Discretization .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discretization or Binning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1)\tConvert the continuous data into discrete classes on iris dataset\n",
    "I have used two methods for Dicretization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris = pd.read_csv(\"iris.csv\",index_col='Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sepal.Length    0\n",
       "Sepal.Width     0\n",
       "Petal.Length    0\n",
       "Petal.Width     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sepal.Length    float64\n",
       "Sepal.Width     float64\n",
       "Petal.Length    float64\n",
       "Petal.Width     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal.Length  Sepal.Width  Petal.Length  Petal.Width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Species'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_iris[['Sepal.Length','Sepal.Width']].plot()  #rough look diamensions of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Species'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KtZTymBDisBBippRyN3ARsGOwB3I6nUydOnUoY9RoNAnwBkMEwxKPL0ie28ldr+zl2S311Hz/Ehx2myXU8RG114hsK0uyONrey6xxebT3+IfF+gCVoudL5FEHQmRm2BHEetRmpAzQ5QuSn5m4FUJ1bStVUwqxGUJuRtTNcZF4ty9Ik8fHzPJcdjd4ONzay/SywWWzAHz1kU2U5rp4/JZl/W88wgw06+PLwMNCiC3AIuC/RmxEGo1mQJg+b5sRUR7r9NLlC1oZD6Z3W5KTYSyRFalMzHTarUhxSWUh+ZlO2odgfcRPJgJ9rA1rvNHWhz9aqNui9pd4DG3dfvY2dln+NEB+phO7TcSUyQMcNKLp82eWGo8H71NLKTnW6WV9bRtH23sH/f7hZkBCLaWsMfznBVLKq6WUbf2/S6PRjCRmhGx60WaVnhmhtkRF1C6HLabXR2aGncriLACqKosoyHIOqZteZ2+CiNqRxKM2ThDx1kh1bSsOI0o2hT+eDQeV5ET3JrHZBMXZGTFl8hAR5vON/OmhZH70+EPW9/uPzXWDfv9wc8pXJmo0YxVTDNsMb9m0OqqNCHXL0Q5KcjLIyrDHTPD1BkK4nXaWTithWmk2y04rpiAzY8jpeTYB2RkRDzhVRO02FhfoNUTQjJRNAU4m1K/ubiTTaWfBxPyY54tzXH0ialOYF04qIM/tGFJEHV3xuLJGC7VGoxkipvC2dgdUVk9URN3lC/LyjgYun1+BEAKXw4Y/GCYUlviDYTKdduZPzOfV21ZQkuOiIMs5ZI/aXDTAxOWwJ86jDoTIdNrIdNrxGtaHGSlfOKvM2l88/mCY1VvruWROudX0yaQkJ4Omrr4RdUmOixyXg8qS7CFF1KZttGJmKTvrO9nTMLq9drRQazRjECmlFbW2dfvp9Abxh8JUFmfR6PFx/5v78QXDXLVoPBBJmTPFPTMj9l8/P0t51INNn/VE9aI2ycyIRO/Pbannnf2q8jFRet76g61k2G0sn15i7S+eN/c20d4TsD5LNCU5rj7pebUt3ZatM6U4+7gi6k8tq8QmYGXN0UHvYzjRQq3RjEECIYlZOd3a47fE6rJ5FQD8fs0+JhZmcuZklapnetSmQGbGRaYFmRn4g+GEkXAqOqP6fJi4HTZLqP9r9U7ueWM/YHjUGbEe9XsNXUwrzaYkV6XaJYqoV9bUUZDl5LwZfeszirMzaO7yxZxgDrb0MNkQ6sriLI609RIIDe5zmVcns8blsmhSAesPjO60nBZqjWYMEu0Bt3X7rUv1pacVk+d24AuGuXLheMuSMCNqM9si3kIwKxUH61N7vAHy4iJqU4illDR6vNZJxPTGo62P5m4/pbkuax+dcRF1ty/IS4aFk+HoK1cluS68gTA9xv68gRD1HV4qjYyWKcXZhMKSo22Dy9ywctCzXUwtyeFg6+hWOGqh1mjGINFLXbV2RyLq0hyXVfBy1aIJ1jYuhw1vMBxlfcRH1IZQD9Kn9iSIqE3ro60nQCAkrZOI17I+bNaJptljNo2y4bSLPtbHyzsb6A2EuDrqs0RTnG0UvRif31wMYUpURA1q+bFESCn5nxd2WV65SUuXjzy3gwyHjcriLBo6ffT4R29xDi3UGs0YJCai7vFHFbdk8MmllXx6WSUzx+Va27iddkJGcQz0tT7ys4Yo1L5AEusjTEOnKm9v7vIRCIUJhKTV6yNoTGq2dPsozslACEGu29nH+the14nLYaPKOPnEY/barjM6/5l9tc2IenZFHiU5Ln7x4u6EKYM76jv53Zp9PLnxSMzzzV1+SnJVQc2UErWv4VoRZyhoodZoxiCml2y3CVqjrI+i7AwumFXGHVfGriBkNvQ3y8T7WB+ZKjLtGLT10Xcy0exJbQqbLxi2TiSmRw1q9RlvIGxVGOa6HX0i6sZOL+V5bqsaMR4zcjYnDM1iF1Oos10Ofn7dAnYd8/C/L+7u8/5VRupd/IRjc5ePkmyXsS/zGFqoNRrNIDAj6vJcF209AVq6fRRmOXHYE/9Lm70uzIg5qUcdFVG39/i56U/VHEoiUOGwpCvhZKIh1FHvO2J4xG4j6yP6ueIYoY6NqBs6fZTlukjG+IJMnHZhpeDVtnRTkOW0rhAALphVxifOmcJ9bx5g46GIxREOS1YZxSzxzZtauv3WBOeUIiX6o9mJTwu1RjMGMS/jKwoyae/x09jpsyLTRJgRdZshxH2yPqzJxIhQrtpcx8s7G3h9b+JGau/WthIMS2ZV5MU8bwpx9ASc2TfbtD6inzObK+W6nH0i6gaPiqiTYbcJJhVlxUTUiZoo3Xbp6YCqgjRZX9tKfYeX00qzreZNJs1dPoqNiDo/y0lhlnNUe1trodZoxiCmUI8vyCQsYX9zN8WG4CXCjKg7DOsjfjIx02knw26LiajNiryDSdZTXFlTR1aGnYtnl8U8b54UDiaIqDMzIkJtPpfK+mjq9FGWl/wEBMrmiI6oTasimvxMJ5lOu9WlD2Dl5joynXY+e+5UpITDrWo8gVCY9p5AzPc51Hzs4UILtUYzBrGEOl9Fm7XN3ccVUQshyM9yWh714dYeKxMiUSRpVgteOqecrIy4rA9nxKM2O+FFR9Smhx2JqE2hjp1M7PYF8fiClOWm7qE+pVhF1L5giLr23oQRtRCC8jwXjZ5IO9XVW+u5dG45s40rAlOIo/t4m1QWZyXtbf3A2gN884nNCV8bLrRQazRjEHMycXyBynoIhmVKobY86t7EQg0qRc+MqE3vdu74vISR5Bt7mujoDcSkAFrHioqYZxmZJ2b07HLaLA/bfK7ISLGLj6hNUS0fQETd4w+x6VA7YUnCiBqgLM9tZaI0eXy09wSoqiyyJh7NE1J0Bo3JlOK+9ggowf/9mn08seEIXb6RS9/TQq3RjEHMycSK/Ei0WZLK+ojP+sjo+68f3e9jVU0dVVMKWT69hIOtPYTjFpBdubmOwiwn584o6bMf8yQQCksmFGZSkOXkcFuUR21G1G0q4jYLWfLcDrr8QetYjYaopvKoIZL58fqeJuNx4kb/5Xlua59m69KJBZkUZjnJjWreFGkPGxVRl2TF2CMmb+9robnLR1jCpkMjV72ohVqjGYNEe9QmxSmtj0jWh01ARoLskPzMDNp7A+w61snuBg9XLhrPlOIs/MEwxzojS36pasFjfHBBBc4E+4nOKCnPc1OcnUG9sWRYtEdd3+GN8YFz3U6khC6jsKTBiKhTZX1AJBXv9d1NxuMkEXWui4ZOVW5eZwj1+IJMhBAxPneLtTJO5Lim+MdfXaysOUp2hh2biO2rPdxoodZoxiC9CYQ6tfVhRNS9fjKd9phudyaqJ7WflTV12G2Cy+dXRNkCEYF6aUcD3kA4oe0BsbZKea6LkhyXtS5jdNZHKM6uyctUXrdpf5jRb1k/EfWEwkzsNsGO+k5yXQ7LSomnPM9FbyBEly9IfYcp1Grfps8Nia2PeHsE1MnyhW3H+MD8CmZX5MVklAw3Wqg1mjGI6VEXZjktEU6V9WFF1N2BPhkfJgWZTtp6AqyqqePc6SWU5LiiCkoiArWy5ijj890snpy4WtCcuAQlstFirCYTI6+XxEXUEGnM1NDpxe20xazHmAin3cbEQnXCmlKSlfAkBBELpaHTR127l1y3wzpmZXG21byppctPhsNGjity3Hh7BGDN7iY8viBXLRrPksoiNh1qH3Tzp4GihVqjGYN4AyHcThtCCCuCLB1ARO3xBfsUu5gUZDnpDYQ42t5rtRStyM8kw26zIuqWLh9v7G3mQ4vGJ60WjLU+XDFi7I6yPiD2KsAsnLEiao+Pslx3UuGNZkpUE6ZklBoWSmOnl6PtvUyIuhqZUpxlNW9q6lL9R6KPK4Rgakk2u49F+lKv2nyUkhwXS6cVU1VZSG8gxI66zn7HOhS0UGs0YxCzwRFAYZYSwoFE1JA44wMg39iPy2Hj0rnjALOgJJODRmra6m3HCIUlVy1MbHvEH6ss1x3j9ZpLcZmYRSWQOKLuL+PDpDKuCVMirIja46WuvTfGNqosiVg8LV3+hN/l+2aUsr62lSaPD483wMs7G7liQQUOu42qKWqFmvUjZH9oodZoBkE4LK3MidGk1x+yBK8oWy23FZ/PHI0rqkVoKusD4OI55TGX/WqiTUXUq2qOMqMsh9kVuQn3AbHWR6nhUQM4bAKn3Wb8GCuJ50ZbH/Eeta9ff9pkIBG1KdSNnT5DqCP7Ni2e9bWt1LX3JvT7r1o0nrCE57bU8c/tDfijFmYYl+9mUlGmtQzacDMgoRZC1AohtgohaoQQ1SMyEo1mDLB6Wz1L//vVIS0EO5x4jeW0QE2ImR5tMqKj2GTWxzgj1e+auElCVZXXw4aDrayvbeOqReNT2hEZdhs2oawUt9NuRaeZCcYQG1Eroe6MsT4GFlHPLFcnjtPLk59AclwOsjPs1LZ009YTiImoS3Nc5Gc6+e1r+9jb2BWT9mgyozyX2RV5rNpcx8qao0wuymLRpALr9SVTiqg+2DroVXIGQmqXPpYLpJTNwz4CjWYMcbClh95AiEOtPczPyu//DSNErz9kFZZ8+wOz6e6n2MJpt2G3CUJhmdT6qJpSyKovLWf+hNjPVVmSRW8gxBf+upGJhZl8alllymMJIXA77ZQbFYVmdOrOiLVfPN4gpVERdV6U9dHlC9LlC/abQ22yfHox//jSucyfmPp3UpbnZtOhdoAYj1oIwSM3n2NMFgrOnlqU8P1XLhzPz17YhU3ArRdMjzlhfWb5VK5bPBEpYQC2+qDQ1ofmlKajJ2BVqw0E87LcLJgYLXxBtVAsKOtjUlFyb9bEtD+SCbUQggUTC/pEy6ad0Nzl4/8+sqhPW9NEZDrtVo+OkgQRtWm/REfU0YsHRIpdBhZRCyH6FWlQudTmQrVmL2uT2RV5XDavgsvmjaMwSYrfhxaqpc7CUol2NPMn5rNseknSSdbjYaBCLYEXhRAbhBA3J9pACHGzEKJaCFHd1JS425ZGk2589dFNfOaP6we8vTnRVTfKQh3tUQ8Uc/tkHnUyZpTlYBPwhRWncVaSSDOe8jw3p5XmAJGIOjPBhGZJlLURvXhAo1XsMrCIeqCU57mttSajPeqBMrEwi2WnFbNgYj4zUtgsw81ArY/lUso6IUQZ8JIQYpeU8o3oDaSU9wL3AlRVVQ2/SaPRDDNNHh+v72nCYbMRCkvsA4iEzIh6tIXaGwxZDY8GihlRD1bgxxdk8sY3L4ixCvrjb58/2+ovkpVhx+20xVgfbqcdl8NGdtxJw+z30TDIiHqgmJ63TfRfmp6MP3xiMXJk0qWTMqCIWkpZZ9w2Ak8DZ43koDTpzeHWnqTN5NOF3cc8NHl8Kbd5bksdYQn+UNiqVOsPK6Ie4PYjxXFF1IN8H6hIciD5zCYFWRlW5C6EoCTHZVk15hhK4nKVISLUjUY70oFmfQwUU5zL89wJy98HQp47dmGCE0G/IxVCZAshcs37wKXAtpEemCZ9ue3xzXzzyZFt63i83PTn9Xz7yS0pt1m5uc5qCDTQZZYiHvXAfe2RwBsID1qoLY86QUOmkWbWuFxrpRRQ6XDRazqa5LqctPf4Wb2tnuLsDHJdg8l36B/TNx8/iKuDdGAg30I58LRx5nMAf5NSvjCio9KkLVJKdtR1WlVe6YiUkoYOH/XtXtq6/Qknhg619LDpUDufXlbJQ2/VUtvSzfLpfTvBxWO2shxt68MXDMXkKw8E13FE1MfL729cjC0qev7JNfMJJ0hjy3U7eHtHCwC//uiiQUXxA8H0vMeaUPf7m5ZS7pdSLjR+5kopf3IiBqZJT4609dLlC1rN1dOFt95rtjIFuv0h/KEwwbBk9bb6hNuv2nwUgJvOm4rLYRt0RN3k8fXpTXwi6fWHBi247iF61MOBmR5oYjeKX+IxM0quWjQ+adOn46HciqiH11IZaXR6nmZQmL0OOnoDBEeoAc1gCYcln35oPfevPQBAW9RJxFxOKp7X9zSxcFIBEwuzmFKcRW2S5abi6fQGrMKMYx2jY39IKfEGh2B9DDHr40QyuyKXaaXZ/OiqeSOy//EFmUwszEzaUCpd0UKtGRS7GyJNaaIXQh1NPN4g/mDYiqjNaP+MyQW8e6C1j00hpWTXMQ/zJ6glmMzKu/4IhyVdvqBVBTdaudSBkFSFK4MUXHc/edTpwE3nTeOV/zh/0BktA8XttLP2WxdavUzGClqoNf3y+p4mNhqrV0R3D2tLYn+81+jh2S2JI9lU+IIhHlx7YNCReqvRe8NcmcN8/Gmjgs5cVsqkvsOLxxtk5jgl1JXFWRxs7e6zikk83f4gUmJNgtW1xzbTv//N/SPW5jIar2G5RPfvGAij6VEPhuH2pU8GtFBr+uW/V+/kW09sQUrJ7mMeq2FPMp/6R8/u5NtPbh30cdbubeZHz+6g+uDgGtuY4zAbvpsnkIUTC5hdkce/3ovtfGCebMz1/CYXZ+MNhK0ii2SY/nREqCMR9SPrD3Pnczt5bVfjoMY+FLx+JdSDTs8zPeo0tj40idFCrekXXzDM3sYuth7tYF9TF0sqlb/XlqCLXJPHx7/ea6bLF+w3Qo3HXO6pfZANj9q64yJq43FhdgZzKvJirgIAdhmPzQY+ZmvM2gSLuEZjCnVxtuoIFy3Uq2rU5OSGQZ5khoK5aMBgI2Nz3cR0j6g1fdFCrekX83L+ly/tIRiWLD2tGIDWbiWo/3qvmb+sOwjA6q311rJLPcZyUfuaurjvjf39HqfBKHLo9A5OqE2ro7XbRzgsaevxY7cJ8twOZo3LpdHji7Fpdh/rZHy+2/JBK5OshxePWeyS63YwocBtedQHmrvZfKQD6L8fcSgs+dXLe46rYMhchmvwEfXYsD40fdFCrekXU6hfMxYPPWeaEmozon5g7QFuf2Ybr+xsYKURWQJWR7eVNXX8ZPVOOvqZfDQnAzsHOUlpinBYqjG1dgcozMpACGHZFLuioupdxzwxxRYV+W6cdhGzHl4izIg61+1gfEGmFVGvqqlDCJVStvVoh7XwbCLe2d/Cr17eG/M9DRZz/4MtXLEiam19jDm0UGv6JRiSFBolsw6bYNa4PLIz7JbFYArWbY9vZuOhdsv7NYtDuqzc49TpbKZHPFihbo2yYFq6/bR1+ynKVuM1x7L7mFoiKRAKs6+py5pIBHDYbUwqzOo3ou60ImqnIdRepJSs3HyUs6cW8aEF4wmEJJsPtyfdhzmxWXccqX1WRO3QEfWpghZqTb/4Q2EunTMOt9PGaaU5ZDhsFGZnWJHs0fZelk8vpseY5PrYWZMB6PGpx10+c2ml1JN1ZiMes3G8SUdPgNse22zlOofCkjtWbWdnvRLfaFuj2eOjtcdvLU9VmuuiMMtppRUeaO4mEJKWgJtMKc7iQPPAIuo8t4MJBZn0BkJc/4e32d/UzZULJ7B4ivLuk02G+oIhVm9VBTjHU9loRtSDnRQ0rZLRKHjRHB/DW0ivOSkJhiT5WU6++f5ZVsZHYVYGrT1+Or0BPN4g75tRyvWLJ7G7wWNN0lkRtXHbX99nU8jjLZLbV25j1eY6ZpTncMv5p3GguZuH3qolz+1gdkUerd0BMhw2/MEwzUZEPb1Mtdg07Q/T+jBv4/tMVBRksvVoR8rxRawPJ+fPLGX5rmL8wTAXzSrjioUV5LmdzCjLSepTv767iU5vkMIs5/AI9SAj6vNnlnKwtZviJL2WNemLFmpNvwRCYRw2wWfPnWo9Z0bU9UYu8fiCTD5kNFLfcqQdiHjUpsCliqiDoTAt3X2tj5U1Ry27wMzeMG/NxkhtPX5OK81hZ30nzR4fbT2x/T1mjcvj8erDhMOSXfWd2G2CaaWxa+sVZDpp7wkgpUyax+vxBnDYhHVl8fBN5/TZpqqyiGe31CVsm7pycx3F2RlcPr+CpzYeSXmsVFhZH4OMqE8vz+XOq+cP+nia0UdbH5qUSCkJhmWfvgxFWU5ae/xWZBjd5CbbiLq7/bERdWMKj7q5y4/Zo8f0gtt7/HzvmW1UTSnkvBklVjRs2hjmsdu6/UwtycJuEzR1+WjrCVCUFRHqmeNy6faHONrey+Yj7UwrybZ6JZsUZDkJhiXd/uQTgR5vkFy3I6W4LqksxOMNWquImHT7gry8o4EPLqhgSnEW3f4Qnb2pl89KRiTrQ//7niro37QmJYGQUk9z1WgTFVEHrBS16Kbypj0SP5nYmCKiNm2RDIfNsj72NHTh8Qb50oXTmVORx77GLgKhsDUxaPaEbu3xU5ztojg7g9rmbkJhGRNRmzbHL1/aw7/ea+mzhBJAQabaPtUK4x5voN9lqJZUqhVQquPsj531nfiCYc4/vdQ6qQ21BN3K+tBe8ymDFmpNSoJhdZndN6LOoMsXpLa5G4dNxLQ9tSJqQ6i7B+BRm6+dVppjRZpmpWF5npuZ43Lxh8LUNndb1kd9u5dAKExHb4DC7AyKcyLr4ZlZHxApbHlq01EWTSrglhWn9Tm+2Qg+VbGNGVGnYmJhJuV5LtbXxk4omlcDsyryLKEeqk891DxqzdhFC7UmJYGgiqgdcUJtRqzb6zoZl++O8WOzDAHpMrI+PJb1kTyiNl+bUZZjRdQthlAX52RYUfGmw+0cbO2hJMeF30i1k1JZMSU5GVYudGGU9ZHjcjCpKJOsDDu/vGFRwvaaBUbxS6pc74EItRCCqsqiPhH17mMecl0Oxue7rRabA1klJhSW3Pq3jfxuzXvWc6ZHPdheH5qxi/5Na1ISMCLqjDjro8gQ6m11HX2asNtsguwMOz2+IFLKmKwPmaBZPKhiF5uAqSXZ9AZC+INhmrr8CKGi99NKc7DbBM9uqUdKuGBmqTr+UWWDFGZnUJLjsqoii+IyG+740Fzu/UQVU0tiJxFNCrJM6yO5UHcOwPoAWDKlkLoOb4y1sfuYh9PH5aplqbJdZNhtA7I+7n9zP89tqWfd/ojw+wJq0QDdvOjUQQu1JiXBUJKI2hA2jzeYcNHTbJeDbn+QHn8IKVU+sy8YTjqB1tDpoyTHZQmsxxugpctHYVYGDrsNt9NOZXGW1WDpwlllAGwzUuqKsjMoyYmIc3REDXDR7HLOnZF8BZeCrOGJqEFlfkDEp1ZtVTutqwKbTVBR4I7pvgeqjWogFLZ+th7p4Bcv7gagK6qsvjcw+EUDNGMbnZ6nSYlZPt7Ho46KWBOtlpHtctDlC1nR9Gml2TR5fDR6vJYfvOFgK7c+vImnb11Go8dLWZ6LvEz1J9nRG6C5yxcjvrPG5bGvqRu302aVsW+vU0JdmKU8apNEy2+lwuz70d6bejIxbwAR9axxueS4HKyvbeWqRRM41uml0xuMKbIZn5/Zx6P+0N1r2V7XGfNcWa6LaaXZMZ0KvYHBL2yrGdtoodakJCLU8VkfEcFKtP5ctstOty9o5VBPK81h3f5WGjp9zDAm9zYf7uBYp5eVNXU0dPqoiGqU1OkN0tKlsjlMZo7L5bmt9ZxenktBlpMcl4MdhrAVGdYHQIbdRvYQqvbcThsdSawP08IZSETtsNs4Y3IB1caEolVkUx4l1AWZvLUv0n41GAqzva6Tc6eXcM60Iuv5y+aN457X98c0ceoNhHVEfYqhhVqTEjM9z2FLbH1AEqHOcNDlC1oZH6eVqkrB6MyPBiOvemVNHU0eLwsnFVgRa2dvgJZuP/Mm5Fvbm9bBzHLl9Y4vcLOnocsaT7ERfRdmO4fk3xZkZiT1qLv9IcKSAQk1qDS9X768h46eQFT/60h/kQkFbho6VdaK026zVsu5dG45n1xaGbOvHLfDmpAFFVG7tFCfUgzYoxZC2IUQm4QQz47kgDTpRbKI2mm3WaKVyKPOcTno9gUt68OsBIzO/DDzqnfWd9Lc5acs10VeVPZFs8cXU+48p0IJ3dzx6tY8QWQ67WRm2Ckxou94f3qgFGQ5k1ofnqiGTANhSWURUsJzW+vZfczDuDy3ZfmYYw/LyInL7FeSaOw5LnXSMydivYHBr0CuGdsM5rf9FWDnSA1Ek54k86gh4lNX5Cf2qHv8Icv6KMt1ket2xETUjR4vU4qzMDP7yvMi1keTx4fHF4zJz55UlMXfP38OHzWaPlXkZ8aMoyQ3I+bxYMk3ysgTEd3idCCcNbWIs6YW8ZPndvDO/pY+vUUiudSx6zwmGnuOy4GUWE2vOr3BAXnlmpOHAQm1EGIi8EHg/pEdjibdCIbNysS+fyqFWRnkuR0Jo8xsIwo0I+pcl5OyXFdMGXlDp485FXksn66yMcrzXJYAHTA65cU3EFp6WrE1kTbBmMQ0/XJT5AY7kWhSkOVMmvUx2IjabhP830cWYhOCug5vn2598UUvZm/vhBG1O7bSs7M3YF15aE4NBhpR/wr4JjDyK3dq0opAUP3KHfa+nu+EwkxOM7rUxZNjTCaaaWXZLjvlee6YMvKGTi/leW6uPXMCAJOLsnA7bWTYbexvVt5zSVQmRzym2Jni5nLYGZfnZmICK2YgpIqoOwcZUQNMLMzix1fPA4jx2iGSKWPmUpur5SSLqCFWqPMz9fTSqUS/v20hxBVAo5RygxBiRYrtbgZuBpg8efJwjU8zygRSRNQ/vmpe0lW3szKU9WEKXI7bQXmem+qDKre417BFSnNdXL1oArPG5VnZIHmZDvY3GRF1TvLo2BTqaHF7/JalVk70YCnIykjhUUd6UQ+Gq8+YwIzynJiMD1DfT67bQZPh2ZsRdaKxmyeHLq/yqTsHmCaoOXkYSES9HLhSCFELPAJcKIT4a/xGUsp7pZRVUsqq0tLSYR6mZrQwI+r4yURQAlme19efhkgU2OjxkmG34XLYKct10dDpQ0ppWSDleW6EEMyuiGRE5Lmd1BsroKSKqCfERdSgfOyB2hPx5Gc68QbCCZfSGqz1Ec3c8fl9CoZAfXbTs2/t9pOdYU+YH53jUsfs8gXpDYQIhKS2Pk4x+hVqKeV3pJQTpZSVwEeBV6WUN474yDRpQbKmTP1hNmY61uGzPNZx+W78wTAt3X6rN3V5Xl8hjhahVBG1OfmYrCx8sKSqTjzc2otNRApjhgN14opkfSTz1rNdSrw93qBV2Tmc49CkP9ro0qQkWZvT/jDFpdHjtaLrGWXq8n/PMQ8tRpZDWW7fiNwU6qwMO1kZyf9EMxw21nx9xaB841REWp0GYq4UpJQ8t7WO5dNLhrUisDzPba0G09rjT5qtkhsVUZu9urX1cWoxqDBJSrlGSnnFSA1Gk36YHnR8wUt/5FgRdUSoTx+nJh53HfNY+dQJI2pDeFPZHiaF2RkJbYWhUGC1OvXT3OWzenVsPNTO4dZerlo0YViOY1KW56LRsILauv1J87+trA9vwIr28/Rk4imFzprXpMRsyuQcZEtN0/po7opYH6VG06Xdxzw0dnrJcNgSXsKbz6WyPUaCSL+PAL96eQ/X3/M27x5oZVXNUTIcNt4/t3xYj1eW68YfCtPeE0gZUZtXJ12+oLVMmbY+Ti30aVmTEr9Z8GIbpPVhWBZhGYmuhRDMLM9lV4OHqcVZlOW6EpZ6m9bHQCLq4cTyqHsCvHugFSnha4/W4A2EuHh22ZAnKZNhXk00eny0dQeSRtQuh50Mhw2Ptj5OWXRErUlJMEVlYirMKBAiQg2qX8feBg/1Hd6kGSOmCJWc4Ija7Eld29LNnoYuLp1TzrFOLy3dfq5cOLy2B2B9/sOtPXT5gjGr0sSTa5Tkm02jdNbHqYUWak1KrKZMg5xMjBbnnKjJvlnjcunxh9h6tCOhPw2Ry/oTHVFnZ9hx2ASv7GwE4HPnTuW2S09nSnEWK2YOf8ppuTGRai7Wm6qiMsftoMsbtPLSB5vPrRnb6N+2JiWB40zPAxUNmpg9L3r8oYQZHxCZKIsvHx9phBAUZDnZ3eDBaRcsnFTA2dOK+eKK6SNyvDLjRGW2QS1K0UzK7EbY2RtQJ5RhmkDVjA30b1uTEnPNxMEKdVaGHdN+jo6uT4+q0CtLElFb1kfuiY2oIRLNz5+QP+LN+d1OO3luh7Wqen8RtccbpEP3+Tgl0UKtSUkwHMYmiFm8diAIIawJxWjrI9vlYHJRFhC59I9nVkUu8ybksXBiwdAGfRyYPvWSyqJ+thweyvPc7DPK5VN1/cs1mlx1egM64+MURAu1JiX+UHjIl9nmhGK0DQIR+yPZZGJZrptnv3wekwxBP5GYq5FXnSChLsuLLMibqo92jlsJdUev7vNxKqKFWpOSYEgOOjXPxIyoc+OE2mz5mcz6GE3M5v6LpxSekONFX1WkaiaV4zImE3uDutjlFET/xjUpCYTCgy52MTEj6Zy4DIUPzKtg1zEPU4pPfMTcH5fOKSfH5Rjy4gODpcy4qshzO1LOA5gRdaY3wKzM3KTbaU5OtFBrUhIIyUGXj5uY1kdOXEQ9Z3we932y6rjHNhJcNq+Cy+ZVnLDjlRkTpv2dGHIyHPiCYVq6/Nr6OAXR1ocmJcFQmIxB5lCbmAI9XE2TTkZMn76gn3UezauS3kBIZ32cgmih1qQkcFyTiYb14dLCkgyz6KffiDrqqkRnfZx6aKHWpCQQloNucWqSzKPWRDAj6v5WTo++KtFViaceWqg1KQkEw4MudjHJcTkQArJGuHBkLFNqedSpo+ToqxJtfZx66FOzJiXBsByyUH/4zIlMKMjENsT0vlMBt9PODz40x1qJPRnRVyXa+jj10EKtSYnyqIcmtDPH5VrFLZrkfGb51H63yYnqRqizPk49tPWhSUkgFMY5xPQ8zfARbX3kD3GVdc3YRf8HalISCEmcDm1djDY5ejLxlEYLtSYlwVB4yAUvmuEjy6m6EdpEpDRfc+rQ729cCOEG3gBcxvZPSCl/MNID06QH/tDQJxM1w4fNJsjJcGC3Cz05ewoykFOzD7hQStklhHACa4UQz0sp143w2DRpQDAUHnIetWZ4yemnH4jm5KVfoZZSSqDLeOg0fuRIDkqTPhxPep5meMl2OXA79e/iVGRAv3UhhF0IUQM0Ai9JKd8Z0VFp0gZ/cOjpeZrhpSQnI+nyZZqTmwHNSkgpQ8AiIUQB8LQQYp6Uclv0NkKIm4GbASZPnjzc49SMEsFwmAwdUacFP79uofanT1EG9R8opWwH1gCXJXjtXilllZSyqrR0+Fds1owOgZDUEXWaMKkoiwkFmaM9DM0o0K9QCyFKjUgaIUQmcDGwa4THpUkTAqGh9/rQaDTDw0CsjwrgT0IIO0rYH5NSPjuyw9KkC1qoNZrRZyBZH1uAM07AWDRpSDAkcWhfVKMZVXSopEmKlFKn52k0aYD+D9QkJRBS6fK64EWjGV20UGuSEgiFAXRErdGMMvo/UJOUoBFRD3XNRI1GMzzo/0BNUgJhFVEPdRVyjUYzPGih1iTFtD50RK3RjC76P1CTlKA1maj/TDSa0UT/B2qS4rcmE7X1odGMJlqoNUmxJhP1Ci/pycG3YNfq49+PzwNv/BxCgePf18nIjpWw77VRHYL+D9QkJaAj6vRm7S/hn/95/PvZ+yK8eiccevv493WyEeiFZ26FZ78GcvTa8Guh1iRF51GnOd4O6DwKRnbOkPEcU7et+49/TCcbe/4Jfg+0HYCjG0dtGPo/UJOUgJ5MTG+8nRDyQ3fT8e3HU69u+xPqcAie/xYc25Z6u3TC2wnPfR3aDyff5pUfQ+3axK9tfRyyS8HuUvdHCf0fqElK0ErP09ZHWuLrVLcdR45vPwONqBt3wDt/gMc+Cf7u4zvmieL5b8L6+5S9k4jOOnjzF/DYp6CrMfa13nb1vvnXw+mXwrYn1clqFNBCrUmKX1sf6Y3XEOrO4RLqA6m3a9hhbLcPXvze8R3zRLDtKdj8d3U/2cnskLFGd08LrPxSrA+98x/qimX+dUqsuxvhwBsjO+Yk6P9ATVKCuilT+hIOKe8UhjGiPpB6wqxxB9iccM4XofpB2L+m75ie/Rrsfj7y3Jv/B5sfOb7xDYXeNjWWCYshb2JqoXZmw6V3wt5/Qs3fIq9tewKKpsH4M2HGpeDKg61P9N3H1idgzU9H5nMYaKHWJCUY1hF12uLzRO4Ph1A7MiHQ3ffyP5rGHVA6Ey6+Q3m2770S+/pbv1ECvmNl5Ln1D8Bbdx/f+IZC4y7wtsOK70BhZQqhfhsmVqmTT8EU2POCej4chsPrYfolIAQ4M2Hq++BwgnW91/1OfcYRzArR/4GapPh1RJ2+mP40QEeKibJ+99OlIvOJVepxKp+6YQeUzQGHS4lfW5RVUlcDr/5E3TfFXkplFzRsUxkqJ5KeFnWbUw75SSJqb6ca2+SlYLPBuPnqZATQXqtOXOVzI9uXzVG2T6A38py/G+o3q++wu3nEPo4Wak1SrMlEXfCSfnijhTpJtLjzWfjLtakn/roa1O2U5eo2mVD3tisvvHyOelw0NdbTXnkrZJfApLMjWSg+IysFqaLTkaBhBzzwfmV1RGMKdVYx5E8w0hjjJgKPrAcZhsnnqMflc9XnD/RG/PhooS6fo7Zv2h157ugGCAfV/RFMb9T/gZqkWHnUDv1nknaYEXWqy/odK2HfK/DP7ybfj5maN2kJ2BzJxaZxp7otM4SraJraVkroalKR6dJboWRGRKi7otIGR6qYZv19cHgdNGyPfd4S6iIVUctQxIu3xrQOhD1yNVFmCvGuyOctnRnZvsw4SZlRt7kPEy3UmtHAyqPWayamH2ZEXTZXCWP05bhJ4w4lvhv+CLtfSLwfU7zyJkLB5BRCbQhh2Wx1WzQNAj0qIjdfK58H2WVqPFJGBFvYYgVtuAgFYPszsZ/DpKdFTRI6MyF/knou/oR26G1ld7hyjfEbJ6GGHeozFUyJvAZQdJry5mOE+m0onqE+oxZqzWigKxPTGDOiNq2Izjr416/hr9epx6GAukQ/62Yonw+rvpy4l4cpcLnjIlGySccR+NV82POiEi9XvopOQVkfoLaPtgmyS5UV0Num/GlQtsrRagj6+x5/zc/g8c8k/5zbnoJ7L0icv7zvNehtjf0cJj2tyvaAyJijvfxQAI5UK3/apGgaONxKiBt2xNoeAHYHlJ4e+bzhkLJ0pr5PnQxGU6iFEJOEEK8JIXYKIbYLIb4yYqPRpBWRFV50RJ12mJNz5uV4Wy2s+z2895KazGt5D8IBqFgEy76kRLNlX9/9eOpVxoc73xDqqBS9zY9A+yF45guqAVTZbJUBAWpbUOLUuB2ySiCnTP2AmlgzI+o5V0HQqybd4tnyqLJofF2JP+e2J6FuozoRxbP1ccgsVOJqWjgmPS3K9gDIm6BuoyPq+i0Q7I340wA2u7I66jap78/8bqMpmxuJqBu2q0nEyUv7nuSGmYGESkHgNinlbOAc4FYhRIJPoDnZ0AUvaYwVURtR35bHImJ1KMqzLZ8T5a3G+bigItHcciXARdPA16GiUSmVEBZPV6mATTsj0TtA/uSIp92wI/Jadom67W40PGoBMy9Xz+1foyJtszdJV6PKopAhFXHHI2XEMokXQX837HoO5lytrgYSWR9mRO3OUyeiaKE2PfNooQYlxAffUmMqTyBz5XPU99zTGhnb5HNGX6illPVSyo3GfQ+wE5gwYiPSpA164YA0xtsJ9gwonAoIJaoZOSq6PLRORX3CDiWnqyhR2COX7NF4jkFuhbpfdJq6PfgvJfRNu+DsW+CSH6rny+dF3md3KE+75T21nTnJmG1G1E3qJ6tIZV0UnQav3Qk/q4RHb1TbRPvWiTzsln3QY6S8xYvgpodV+tz869T4zewVk57miFCDsibihbpwqhL5aMrnAMYVRVmc9RH9XMN22LnK8PYnKaH2tisBHwEcg9lYCFEJnAH0yfoWQtwM3AwwefLk4RibZpQJhMLYBNj1ZGL64etUlXKODCOirIdZVygxOvS2yh8umaFynkFFxo0JhLrrmJpQA5h2voq+V38dTn+/Eve510BmkbIPTrsw9r1F01RJdaAnKqIuNfbbpKJq8/GH74PD76rmR7tXqxPEoXXqxFJYmTgrJPq5aKFu3gsvfR+mXaD879xxcGxr7HujPWqIzaU2I/UZl/Y9pnn1Yc+A4tP6vm5+zhe/q6ycy38R+S5AWUem5TKMDDhUEkLkAE8CX5VSdsa/LqW8V0pZJaWsKi0tHc4xjim6fEEr/zhd8XgDyAFUUQXCYb1eYrri7VSX9BCZLJt/PUw+WwlI3cZYj7V8Tt8UNoiNqB0uuPZeZU9s/LMS5uwSVQwy50pw5cS+t2haJH/ZjDSzilQGRHeT8qlNoZ6wGM75Alz0fZUCt/1pJcQTFkPleWpSLhSM3f+hdeokUXJ6RKhDAXjq8+B0w9W/V5ZNTpz1EfSrE1m0YOZPjEwmmpF6vO0BESupZCbYnX1fz60Ad4H6jqdfAktuinwXMGL2x4D+C4UQTpRIPyylfGpERnIS4A+Gufh/X+eXL+8Z7aEkpbXbz9n/9QqrNieYnIkjEJQ6NS9dMSNqULZCdqmKiCcvVf5qV0Osx1o2F9oPxpae+zzg71LRt8m4+UpMARZ8JPUYTHFCQNksdddmV5Fsd6PyoLPjgrbSmTBuAWz8ixK7yeeon0A3NMRFxYfeNibqTosU12z+u5rsu+JXkGecYHLHqc9hfjYzEyReqL3tahvLn47K+DDJKVfCX7Ew8WcWQr2WVQxX/TYyuVpYqb6H0RJqIYQAHgB2Sin/b0RGcZLwxp4mjnV6eWPPyJWSHi/ra1vp8YfYcqT/kt5gOKyLXdKV6Ij60h/DZ/+pIsCJSwBDPKI9VlO0G3dFnvMYvq4ZUZuccyt8+jmYd13qMZhCXVgJGdmR57PLjKyP5kgWSDTzr1cTmzKkxNIUzGif2pxojJ6okxIOvKnEdM5VkW3N8ZufJ7oq0cTKpT4aFanP6Ds2IeDTz8IlP0r+ua/6Ldz0spqENXG6lT00ihH1cuATwIVCiBrj5/IRGc0YZ6URpW6v66DLF+xn69GhulZFGwdb+u8nHAjJsVM+3lkHAe9oj+LEER1R55RF/NTMgsjle0xEHZX5YTYceu9l9Vz8hJrNBpXnqttUmEIdn2+cXaJsBl9HJAskmnkfRp1MhDqx5E9QWSR7XlBCfOBN2PRXte3kpSpnO9gb8bUnnxOJZKPHb2a9JBRqwx7a+yLUvtl3H9GUzIDs4sSvQWTyMJ6iqSMm1P1OJkop12KdojXJ6PYFeWnHMaaVZrO/qZuaQ+2cOyPBH+kos75WeYq1LT39bhsIhckYCznUUsLvl8PZ/wYrvj3aozkxeDtVylkipq1Q1kd+1KR+wRRVqdewQ/WSXvfbyGuFU4Y2hoLJkJGrfOZocsoiXeayE0TU+ROUTePtVCcWUEUjNX+NbZ2akatsBjMVsfZN6DgES78Yuz9TqLtSRNSFU5V3/tLt6vHZtwzmkw6MomkqZXAEGFTWhyY5L+9swBsI870PzuamP1Xzbm1r2gl1rz/EtqMdOGyCQy09hMIyZUZHMDRGJhN725QvGT/zfzITHVHHc+H3YOmXYiNim00VrOxYqTI9Ft0ICz+qCkYKK4c2BocLbn2nb9ScXaoKXMz7ibj+T5FmRgCX/bcaTzT5E1RWixm9mr2i4ycBBxJR55bDF95SdozNEenvMZwsvRUWf0oFDsmi9SEyJoX6aHsvUkomFmaN+LHW17ayeHIhNkPQNh5qY974fDLivNuVNXWMz3ez4vQyZlfkWRZDOlFzuJ1gWHLx7HJe3tnAsU4vEwoyk24fCMmx0eLUWvOvnxVKEr73mCqeSJSKla6Ew2pSLLoPRTTOTPUTT/kcVVhSMhMu/zlkDMP/T36CkopocU7kUUMkkjZx58HU85IcY5IS1/1r1FVB+fzY11154MyKZH6YucyZcWlyZp+SkSK6gdMwMwbCpb78+983cevfNo34cTYeauP6P7zNExtU/uXO+k6u/d1b3L821ocKhSVv7m3isnkV2GyCJZVFbDrUbvXKSBfMk8eHz1T/XAebU/vUgVB4bBS7RC/OOtjm7f/4Cjz0wVFbC29I+D2AjEwmDpRJZ6u85WvvHR6RTka0UCfyqAeL3aGsG6Tq8mePiy+FUBOM0RG1mWN+kjAG/gtj6fEH2Xy4na1H2kd8wm7dfnUJ9UzNUUBFzQArN8WmtrV0+wiEJJUl6o+/qrKQ3kCIHXV90s1HlfUH25hZnsuCSQVA/z712BFqw5s0J5wGSjgEB99W/+DJVqFOR8zOecmsj2Qs+jh8fQ+MXzTsQ4ohOopOZn0MFrMJVKKUOlCZH9FZHyNQdDKajIH/wljMy/ewhJpD7QN6T5PHx2u7UiwxlIRqY+Lt7f0t1Hf08o/NdbgcNnY3eNh1LCLCjZ0+AMpy3QBUTVF/JPe+uZ8H1h5ImGGxo66TB9Ye4IG1B6g5PLDPcTyEwpKNB9uoqiykIs9NhsNmjevNvU00eXx93hMMy6E1ZOqsUwJ4oohuyDOYWffGnSozAVQJ9kDobVcN+c3I3d8Du1af2IjcnFwbbEQtRPIJyOHEjKKd2bFpe8eD6VMnKlKBSHUmxPb5OEkYc0JdXduGEGATyj/uj2AozM1/qeYzD63ncGv/mQ4m4bCkuraVs6YWISXcsWo7R9t7+eZls7DbhBVdAzR61MRJeZ4q1x2X72bu+Dye21LPj5/dwUfvXUdHT6TFpJSSrzyyiR8/u4MfP7uDG+55m/caPYwku4510uULsqSyCJtNMLkoi9qWburae/nkg+/yPy/s6vMefzCMcyjpea/8CB6+bkTXkIshOooejFCbhQ9TlsOOVRDse7Lqw8Y/w6Mfh01/UY+f+w945GOqxeiJYqgR9YnCzPTIGcYK5cnnqKh5QpJJwNyKKI9aC/Wos762lZnluWrC7mD/Qv27NfvYZETeA6nGM9nb2EWnN8hHqiYxf0I+/9zegNtp46NLJnHejBJW1dQRDishajAj6jy39f6Vty5n8w8u5YlbltLk8fHdZ7ZaZds76z3sbezi9ivm8Po3VpDtcvCVR2rwB0fO0zavDqoqCwGoLM7iYEsP/9hch5TwwrZjeAOxUWEwLHE6hhBRH3xLVYqNUIOaPnQdU70sUq1QkohD6yB3PJz7Hyqy3vtS/+9p2KZun/82rP2lqpTLGQev/UStG3gisCLqExAdDwXT7hgu2wNU7vVtu/qWsZvklqvqRp+nb5+Pk4AxIdSv7Wpkw8E2gqEwGw+2saSyKGbCbuuRDl7dFeme9V6jh/95YRc/fnYHv35lL1cvGk/VlEJW1SQW6m5fkD+/XRvTo8OM1pdUFnLVovEAXDJnHNkuB1ctGs/R9l42HFLiZ1ofpTku6/0Ou438TCdVlUV87ZLTeXZLfcTj3nwUh01wzRkTmFKczU+vnc/2uk6++PAGfvr8Lp7bErmUr23u5skN/a8y3eMP8tC/DiQV+/W1rVTku60sjynF2dS2dPNMTR25bgceX5A1u5U99PzWevY0eAiEwrEFL/tfh5d+oH72vRp5vm4T7Pmnut9Zp0qVof9FV30eqP5j/5F3KADv3hcr/JsfiSyi6jmmChoKpkSEetfqxL0tojGLJ6atUP2Utz6WentQecjjFqgJrZfvUDnEt6xVUeRTn0+80spwk+4RtdOtxpYoh3qkMKsTO+t1RD0aeAMhvvz3TXz2ofWs2d1Etz9EVWUhVZWF9PhDrNndxCcefIeb/lTNhoNtdPQG+NSD6/nD6/v4y7qDzKnI44dXzeOqReP7eMsmT286yvdXbuelHRGxr65tpTTXxeSiLK5cNJ6pJdl84hxVGHDxbFU6+vY+NdnY4PFSnJ3RJ2XP5JbzT6NqSiG3P7ONw609PLu5nvedXkpRtpqVvnTuOP7t/Gm8sbeZB9bu59a/bWTN7kZ6/SE+96f13Pb4Zpq7Ul+W//3dw9zxjx08v62+z2tSStbXtlJVWYQw8jsri7PwBsLsrO/kKxfNoCTHxarNdazZ3cgXHt7IJx94l7Yef2QysWE7PHw9vH03vPUbePoLEV/2H19Vq3T4u2PLgJOt5Wey9Ql49qtwbEvq7d74uerotv1p9bizHp7+NyXeoCaRcqJWKOlth8c/DS//MPk+2w+rxVonL1WiO/MyVRGXilAAmnerYo2rfguls+Da+9Ql/od+Dc17YOc/Uu9jODB99cF61CeS6RcnT7cbCcrnAgL+9SvVzU9PJp5YXtvVSJcviMcb4N8fUSl5SyqLrAm7rzyyCW8gxLg8N197tIbvPLWFY51envricvbc+QH+8eVzyc90cvn8ij7esomZthb92vraNpZUFiKEoCzXzWtfX8FZU9Uxc91OxuW5OWhkTTR2einNdfXZr4ndJvjlDYuQwP+7fx1H23utKN3kOx+YzZ47P8DWO97P6eU5fOOJLXz3ma3sa+o2xtiWYM8RVsVlpkRzpK2Xhk4fSwzbA1REDcrrv3LReK5YUMHLOxv5xhNbmFyURXOXj8OtvSqPOuCFJz+vLrVv2w3XPajshtq1quVkfY267Nz9vBJqm5E+1Z9Qm9FvKrvi8LtKqBNt37hDReOe+qilpA4osQz51KKn4SR2UnTTd1Ad2npblcgno2WfWlW7bC7M/pAq9jDzr6dfrPJ9BzopeTyke0QNcP0fVbe8E0X5XFVwUvOweqwj6pHjO09t4bbHNvP1xzez4aASppU1dZTmuvjRVfPo8YeYUJDJ+IJMxuW7mVSUSY8/xPc+OIdfffQMDrf1sHrrMf79whksMlLQTIpzXJa3HN/is9o41qu7G+noDXC0vZej7b3WySARU4qzrKyJRo+P8ih/OhGTirK448q5HG7tJdNpt6LyeNxOO7+64Qw6egI8tfEoN54zmQyHrU8BzZG2Hn7zyl68gRAHmrvZfKSDkhwXb+xporU7dm0608uP/jyVhlAvn15CWa6bqxaNxx8M09ET4A83LuZrl5wOGIsGrPlv1SPi6t+pGf3TL1NN6rc+rqJihPrH2PpEpOOZw60i1mia9sDrP49YHfHC6+1UUbDZOtPnUXZC/kRVPWcWtJjbN2xXdkg4oC59i6apHON37jH216Ga2kezY5W6Glj7S1WibPapMLMK2lIUzViLuCZY+cNmUz7qe6+o6rdkhMPw+v8MrTjHxNepToaJilpOZS76flS71ZNLqNOqMvHdA614A2E6ewO8srOBx29Zxqu7Gvn4OZP5+NmT2VHfGVNJ9//OmsLBlm4+fvZkhBB89/LZbD7Swa0XJK4y+8C8cazZvZV9TV1ML1NVXfUdvRxpUxHuypo6/rntGGv2NOKwCVbMTD4ZUlmczStGyl9Dp5dZ45JUiUXx4TMnsOVIOwWZTrJdyb/6OePzuPPqeby0s4HvfXAOe451WScTUPnNtz68kc1HOujsDZDrdiIE/OzD8/ncn6pZvbWeG8+J9G9YX9tGrsvBzKgxTijMZMXMUj67XOWnLppUwCVzyrlgZhlzxucxc1wuexo8LJ1WDO88CzPeDzMuUW92ZqqIcscqdYk59Tzl275zj+qI9r5vqCg3OqIO9KqVPZp3w7xrVSQaL7x7X4S1/6ceX/8QvPBttWbfp1fDW3f1Ffa22sj93HKVDgaqXeb869WJ5PC6iLAe3QBPfEZdGTizVbmvza5ei+4nPP6MxL+YBnPVlCQVaPOvV5feO56J9CmO59BbauKx4whc+ZvE2/SH1ygfH+Yy5TGPwwXXPaCKmCoWjfZohpW0EupXblsBwHuNXVxx15vccM/b+ENhrlo0ASEE/3VNbOnoF1bECvJN5yXoaBXFWVPVWXZ9bZsl1Kal8Llzp1JzuJ2fvbCLlm4/37xsJtNKk8wwA1NKlD3Q6Q3Q5PFZOdSpEELwo6vm9bsdwEeWTOIjS1RrxqrKQu59Yz+9/hCZGXbuemUvm490cObkAu5fe4DCLCdnTy3iwlllTC/LYdXmuhihrq5t5cwphTF9Pew2wUOfOStmbPd9sirm9V9/1BCs1zsjvX9N5l2nMh58HXDefyihfvtu9drkc1RTnmihfukHSqRBWRbRa8yZgm1O/u14Bp6+BbY8AufdBlOWwq5nYd8rKiK1rBIJ+19Td3Mr1ISgyfu+oVYfObQOqj6r/POnblZe9hfWqh4X0Zj9LlLZMI071AnGmeR3XT4XSmerK4tkQm1aIztWqjJuR3LLLCm+zvT2p0eTstnwuRdHexTDTlpZHybTy3L47uWzaen2M6U4i4UThycNqbI4i5KcjJj86+raVrIy7MypyOOqheNp6fZzVmUR//a+1L0fTOtg48E2wjKSQz0SLKksIhiW1BxuZ8PBVu5+7T2uWzyRh286h9NKs2nrCXDlQnUyu2rheN490MonHniHTz74Lp944B32NHTF+NODJlEDoGnnK2G0OVV0XbEQimeoDmUTl8QufbT/dXj3HjjzU4BQkannmKokjE6pa9yhvOLJy5RIVyyC841ueEXTVKMfT73a3lzfz8w+yR2nurkJmzpplM5UJwwzV/rF7ymP+Zrf9xVpUIUZuRWpLYmG7YlXpjYRQq3hd+ht+Ms18Jdr1c8jH1deftAP259R2SnedmWTRPPKj2Hvy5HHb/wiso8tURkp3gS/D81JTVpF1NHceM4U9jd3s2hSgZWpcLwIIaiaUhQzMbe+to0zJxfisNv46FmT2XnMww8+NKffdQKnFKty8XcPKNEv68ejPh7OnFyIELBmTyOrt9YzoTCTH3xojoquP3Ymv35lD1csVBHvR5ZM4q19LXi8kfL6s6YWcfn8imS7T03QrwQyPoKzO+Gi25UfawrfhcY6cq5cNbHmOabev/UxtXzRB/5HRbmN2yPiPHmpal/p71YCPmkJXHyHSn1b8Z+Rfg2WNbFPienCjyrRPvyuej5nnNp26a1K6M1971gJ1Q+qn2X/rtppJiPVStI+j0o7POPG1N/XGTfCgdcjE34AR9arrJj3fV0J9NW/h1VfVtH1LKO1e3czvPkLcOXDF/6lrgRe/bGK0HtaVBXlvOtUx7kj78JpF6Ueh+akIm2FWgjBDz6UYBXg46SqspAXth+jodNLZoadXcc6+feL1EoP4wsyYy7/U2FmTVhCnSLr43jJz3IyszyXe9/YjwAe+7el5LrVem5zxudxzyciYy7Pc/P3m5OU2Q4Fs7jCleCqZvGnYx/PvUb9gNGoXYKnzshXXqosg/K5SpBNQZx+kRLqY1tVr+HFn1KR8XUPxu7bFOrD76oJw+LpKj2ubqM6CZh2xKV3Rt5jZnQ8+x+q49qF30v9WYumJi96MVdGSRVRg4rsPxWXorf7Bfj7DbDyVjXJNeMS9T1t+mukC57Zv9nvUamFzXth0jnwmdUqLfHJzyl/29+tJlvn97P6iuakIi2tj5FkSaXKfKiubWPdvhbCMvLcYMhxOSjJcVlLWvWX9XG8VFUWIiXcesF0qoYw3iHjHWLOrrmiRt0maHkvIpplc1RU3LhD2SaVRoS761l1G79aSPT+bM6IXVA0LTJJGL+UlEn5fDVpaM9Qq2D35wcXTVPN531dfV8zKxITZXz0x8zLDJ+8Swm03amENtirCnNA2SX2DLWq9dFqtQDstfeoyc6ZH1Cfw8yycRfoiPoUI20j6pFizvg8Mp12XtvdyLr9LUwuymLxlKH5t5XFWVY2Rqo86uHg/501BbfDbkX/Jwwroh6sUBtr1G19Qt2aXc/K5ygR2v28WlmkZLp6fqch1MkiVptdTfiZkWfRtEgqVm7iVEfsDrjkhyqlcCC9iKNT9MbF9Tze84JaE6+gsv/9JOLSO1XGiJlbPPEstQLL1sdh4Q3qqmP8mUrQve3K8zcnODOyYdYHlb8dCsCC60+qFp6a/jnlImqn3caiSQU8seEIde29/PKGRbid9iHty7Q/irMzRrwd6JzxeXzvijknvu2o6bUONqLOMwp69r4IdlektaYprm0HlDC689WkZNsBlddcMDnh7gC1vQypCcOCyf1H1ABnfT5ix/RHdIpeND2tan3Bedf2v45gMjKy4YO/iBTI2Gww/8NqMrT9sOoTYq7jd95tqoAmmvnXKQEPdKs0QM0pxSkn1ICVAfGlC2cMOZoGFVHDyE4kjjpDjaidmaopT8iv+mGYtkPRNCXc5v3o27LZqXODze3yJ6mI0hT9nCQR9WApNHoet+5Xk49/uVZ5wjueUZN4wy2Q869XJ56XbldFO8l6LQOcdqFasSR3fGSyVHPK0K/1IYR4ELgCaJRSDiwJOM352NmTcTnt3Py+1HnX/TGlREXUI5maN+oMNaIG5St3N8X2ELY7VOrcsS2xQn3k3f7933hhzymFK36pmioNB+48dXLZ9ZyKcMMBePF2Vd1YcrpK+xtOyucqq8fsYTLprOTb2p2qv4g9Y+hRvWbMMpDf+EPAZSM8jhNKRX4mt14w/bhtBCuiHmF/elQZakQNkQnF+Egxvmzbiqj7yfKJ3x6Up1t0fCfc2GOcptLpsktU3nf1A3DwXyr6HYlKQDN7o3R2/42EZl0OMy5OvY3mpKRfpZJSvgGk30qtaYDpUY87ma2P42kAVDAFECo3Opp4oTZ922QZHybFCYR6uDHHcvXvVOWgefKY9+GROZ6532Qrl2g0DGPWhxDiZuBmgMmTU0wInUTkZzr5w41ncuZx+Nxpj6/TSHEbwp/K2bfAlGV9KwHP/KSyGExRnHUFXHlXao8WlEBfe1+k58hIcO5/wMzLlScM8LG/qwh7pFYpL6yEj/xZZXxoNEkQ8Z3kEm4kRCXw7EA96qqqKlldXX2cQ9OkBSu/pDIebuu7VJdGoxk+hBAbpJQJK+70rIQmNYn6fGg0mhOKFmpNary6U5tGM9r0K9RCiL8DbwMzhRBHhBCfG/lhadIGHVFrNKNOvzNEUsqPnYiBaNIUb2fqakGNRjPiaOtDkxodUWs0o44Wak1qtEet0Yw6Wqg1yQkFVCvORL2oNRrNCUMLtSY5x9PnQ6PRDBtaqE8lav+llsYaKD5j0QDtUWs0o4oW6lOFln3w0OWw7ncDf4+OqDWatEAL9anCsS3q1lxxZSAcT+c8jUYzbGihHi7eewU660d7FMlp2GHcblUrWg8EHVFrNGmBFurhwNsBD18Pb/zPaI8kOY071EoowjbwqFpH1BpNWqCFejg4sl4tqXTw7dEeSXIatquex9NWqAVVB9A1MRJR6/Q8jWY0GZtCve812PvS8O2vtw1evgNe+E94/edqnbxUSAnv3gcdR9XjQ+vUbdNOtRBqNLuegyMDaPl6dGNkJe7hxt8NbbWqCf7866H9IBx+t//3WRF17siMS6PRDIixJ9ThMDzzRXjk4xHf9Xip/iOs/SVs/DO8die88O3U2x/dCKu/Dm/+Qj0+tA4cmep+tAD6uuDJm+C5/+h/DKu/Dk//G4SCQ/sMqWjcBUi1JuGsK1Qj/39+RxW0pMLbAc4stV6fRqMZNcaeUB96Czx1alXopz4PQd/x73PrEzDpbPjPI3Du15Rgp4putz6mbrc/raLVI9Ww8KNgc8KhKPtj9/MQ6IH6zdC0J/n+WvbB0Q3g74LG7cf/eeIx91k2R00MXvErdbw3fp76fbrPh0aTFow9od76uFoa6sP3Q8M2ePXO49tfw3YlZPOvV49X/KdabXrVl+CJz0V+Vn4J2g6qiHfbU5AzTlkma3+lyqxPuwDGnxGxQcyxZpeqCbxtKSbwtj0ZuR/9/qHQ3QzP/oca8z++Ap5j6srDmQWFU9U2c6+Ghf9PCXUqC0T3+dBo0oKxJdRBP2x/BmZ9EOZdC4s/A2/dBQfeGPo+tz4Bwg5zrlaPHRnw4QfU+nx1myI/W5+AJz4D+16F7kZ4/0+UhfCvX6v3TTpHTdbVbYSAF7pbYN8rsOj/QeV5ySfwpIQtj8GUcyFvYmxEPlikhJW3qiuCuk1Q8zd46mZ1QiudBbaoX/cHfqZWCX/q8+DzJN6fjqg1mrRgbAn1vlfA2x6Jft//EyWoT9+iotvBIqUS4NMugJzSyPOlp8PnX4V/3xj5ueb3yi54+t+UeM36oBL3kE+NIbdcLc4a8iuR3PGMsmfmX69+WvcrEY/n2BZo2Qvzr1NCf2jdwDIyErHhIdjzAlz6YzXmy38BB16H2jeVPx2NOw+uuQfaDyX35HVErdGkBcO2Cvmw8NfrIOhN/npbLWQWKWEFyMiGD98H91+ifnLHwaSz4MLbQQhVhPLWbyAcguLp8IH/URGzycF/QcchuPC7/Y9t7jWw55+w+e+w6OPgzFTiuuGPkdWzJ52tbld9WU3ElcyE8nmQP0lNKD55E+RNiN1vZ53ytudcpYR92xMqK6P5PTV2Ge47lvxJ8MFfqM/ftBte+n7EK5+2As76N7XdmZ9Uwr17tcr4iGfKMuXJv/m/yie3xf05NO4c2RW/NRrNgEgvoQ4HlagmI3+SEsfoLIQJi+Gqu2HjX9Sl+pv/q1YkmX6xsioyctQl/oY/QlYRXPR99T5/Dzz7NcitUJkQA+ED/6PGeM4X1ePJy5T9suj/qcfZxXD2F9TkYXYpnH2zOmFkFsCKb8Pel/t+vpxyNRGZVRQR/C2PK5F25SVYXUWqk4XTDe//b3j8M9B5RAnx9Ivg8p9HLA4h4Mq74PlMmPmBxJ/p/G8rX7t5b9+xVSxUJyiNRjOqCDnUy+wUVFVVyerqAeQODzfhMPz1GjVBVjZbpaV9Ya2yJlZ9WYn5Z1arSHL1N+Dde+ETz0Qi9NEmHIKfTVVd6zJy1dgLK/tu9+LtSsgrz1O2xsef0JGvRjPGEUJskFJWJXptbHnU/WGzwdW/B3uG8pM/8FMl0qCiz8JK+NsNcPdZSqTPuTV9RBrAZlfWDajIOJFIA1z4PSifr0R6yU1apDWak5wBRdRCiMuAXwN24H4p5U9TbT9qEbVJ7VpV1r38q+ry36RhuypsCfmVV3zRD5SFkE7UrlVXBOd+LXbs8bTsU9kd538LMrJO3Pg0Gs2IkCqi7leohRB2YA9wCXAEWA98TEqZtCxw1IVao9FoxhjHa32cBbwnpdwvpfQDjwBXDecANRqNRpOcgQj1BOBw1OMjxnMxCCFuFkJUCyGqm5qahmt8Go1Gc8ozEKFOZJT28UuklPdKKauklFWlpaUJ3qLRaDSaoTAQoT4CTIp6PBGoG5nhaDQajSaegQj1emCGEGKqECID+CiwamSHpdFoNBqTfisTpZRBIcSXgH+i0vMelFKOQC9OjUaj0SRiQCXkUsrVwOoRHotGo9FoEnByVSZqNBrNSciI9PoQQjQBB4f49hKgeRiHMxLoMR4/6T4+0GMcLvQYB8YUKWXClLkREerjQQhRnaw6J13QYzx+0n18oMc4XOgxHj/a+tBoNJo0Rwu1RqPRpDnpKNT3jvYABoAe4/GT7uMDPcbhQo/xOEk7j1qj0Wg0saRjRK3RaDSaKLRQazQaTZozqkIthPi0EGL8aI6hP/QYhw8hxI+EEBcP4X0rhBDPjsSYEhzrhI5RCDFeCPHEEN63WghR0M82Q/osCfajxzgMYzweRtWjFkKsAb4upUzb5WD0GAc9FoH6uwoP4z5XoD7fgJaLF0I4pJTBFK+PhTGmfD0d0GM8gUgph/UHyAaeAzYD24AbgMXA68AGVHOnCuA6oAvYDdQAmcBFwCZgK/Ag4DL2+VNgB7AF+IXx3IeAd4ztXwbK9RiHb4zAz4AvRo3nDuA24BuojopbgB8ar1UCO4HfGfuZAjxkjHsr8DVju4eA64z7S4C3jM/3LpALuIE/Gu/ZBFxgbLsCeNa4XwQ8Yxz/KHBn1Pg2GN/DpjQa4zpggfH8WuPnReBvUd/pNuP1TwOPA/8AXgWygMeM/Txq/J6qjG1rUdV05ue6D9hu7DtzgJ+lEngT2Gj8LCP5712PcRBjHHZdHQGh/jBwX9TjfOODlRqPb0B14ANYE/WFuVEryZxuPP4z8FXUH/1uItF/gXFbGPXcTcD/6jEO3xiBM4DXo46/A/gkKo1JoGyzZ4H3GX+oYeAcY9vFwEtR7zWP9RDqxJIB7AeWGM/noRqE3Qb80XhuFnDI+DwriIjgXcAPjPs3A11R/6y9wOfSbIwXAjXG/T8AHiICsMMYW7TAHAGKjMdfB+4x7s8DgiQWmCCwyHj+MeDGAX6WLMBtPDcDqCbx712PcZBjHG5dHVD3vEGyFfiFEOJnqH+SNuPLeUldcWIH6hO8byZwQEq5x3j8J+BW4G7AC9wvhHjO2CeoBQweFUJUoL7AA3qMwzdGKeUmIUSZ4X2XGsdfAFyKiiQBclB/mIeAg1LKdcbz+4FpQoi7UFcFLyYYY72Ucr1xrE4AIcS5KJFDSrlLCHEQOD3uveeiTmJIKe8VQvxWCDETKAeagNlpNsZXhRDFQoh84Bjqd1AohDjd+E4Pxb33JSlla9R+fm3sZ5sQYguJOSClrDHub0CJzkA+SzZwtxBiERBCndwT/d71GAc5xiT7HzLDLtRSyj1CiMXA5cB/Ay8B26WUS/t5a6Ilv5CqH/ZZqMv5jwJfQkUpdwH/J6VcZfiDd+gxDvsYn0BFEuNQixpXAv8tpbwn5oBCVALdUcdqE0IsBN6POkl8BPhs3BjlQMfezzY9wJWok9gG4/V0GyNR+6oh9juNpzvq/kCOBeCLuh9C2V/x40n0Wb4GNAALUVcfXuP5+N+7HuPQxjhsDHvWh3GW65FS/hX4BXA2UCqEWGq87hRCzDU296A8HoBdQKUQYrrx+BPA60KIHCBfqp7YXwUWGa/nozxKgE/pMY7IGB9Bifp1qH+MfwKfNfaFEGKCEKIswdhLAJuU8kngduDMuE12AeOFEEuM7XOFEA7gDeDjxnOnA5NRdk000dusQC0Ldw0wB+VBpuMYm83oCyXU0d9pKtaiTiAIIeYA8/vZPhnJPks+KkIMo/5O7Mb28b93PcahjXHYGAnrYz7wcyFEGAgAX0B5P78xLv8cwK9Qhv1DwB+EEL3AUuAzwOPGh1+P8vSKgJVCCDfqjPY14zh3GNseRU3YTNVjHN4xSim3CyFygaNSynqgXggxG3jbsF+6gBtR0Uc0E4A/CiHMQOA70S9KKf1CiBuAu4QQmShv+WLURN8fhBBbjc/6aSmlzziWyR3GvregoukbgIeBTsAjpXwxDccYfQJsQJ1Uj0op641IPxm/A/5k7GcT6kTUkWL7hPTzWZ4UQlwPvIYRhcb/3vUYhzbG4USXkGs0aYoQwg44pZReIcRpwCsoj9Y/ykOz0GM8MYxERK3RaIaHLOA1IYQTdRX0hTQUFz3GE4COqDUajSbN0b0+NBqNJs3RQq3RaDRpjhZqjUajSXO0UGvGBEKI7wohtgshtgghaoQQZw/jvvvtoKbRjCY660OT9hhFPlcAZxo5yyWocvdhQUp5+XDtS6MZCXRErRkLVKCq+3wAUspmKWWdEKJWCPEzIcS7xs90ACFEqRDiSSHEeuNnufF8jhDij0KIrUZk/mHj+VpD/BFC3Gjsq0YIcY8Qwm78PCSE2Ga892tJxqnRjAhaqDVjgReBSUKIPUKI3wkhzo96rVNKeRaq6dSvjOd+DfxSSrkE1RzpfuP524EOKeV8KeUCVAtMC6Oi8QZguZRyEaqa8eOocvsJUsp5Usr5qDanGs0JQ1sfmrRHStklVIOq84ALUN3+vm28/Peo218a9y8G5kSVdecZ5cYXo/pDmPttizvURaj2p+uN92YCjai+xqk67Wk0I4oWas2YQEoZQvXdXmP02TD7Z0RXbJn3bcBSKWVv9D6EUt9UFV4C+JOU8jt9XkjdaU+jGVG09aFJe4QQM4UQM6KeWgQcNO7fEHX7tnH/RVQbV/P9i5I8Xxh3qFeA64TRbU8IUSSEmCL677Sn0YwoOqLWjAVyUB3LClAd695Dre5yBeASQryDCjo+Zmz/78BvjW5pZmvSW4A7jee3ofznHwJPmQeRUu4QQnwPeNHoqhdARdC9pOi0p9GMNLrXh2bMIoSoRS2p1DzaY9FoRhJtfWg0Gk2aoyNqjUajSXN0RK3RaDRpjhZqjUajSXO0UGs0Gk2ao4Vao9Fo0hwt1BqNRpPm/H++XBdIITysfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_iris[['Petal.Length','Petal.Width']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('max_rows', 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sepal.Length  Sepal.Width  Petal.Length  Petal.Width\n",
       "Species                                                      \n",
       "setosa            5.1          3.5           1.4          0.2\n",
       "setosa            4.9          3.0           1.4          0.2\n",
       "setosa            4.7          3.2           1.3          0.2\n",
       "setosa            4.6          3.1           1.5          0.2\n",
       "setosa            5.0          3.6           1.4          0.2\n",
       "...               ...          ...           ...          ...\n",
       "setosa            5.0          3.0           1.6          0.2\n",
       "setosa            5.0          3.4           1.6          0.4\n",
       "setosa            5.2          3.5           1.5          0.2\n",
       "setosa            5.2          3.4           1.4          0.2\n",
       "setosa            4.7          3.2           1.6          0.2\n",
       "\n",
       "[30 rows x 4 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = [df_iris['Sepal.Length','Sepal.Width']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": [
    "plt.hist(df_iris['Sepal.Length'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": [
    "plt.hist(df_iris['Sepal.Width'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOSklEQVR4nO3df4hl5X3H8fen6wZDTYmyVztEzbRBSm0gqwxbi1BsjMVqqQoNKNQuRVhbKigEytY/GvPfFvKjFErKpkq2bZoiaKr4o+2yTRAhNR3tqitrqg3bVF12RyVRaUlRv/1jzsJknOs9O/femTx33i+43HOec+6c78PDfjicc549qSokSe35qc0uQJK0Pga4JDXKAJekRhngktQoA1ySGnXGRh5sx44dNT8/v5GHlKTmPfnkk69W1WB1+4YG+Pz8PIuLixt5SElqXpL/WqvdSyiS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktSoDZ2JOY75vQ9v2rGP7bt2044tScN4Bi5Jjeod4Em2Jfn3JA916+ckOZjkhe777OmVKUla7XTOwG8Hjq5Y3wscqqqLgEPduiRpg/QK8CTnA9cCf7Wi+TrgQLd8ALh+opVJkt5X3zPwPwP+CHh3Rdt5VXUcoPs+d60fJtmTZDHJ4tLS0ji1SpJWGBngSX4TOFlVT67nAFW1v6oWqmphMHjP/0cuSVqnPo8RXg78VpJrgDOBn0nyt8CJJHNVdTzJHHBymoVKkn7cyDPwqvrjqjq/quaBG4F/qarfAR4Edne77QYemFqVkqT3GOc58H3AVUleAK7q1iVJG+S0ZmJW1beAb3XLrwFXTr4kSVIfzsSUpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDWqz0uNz0zynSRPJ3kuyee69ruSvJzkcPe5ZvrlSpJO6fNGnh8Bn6yqt5JsBx5P8mi37UtV9fnplSdJGmZkgFdVAW91q9u7T02zKEnSaL2ugSfZluQwcBI4WFVPdJtuS/JMknuSnD3kt3uSLCZZXFpamkzVkqR+AV5V71TVTuB8YFeSjwNfBj4G7ASOA18Y8tv9VbVQVQuDwWAiRUuSTvMplKr6Actvpb+6qk50wf4u8BVg1+TLkyQN0+cplEGSD3fLHwQ+BTyfZG7FbjcAR6ZSoSRpTX2eQpkDDiTZxnLg31tVDyX5myQ7Wb6heQy4dWpVSpLeo89TKM8Al6zRfvNUKpIk9eJMTElqlAEuSY0ywCWpUQa4JDWqz1Mo0syb3/vwph372L5rN+3Yaptn4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqP6vFLtzCTfSfJ0kueSfK5rPyfJwSQvdN9rvpVekjQdfc7AfwR8sqo+wfIb6K9OchmwFzhUVRcBh7p1SdIGGRngteytbnV79yngOuBA134AuH4aBUqS1tbrGniSbUkOAyeBg1X1BHBeVR0H6L7PHfLbPUkWkywuLS1NqGxJUq8Ar6p3qmoncD6wK8nH+x6gqvZX1UJVLQwGg3WWKUla7bSeQqmqHwDfAq4GTiSZA+i+T066OEnScH2eQhkk+XC3/EHgU8DzwIPA7m633cADU6pRkrSGPq9UmwMOJNnGcuDfW1UPJfk2cG+SW4DvA5+eYp2SpFVGBnhVPQNcskb7a8CV0yhKkjSaMzElqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY3q80q1C5J8M8nRJM8lub1rvyvJy0kOd59rpl+uJOmUPq9Uexv4TFU9leRDwJNJDnbbvlRVn59eeZKkYfq8Uu04cLxbfjPJUeAj0y5MkvT+TusaeJJ5lt+P+UTXdFuSZ5Lck+TsSRcnSRqud4AnOQu4D7ijqt4Avgx8DNjJ8hn6F4b8bk+SxSSLS0tL41csSQJ6BniS7SyH99eq6n6AqjpRVe9U1bvAV4Bda/22qvZX1UJVLQwGg0nVLUlbXp+nUALcDRytqi+uaJ9bsdsNwJHJlydJGqbPUyiXAzcDzyY53LXdCdyUZCdQwDHg1inUJ0kaos9TKI8DWWPTI5MvR5LUlzMxJalRBrgkNcoAl6RGGeCS1CgDXJIa1ecxQkkzaH7vw5t27GP7rt20Y88Sz8AlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RG9Xkn5gVJvpnkaJLnktzetZ+T5GCSF7rvs6dfriTplD5n4G8Dn6mqXwQuA/4wycXAXuBQVV0EHOrWJUkbZGSAV9XxqnqqW34TOAp8BLgOONDtdgC4fko1SpLWcFrXwJPMA5cATwDnVdVxWA554Nwhv9mTZDHJ4tLS0pjlSpJO6R3gSc4C7gPuqKo3+v6uqvZX1UJVLQwGg/XUKElaQ68AT7Kd5fD+WlXd3zWfSDLXbZ8DTk6nREnSWvo8hRLgbuBoVX1xxaYHgd3d8m7ggcmXJ0kaps8r1S4HbgaeTXK4a7sT2Afcm+QW4PvAp6dSoSRpTSMDvKoeBzJk85WTLUeS1JczMSWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjerzSrV7kpxMcmRF211JXk5yuPtcM90yJUmr9TkD/ypw9RrtX6qqnd3nkcmWJUkaZWSAV9VjwOsbUIsk6TSMcw38tiTPdJdYzh62U5I9SRaTLC4tLY1xOEnSSusN8C8DHwN2AseBLwzbsar2V9VCVS0MBoN1Hk6StNq6AryqTlTVO1X1LvAVYNdky5IkjbKuAE8yt2L1BuDIsH0lSdNxxqgdknwduALYkeQl4LPAFUl2AgUcA26dXomSpLWMDPCqummN5runUIskTdX83oc37djH9l078b/pTExJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqNGBnj31vmTSY6saDsnycEkL3TfQ99KL0majj5n4F8Frl7Vthc4VFUXAYe6dUnSBhoZ4FX1GPD6qubrgAPd8gHg+smWJUkaZb3XwM+rquMA3fe5w3ZMsifJYpLFpaWldR5OkrTa1G9iVtX+qlqoqoXBYDDtw0nSlrHeAD+RZA6g+z45uZIkSX2sN8AfBHZ3y7uBByZTjiSprz6PEX4d+DbwC0leSnILsA+4KskLwFXduiRpA50xaoequmnIpisnXIukLWJ+78ObXcJMcCamJDXKAJekRhngktQoA1ySGjXyJqY274bLsX3XbspxN5M3t6T+PAOXpEYZ4JLUKANckhplgEtSo7yJqffwRqLUBs/AJalRBrgkNcoAl6RGGeCS1ChvYkqbzJvGWi/PwCWpUWOdgSc5BrwJvAO8XVULkyhKkjTaJC6h/FpVvTqBvyNJOg1eQpGkRo0b4AX8c5Ink+yZREGSpH7GvYRyeVW9kuRc4GCS56vqsZU7dMG+B+DCCy8c83CSpFPGOgOvqle675PAN4Bda+yzv6oWqmphMBiMczhJ0grrDvAkP53kQ6eWgV8HjkyqMEnS+xvnEsp5wDeSnPo7f1dV/ziRqiRJI607wKvqe8AnJliLJOk0+BihJDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjRr3nZiaovm9D292CZJ+gnkGLkmNGivAk1yd5LtJXkyyd1JFSZJGG+elxtuAvwB+A7gYuCnJxZMqTJL0/sY5A98FvFhV36uq/wP+HrhuMmVJkkYZ5ybmR4D/XrH+EvDLq3dKsgfY062+leS7wA7g1TGO3bqt3P+t3HfY2v3fyn0nfzpW/z+6VuM4AZ412uo9DVX7gf0/9sNksaoWxjh207Zy/7dy32Fr938r9x2m0/9xLqG8BFywYv184JXxypEk9TVOgP8bcFGSn0vyAeBG4MHJlCVJGmXdl1Cq6u0ktwH/BGwD7qmq53r+fP/oXWbaVu7/Vu47bO3+b+W+wxT6n6r3XLaWJDXAmZiS1CgDXJIaNdUAHzXVPsv+vNv+TJJLp1nPRuvR/yuS/DDJ4e7zJ5tR56QluSfJySRHhmyf9XEf1f+ZHHeAJBck+WaSo0meS3L7GvvM5Pj37Ptkx76qpvJh+cbmfwI/D3wAeBq4eNU+1wCPsvxM+WXAE9OqZ6M/Pft/BfDQZtc6hb7/KnApcGTI9pkd9579n8lx7/o2B1zaLX8I+I+t8u++Z98nOvbTPAPvM9X+OuCva9m/Ah9OMjfFmjbSlv2vBqrqMeD199lllse9T/9nVlUdr6qnuuU3gaMsz9peaSbHv2ffJ2qaAb7WVPvVnemzT6v69u1Xkjyd5NEkv7QxpW26WR73vmZ+3JPMA5cAT6zaNPPj/z59hwmO/TRf6NBnqn2v6fiN6tO3p4CPVtVbSa4B/gG4aNqF/QSY5XHvY+bHPclZwH3AHVX1xurNa/xkZsZ/RN8nOvbTPAPvM9V+lqfjj+xbVb1RVW91y48A25Ps2LgSN80sj/tIsz7uSbazHGBfq6r719hlZsd/VN8nPfbTDPA+U+0fBH63uyt9GfDDqjo+xZo20sj+J/nZJOmWd7E8Hq9teKUbb5bHfaRZHveuX3cDR6vqi0N2m8nx79P3SY/91C6h1JCp9kl+v9v+l8AjLN+RfhH4H+D3plXPRuvZ/98G/iDJ28D/AjdWd6u6ZUm+zvLd9h1JXgI+C2yH2R936NX/mRz3zuXAzcCzSQ53bXcCF8LMj3+fvk907J1KL0mNciamJDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmN+n/75fBUzBjpkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_iris['Petal.Width'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_iris['Petal.Length'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are using equal frq discretization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from feature_engine.discretisation import KBinsDiscretisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from feature_engine.discretisation import EqualFrequencyDiscretiser\n",
    "from feature_engine.discretisation import EqualWidthDiscretiser\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc = EqualWidthDiscretiser(bins=5, variables=['Sepal.Length','Sepal.Width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EqualWidthDiscretiser(bins=5, variables=['Sepal.Length', 'Sepal.Width'])\n"
     ]
    }
   ],
   "source": [
    "print(disc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EqualWidthDiscretiser(bins=5, variables=['Sepal.Length', 'Sepal.Width'])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc.fit(df_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sepal.Length  Sepal.Width  Petal.Length  Petal.Width\n",
       "Species                                                        \n",
       "setosa              5.1          3.5           1.4          0.2\n",
       "setosa              4.9          3.0           1.4          0.2\n",
       "setosa              4.7          3.2           1.3          0.2\n",
       "setosa              4.6          3.1           1.5          0.2\n",
       "setosa              5.0          3.6           1.4          0.2\n",
       "...                 ...          ...           ...          ...\n",
       "virginica           6.7          3.0           5.2          2.3\n",
       "virginica           6.3          2.5           5.0          1.9\n",
       "virginica           6.5          3.0           5.2          2.0\n",
       "virginica           6.2          3.4           5.4          2.3\n",
       "virginica           5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "discre = disc.transform(df_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('max_rows', 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sepal.Length  Sepal.Width  Petal.Length  Petal.Width\n",
       "Species                                                        \n",
       "virginica             3            2           5.6          2.4\n",
       "virginica             3            2           5.1          2.3\n",
       "virginica             2            1           5.1          1.9\n",
       "virginica             3            2           5.9          2.3\n",
       "virginica             3            2           5.7          2.5\n",
       "virginica             3            2           5.2          2.3\n",
       "virginica             2            1           5.0          1.9\n",
       "virginica             3            2           5.2          2.0\n",
       "virginica             2            2           5.4          2.3\n",
       "virginica             2            2           5.1          1.8"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discre.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Sepal.Length': [-inf, 5.02, 5.74, 6.46, 7.18, inf],\n",
       " 'Sepal.Width': [-inf,\n",
       "  2.48,\n",
       "  2.96,\n",
       "  3.4400000000000004,\n",
       "  3.9200000000000004,\n",
       "  inf]}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc.binner_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc1 = EqualWidthDiscretiser(bins=5, variables=['Petal.Length','Petal.Width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EqualWidthDiscretiser(bins=5, variables=['Petal.Length', 'Petal.Width'])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc1.fit(df_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "discre1 = disc1.transform(df_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sepal.Length  Sepal.Width  Petal.Length  Petal.Width\n",
       "Species                                                        \n",
       "virginica           6.7          3.0             3            4\n",
       "virginica           6.3          2.5             3            3\n",
       "virginica           6.5          3.0             3            3\n",
       "virginica           6.2          3.4             3            4\n",
       "virginica           5.9          3.0             3            3"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discre1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal.Length  Sepal.Width  Petal.Length  Petal.Width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Petal.Length': [-inf,\n",
       "  2.18,\n",
       "  3.3600000000000003,\n",
       "  4.540000000000001,\n",
       "  5.720000000000001,\n",
       "  inf],\n",
       " 'Petal.Width': [-inf, 0.58, 1.06, 1.54, 2.02, inf]}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc1.binner_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal.Length  Sepal.Width  Petal.Length  Petal.Width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_iris['S_Len'] = pd.cut(df_iris['Sepal.Length'], bins=[8, 4, 0], labels= ['max_length','min_length','Avg_len','micro_len'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris['S_Len'] = pd.cut(df_iris['Sepal.Length'], bins=[ 4.3, 5.84, 7.9, 8], \n",
    "                          labels= ['min_length','max_length','Avg_len'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris['S_Wid'] = pd.cut(df_iris['Sepal.Width'], bins=[2,3.05,4.40,7], \n",
    "                          labels= ['min_Width','Avg_Width','max_Width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris['P_len'] = pd.cut(df_iris['Petal.Length'], bins=[1, 3.75, 6.9,8], \n",
    "                          labels= ['minP_len','AvgP_len','maxP_len'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris['P_Wid'] = pd.cut(df_iris['Petal.Width'], bins=[0.1, 1.1, 2.5 ,4], \n",
    "                          labels= ['minP_wed','AvgP_wed','maxP_wed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>S_Len</th>\n",
       "      <th>S_Wid</th>\n",
       "      <th>P_len</th>\n",
       "      <th>P_Wid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>min_length</td>\n",
       "      <td>Avg_Width</td>\n",
       "      <td>minP_len</td>\n",
       "      <td>minP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>min_length</td>\n",
       "      <td>min_Width</td>\n",
       "      <td>minP_len</td>\n",
       "      <td>minP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>min_length</td>\n",
       "      <td>Avg_Width</td>\n",
       "      <td>minP_len</td>\n",
       "      <td>minP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>min_length</td>\n",
       "      <td>Avg_Width</td>\n",
       "      <td>minP_len</td>\n",
       "      <td>minP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>min_length</td>\n",
       "      <td>Avg_Width</td>\n",
       "      <td>minP_len</td>\n",
       "      <td>minP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>max_length</td>\n",
       "      <td>min_Width</td>\n",
       "      <td>AvgP_len</td>\n",
       "      <td>AvgP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>max_length</td>\n",
       "      <td>min_Width</td>\n",
       "      <td>AvgP_len</td>\n",
       "      <td>AvgP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>max_length</td>\n",
       "      <td>min_Width</td>\n",
       "      <td>AvgP_len</td>\n",
       "      <td>AvgP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>max_length</td>\n",
       "      <td>Avg_Width</td>\n",
       "      <td>AvgP_len</td>\n",
       "      <td>AvgP_wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>max_length</td>\n",
       "      <td>min_Width</td>\n",
       "      <td>AvgP_len</td>\n",
       "      <td>AvgP_wed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sepal.Length  Sepal.Width  Petal.Length  Petal.Width       S_Len  \\\n",
       "Species                                                                       \n",
       "setosa              5.1          3.5           1.4          0.2  min_length   \n",
       "setosa              4.9          3.0           1.4          0.2  min_length   \n",
       "setosa              4.7          3.2           1.3          0.2  min_length   \n",
       "setosa              4.6          3.1           1.5          0.2  min_length   \n",
       "setosa              5.0          3.6           1.4          0.2  min_length   \n",
       "...                 ...          ...           ...          ...         ...   \n",
       "virginica           6.7          3.0           5.2          2.3  max_length   \n",
       "virginica           6.3          2.5           5.0          1.9  max_length   \n",
       "virginica           6.5          3.0           5.2          2.0  max_length   \n",
       "virginica           6.2          3.4           5.4          2.3  max_length   \n",
       "virginica           5.9          3.0           5.1          1.8  max_length   \n",
       "\n",
       "               S_Wid     P_len     P_Wid  \n",
       "Species                                   \n",
       "setosa     Avg_Width  minP_len  minP_wed  \n",
       "setosa     min_Width  minP_len  minP_wed  \n",
       "setosa     Avg_Width  minP_len  minP_wed  \n",
       "setosa     Avg_Width  minP_len  minP_wed  \n",
       "setosa     Avg_Width  minP_len  minP_wed  \n",
       "...              ...       ...       ...  \n",
       "virginica  min_Width  AvgP_len  AvgP_wed  \n",
       "virginica  min_Width  AvgP_len  AvgP_wed  \n",
       "virginica  min_Width  AvgP_len  AvgP_wed  \n",
       "virginica  Avg_Width  AvgP_len  AvgP_wed  \n",
       "virginica  min_Width  AvgP_len  AvgP_wed  \n",
       "\n",
       "[150 rows x 8 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris #Discretization dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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