IS362Project4.ipynb

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   "source": [
    "<h1> IS362 Week 12 Assignment 8 </h1>\n",
    "\n",
    "Start with the mushroom data in the pandas DataFrame that you constructed in your “Assignment –\n",
    "Preprocessing Data with sci-kit learn.”\n",
    "• Use scikit-learn to determine which of the two predictor columns that you selected (odor and one \n",
    "other column of your choice) most accurately predicts whether or not a mushroom is poisonous. There is\n",
    "an additional challenge here—to use scikit-learn’s predictive classifiers, you’ll want to convert each of\n",
    "your two (numeric categorical) predictor columns into a set of columns. See for one approach pandas\n",
    "get_dummies() method.\n",
    "• Clearly state your conclusions along with any recommendations for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "decd6b62-d56a-4b43-a619-45572f250101",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'uci_id': 73, 'name': 'Mushroom', 'repository_url': 'https://archive.ics.uci.edu/dataset/73/mushroom', 'data_url': 'https://archive.ics.uci.edu/static/public/73/data.csv', 'abstract': 'From Audobon Society Field Guide; mushrooms described in terms of physical characteristics; classification: poisonous or edible', 'area': 'Biology', 'tasks': ['Classification'], 'characteristics': ['Multivariate'], 'num_instances': 8124, 'num_features': 22, 'feature_types': ['Categorical'], 'demographics': [], 'target_col': ['poisonous'], 'index_col': None, 'has_missing_values': 'yes', 'missing_values_symbol': 'NaN', 'year_of_dataset_creation': 1981, 'last_updated': 'Thu Aug 10 2023', 'dataset_doi': '10.24432/C5959T', 'creators': [], 'intro_paper': None, 'additional_info': {'summary': \"This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525).  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.\", 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': '     1. cap-shape:                bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s\\r\\n     2. cap-surface:              fibrous=f,grooves=g,scaly=y,smooth=s\\r\\n     3. cap-color:                brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y\\r\\n     4. bruises?:                 bruises=t,no=f\\r\\n     5. odor:                     almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s\\r\\n     6. gill-attachment:          attached=a,descending=d,free=f,notched=n\\r\\n     7. gill-spacing:             close=c,crowded=w,distant=d\\r\\n     8. gill-size:                broad=b,narrow=n\\r\\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\\r\\n    10. stalk-shape:              enlarging=e,tapering=t\\r\\n    11. stalk-root:               bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?\\r\\n    12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s\\r\\n    13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\\r\\n    14. stalk-color-above-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\\r\\n    15. stalk-color-below-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\\r\\n    16. veil-type:                partial=p,universal=u\\r\\n    17. veil-color:               brown=n,orange=o,white=w,yellow=y\\r\\n    18. ring-number:              none=n,one=o,two=t\\r\\n    19. ring-type:                cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z\\r\\n    20. spore-print-color:        black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y\\r\\n    21. population:               abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y\\r\\n    22. habitat:                  grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d', 'citation': None}}\n",
      "                        name     role         type demographic  \\\n",
      "0                  poisonous   Target  Categorical        None   \n",
      "1                  cap-shape  Feature  Categorical        None   \n",
      "2                cap-surface  Feature  Categorical        None   \n",
      "3                  cap-color  Feature       Binary        None   \n",
      "4                    bruises  Feature  Categorical        None   \n",
      "5                       odor  Feature  Categorical        None   \n",
      "6            gill-attachment  Feature  Categorical        None   \n",
      "7               gill-spacing  Feature  Categorical        None   \n",
      "8                  gill-size  Feature  Categorical        None   \n",
      "9                 gill-color  Feature  Categorical        None   \n",
      "10               stalk-shape  Feature  Categorical        None   \n",
      "11                stalk-root  Feature  Categorical        None   \n",
      "12  stalk-surface-above-ring  Feature  Categorical        None   \n",
      "13  stalk-surface-below-ring  Feature  Categorical        None   \n",
      "14    stalk-color-above-ring  Feature  Categorical        None   \n",
      "15    stalk-color-below-ring  Feature  Categorical        None   \n",
      "16                 veil-type  Feature       Binary        None   \n",
      "17                veil-color  Feature  Categorical        None   \n",
      "18               ring-number  Feature  Categorical        None   \n",
      "19                 ring-type  Feature  Categorical        None   \n",
      "20         spore-print-color  Feature  Categorical        None   \n",
      "21                population  Feature  Categorical        None   \n",
      "22                   habitat  Feature  Categorical        None   \n",
      "\n",
      "                                          description units missing_values  \n",
      "0                                                None  None             no  \n",
      "1   bell=b,conical=c,convex=x,flat=f, knobbed=k,su...  None             no  \n",
      "2                fibrous=f,grooves=g,scaly=y,smooth=s  None             no  \n",
      "3   brown=n,buff=b,cinnamon=c,gray=g,green=r, pink...  None             no  \n",
      "4                                      bruises=t,no=f  None             no  \n",
      "5   almond=a,anise=l,creosote=c,fishy=y,foul=f, mu...  None             no  \n",
      "6            attached=a,descending=d,free=f,notched=n  None             no  \n",
      "7                         close=c,crowded=w,distant=d  None             no  \n",
      "8                                    broad=b,narrow=n  None             no  \n",
      "9   black=k,brown=n,buff=b,chocolate=h,gray=g, gre...  None             no  \n",
      "10                             enlarging=e,tapering=t  None             no  \n",
      "11  bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,...  None            yes  \n",
      "12                 fibrous=f,scaly=y,silky=k,smooth=s  None             no  \n",
      "13                 fibrous=f,scaly=y,silky=k,smooth=s  None             no  \n",
      "14  brown=n,buff=b,cinnamon=c,gray=g,orange=o, pin...  None             no  \n",
      "15  brown=n,buff=b,cinnamon=c,gray=g,orange=o, pin...  None             no  \n",
      "16                              partial=p,universal=u  None             no  \n",
      "17                  brown=n,orange=o,white=w,yellow=y  None             no  \n",
      "18                                 none=n,one=o,two=t  None             no  \n",
      "19  cobwebby=c,evanescent=e,flaring=f,large=l, non...  None             no  \n",
      "20  black=k,brown=n,buff=b,chocolate=h,green=r, or...  None             no  \n",
      "21  abundant=a,clustered=c,numerous=n, scattered=s...  None             no  \n",
      "22  grasses=g,leaves=l,meadows=m,paths=p, urban=u,...  None             no  \n"
     ]
    }
   ],
   "source": [
    "#From the ucimlrepo https://archive.ics.uci.edu/dataset/73/mushroom download the dataset into Pandas.\n",
    "from ucimlrepo import fetch_ucirepo \n",
    "  \n",
    "# fetch dataset \n",
    "mushroom = fetch_ucirepo(id=73) \n",
    "  \n",
    "# data (as pandas dataframes) \n",
    "X = mushroom.data.features \n",
    "y = mushroom.data.targets \n",
    "  \n",
    "# metadata \n",
    "print(mushroom.metadata) \n",
    "  \n",
    "# variable information \n",
    "print(mushroom.variables) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670e3474-9897-4e98-9a42-d22bdf772e9f",
   "metadata": {},
   "source": [
    "<h2> Lets focus on Cap-color and habitat</h2>\n",
    "Using seaborn to plot the basic relationships for count and poisonous.\n",
    "To prepare data for Kmeans clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "e78e8f37-0116-4337-a602-f2027d3da859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\turin\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n",
      "C:\\Users\\turin\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n",
      "C:\\Users\\turin\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n",
      "C:\\Users\\turin\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ba6b1ca50>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 581.111x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rLfbZbDa3+y6Xq8W2K125prX1V3ueFStWyOFwWLeTJ09+k8MAAKBTGRHqtLQ0vfXWW9q5c6duuukma3toaKgktTjrra6uts6yQ0ND1dTUpNra2quuOX36dIvfe+bMmRZn65f5+voqICDA7QYAwI3m0VC7XC4tWrRIv//97/Xee+8pMjLSbX9kZKRCQ0NVUFBgbWtqatLu3bs1YcIESdLYsWPl4+PjtqayslIlJSXWmvj4eDkcDh08eNBac+DAATkcDmsNAAAm8uhV34888oi2bt2qN998U/7+/taZs91ul5+fn2w2m9LT07Vq1SpFRUUpKipKq1atUt++fTV79mxr7bx587R06VINGjRIgYGBWrZsmWJjY62rwEeOHKkZM2YoNTVVL7/8siRp/vz5SkpK4opvAIDRPBrqdevWSZISEhLctm/cuFFz586VJC1fvlyNjY1auHChamtrFRcXp3fffVf+/v7W+szMTHl7e2vWrFlqbGzU5MmTlZOTIy8vL2vNli1btHjxYuvq8OTkZGVnZ1/fAwQAoIM8GmqXy/W1a2w2mzIyMpSRkdHmmj59+igrK0tZWVltrgkMDNTmzZvbMyYAAB5jxMVkAACgdYQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMFi7Qn3zzTfr7NmzLbafO3dON998c4eHAgAAX2pXqD/99FNdvHixxXan06nPPvusw0MBAIAveV/L4rfeesv6+Z133pHdbrfuX7x4UTt27NCwYcM6bTgAAHq6awr1vffeK0my2WyaM2eO2z4fHx8NGzZML7zwQqcNBwBAT3dNob506ZIkKTIyUocOHVJQUNB1GQoAAHzpmkJ9WXl5eWfPAQAAWtGuUEvSjh07tGPHDlVXV1tn2pf99re/7fBgAACgnaF++umn9cwzz2jcuHEKCwuTzWbr7LkAAIDaGerf/OY3ysnJUUpKSmfPAwAAvqJdf4+6qalJEyZM6OxZAADAFdoV6p/+9KfaunVrZ88CAACu0K6Xvv/+97/rlVde0fbt2zV69Gj5+Pi47V+zZk2nDAcAQE/XrlAfPXpUY8aMkSSVlJS47ePCMgAAOk+7Qr1z587OngMAALSCr7kEAMBg7TqjnjRp0lVf4n7vvffaPRAAAPg/7Qr15fenL2tublZxcbFKSkpafFkHAABov3aFOjMzs9XtGRkZamho6NBAAADg/3Tqe9QPPvggn/MNAEAn6tRQ79+/X3369OnMpwQAoEdr10vfM2fOdLvvcrlUWVmpw4cP68knn+yUwQAAQDtDbbfb3e736tVL0dHReuaZZzRt2rROGQwAALQz1Bs3buzsOQAAQCvaFerLioqKVFpaKpvNplGjRukf/uEfOmsuAACgdoa6urpaDzzwgHbt2qUBAwbI5XLJ4XBo0qRJysvL0+DBgzt7TgAAeqR2XfWdlpamuro6HT9+XJ9//rlqa2tVUlKiuro6LV68uLNnBACgx2rXGXV+fr62b9+ukSNHWttGjRqlX//611xMBgBAJ2rXGfWlS5dafAe1JPn4+OjSpUsdHgoAAHypXaG+66679LOf/Ux/+9vfrG2fffaZHn30UU2ePLnThgMAoKdrV6izs7NVX1+vYcOG6ZZbbtGtt96qyMhI1dfXKysrq7NnBACgx2rXe9QRERE6cuSICgoK9NFHH8nlcmnUqFGaMmVKZ88HAECPdk1n1O+9955GjRqluro6SdLUqVOVlpamxYsX6/bbb9e3v/1t7dmz57oMCgBAT3RNoV67dq1SU1MVEBDQYp/dbteCBQu0Zs2aThsOAICe7ppC/eGHH2rGjBlt7p82bZqKioo6PBQAAPjSNYX69OnTrf61rMu8vb115syZb/x8f/rTn3T33XcrPDxcNptNb7zxhtv+uXPnymazud3Gjx/vtsbpdCotLU1BQUHq16+fkpOTderUKbc1tbW1SklJkd1ul91uV0pKis6dO/eN5wQAwFOuKdTf+ta3dOzYsTb3Hz16VGFhYd/4+c6fP6/bbrtN2dnZba6ZMWOGKisrrdvbb7/ttj89PV3btm1TXl6e9u7dq4aGBiUlJenixYvWmtmzZ6u4uFj5+fnKz89XcXGxUlJSvvGcAAB4yjVd9f3d735Xv/jFL5SYmKg+ffq47WtsbNRTTz2lpKSkb/x8iYmJSkxMvOoaX19fhYaGtrrP4XBow4YN2rRpk3XF+ebNmxUREaHt27dr+vTpKi0tVX5+vgoLCxUXFydJWr9+veLj41VWVqbo6OhWn9vpdMrpdFr3L19ABwDAjXRNZ9RPPPGEPv/8cw0fPlzPPfec3nzzTb311lv61a9+pejoaH3++edauXJlpw64a9cuBQcHa/jw4UpNTVV1dbW1r6ioSM3NzW4fWxoeHq6YmBjt27dPkrR//37Z7XYr0pI0fvx42e12a01rVq9ebb1UbrfbFRER0anHBQDAN3FNZ9QhISHat2+fHn74Ya1YsUIul0uSZLPZNH36dL300ksKCQnptOESExP1/e9/X0OHDlV5ebmefPJJ3XXXXSoqKpKvr6+qqqrUu3dvDRw4sMWcVVVVkqSqqioFBwe3eO7g4GBrTWtWrFihJUuWWPfr6uqINQDghrvmDzwZOnSo3n77bdXW1urjjz+Wy+VSVFRUi1h2hvvvv9/6OSYmRuPGjdPQoUP1hz/8QTNnzmzzcS6XSzabzbr/1Z/bWnMlX19f+fr6tnNyAAA6R7s+mUySBg4cqNtvv70zZ/laYWFhGjp0qE6cOCFJCg0NVVNTk2pra93+oFBdXa0JEyZYa06fPt3iuc6cOdOpZ/8AAFwP7fqsb085e/asTp48aV1ZPnbsWPn4+KigoMBaU1lZqZKSEivU8fHxcjgcOnjwoLXmwIEDcjgc1hoAAEzV7jPqztDQ0KCPP/7Yul9eXq7i4mIFBgYqMDBQGRkZuu+++xQWFqZPP/1Ujz/+uIKCgvS9731P0pefhjZv3jwtXbpUgwYNUmBgoJYtW6bY2FjrKvCRI0dqxowZSk1N1csvvyxJmj9/vpKSktq84hsAAFN4NNSHDx/WpEmTrPuXL96aM2eO1q1bp2PHjunVV1/VuXPnFBYWpkmTJun111+Xv7+/9ZjMzEx5e3tr1qxZamxs1OTJk5WTkyMvLy9rzZYtW7R48WLr6vDk5OSr/t1tAABM4dFQJyQkWFeOt+add9752ufo06ePsrKyrvr1moGBgdq8eXO7ZgQAwJO61HvUAAD0NIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYN6eHgAAOqK0tNTTI3SaoKAgDRkyxNNjwDCEGkCX1Og4K8mmBx980NOjdBo/v7766KNSYg03hBpAl9T8Rb0kl8bM/rkGR47w9DgdVlf5qQ789mnV1NQQargh1AC6tP7BQxQ4JNrTYwDXDReTAQBgMEINAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDBCDUAAAYj1AAAGMyjof7Tn/6ku+++W+Hh4bLZbHrjjTfc9rtcLmVkZCg8PFx+fn5KSEjQ8ePH3dY4nU6lpaUpKChI/fr1U3Jysk6dOuW2pra2VikpKbLb7bLb7UpJSdG5c+eu89EBANBxHg31+fPnddtttyk7O7vV/c8995zWrFmj7OxsHTp0SKGhoZo6darq6+utNenp6dq2bZvy8vK0d+9eNTQ0KCkpSRcvXrTWzJ49W8XFxcrPz1d+fr6Ki4uVkpJy3Y8PAICO8ujXXCYmJioxMbHVfS6XS2vXrtXKlSs1c+ZMSVJubq5CQkK0detWLViwQA6HQxs2bNCmTZs0ZcoUSdLmzZsVERGh7du3a/r06SotLVV+fr4KCwsVFxcnSVq/fr3i4+NVVlam6Gi+Hg8AYC5j36MuLy9XVVWVpk2bZm3z9fXVxIkTtW/fPklSUVGRmpub3daEh4crJibGWrN//37Z7XYr0pI0fvx42e12a01rnE6n6urq3G4AANxoxoa6qqpKkhQSEuK2PSQkxNpXVVWl3r17a+DAgVddExwc3OL5g4ODrTWtWb16tfWett1uV0RERIeOBwCA9jA21JfZbDa3+y6Xq8W2K125prX1X/c8K1askMPhsG4nT568xskBAOg4Y0MdGhoqSS3Oequrq62z7NDQUDU1Nam2tvaqa06fPt3i+c+cOdPibP2rfH19FRAQ4HYDAOBGMzbUkZGRCg0NVUFBgbWtqalJu3fv1oQJEyRJY8eOlY+Pj9uayspKlZSUWGvi4+PlcDh08OBBa82BAwfkcDisNQAAmMqjV303NDTo448/tu6Xl5eruLhYgYGBGjJkiNLT07Vq1SpFRUUpKipKq1atUt++fTV79mxJkt1u17x587R06VINGjRIgYGBWrZsmWJjY62rwEeOHKkZM2YoNTVVL7/8siRp/vz5SkpK4opvAIDxPBrqw4cPa9KkSdb9JUuWSJLmzJmjnJwcLV++XI2NjVq4cKFqa2sVFxend999V/7+/tZjMjMz5e3trVmzZqmxsVGTJ09WTk6OvLy8rDVbtmzR4sWLravDk5OT2/y72wAAmMSjoU5ISJDL5Wpzv81mU0ZGhjIyMtpc06dPH2VlZSkrK6vNNYGBgdq8eXNHRgUAwCOMfY8aAAAQagAAjEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIN59NuzAADdW0VFhWpqajw9RqcKCgrSkCFDbtjvI9QAgOuioqJCI0aMVGPjF54epVP5+fXVRx+V3rBYE2oAwHVRU1OjxsYvFPeTpxQQNszT43SKuspPdeC3T6umpoZQAwC6h4CwYQocEu3pMbosLiYDAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBgRoc6IyNDNpvN7RYaGmrtd7lcysjIUHh4uPz8/JSQkKDjx4+7PYfT6VRaWpqCgoLUr18/JScn69SpUzf6UAAAaBejQy1J3/72t1VZWWndjh07Zu177rnntGbNGmVnZ+vQoUMKDQ3V1KlTVV9fb61JT0/Xtm3blJeXp71796qhoUFJSUm6ePGiJw4HAIBr4u3pAb6Ot7e321n0ZS6XS2vXrtXKlSs1c+ZMSVJubq5CQkK0detWLViwQA6HQxs2bNCmTZs0ZcoUSdLmzZsVERGh7du3a/r06W3+XqfTKafTad2vq6vr5CMDAODrGX9GfeLECYWHhysyMlIPPPCAPvnkE0lSeXm5qqqqNG3aNGutr6+vJk6cqH379kmSioqK1Nzc7LYmPDxcMTEx1pq2rF69Wna73bpFRERch6MDAODqjA51XFycXn31Vb3zzjtav369qqqqNGHCBJ09e1ZVVVWSpJCQELfHhISEWPuqqqrUu3dvDRw4sM01bVmxYoUcDod1O3nyZCceGQAA34zRL30nJiZaP8fGxio+Pl633HKLcnNzNX78eEmSzWZze4zL5Wqx7UrfZI2vr698fX3bOTkAAJ3D6DPqK/Xr10+xsbE6ceKE9b71lWfG1dXV1ll2aGiompqaVFtb2+YaAABM1qVC7XQ6VVpaqrCwMEVGRio0NFQFBQXW/qamJu3evVsTJkyQJI0dO1Y+Pj5uayorK1VSUmKtAQDAZEa/9L1s2TLdfffdGjJkiKqrq/Uv//Ivqqur05w5c2Sz2ZSenq5Vq1YpKipKUVFRWrVqlfr27avZs2dLkux2u+bNm6elS5dq0KBBCgwM1LJlyxQbG2tdBQ4AgMmMDvWpU6f0gx/8QDU1NRo8eLDGjx+vwsJCDR06VJK0fPlyNTY2auHChaqtrVVcXJzeffdd+fv7W8+RmZkpb29vzZo1S42NjZo8ebJycnLk5eXlqcMCAOAbMzrUeXl5V91vs9mUkZGhjIyMNtf06dNHWVlZysrK6uTpAAC4/rrUe9QAAPQ0hBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwmNEfeNJdVVRUqKamxtNjdJrS0lJPjwAA3RahvsEqKio0YsRINTZ+4elROl2zs8nTIwBAt0Oob7Camho1Nn6huJ88pYCwYZ4ep1NUHtuvkrde0YULFzw9CgB0O4TaQwLChilwSLSnx+gUdZWfenoEAOi2uJgMAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAzGX88C2tCdPnGtOx0L0NMQauAKjY6zkmx68MEHPT1Kp+PT44Cuh1ADV2j+ol6SS2Nm/1yDI0d4epxOwafHAV0XoQba0D94CJ8eB8DjuJgMAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACD8clkAGCQ7vQFKt3pWDyJUAOAAfgyGLSFUAOAAfgyGLSFUAOAQfgyGFyJi8kAADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGA9KtQvvfSSIiMj1adPH40dO1Z79uzx9EgAAFxVjwn166+/rvT0dK1cuVIffPCB/t//+39KTExURUWFp0cDAKBNPSbUa9as0bx58/TTn/5UI0eO1Nq1axUREaF169Z5ejQAANrk7ekBboSmpiYVFRXpn//5n922T5s2Tfv27Wv1MU6nU06n07rvcDgkSXV1dR2apaGhQZL0+V/LdMHZ2KHnMkVd5V8lSY7PTsjH2+bhaTquux2PxDF1Bd3teKRuekxVX74K29DQ0OEeSJK/v79stq/5Z+PqAT777DOXJNf777/vtv3ZZ591DR8+vNXHPPXUUy5J3Lhx48aN23W7ORyOr21YjzijvuzKP7W4XK42/ySzYsUKLVmyxLp/6dIlff755xo0aNDX/+nHEHV1dYqIiNDJkycVEBDg6XE6rLsdj9T9jqm7HY/EMXUFXfl4/P39v3ZNjwh1UFCQvLy8VFVV5ba9urpaISEhrT7G19dXvr6+btsGDBhwvUa8rgICArrcf7xX092OR+p+x9TdjkfimLqC7nY8l/WIi8l69+6tsWPHqqCgwG17QUGBJkyY4KGpAAD4ej3ijFqSlixZopSUFI0bN07x8fF65ZVXVFFRoYceesjTowEA0KYeE+r7779fZ8+e1TPPPKPKykrFxMTo7bff1tChQz092nXj6+urp556qsVL+F1VdzseqfsdU3c7Holj6gq62/FcyeZyuVyeHgIAALSuR7xHDQBAV0WoAQAwGKEGAMBghBrAdZOQkKD09HRPj4EeoDv/t0aoAQAwGKEGAMBghLqbGTZsmNauXeu2bcyYMcrIyPDIPJ2hvr5eP/zhD9WvXz+FhYUpMzOzS77MlZCQoLS0NKWnp2vgwIEKCQnRK6+8ovPnz+vHP/6x/P39dcstt+iPf/yjp0dtl/Pnz+tHP/qR+vfvr7CwML3wwgueHqlDEhIStGjRIi1atEgDBgzQoEGD9MQTT6gr/o3W//7v/9aAAQN06dIlSVJxcbFsNpsee+wxa82CBQv0gx/8wFMj4ioINYy3ZMkSvf/++3rrrbdUUFCgPXv26MiRI54eq11yc3MVFBSkgwcPKi0tTQ8//LC+//3va8KECTpy5IimT5+ulJQUffHFF54e9Zo99thj2rlzp7Zt26Z3331Xu3btUlFRkafH6pDc3Fx5e3vrwIEDevHFF5WZman/+I//8PRY1+zOO+9UfX29PvjgA0nS7t27FRQUpN27d1trdu3apYkTJ3pqRFwFoYbR6uvrlZubq+eff16TJ09WTEyMNm7cqIsXL3p6tHa57bbb9MQTTygqKkorVqyQn5+fgoKClJqaqqioKP3iF7/Q2bNndfToUU+Pek0aGhq0YcMGPf/885o6dapiY2OVm5vbZf89XRYREaHMzExFR0frhz/8odLS0pSZmenpsa6Z3W7XmDFjtGvXLklfRvnRRx/Vhx9+qPr6elVVVenPf/6zEhISPDonWkeoYbRPPvlEzc3NuuOOO6xtdrtd0dHRHpyq/UaPHm397OXlpUGDBik2Ntbadvnb3Kqrq2/4bB3xl7/8RU1NTYqPj7e2BQYGdtl/T5eNHz/e7Wtt4+PjdeLEiS75B5CEhATt2rVLLpdLe/bs0T333KOYmBjt3btXO3fuVEhIiEaMGOHpMdGKHvNZ3z1Fr169WryH1tzc7KFpOu7ysbT2XeJdkY+Pj9t9m83mtu3ycV5+L7Gr6Kr/PnqShIQEbdiwQR9++KF69eqlUaNGaeLEidq9e7dqa2t52dtgnFF3M4MHD1ZlZaV1v66uTuXl5R6cqGNuueUW+fj46ODBg9a2uro6nThxwoNT4Uq33nqrfHx8VFhYaG2rra3Vn//8Zw9O1XFfPZ7L96OiouTl5eWhidrv8vvUa9eu1cSJE2Wz2TRx4kTt2rWL96cNR6i7mbvuukubNm3Snj17VFJSojlz5nTJ/6lc5u/vrzlz5lgXKh0/flw/+clP1KtXrxZn2fCc/v37a968eXrssce0Y8cOlZSUaO7cuerVq2v/L+bkyZNasmSJysrK9NprrykrK0s/+9nPPD1Wu1x+n3rz5s3We9F33nmnjhw5wvvThuOl725mxYoV+uSTT5SUlCS73a5f/vKXXfqMWpLWrFmjhx56SElJSQoICNDy5ct18uRJ9enTx9Oj4Sv+7d/+TQ0NDUpOTpa/v7+WLl0qh8Ph6bE65Ec/+pEaGxt1xx13yMvLS2lpaZo/f76nx2q3SZMm6ciRI1aUBw4cqFGjRulvf/ubRo4c6dnh0Ca+5hJdzvnz5/Wtb31LL7zwgubNm+fpcdBNJSQkaMyYMS0+lwC40TijhvE++OADffTRR7rjjjvkcDj0zDPPSJLuueceD08GANcfoUaX8Pzzz6usrEy9e/fW2LFjtWfPHgUFBXl6LAC47njpGwAAg3XtSzIBAOjmCDUAAAYj1AAAGIxQAwBgMEINAIDBCDXQAyUkJCg9Pb3dj8/JydGAAQOuuiYjI0Njxoxp9+8A8CVCDeC6WLZsmXbs2GHdnzt3ru69995rfh6Cj56ODzwBcF30799f/fv39/QYQJfHGTXQQ126dEnLly9XYGCgQkNDlZGRYe1bs2aNYmNj1a9fP0VERGjhwoVqaGho8RxvvPGGhg8frj59+mjq1Kk6efKkte+rZ8IZGRnKzc3Vm2++KZvNJpvNpl27dkmSfv7zn2v48OHq27evbr75Zj355JPWd6jn5OTo6aef1ocffmg9Licn53r9IwGMxBk10EPl5uZqyZIlOnDggPbv36+5c+fqn/7pnzR16lT16tVLL774ooYNG6by8nItXLhQy5cv10svvWQ9/osvvtCzzz6r3Nxc9e7dWwsXLtQDDzyg999/v8XvWrZsmUpLS1VXV6eNGzdKkgIDAyV9+VWmOTk5Cg8P17Fjx5Samip/f38tX75c999/v0pKSpSfn6/t27dL+vLrGoGehFADPdTo0aP11FNPSZKioqKUnZ2tHTt2aOrUqW4XmkVGRuqXv/ylHn74YbdQNzc3Kzs7W3FxcZK+DP/IkSN18OBB3XHHHW6/q3///vLz85PT6VRoaKjbvieeeML6ediwYVq6dKlef/11LV++XH5+furfv7+8vb1bPA7oKQg10EONHj3a7X5YWJiqq6slSTt37tSqVav0v//7v6qrq9OFCxf097//XefPn1e/fv0kSd7e3ho3bpz1+BEjRmjAgAEqLS1tEeqr+a//+i+tXbtWH3/8sRoaGnThwgUFBAR0whEC3QPvUQM9lI+Pj9t9m82mS5cu6a9//au++93vKiYmRr/73e9UVFSkX//615JkvXf81cdcqbVtbSksLNQDDzygxMRE/c///I8++OADrVy5Uk1NTe04IqB74owagJvDhw/rwoULeuGFF9Sr15d/lv/P//zPFusuXLigw4cPW2fPZWVlOnfunEaMGNHq8/bu3VsXL1502/b+++9r6NChWrlypbXtr3/969c+DuhJOKMG4OaWW27RhQsXlJWVpU8++USbNm3Sb37zmxbrfHx8lJaWpgMHDujIkSP68Y9/rPHjx7f5svewYcN09OhRlZWVqaamRs3Nzbr11ltVUVGhvLw8/eUvf9GLL76obdu2tXhceXm5iouLVVNTI6fTeV2OGzAVoQbgZsyYMVqzZo1+9atfKSYmRlu2bNHq1atbrOvbt69+/vOfa/bs2YqPj5efn5/y8vLafN7U1FRFR0dr3LhxGjx4sN5//33dc889evTRR7Vo0SKNGTNG+/bt05NPPun2uPvuu08zZszQpEmTNHjwYL322mudfsyAyWwul8vl6SEAAEDrOKMGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADPb/ATdyLrJgrFlwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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766uvvtKyZct0/fXXKzg4WF988YXS09PVu3dvXX311ZKkbt26aeTIkUpOTtbLL78s6dRb6xMTE3knGQAAMDcMbd++XYMHDza2T6/RGT9+vDIyMoyHJPbq1cvluHXr1ik+Pl7e3t5as2aN/vd//1dlZWWKjIzUr3/9az3xxBPy8PAw+i9btkxTp07V8OHDJZ166OL8+fObeHQAAKA5MDUMxcfHy+l01rv/5/ZJUmRkZJ2nT59JYGCgli5det71AQCAls+t1wwBAAA0NcIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwNMIQAACwtAaFoS5duujo0aN12ktKStSlS5cLLgoAAOBiaVAY+vrrr1VTU1OnvbKyUt9+++0FFwUAAHCxeJ5P57ffftv4/oMPPpDdbje2a2pqtGbNGnXu3LnRigMAAGhq5xWGbrrpJkmSzWbT+PHjXfZ5eXmpc+fOmjNnTqMVBwAA0NTOKwzV1tZKkqKiorRt2zYFBwc3SVEAAAAXy3mFodMOHDjQ2HUAAACYokFhSJLWrFmjNWvWqKioyJgxOu2vf/3rBRcGAABwMTQoDD355JOaNWuW+vXrp/DwcNlstsauCwAA4KJoUBj605/+pMWLFyspKamx6wEAALioGvScoaqqKg0cOLCxawEAALjoGhSG7rnnHr322muNXQsAAMBF16AwdOLECc2dO1eDBg1SSkqK0tLSXF7n6qOPPtINN9ygiIgI2Ww2vfXWWy77nU6nMjIyFBERIV9fX8XHx+vzzz936VNZWamUlBQFBwfLz89Po0aN0qFDh1z6FBcXKykpSXa7XXa7XUlJSSopKWnI0AEAQAvToDVDn332mXr16iVJys/Pd9l3Poupy8vL1bNnT911110aPXp0nf3PPvus5s6dq8WLF6tr1676/e9/r2HDhmnPnj3y9/eXJKWmpuqdd97R8uXLFRQUpPT0dCUmJio3N1ceHh6SpHHjxunQoUNauXKlJGnSpElKSkrSO++805DhAzDRvv+clL51mF1Go/Dz8VRUsJ/ZZcDiFi9erNTUVEtPEjQoDK1bt65RLp6QkKCEhIQz7nM6nZo3b55mzJihW265RZKUnZ2t0NBQvfbaa7r33nvlcDi0cOFCLVmyREOHDpUkLV26VJGRkVq9erVGjBih3bt3a+XKldq8ebP69+8vSXrllVcUFxenPXv2KCYmplHGAqBptfZ0SpJSPyyWPvzY5Goaz7pp8QQimGrs2LG6/vrrzS7DVA1+zlBTO3DggAoLCzV8+HCjzcfHR4MGDdLGjRt17733Kjc3V9XV1S59IiIiFBsbq40bN2rEiBHatGmT7Ha7EYQkacCAAbLb7dq4cWO9YaiyslKVlZXGdmlpaROMEsC5Cvet1VzPF3XimhlSQKTZ5Vywb0sq9OK6fSqvPGl2KbA4X19f+fr6ml2GqRoUhgYPHvyzt8PWrl3b4IJOKywslCSFhoa6tIeGhuqbb74x+nh7e6t9+/Z1+pw+vrCwUCEhIXXOHxISYvQ5k9mzZ+vJJ5+8oDEAaFzhtv9I7T2lIGZSgNPi4+MVGxsr6dTdEQ8PD91333166qmnZLPZVFxcrAceeEDvvPOOKisrNWjQIL3wwguKjo6WVPc22c6dO5Wamqrt27fLZrMpOjpaL7/8svr16ydJ+uc//6nHH39c+/btU3h4uFJSUpSenm7U07lzZ02aNEn79u3T3//+d7Vv314zZ87UpEmTjD67du3SAw88oE2bNqlNmzYaPXq05s6dq7Zt2xpj6tWrl+bNm2ccc9NNNykgIECLFy+WJC1YsECZmZkqKCiQ3W7XNddco3/84x8N+hk2aAF1r1691LNnT+PVvXt3VVVVKS8vTz169GhQIfX5aehyOp1nXZf00z5n6n+280yfPl0Oh8N4FRQUnGflAABcHNnZ2fL09NSWLVv0wgsvKDMzU3/5y18kSRMmTND27dv19ttva9OmTXI6nbr++utVXV19xnPdcccd+sUvfqFt27YpNzdXjzzyiLy8vCRJubm5GjNmjG677Tbt2rVLGRkZeuyxx4yActqcOXPUr18/ffrpp5o8ebLuu+8+ffnll5Kk48ePa+TIkWrfvr22bdumv//971q9erWmTJlyzuPdvn27pk6dqlmzZmnPnj1auXKlrr322gb85E5p0MxQZmbmGdszMjJUVlbW4GJ+LCwsTNKpmZ3w8HCjvaioyJgtCgsLU1VVlYqLi11mh4qKioznIIWFhen777+vc/4jR47UmXX6MR8fH/n4+DTKWAAAaEqRkZHKzMyUzWZTTEyMdu3apczMTMXHx+vtt9/WJ598Yvy7uGzZMkVGRuqtt97SrbfeWudcBw8e1EMPPaTLLrtMkowZJEmaO3euhgwZoscee0yS1LVrV33xxRd67rnnNGHCBKPf9ddfr8mTJ0uSfve73ykzM1M5OTm67LLLtGzZMlVUVOjVV1+Vn9+pWd758+frhhtu0DPPPPOz/zb/uEY/Pz8lJibK399fnTp1Uu/evRv2w1MDZ4bq89vf/rbRPpcsKipKYWFhWrVqldFWVVWl9evXG3+gffv2lZeXl0ufw4cPKz8/3+gTFxcnh8OhrVu3Gn22bNkih8PBgyMBAC3CgAEDXO52xMXFae/evfriiy/k6enpsm42KChIMTEx2r179xnPlZaWpnvuuUdDhw7VH//4R3311VfGvt27d+vqq6926X/11Vdr7969qqmpMdquuOIK43ubzaawsDAVFRUZ5+jZs6cRhE6fo7a2Vnv27Dmn8Q4bNkydOnVSly5dlJSUpGXLlun48ePndOyZNGoY2rRpk1q3bn3O/cvKyrRjxw7t2LFD0qlF0zt27NDBgwdls9mUmpqqp59+WitWrFB+fr4mTJigNm3aaNy4cZIku92uiRMnKj09XWvWrNGnn36q3/72t+rRo4fx7rJu3bpp5MiRSk5O1ubNm7V582YlJycrMTGRd5IBACzp55aKZGRk6PPPP9evf/1rrV27Vt27d9eKFSvqPc7pdNY5x+nbaqfZbDbjQ91/7tqn21u1alXnvD++refv76+8vDy9/vrrCg8P1+OPP66ePXs2+PEADbpNdvqt7qc5nU4dPnxY27dvN6bOzsX27ds1ePBgY/v0AxvHjx+vxYsX6+GHH1ZFRYUmT56s4uJi9e/fXx9++KHxjCHp1C07T09PjRkzRhUVFRoyZIgWL15sPGNIOjUlOHXqVONdZ6NGjdL8+fMbMnQAANzO5s2b62xHR0ere/fuOnnypLZs2WLcDTl69Kj+/e9/q1u3bvWer2vXruratasefPBB3X777Vq0aJFuvvlmde/eXR9/7Ppoi40bN6pr164u/+7+nO7duys7O1vl5eXG7NAnn3yiVq1aqWvXrpKkDh066PDhw8YxNTU1ys/Pd8kMnp6eGjp0qIYOHaonnnhCAQEBWrt2bZ2Mci4aFIbsdrvLdqtWrRQTE6NZs2a5vM39bOLj48+YKE+z2WzKyMhQRkZGvX1at26trKwsZWVl1dsnMDBQS5cuPee6AABoTgoKCpSWlqZ7771XeXl5ysrK0pw5cxQdHa0bb7xRycnJevnll+Xv769HHnlEHTt21I033ljnPBUVFXrooYf0m9/8RlFRUTp06JC2bdtmPBg5PT1dV155pZ566imNHTtWmzZt0vz587VgwYJzrvWOO+7QE088ofHjxysjI0NHjhxRSkqKkpKSjPVC1113ndLS0vTee+/pkksuUWZmpsusz7vvvqv9+/fr2muvVfv27fX++++rtra2wXd8GhSGFi1a1KCLAQCAxnfnnXeqoqJCV111lTw8PJSSkmK8lX3RokV64IEHlJiYqKqqKl177bV6//3369zKkiQPDw8dPXpUd955p77//nsFBwfrlltuMR4106dPH/3tb3/T448/rqeeekrh4eGaNWuWy+Lps2nTpo0++OADPfDAA7ryyitd3lp/2t13362dO3fqzjvvlKenpx588EGXWaGAgAC9+eabysjI0IkTJxQdHa3XX39dl19+eYN+fjbnz03NnEVubq52794tm82m7t27X9BKbndXWloqu90uh8Ohdu3amV2O+/huh/TnQVLiPCnoUrOruXBH90nvpkqT1ksRvcyu5oLl79iqxOVH9HTvUkX9Iszsci6M41tp03zphv9tEX/XDvxQrkdX7NK7Kb9SbEf72Q8A6nGmZ/Lg/DRoZqioqEi33XabcnJyFBAQIKfTKYfDocGDB2v58uXq0KFDY9cJAADQJBr0brKUlBSVlpbq888/13/+8x8VFxcrPz9fpaWlmjp1amPXCAAA0GQaNDO0cuVKrV692mUlevfu3fXiiy+e1wJqAABwYXJycswuodlr0MxQbW3tGRdeeXl5Gc8RAAAAaA4aFIauu+46PfDAA/ruu++Mtm+//VYPPvighgwZ0mjFAQAANLUGhaH58+fr2LFj6ty5sy655BJdeumlioqK0rFjx372eT8AAADupkFrhiIjI5WXl6dVq1bpyy+/lNPpVPfu3Y2PwAAAAGguzmtm6PRnlJSWlko69UFpKSkpmjp1qq688kpdfvnl2rBhQ5MUCgAA0BTOa2Zo3rx5Sk5OPuNDB+12u+69917NnTtX11xzTaMVCAAALty3JRUqLq+6aNdr7+etjgG+F+16F+K8wtDOnTv1zDPP1Lt/+PDhev755y+4KAAA0Hi+LanQkDk5OlF98d7x3dqrldakxzeLQHReYej7778/41vqjZN5eurIkSMXXBQAAGg8xeVVOlFdq/sHX3pRwsm3JRV6cd0+FZdXtbww1LFjR+3atUuXXnrmzwX67LPPFB4e3iiFAQCAxtUxwFdRwX5ml+F2zmsB9fXXX6/HH39cJ06cqLOvoqJCTzzxhBITExutOAAAYA3x8fGaMmWKpkyZooCAAAUFBWnmzJm6gM+TP2fnNTM0c+ZMvfnmm+rataumTJmimJgY2Ww27d69Wy+++KJqamo0Y8aMpqoVAAC0YNnZ2Zo4caK2bNmi7du3a9KkSerUqZOSk5Ob9LrnFYZCQ0O1ceNG3XfffZo+fbqR1mw2m0aMGKEFCxYoNDS0SQoFAAAtW2RkpDIzM2Wz2RQTE6Ndu3YpMzPTvcKQJHXq1Envv/++iouLtW/fPjmdTkVHR6t9+/ZNUR8AALCIAQMGyGazGdtxcXGaM2eOampq5OHh0WTXbdATqCWpffv2uvLKKxuzFgAAgIuuQZ9NBgAA0Ng2b95cZzs6OrpJZ4WkC5gZAgAAzcu3JRVufZ2CggKlpaXp3nvvVV5enrKysjRnzpxGrq4uwhAAAC1cez9vtfZqpRfX7bto12zt1Urt/bzP65g777xTFRUVuuqqq+Th4aGUlBRNmjSpiSr8L8IQAAAtXMcAX61Jj3f7zybz8vLSvHnz9NJLLzVRVWdGGAIAwAI6Bvg2i4/GMAMLqAEAgKUxMwQAAEyXk5Nj2rWZGQIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJbGu8kAALCCkgLp+NGLd702QVJA5MW73gUgDAEA0NKVFEgvXilVX5zPJpMkeflK929rFoGIMAQAQEt3/OipIHRNumS/COHEUSBtmHPquoQhAADgNuyRUtClZlfhdlhADQAATOd0OvXss8+qS5cu8vX1Vc+ePfWPf/zjolybmSEAAGC6mTNn6s0339RLL72k6OhoffTRR/rtb3+rDh06aNCgQU16bcIQAAAwVXl5uebOnau1a9cqLi5OktSlSxd9/PHHevnllwlDAACgZfviiy904sQJDRs2zKW9qqpKvXv3bvLrE4YAAICpamtrJUnvvfeeOnbs6LLPx8enya9PGAIAAKbq3r27fHx8dPDgwSa/JXYmhCEAAKzCUeCW1/H399e0adP04IMPqra2Vr/61a9UWlqqjRs3qm3btho/fnwTFXoKYQgAgJauTdCpJ0JvmHPxrunle+q65+ipp55SSEiIZs+erf379ysgIEB9+vTRo48+2oRFnuL2Yahz58765ptv6rRPnjxZL774oiZMmKDs7GyXff3799fmzZuN7crKSk2bNk2vv/66KioqNGTIEC1YsEC/+MUvmrx+AABMFxB56qMx3PizyWw2m6ZOnaqpU6c2YVFn5vZhaNu2baqpqTG28/PzNWzYMN16661G28iRI7Vo0SJj29vb2+Ucqampeuedd7R8+XIFBQUpPT1diYmJys3NlYeHR9MPAgAAswVENouPxjCD24ehDh06uGz/8Y9/1CWXXOKywMrHx0dhYWFnPN7hcGjhwoVasmSJhg4dKklaunSpIiMjtXr1ao0YMaLpigcAAG6vWX0cR1VVlZYuXaq7775bNpvNaM/JyVFISIi6du2q5ORkFRUVGftyc3NVXV2t4cOHG20RERGKjY3Vxo0b671WZWWlSktLXV4AAKDlaVZh6K233lJJSYkmTJhgtCUkJGjZsmVau3at5syZo23btum6665TZWWlJKmwsFDe3t5q3769y7lCQ0NVWFhY77Vmz54tu91uvCIjmVoEAKAlcvvbZD+2cOFCJSQkKCIiwmgbO3as8X1sbKz69eunTp066b333tMtt9xS77mcTqfL7NJPTZ8+XWlpacZ2aWkpgageB2rDVF7sKdXWnL2zu3N4yq82TFFm1wEAuGiaTRj65ptvtHr1ar355ps/2y88PFydOnXS3r17JUlhYWGqqqpScXGxy+xQUVGRBg4cWO95fHx8LspTL5u7AyUnNbhqrrRGksrNLqcRBEqaq3UlJxUVcdbOAIAWoNmEoUWLFikkJES//vWvf7bf0aNHVVBQoPDwcElS37595eXlpVWrVmnMmDGSpMOHDys/P1/PPvtsk9fd0pVXnXqE+v0x5eoYcu7Pk3BX3xYd1Yt7/IxxAQBavmYRhmpra7Vo0SKNHz9enp7/LbmsrEwZGRkaPXq0wsPD9fXXX+vRRx9VcHCwbr75ZkmS3W7XxIkTlZ6erqCgIAUGBmratGnq0aOH8e4yXLiObWoUFdCslqCdWVkLuNUHADgvzSIMrV69WgcPHtTdd9/t0u7h4aFdu3bp1VdfVUlJicLDwzV48GC98cYb8vf3N/plZmbK09NTY8aMMR66uHjxYp4xBAAAmkcYGj58uJxOZ512X19fffDBB2c9vnXr1srKylJWVlZTlAcAAJqxFnBfAwAAoOEIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNI8zS4AANAyHPihXOWVJ80uo9H4+XgqKtjP7DJwERCGAAAX7MAP5Rr8fI7ZZTS6ddPiCUQWQBgCAFyw0zNC9w++VB0DfE2u5sJ9W1KhF9fta1EzXagfYQgA0Gg6Bvgyk4JmhwXUAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0tw6DGVkZMhms7m8wsLCjP1Op1MZGRmKiIiQr6+v4uPj9fnnn7uco7KyUikpKQoODpafn59GjRqlQ4cOXeyhAAAAN+XWYUiSLr/8ch0+fNh47dq1y9j37LPPau7cuZo/f762bdumsLAwDRs2TMeOHTP6pKamasWKFVq+fLk+/vhjlZWVKTExUTU1NWYMBwAAuBlPsws4G09PT5fZoNOcTqfmzZunGTNm6JZbbpEkZWdnKzQ0VK+99pruvfdeORwOLVy4UEuWLNHQoUMlSUuXLlVkZKRWr16tESNGXNSxAAAA9+P2M0N79+5VRESEoqKidNttt2n//v2SpAMHDqiwsFDDhw83+vr4+GjQoEHauHGjJCk3N1fV1dUufSIiIhQbG2v0qU9lZaVKS0tdXgAAoOVx6zDUv39/vfrqq/rggw/0yiuvqLCwUAMHDtTRo0dVWFgoSQoNDXU5JjQ01NhXWFgob29vtW/fvt4+9Zk9e7bsdrvxioyMbMSRAQAAd+HWYSghIUGjR49Wjx49NHToUL333nuSTt0OO81ms7kc43Q667T91Ln0mT59uhwOh/EqKCho4CgAAIA7c+sw9FN+fn7q0aOH9u7da6wj+ukMT1FRkTFbFBYWpqqqKhUXF9fbpz4+Pj5q166dywsAALQ8zSoMVVZWavfu3QoPD1dUVJTCwsK0atUqY39VVZXWr1+vgQMHSpL69u0rLy8vlz6HDx9Wfn6+0QcAAFibW7+bbNq0abrhhhv0y1/+UkVFRfr973+v0tJSjR8/XjabTampqXr66acVHR2t6OhoPf3002rTpo3GjRsnSbLb7Zo4caLS09MVFBSkwMBATZs2zbjtBgAA4NZh6NChQ7r99tv1ww8/qEOHDhowYIA2b96sTp06SZIefvhhVVRUaPLkySouLlb//v314Ycfyt/f3zhHZmamPD09NWbMGFVUVGjIkCFavHixPDw8zBoWAABwI24dhpYvX/6z+202mzIyMpSRkVFvn9atWysrK0tZWVmNXB0AAGgJmtWaIQAAgMZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJbmaXYBVnTgh3KVV540u4xGse8/LWMcAADrIgxdZAd+KNfg53PMLqPRtfZ0ml0CAAAN4tZhaPbs2XrzzTf15ZdfytfXVwMHDtQzzzyjmJgYo8+ECROUnZ3tclz//v21efNmY7uyslLTpk3T66+/roqKCg0ZMkQLFizQL37xi4s2ltNOzwjdP/hSdQzwvejXb3QlBWq94Q8K9x1ndiUAADSIW4eh9evX6/7779eVV16pkydPasaMGRo+fLi++OIL+fn5Gf1GjhypRYsWGdve3t4u50lNTdU777yj5cuXKygoSOnp6UpMTFRubq48PDwu2nh+rGOAr6KC/c7e0d3ZPCXbf8yuovGVHJS+8z57P3dXclBSCwjdANCE3DoMrVy50mV70aJFCgkJUW5urq699lqj3cfHR2FhYWc8h8Ph0MKFC7VkyRINHTpUkrR06VJFRkZq9erVGjFiRNMNAM2Pp9epr2ufknK+NrWURlHbWdLT/x0XAKAOtw5DP+VwOCRJgYGBLu05OTkKCQlRQECABg0apD/84Q8KCQmRJOXm5qq6ulrDhw83+kdERCg2NlYbN26sNwxVVlaqsrLS2C4tLW3s4cAdtW4v6YR0zTSpfQtYHF7sKa3R/40LAHAmzSYMOZ1OpaWl6Ve/+pViY2ON9oSEBN16663q1KmTDhw4oMcee0zXXXedcnNz5ePjo8LCQnl7e6t9e9d/DEJDQ1VYWFjv9WbPnq0nn3yyycYDN2ePlILMuYXaqGprJJWbXQUAuLVmE4amTJmizz77TB9//LFL+9ixY43vY2Nj1a9fP3Xq1EnvvfeebrnllnrP53Q6ZbPZ6t0/ffp0paWlGdulpaWKjIy8gBEAgAWUFJxaS9jclbSAmWGcs2bxNzYlJUVvv/22Pvroo7O+Ayw8PFydOnXS3r17JUlhYWGqqqpScXGxy+xQUVGRBg4cWO95fHx85OPj0zgDAICWznHo1NcNz0m2+mfdmw1nmKTkU+PqaDe7GjQxtw5DTqdTKSkpWrFihXJychQVFXXWY44ePaqCggKFh4dLkvr27SsvLy+tWrVKY8aMkSQdPnxY+fn5evbZZ5u0fgCwjKr/ux0bPUzq0ALCwxGHtFv/HRdaNLcOQ/fff79ee+01/etf/5K/v7+xxsdut8vX11dlZWXKyMjQ6NGjFR4erq+//lqPPvqogoODdfPNNxt9J06cqPT0dAUFBSkwMFDTpk1Tjx49jHeXAQAaSetAyX7md/c2K8dawJpBnDO3DkMvvfSSJCk+Pt6lfdGiRZowYYI8PDy0a9cuvfrqqyopKVF4eLgGDx6sN954Q/7+/kb/zMxMeXp6asyYMcZDFxcvXmzaM4YAAID7cOsw5HT+/Ec8+Pr66oMPPjjreVq3bq2srCxlZWU1VmkAAKCF4FPrAQCApRGGAACApRGGAACApRGGAACApbn1AmoAaPGO7JFs3mZXceFKDkryNbsKoEEIQwBghrKiU1/fTJZafW1qKY2itrOkpyVPL7MrAc4bYQgAzHCy8tTX3r+VIoPNraUxFHtKayS1bn/WroC7IQwBgJnahklBnc2u4sLV1kjioyvQPLGAGgAAWBphCAAAWBphCAAAWBphCAAAWBphCAAAWBphCAAAWBphCAAAWBphCAAAWBoPXQTQvDgKzK6gcZQVSmptdhUARBgC0Fx4+pz6+tHz5tbRWJxhkpL/Oy4ApiEMAWge/IKla9L/+5lezd0xD+lTSX4hZlcCWB5hCEDz4dcCPtD0NGetpBNmVwFALKAGAAAWRxgCAACWRhgCAACWRhgCAACWRhgCAACWxrvJzFJSINlawI+/pTwADwBgWS3gX+NmxnHo1NcNz0m2QnNraUw8OA4A0EwRhi62qvJTX6OHSR3s5tbSWDx9WtbzXwAAlkIYMkvrQMkeZnYVAABYHguoAQCApRGGAACApRGGAACApRGGAACApRGGAACApRGGAACApRGGAACApRGGAACApRGGAACApfEEauAM9pXUml1Co2gp4wCApkQYAn6k9f/9F5G6tsLcQhpZa/5LB4B68SsS+JHwtq00d3BrnThpdiWNp7XnqXEBAM6MMAT8BMEBAKyF3/oAAMDSLBWGFixYoKioKLVu3Vp9+/bVhg0bzC4JAACYzDJh6I033lBqaqpmzJihTz/9VNdcc40SEhJ08OBBs0sDAAAmskwYmjt3riZOnKh77rlH3bp107x58xQZGamXXnrJ7NIAAICJLLGAuqqqSrm5uXrkkUdc2ocPH66NGzee8ZjKykpVVlYa2w6HQ5JUWlp6QbWUlZWptvK4vvruiI4fP35B5wLQfH1T7qHaSj8t3/qDuvg3/7cv7j/mqdrKNtp94IiO+9WYXc4FO1xSrtpKT5WVlV3w731J8vf3l81ma4TK0BQsEYZ++OEH1dTUKDQ01KU9NDRUhYWFZzxm9uzZevLJJ+u0R0ZGNkpNjzfKWQA0d38wu4BG9nuzC2hkcfMa5zwOh0Pt2rVrnJOh0VkiDJ3201TudDrrTerTp09XWlqasV1bW6v//Oc/CgoKahbpvrS0VJGRkSooKGgR/wEyHvfVksYiMR5311zH4+/vb3YJ+BmWCEPBwcHy8PCoMwtUVFRUZ7boNB8fH/n4+Li0BQQENFWJTaZdu3bN6hfG2TAe99WSxiIxHnfX0sYDc1liAbW3t7f69u2rVatWubSvWrVKAwcONKkqAADgDiwxMyRJaWlpSkpKUr9+/RQXF6c///nPOnjwoP7nf/7H7NIAAICJLBOGxo4dq6NHj2rWrFk6fPiwYmNj9f7776tTp05ml9YkfHx89MQTT9S51ddcMR731ZLGIjEed9fSxgP3YHM6nU6ziwAAADCLJdYMAQAA1IcwBAAALI0wBAAALI0wBKBRxcfHKzU11ewy0ELx9wtNgTAEAAAsjTAEAAAsjTDUwnTu3Fnz5s1zaevVq5cyMjJMqedCHTt2THfccYf8/PwUHh6uzMzMZjdNHh8fr5SUFKWmpqp9+/YKDQ3Vn//8Z5WXl+uuu+6Sv7+/LrnkEv2///f/zC71vJWXl+vOO+9U27ZtFR4erjlz5phdUoPFx8drypQpmjJligICAhQUFKSZM2equT195J133lFAQIBqa2slSTt27JDNZtNDDz1k9Ln33nt1++23m1Ui4HYIQ3BraWlp+uSTT/T2229r1apV2rBhg/Ly8swu67xlZ2crODhYW7duVUpKiu677z7deuutGjhwoPLy8jRixAglJSXp+PHjZpd6Xh566CGtW7dOK1as0IcffqicnBzl5uaaXVaDZWdny9PTU1u2bNELL7ygzMxM/eUvfzG7rPNy7bXX6tixY/r0008lSevXr1dwcLDWr19v9MnJydGgQYPMKhFwO4QhuK1jx44pOztbzz//vIYMGaLY2FgtWrRINTU1Zpd23nr27KmZM2cqOjpa06dPl6+vr4KDg5WcnKzo6Gg9/vjjOnr0qD777DOzSz1nZWVlWrhwoZ5//nkNGzZMPXr0UHZ2drP88zktMjJSmZmZiomJ0R133KGUlBRlZmaaXdZ5sdvt6tWrl3JyciSdCj4PPvigdu7cqWPHjqmwsFD//ve/FR8fb2qdgDshDMFt7d+/X9XV1brqqquMNrvdrpiYGBOrapgrrrjC+N7Dw0NBQUHq0aOH0RYaGipJKioquui1NdRXX32lqqoqxcXFGW2BgYHN8s/ntAEDBshmsxnbcXFx2rt3b7MLePHx8crJyZHT6dSGDRt04403KjY2Vh9//LHWrVun0NBQXXbZZWaXCbgNy3w2mVW0atWqzhqH6upqk6q5MKfH8eN/nH7c3px4eXm5bNtsNpe202M8vc6jOWiOfw5WER8fr4ULF2rnzp1q1aqVunfvrkGDBmn9+vUqLi7mFhnwE8wMtTAdOnTQ4cOHje3S0lIdOHDAxIoa7pJLLpGXl5e2bt1qtJWWlmrv3r0mVoXTLr30Unl5eWnz5s1GW3Fxsf7973+bWNWF+fFYTm9HR0fLw8PDpIoa5vS6oXnz5mnQoEGy2WwaNGiQcnJyWC8EnAFhqIW57rrrtGTJEm3YsEH5+fkaP358s/tFfpq/v7/Gjx9vLNL9/PPPdffdd6tVq1Z1Zotw8bVt21YTJ07UQw89pDVr1ig/P18TJkxQq1bN99dKQUGB0tLStGfPHr3++uvKysrSAw88YHZZ5+30uqGlS5caa4OuvfZa5eXlsV4IOANuk7Uw06dP1/79+5WYmCi73a6nnnqq2c4MSdLcuXP1P//zP0pMTFS7du308MMPq6CgQK1btza7NEh67rnnVFZWplGjRsnf31/p6elyOBxml9Vgd955pyoqKnTVVVfJw8NDKSkpmjRpktllNcjgwYOVl5dnBJ/27dure/fu+u6779StWzdziwPcjM3JjX80I+Xl5erYsaPmzJmjiRMnml0OWpD4+Hj16tWrznO6ALR8zAzBrX366af68ssvddVVV8nhcGjWrFmSpBtvvNHkygAALQVhCG7v+eef1549e+Tt7a2+fftqw4YNCg4ONrssAEALwW0yAABgac33bR8AAACNgDAEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEWER8fLxSU1MbfPzixYsVEBDws30yMjLUq1evBl8DAMxAGALQaKZNm6Y1a9YY2xMmTNBNN9103uchVAG4mHjoIoBG07ZtW7Vt29bsMgDgvDAzBFhIbW2tHn74YQUGBiosLEwZGRnGvrlz56pHjx7y8/NTZGSkJk+erLKysjrneOutt9S1a1e1bt1aw4YNU0FBgbHvxzM6GRkZys7O1r/+9S/ZbDbZbDbl5ORIkn73u9+pa9euatOmjbp06aLHHntM1dXVkk7djnvyySe1c+dO47jFixc31Y8EAJgZAqwkOztbaWlp2rJlizZt2qQJEybo6quv1rBhw9SqVSu98MIL6ty5sw4cOKDJkyfr4Ycf1oIFC4zjjx8/rj/84Q/Kzs6Wt7e3Jk+erNtuu02ffPJJnWtNmzZNu3fvVmlpqRYtWiRJCgwMlCT5+/tr8eLFioiI0K5du5ScnCx/f389/PDDGjt2rPLz87Vy5UqtXr1akmS32y/CTweAVRGGAAu54oor9MQTT0iSoqOjNX/+fK1Zs0bDhg1zWVwdFRWlp556Svfdd59LGKqurtb8+fPVv39/SafCVbdu3bR161ZdddVVLtdq27atfH19VVlZqbCwMJd9M2fONL7v3Lmz0tPT9cYbb+jhhx+Wr6+v2rZtK09PzzrHAUBTIAwBFnLFFVe4bIeHh6uoqEiStG7dOj399NP64osvVFpaqpMnT+rEiRMqLy+Xn5+fJMnT01P9+vUzjr/ssssUEBCg3bt31wlDP+cf//iH5s2bp3379qmsrEwnT55Uu3btGmGEAHD+WDMEWIiXl5fLts1mU21trb755htdf/31io2N1T//+U/l5ubqxRdflCRjLc+Pj/mpM7XVZ/PmzbrtttuUkJCgd999V59++qlmzJihqqqqBowIAC4cM0MAtH37dp08eVJz5sxRq1an/h/pb3/7W51+J0+e1Pbt241ZoD179qikpESXXXbZGc/r7e2tmpoal7ZPPvlEnTp10owZM4y2b7755qzHAUBTYWYIgC655BKdPHlSWVlZ2r9/v5YsWaI//elPdfp5eXkpJSVFW7ZsUV5enu666y4NGDCg3ltknTt31meffaY9e/bohx9+UHV1tS699FIdPHhQy5cv11dffaUXXnhBK1asqHPcgQMHtGPHDv3www+qrKxsknEDgEQYAiCpV69emjt3rp555hnFxsZq2bJlmj17dp1+bdq00e9+9zuNGzdOcXFx8vX11fLly+s9b3JysmJiYtSvXz916NBBn3zyiW688UY9+OCDmjJlinr16qWNGzfqscceczlu9OjRGjlypAYPHqwOHTro9ddfb/QxA8BpNqfT6TS7CAAAALMwMwQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACzt/wP85YQcFKVuzAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 581.111x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "\n",
    "df= pd.concat([X, y], axis=1, join='inner')\n",
    "sns.displot(X, x=\"cap-color\", bins=[1, 2, 3, 4, 5, 6, 7])\n",
    "sns.displot(df, x=\"cap-color\", hue=\"poisonous\", element=\"step\")\n",
    "sns.displot(X,x=\"habitat\",bins=[1, 2, 3, 4, 5, 6, 7])\n",
    "sns.displot(df, x=\"habitat\", hue=\"poisonous\", element=\"step\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f54e15c-1d77-45a9-b3b1-06e75e8fd25b",
   "metadata": {},
   "source": [
    "<h2> Nearest neighbor in scikit learn </h2> \n",
    "\n",
    "Using the tutorial: https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py\n",
    "\n",
    "Develop a nearest neighbor plot for cap-color and habitat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "05e0f395-65e8-4885-9e13-6f1c885aca09",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\turin\\AppData\\Local\\Temp\\ipykernel_22720\\3843034751.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X[\"cap-color\"]=X[\"cap-color\"].replace({\"n\":.1,\"b\":.2,\"c\":.3,\"g\":.4,\"r\":.5, \"p\":.6,\"u\":.7,\"e\":.8,\"w\":.9,\"y\":1}).fillna(0)\n",
      "C:\\Users\\turin\\AppData\\Local\\Temp\\ipykernel_22720\\3843034751.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X[\"habitat\"]=X[\"habitat\"].replace({\"g\":1,\"l\":2,\"m\":3,\"p\":4,\"u\": 5,\"w\":6,\"d\":7,\"g\":8,\"m\":9}).fillna(0)\n",
      "C:\\Users\\turin\\AppData\\Local\\Temp\\ipykernel_22720\\3843034751.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  y[\"poisonous\"]=y[\"poisonous\"].replace({\"p\":10,\"e\":5}).fillna(0)\n"
     ]
    }
   ],
   "source": [
    "# Develop test and training data set from cap-color and habitat.\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X[\"cap-color\"]=X[\"cap-color\"].replace({\"n\":.1,\"b\":.2,\"c\":.3,\"g\":.4,\"r\":.5, \"p\":.6,\"u\":.7,\"e\":.8,\"w\":.9,\"y\":1}).fillna(0)\n",
    "X[\"habitat\"]=X[\"habitat\"].replace({\"g\":1,\"l\":2,\"m\":3,\"p\":4,\"u\": 5,\"w\":6,\"d\":7,\"g\":8,\"m\":9}).fillna(0)\n",
    "y[\"poisonous\"]=y[\"poisonous\"].replace({\"p\":10,\"e\":5}).fillna(0)\n",
    "\n",
    "#In order to solve many types of errors in the plot, y or target variable must be converted to 1D array.\n",
    "y=y.squeeze().ravel()\n",
    "X1=X[[\"cap-color\", \"habitat\"]]\n",
    "\n",
    "\n",
    "X1_train, X1_test, y_train, y_test = train_test_split(X1, y, stratify=y, random_state=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "64875359-39eb-448d-9cc2-12453edc1f71",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Scale the data beforehand with classifier. Following the tutorial above.\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "clf = Pipeline(\n",
    "    steps=[(\"scaler\", StandardScaler()), (\"knn\", KNeighborsClassifier(n_neighbors=11))]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "28bed91d-ce5a-4e70-b59c-793d06c9e0e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\turin\\anaconda3\\Lib\\site-packages\\matplotlib\\text.py:1279: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if s != self._text:\n",
      "C:\\Users\\turin\\anaconda3\\Lib\\site-packages\\matplotlib\\text.py:1279: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if s != self._text:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot boundary conditions and data point using the tutorial format\n",
    "\n",
    "#Note a lot of data transformations had to be done to get this to work. Using the .squeeze().ravel() for y. And using .unique() for X1\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.inspection import DecisionBoundaryDisplay\n",
    "\n",
    "_, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "for ax, weights in zip(axs, (\"uniform\", \"distance\")):\n",
    "    clf.set_params(knn__weights=weights).fit(X1_train, y_train)\n",
    "    disp = DecisionBoundaryDisplay.from_estimator(\n",
    "        clf,\n",
    "        X1_test,\n",
    "        response_method=\"predict\",\n",
    "        plot_method=\"pcolormesh\",\n",
    "        xlabel=X1[\"cap-color\"].unique(),\n",
    "        ylabel=X1[\"habitat\"].unique(),\n",
    "        shading=\"auto\",\n",
    "        alpha=0.5,\n",
    "        ax=ax,\n",
    "    )\n",
    "    scatter = disp.ax_.scatter(X1.iloc[:, 0], X1.iloc[:, 1], c=y, edgecolors=\"k\")\n",
    "    disp.ax_.legend(\n",
    "        scatter.legend_elements()[0],\n",
    "        y, #Note we use the squeeze.ravel y here.\n",
    "        loc=\"lower left\",\n",
    "        title=\"Classes\",\n",
    "    )\n",
    "    _ = disp.ax_.set_title(\n",
    "        f\"3-Class classification\\n(k={clf[-1].n_neighbors}, weights= {weights!r})\"\n",
    "    )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ab777d-2209-48f0-a751-e66dbfb3e837",
   "metadata": {
    "tags": []
   },
   "source": [
    "<h2> Conclusion from the nearest neighbor plot using scikit learn</h2>\n",
    "\n",
    "Red and Brown cap-color in wet, woods, and waste habitats are very likely poisonous. Habitat and cap-color have a strong relationship for specific variables. To build a thorough test of this relationship could be done through more multivariate visualizations or statistical relationships. But the graphs below largely match the trend shown in the nearest neighbor plot.\n",
    "\n",
    "Finally we will try Nearest Centroid classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "7ba91a88-eff3-4ac7-b184-4917ef042c35",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\turin\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "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-color</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cap-color</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat</th>\n",
       "      <td>0.200399</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cap-color   habitat\n",
       "cap-color   1.000000  0.200399\n",
       "habitat     0.200399  1.000000"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 581.111x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(\n",
    "    data=df,\n",
    "    x=\"cap-color\", y=\"habitat\", hue=\"poisonous\", style=\"poisonous\"\n",
    ")\n",
    "\n",
    "X1.corr()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d3872e5-4562-4ef7-a143-6a73f0e9c45f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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