeJ2mYbIwM2YbL6TUiyqaBsC6mZlse2tPckGAaft6UAABxISURBVB8PbrYMi/egss/oaLTQtfeg6uM9KEOMjgZFXU1hW5JE1Lvi43hQ7e3x7ry3Be9B5YOomYsZMVqOPfZYXn311brH/NM//RM///nPw5+0TEGVP6ojSbJ5HxSYvd/FNg8qKnE8qNLFvMsuZmQyTU8P/OUvaUuRHHHmnEgQ1gl7f5zr9PRAxf1ErqGUQinFqlWrGh57ySWXRDu596AcxbYkiajEkcemcMjUVPRFNI8hvqhjPGtWUGHEReJknNk0p+tw2WWXse+++7Lvvvvyta99jQ0bNvCWt7yFj33sY/T29rJx40YWLFjAS8Uitl/84hdZtGgRRx11FKeccsq2Bwiefvrp3HDDDUCQvn7RRRfR29vLfvvtt+1m3vvvv5/DDjuMt73tbRx21FGs++Mf02l0kewpqCSqBdiYlNDSEizcpuSxaYEfGoouvw/xNcamMY7KyEi0+77AWIivUIBNm/RkaT/44IN897vf5Xe/+x333Xcf3/rWt9i8eTPr1q3jwx/+MA899NB2JYzWrFnDjTfeyEMPPcRNN93EmjVrap57l1124fe//z1nn332NiW2aNEi7rnnHh566CEuOf98PlvnqbtJkL0Q38iI2f0nsDMpobs7WLh32imcPDXqctU9vy0LfJzwlcuLbxzi9FFpjOfPNyOTSeLOCc1zulCAI46A1avhsMPgrruai5jee++9nHDCCdsql5944on8+te/Zq+99uKQQw6pevzKlSuZUUwWee9731vz3CeeeCIABx544LaHGQ4MDHDaaafx9NNPI1NTTExOxhdeA9nzoJLYHG5vhygDF6WqdFyiKBDXPag4Y2yTgk2CvHlQceeE5vb29wfKaXIy+L+/v7nzqRpuWElhhT2+GtOLWbCtra1MFtezz3/+8xxxxBE8/vjj3P7d7zKW8q0H2VNQtiUklEgi7Bj2YoubJGHLAm+JtWw1zXhQLmLJnJg7N/Cc2tqC/+fObe58hx9+OLfccgujo6OMjIxw8803160Yvnz5cm6//XbGxsYYHh7mxz/+caTfGxgYYI899gDgmh/8IPXiytkL8eU1vTYJD8qWxStuAoCr3kEcXE+EiUqc9nZ1aU8KEQnCev39gXJqdn3v7e3l9NNP5+CDDwbgjDPOYOedd655/EEHHcTxxx/P/vvvz1577cXSpUvpidAv559/PqeddhqXXXYZ7+jtbU54DWRPQSWVMdfWBhMTQbivHlGrSsclyuISd/GyJU07jhHS3h4+iSQLuB7GjUqcOdHSYqTeXEsL7LqrvvOdd955nHfeedu9V1lAtvzxG5/61Ke4+OKLGR0d5fDDD+eTn/wkANdcc03V45cuXcrdd98NwKGHHspTTz0VfLB+PV8sJk+sWLGCFStWaGlPFLKpoJII8ZWSEl73uvrHRa0qHRcf4vOUE6dMTXc39PWZkcc0fk5s48wzz+SJJ55gbGyM0047jd64npAF90FlT0ElFeIrLdiNFFTS8oRh69ZoZYLALut6cDB6FqKnMT09wQPqXGRwML7bkrEHWV577bV6TmSBgvJJEnEJu2DbJk9csuBBlcKynurYNMZRiTsnZswIsmw91UlZcWdPQdnmsdgmT1xs2kCPu8+YodprdRkfb7w3Wg2bxjgqzcyJBm2OkrrtqU7cPsyegkrSYwlzMdsmT1y33abFPW5lDpcX4CjE7R+bxjgqzcyJOm3u6Ojg5Zdf9kqqCZRSvPzyy3TEuBc0e3tQSWXxRQnx2STP0FCQch2VmTODKvE2ELdPbdpHM0kz/eOqAjfU5nnz5tHX10d/s3fcuoZSQb0mTSG+jo4O5s2bF/l7oRSUiBwNfB1oBb6tlKpaoElEDgLuA05WSt0QWRod2BZSs1GeOJamTZvIcfvU5T2WKMTtH5eLxTYzJ+oYLe3t7SxcuLAJwRylvx/OOw/uuCNVMRqG+ESkFfgmcAywGDhFRBbXOO7LwE91CxkJ25ISbJTH9RuZh4bipe677CFEIe6ca20N0tNdZHAwXmQgL3MiKpak7YfZgzoYWK+UelYpNQ5cD6ysctz/Bm4EXtQoX3SSepy5bR5U2Lvim+0fG2LxSsWrwOnyHksU8lhNZXIyXmJIXuZEVCyZQ2Gu8j2AjWWv+4rvbUNE9gBOAK6sdyIROVNE1ojIGmMx3bgTNSq2JUm0tISzfpvxoDo7g2rxrpIXa9kS69cJ8jInomLJHAqjoKptPlSa0V8DPqOUqltLRil1lVJqqVJq6Zw5c8LKaCdhLS9LLJFtNCOPDRdzMx6cT5JozLRpwY3cecGGOW0jlmwFhEmS6APKHxAzD3ih4pilwPUSbKTvAhwrIpNKqVu0SGkjtnlQECQyNLorvhl5Sgv8Hns0PtYUW7fGf3RJnpIkdtst3ndLY+ySATkxEdyEHQcf4quOJYZ1GA/qAWAfEVkoItOADwK3lR+glFqolFqglFoA3AB8LBXl1MxEjYqNSQkdHY2t32bksWGB16Fgs04zfWTDGEelmX1V70FVx5UQn1JqEjiHIDvvD8APlVJrReQsETnLtICRiPMo8Lh0dgaFYBuRVNIGhFtcmr2Y017gm7HsXFx849BsGDftMY5Ks3PCtfYmgSUeVCh3Qym1ClhV8V7VhAil1OnNixWTpMNpYYhTVToupcWlXtHMvHtQacufBHnzoPyc0I8rHpRTWKL1UyOsB+VykkQzCtZF7yAOzfZR2mMclby1NwksSZLIloJKo1NtuC+oRJiLzfU9nGYU7MyZbqfJh8WH+MIzfXq+shbDYomxnz0FlaRbOnMmbNlS+/O4VaXjEmZxyXOIz6ZyTSYZHQ0eIxEHG8Y4KpaEozKFJX2aLQWVtNZvdDHbJg/ELxMEdljXllh21hNXGdswxlHxc0I/lvRpthRU0lq/0cVsmzwQhCTjJm3YYF3r6FObwrK2YcMYR6XZOdHaGjyGxvMaW7bEv99QI9lSULZ5LLbJ0yw2bCg3u8/Y2WnPY0NM0KzytWGMo9LsnHC5irspGt3wnxDZUlBpeCz1Lmbb5GkWG+4Zafa+Mhc9hCiMjARKOC42jHFUdMwJ19qcE7KnoJL0WMKE+GySZ2wsyFqKiw0XcrN96uIeSxR09I9rCjyPbTZJoRDvaQEGsEMKXdgWUrNRnmYszfb2oFp8mjTbp1n3oJrtHxcVuI454VqbTTIyEj+RSjPZUlC2JSXYKI8FmTlN0WyfZt1abrZ/Ojrq3zphI35O6MWSFHPImoKy0WOxTR7XM+DGxprLLsq6tdysEVKqiO8SOvbdvIJ6DUtSzCFrCmp4uLmJGhXbkiQaVTPX4UG1tQVV410l69ZyksWJbaHZjDMXw5om8R6UQZJMjWw0sS2yRAA98qR9MTc7vmnLbxodRkjYpzPbgo45kWWjJSoWbQVkT0ElSaPQgEWWCKBHnjQX+EKh+cUo6+EcHUZIV5c7NQt1hCOzHvaNikWGdXYUVBpxcxuTEkRqW7865ElzgR8aCm6qbIasW8s6jBCXlPjoaFATsxmyPieiYpFhnR0FtWVL8xM1KrYlSUD9u+J17E+k6UHp6M+sW8tZCONGwc8J/XgPygBpaP1p0+onDDRTVTou9axB1z0oXSHKLFvLefOg/JzQj/egDGCR1t+OpOtZ1bN+dVnXaSqoPHkHcdDVR64s2HlrbxL4JAkDWNSpqVLP+nU9SUJXOCfLi5EP8UWnq8sXiy3HImM/WwoqDbe0paV6qf60bnast7j4EF9Qrsnl+7gaMTjYfCKJS0pcx5xobXUrrd40PsRngLS0fq0N1rTqWdVbXHySRPaZnGz+Kc5586A822NRn2ZHQaWl9WvFr22TB5ovEwTue1BgxXNurCZvHhT4OVGODi9cE9lRUGlp/VrWpm3ylHD5rntd+4ytrelXZTeFjtCyS0kDuubE9OmBAecJro22trSlALKkoNJKkqhlbablQZm2ftO8Z0RXnbms3vcyMaFnYXGpf/ycyDTZUlBphdSqTey0FGYteXSUCSqd33UPyiUPIQq6vHaX+sfPCf1YFO7MjoJKM0mi2sS2TZ7hYT1JG52d6dVp09WnWbWWdSooV/rHzwm9jI83n2SjkewoKBs9KNvk0XEhp2ld6erTrFrLuvpn1ix3Fms/J/Ri2eNasqOgbPNYsixPWvd46coucslDiIIuI6StzZ0kEq+g9GJRijlkSUHZltadljy1isXqlicNJVUoBBl4zeJSGnUULLN+E2FiIqiJ2Sw+xBdg0U26kCUFpWuiRsW2NPNad8XrlKezMyiEmzS6woveg2qMRRvlddE5J7JotETFspJx2VFQaVEvzdyigdZqGbm+wGfZg9I159rbgw3zvOA9qAAf4ssYtiVJ1EKnwkxjgd+6VZ+HnFVrWeecc2HBnpzUE/KF7M6JqFi2bmVDQU1NBUVb08C2JAkIFvKtW83Jk4YH5br8SZC3Phoa8lEB3XgPygBpbg53dQX3GFWSZj2ratagbus6aWvTdfmTIG99lLf2JoGLHpSIHC0i60RkvYhcUOXzvxWRR4v/VovI/vpFrUOaWr+lpXpGm46q0nGpZg3qtq7TUFB58g7ioLuPbF+w89beJLBs77yhghKRVuCbwDHAYuAUEVlccdgfgbcrpZYAXwSu0i1oXSzr1NSpZg26niShU8Fm1VrOW4hP95ywvb1J4GCI72BgvVLqWaXUOHA9sLL8AKXUaqXU5uLL+4B5esVsgA1uaVo3r1aj2uLiepKEzjGuFZZ1nbyFvHS2t6MDtmzRcy6XsWEtLSOMgtoD2Fj2uq/4Xi3+F/CTah+IyJkiskZE1vT394eXshFpa/2Oju1L9euqKh2XaouLzn061z0oEbsMCl2MjAT3qOkgbx6UK/d9mSbttbSCMAqq2shVvbpF5AgCBfWZap8rpa5SSi1VSi2dM2dOeCkbkbbWr4xfp31Hf7V4uk6ZXPegILsLkq525c2DguzOiSikvZZWEEZB9QHzy17PA16oPEhElgDfBlYqpV7WI15I0tb6ldambfKAvjJBpfO7nCRRImtelM72uJA0oHtOiFSvwpInRkZg5sy0pdhGGAX1ALCPiCwUkWnAB4Hbyg8QkT2Bm4C/U0o9pV/MBqSdJFFpbaZthWTxoYW6vdIZM7K156Bb2bqQNKB7TmR1bzIqFnmSDTdKlFKTInIO8FOgFbhaKbVWRM4qfn4l8E/AbOByCRo3qZRaak7sCmwIqZVfzGkrzGoelOvWte4+LbXBImuxKUZH9e0/QT49qFKbLQpxJY5FyglCKCgApdQqYFXFe1eW/X0GcIZe0SKQtkKo9FjSDvFVyqOzTFDp/C4nScBrbdhtN33nTBPd/ZO3JAlww2s0iYUh72xUkkjb6qm0Nm2TR/eFPG1akKmYJLr71AUPIQq6+yePSRJZmxNR0ZkFqolsKKi0PRbbkyRMyJO0tWUinJMla1l3/8ycGSxYNmMqxJdX0o5EVSEbCiptj8X2JIm05dHBli3B/Wa6cMFDiILufVjL9iKqMjys1+LPe4gv7b38KmRDQQ0PBxk4aWGbB1V547AJeZJ+LLiI3kUza9ayCevXBSWl8ykGWZsTUfEelCGUSu9xG1DdY0lzoCsXFhMeVHd39UfLu0LWrGVTRpGFG+fGyNqciErahnUVsqGg0sa2JIlKTCjMJENkhYJ+az5r1rKJOdfZae8+lAnFmbU5ERXb1i2yoKBssPBsC/HB9nfFm5AnySSDkRH9IdysJUm4PsZRGRsLbrbWic3tTQIb1q0K3FdQY2N6N8/jYGNSQvld8aZCfElZm67LnwR566O8tTcJbFi3KnBfQdmg9adPh/Hx117bcD9BuTVoyrpOUkG5LH8S5K2P8tbeJEh777wK7isoCzsVSD8DqtwaNGEZJRkOMaFgs7YhnrcQn58T+rHB2K8gGwrKMrfUCsoXF9eTJEwp2CxZy3kLeZlqb54VlIVrqfsKyhat39ICU1N2JG3A9guw69a1CfnTKNdkEhM3WebNg2ptzffjNmxZS8twX0HZovVnzQruCxodtaNCtukQn+seVNaYmNBbEBjy50HlHQv71H0FZYvWL1mbtskDQZkgEym5LidJgD3erg7ydl+QnxP68QrKALYkSZSsTVsGudL61Z20kWS83lSNsNbWICzrqY7NezKm5sS0acHjafLI5CS0t6ctxXa4r6BsKXBY8lhsUZim9w+y4EHZvABHYXIyqI2omzx6UDbvu5km7czjKrivoGxRCCWPxZYQX0keUyEL15MkIDuL0dCQGSPN5v7xcyIXGDC7EsaWkFrJ2mxvt0seU5XeOztfq1RhGlNjbHMSQBTy2D95bLNJJibMeOFNYp9EUbHFYylZXu3tdsljqn9aWpJLyfXhnPqYDIHaulibnBO2ttkktmyVVOBDfLqwNUnCFnmaYXAwSOPXjc0LcBRMLS7t7ck+8ysKptqclX3JqHgFZQhbOta2NPPShWZSHpFk0nILhfwlAUTBpJFm4cY5ENS+nD5d/3mzMieiYouhX4H7Ib6tW81M1KiUrHFbQnylu+JNelAzZph57EFSZMVatsUoygJZmRNRsXQOue9B2ULJ8rItpGbSMnI9RJYVa9nknGtry1ZJqEZkZU5ExbZ1q4hXULqwLcRXwqQ8SVzM4+Pmbh70SRKNsdGjmJoKknRMkFcFZdu6VcRtBWVyokbFxqSE9nbo7zcnTxKLl8k9Rtc9wBKm+8g2BTU0ZCZpBuxsbxLYtG6VYcnqHpPhYXMTNSqlYrG2JG1AYBH19bntQZn0DrJiLeetj/LW3iSwNEnCbQVlU6e2tAQZbSaqSseluxs2bnTbus6bdxCHvPVR3tqbBDYZ1mW4r6As7FRr6OmB555z29r01nJj8tZHeWtvEthk7JfhtoKydGPPGnp6Ag/K5MWchAdlSv6uruTKNZnEdCKMbR6FyfbOmBE8niZvWLqWuq2gbPOgpk+3J2kDgr555RW3kwxMjnEpLOs6w8NBbUQT2JhIYnJO2HpjsmlsW0uLWLSaxsA2rd/TY588YG7iuR7ig2woqEIhX2nXfk7ox4f4DGBbp3Z322WFdHcHj583VaXY9SSJEnlckMJiY9KA6TmRVAkvmxgZMeeFN4HbCsq2zBMbPSiT8mTBgyqVa3IVpcyGpfLoQWVlbzIqFoY3QykoETlaRNaJyHoRuaDK5yIi3yh+/qiI9OoXtQq2eVB5VFAuJ0mAnUkAUTBdC9HG/vFzIjc0VFAi0gp8EzgGWAycIiKLKw47Btin+O9M4ArNclZFvTrAK5Pd1njjhVndDLfYI4/pkGOhq5vxlwYitbdQgE2bwkdQ1KsDvDRurk/VrG5eeiZ8G6LKb/z4zQNsnW6ufwpd3Wx90Vz/xPmObXMiKtbNoSnFxKSdUc0wmxMHA+uVUs8CiMj1wErgibJjVgLfV0op4D4R2UlEdlNK/Vm7xEUKBbjzR4NccGUPPcvgrrvSTaArFOA/vt/Dxj9OcP+D6csDGPWgCgU44ujpXPHgAF/vvZ8rrmjc3kIBzj4bHnsM9tuPht8pFODxVRs57kc9vMHAGBcK8D/39HDLDb+lfcmroeSJKr/p4y/6+4289ckerlhhpn+OPaWHf/nN81wZYoyjyh/nO4UCPHL7c5x4XQ97GpoTt97Vw6rr76NlyXCoNkQ9v21z6NyztnLyozP53ApL1q0ywiioPYCNZa/7gL8KccwewHYKSkTOJPCw2HPPPaPKuh39/XDLS8vpK7yetauD17vu2tQpm5bnmg0rUIUpHrdAHgD23BPOOMPIqfv7YfVq+BEn0fvIdxm7IsjHqMfYKPQ+AgcoaHmEht8ZG4VfDfXyF7ULfzHQp/39cNWL7+O4wm20PHJfKHmiym/6+HlPwg/VSaw21D+/uK+TX/COUGMcVf443xkbhd8MLuF5tSt9htr8rU3H897CLbQ88kCoNkTBxjm076NwNR8xMoeaRilV9x9wEvDtstd/B/x7xTE/BpaXvf4FcGC98x544IGqGQoFpQ4/XKm2tuD/QqGp0zWNbfKYJk57o37HdJ+alse246OShDyut9n0+W07XjfAGlVHT4hqEHgUkUOBi5VS7y6+vrCo2P6l7Jj/BO5WSl1XfL0OWKHqhPiWLl2q1qxZE0upligUAo0/d64dCSi2yWOaOO2N+h3TfWpaHtuOj0oS8rjeZtPnt+14nYjIg0qppTU/D6Gg2oCngCOB54EHgFOVUmvLjjkOOAc4liD89w2l1MH1zqtDQXk8Ho/HXRopqIZ7UEqpSRE5B/gp0ApcrZRaKyJnFT+/ElhFoJzWA6PAR3QI7/F4PJ78EqrEgFJqFYESKn/vyrK/FfBxvaJ5PB6PJ89YlFDo8Xg8Hs9reAXl8Xg8HivxCsrj8Xg8VuIVlMfj8XisxCsoj8fj8VhJw/ugjP2wSD/wJw2n2gV4ScN5XCFv7YX8tTlv7YX8tTlv7YXqbd5LKTWn1hdSU1C6EJE19W70yhp5ay/kr815ay/kr815ay/Ea7MP8Xk8Ho/HSryC8ng8Ho+VZEFBXZW2AAmTt/ZC/tqct/ZC/tqct/ZCjDY7vwfl8Xg8nmySBQ/K4/F4PBnEKyiPx+PxWImzCkpEjhaRdSKyXkQuSFueJBCRDSLymIg8LCKZe5iWiFwtIi+KyONl771ORH4mIk8X/985TRl1U6PNF4vI88VxflhEjk1TRp2IyHwRuUtE/iAia0Xk3OL7mRznOu3N8hh3iMj9IvJIsc1fKL4feYyd3IMSkVaChygeBfQRPETxFKXUE6kKZhgR2QAsVUpl8gY/ETkcGAa+r5Tat/je/wVeUUpdWjREdlZKfSZNOXVSo80XA8NKqa+mKZsJRGQ3YDel1O9FZBbwIPA+4HQyOM512vs3ZHeMBehUSg2LSDtwL3AucCIRx9hVD+pgYL1S6lml1DhwPbAyZZk8TaKUugd4peLtlcD3in9/j+Dizgw12pxZlFJ/Vkr9vvj3EPAHYA8yOs512ptZVMBw8WV78Z8ixhi7qqD2ADaWve4j44NeRAF3isiDInJm2sIkxK5KqT9DcLEDc1OWJynOEZFHiyHATIS7KhGRBcDbgN+Rg3GuaC9keIxFpFVEHgZeBH6mlIo1xq4qKKnynnuxyugsU0r1AscAHy+GhzzZ4wrgjcABwJ+Bf01XHP2ISBdwI/AJpdRg2vKYpkp7Mz3GSqkppdQBwDzgYBHZN855XFVQfcD8stfzgBdSkiUxlFIvFP9/EbiZINSZdTYV4/ileP6LKctjHKXUpuIFXgC+RcbGubgvcSPw/5RSNxXfzuw4V2tv1se4hFLqVeBu4GhijLGrCuoBYB8RWSgi04APArelLJNRRKSzuMmKiHQC7wIer/+tTHAbcFrx79OAW1OUJRFKF3GRE8jQOBc30L8D/EEpdVnZR5kc51rtzfgYzxGRnYp/zwDeCTxJjDF2MosPoJiW+TWgFbhaKfWllEUyioi8gcBrAmgDrs1am0XkOmAFQVn+TcBFwC3AD4E9geeAk5RSmUkqqNHmFQShHwVsAD5ait27jogsB34NPAYUim9/lmBfJnPjXKe9p5DdMV5CkATRSuAE/VApdYmIzCbiGDuroDwej8eTbVwN8Xk8Ho8n43gF5fF4PB4r8QrK4/F4PFbiFZTH4/F4rMQrKI/H4/FYiVdQHo/H47ESr6A8Ho/HYyX/HwY1fxiR63z/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a preduction vs original chart\n",
    "x_ax = range(len(y_test[:30]))\n",
    "plt.scatter(x_ax, y_test[:30], s=5, color=\"blue\", label=\"original\")\n",
    "plt.plot(x_ax, predictions[:30], lw=0.8, color=\"red\", label=\"predicted\")\n",
    "plt.title('First 30 predictions')\n",
    "plt.legend()\n",
    "# Save the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(\"images\", \"random_forest_plot.png\"))\n",
    "# Show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models\\\\model_random_forest.sav']"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the model\n",
    "filename = os.path.join(\"models\", \"model_random_forest.sav\")\n",
    "joblib.dump(best_rf, filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models\\\\grid_random_forest.sav']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the grid\n",
    "filename = os.path.join(\"models\", \"grid_random_forest.sav\")\n",
    "joblib.dump(grid, filename)"
   ]
  }
 ],
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