Dry_beans_ver2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn.utils import class_weight\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"train.csv\")\n",
    "data_test=pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ID</th>\n",
       "      <th>Area</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>MajorAxisLength</th>\n",
       "      <th>MinorAxisLength</th>\n",
       "      <th>AspectRation</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>ConvexArea</th>\n",
       "      <th>EquivDiameter</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>roundness</th>\n",
       "      <th>Compactness</th>\n",
       "      <th>ShapeFactor1</th>\n",
       "      <th>ShapeFactor2</th>\n",
       "      <th>ShapeFactor3</th>\n",
       "      <th>ShapeFactor4</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>58238</td>\n",
       "      <td>971.303</td>\n",
       "      <td>397.202654</td>\n",
       "      <td>186.945510</td>\n",
       "      <td>2.124697</td>\n",
       "      <td>0.882317</td>\n",
       "      <td>58977</td>\n",
       "      <td>272.306674</td>\n",
       "      <td>0.604756</td>\n",
       "      <td>0.987470</td>\n",
       "      <td>0.775723</td>\n",
       "      <td>0.685561</td>\n",
       "      <td>0.006820</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.469994</td>\n",
       "      <td>0.998595</td>\n",
       "      <td>HOROZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>44515</td>\n",
       "      <td>757.601</td>\n",
       "      <td>265.590470</td>\n",
       "      <td>213.967453</td>\n",
       "      <td>1.241266</td>\n",
       "      <td>0.592420</td>\n",
       "      <td>44780</td>\n",
       "      <td>238.071960</td>\n",
       "      <td>0.771745</td>\n",
       "      <td>0.994082</td>\n",
       "      <td>0.974620</td>\n",
       "      <td>0.896387</td>\n",
       "      <td>0.005966</td>\n",
       "      <td>0.002376</td>\n",
       "      <td>0.803510</td>\n",
       "      <td>0.997370</td>\n",
       "      <td>SEKER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>31662</td>\n",
       "      <td>653.783</td>\n",
       "      <td>239.902428</td>\n",
       "      <td>168.421505</td>\n",
       "      <td>1.424417</td>\n",
       "      <td>0.712136</td>\n",
       "      <td>32034</td>\n",
       "      <td>200.781748</td>\n",
       "      <td>0.801407</td>\n",
       "      <td>0.988387</td>\n",
       "      <td>0.930853</td>\n",
       "      <td>0.836931</td>\n",
       "      <td>0.007577</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.700453</td>\n",
       "      <td>0.997737</td>\n",
       "      <td>DERMASON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>38778</td>\n",
       "      <td>734.020</td>\n",
       "      <td>268.446281</td>\n",
       "      <td>184.061923</td>\n",
       "      <td>1.458456</td>\n",
       "      <td>0.727925</td>\n",
       "      <td>39208</td>\n",
       "      <td>222.201897</td>\n",
       "      <td>0.766137</td>\n",
       "      <td>0.989033</td>\n",
       "      <td>0.904439</td>\n",
       "      <td>0.827733</td>\n",
       "      <td>0.006923</td>\n",
       "      <td>0.002005</td>\n",
       "      <td>0.685142</td>\n",
       "      <td>0.999250</td>\n",
       "      <td>DERMASON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>42530</td>\n",
       "      <td>775.403</td>\n",
       "      <td>281.212824</td>\n",
       "      <td>193.236878</td>\n",
       "      <td>1.455275</td>\n",
       "      <td>0.726511</td>\n",
       "      <td>43028</td>\n",
       "      <td>232.703412</td>\n",
       "      <td>0.729816</td>\n",
       "      <td>0.988426</td>\n",
       "      <td>0.888895</td>\n",
       "      <td>0.827499</td>\n",
       "      <td>0.006612</td>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.684755</td>\n",
       "      <td>0.996507</td>\n",
       "      <td>SIRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID   Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n",
       "0   0  58238    971.303       397.202654       186.945510      2.124697   \n",
       "1   1  44515    757.601       265.590470       213.967453      1.241266   \n",
       "2   2  31662    653.783       239.902428       168.421505      1.424417   \n",
       "3   3  38778    734.020       268.446281       184.061923      1.458456   \n",
       "4   4  42530    775.403       281.212824       193.236878      1.455275   \n",
       "\n",
       "   Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n",
       "0      0.882317       58977     272.306674  0.604756  0.987470   0.775723   \n",
       "1      0.592420       44780     238.071960  0.771745  0.994082   0.974620   \n",
       "2      0.712136       32034     200.781748  0.801407  0.988387   0.930853   \n",
       "3      0.727925       39208     222.201897  0.766137  0.989033   0.904439   \n",
       "4      0.726511       43028     232.703412  0.729816  0.988426   0.888895   \n",
       "\n",
       "   Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  \\\n",
       "0     0.685561      0.006820      0.000929      0.469994      0.998595   \n",
       "1     0.896387      0.005966      0.002376      0.803510      0.997370   \n",
       "2     0.836931      0.007577      0.002293      0.700453      0.997737   \n",
       "3     0.827733      0.006923      0.002005      0.685142      0.999250   \n",
       "4     0.827499      0.006612      0.001912      0.684755      0.996507   \n",
       "\n",
       "          y  \n",
       "0     HOROZ  \n",
       "1     SEKER  \n",
       "2  DERMASON  \n",
       "3  DERMASON  \n",
       "4      SIRA  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check for nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                 0\n",
       "Area               0\n",
       "Perimeter          0\n",
       "MajorAxisLength    0\n",
       "MinorAxisLength    0\n",
       "AspectRation       0\n",
       "Eccentricity       0\n",
       "ConvexArea         0\n",
       "EquivDiameter      0\n",
       "Extent             0\n",
       "Solidity           0\n",
       "roundness          0\n",
       "Compactness        0\n",
       "ShapeFactor1       0\n",
       "ShapeFactor2       0\n",
       "ShapeFactor3       0\n",
       "ShapeFactor4       0\n",
       "y                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10834 entries, 0 to 10833\n",
      "Data columns (total 18 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ID               10834 non-null  int64  \n",
      " 1   Area             10834 non-null  int64  \n",
      " 2   Perimeter        10834 non-null  float64\n",
      " 3   MajorAxisLength  10834 non-null  float64\n",
      " 4   MinorAxisLength  10834 non-null  float64\n",
      " 5   AspectRation     10834 non-null  float64\n",
      " 6   Eccentricity     10834 non-null  float64\n",
      " 7   ConvexArea       10834 non-null  int64  \n",
      " 8   EquivDiameter    10834 non-null  float64\n",
      " 9   Extent           10834 non-null  float64\n",
      " 10  Solidity         10834 non-null  float64\n",
      " 11  roundness        10834 non-null  float64\n",
      " 12  Compactness      10834 non-null  float64\n",
      " 13  ShapeFactor1     10834 non-null  float64\n",
      " 14  ShapeFactor2     10834 non-null  float64\n",
      " 15  ShapeFactor3     10834 non-null  float64\n",
      " 16  ShapeFactor4     10834 non-null  float64\n",
      " 17  y                10834 non-null  object \n",
      "dtypes: float64(14), int64(3), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target_variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DERMASON    2837\n",
       "SIRA        2109\n",
       "SEKER       1621\n",
       "HOROZ       1488\n",
       "CALI        1304\n",
       "BARBUNYA    1057\n",
       "BOMBAY       418\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imabalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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(data['y'],bins=int(np.sqrt(len(data['y']))))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# oversample = RandomOverSampler(sampling_strategy='all')\n",
    "# X=data.drop('y',axis=1)\n",
    "# X_train_copy,y_train_copy = oversample.fit_resample(X,data[['y']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data=pd.concat([X_train_copy,y_train_copy],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://towardsdatascience.com/how-to-handle-multiclass-imbalanced-data-say-no-to-smote-e9a7f393c310"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_data(data):\n",
    "    data_visualize=data.copy()\n",
    "    target_visualize=data_visualize['y']\n",
    "    data_visualize.drop(['ID','y'],axis=1,inplace=True)\n",
    "    for col in data_visualize.columns:\n",
    "            sns.boxplot(target_visualize,data_visualize[col])\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEGCAYAAABYV4NmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAnRUlEQVR4nO3de5hU1Znv8e/LRYV45S7dKiZgovFMTOiDzkk0MiJwZhLRJ2aAmTE4jwmMx4gxMaMhPBFwSHRiYg5xYtDRsfWYiKNxgkkQGgNqJoyICihehjKCtFwbEFEU+vKeP/Yq3F1UV1d31a7qan6f56mna6+919pr167eb61L7TJ3R0REpNh6lLsCIiLSPSnAiIhIIhRgREQkEQowIiKSCAUYERFJRK9yV6CrGDBggA8bNqzc1RARqSjPPfdcg7sPzLZOASYYNmwYq1atKnc1REQqipltbGudushERCQRCjAiIpIIBRgREUmEAoyIiCRCAUZEpJtqaGjg6quvZufOnWXZvwKMiEg3VVtby9q1a6mtrS3L/hVgRES6oYaGBhYtWoS7s2jRorK0YhRgRES6odraWlpaWgBobm4uSytGAUZEpBuqq6ujqakJgKamJpYsWVLyOiQWYMzsJDNbZmavmNk6M7smpM8ys7fMbHV4/GUsz3fMLGVmr5nZuFj6SDN7MaybZ2YW0o80swUh/RkzGxbLM8XM1ofHlKSOU0SkKzr33HNbLZ933nklr0OSt4ppAr7l7s+b2THAc2ZWF9bd5u63xjc2szOAScAngaHAUjM7zd2bgTuAqcB/Ab8DxgOLgCuA3e4+3MwmAbcAE82sH3AjUAN42PdCd9+d4PGKiEhMYi0Yd9/i7s+H53uBV4CqHFkmAA+6+353fwNIAaPM7ETgWHdf4dHvO98HXBzLk+5YfBi4ILRuxgF17r4rBJU6oqAkInJYeOqpp1otP/nkkyWvQ0nGYELX1aeBZ0LS181srZndY2YnhLQqYFMsW31IqwrPM9Nb5XH3JmAP0D9HWZn1mmpmq8xs1Y4dOzp/gCIiXczgwYNzLpdC4gHGzI4GHgG+4e7vEHV3fQw4C9gC/Ci9aZbsniO9s3k+THC/091r3L1m4MCsd5sWEalI27Zty7lcCokGGDPrTRRcHnD3XwG4+zZ3b3b3FuAuYFTYvB44KZa9Gtgc0quzpLfKY2a9gOOAXTnKEhE5LIwdO5YwHwozY9y4ce3kKL4kZ5EZcDfwirv/OJZ+YmyzS4CXwvOFwKQwM+xUYASw0t23AHvN7JxQ5leAX8fypGeIXQr8PozTLAbGmtkJoQtubEgTETksTJkyhV69onlcvXr1YsqU0k+mTXIW2WeBy4AXzWx1SJsBTDazs4i6rDYA0wDcfZ2ZPQS8TDQD7aowgwzgSuBeoA/R7LFFIf1u4H4zSxG1XCaFsnaZ2U3As2G7Oe6+K5GjFBHpggYMGEBVVRUbNmygqqqK/v37l7wOFn3gl5qaGtcvWopId9HQ0MDEiRNpbGzkiCOOYMGCBYkEGTN7zt1rsq3TN/lFRLqh+K1h3F23ihERkeKoq6ujsbERgMbGxu51qxgRESmfCy+8sNUssrFjx5a8DgowIiLd0Be/+EXSY+zuzkUXXVTyOijAiIh0Q4899lir5YULF5a8DgowIiLdUOaYy+LFpf8qoAKMiEg3lDkluRzfg1GAERHphrZs2ZJzuRQUYEREuqH0DLK2lkshyVvFiIhIQubNm0cqlWpz/THHHMPu3btbLU+fPr3N7YcPH55zfWeoBSMi0g0NHTo053IpqAUjIlKB8mltTJgwgd27dzN+/HhmzJhRglq1pgAjItJNDR06lAMHDjBt2rSy7F9dZCIi3VTv3r0ZMWJEWaYogwKMiIgkRAFGREQSoQAjIiKJUIAREZFEKMCIiEgiFGBERCQRCjAiIpIIBRgREUmEAoyIiCRCAUZERBKhACMiIolQgBERkUQowIiISCIUYEREJBEKMCIikggFGBERSYQCjIiIJEIBRkREEqEAIyIiiUgswJjZSWa2zMxeMbN1ZnZNSO9nZnVmtj78PSGW5ztmljKz18xsXCx9pJm9GNbNMzML6Uea2YKQ/oyZDYvlmRL2sd7MpiR1nCIikl2SLZgm4FvufjpwDnCVmZ0B3AA84e4jgCfCMmHdJOCTwHjgZ2bWM5R1BzAVGBEe40P6FcBudx8O3AbcEsrqB9wInA2MAm6MBzIREUleYgHG3be4+/Ph+V7gFaAKmADUhs1qgYvD8wnAg+6+393fAFLAKDM7ETjW3Ve4uwP3ZeRJl/UwcEFo3YwD6tx9l7vvBur4MCiJiEgJlGQMJnRdfRp4Bhjs7lsgCkLAoLBZFbAplq0+pFWF55nprfK4exOwB+ifo6zMek01s1VmtmrHjh0FHKGIiGRKPMCY2dHAI8A33P2dXJtmSfMc6Z3N82GC+53uXuPuNQMHDsxRNRER6ahEA4yZ9SYKLg+4+69C8rbQ7UX4uz2k1wMnxbJXA5tDenWW9FZ5zKwXcBywK0dZIiJSIknOIjPgbuAVd/9xbNVCID2rawrw61j6pDAz7FSiwfyVoRttr5mdE8r8SkaedFmXAr8P4zSLgbFmdkIY3B8b0kREpER6JVj2Z4HLgBfNbHVImwHcDDxkZlcAbwJfBnD3dWb2EPAy0Qy0q9y9OeS7ErgX6AMsCg+IAtj9ZpYiarlMCmXtMrObgGfDdnPcfVdCxykiIlkkFmDc/Q9kHwsBuKCNPHOBuVnSVwFnZkn/gBCgsqy7B7gn3/qKiEhx6Zv8IiKSCAUYERFJhAKMiIgkQgFGREQSkeQsMhER6aB58+aRSqWKUtb69esBmD59elHKGz58eIfKUoAREelCUqkUr730CicdM6Tgsno3RZ1U+zbuLrisTXu3djiPAoyISBdz0jFD+Naovy93NVr50cp/63AejcGIiEgiFGBERCQRCjAiIpIIBRgREUmEAoyIiCRCAUZERBKhACMiIolQgBERkUQowIiISCL0TX4RkS6kvr6e9/bu7dQ355O0ae9WPlL/XofyqAUjIiKJUAtGRKQLqa6uZl/z7i55L7K+1Sd0KI9aMCIikggFGBERSYQCjIiIJEIBRkREEqEAIyIiiVCAERGRRCjAiIhIIhRgREQkEQowIiKSCAUYERFJhG4VIyLSxWzau7UoN7vcvm8XAIP69iu4rE17t/JxOnarGAUYEZEuZPjw4UUrq3F9AwB9T+lYYMjm45zQ4bopwIiIdCHTp08velnz5s0rWpkdoTEYERFJRGIBxszuMbPtZvZSLG2Wmb1lZqvD4y9j675jZikze83MxsXSR5rZi2HdPDOzkH6kmS0I6c+Y2bBYnilmtj48piR1jCIi0rYkWzD3AuOzpN/m7meFx+8AzOwMYBLwyZDnZ2bWM2x/BzAVGBEe6TKvAHa7+3DgNuCWUFY/4EbgbGAUcKOZFd4BKSIiHZJYgHH3p4BdeW4+AXjQ3fe7+xtAChhlZicCx7r7Cnd34D7g4lie2vD8YeCC0LoZB9S5+y533w3UkT3QiYhIgsoxBvN1M1sbutDSLYsqYFNsm/qQVhWeZ6a3yuPuTcAeoH+OskREpIRKHWDuAD4GnAVsAX4U0i3Ltp4jvbN5WjGzqWa2ysxW7dixI0e1RUSko/IOMGY2yMxOTj86szN33+buze7eAtxFNEYCUSvjpNim1cDmkF6dJb1VHjPrBRxH1CXXVlnZ6nOnu9e4e83AgQM7c0giItKGdgOMmV1kZuuBN4AngQ3Aos7sLIyppF0CpGeYLQQmhZlhpxIN5q909y3AXjM7J4yvfAX4dSxPeobYpcDvwzjNYmCsmZ0QuuDGhjQRESmhfL5oeRNwDrDU3T9tZqOBye1lMrNfAucDA8ysnmhm1/lmdhZRl9UGYBqAu68zs4eAl4Em4Cp3bw5FXUk0I60PUWBLB7e7gfvNLEXUcpkUytplZjcBz4bt5rh7vpMNRESkSPIJMI3uvtPMephZD3dfZma3tJfJ3bMFobtzbD8XmJslfRVwZpb0D4Avt1HWPcA97dVRRESSk0+AedvMjgaeBh4ws+1ErQwREZE25TPIPwHYB3wDeBx4HfhignUSEZFuoN0WjLu/Z2anACPcvdbM+gI928snIiKHt3xmkX2N6Jvy80NSFfAfCdZJRES6gXy6yK4CPgu8A+Du64FBSVZKREQqXz4BZr+7H0gvhC81Zv1mvIiISFo+AeZJM5sB9DGzC4F/Bx5LtloiIlLp8gkw1wM7gBeJvhj5O2BmkpUSEZHKl3MWmZn1ANa6+5lE9w4TERHJS84WTLgp5ZrO3txSREQOX/l8k/9EYJ2ZrQTeC2nu7hOSq5aIiFS6fALM7NhzAz5HHje7FBGRw1u7g/zu/iTRr0X+FdFdjS8Afp5stUREpNK12YIxs9OIboE/GdgJLADM3UeXqG4iIlLBcnWRvUp0B+UvunsKwMyuLUmtRESk4uXqIvsSsBVYZmZ3mdkFZP+9exERkUO0GWDc/VF3nwh8AlgOXAsMNrM7zGxsieonIiIVKp9B/vfc/QF3/wJQDawGbki6YiIiUtnyuVXMQe6+y93nu/tfJFUhERHpHjoUYEREDjcNDQ1cffXV7Ny5s9xVqTgKMCIiOcyfP581a9Ywf/789jeWVhRgRETa0NDQQF1dHQBLlixRK6aDFGBERNowf/58WlpaAGhpaVErpoMUYERE2vDEE0+0Wl66dGmZalKZ8rnZpYjIYcndcy6X07x580ilUjm3Wb9+PQDTp09vt7zhw4fntV1HqAUjItKGMWPGtFq+8MILy1STzunTpw99+vQp2/6tK0XkcqqpqfFVq1aVuxoi0oU0NDTwpS99CXenR48ePPLII/Tv37/c1epSzOw5d6/Jtk4tGBGRNgwYMIBBgwYBMHDgQAWXDlKAEZFEVfIXFRsaGti2bRsA27Ztq8hjKCcFGBFJVG1tLWvXrqW2trbcVemwefPm5VyW3BRgRCQxDQ0NLFq0CHdn0aJFFdcCWL58eavlZcuWlaciFUoBRkQSU1tbS3NzMwBNTU0V2YqRzlOAEZHE1NXVHQwwzc3NLFmypMw1klJSgBGRxIwaNarV8tlnn12mmkg5JBZgzOweM9tuZi/F0vqZWZ2ZrQ9/T4it+46ZpczsNTMbF0sfaWYvhnXzzMxC+pFmtiCkP2Nmw2J5poR9rDezKUkdo4jk9tprr+Vclu4tyRbMvcD4jLQbgCfcfQTwRFjGzM4AJgGfDHl+ZmY9Q547gKnAiPBIl3kFsNvdhwO3AbeEsvoBNwJnA6OAG+OBTERKZ8uWLa2WN2/eXKaaSDkkdi8yd38q3qoIJgDnh+e1wHLg+pD+oLvvB94wsxQwysw2AMe6+woAM7sPuBhYFPLMCmU9DNweWjfjgDp33xXy1BEFpV8W+xhFpHLlcy+vbNq6X1cS9/KqdKUegxns7lsAwt9BIb0K2BTbrj6kVYXnmemt8rh7E7AH6J+jrEOY2VQzW2Vmq3bs2FHAYYlINulvwacNHjy4TDWRcugqd1O2LGmeI72zeVonut8J3AnRvcjar6aIdETm914aGhrKVJND5dPaWLp0KXPmzDm4PHv2bEaPHp1ktbqVUgeYbWZ2ortvMbMTge0hvR44KbZdNbA5pFdnSY/nqTezXsBxwK6Qfn5GnuXFPQwRyaeLKT1FOb5cSV1MY8aMORhgevTooeDSQaXuIlsIpGd1TQF+HUufFGaGnUo0mL8ydKPtNbNzwvjKVzLypMu6FPi9R7eGXgyMNbMTwuD+2JAmItJhJ598MgA33nhjmWtSeRJrwZjZL4laEgPMrJ5oZtfNwENmdgXwJvBlAHdfZ2YPAS8DTcBV7p7+6HMl0Yy0PkSD+4tC+t3A/WFCwC6iWWi4+y4zuwl4Nmw3Jz3gLyLFk09rY+7cuSxe/OHnu/HjxzNjxowkq1V0/fr1o1+/fmq9dEKSs8gmt7Hqgja2nwvMzZK+CjgzS/oHhACVZd09wD15V1ZEEjFt2rSDAcbMmDZtWplrJKWkb/KLSGIGDBjACSdEX0MbN26cfk/lMNNVZpGJSDc1dOhQDhw4oNbLYUgtGBFJVO/evRkxYoRaL4chBRgREUmEAoyIiCRCAaYEKvk3yUVEOksBpgQq+TfJRUQ6SwEmYZX+m+QiIp2lacoJq62tJbqDDbS0tFBbW8s3v/nNMtdKpPvq7G3427J+/XogvzsX5KMr3nMtKQowCaurq6OxsRGAxsZGlixZogAjkqBUKsVLa9ZwzBHFubw1NUV3rdr4yrqCy9p7oKngMiqJAkzCLrzwQn7729/S1NREr169GDt2bLmrJNLtHXNEL0YN7no/ZLty2+5yV6GkNAaTsClTptDUFH1qaWpqYsqUKe3kEBHpHhRgREQkEeoiS9gPf/jDQ5ZvvvnmMtVGJD/FHCjXIPnhSwEmYStWrGi1/Mc//rFMNRHJXyqVYt2Lr3B830EFl9VyIPoV87deL3yK/tv7tre7TX19PXsPNHXJ8Y69B5qor68vdzVKRgFGRLI6vu8gRn9iUrmr0cqyVx8sdxWkAxRgCtSZroRczXs1/0UKU11dTfPePV12Fll1dXW5q1EyGuQXEZFEqAVToPZaGytXruS66647uHzbbbcxcuTIpKslUpD6+nr27Nvb5bqk3t63Ha9/v9zVkDwpwCRs1KhRB58fddRRCi4iJVDMQf594Zv8fXv1LLgsfZNfiu6jH/0of/rTn/jBD35Q7qpIBWpoaGD27NnMmjWrZL8KWV1dje3f2SUH+auqc78Gw4cPL+o+09OsTxkxoijlFbt+XZkCTAkce+yxnHXWWWq9SKfEf+5B97FrX7EnyaTLmzdvXlHLPRxokF+kC9PPPUglU4CRdukXOcuntraWlpYWAJqbm/WjdVJR1EUm7Zo/fz5r1qxh/vz5zJgxo9zVOazU1dW1ullqKX/u4e1924syi+zdD6LB9qOPKvx7KW/v204VpRmHksIpwEhODQ0NLFmyBIDFixczbdq0kg00C5x77rksXrz44PJ5551Xkv0WcyB6/fpdAFR9rPD3TRX9D6tB8kqnACM5zZ8//+Avcrq7WjEltn///pzLSSnmQLkGyQ9fGoORnOrq6lotp1szUhrLly9vtbxs2bLyVESkE9SCaYNuVx5JDzC3tSwi0hYFmDakUileePFlWvr2K7gsOxB1MT33+taCy+qxb1fBZUjX0NkPMW19uNCNUqWrUYDJoaVvPz444wvlrkYrR738m3JXQUQkLwowImWST2tj9OjRNDc3H1zu2bOnBsulYijAtKG+vp4e+/Z0uRZDj307qa8/vG6Ydzj77ne/y5w5cw4uf+973ytjbQ5PjY2NbNiwgZ07d2qKfgeVJcCY2QZgL9AMNLl7jZn1AxYAw4ANwF+7++6w/XeAK8L20919cUgfCdwL9AF+B1zj7m5mRwL3ASOBncBEd99QosOrKPrBtK5tzJgxBwNMz549GT16dJlrdPipr6/nvffeY968ecyePbvc1ako5WzBjHb3htjyDcAT7n6zmd0Qlq83szOAScAngaHAUjM7zd2bgTuAqcB/EQWY8cAiomC0292Hm9kk4BZgYkcqV11dzbb9vbrkGEx19ZByV0NK6OSTT+bNN99U66XI8vlw1djYyJ49e4Boivj27dvp3bt31m314epQXamLbAJwfnheCywHrg/pD7r7fuANM0sBo0Ir6Fh3XwFgZvcBFxMFmAnArFDWw8DtZmae/sagHNTeP0S2b45rDKC0+vXrR79+/dR6KYP6+vpDlk899dQy1abylCvAOLDEzByY7+53AoPdfQuAu28xs0Fh2yqiFkpafUhrDM8z09N5NoWymsxsD9AfiLeYMLOpRC0gTj755EMq2WPfrqKMwdgH7wDgRx1bcFnRNOXStWC+9rWvcddddx1cvvLKK0u2b5Ek5dPa+PznP99q+Z133tEHrA4oV4D5rLtvDkGkzsxezbGtZUnzHOm58rROiALbnQA1NTWt1hf3Xkx7ARjxsWIEhiElvRfTZZdd1irATJ48uWT7rmT6om73kNnpoU6QjilLgHH3zeHvdjN7FBgFbDOzE0Pr5URge9i8Hjgplr0a2BzSq7Okx/PUm1kv4DigQ99Q1L2YPjRkyBC2bt2q1ksHpFIpXl29uihtzfT9nN5evbrgsgr/qu/hZdCgQWzfvv3g8uDBg8tYm8pT8gBjZh8Berj73vB8LDAHWAhMAW4Of38dsiwEfmFmPyYa5B8BrHT3ZjPba2bnAM8AXwF+GsszBVgBXAr8XuMvnTdkyBCGDBmi1ksHDQGuyNqYLp+7D23IJ27fvn2kUilSqVTF3Qm5Z8+eOZclt3K0YAYDj5pZev+/cPfHzexZ4CEzuwJ4E/gygLuvM7OHgJeBJuCqMIMM4Eo+nKa8KDwA7gbuDxMCdhHNQhMpmfr6evZSngt6LluAdzMGrpO2ceNGWlpamDNnDvfdd19J912oLVu2tFrevHlzG1tKNiUPMO7+J+BTWdJ3Ahe0kWcuMDdL+irgzCzpHxAC1OFKYwCStHzeY/v27Tv4EwMbNmzgq1/9Kn379s26bVd8XwwbNowNGza0Wpb8daVpylJEqVSK/37peU4+urn9jdtxRGM0CvDBhmcLLuvNd4vXxZBvEE1PNa2urm5ny+Jd5Kqrq3m1oaH9DfOQ/qHqYnyH3MjvdSiWjRs3HrJ8+umnl2z/hZo5cyZf/epXDy7ru0gdowDTjZ18dDMza94tdzVa+adVR5d8n++//37J91nMsYYdoQV5/IgRBZd1PMWrWz6BOPN7VPv376+oyS6nnXbawVbMsGHDKm4MqdwUYLqp+vp63tvbsywX9Fw27u3JR4o0BpBvS6Mcs/g0CzHSHbqYZs6cyTXXXKPWSyfoFy1LYMeOHaxevZqFCxeWuyoiJTVz5sxWy5V4kT7ttNNYtGiRWi+doABTAm+99RYAt956a8n2WV1djRVphuy2fT3Ytq84bxWz0o4BSHmlu5gAdTEdhtRFlrBHH3201fLChQu56KKLEt9vMf+RD4QxgKOGFT4GcBr51U2z4D5U6beLVxfT4UsBpkDtXQhXZ3z7+tZbb2Xp0qVtbl+si1eljwGkUileWPdCNCpdqJbozwtvvVB4WW8XXkRHbd68mffee4/58+czY8aM0legQOkuJjn8KMBI13U8tJzfUu5atNJjefF6lfO9Xfzu3bsBePzxx9m0aZNuFy8VQwGmQLrdfTLq6+thT3Ev6EXxNtR76b4Jn/nN8c2bN3PKKaeUbP8ihVCAESmTfFobmb8Bo9vFSyVRgEnYtddey2233XZw+brrritjbSpHdXU1O2xHl+wiq64q3Sw4y5gKmLks0pUpwCTskksuaRVgSjGDrCPyGQfoyCysoo4DvF2kLrL0zQyK8Z3Tt/nwZ+1K4IILLmDx4sUHl8eMGVO6nYsUSAGmBNKtmEptvfTp06fk+yzuD75FAXJEVeHTrKkqbt3aM23aNOrq6mhpaaFHjx5MmzatZPsWKZTpZ1IiNTU1vmrVqnJXQxJQybdaAZg7dy6LFy9m/PjxFTlNWbo3M3vO3WuyrVMLRqSLmzZtGlu3blXrRSqOAoxIFzdgwAB++tOftr+hSBfTxb5kICIi3YUCjIiIJEJdZFKx8r0hZtmmWYsc5hRgpNsrxzRrEVGAkQqmloZI16YxGBERSYQCjIiIJEIBRkREEqEAIyIiiVCAERGRRCjAiIhIIhRgREQkEQowIiKSCP0eTGBmO4CNCe5iANCQYPlJU/3LS/Uvr0quf9J1P8XdB2ZboQBTIma2qq0f5akEqn95qf7lVcn1L2fd1UUmIiKJUIAREZFEKMCUzp3lrkCBVP/yUv3Lq5LrX7a6awxGREQSoRaMiIgkQgFGREQSoQCTBzN7N2P5cjO7PbY81cxeDY+VZva52LrlZvaama0xs2fN7KzYuuPM7D4zez087jOz48K6q8xsdezxkpm5mZ1e4LF818zWmdnaUO7ZsTqm9/Vw2HaWmV0Xnh9lZnVmdmNYbs6o3w3tHW9GPdL514Vtv2lmPcK6881sT0b5YzLyvWRmj5nZ8SF9WHh9bortY4CZNcbPVUhfY2a/zEg7x8yeCWW/YmazYusuDq/Xq2b2opldHFt3r5m9ZWZHxva5oQjnoyas3xD2udbMnjSzUzLy/9rMVuS7v4y86ddyjZk9b2b/K2P9tWb2Qfo9GdLS5+aF8HrcGlt3uZntiJ3Xh82sb+x1ujSj/HfD3/S5uzq27vZQ3lQzWxBLPzb8r5xa6PGH/EPM7MFQ5stm9jszO62d4/9NlnIOnrNC5TovZvY5i64x6evN1Ni6WeF1HB5LuzakZb6fVoe/EzL2fUnY/hNh+RdmdmVs/dnhvZjfj1W6ux7tPIB3M5YvB24Pz78APAcMCMufAd4EhoTl5UBNeP73QF2snIeBWbHl2cC/t1GH7wP/r8Dj+HNgBXBkWB4ADI3XMWP7WcB1wBHAb4Gb23pNYultHm9brykwCFgKzA7L5wO/ySNfLfDd8HwY8DrwQmz9lcDq9LkKaacDLwJvAR+Jpb8GfCo87wmcEZ5/CkgBp4blU8Pyn4Xle8P5vjL2mm4o1vkANsTeW7OBu2L5jwc2Aa+k69fZ9zUwDngyY/1K4Gng8ljawXMD9AFeBT6b+X8Rln8B/H3sdbo02/7DudsWXtcjQtrtoTwD/hMYE9J/EjvnhR6/hdf/H2JpZwHn5nP8bb3vC320dV6AIeG99pnY++U54K9i/69rgZmx/P8JrGvj/fRxYGPGvh8KxzwrLA8G/gQMJGqQPAt8Lt9jUQumcNcD33b3BgB3f57owndVlm1XAFUA4VPGSOCm2Po5QI2ZfSyeyczOA/4a+D8F1vVEoMHd94e6Nrj75nby9AIeBNa7+w0d3N/B483F3bcDU4Gvm5kVUP77wCuxT5ITif5h4v4GuB9YAlwUSx8EbAn1aXb3l0P6dcD33f2NsO4N4AfAt2N5fwJcm/enug919HxkHu+XgMeIzs+kDu4707HA7vRCeA8eDcwEJmfL4O7vEwXwQ85xeC0+Ei+zHTuAJ4ApGftwog8KPwnn9QLgh2F1occ/Gmh095/H9rfa3Z/O5/hLJH5ergLuDdcYwjXnH4H4/+V/ABMAzOyjwB6i17a9sjGzo4HPAlcQXk933wbcCvwz8A/AWnf/Q76VV4DJTx+LddcQBYK0TxJ9iohbFdIzjSd6AwCcAax29+b0yvB8dTyvRV1A/wZMcfd3CjqK6KJ6kpn9t5n9zMw+H1v3QOwYfxhL/0egyd2/kVFWq9fEzCZm2V/8eHNy9z8RvR8HhaRzM8rPDLo9iS42CzOKehCYZGbVQDOQecGeCCwAfknrC8dtwGtm9qiZTTOzo0J6Puf3TeAPwGX5HGtMrvORTebrOZnoODKPJV/pc/gq8K+0/rCTLvtp4ONmNigzs5mdAIwAnoolTwz/I28B/YgCQL5uBr4Vzu1B7r4WWEwUgKa7+4GMOnb2+M/k0HOb1u7xJ6it85LPe/EdYJOZnUl0DAs41DIzewl4kiiApl0MPO7u/w3sMrPPhPSfE12vvk10PcibAkx+3nf3s9IP4HvtbG9AfP73A2ZWT9Ta+Wkb27SV9w6irrH/7FTNY9z9XaJW01SiTzULzOzysPpvY8cY/3T+B+DP0/3SMa1eE3ePv5GzHW8+4q2XpzPKfz2k9wkXsJ1EF7C6jDIeBy4kyz+Xmf1PYIe7byS6WH0mXCRx9zlADdFF/29COek6ZZ6nbGnfJ/oHzPt/qp3zEbfMzLYDY4i6nTCzwcBw4A/hgtAULiodkT6HnyAKXvfFWpCTgAfdvQX4FfDlWL5zzWwtsJWou2hrbN2C8D8yhKgrMv1eyvZeb5UWWocriV7/TP8CvOXuy6Box59LruNPWlvnpa1rRmZaukV3MfBolu1Hu/uZwP8Abg8tF4j+Zx6MlTEZILwG84FF7r6zIweiAFO4l4kuEnGfCelpf0vUd/8Lon8UiPpFP21hYBsgPP8UUZ8yZjaFqH86/smyIKH7Z7m73wh8naibIZengG8Ai8xsaJ67yXa8OYXmfDOwvZ1N3w8XsFOIxoZadUWGT7fPAd8CHsnIOxn4hEWD8K8TdRF8KZb3dXe/g6hl9Ckz60/ov84oJ/P84u4potbnX7dT/1byPB+jiY53HR+2nicCJwBvhOMZRgHdZO6+gqhPf6CZ/RlRy6QulD2J1i2Ep939z4guUFdalokcoWvrMeC8kLQz1BcAM+tH9hswfp/og0nmtaklPNKKcfzrOPR/lzyOv2Ti54Xs78WRZLwXiV73y4A3c/V6hA9t24Azwnv9L4B/Dcf8baLWaPoDR+brnxcFmML9M3BLOEGEf7bLgZ/FN3L3RqLm6Dlmdnq4IL1A6ybqTOB5d0+FC+5copZFUzEqamYfN7MRsaSzyOMO0u7+CFG/9+Ohy65dmcfbTr0GEjXDbw8XpnzK3wNMB64zs94Zq38EXB//tBWC95eJBueHufswor7qyWH9X8X+mUYQBbu3ifqfv2Nmw8J2w4AZYR+Z5hKN2eSlI+cjjHd8A/hKuDhPBsbHjmUkBQSYMGuoJ1EgmEw0yDssPIYCVZYxgy20HH5AFBCy+RxRIIdoEHyimR0Rli8HlmVmcPdXiS6YX2inysU4/t8DR5rZ19IJoZX7f8nj+Esh47z8C3B5OqCHa84tRNegg8J75Xqi92OusgcRfRDcCFwK3Ofup4RjPgl4g+gcdlpHByUlg7svNLMq4I9m5sBe4O/cfUuWbd83sx8RXYSuCI+fmlmKD2e0XBE2v55okPRXGePeV7v7052s7tFhf8cDTUSzdqYSzWZ7wMzeD9s1uPuYjLr/3MyGAAvNbCwfdlWlPZ45CSDL8cal8/cOdbkf+HFs/bkZ5f+Tuz+cUf4LZraG6MLydCx9HdGnvbjziLpY3oqlPUX06e1Eok98t5nZvlCfv02PiZnZ9cBjIZA1Av/o7qvJ4O7rzOx5ohZOPnKdj0O4+xaLpldfBZwM/Fds3Rtm9o6Zne3uz+S5//g5NKJxvmYzmwT874xtHyV6nTPL/jlRkD81LE+0aJp+D6CeKJDg7r8xs5HAc2bWTBR4/qGNes0l+vCVVQjyBR+/u7uZXUI0geAG4AOiWVbnE00siIsf/wWhCzit2N1nWc8LsMXM/g64y8yOCet+4u6HjHO5+4OZaTHLwjnoDdzg7tvMbDLRGFjcI0TdlZ293uhWMSIikgx1kYmISCIUYEREJBEKMCIikggFGBERSYQCjIiIJEIBRkREEqEAIyIiiVCAEemizOwmM7smtjzXzKaXs04iHaEvWop0UeEb679y98+EW92sB0Z19IaDIuWiW8WIdFHuvsHMdprZp4l++OkFBRepJAowIl3bvxLdz2sIcE95qyLSMeoiE+nCwt2HXyS6MeEIj/1AnUhXpxaMSBfm7gfMbBnwtoKLVBoFGJEuLAzun0Npf1FRpCg0TVmkizKzM4h+I+YJd19f7vqIdJTGYEREJBFqwYiISCIUYEREJBEKMCIikggFGBERSYQCjIiIJOL/AxIAE3O9OY57AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAsLklEQVR4nO3de3wV9Zn48c+TEKSgiBC8QMBYQat10WrqpVqVbYPGLdpu3apt9eBPf6jralt7seuyKlbrdrvtKrotUG2Jq632Ymu0xBK7VPBSXVTECxaiBomgcBABiZeT5Nk/Zk4653Auk+TMmTOZ5/16nRd8Z+bMeebMyTzznfnO9yuqijHGmPiqCjsAY4wx4bJEYIwxMWeJwBhjYs4SgTHGxJwlAmOMiblhYQfQX7W1tVpfXx92GMYYEylPPfVUUlXH55oXuURQX1/PihUrwg7DGGMiRUTW5Ztnl4aMMSbmLBEYY0zMWSIwxpiYCywRiMgkEVkqIqtF5AUR+UqBZT8uIj0icmZQ8RhjjMktyBpBN/B1VT0EOBa4VEQOzV5IRKqB7wF/CDAWX9asWUNTUxPt7e1hh2KMMWUTWCJQ1Y2q+rT7/x3AamBijkUvA34DbAoqFr/mzp3Lzp07ueaaa8IOxRhjyqYs9whEpB74GPBE1vSJwOeA+eWIo5A1a9awfv16ANavX2+1AmNMbASeCERkd5wz/q+q6vas2TcBV6pqT5F1zBaRFSKyYvPmzYHEOXfu3Iyy1QqMMXER6ANlIlKDkwTuUtV7cyzSANwtIgC1wGki0q2qv/MupKoLgYUADQ0NgQygkK4N5CsbY8xQFVgiEOfofjuwWlV/mGsZVT3As/wi4IHsJFAuIoJ3kB43ORljzJAX5KWh44Fzgb8VkZXu6zQRuVhELg7wcwfkpJNOyiiffPLJ4QRijDFlFliNQFUfAXyfVqvqrKBiAZg3b17BG8CpVCqjvGnTJi6//PK8y0+ZMqXgfGOMiQp7sthVU1NDVZXzdey5557U1NSEHJExxpRH5HofHSg/Z++XXHIJHR0dLFq0iHHjxpUhKmOMCZ/VCDxqamqYOnWqJQFjTKxYIjDGmJizRGCMMTFnicAYY2LOEoExxsScJQJjjIk5SwTGGBNzlgiMMSbmLBEYY0zMWSIwxpiYs0RgjDExZ4nAGGNizhKBMcbEnCUCY4yJOUsExhgTc5YIjDEm5iwRGGNMzFkiMMaYmAssEYjIJBFZKiKrReQFEflKjmW+JCKr3NdjInJ4UPEYY4zJLcgxi7uBr6vq0yKyB/CUiLSp6oueZV4FTlLVrSLSBCwEjgkwJmOMMVkCSwSquhHY6P5/h4isBiYCL3qWeczzlj8DdUHFY4wxJrey3CMQkXrgY8ATBRa7AGgtRzzGGGP+KshLQwCIyO7Ab4Cvqur2PMtMx0kEJ+SZPxuYDTB58uSAIjXGmHgKtEYgIjU4SeAuVb03zzLTgNuAM1R1S65lVHWhqjaoasP48eODC9gYY2IoyFZDAtwOrFbVH+ZZZjJwL3Cuqq4JKhZjjDH5BXlp6HjgXOA5EVnpTrsKmAygqvOBq4FxwI+cvEG3qjYEGJMxxpgsQbYaegSQIstcCFwYVAzGGGOKsyeLjTEm5iwRGGNMzFkiMMaYmLNEYIwxMWeJwBhjYs4SgTHGxFzgXUwEbd68ebS3t5dkXWvXrgXg8ssvL8n6pkyZUrJ1GWNMUCKfCNrb23nmuRfpHTl20OuSDxSAp15+Y9Drqup6a9DrMMaYcoh8IgDoHTmW9w79TNhhZBjx4gNhh2CMMb7YPQJjjIk5SwTGGBNzlgiMMSbmLBEYY0zMWSIwxpiYs0RgjDExZ4nAGGNizhKBMcbEnCUCY4yJucg/WdzZ2UlV17aKe5K3qmsLnZ3dYYdhTCwkk0nmzp3Ltddey7hx48IOJ3KsRmBMiSSTSS677DK2bNkSdiixs2DBAp599lkWLFgQdiiRFFiNQEQmAXcA+wK9wEJVvTlrGQFuBk4DuoBZqvp0fz6nrq6ON98fVpF9DdXV7Rt2GKaMmpubWbVqFc3NzVxxxRVhhxMbyWSStrY2AJYsWcJFF11ktYJ+CrJG0A18XVUPAY4FLhWRQ7OWaQKmuq/ZwI8DjMeYwCSTSVpbW1FVWltbrVZQRgsWLKC3txeA3t5eqxUMQGCJQFU3ps/uVXUHsBqYmLXYGcAd6vgzMEZE9gsqJmOC0tzcjKrTjXlvby/Nzc0hRxQfDz30UEY5XTsw/pXlHoGI1AMfA57ImjURWO8pd7JrsjCm4rW1tZFKpQBIpVIsWbIk5IiM8S/wRCAiuwO/Ab6qqtuzZ+d4i+ZYx2wRWSEiKzZv3hxEmMYMSmNjIzU1NQDU1NQwY8aMkCOKj+HDhxcsm+ICTQQiUoOTBO5S1XtzLNIJTPKU64AN2Qup6kJVbVDVhvHjxwcTrAldlFvdJBIJnLYPICIkEomQI4qPd999t2DZFBdYInBbBN0OrFbVH+ZZrAU4TxzHAttUdWNQMZnK5m11EzW1tbVMmDABgAkTJlirFRMpQT5QdjxwLvCciKx0p10FTAZQ1fnAYpymo+04zUfPDzAeU8GyW90kEolIHUyTySSvv/46ABs2bGDLli2Ri79SH8iaN28e7e3t/XrP5ZdfnnfelClTCs6PoyBbDT2iqqKq01T1CPe1WFXnu0kAt7XQpap6oKr+jaquCCoeU9mi3urGG6+qRjL+qNbG7B7B4EW+iwkzNORqdROlh7KiHH+l18aKnb2vWbOGCy+8sK88f/58pkyZEnRYJRV2jWxIJIKqrrdK0teQvOc0atIRowe9rqqut3AeqjZ+NDY2snjxYlKpVCRb3UQ5/ly1sagkMYCDDjqI4cOH88EHH1BXVxe5JADhP5Ue+URQyp2+du0OAKYeWIoD+L6R/EGGJZFI0NraCkBVVVXkWt1EOf4o12bS6uvraW9v57rrrgs7lH5LJpMsXrwYVWXx4sWh1MginwhKedMnva558+aVbJ3Gn9raWpqammhpaaGpqamiLk34EeX4GxsbaWlpQVURkUjVZtJGjhzJtGnTInny1dzcTHe301NxKpUKpVZgvY+aijFz5kxGjhzJ6aefHnYoAxLV+GfOnNl3aUhVIxd/1C1ZsiTj+//DH/5Q9hgsEZiKcf/999PV1UVLS0vYoQxIVOO///77M8pRiz/q9tlnn4LlcrBEYCpC1HvvjHL82f0ihXFGGmdvvvlmwXI5WCIwFWEoPEcQ1fhra2sLlk2wZsyYkdE9ySmnnFL2GIomAhE5XkTaRGSNiLwiIq+KyCvlCM7ER9R774xy/OknovOVTbASiURGh4VhtDjzUyO4HfghcALwcaDB/deYkol6752NjY0ZZ3VRij9dk8lXNsFKtzgTEU477bRQWpz5SQTbVLVVVTep6pb0K/DITKxEvffOKLe8sUQQvkQiwbRp00L73ft5jmCpiHwfuBd4Pz2xv2MLG1NIuvfOjo6OSPbemavlTVQeyqqqquob6jFdNqXjp9O8zs5OAObOnVt0fUF0mucnERzj/tvgmabA35Y0EhNrUe+9M1fLm0pJBMUORHvuuSdbt27NKFvvneUV9hgKRROBqk4vRyAm3pqbm/vOSnt6eiLX382YMWMy/pj32muvEKPpnwkTJmQkgvS4CqY0/CTNsHs1KJoIRGRP4BrgRHfSw8B1qrotyMBMvLS1tdHT0wM4iSBq/d288cYbGeWNGytnfCU/B6IzzjiDrVu3cuqpp3LVVVeVISpTSfxcDPwpsAP4gvvaDvwsyKBM/Bx99NEZ5WOOOSbPkpUp6jdcJ0yYwKhRo7jooovCDsWEwM89ggNV9fOe8lzPiGPGlMTLL7+cUe7viFRmcGpqapg6dWqk7suY0vFTI3hXRE5IF0TkeMBGhzYltX79+oJlY0xw/NQILgGa3XsFArwFzAoyKBM/9fX1dHR0ZJSNMeVRtEagqitV9XBgGvA3qvoxVX02+NBMnJx33nkZ5fPPPz+kSIyJn7w1AhH5sqreKSJXZE0HQFV/WGjFIvJT4DPAJlU9LMf8PYE7gcluHP+hqnYTOqZ+9rPMXX/bbbcxfXpltFz280BQLvla61g7fFNpCtUIRrn/7pHjtbuPdS8CTi0w/1LgRbe2cTLwAxEZ7mO9ZgiyewTGhCdvjUBVF7j/fUhVH/XOc28YF6Sqy0SkvtAiwB7iVDF2x7n30F00YmPKzM/Ze2NjI++/39cDCyNGjLAhT01k+Gk1dIvPaf11K3AIsAF4DviKqvYWfosxlemGG27IKN94440hRWJM/xW6R3Ac8AlgfNZ9gtFAdQk++xRgJU6fRQcCbSKyXFW354hlNjAbYPLkySX4aGNK6+ijj0ZEUFVGjBjBUUcdFXZIFW+g915yWbt2LeCv9uZH3O7jFGo+Ohznks0wnPsCaduBM0vw2ecD/6bOI5jtIvIq8BHgyewFVXUhsBCgoaEhWo9sGl+GQg+YBxxwAK+88orVBnxqb2/n+WefZY/hflqxF9bd7XRPsm71C4Ne144P4neFutA9goeBh0VkkaquC+CzXwM+BSwXkX2AgwEb+Sym0q3R8pWjYPTo0RxxxBFWG+iHPYYP4+h9KquDviff3Fp8oSHGTyrucscj+CgwIj1RVQt2Qy0iv8BpDVQrIp04HdfVuO+dD3wHWCQiz+E8qHalqiYHshEm+qLeV48xUeYnEdwF3IPzTMDFQALYXOxNqnpOkfkbgOiM52cC5b0slKtsjAmOnwux41T1diClqg+r6v8Djg04LmOMMWXip0aQcv/dKCJ/h9Pcsy64kIwxxpSTn0RwvdsdxNdxnh8YDXw1yKCMMcaUj5+hKh9w/7sNmA7+niw2Js366jG5dHZ2suOD7oprpbPjg+6+weTjIu89AhGpFpFzROQbInKYO+0zIvIYzlPBxpRM9oOC9uCgMeVTqEZwOzAJ5wGveSKyDjgO+Laq/q4MsZkhwu/Z+4knOsNiiwh33nlnkCGZClBXV0fPjm0V+RxBXV28boMWSgQNwDRV7RWREUASmKKqbxR4T6SlUik6OjrYsmWLDdkXgsmTJ/Paa69x7bXXhh2KMbFSqPnoB+lO4FT1PWDNUE4C4Fyz3Llzp/UaGZKxY8dyxBFHVMw4BMbERaFE8BERWeW+nvOUnxORVeUKsFySySTbtm0DYOnSpWzZsiXkiPovmUxy2WWXRTJ2Y0x4Cl0aOqRsUZRBsZYrr776akZ51qxZHHDAAXmXr8SWK83NzaxatYrm5mauuOKK4m8wxhgK1AhUdZ3b2dw/pv/vnVa+EMsjXRvIV650yWSS1tZWVJXW1larFRhjfPPzQFkjcGXWtKYc0ypasbP3dIsVryjdK2hubu7rqK23t9dqBcYY3woNTHMJzpn/gVn3BPYAHgs6MNM/bW1tpFJObyCpVIolS5ZYIjAVr1QPlHW54xGMHDb4MbNsPIJMPwdagRuBb3um71DVtwKNyvRbY2MjLS0tqCoiwowZ1rGrqWxTpkwp2brSI5TtP3VqSdZXytiioNDANNuAbSJyM/CWqu4AEJE9ROQYVX2iXEGa4mbOnMl9990HOH35n3766SFHZExhpWxskV5XOS/nDqWhNv3cI/gxcKSnvDPHNBOyX/3qVxnlX/7yl1x11VUhRWPM0Nfe3s5fnl/NpD32HfS6arqddjtd6wZ/mWz9jv4/7uUnEYh6hotynzQe/CCjFSbqY+b+8Y9/zCg/9NBDlgiMCdikPfbl60efH3YYGX7w5M/6/R4/R7tXRORyEalxX19hCI4tHPURsmyoR2PMQPk5s78YmAfMART4IzA7yKBM/+222250dXVllE1hpbzGC+W/zlvJ16ihMh+6NLn5GY9gE3B2GWIJlYhknEWLSIjR9J83CeQqm121t7fz0sqVDP4KryNdvX575cpBr8vPVd729nZeeG41Y0buPejP6/3A+b2//nJpHkR8u2tTSdZjyqNoIhCRg3BuDu+jqoeJyDTgdFW9vsj7fooz4P0mVT0szzInAzcBNUBSVU/qV/QldOyxx/L444/3lY877riwQhmQCRMmsGHDhoyyKW5f4AIqL+nfjr9Le2NG7s30j1TeedrSl+4OOwTTD37uEfwE+GfcsYtVdRX+agiLgFPzzRSRMcCPcJLKR4F/8LHOwIwePbpgudLZPQFjzED5SQQjVfXJrGlFH71T1WVAoQfPvgjcq6qvucuHWpdctmxZRvnhhx8OKZKB2bhxY0bZWzswxphC/CSCpIgciHOjGBE5E9hY+C2+HATsJSJ/EpGnROS8EqxzwMaMGZNR3muvyho1qZj6+vqCZWOMycdPq6FLgYU44xG8DrwKfKlEn30U8CngQ8DjIvJnVV2TvaCIzMZtqRTUWLZRP6OeM2cOF154YV/56quvDjEaY4a+zs5Odu7YMaB2+0Fav+MNRnXu7Nd7/LQaegX4tIiMAqrSXU2UQCfODeKdwE4RWQYcDuySCFR1IU4yoqGhwS6G5zB27NiMctRqNMaY8PhpNTQOuAY4AVAReQS4TlUH287sPuBW9ynl4cAxwH8Ocp2x1dzcvEvZeh81Jjh1dXV09WytyCeLR9b170TQzz2Cu4HNwOeBM93/31PsTSLyC+Bx4GAR6RSRC0TkYhG5GEBVVwMPAquAJ4HbVPX5fkVv+jz44IMZ5dbW1pAiMcZEjZ97BGNV9Tue8vUi8tlib1LVc3ws833g+z5iCFzUHyjr6ekpWDa76uzsZAf+2+yX00bgnc7OsMMwMeGnRrBURM4WkSr39QXg90EHVm5R76unu7u7YNkYY/LxUyO4CLgC+G9AcJLHThG5AlBVjdaTV3lUV1dnnEVXVw9+pKNyCiv+Su7vplhfN3V1dbydTFbsk8Vj6uoKLtPZ2cm2rh0V+RTv212b0M53ww7D+OSn1dAe5QgkbEcffXRGFxPHHHNMiNH0X1iXhtrb23nmhWdgTAlW5nb4+szrzwx+XW8PfhXGxIWfVkPHAytVdaeIfBlnQJqb0k8EDxWdWddj169fH1IkETQGek+urG67q/4UrfEkBqKurg55f0vF9jU0sW5c2GEYn/z8tfwY6BKRw4FvAetwLhMNKdkHfksExpi48JMIut0Rys4AblbVm4Ehd7nIumgwxsSVn0SwQ0T+GTgX+L2IVOPvJnOkzJkzJ6MctS4asofWjNpQm8YMRiqVYu3atWzZUprxFOLGzwH9LJyeQs9X1TdE5ERgVLBhld/YsWP7niUQkch10bDbbrvx7rvvZpRNcW9QuucI0oegUlwZf4PS3H+Piw0bNrBz504WLFhQ1rG61+94oyR9DW3qcjpq3nvk2CJLFrd+xxscTP+OX35aDb0hIv8DfFFE7sTpdO6mAUVYwZqbm6mqqqKnp4eqqqrIddHgTQK5ymZXU6ZMKen6NrvNX8dMnTrodY2h9PFFVbEmyqlUiq1btwLOE/br16+npqYm7/KlGkKzlPsntTYJwMj9B38CejB79Tu2vInAHZnsbOAcnJOdewBR1emDCbJStbW19TW57OnpYcmSJZFKBKb/Sj2ebnp98+bNK+l6TWHZPQVv2LCB/fffP/DPLeXvJ+zfTqEawUvAcmCmqrYDiMjXyhJVCBobG1m8eDGpVIqamhpmzJgRdkiR0NnZCdsqsLnm29Cp1kXDUFDsgDt9eua56fbt2y0Z91Ohv97P41yqXCoiPxGRT0EFPoJZIolEoq9/IREhkUiEHJExxpRH3hqBqv4W+K07DsFnga8B+4jIj4HfquqS8oRYHrW1tUyYMIGOjg4mTJjAuHH2MIwfdXV1bJbNFflAWd3Ewl00mKFh7733zhhYap999gkxmmjyc7N4J3AXcJeIjMUZZP7bwJBKBMlkktdffx1wrjFu2bLFkoGpeG93bSpJX0PvvOfcbN19RGlay73dtYmJJWk/VdymTZnDnb/55ptl+dyhpF/PA6jqW8AC9zWkeAd2UdXItRoy8VPKVitr1zrNFyceWJqD90TGWaunCBlyD4YNVFtbG6lUCnCao1mrIVPphlKrlcGoqanJ6GSxUNNRk1uFNfUIT2NjY98PyFoNGRMd7733XsGyKc4SgcvbaqiqqspaDRljYsMuDblqa2tpamqipaWFpqamirpRPNDBXwpdOijV05XGmOizROCRSCTo6Oiw2kB/vV2iB8recf/dffCr4m1gYgnWYyrecccdlzGo1Cc+8YkQo4mmwBKBiPwU+AywSVUPK7Dcx4E/A2ep6q+DiseP2tpabrnlljBDyMnPmftFF13E6tWr+8of/ehHy3Ljr7QtV5y+eqZOHHxfPUy0vnriYvTo0QXLprggawSLgFuBO/It4HZp/T3gDwHGEQs33HADf//3f99Xvv7668vyudZyxYRt+fLlGeVly5aVtQfSoSCwm8Wqugx4q8hilwG/ATYVWc4UUVtby4c+9CHAqQ1U0j0OY4LU2NjIsGHOOe2wYcOsxd8AhNZqSEQmAp8D5ocVw1Dz4Q9/mFGjRpWtNmBMJUgkEn0DMVVXV9s9vgEIs/noTcCVqtpTbEERmS0iK0RkxebNm4OPLKJqamqYOnWq1QZMrKRb/IlIxbX4i4owWw01AHe7bfdrgdNEpFtVf5e9oKouBBYCNDQ0lGY4KWPMkGEt/gYntESgqgek/y8ii4AHciUBY4wpplJb/EVFkM1HfwGcDNSKSCdwDVADoKp2X8AYYypEYIlAVc/px7KzgorDGGNMYdbXkDEm8pLJJJdddhlbtmwJO5RIskRgjIm85uZmVq1alTGuiPHPEoExJtKSySStra2oKq2trVYrGABLBMaUSCqVYu3atXYgKrPm5mZUnVblvb29VisYAEsExpTIhg0b2LlzJwsWDLmRXCtartEFTf9YN9TGFOFnPIhUKsXWrc4A8A8++CDr16/PO2SijQVRWo2Njfz+97+nu7vb+hoaIKsRGFMCGzZsKFg2wUkkEvT29gLOpSF7urj/rEZgTBF+zt6nT5+eUd6+fbt1p20iw2oExpRAT09PwbIJTnNzc1/vo1VVVXazeACsRmCMibS2tja6u7sB6O7uZsmSJVxxxRUhR/VXfu4xpUfn81P7DOIekyWCkA10YPpc+vNj8sNuavo3cuRIurq6MsqmPBobG1m8eDGpVIqamppI3ixODyoVFksEIWtvb2fN808zeffBX0oYnnKqx+91/O+g1/XaO9WDXkecpG9W5iub4CQSCVpbWwHn0lCl3Sz2czKVTCaZO3cu11xzTSjjKVgiqACTd+9hTsM7YYeR4foVu4cdQqSccsop3HfffX3lU089NcRo4iU9ME1LS0tkB6bxdpERxmUtu1lsTAnMnDkzo3z66aeHFEk8JRIJpk2bVnG1AT8qoYsMSwSmYkS5i4b7778fd7Q9RISWlpaQI4qX9MA0Ua0NpC8l9vT0hNLqyS4NmcD5vSG+evVqent7Oe+88zjwwAPzLleJN7Hb2tr6+rtR1YpruWIqVyW0erIagakIqVSq76xox44dfX3HREVjY2NflxJRbbliwvHJT34yo3ziiSeWPQarEYSss7OTnTuqK+7m7Lod1Yzq7CzJuvycvV955ZUZ5TFjxnDjjTeW5PPLodJbrhSzfft2XnnlFZ566imOOuqosMMxZWY1AlMRHn/88Yzyo48+GlIkA5NuuSIikWy50tHRAcC//uu/hhtIDC1fvjyjvGzZsrLHYDWCkNXV1fFe98aKbD46oq4u7DAiJZFI0NHRUXG1gWL3aLZv3953We6dd95h1qxZjB49Ou/ylXiPJsoaGxtpaWlBVRGRUC4rBlYjEJGfisgmEXk+z/wvicgq9/WYiBweVCzGlENUW66kawP5yiZYM2fOzGhoEEbT4yBrBIuAW4E78sx/FThJVbeKSBOwEDgmwHhMBautrSWZTPaVx48fH2I0Q0uxs/fsm5O9vb3Wc2oZpZsep2sELS0tQ6fVkKouA94qMP8xVd3qFv8M2HWIGEsP6pL21lt5fzqmxIYNG1awbIKVq+lxuVXKzeILgNawgzAmjqqrqwuWTbAqoelx6KlfRKbjJIITCiwzG5gNMHny5DJFVj6vvVOa5qNvdjl5fZ+Rg+/w7LV3qjlo0Gvxb7/99qPT01x1v/32K+Onx9upp56a0U9SU1NTiNHETyU0PQ41EYjINOA2oElV8/YroKoLce4h0NDQoGUKryymTJlSsnV94HZDPaJ+6qDXdRClja2Y7G4lotjNRFQlEgkeeOABenp6qK6urrhWT0NdJXSaF1oiEJHJwL3Auaq6Jqw4wlbKZnjpdUXxRt+MGTMyzkpPOeWUEKOJl9raWiZNmkRHRweTJk2KXKunoSDspsdBNh/9BfA4cLCIdIrIBSJysYhc7C5yNTAO+JGIrBSRFUHFYipfIpFg+PDhAAwfPtzOSssomUyyYcMGADZs2GC1sRCE3fQ4yFZD56jqfqpao6p1qnq7qs5X1fnu/AtVdS9VPcJ9NQQVi6l83idzTzvtNDsrLaPm5uaMVis25m/8VEqrIWMi3ad8lLW1tfV18pdKpUJpvmjCZYnAVIywq8dx1djYmFG2nlPjxxKBMTGX3Q3ySSedFFIkJiyWCIyJuVtvvTWjfPPNN4cUiQmLJQJjYs46nTOWCIyJufr6+oJlM/RZIjAm5ubMmZNRvvrqq0OKxITFEoExMXfQQQf11QLq6+vL2rWIqQyWCIwxzJkzh1GjRlltIKZC733UGBO+gw46qK8HTBM/ViMwxpiYs0RgjDExZ4nAGGNizhLBENLV1cWqVatob28POxRjTIRYIhhC1q1bR29vL9ddd13YoRhjIsRaDUXAvHnzip7ld3V18f777wNOFwEXXnghI0eOzLv8lClTSjo6mjEmuqxGMESsW7euYNkYY/KR9MhEUdHQ0KArVtioltlOPPHEXaYtW7YshEiMMZVIRJ7KNxKk1QiGiLq6uoJlY4zJxxLBEJHdP8zUqVNDisQYEzWBJQIR+amIbBKR5/PMFxGZJyLtIrJKRI4MKpY4ePLJJzPKTzzxREiRGGOiJsgawSLg1ALzm4Cp7ms28OMAYxnyGhsbqa6uBqC6utrGnTXG+BZYIlDVZcBbBRY5A7hDHX8GxojIfkHFM9QlEom+RDBs2DASiUTIERljoiLMewQTgfWecqc7zQxAbW0tTU1NiAhNTU2MGzcu7JCMMRER5gNlkmNazrasIjIb5/IRkydPDjKmSEskEnR0dFhtwBjTL2HWCDqBSZ5yHbAh14KqulBVG1S1Yfz48WUJLopqa2u55ZZbrDZgjOmXMBNBC3Ce23roWGCbqm4MMR5jjImlwC4NicgvgJOBWhHpBK4BagBUdT6wGDgNaAe6gPODisUYY0x+gSUCVT2nyHwFLg3q840xxvhjTxYbY0zMRa7TORHZDATZtWYtkAxw/UGz+MMV5fijHDtY/MXsr6o5W9tELhEETURW5OuhLwos/nBFOf4oxw4W/2DYpSFjjIk5SwTGGBNzlgh2tTDsAAbJ4g9XlOOPcuxg8Q+Y3SMwxpiYsxqBMcbEnCUCY4yJuSGVCETknazyLBG51VOeLSIvua8nReQEz7w/ichfRORZEflfETnCM29PEblDRF52X3eIyJ7uvEtFZKXn9byIqIgcMsht+RcRecEdvW2liBzjiTH9Wb92l71WRL7h/n+EiLSJyDVuuScrvm8X296sONLvf8Fd9goRqXLnnSwi27LW/+ms9z0vIveLyBh3er37/XzH8xm1IpLy7it3+rNuVyXeaceKyBPuuleLyLWeeZ91v6+XROQ5EfmsZ94iEXldRHbzfGZHCfZHgzu/w/3MVSLysIjsn/X++0Tkcb+fl/Xe9Hf5rIg8LSKfyJr/NRF5L/2bdKel980z7vfxH555s0Rks2e//lpERnq+pzOz1v+O+296313mmXeru77ZInKPZ/po92/lgMFuv/v+fUXkbnedL4rIYhE5qMj2P5BjPX37bLAK7RcROUGcY0z6eDPbM+9a93uc4pn2NXda9u9ppfvvGVmf/Tl3+Y+45Z+LyCWe+ce4v0V/vUeo6pB5Ae9klWcBt7r//wzwFFDrlo8EXgP2dct/Ahrc/58PtHnW82vgWk95LvCrPDF8F7hzkNtxHPA4sJtbrgUmeGPMWv5a4BvAcOD3wL/l+0480/Nub77vFNgbeAiY65ZPBh7w8b5m4F/c/9cDLwPPeOZfAqxM7yt32iHAc8DrwCjP9L8Ah7v/rwYOdf9/OE6/VQe45QPc8jS3vMjd35d4vtOOUu0PoMPz25oL/MTz/jE4Y2+sTsc30N81cArwcNb8J4HlwCzPtL59A3wIeAk4Pvvvwi3/HDjf8z2dmevz3X33pvu9Dnen3equT4BHgU+702/y7PPBbr+43//FnmlHAJ/0s/35fveDfeXbL8C+7m/tSM/v5Sng7zx/r6uAOZ73Pwq8kOf3dDCwLuuzf+lu87VueR/gFWA8zgn+/wIn+N2WIVUjKOJK4JuqmgRQ1adxDlC5+jt6HHeQHDdrHwV8xzP/OqBBRA70vklETgS+APzjIGPdD0iq6vturElVzdlFt8cw4G5grap+u5+f17e9hajqJpxxIf5JRHKNJ+F3/e8Cqz1nZmfh/LC9vgj8N7AEON0zfW9goxtPj6q+6E7/BvBdVX3VnfcqcCPwTc97bwK+5vss6a/6uz+yt/fzwP04++fsfn52ttHA1nTB/Q3uDswBcvbvparv4iTaXfax+12M8q6ziM3AH4GMQS/UORpdAtzk7tdPAd93Zw92+6cDKXU6q0x/3kpVXe5n+8vEu18uBRa5xxjcY863AO/f5e9wRmlERD4MbMP5boutGxHZHTgeuAD3+1TVN4H/AP4duBhYpaqP+A1+qCWCD4nnMgXOATvtozhZ2WuFOz3bqTg7CuBQYKWq9qRnuv9f6X2vOJc+fgYkVHX7oLbCOfhNEpE1IvIjETnJM+8uzzZ+3zP9W0C3qn41a10Z34mInJXj87zbW5CqvoLzu9nbnfTJrPVnJ8dqnINCS9aq7gbOFpE6oIddx6I4C7gH+AWZf+D/CfxFRH4rIheJyAh3up/9+xrwCHCun231KLQ/csn+Ps/B2Y7sbfErvQ9fAm4j86Qkve7lwMEisnf2m0VkL5yxwZd5Jp/l/o28DozFOVD79W/A191920dVVwF/wEkUl6vqB1kxDnT7D2PXfZtWdPsDlG+/+PktbgfWi8hhONtwD7taKiLPAw/jJLq0zwIPquoa4C0ROdKdPh/nePVNnOOBb0MtEbyrqkekX8DVRZYXMkdFu0ucLrOvBG7Js0y+9/4Y55LQowOK3ENV38GphczGOUu4R0RmubO/5NlG79nuI8Bx6eumHhnfiap6f3C5ttcPb21gedb6X3anf8g90GzBOdC0Za3jQaCRHH8EIvJxYLOqrsM5qBzpHsxQ1euABpyD8xfd9aRjyt5PuaZ9F+cPxfdvv8j+8FoqIpuAT+NcbkFE9gGmAI+4f7jd7h9/f6T34UdwkswdnhrZ2cDdqtoL3Av8g+d9nxSRVcAbOJdJ3vDMu8f9G9kX5xJc+reU67eeMc2tbT2J8/1n+y/gdVVdCiXb/kIKbX/Q8u2XfMeM7GnpGtJngd/mWH66qh4G/A1wq1sTAOdv5m7POs4BcL+DBUCrqm7pz4YMtURQyIs4f8xeR7rT076Ec2355zg/aHCu231M3BukAO7/D8e55omIJHCun3rP1AbFvezxJ1W9BvgnnOp1IcuArwKtIjLB58fk2t6C3GpsD7CpyKLvugea/XHuXWRcgnPPFp8Cvg78Juu95wAfEedm7ss4VePPe977sqr+GKemcbiIjMO9vpq1nuz9i6q249TmvlAk/gw+98d0nO19gb/WRs8C9gJedbennkFcHlLVx3GuOY8XkWk4Z/pt7rrPJvOMe7mqTsM5kFwiORoEuJd07gdOdCdtceMFQETGkrsjtO/inEBkH0N63VdaKbb/BXb928XH9peNd7+Q+7d4FFm/RZzv/VzgtUJXEdyTqzeBQ93f+t8Ct7nb/E2c2l36xCD7+/clTong34HvuV8k7h/FLOBH3oVUNYVTDTtWRA5xDxzPkFk1mwM8rart7oHxBpwz9e5SBCoiB4vIVM+kI/DR46qq/gbnuuyD7qWqorK3t0hc43Gqn7e6BxA/698GXA58Q0Rqsmb/ALjSe/biJtl/wLnJW6+q9TjXUs9x5/+d50c/FScpvY1zffSfRaTeXa4euMr9jGw34NxT8KU/+8O9Hv9VnNH3xrpxn+rZlqMYRCJwW4lU4xywz8G5WVjvviYAEyWrxZJ7Jn4jzoE7lxNwEi44N1PPEpHhbnkWsDT7Dar6Es6B7TNFQi7F9v8PsJuI/P/0BLfWeDM+tr8csvbLfwGz0onXPeZ8D+cY1Mf9rVyJ83sstO69cU7Y1gFnAneo6v7uNk8CXsXZhwMW5uD1ZaWqLSIyEXhMRBTYAXxZcwyPqarvisgPcA4WF7ivW0Sknb+2YLjAXfxKnJtt92bdP71MVZcPMNzd3c8bA3TjtNKYjdN66S4RedddLqmqn86Kfb6I7Au0iMgM/nqJJu3B7JvJObbXK/3+GjeW/wZ+6Jn/yaz1X6+qv85a/zMi8izOAWC5Z/oLOGdPXifiXFp43TNtGc7Z0H44Z1D/KSJdbjxfSt+zEZErgfvdhJMCvqWqK8miqi+IyNM4NQY/Cu2PXajqRnGavV4KTAb+7Jn3qohsF5FjVPUJn5/v3YeCcx+qR0TOBpqylv0tzvecve75OMn4ALd8ljjNp6twxg+f5cb3gIgcBTwlIj04CeLiPHHdgHOSlJObjAe9/aqqIvI5nBvR3wbew2lVczLODWov7/Z/yr30mVbqy0Y59wuwUUS+DPxERPZw592kqrvch1HVu7OneSx190EN8G1VfVNEzsG5R+P1G5zLdAM93lgXE8YYE3dxujRkjDEmB0sExhgTc5YIjDEm5iwRGGNMzFkiMMaYmLNEYIwxMWeJwBhjYs4SgTGDJCLfEZGveMo3iMjlYcZkTH/YA2XGDJL7BO29qnqk20XGWuDo/nb8ZUxYYtPFhDFBUdUOEdkiIh/DGSDkGUsCJkosERhTGrfh9NezL/DTcEMxpn/s0pAxJeD21vkcTgdhU9UzkJExlc5qBMaUgKp+ICJLgbctCZiosURgTAm4N4mPpbwjZBlTEtZ81JhBEpFDccYo+KOqrg07HmP6y+4RGGNMzFmNwBhjYs4SgTHGxJwlAmOMiTlLBMYYE3OWCIwxJub+Dw49rA0JPt1pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEGCAYAAABYV4NmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAo6UlEQVR4nO3de5wU1Z338c9vABW8IuCNQUYFkhgfQ3SCRqPRKMizm13NxkSMG8ddDKyPES/RqFlXQZesmjXmQTcGE41jHq9r4oa4cplVNCYPq4IiiqiMijKCwADqKF7m8ts/6jTWND09NUxX9/TM9/169Wu6TtU5daqrp3596pyqMndHRESk0CpKXQEREemdFGBERCQVCjAiIpIKBRgREUmFAoyIiKSif6kr0FMMHTrUq6qqSl0NEZGysmTJkkZ3H5ZrngJMUFVVxeLFi0tdDRGRsmJmb3Q0T6fIREQkFQowIiKSCgUYERFJhQKMiIikQgFGRKSXamxs5LzzzmPjxo0lWb8CjIhIL1VbW8uyZcuora0tyfoVYEREeqHGxkbmzp2LuzN37tyStGIUYEREeqHa2loyj2Npa2srSStGAUZEpBeqq6ujubkZgObmZhYsWFD0OijAiIj0QuPHj6d//+hmLf3792fChAlFr4MCjIhIL1RTU0NbWxsQnSKrqakpeh0UYEREJBUKMCIivVBtbS0VFdEhvqKiQp38IiJSGHV1dbS0tADQ0tKiTn4RESkMdfKLiEgqenUnv5mNMLOFZrbCzJab2fkhfbqZvWVmS8PrL2J5LjezejN72cxOiqUfbmbPh3mzzMxC+o5mdl9If9LMqmJ5asxsZXgV/5MVEenj0mzBtAA/cPfPAUcC55rZwWHeje4+NrweBgjzJgGfByYCPzezfmH5W4ApwOjwmhjSJwOb3X0UcCNwXShrT+Aq4AhgHHCVmQ1OcVtFRHqUXt3J7+5r3f2Z8L4JWAEMz5PlZOBed//Y3V8H6oFxZrYvsJu7L/Lovgd3AqfE8mQ+tQeAE0Lr5iSgzt03uftmoI5Pg5KISK/XZzr5w6mrLwJPhqTvm9kyM7s91rIYDqyOZWsIacPD++z0dnncvQV4FxiSp6zsek0xs8VmtnjDhg3bv4EiIj3M+PHjGTBgAAADBgzonZ38ZrYL8FvgAnd/j+h010HAWGAtcENm0RzZPU/69ub5NMH9VnevdvfqYcOG5dsMEZGyUlNTQ+iuxsx6Vyc/gJkNIAoud7n77wDcfZ27t7p7G/BLoj4SiFoZI2LZK4E1Ib0yR3q7PGbWH9gd2JSnLBGRPmHo0KHst99+AOy3334MGTKk6HVIcxSZAbcBK9z9p7H0fWOLfQN4IbyfA0wKI8MOIOrMf8rd1wJNZnZkKPNM4PexPJmwfCrwaOinmQ9MMLPB4RTchJAmItInNDY28tZbbwGwZs2aXvc8mKOB7wJfyxqSfH0YcrwMOB64EMDdlwP3Ay8C84Bz3b01lHUO8Cuijv9Xgbkh/TZgiJnVAxcBl4WyNgHXAE+H19UhTUSkT+gJz4OxTAX6uurqal+8eHGpqyEiUhATJ05ky5YtW6cHDRrEvHnzCr4eM1vi7tW55ulKfhGRXuiYY45pN33ssccWvQ4KMCIikgoFGBGRXujxxx9vN/3YY48VvQ4KMCIivVDmTsodTReDAoyISC/0/vvv550uBgUYEZFeqKqqKu90MSjAiIj0QldccUW76SuvvLLodSj+STkREem2WbNmUV9fn3eZiooK2tra2HHHHZk1a1beZUeNGsW0adMKWUW1YEREeqsddtgBgJEjR5Zk/WrBiIiUoSStjcwynbVe0qIWjIiIpEIBRkREUqEAIyIiqVCAERGRVCjAiIhIKhRgREQkFQowIiKSCgUYERFJhQKMiIikQgFGRERSoQAjIiKpUIAREZFUKMCIiEgqFGBERCQVCjAiIpIKBRgREUmFAoyIiKRCAUZERFKhACMiIqlQgBERkVQowIiISCoUYEREJBWpBRgzG2FmC81shZktN7PzQ/qeZlZnZivD38GxPJebWb2ZvWxmJ8XSDzez58O8WWZmIX1HM7svpD9pZlWxPDVhHSvNrCat7RQRkdzSbMG0AD9w988BRwLnmtnBwGXAI+4+GngkTBPmTQI+D0wEfm5m/UJZtwBTgNHhNTGkTwY2u/so4EbgulDWnsBVwBHAOOCqeCATEZH0pRZg3H2tuz8T3jcBK4DhwMlAbVisFjglvD8ZuNfdP3b314F6YJyZ7Qvs5u6L3N2BO7PyZMp6ADghtG5OAurcfZO7bwbq+DQoiYhIERSlDyacuvoi8CSwt7uvhSgIAXuFxYYDq2PZGkLa8PA+O71dHndvAd4FhuQpK7teU8xssZkt3rBhQze2UEREsqUeYMxsF+C3wAXu/l6+RXOkeZ707c3zaYL7re5e7e7Vw4YNy1M1ERHpqlQDjJkNIAoud7n770LyunDai/B3fUhvAEbEslcCa0J6ZY70dnnMrD+wO7ApT1kiIlIkaY4iM+A2YIW7/zQ2aw6QGdVVA/w+lj4pjAw7gKgz/6lwGq3JzI4MZZ6ZlSdT1qnAo6GfZj4wwcwGh879CSFNRESKpH+KZR8NfBd43syWhrQfAdcC95vZZOBN4FsA7r7czO4HXiQagXauu7eGfOcAdwADgbnhBVEA+42Z1RO1XCaFsjaZ2TXA02G5q919U0rbKSIiOaQWYNz9T+TuCwE4oYM8M4GZOdIXA4fkSP+IEKByzLsduD1pfUVEpLB0Jb+IiKQizVNkIiLSRbNmzaK+vr4gZa1cuRKAadOmFaS8UaNGdaksBRgRkR6kvr6el19YwYhd9+l2WQNaopNUW97Y3O2yVje93eU8CjAiIj3MiF334Qfj/q7U1Wjnhqd+3eU86oMREZFUKMCIiEgqFGBERCQVCjAiIpIKBRgREUmFAoyIiKRCAUZERFKhACMiIqlQgBERkVToSn4RkR6koaGBD5qatuvK+TStbnqbnRs+6FKeLgUYM9sL2Ckz7e5vdmltIiLSZyQKMGb218ANwH5EjzgeCawAPp9e1URE+p7Kykq2tG7ukfciG1Q5uEt5kvbBXAMcCbzi7gcQPTDsz12rnoiI9CVJA0yzu28EKsyswt0XAmPTq5aIiJS7pH0w75jZLsATwF1mth5oSa9aIiJS7pK2YE4GtgAXAPOAV4G/SqlOIiLSCyRqwbj7B2Y2Ehjt7rVmNgjol27VRESknCVqwZjZ94AHgNkhaTjwHynVSUREeoGkp8jOBY4G3gNw95XAXmlVSkREyl/SAPOxu3+SmTCz/oCnUyUREekNkgaYx83sR8BAMxsP/Dvwh/SqJSIi5S7pMOVLgbOB54GpwMPAr9KqlIhIX7a66e2C3Its/ZZNAOw1aM9ul7W66W0+Q9eu5O80wJhZBbDM3Q8BfrmddRMRkQRGjRpVsLKaVzYCMGhk1wJDLp9hcJfr1mmAcfc2M3vOzPbXzS1FRNI1bdq0gpc1a9asgpXZFUlPke0LLDezp4Ct92t2979OpVYiIlL2kgaYGanWQkREep2kV/I/Hp82s6OB7wCP584hIiJ9XeJHJpvZWDO73sxWAf9M9DyYfMvfbmbrzeyFWNp0M3vLzJaG11/E5l1uZvVm9rKZnRRLP9zMng/zZpmZhfQdzey+kP6kmVXF8tSY2crwqkm6jSIiUjh5A4yZjTGzK81sBXAzsBowdz/e3W/upOw7gIk50m9097Hh9XBYz8HAJKIHmE0Efm5mmXud3QJMAUaHV6bMycBmdx8F3AhcF8raE7gKOAIYB1xlZt0fQiEiIl3SWQvmJaKHi/2Vu3/F3W8CWpMU7O5/BDYlrMfJwL3u/rG7vw7UA+PMbF9gN3df5O4O3AmcEstTG94/AJwQWjcnAXXuvsndNwN15A50IiKSos4CzDeBt4GFZvZLMzsBsG6u8/tmtiycQsu0LIYTtY4yGkLa8PA+O71dHndvAd4FhuQpS0REiihvgHH3B939NOCzwGPAhcDeZnaLmU3YjvXdAhxE9DTMtcANIT1X0PI86dubpx0zm2Jmi81s8YYNG/JUW0REuipRJ7+7f+Dud7n714FKYClwWVdX5u7r3L3V3duI7gowLsxqAEbEFq0E1oT0yhzp7fKEm2/uTnRKrqOyctXnVnevdvfqYcOGdXVzREQkj6TPgzkx8z70bcwm6g/pktCnkvENIDPCbA4wKYwMO4CoM/8pd18LNJnZkaF/5Uzg97E8mRFipwKPhn6a+cAEMxscTsFNCGkiIlJESS+0vNLMvglcDOxCdKPLj4lGiuVkZvcAxwFDzayBaGTXcWY2luiU1SqiG2fi7svN7H7gRaAFONfdM4MJzgnrGQjMDS+A24DfmFk9UctlUihrk5ldAzwdlrva3ZMONhARkQJJGmC+CvyA6NQYwJXufk++DO5+eo7k2/IsPxOYmSN9MXBIjvSPgG91UNbtwO356iciIulKeqHlYKLrSl4larmMzFzwKCIikkvSAPPfwFx3nwh8CdgP+HNqtRIRkbKX9BTZiZlb9bv7h8A0Mzs2vWqJiEi5S9qCaTSzfzKzXwKY2Whgt/SqJSIi5S5pgPk1Ud/Ll8N0A9ENL0VERHJKGmAOcvfrgWbYeppMnfwiItKhpAHmEzMbSLjlipkdRNSiERERySlpJ/90YB4wwszuAo4GzkqpTiIi0gskfaLlAjNbAhxJdGrsfHdvTLVmIiJS1hIFGDObA9wDzHH3D9KtkoiI9AZJ+2BuAI4BXjSzfzezU81spxTrJSIiZS7pKbLHgcfDY4y/BnyP6F5fuhZGRERyStrJTxhF9lfAacBhfPq4YhERkW0k7YO5j+hml/OAfwMeCw8NExERySlpC+bXwHdiz2gRERHJK2kfzDwzO8rMquJ53L3LT7UUEZG+Iekjk38D/CvwFaLb9X8JqE6xXiIiPUJjYyPnnXceGzduLHVVyk7SU2TVwMHhmfciIn1GbW0ty5Yto7a2losuuqjU1SkrSa+DeQHYJ82KiIj0NI2NjcydOxd35+GHH1YrpouSBpihRBdZzjezOZlXmhUTESm12tpampubAWhubqa2VldndEVXbnYpItKnLFiwgEzPgLszf/58nSbrgkQtmHAl/0vAruG1IqSJiPRae++9d95pyS/phZbfBn4CPEZ0N+WbzOwSd38gxbqJiJTUunXr8k6X0qxZs6ivr8+7zMqVKwGYNm1ap+WNGjUq0XJdkbQP5h+BL7l7jbufCYwD/qmgNRER6WEmTJiAWfTwXjPjpJNOKnGNumbgwIEMHDiwZOtP2gdT4e7rY9MbSR6cRETKUk1NDXPmzGk33VMUurWRhqRBYl4YQXaWmZ0F/CfwcHrVEpHeotwvVIx38kvX5A0wZjbKzI5290uA2cChwBeARcCtRaifiJS5+IWK5Sa7zuW4DaXUWQvmZ0ATgLv/zt0vcvcLiVovP0u3aiJS7uIXKs6dO7fsWjHz5s1rNz137twS1aQ8dRZgqtx9WXaiuy8GqlKpkYj0GrW1tbS2Rjdhb2lpKbsWQKbuHU1Lfp0FmHyPRS7d0AQRKQt1dXVbD8qtra0sWLCgxDXqmpaWlrzTkl9nAeZpM/tedqKZTQaWpFMlEektxo0b1276iCOOKFFNpBQ6G6Z8AfCgmZ3BpwGlGtgB+EaK9RKRXuDVV19tN93ZhYHSu+Rtwbj7Onc/CpgBrAqvGe7+ZXd/O19eM7vdzNab2QuxtD3NrM7MVoa/g2PzLjezejN72cxOiqUfbmbPh3mzLFz1ZGY7mtl9If3J8DC0TJ6asI6VZtZzBq6L9DGrV6/OOy29W9J7kS1095vC69GEZd8BTMxKuwx4xN1HA4+EaczsYGAS8PmQ5+dm1i/kuQWYAowOr0yZk4HN7j4KuBG4LpS1J3AVcATRHQeuigcyESmenXfeOe+09G5Jr+TvMnf/Y7xVEZwMHBfe1xLd2+zSkH6vu38MvG5m9cA4M1sF7ObuiwDM7E7gFGBuyDM9lPUAcHNo3ZwE1Ln7ppCnjigo3VPobRSR/D788MO806WU5F5euXR0BX0a9/Iqd8W+3cve7r4WIPzdK6QPB+Jt54aQNjy8z05vl8fdW4B3gSF5ytqGmU0xs8VmtnjDhg3d2CwRySX76vdyuxq+f//+eaclv57yaVmONM+Tvr152ie630q4I0F1dXV5ffNFSixJCyBXgOkpLYAk63rllVc4++yzt07feuutjBo1Ks1q9SrFbsGsM7N9AcLfzA00G4ARseUqgTUhvTJHers8ZtYf2B3YlKcsEZEuGTNmzNZWy7BhwxRcuqjYLZg5QA1wbfj7+1j63Wb2U2A/os78p9y91cyazOxI4EngTOCmrLIWAacCj7q7m9l84Mexjv0JwOXpb5pI35KkBXDGGWe0Gzk2YsQIZs2alWa1Cu7AAw+kvr6e6667rtRVKTuptWDM7B6ig/9nzKwhXJx5LTDezFYC48M07r4cuB94EZgHnOvumXsynAP8CqgHXiXq4Ae4DRgSBgRcRBiRFjr3rwGeDq+rMx3+IlJcV111VbvpGTNmlKgm22/QoEEceuihar1shzRHkZ3ewawTOlh+JjAzR/pi4JAc6R8B3+qgrNuB2xNXVkRSMWbMGHbYYQc++eQTRowYoYN0H6OHholIqqqqqqioqCjL1ot0jwKMiKRKp5j6LgUYERFJhQKMiIikQgGmCMr9meQiIttDAaYIyvmZ5CIi20sBJmXl/kxyEZHt1VPuRdZr1dbWbr0fU1tbG7W1tVx00UUlrpVI77W9d0nuyMqVK4Fkdy5Ioi/ddVkBJmV1dXU0NzcD0NzczIIFCxRgRFJUX1/PC889x647FObw1tIS3VTkjRXLu11W0yct3S6jnCjApGz8+PE89NBDtLa20q9fPyZMmFDqKon0ervu0J9xe/e85ww+tW5zqatQVOqDSVlNTQ2trdEvoNbWVmpq9ARnEekbFGBStmlT+/tsbt7ct37BiEjfpVNkKZs+fXq76SuvvJK77767NJURSaiQHeXqJO+7FGBS1tDQkHdapCeqr69n+fMr2GPQXp0v3Im2T6KHzL71aveH6L+zZX2nyzQ0NND0SUuP7O9o+qSlTx0DFGBEJKc9Bu3F8Z+dVOpqtLPwpXtLXQXpAgWYbtqeUwn5mvdq/ot0T2VlJa1N7/bYUWSVlZWdL9hLqJNfRERSoRZMN3XW2rj00ktZtGjR1umjjjqKa6+9Nu1qiXRLQ0MD725p6nGnpN7Zsh5v+LDU1ZCEFGBSdskll/A3f/M37aZFJF2F7OTfEq7kH9S/X7fL0pX8UlBDhw5l1113pampiaOOOoohQ4aUukpSZhobG5kxYwbTp08v2vensrIS+3hjj+zkH16Z/zMo9JMzM8OsR44eXZDy+tKTPRVgimD//fdn1apVar3Idok/7kH3setcoQfJZMqbNWtWQcvtC9TJXwQDBgxg9OjRar1Il+lxD1LOFGCkU3oiZ+nU1tbS1tYGRPey00PrpJzoFJl0avbs2Tz33HPMnj2bH/3oR6WuTp9SV1dHS0vUMdzS0lLUxz28s2V9QUaRvf9R1Nm+y07dvy7lnS3rGY7OBJQLBRjJq7GxkQULFgAwf/58pk6dqlN9RXTMMccwf/78rdPHHntsUdZbyI7olSujG74OP6j735vhDOlTneTlTgFG8po9e/bWJ3K6u1oxRfbxxx/nnU5LITvK1UnedynAdEB3k43U1dW1m16wYIECTBE99thj7aYXLlzIjBkzSlMZkS5SgOlAfX09zz7/Im2D9ux2WfZJ1AJY8urb3S6rYsumzhcqoEwHc0fTIiIdUYDJo23Qnnx08NdLXY12dnrxoVJXQQpke1vJHbVedaNU6Wk0TFlERFKhFkwHGhoaqNjybo9rMVRs2UhDQ9+6n1FvlaS1cfLJJ7d7zPbgwYPVWV5kzc3NrFq1io0bN2oEZRepBSPSg/3kJz9pN33DDTeUqCZ915o1a/jggw+YPXt2qatSdkrSgjGzVUAT0Aq0uHu1me0J3AdUAauAb7v75rD85cDksPw0d58f0g8H7gAGAg8D57u7m9mOwJ3A4cBG4DR3X9WVOlZWVrLu4/49sg+msnKfgpWnB6b1bGPGjKGiooK2tjYGDx6sa0CKrLGxcWsLcsGCBboOrItKeYrseHdvjE1fBjzi7tea2WVh+lIzOxiYBHwe2A/4LzMb4+6twC3AFOC/iQLMRGAuUTDa7O6jzGwScB1wWrE2TKSQRo0aRX19vVovBZbkx9Ubb7yx9X1bWxt///d/z8iRI3Muqx9X2+pJfTAnA8eF97XAY8ClIf1ed/8YeN3M6oFxoRW0m7svAjCzO4FTiALMycD0UNYDwM1mZp65YjChii2bCtIHYx+9B4DvtFu3y4qGKReuBdPZP8Rxxx3XbmhyRUWF+gCKbNCgQRx66KFqvZRAvP8rM91RgJFtlSrAOLDAzByY7e63Anu7+1oAd19rZnuFZYcTtVAyGkJac3ifnZ7JszqU1WJm7wJDgHiLCTObQtQCYv/9929XwcLeKqMJgNEHFSIw7FPUA83111/PxRdfvHVav6KT0YW6PV+Sz+CrX/0q8d+lZqYfWF1QqgBztLuvCUGkzsxeyrOs5UjzPOn58rRPiALbrQDV1dXt5utWGZFx48ZtfV9RUcHhhx9ewtqUj/r6el5aurQgbc3MSJx3li7tdlndv9S3bzGzbQKMJFeSAOPua8Lf9Wb2IDAOWGdm+4bWy77A+rB4AzAilr0SWBPSK3Okx/M0mFl/YHeguJfA9yIHHnggr732mlovXbQPMDnnb53SuW3b31mpK+dhvgMHDuSDDz5oNy3JFT3AmNnOQIW7N4X3E4CrgTlADXBt+Pv7kGUOcLeZ/ZSok3808JS7t5pZk5kdCTwJnAncFMtTAywCTgUe7Wr/i3xqt912Y+zYsWq9dEFDQwNNlOaAns9a4P2Ghk6XK6T4MN9yu49dPLjkmpb8StGC2Rt4MDQ1+wN3u/s8M3sauN/MJgNvAt8CcPflZnY/8CLQApwbRpABnMOnw5TnhhfAbcBvwoCATUSj0ESkgJL0MzU3N2/tKJ83bx6rV69mwIABOZftiX1DVVVVrFq1qt20JFf0AOPurwFfyJG+ETihgzwzgZk50hcDh+RI/4gQoPqqvtDJnHQbG8Iv9srKyk6WLFzdKisreamxsfMFE8g8R7QQJ5eMZJ9DoaxZs2ab6XIahXXFFVdw9tlnb52+8sorS1ib8tOThilLAdXX1/PKC8+w/y6tnS/ciR2ao27mj1Y93e2y3ny/X7fL6KoPP/yw6Oss5Ei/DSHA7zF6dLfL2oPC1S1JID7++OPbTb/33ntlNdhlzJgxW1sxVVVVGireRQowvdj+u7RyRfX7pa5GO/+8eJeClZW0pVGKUXwahRjJHnVVjqOwrrjiCs4//3y1XraDAkwRvPfee7z22mssWbKkaB3lDQ0NfNDUr6AH9EJ4o6kfOxe5k1lK54QTTmj3yOcTTzyxhLXZPmPGjGHu3LmdLyjb0M0ui+C1114D4Ic//GGJayJSXFOnTqWiIjrMVFRUMHXq1BLXSIpJLZiUPfXUU1vfNzc3F60VU1lZySvvrO98wQTWbYkOEHsP6v7TLM2SdTL3hUEKSZXzdSRDhw5l/PjxzJ8/nwkTJpRd/aV7FGC6qbMD4dKsq68vvPBCxo4d2+HyhTp4FbIz8pNwgN6pqvudzGNIVrf6+nqeXf5s1CvdXSEuPvvWs90v653uF9FV5XwdCUStmLffflutlz5IAaaX6hWdzHtA23HdbzUVUsVjxT2r3BtuFz906FBuuummzheUXkcBpps6O5Afe+yx26SV42igYmtoaIB3i39A79Q70OCFGaSg28VLb9fD/ntFJC7X7eJFyoVaMCk744wzuOuuu7ZO19TUlLA25aOyspINtqFHniKrHF6YK+GTtDa+9rWv0dLSsnW6f//+agFL2VCASdnUqVPbBZjJkyeXsDbbSnKapiujsAp6muadAp0iy1xrWohLgt7h06cOFUFvuI5E+i4FmCLItGLKtfVSiluUF/aBb1GAHD28+6PgGF7YunVm6tSp1NXV0dbWputIpOyY7mIfqa6u9sWLF5e6GpKCcr7VCsDMmTOZP38+EydOLMthytK7mdkSd6/ONU8tGJEeTteRSLlSgBHp4XQdiZQrDVMWEZFUKMCIiEgqdIpMylbSG2KWbJi1SB+nACO9XimGWYuIAoyUMbU0RHo29cGIiEgqFGBERCQVCjAiIpIKBRgREUmFAoyIiKRCAUZERFKhACMiIqlQgBERkVToeTCBmW0A3khxFUOBxhTLT5vqX1qqf2mVc/3TrvtIdx+Wa4YCTJGY2eKOHspTDlT/0lL9S6uc61/KuusUmYiIpEIBRkREUqEAUzy3lroC3aT6l5bqX1rlXP+S1V19MCIikgq1YEREJBUKMCIikgoFmATM7P2s6bPM7ObY9BQzeym8njKzr8TmPWZmL5vZc2b2tJmNjc3b3czuNLNXw+tOM9s9zDvXzJbGXi+YmZvZ57q5Lf9oZsvNbFko94hYHTPreiAsO93MLg7vdzKzOjO7Kky3ZtXvss62N6semfzLw7IXmVlFmHecmb2bVf6JWfleMLM/mNkeIb0qfD7XxNYx1Mya4/sqpD9nZvdkpR1pZk+GsleY2fTYvFPC5/WSmT1vZqfE5t1hZm+Z2Y6xda4qwP6oDvNXhXUuM7PHzWxkVv7fm9mipOvLypv5LJ8zs2fM7Kis+Rea2UeZ72RIy+ybZ8Pn8a+xeWeZ2YbYfn3AzAbFPqdTs8p/P/zN7LvzYvNuDuVNMbP7Yum7hf+VA7q7/SH/PmZ2byjzRTN72MzGdLL9D+UoZ+s+6658+8XMvmLRMSZzvJkSmzc9fI6jYmkXhrTs79PS8PfkrHV/Iyz/2TB9t5mdE5t/RPguJntYpbvr1ckLeD9r+izg5vD+68ASYGiYPgx4E9gnTD8GVIf3fwfUxcp5AJgem54B/HsHdfgx8P+6uR1fBhYBO4bpocB+8TpmLT8duBjYAfhP4NqOPpNYeofb29FnCuwF/BcwI0wfBzyUIF8t8I/hfRXwKvBsbP45wNLMvgppnwOeB94Cdo6lvwx8IbzvBxwc3n8BqAcOCNMHhOlDw/QdYX+fE/tMVxVqfwCrYt+tGcAvY/n3AFYDKzL1297vNXAS8HjW/KeAJ4CzYmlb9w0wEHgJODr7/yJM3w38XexzOjXX+sO+Wxc+1x1C2s2hPAP+DJwY0n8W2+fd3X4Ln/8/xNLGAsck2f6OvvfdfXW0X4B9wnftsNj3ZQnwl7H/12XAFbH8fwaWd/B9+gzwRta67w/bPD1M7w28BgwjapA8DXwl6baoBdN9lwKXuHsjgLs/Q3TgOzfHsouA4QDhV8bhwDWx+VcD1WZ2UDyTmR0LfBv4P92s675Ao7t/HOra6O5rOsnTH7gXWOnul3VxfVu3Nx93Xw9MAb5vZtaN8j8EVsR+SZ5G9A8T9x3gN8AC4K9j6XsBa0N9Wt39xZB+MfBjd389zHsd+BfgkljenwEXJv5V96mu7o/s7f0m8Aei/TOpi+vOthuwOTMRvoO7AFcAp+fK4O4fEgXwbfZx+Cx2jpfZiQ3AI0BN1jqc6IfCz8J+PQH4SZjd3e0/Hmh291/E1rfU3Z9Isv1FEt8v5wJ3hGMM4ZjzQyD+f/kfwMkAZnYg8C7RZ9tZ2ZjZLsDRwGTC5+nu64B/Ba4H/gFY5u5/Slp5BZhkBlrsdA1RIMj4PNGviLjFIT3bRKIvAMDBwFJ3b83MDO+XxvNadAro10CNu7/Xra2IDqojzOwVM/u5mX01Nu+u2Db+JJb+Q6DF3S/IKqvdZ2Jmp+VYX3x783L314i+j3uFpGOyys8Ouv2IDjZzsoq6F5hkZpVAK5B9wD4NuA+4h/YHjhuBl83sQTObamY7hfQk+/dN4E/Ad5Nsa0y+/ZFL9ud5OtF2ZG9LUpl9+BLwK9r/2MmU/QTwGTPbKzuzmQ0GRgN/jCWfFv5H3gL2JAoASV0L/CDs263cfRkwnygATXP3T7LquL3bfwjb7tuMTrc/RR3tlyTfxfeA1WZ2CNE23Me2FprZC8DjRAE04xRgnru/Amwys8NC+i+IjleXEB0PElOASeZDdx+beQFXdrK8AfHx33eZWQNRa+emDpbpKO8tRKfG/rxdNY9x9/eJWk1TiH7V3GdmZ4XZZ8S2Mf7r/E/AlzPnpWPafSbuHv8i59reJOKtlyeyyn81pA8MB7CNRAewuqwy5gHjyfHPZWZfAja4+xtEB6vDwkESd78aqCY66H8nlJOpU/Z+ypX2Y6J/wMT/U53sj7iFZrYeOJHotBNmtjcwCvhTOCC0hINKV2T24WeJgtedsRbkJOBed28Dfgd8K5bvGDNbBrxNdLro7di8+8L/yD5EpyIz36Vc3/V2aaF1+BTR55/t34C33H0hFGz788m3/WnraL90dMzITsu06E4BHsyx/PHufgjwv4CbQ8sFov+Ze2NlnA4QPoPZwFx339iVDVGA6b4XiQ4ScYeF9IwziM7d3030jwLRedEvWujYBgjvv0B0ThkzqyE6Px3/Zdkt4fTPY+5+FfB9otMM+fwRuACYa2b7JVxNru3NKzTnW4H1nSz6YTiAjSTqG2p3KjL8ul0C/AD4bVbe04HPWtQJ/yrRKYJvxvK+6u63ELWMvmBmQwjnr7PKyd6/uHs9Uevz253Uv52E++N4ou1dzqet59OAwcDrYXuq6MZpMndfRHROf5iZHUrUMqkLZU+ifQvhCXc/lOgAdY7lGMgRTm39ATg2JG0M9QXAzPYk9w0Yf0z0wyT72NQWXhmF2P7lbPu/S4LtL5r4fiH3d/Fwsr6LRJ/7d4E38531CD/a1gEHh+/614BfhW2+hKg1mvnBkf35J6IA033XA9eFHUT4ZzsL+Hl8IXdvJmqOHmlmnwsHpGdp30S9AnjG3evDAXcmUcuipRAVNbPPmNnoWNJYEtxB2t1/S3Tee144Zdep7O3tpF7DiJrhN4cDU5Ly3wWmAReb2YCs2TcAl8Z/bYXg/S2izvkqd68iOld9epj/l7F/ptFEwe4dovPPl5tZVViuCvhRWEe2mUR9Nol0ZX+E/o4LgDPDwfl0YGJsWw6nGwEmjBrqRxQITifq5K0Kr/2A4ZY1gi20HP6FKCDk8hWiQA5RJ/hpZrZDmD4LWJidwd1fIjpgfr2TKhdi+x8FdjSz72USQiv3/5Jg+4sha7/8G3BWJqCHY851RMegrcJ35VKi72O+svci+iH4BnAqcKe7jwzbPAJ4nWgfbreudkpKFnefY2bDgf9vZg40AX/r7mtzLPuhmd1AdBCaHF43mVk9n45omRwWv5Sok/R3Wf3e57n7E9tZ3V3C+vYAWohG7UwhGs12l5l9GJZrdPcTs+r+CzPbB5hjZhP49FRVxrzsQQA5tjcuk39AqMtvgJ/G5h+TVf4/u/sDWeU/a2bPER1YnoilLyf6tRd3LNEplrdiaX8k+vW2L9EvvhvNbEuozxmZPjEzuxT4QwhkzcAP3X0pWdx9uZk9Q9TCSSLf/tiGu6+1aHj1ucD+wH/H5r1uZu+Z2RHu/mTC9cf3oRH187Wa2STgf2ct+yDR55xd9i+IgvwBYfo0i4bpVwANRIEEd3/IzA4HlphZK1Hg+YcO6jWT6MdXTiHId3v73d3N7BtEAwguAz4iGmV1HNHAgrj49p8QTgFnFPr0Wc79Aqw1s78Ffmlmu4Z5P3P3bfq53P3e7LSYhWEfDAAuc/d1ZnY6UR9Y3G+JTldu7/FGt4oREZF06BSZiIikQgFGRERSoQAjIiKpUIAREZFUKMCIiEgqFGBERCQVCjAiIpIKBRiRHsrMrjGz82PTM81sWinrJNIVutBSpIcKV6z/zt0PC7e6WQmM6+oNB0VKRbeKEemh3H2VmW00sy8SPfjpWQUXKScKMCI926+I7ue1D3B7aasi0jU6RSbSg4W7Dz9PdGPC0R57QJ1IT6cWjEgP5u6fmNlC4B0FFyk3CjAiPVjo3D+S4j5RUaQgNExZpIcys4OJnhHziLuvLHV9RLpKfTAiIpIKtWBERCQVCjAiIpIKBRgREUmFAoyIiKRCAUZERFLxPx/PFqDW1CDTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "visualize_data(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "notes: \n",
    "\n",
    "1-some features have an overlapping between boxes which indicates that they are not doing well in differentating between \n",
    "\n",
    "classes.\n",
    "\n",
    "2-there is huge amount of oultiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importnace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1-for decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Decision_tree_importance(data):\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    y=data['y']\n",
    "    X=data.drop('y',axis=1)\n",
    "    clf=DecisionTreeClassifier()\n",
    "    clf.fit(X,y)\n",
    "    importances=clf.feature_importances_\n",
    "    forest_importances = pd.Series(importances, index=X.columns)\n",
    "    fig, ax = plt.subplots()\n",
    "    forest_importances.plot.bar(yerr=forest_importances, ax=ax)\n",
    "    ax.set_title(\"Feature importances using MDI\")\n",
    "    ax.set_ylabel(\"Mean decrease in impurity\")\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "Decision_tree_importance(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Skewness and oultiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(bins=int(np.sqrt(len(data))),figsize=(15,15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pt/jyznl8l11dnchkwt6_m3kqy80000gn/T/ipykernel_5664/1188251951.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  data.skew()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID                 0.000000\n",
       "Area               2.970246\n",
       "Perimeter          1.650773\n",
       "MajorAxisLength    1.390286\n",
       "MinorAxisLength    2.251468\n",
       "AspectRation       0.589186\n",
       "Eccentricity      -1.075626\n",
       "ConvexArea         2.960315\n",
       "EquivDiameter      1.971378\n",
       "Extent            -0.900892\n",
       "Solidity          -2.507634\n",
       "roundness         -0.659566\n",
       "Compactness        0.038242\n",
       "ShapeFactor1      -0.544873\n",
       "ShapeFactor2       0.292677\n",
       "ShapeFactor3       0.245589\n",
       "ShapeFactor4      -2.628540\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_skew_kurt(df):\n",
    "    skew= df.select_dtypes(np.number).skew()\n",
    "    abs_skew = abs(skew)\n",
    "    kurt = df.select_dtypes(np.number).kurt()\n",
    "    skew_kurt_table = pd.concat([skew,abs_skew,kurt], axis = 1,\n",
    "                                keys =['Skew','Absolute Skew','Kurtosis']).sort_values(\"Skew\",ascending = False)\n",
    "    skew_kurt_table['Skewed'] = skew_kurt_table['Absolute Skew'].apply(lambda x: True if x >= 0.5 else False)\n",
    "    return skew_kurt_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Skew</th>\n",
       "      <th>Absolute Skew</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Skewed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>2.970246</td>\n",
       "      <td>2.970246</td>\n",
       "      <td>10.846326</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvexArea</th>\n",
       "      <td>2.960315</td>\n",
       "      <td>2.960315</td>\n",
       "      <td>10.800252</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MinorAxisLength</th>\n",
       "      <td>2.251468</td>\n",
       "      <td>2.251468</td>\n",
       "      <td>6.689757</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EquivDiameter</th>\n",
       "      <td>1.971378</td>\n",
       "      <td>1.971378</td>\n",
       "      <td>5.267857</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perimeter</th>\n",
       "      <td>1.650773</td>\n",
       "      <td>1.650773</td>\n",
       "      <td>3.673793</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MajorAxisLength</th>\n",
       "      <td>1.390286</td>\n",
       "      <td>1.390286</td>\n",
       "      <td>2.640599</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AspectRation</th>\n",
       "      <td>0.589186</td>\n",
       "      <td>0.589186</td>\n",
       "      <td>0.169588</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShapeFactor2</th>\n",
       "      <td>0.292677</td>\n",
       "      <td>0.292677</td>\n",
       "      <td>-0.847298</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShapeFactor3</th>\n",
       "      <td>0.245589</td>\n",
       "      <td>0.245589</td>\n",
       "      <td>-0.112545</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Compactness</th>\n",
       "      <td>0.038242</td>\n",
       "      <td>0.038242</td>\n",
       "      <td>-0.190212</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.200000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShapeFactor1</th>\n",
       "      <td>-0.544873</td>\n",
       "      <td>0.544873</td>\n",
       "      <td>0.730050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roundness</th>\n",
       "      <td>-0.659566</td>\n",
       "      <td>0.659566</td>\n",
       "      <td>0.460578</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extent</th>\n",
       "      <td>-0.900892</td>\n",
       "      <td>0.900892</td>\n",
       "      <td>0.664159</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eccentricity</th>\n",
       "      <td>-1.075626</td>\n",
       "      <td>1.075626</td>\n",
       "      <td>1.460951</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solidity</th>\n",
       "      <td>-2.507634</td>\n",
       "      <td>2.507634</td>\n",
       "      <td>12.915000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShapeFactor4</th>\n",
       "      <td>-2.628540</td>\n",
       "      <td>2.628540</td>\n",
       "      <td>11.691832</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Skew  Absolute Skew   Kurtosis  Skewed\n",
       "Area             2.970246       2.970246  10.846326    True\n",
       "ConvexArea       2.960315       2.960315  10.800252    True\n",
       "MinorAxisLength  2.251468       2.251468   6.689757    True\n",
       "EquivDiameter    1.971378       1.971378   5.267857    True\n",
       "Perimeter        1.650773       1.650773   3.673793    True\n",
       "MajorAxisLength  1.390286       1.390286   2.640599    True\n",
       "AspectRation     0.589186       0.589186   0.169588    True\n",
       "ShapeFactor2     0.292677       0.292677  -0.847298   False\n",
       "ShapeFactor3     0.245589       0.245589  -0.112545   False\n",
       "Compactness      0.038242       0.038242  -0.190212   False\n",
       "ID               0.000000       0.000000  -1.200000   False\n",
       "ShapeFactor1    -0.544873       0.544873   0.730050    True\n",
       "roundness       -0.659566       0.659566   0.460578    True\n",
       "Extent          -0.900892       0.900892   0.664159    True\n",
       "Eccentricity    -1.075626       1.075626   1.460951    True\n",
       "Solidity        -2.507634       2.507634  12.915000    True\n",
       "ShapeFactor4    -2.628540       2.628540  11.691832    True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skewness_check=display_skew_kurt(data)\n",
    "skewness_check   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skewed_columns=skewness_check.index[skewness_check['Skewed'] == True].tolist()\n",
    "len(skewed_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from \n",
    "\n",
    "-0.5 to 0.5..................no skewness\n",
    "\n",
    "0.5 to 1 OR -0.5 to -1.........moderate skewness\n",
    "\n",
    "if > 1  or <-1 .....................high skewness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_target(data):\n",
    "    data['y'].replace({'DERMASON':0,'SIRA':1,'SEKER':2,'HOROZ':3,'CALI':4,'BARBUNYA':5,'BOMBAY':6},inplace=True)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_target(data):\n",
    "    data['y'].replace({0:'DERMASON',1:'SIRA',2:'SEKER',3:'HOROZ',4:'CALI',5:'BARBUNYA',6:'BOMBAY'},inplace=True)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>ID</th>\n",
       "      <th>Area</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>MajorAxisLength</th>\n",
       "      <th>MinorAxisLength</th>\n",
       "      <th>AspectRation</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>ConvexArea</th>\n",
       "      <th>EquivDiameter</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>roundness</th>\n",
       "      <th>Compactness</th>\n",
       "      <th>ShapeFactor1</th>\n",
       "      <th>ShapeFactor2</th>\n",
       "      <th>ShapeFactor3</th>\n",
       "      <th>ShapeFactor4</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>58238</td>\n",
       "      <td>971.303</td>\n",
       "      <td>397.202654</td>\n",
       "      <td>186.945510</td>\n",
       "      <td>2.124697</td>\n",
       "      <td>0.882317</td>\n",
       "      <td>58977</td>\n",
       "      <td>272.306674</td>\n",
       "      <td>0.604756</td>\n",
       "      <td>0.987470</td>\n",
       "      <td>0.775723</td>\n",
       "      <td>0.685561</td>\n",
       "      <td>0.006820</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.469994</td>\n",
       "      <td>0.998595</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>44515</td>\n",
       "      <td>757.601</td>\n",
       "      <td>265.590470</td>\n",
       "      <td>213.967453</td>\n",
       "      <td>1.241266</td>\n",
       "      <td>0.592420</td>\n",
       "      <td>44780</td>\n",
       "      <td>238.071960</td>\n",
       "      <td>0.771745</td>\n",
       "      <td>0.994082</td>\n",
       "      <td>0.974620</td>\n",
       "      <td>0.896387</td>\n",
       "      <td>0.005966</td>\n",
       "      <td>0.002376</td>\n",
       "      <td>0.803510</td>\n",
       "      <td>0.997370</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>31662</td>\n",
       "      <td>653.783</td>\n",
       "      <td>239.902428</td>\n",
       "      <td>168.421505</td>\n",
       "      <td>1.424417</td>\n",
       "      <td>0.712136</td>\n",
       "      <td>32034</td>\n",
       "      <td>200.781748</td>\n",
       "      <td>0.801407</td>\n",
       "      <td>0.988387</td>\n",
       "      <td>0.930853</td>\n",
       "      <td>0.836931</td>\n",
       "      <td>0.007577</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.700453</td>\n",
       "      <td>0.997737</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>38778</td>\n",
       "      <td>734.020</td>\n",
       "      <td>268.446281</td>\n",
       "      <td>184.061923</td>\n",
       "      <td>1.458456</td>\n",
       "      <td>0.727925</td>\n",
       "      <td>39208</td>\n",
       "      <td>222.201897</td>\n",
       "      <td>0.766137</td>\n",
       "      <td>0.989033</td>\n",
       "      <td>0.904439</td>\n",
       "      <td>0.827733</td>\n",
       "      <td>0.006923</td>\n",
       "      <td>0.002005</td>\n",
       "      <td>0.685142</td>\n",
       "      <td>0.999250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>42530</td>\n",
       "      <td>775.403</td>\n",
       "      <td>281.212824</td>\n",
       "      <td>193.236878</td>\n",
       "      <td>1.455275</td>\n",
       "      <td>0.726511</td>\n",
       "      <td>43028</td>\n",
       "      <td>232.703412</td>\n",
       "      <td>0.729816</td>\n",
       "      <td>0.988426</td>\n",
       "      <td>0.888895</td>\n",
       "      <td>0.827499</td>\n",
       "      <td>0.006612</td>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.684755</td>\n",
       "      <td>0.996507</td>\n",
       "      <td>1</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10829</th>\n",
       "      <td>10829</td>\n",
       "      <td>37777</td>\n",
       "      <td>716.007</td>\n",
       "      <td>250.174662</td>\n",
       "      <td>192.704033</td>\n",
       "      <td>1.298233</td>\n",
       "      <td>0.637708</td>\n",
       "      <td>38174</td>\n",
       "      <td>219.315230</td>\n",
       "      <td>0.770519</td>\n",
       "      <td>0.989600</td>\n",
       "      <td>0.925983</td>\n",
       "      <td>0.876648</td>\n",
       "      <td>0.006622</td>\n",
       "      <td>0.002413</td>\n",
       "      <td>0.768513</td>\n",
       "      <td>0.997708</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10830</th>\n",
       "      <td>10830</td>\n",
       "      <td>32187</td>\n",
       "      <td>667.621</td>\n",
       "      <td>247.778775</td>\n",
       "      <td>165.786862</td>\n",
       "      <td>1.494562</td>\n",
       "      <td>0.743179</td>\n",
       "      <td>32582</td>\n",
       "      <td>202.439525</td>\n",
       "      <td>0.731805</td>\n",
       "      <td>0.987877</td>\n",
       "      <td>0.907466</td>\n",
       "      <td>0.817017</td>\n",
       "      <td>0.007698</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.667517</td>\n",
       "      <td>0.997646</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10831</th>\n",
       "      <td>10831</td>\n",
       "      <td>81837</td>\n",
       "      <td>1095.937</td>\n",
       "      <td>433.838186</td>\n",
       "      <td>241.527026</td>\n",
       "      <td>1.796230</td>\n",
       "      <td>0.830699</td>\n",
       "      <td>82554</td>\n",
       "      <td>322.797312</td>\n",
       "      <td>0.771996</td>\n",
       "      <td>0.991315</td>\n",
       "      <td>0.856226</td>\n",
       "      <td>0.744050</td>\n",
       "      <td>0.005301</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.553610</td>\n",
       "      <td>0.994412</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10832</th>\n",
       "      <td>10832</td>\n",
       "      <td>48212</td>\n",
       "      <td>826.362</td>\n",
       "      <td>309.639462</td>\n",
       "      <td>199.285811</td>\n",
       "      <td>1.553746</td>\n",
       "      <td>0.765357</td>\n",
       "      <td>48777</td>\n",
       "      <td>247.760822</td>\n",
       "      <td>0.677002</td>\n",
       "      <td>0.988417</td>\n",
       "      <td>0.887206</td>\n",
       "      <td>0.800159</td>\n",
       "      <td>0.006422</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>0.640255</td>\n",
       "      <td>0.994793</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10833</th>\n",
       "      <td>10833</td>\n",
       "      <td>28026</td>\n",
       "      <td>610.943</td>\n",
       "      <td>215.446569</td>\n",
       "      <td>165.740377</td>\n",
       "      <td>1.299904</td>\n",
       "      <td>0.638903</td>\n",
       "      <td>28302</td>\n",
       "      <td>188.901592</td>\n",
       "      <td>0.776688</td>\n",
       "      <td>0.990248</td>\n",
       "      <td>0.943560</td>\n",
       "      <td>0.876791</td>\n",
       "      <td>0.007687</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>0.768762</td>\n",
       "      <td>0.999317</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10834 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID   Area  Perimeter  MajorAxisLength  MinorAxisLength  \\\n",
       "0          0  58238    971.303       397.202654       186.945510   \n",
       "1          1  44515    757.601       265.590470       213.967453   \n",
       "2          2  31662    653.783       239.902428       168.421505   \n",
       "3          3  38778    734.020       268.446281       184.061923   \n",
       "4          4  42530    775.403       281.212824       193.236878   \n",
       "...      ...    ...        ...              ...              ...   \n",
       "10829  10829  37777    716.007       250.174662       192.704033   \n",
       "10830  10830  32187    667.621       247.778775       165.786862   \n",
       "10831  10831  81837   1095.937       433.838186       241.527026   \n",
       "10832  10832  48212    826.362       309.639462       199.285811   \n",
       "10833  10833  28026    610.943       215.446569       165.740377   \n",
       "\n",
       "       AspectRation  Eccentricity  ConvexArea  EquivDiameter    Extent  \\\n",
       "0          2.124697      0.882317       58977     272.306674  0.604756   \n",
       "1          1.241266      0.592420       44780     238.071960  0.771745   \n",
       "2          1.424417      0.712136       32034     200.781748  0.801407   \n",
       "3          1.458456      0.727925       39208     222.201897  0.766137   \n",
       "4          1.455275      0.726511       43028     232.703412  0.729816   \n",
       "...             ...           ...         ...            ...       ...   \n",
       "10829      1.298233      0.637708       38174     219.315230  0.770519   \n",
       "10830      1.494562      0.743179       32582     202.439525  0.731805   \n",
       "10831      1.796230      0.830699       82554     322.797312  0.771996   \n",
       "10832      1.553746      0.765357       48777     247.760822  0.677002   \n",
       "10833      1.299904      0.638903       28302     188.901592  0.776688   \n",
       "\n",
       "       Solidity  roundness  Compactness  ShapeFactor1  ShapeFactor2  \\\n",
       "0      0.987470   0.775723     0.685561      0.006820      0.000929   \n",
       "1      0.994082   0.974620     0.896387      0.005966      0.002376   \n",
       "2      0.988387   0.930853     0.836931      0.007577      0.002293   \n",
       "3      0.989033   0.904439     0.827733      0.006923      0.002005   \n",
       "4      0.988426   0.888895     0.827499      0.006612      0.001912   \n",
       "...         ...        ...          ...           ...           ...   \n",
       "10829  0.989600   0.925983     0.876648      0.006622      0.002413   \n",
       "10830  0.987877   0.907466     0.817017      0.007698      0.002116   \n",
       "10831  0.991315   0.856226     0.744050      0.005301      0.001002   \n",
       "10832  0.988417   0.887206     0.800159      0.006422      0.001624   \n",
       "10833  0.990248   0.943560     0.876791      0.007687      0.002802   \n",
       "\n",
       "       ShapeFactor3  ShapeFactor4  y  \n",
       "0          0.469994      0.998595  3  \n",
       "1          0.803510      0.997370  2  \n",
       "2          0.700453      0.997737  0  \n",
       "3          0.685142      0.999250  0  \n",
       "4          0.684755      0.996507  1  \n",
       "...             ...           ... ..  \n",
       "10829      0.768513      0.997708  2  \n",
       "10830      0.667517      0.997646  0  \n",
       "10831      0.553610      0.994412  4  \n",
       "10832      0.640255      0.994793  1  \n",
       "10833      0.768762      0.999317  0  \n",
       "\n",
       "[10834 rows x 18 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded=encode_target(data)\n",
    "data_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y                  1.000000\n",
       "Perimeter          0.856849\n",
       "EquivDiameter      0.834189\n",
       "MajorAxisLength    0.814923\n",
       "MinorAxisLength    0.784747\n",
       "ConvexArea         0.772137\n",
       "Area               0.770679\n",
       "AspectRation       0.307983\n",
       "Eccentricity       0.238923\n",
       "ID                 0.012671\n",
       "Extent            -0.012577\n",
       "ShapeFactor3      -0.288999\n",
       "Compactness       -0.297618\n",
       "Solidity          -0.328793\n",
       "ShapeFactor4      -0.365705\n",
       "roundness         -0.555520\n",
       "ShapeFactor2      -0.637964\n",
       "ShapeFactor1      -0.819143\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded.corr()['y'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_bacdd_row0_col0, #T_bacdd_row1_col1, #T_bacdd_row1_col7, #T_bacdd_row2_col2, #T_bacdd_row3_col3, #T_bacdd_row4_col4, #T_bacdd_row5_col5, #T_bacdd_row6_col6, #T_bacdd_row7_col1, #T_bacdd_row7_col7, #T_bacdd_row8_col8, #T_bacdd_row9_col9, #T_bacdd_row10_col10, #T_bacdd_row11_col11, #T_bacdd_row12_col12, #T_bacdd_row12_col15, #T_bacdd_row13_col13, #T_bacdd_row14_col14, #T_bacdd_row15_col12, #T_bacdd_row15_col15, #T_bacdd_row16_col16, #T_bacdd_row17_col17 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row0_col1, #T_bacdd_row0_col7, #T_bacdd_row9_col2 {\n",
       "  background-color: #d1dae9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col2, #T_bacdd_row0_col3 {\n",
       "  background-color: #d3dbe7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col4, #T_bacdd_row4_col12, #T_bacdd_row5_col4, #T_bacdd_row15_col13 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col5, #T_bacdd_row5_col13, #T_bacdd_row6_col4 {\n",
       "  background-color: #dcdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col6, #T_bacdd_row0_col12, #T_bacdd_row0_col15, #T_bacdd_row4_col5, #T_bacdd_row6_col13, #T_bacdd_row9_col1, #T_bacdd_row13_col12, #T_bacdd_row13_col15 {\n",
       "  background-color: #dbdcde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col8 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col9, #T_bacdd_row11_col7, #T_bacdd_row17_col9 {\n",
       "  background-color: #92b4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col10 {\n",
       "  background-color: #88abfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col11, #T_bacdd_row9_col3, #T_bacdd_row9_col16 {\n",
       "  background-color: #c7d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col13, #T_bacdd_row4_col15, #T_bacdd_row12_col13 {\n",
       "  background-color: #d8dce2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col14, #T_bacdd_row0_col17 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row0_col16, #T_bacdd_row8_col12 {\n",
       "  background-color: #a9c6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row1_col0, #T_bacdd_row5_col14, #T_bacdd_row6_col12, #T_bacdd_row7_col0, #T_bacdd_row13_col0, #T_bacdd_row14_col0 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col2, #T_bacdd_row2_col1, #T_bacdd_row2_col7, #T_bacdd_row7_col2 {\n",
       "  background-color: #ba162b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col3, #T_bacdd_row3_col1, #T_bacdd_row3_col7, #T_bacdd_row5_col6, #T_bacdd_row6_col5, #T_bacdd_row7_col3 {\n",
       "  background-color: #c12b30;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col4, #T_bacdd_row4_col1, #T_bacdd_row4_col7, #T_bacdd_row4_col8, #T_bacdd_row7_col4, #T_bacdd_row8_col4 {\n",
       "  background-color: #bd1f2d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col5, #T_bacdd_row5_col17, #T_bacdd_row7_col5, #T_bacdd_row16_col13 {\n",
       "  background-color: #f4c6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row1_col6, #T_bacdd_row7_col6 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row1_col8, #T_bacdd_row8_col1, #T_bacdd_row8_col7 {\n",
       "  background-color: #b70d28;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col9, #T_bacdd_row4_col11, #T_bacdd_row7_col9, #T_bacdd_row12_col1 {\n",
       "  background-color: #a3c2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row1_col10, #T_bacdd_row14_col8 {\n",
       "  background-color: #5875e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col11 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col12, #T_bacdd_row7_col12, #T_bacdd_row10_col5 {\n",
       "  background-color: #b3cdfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row1_col13, #T_bacdd_row2_col14, #T_bacdd_row5_col10, #T_bacdd_row7_col13 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col14, #T_bacdd_row17_col11, #T_bacdd_row17_col14 {\n",
       "  background-color: #5f7fe8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col15, #T_bacdd_row7_col15 {\n",
       "  background-color: #b2ccfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row1_col16, #T_bacdd_row17_col16 {\n",
       "  background-color: #5470de;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row1_col17, #T_bacdd_row7_col17, #T_bacdd_row14_col11 {\n",
       "  background-color: #de614d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row2_col0, #T_bacdd_row2_col10, #T_bacdd_row6_col0 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row2_col3, #T_bacdd_row3_col2 {\n",
       "  background-color: #b8122a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row2_col4, #T_bacdd_row4_col2 {\n",
       "  background-color: #c43032;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row2_col5, #T_bacdd_row14_col16 {\n",
       "  background-color: #f7b093;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row2_col6 {\n",
       "  background-color: #f7af91;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row2_col8, #T_bacdd_row7_col8, #T_bacdd_row8_col2 {\n",
       "  background-color: #b50927;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row2_col9, #T_bacdd_row16_col1, #T_bacdd_row16_col7, #T_bacdd_row16_col8 {\n",
       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row2_col11 {\n",
       "  background-color: #6180e9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row2_col12, #T_bacdd_row15_col17 {\n",
       "  background-color: #9bbcff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row2_col13 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row2_col15, #T_bacdd_row12_col17 {\n",
       "  background-color: #9abbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row2_col16, #T_bacdd_row3_col10 {\n",
       "  background-color: #445acc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row2_col17 {\n",
       "  background-color: #d0473d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col0, #T_bacdd_row4_col0, #T_bacdd_row8_col0, #T_bacdd_row12_col0, #T_bacdd_row15_col0, #T_bacdd_row16_col0 {\n",
       "  background-color: #3e51c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col4, #T_bacdd_row17_col8 {\n",
       "  background-color: #d24b40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col5, #T_bacdd_row16_col10 {\n",
       "  background-color: #f29072;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col6 {\n",
       "  background-color: #f29274;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col8, #T_bacdd_row8_col3 {\n",
       "  background-color: #bb1b2c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col9 {\n",
       "  background-color: #82a6fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col11, #T_bacdd_row14_col17 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col12 {\n",
       "  background-color: #80a3fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col13, #T_bacdd_row7_col10 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col14, #T_bacdd_row3_col16, #T_bacdd_row4_col13, #T_bacdd_row5_col9, #T_bacdd_row5_col11, #T_bacdd_row5_col12, #T_bacdd_row5_col15, #T_bacdd_row6_col14, #T_bacdd_row6_col15, #T_bacdd_row10_col0, #T_bacdd_row11_col0, #T_bacdd_row12_col5, #T_bacdd_row13_col1, #T_bacdd_row13_col2, #T_bacdd_row13_col4, #T_bacdd_row13_col7, #T_bacdd_row13_col8, #T_bacdd_row13_col17, #T_bacdd_row14_col3, #T_bacdd_row15_col6, #T_bacdd_row17_col10 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col15 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row3_col17 {\n",
       "  background-color: #d75445;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row4_col3, #T_bacdd_row8_col17 {\n",
       "  background-color: #d44e41;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row4_col6, #T_bacdd_row13_col5, #T_bacdd_row14_col10 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row4_col9 {\n",
       "  background-color: #bad0f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row4_col10 {\n",
       "  background-color: #6282ea;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row4_col14 {\n",
       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row4_col16, #T_bacdd_row11_col17 {\n",
       "  background-color: #688aef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row4_col17, #T_bacdd_row11_col12, #T_bacdd_row11_col14 {\n",
       "  background-color: #dc5d4a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row5_col0 {\n",
       "  background-color: #4055c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row5_col1, #T_bacdd_row5_col7 {\n",
       "  background-color: #f1cdba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row5_col2, #T_bacdd_row9_col15 {\n",
       "  background-color: #f7b79b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row5_col3 {\n",
       "  background-color: #f39778;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row5_col8 {\n",
       "  background-color: #f5c1a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row5_col16, #T_bacdd_row6_col10, #T_bacdd_row6_col11, #T_bacdd_row6_col16 {\n",
       "  background-color: #4257c9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row6_col1, #T_bacdd_row6_col7 {\n",
       "  background-color: #f2cab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row6_col2, #T_bacdd_row9_col12 {\n",
       "  background-color: #f7b599;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row6_col3, #T_bacdd_row16_col14 {\n",
       "  background-color: #f4987a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row6_col8 {\n",
       "  background-color: #f6bfa6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row6_col9 {\n",
       "  background-color: #455cce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row6_col17 {\n",
       "  background-color: #efcfbf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row7_col11, #T_bacdd_row16_col2 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row7_col14, #T_bacdd_row11_col5 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row7_col16 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row8_col5, #T_bacdd_row10_col12, #T_bacdd_row10_col15, #T_bacdd_row11_col16, #T_bacdd_row17_col5 {\n",
       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row8_col6, #T_bacdd_row12_col16, #T_bacdd_row15_col16 {\n",
       "  background-color: #f7ba9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row8_col9, #T_bacdd_row10_col2, #T_bacdd_row12_col8 {\n",
       "  background-color: #9ebeff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row8_col10 {\n",
       "  background-color: #506bda;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row8_col11 {\n",
       "  background-color: #7699f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row8_col13, #T_bacdd_row17_col0 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row8_col14, #T_bacdd_row14_col5 {\n",
       "  background-color: #516ddb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row8_col15 {\n",
       "  background-color: #a7c5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row8_col16, #T_bacdd_row14_col6 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row9_col0, #T_bacdd_row12_col6, #T_bacdd_row15_col5 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row9_col4, #T_bacdd_row13_col11 {\n",
       "  background-color: #ecd3c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row9_col5, #T_bacdd_row12_col7, #T_bacdd_row15_col1, #T_bacdd_row15_col7 {\n",
       "  background-color: #a2c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row9_col6 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row9_col7, #T_bacdd_row9_col8 {\n",
       "  background-color: #dadce0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row9_col10 {\n",
       "  background-color: #bbd1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row9_col11 {\n",
       "  background-color: #f5c4ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row9_col13 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row9_col14 {\n",
       "  background-color: #f0cdbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row9_col17, #T_bacdd_row14_col9 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row10_col1, #T_bacdd_row10_col8, #T_bacdd_row17_col15 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row10_col3 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row10_col4, #T_bacdd_row10_col9 {\n",
       "  background-color: #c1d4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row10_col6, #T_bacdd_row10_col7, #T_bacdd_row16_col4, #T_bacdd_row17_col12 {\n",
       "  background-color: #aec9fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row10_col11 {\n",
       "  background-color: #f08b6e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row10_col13 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row10_col14 {\n",
       "  background-color: #f6bea4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row10_col16 {\n",
       "  background-color: #ee8669;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row10_col17 {\n",
       "  background-color: #94b6ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row11_col1, #T_bacdd_row16_col5, #T_bacdd_row16_col6 {\n",
       "  background-color: #93b5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row11_col2 {\n",
       "  background-color: #7295f4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row11_col3 {\n",
       "  background-color: #6788ee;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row11_col4, #T_bacdd_row16_col9 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row11_col6 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row11_col8, #T_bacdd_row12_col2, #T_bacdd_row15_col2 {\n",
       "  background-color: #8badfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row11_col9 {\n",
       "  background-color: #e2dad5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row11_col10 {\n",
       "  background-color: #f7a98b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row11_col13 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row11_col15, #T_bacdd_row17_col1, #T_bacdd_row17_col7 {\n",
       "  background-color: #dd5f4b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row12_col3, #T_bacdd_row15_col3 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row12_col4, #T_bacdd_row12_col10, #T_bacdd_row15_col4 {\n",
       "  background-color: #d6dce4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row12_col9 {\n",
       "  background-color: #e4d9d2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row12_col11 {\n",
       "  background-color: #e0654f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row12_col14 {\n",
       "  background-color: #cd423b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row13_col3 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row13_col6, #T_bacdd_row13_col16 {\n",
       "  background-color: #dddcdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row13_col9 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row13_col10 {\n",
       "  background-color: #b5cdfa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row13_col14 {\n",
       "  background-color: #f7a688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row14_col1, #T_bacdd_row14_col7 {\n",
       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row14_col2 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row14_col4 {\n",
       "  background-color: #89acfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row14_col12, #T_bacdd_row14_col15 {\n",
       "  background-color: #cb3e38;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row14_col13 {\n",
       "  background-color: #f6a283;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row15_col8 {\n",
       "  background-color: #9dbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row15_col9 {\n",
       "  background-color: #e3d9d3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row15_col10 {\n",
       "  background-color: #d7dce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row15_col11 {\n",
       "  background-color: #e16751;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row15_col14 {\n",
       "  background-color: #cc403a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row16_col3 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row16_col11 {\n",
       "  background-color: #f7aa8c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row16_col12, #T_bacdd_row16_col15 {\n",
       "  background-color: #f59c7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row16_col17 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row17_col2 {\n",
       "  background-color: #cf453c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row17_col3 {\n",
       "  background-color: #d65244;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row17_col4 {\n",
       "  background-color: #d95847;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bacdd_row17_col6 {\n",
       "  background-color: #f3c7b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bacdd_row17_col13 {\n",
       "  background-color: #4e68d8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_bacdd\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bacdd_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
       "      <th id=\"T_bacdd_level0_col1\" class=\"col_heading level0 col1\" >Area</th>\n",
       "      <th id=\"T_bacdd_level0_col2\" class=\"col_heading level0 col2\" >Perimeter</th>\n",
       "      <th id=\"T_bacdd_level0_col3\" class=\"col_heading level0 col3\" >MajorAxisLength</th>\n",
       "      <th id=\"T_bacdd_level0_col4\" class=\"col_heading level0 col4\" >MinorAxisLength</th>\n",
       "      <th id=\"T_bacdd_level0_col5\" class=\"col_heading level0 col5\" >AspectRation</th>\n",
       "      <th id=\"T_bacdd_level0_col6\" class=\"col_heading level0 col6\" >Eccentricity</th>\n",
       "      <th id=\"T_bacdd_level0_col7\" class=\"col_heading level0 col7\" >ConvexArea</th>\n",
       "      <th id=\"T_bacdd_level0_col8\" class=\"col_heading level0 col8\" >EquivDiameter</th>\n",
       "      <th id=\"T_bacdd_level0_col9\" class=\"col_heading level0 col9\" >Extent</th>\n",
       "      <th id=\"T_bacdd_level0_col10\" class=\"col_heading level0 col10\" >Solidity</th>\n",
       "      <th id=\"T_bacdd_level0_col11\" class=\"col_heading level0 col11\" >roundness</th>\n",
       "      <th id=\"T_bacdd_level0_col12\" class=\"col_heading level0 col12\" >Compactness</th>\n",
       "      <th id=\"T_bacdd_level0_col13\" class=\"col_heading level0 col13\" >ShapeFactor1</th>\n",
       "      <th id=\"T_bacdd_level0_col14\" class=\"col_heading level0 col14\" >ShapeFactor2</th>\n",
       "      <th id=\"T_bacdd_level0_col15\" class=\"col_heading level0 col15\" >ShapeFactor3</th>\n",
       "      <th id=\"T_bacdd_level0_col16\" class=\"col_heading level0 col16\" >ShapeFactor4</th>\n",
       "      <th id=\"T_bacdd_level0_col17\" class=\"col_heading level0 col17\" >y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row0\" class=\"row_heading level0 row0\" >ID</th>\n",
       "      <td id=\"T_bacdd_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row0_col1\" class=\"data row0 col1\" >-0.006442</td>\n",
       "      <td id=\"T_bacdd_row0_col2\" class=\"data row0 col2\" >0.000679</td>\n",
       "      <td id=\"T_bacdd_row0_col3\" class=\"data row0 col3\" >-0.000639</td>\n",
       "      <td id=\"T_bacdd_row0_col4\" class=\"data row0 col4\" >-0.003572</td>\n",
       "      <td id=\"T_bacdd_row0_col5\" class=\"data row0 col5\" >0.004579</td>\n",
       "      <td id=\"T_bacdd_row0_col6\" class=\"data row0 col6\" >0.001265</td>\n",
       "      <td id=\"T_bacdd_row0_col7\" class=\"data row0 col7\" >-0.006194</td>\n",
       "      <td id=\"T_bacdd_row0_col8\" class=\"data row0 col8\" >-0.002186</td>\n",
       "      <td id=\"T_bacdd_row0_col9\" class=\"data row0 col9\" >-0.008626</td>\n",
       "      <td id=\"T_bacdd_row0_col10\" class=\"data row0 col10\" >-0.015873</td>\n",
       "      <td id=\"T_bacdd_row0_col11\" class=\"data row0 col11\" >-0.015406</td>\n",
       "      <td id=\"T_bacdd_row0_col12\" class=\"data row0 col12\" >-0.003421</td>\n",
       "      <td id=\"T_bacdd_row0_col13\" class=\"data row0 col13\" >-0.004321</td>\n",
       "      <td id=\"T_bacdd_row0_col14\" class=\"data row0 col14\" >-0.004543</td>\n",
       "      <td id=\"T_bacdd_row0_col15\" class=\"data row0 col15\" >-0.002997</td>\n",
       "      <td id=\"T_bacdd_row0_col16\" class=\"data row0 col16\" >-0.001742</td>\n",
       "      <td id=\"T_bacdd_row0_col17\" class=\"data row0 col17\" >0.012671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row1\" class=\"row_heading level0 row1\" >Area</th>\n",
       "      <td id=\"T_bacdd_row1_col0\" class=\"data row1 col0\" >-0.006442</td>\n",
       "      <td id=\"T_bacdd_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row1_col2\" class=\"data row1 col2\" >0.966881</td>\n",
       "      <td id=\"T_bacdd_row1_col3\" class=\"data row1 col3\" >0.933198</td>\n",
       "      <td id=\"T_bacdd_row1_col4\" class=\"data row1 col4\" >0.952536</td>\n",
       "      <td id=\"T_bacdd_row1_col5\" class=\"data row1 col5\" >0.243893</td>\n",
       "      <td id=\"T_bacdd_row1_col6\" class=\"data row1 col6\" >0.268565</td>\n",
       "      <td id=\"T_bacdd_row1_col7\" class=\"data row1 col7\" >0.999940</td>\n",
       "      <td id=\"T_bacdd_row1_col8\" class=\"data row1 col8\" >0.984987</td>\n",
       "      <td id=\"T_bacdd_row1_col9\" class=\"data row1 col9\" >0.061387</td>\n",
       "      <td id=\"T_bacdd_row1_col10\" class=\"data row1 col10\" >-0.200078</td>\n",
       "      <td id=\"T_bacdd_row1_col11\" class=\"data row1 col11\" >-0.354127</td>\n",
       "      <td id=\"T_bacdd_row1_col12\" class=\"data row1 col12\" >-0.270134</td>\n",
       "      <td id=\"T_bacdd_row1_col13\" class=\"data row1 col13\" >-0.848371</td>\n",
       "      <td id=\"T_bacdd_row1_col14\" class=\"data row1 col14\" >-0.640619</td>\n",
       "      <td id=\"T_bacdd_row1_col15\" class=\"data row1 col15\" >-0.274081</td>\n",
       "      <td id=\"T_bacdd_row1_col16\" class=\"data row1 col16\" >-0.365265</td>\n",
       "      <td id=\"T_bacdd_row1_col17\" class=\"data row1 col17\" >0.770679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row2\" class=\"row_heading level0 row2\" >Perimeter</th>\n",
       "      <td id=\"T_bacdd_row2_col0\" class=\"data row2 col0\" >0.000679</td>\n",
       "      <td id=\"T_bacdd_row2_col1\" class=\"data row2 col1\" >0.966881</td>\n",
       "      <td id=\"T_bacdd_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row2_col3\" class=\"data row2 col3\" >0.977830</td>\n",
       "      <td id=\"T_bacdd_row2_col4\" class=\"data row2 col4\" >0.915162</td>\n",
       "      <td id=\"T_bacdd_row2_col5\" class=\"data row2 col5\" >0.386119</td>\n",
       "      <td id=\"T_bacdd_row2_col6\" class=\"data row2 col6\" >0.390878</td>\n",
       "      <td id=\"T_bacdd_row2_col7\" class=\"data row2 col7\" >0.967823</td>\n",
       "      <td id=\"T_bacdd_row2_col8\" class=\"data row2 col8\" >0.991439</td>\n",
       "      <td id=\"T_bacdd_row2_col9\" class=\"data row2 col9\" >-0.013889</td>\n",
       "      <td id=\"T_bacdd_row2_col10\" class=\"data row2 col10\" >-0.307817</td>\n",
       "      <td id=\"T_bacdd_row2_col11\" class=\"data row2 col11\" >-0.543906</td>\n",
       "      <td id=\"T_bacdd_row2_col12\" class=\"data row2 col12\" >-0.407622</td>\n",
       "      <td id=\"T_bacdd_row2_col13\" class=\"data row2 col13\" >-0.866250</td>\n",
       "      <td id=\"T_bacdd_row2_col14\" class=\"data row2 col14\" >-0.768104</td>\n",
       "      <td id=\"T_bacdd_row2_col15\" class=\"data row2 col15\" >-0.409079</td>\n",
       "      <td id=\"T_bacdd_row2_col16\" class=\"data row2 col16\" >-0.439272</td>\n",
       "      <td id=\"T_bacdd_row2_col17\" class=\"data row2 col17\" >0.856849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row3\" class=\"row_heading level0 row3\" >MajorAxisLength</th>\n",
       "      <td id=\"T_bacdd_row3_col0\" class=\"data row3 col0\" >-0.000639</td>\n",
       "      <td id=\"T_bacdd_row3_col1\" class=\"data row3 col1\" >0.933198</td>\n",
       "      <td id=\"T_bacdd_row3_col2\" class=\"data row3 col2\" >0.977830</td>\n",
       "      <td id=\"T_bacdd_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row3_col4\" class=\"data row3 col4\" >0.830452</td>\n",
       "      <td id=\"T_bacdd_row3_col5\" class=\"data row3 col5\" >0.548912</td>\n",
       "      <td id=\"T_bacdd_row3_col6\" class=\"data row3 col6\" >0.539787</td>\n",
       "      <td id=\"T_bacdd_row3_col7\" class=\"data row3 col7\" >0.933934</td>\n",
       "      <td id=\"T_bacdd_row3_col8\" class=\"data row3 col8\" >0.962754</td>\n",
       "      <td id=\"T_bacdd_row3_col9\" class=\"data row3 col9\" >-0.070935</td>\n",
       "      <td id=\"T_bacdd_row3_col10\" class=\"data row3 col10\" >-0.285916</td>\n",
       "      <td id=\"T_bacdd_row3_col11\" class=\"data row3 col11\" >-0.589910</td>\n",
       "      <td id=\"T_bacdd_row3_col12\" class=\"data row3 col12\" >-0.566905</td>\n",
       "      <td id=\"T_bacdd_row3_col13\" class=\"data row3 col13\" >-0.777222</td>\n",
       "      <td id=\"T_bacdd_row3_col14\" class=\"data row3 col14\" >-0.858387</td>\n",
       "      <td id=\"T_bacdd_row3_col15\" class=\"data row3 col15\" >-0.566625</td>\n",
       "      <td id=\"T_bacdd_row3_col16\" class=\"data row3 col16\" >-0.491591</td>\n",
       "      <td id=\"T_bacdd_row3_col17\" class=\"data row3 col17\" >0.814923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row4\" class=\"row_heading level0 row4\" >MinorAxisLength</th>\n",
       "      <td id=\"T_bacdd_row4_col0\" class=\"data row4 col0\" >-0.003572</td>\n",
       "      <td id=\"T_bacdd_row4_col1\" class=\"data row4 col1\" >0.952536</td>\n",
       "      <td id=\"T_bacdd_row4_col2\" class=\"data row4 col2\" >0.915162</td>\n",
       "      <td id=\"T_bacdd_row4_col3\" class=\"data row4 col3\" >0.830452</td>\n",
       "      <td id=\"T_bacdd_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row4_col5\" class=\"data row4 col5\" >-0.003095</td>\n",
       "      <td id=\"T_bacdd_row4_col6\" class=\"data row4 col6\" >0.024388</td>\n",
       "      <td id=\"T_bacdd_row4_col7\" class=\"data row4 col7\" >0.952268</td>\n",
       "      <td id=\"T_bacdd_row4_col8\" class=\"data row4 col8\" >0.949832</td>\n",
       "      <td id=\"T_bacdd_row4_col9\" class=\"data row4 col9\" >0.152157</td>\n",
       "      <td id=\"T_bacdd_row4_col10\" class=\"data row4 col10\" >-0.160567</td>\n",
       "      <td id=\"T_bacdd_row4_col11\" class=\"data row4 col11\" >-0.211275</td>\n",
       "      <td id=\"T_bacdd_row4_col12\" class=\"data row4 col12\" >-0.021042</td>\n",
       "      <td id=\"T_bacdd_row4_col13\" class=\"data row4 col13\" >-0.947040</td>\n",
       "      <td id=\"T_bacdd_row4_col14\" class=\"data row4 col14\" >-0.476659</td>\n",
       "      <td id=\"T_bacdd_row4_col15\" class=\"data row4 col15\" >-0.025152</td>\n",
       "      <td id=\"T_bacdd_row4_col16\" class=\"data row4 col16\" >-0.273554</td>\n",
       "      <td id=\"T_bacdd_row4_col17\" class=\"data row4 col17\" >0.784747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row5\" class=\"row_heading level0 row5\" >AspectRation</th>\n",
       "      <td id=\"T_bacdd_row5_col0\" class=\"data row5 col0\" >0.004579</td>\n",
       "      <td id=\"T_bacdd_row5_col1\" class=\"data row5 col1\" >0.243893</td>\n",
       "      <td id=\"T_bacdd_row5_col2\" class=\"data row5 col2\" >0.386119</td>\n",
       "      <td id=\"T_bacdd_row5_col3\" class=\"data row5 col3\" >0.548912</td>\n",
       "      <td id=\"T_bacdd_row5_col4\" class=\"data row5 col4\" >-0.003095</td>\n",
       "      <td id=\"T_bacdd_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row5_col6\" class=\"data row5 col6\" >0.924040</td>\n",
       "      <td id=\"T_bacdd_row5_col7\" class=\"data row5 col7\" >0.245408</td>\n",
       "      <td id=\"T_bacdd_row5_col8\" class=\"data row5 col8\" >0.305557</td>\n",
       "      <td id=\"T_bacdd_row5_col9\" class=\"data row5 col9\" >-0.372383</td>\n",
       "      <td id=\"T_bacdd_row5_col10\" class=\"data row5 col10\" >-0.266038</td>\n",
       "      <td id=\"T_bacdd_row5_col11\" class=\"data row5 col11\" >-0.762150</td>\n",
       "      <td id=\"T_bacdd_row5_col12\" class=\"data row5 col12\" >-0.987664</td>\n",
       "      <td id=\"T_bacdd_row5_col13\" class=\"data row5 col13\" >0.019197</td>\n",
       "      <td id=\"T_bacdd_row5_col14\" class=\"data row5 col14\" >-0.837526</td>\n",
       "      <td id=\"T_bacdd_row5_col15\" class=\"data row5 col15\" >-0.978550</td>\n",
       "      <td id=\"T_bacdd_row5_col16\" class=\"data row5 col16\" >-0.456479</td>\n",
       "      <td id=\"T_bacdd_row5_col17\" class=\"data row5 col17\" >0.307983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row6\" class=\"row_heading level0 row6\" >Eccentricity</th>\n",
       "      <td id=\"T_bacdd_row6_col0\" class=\"data row6 col0\" >0.001265</td>\n",
       "      <td id=\"T_bacdd_row6_col1\" class=\"data row6 col1\" >0.268565</td>\n",
       "      <td id=\"T_bacdd_row6_col2\" class=\"data row6 col2\" >0.390878</td>\n",
       "      <td id=\"T_bacdd_row6_col3\" class=\"data row6 col3\" >0.539787</td>\n",
       "      <td id=\"T_bacdd_row6_col4\" class=\"data row6 col4\" >0.024388</td>\n",
       "      <td id=\"T_bacdd_row6_col5\" class=\"data row6 col5\" >0.924040</td>\n",
       "      <td id=\"T_bacdd_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row6_col7\" class=\"data row6 col7\" >0.270283</td>\n",
       "      <td id=\"T_bacdd_row6_col8\" class=\"data row6 col8\" >0.319497</td>\n",
       "      <td id=\"T_bacdd_row6_col9\" class=\"data row6 col9\" >-0.319550</td>\n",
       "      <td id=\"T_bacdd_row6_col10\" class=\"data row6 col10\" >-0.295614</td>\n",
       "      <td id=\"T_bacdd_row6_col11\" class=\"data row6 col11\" >-0.716765</td>\n",
       "      <td id=\"T_bacdd_row6_col12\" class=\"data row6 col12\" >-0.970173</td>\n",
       "      <td id=\"T_bacdd_row6_col13\" class=\"data row6 col13\" >0.015644</td>\n",
       "      <td id=\"T_bacdd_row6_col14\" class=\"data row6 col14\" >-0.859207</td>\n",
       "      <td id=\"T_bacdd_row6_col15\" class=\"data row6 col15\" >-0.980946</td>\n",
       "      <td id=\"T_bacdd_row6_col16\" class=\"data row6 col16\" >-0.453895</td>\n",
       "      <td id=\"T_bacdd_row6_col17\" class=\"data row6 col17\" >0.238923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row7\" class=\"row_heading level0 row7\" >ConvexArea</th>\n",
       "      <td id=\"T_bacdd_row7_col0\" class=\"data row7 col0\" >-0.006194</td>\n",
       "      <td id=\"T_bacdd_row7_col1\" class=\"data row7 col1\" >0.999940</td>\n",
       "      <td id=\"T_bacdd_row7_col2\" class=\"data row7 col2\" >0.967823</td>\n",
       "      <td id=\"T_bacdd_row7_col3\" class=\"data row7 col3\" >0.933934</td>\n",
       "      <td id=\"T_bacdd_row7_col4\" class=\"data row7 col4\" >0.952268</td>\n",
       "      <td id=\"T_bacdd_row7_col5\" class=\"data row7 col5\" >0.245408</td>\n",
       "      <td id=\"T_bacdd_row7_col6\" class=\"data row7 col6\" >0.270283</td>\n",
       "      <td id=\"T_bacdd_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row7_col8\" class=\"data row7 col8\" >0.985223</td>\n",
       "      <td id=\"T_bacdd_row7_col9\" class=\"data row7 col9\" >0.059711</td>\n",
       "      <td id=\"T_bacdd_row7_col10\" class=\"data row7 col10\" >-0.209552</td>\n",
       "      <td id=\"T_bacdd_row7_col11\" class=\"data row7 col11\" >-0.358655</td>\n",
       "      <td id=\"T_bacdd_row7_col12\" class=\"data row7 col12\" >-0.271930</td>\n",
       "      <td id=\"T_bacdd_row7_col13\" class=\"data row7 col13\" >-0.848345</td>\n",
       "      <td id=\"T_bacdd_row7_col14\" class=\"data row7 col14\" >-0.642119</td>\n",
       "      <td id=\"T_bacdd_row7_col15\" class=\"data row7 col15\" >-0.275901</td>\n",
       "      <td id=\"T_bacdd_row7_col16\" class=\"data row7 col16\" >-0.371420</td>\n",
       "      <td id=\"T_bacdd_row7_col17\" class=\"data row7 col17\" >0.772137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row8\" class=\"row_heading level0 row8\" >EquivDiameter</th>\n",
       "      <td id=\"T_bacdd_row8_col0\" class=\"data row8 col0\" >-0.002186</td>\n",
       "      <td id=\"T_bacdd_row8_col1\" class=\"data row8 col1\" >0.984987</td>\n",
       "      <td id=\"T_bacdd_row8_col2\" class=\"data row8 col2\" >0.991439</td>\n",
       "      <td id=\"T_bacdd_row8_col3\" class=\"data row8 col3\" >0.962754</td>\n",
       "      <td id=\"T_bacdd_row8_col4\" class=\"data row8 col4\" >0.949832</td>\n",
       "      <td id=\"T_bacdd_row8_col5\" class=\"data row8 col5\" >0.305557</td>\n",
       "      <td id=\"T_bacdd_row8_col6\" class=\"data row8 col6\" >0.319497</td>\n",
       "      <td id=\"T_bacdd_row8_col7\" class=\"data row8 col7\" >0.985223</td>\n",
       "      <td id=\"T_bacdd_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row8_col9\" class=\"data row8 col9\" >0.035291</td>\n",
       "      <td id=\"T_bacdd_row8_col10\" class=\"data row8 col10\" >-0.234767</td>\n",
       "      <td id=\"T_bacdd_row8_col11\" class=\"data row8 col11\" >-0.432327</td>\n",
       "      <td id=\"T_bacdd_row8_col12\" class=\"data row8 col12\" >-0.329457</td>\n",
       "      <td id=\"T_bacdd_row8_col13\" class=\"data row8 col13\" >-0.893598</td>\n",
       "      <td id=\"T_bacdd_row8_col14\" class=\"data row8 col14\" >-0.714397</td>\n",
       "      <td id=\"T_bacdd_row8_col15\" class=\"data row8 col15\" >-0.332067</td>\n",
       "      <td id=\"T_bacdd_row8_col16\" class=\"data row8 col16\" >-0.402038</td>\n",
       "      <td id=\"T_bacdd_row8_col17\" class=\"data row8 col17\" >0.834189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row9\" class=\"row_heading level0 row9\" >Extent</th>\n",
       "      <td id=\"T_bacdd_row9_col0\" class=\"data row9 col0\" >-0.008626</td>\n",
       "      <td id=\"T_bacdd_row9_col1\" class=\"data row9 col1\" >0.061387</td>\n",
       "      <td id=\"T_bacdd_row9_col2\" class=\"data row9 col2\" >-0.013889</td>\n",
       "      <td id=\"T_bacdd_row9_col3\" class=\"data row9 col3\" >-0.070935</td>\n",
       "      <td id=\"T_bacdd_row9_col4\" class=\"data row9 col4\" >0.152157</td>\n",
       "      <td id=\"T_bacdd_row9_col5\" class=\"data row9 col5\" >-0.372383</td>\n",
       "      <td id=\"T_bacdd_row9_col6\" class=\"data row9 col6\" >-0.319550</td>\n",
       "      <td id=\"T_bacdd_row9_col7\" class=\"data row9 col7\" >0.059711</td>\n",
       "      <td id=\"T_bacdd_row9_col8\" class=\"data row9 col8\" >0.035291</td>\n",
       "      <td id=\"T_bacdd_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row9_col10\" class=\"data row9 col10\" >0.180843</td>\n",
       "      <td id=\"T_bacdd_row9_col11\" class=\"data row9 col11\" >0.341669</td>\n",
       "      <td id=\"T_bacdd_row9_col12\" class=\"data row9 col12\" >0.355279</td>\n",
       "      <td id=\"T_bacdd_row9_col13\" class=\"data row9 col13\" >-0.147888</td>\n",
       "      <td id=\"T_bacdd_row9_col14\" class=\"data row9 col14\" >0.235020</td>\n",
       "      <td id=\"T_bacdd_row9_col15\" class=\"data row9 col15\" >0.348442</td>\n",
       "      <td id=\"T_bacdd_row9_col16\" class=\"data row9 col16\" >0.142275</td>\n",
       "      <td id=\"T_bacdd_row9_col17\" class=\"data row9 col17\" >-0.012577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row10\" class=\"row_heading level0 row10\" >Solidity</th>\n",
       "      <td id=\"T_bacdd_row10_col0\" class=\"data row10 col0\" >-0.015873</td>\n",
       "      <td id=\"T_bacdd_row10_col1\" class=\"data row10 col1\" >-0.200078</td>\n",
       "      <td id=\"T_bacdd_row10_col2\" class=\"data row10 col2\" >-0.307817</td>\n",
       "      <td id=\"T_bacdd_row10_col3\" class=\"data row10 col3\" >-0.285916</td>\n",
       "      <td id=\"T_bacdd_row10_col4\" class=\"data row10 col4\" >-0.160567</td>\n",
       "      <td id=\"T_bacdd_row10_col5\" class=\"data row10 col5\" >-0.266038</td>\n",
       "      <td id=\"T_bacdd_row10_col6\" class=\"data row10 col6\" >-0.295614</td>\n",
       "      <td id=\"T_bacdd_row10_col7\" class=\"data row10 col7\" >-0.209552</td>\n",
       "      <td id=\"T_bacdd_row10_col8\" class=\"data row10 col8\" >-0.234767</td>\n",
       "      <td id=\"T_bacdd_row10_col9\" class=\"data row10 col9\" >0.180843</td>\n",
       "      <td id=\"T_bacdd_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row10_col11\" class=\"data row10 col11\" >0.614744</td>\n",
       "      <td id=\"T_bacdd_row10_col12\" class=\"data row10 col12\" >0.301718</td>\n",
       "      <td id=\"T_bacdd_row10_col13\" class=\"data row10 col13\" >0.158452</td>\n",
       "      <td id=\"T_bacdd_row10_col14\" class=\"data row10 col14\" >0.343114</td>\n",
       "      <td id=\"T_bacdd_row10_col15\" class=\"data row10 col15\" >0.305648</td>\n",
       "      <td id=\"T_bacdd_row10_col16\" class=\"data row10 col16\" >0.695040</td>\n",
       "      <td id=\"T_bacdd_row10_col17\" class=\"data row10 col17\" >-0.328793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row11\" class=\"row_heading level0 row11\" >roundness</th>\n",
       "      <td id=\"T_bacdd_row11_col0\" class=\"data row11 col0\" >-0.015406</td>\n",
       "      <td id=\"T_bacdd_row11_col1\" class=\"data row11 col1\" >-0.354127</td>\n",
       "      <td id=\"T_bacdd_row11_col2\" class=\"data row11 col2\" >-0.543906</td>\n",
       "      <td id=\"T_bacdd_row11_col3\" class=\"data row11 col3\" >-0.589910</td>\n",
       "      <td id=\"T_bacdd_row11_col4\" class=\"data row11 col4\" >-0.211275</td>\n",
       "      <td id=\"T_bacdd_row11_col5\" class=\"data row11 col5\" >-0.762150</td>\n",
       "      <td id=\"T_bacdd_row11_col6\" class=\"data row11 col6\" >-0.716765</td>\n",
       "      <td id=\"T_bacdd_row11_col7\" class=\"data row11 col7\" >-0.358655</td>\n",
       "      <td id=\"T_bacdd_row11_col8\" class=\"data row11 col8\" >-0.432327</td>\n",
       "      <td id=\"T_bacdd_row11_col9\" class=\"data row11 col9\" >0.341669</td>\n",
       "      <td id=\"T_bacdd_row11_col10\" class=\"data row11 col10\" >0.614744</td>\n",
       "      <td id=\"T_bacdd_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row11_col12\" class=\"data row11 col12\" >0.763130</td>\n",
       "      <td id=\"T_bacdd_row11_col13\" class=\"data row11 col13\" >0.232067</td>\n",
       "      <td id=\"T_bacdd_row11_col14\" class=\"data row11 col14\" >0.776929</td>\n",
       "      <td id=\"T_bacdd_row11_col15\" class=\"data row11 col15\" >0.758101</td>\n",
       "      <td id=\"T_bacdd_row11_col16\" class=\"data row11 col16\" >0.479108</td>\n",
       "      <td id=\"T_bacdd_row11_col17\" class=\"data row11 col17\" >-0.555520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row12\" class=\"row_heading level0 row12\" >Compactness</th>\n",
       "      <td id=\"T_bacdd_row12_col0\" class=\"data row12 col0\" >-0.003421</td>\n",
       "      <td id=\"T_bacdd_row12_col1\" class=\"data row12 col1\" >-0.270134</td>\n",
       "      <td id=\"T_bacdd_row12_col2\" class=\"data row12 col2\" >-0.407622</td>\n",
       "      <td id=\"T_bacdd_row12_col3\" class=\"data row12 col3\" >-0.566905</td>\n",
       "      <td id=\"T_bacdd_row12_col4\" class=\"data row12 col4\" >-0.021042</td>\n",
       "      <td id=\"T_bacdd_row12_col5\" class=\"data row12 col5\" >-0.987664</td>\n",
       "      <td id=\"T_bacdd_row12_col6\" class=\"data row12 col6\" >-0.970173</td>\n",
       "      <td id=\"T_bacdd_row12_col7\" class=\"data row12 col7\" >-0.271930</td>\n",
       "      <td id=\"T_bacdd_row12_col8\" class=\"data row12 col8\" >-0.329457</td>\n",
       "      <td id=\"T_bacdd_row12_col9\" class=\"data row12 col9\" >0.355279</td>\n",
       "      <td id=\"T_bacdd_row12_col10\" class=\"data row12 col10\" >0.301718</td>\n",
       "      <td id=\"T_bacdd_row12_col11\" class=\"data row12 col11\" >0.763130</td>\n",
       "      <td id=\"T_bacdd_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row12_col13\" class=\"data row12 col13\" >-0.004101</td>\n",
       "      <td id=\"T_bacdd_row12_col14\" class=\"data row12 col14\" >0.868571</td>\n",
       "      <td id=\"T_bacdd_row12_col15\" class=\"data row12 col15\" >0.998684</td>\n",
       "      <td id=\"T_bacdd_row12_col16\" class=\"data row12 col16\" >0.490530</td>\n",
       "      <td id=\"T_bacdd_row12_col17\" class=\"data row12 col17\" >-0.297618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row13\" class=\"row_heading level0 row13\" >ShapeFactor1</th>\n",
       "      <td id=\"T_bacdd_row13_col0\" class=\"data row13 col0\" >-0.004321</td>\n",
       "      <td id=\"T_bacdd_row13_col1\" class=\"data row13 col1\" >-0.848371</td>\n",
       "      <td id=\"T_bacdd_row13_col2\" class=\"data row13 col2\" >-0.866250</td>\n",
       "      <td id=\"T_bacdd_row13_col3\" class=\"data row13 col3\" >-0.777222</td>\n",
       "      <td id=\"T_bacdd_row13_col4\" class=\"data row13 col4\" >-0.947040</td>\n",
       "      <td id=\"T_bacdd_row13_col5\" class=\"data row13 col5\" >0.019197</td>\n",
       "      <td id=\"T_bacdd_row13_col6\" class=\"data row13 col6\" >0.015644</td>\n",
       "      <td id=\"T_bacdd_row13_col7\" class=\"data row13 col7\" >-0.848345</td>\n",
       "      <td id=\"T_bacdd_row13_col8\" class=\"data row13 col8\" >-0.893598</td>\n",
       "      <td id=\"T_bacdd_row13_col9\" class=\"data row13 col9\" >-0.147888</td>\n",
       "      <td id=\"T_bacdd_row13_col10\" class=\"data row13 col10\" >0.158452</td>\n",
       "      <td id=\"T_bacdd_row13_col11\" class=\"data row13 col11\" >0.232067</td>\n",
       "      <td id=\"T_bacdd_row13_col12\" class=\"data row13 col12\" >-0.004101</td>\n",
       "      <td id=\"T_bacdd_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row13_col14\" class=\"data row13 col14\" >0.474601</td>\n",
       "      <td id=\"T_bacdd_row13_col15\" class=\"data row13 col15\" >-0.003162</td>\n",
       "      <td id=\"T_bacdd_row13_col16\" class=\"data row13 col16\" >0.257126</td>\n",
       "      <td id=\"T_bacdd_row13_col17\" class=\"data row13 col17\" >-0.819143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row14\" class=\"row_heading level0 row14\" >ShapeFactor2</th>\n",
       "      <td id=\"T_bacdd_row14_col0\" class=\"data row14 col0\" >-0.004543</td>\n",
       "      <td id=\"T_bacdd_row14_col1\" class=\"data row14 col1\" >-0.640619</td>\n",
       "      <td id=\"T_bacdd_row14_col2\" class=\"data row14 col2\" >-0.768104</td>\n",
       "      <td id=\"T_bacdd_row14_col3\" class=\"data row14 col3\" >-0.858387</td>\n",
       "      <td id=\"T_bacdd_row14_col4\" class=\"data row14 col4\" >-0.476659</td>\n",
       "      <td id=\"T_bacdd_row14_col5\" class=\"data row14 col5\" >-0.837526</td>\n",
       "      <td id=\"T_bacdd_row14_col6\" class=\"data row14 col6\" >-0.859207</td>\n",
       "      <td id=\"T_bacdd_row14_col7\" class=\"data row14 col7\" >-0.642119</td>\n",
       "      <td id=\"T_bacdd_row14_col8\" class=\"data row14 col8\" >-0.714397</td>\n",
       "      <td id=\"T_bacdd_row14_col9\" class=\"data row14 col9\" >0.235020</td>\n",
       "      <td id=\"T_bacdd_row14_col10\" class=\"data row14 col10\" >0.343114</td>\n",
       "      <td id=\"T_bacdd_row14_col11\" class=\"data row14 col11\" >0.776929</td>\n",
       "      <td id=\"T_bacdd_row14_col12\" class=\"data row14 col12\" >0.868571</td>\n",
       "      <td id=\"T_bacdd_row14_col13\" class=\"data row14 col13\" >0.474601</td>\n",
       "      <td id=\"T_bacdd_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row14_col15\" class=\"data row14 col15\" >0.872534</td>\n",
       "      <td id=\"T_bacdd_row14_col16\" class=\"data row14 col16\" >0.537707</td>\n",
       "      <td id=\"T_bacdd_row14_col17\" class=\"data row14 col17\" >-0.637964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row15\" class=\"row_heading level0 row15\" >ShapeFactor3</th>\n",
       "      <td id=\"T_bacdd_row15_col0\" class=\"data row15 col0\" >-0.002997</td>\n",
       "      <td id=\"T_bacdd_row15_col1\" class=\"data row15 col1\" >-0.274081</td>\n",
       "      <td id=\"T_bacdd_row15_col2\" class=\"data row15 col2\" >-0.409079</td>\n",
       "      <td id=\"T_bacdd_row15_col3\" class=\"data row15 col3\" >-0.566625</td>\n",
       "      <td id=\"T_bacdd_row15_col4\" class=\"data row15 col4\" >-0.025152</td>\n",
       "      <td id=\"T_bacdd_row15_col5\" class=\"data row15 col5\" >-0.978550</td>\n",
       "      <td id=\"T_bacdd_row15_col6\" class=\"data row15 col6\" >-0.980946</td>\n",
       "      <td id=\"T_bacdd_row15_col7\" class=\"data row15 col7\" >-0.275901</td>\n",
       "      <td id=\"T_bacdd_row15_col8\" class=\"data row15 col8\" >-0.332067</td>\n",
       "      <td id=\"T_bacdd_row15_col9\" class=\"data row15 col9\" >0.348442</td>\n",
       "      <td id=\"T_bacdd_row15_col10\" class=\"data row15 col10\" >0.305648</td>\n",
       "      <td id=\"T_bacdd_row15_col11\" class=\"data row15 col11\" >0.758101</td>\n",
       "      <td id=\"T_bacdd_row15_col12\" class=\"data row15 col12\" >0.998684</td>\n",
       "      <td id=\"T_bacdd_row15_col13\" class=\"data row15 col13\" >-0.003162</td>\n",
       "      <td id=\"T_bacdd_row15_col14\" class=\"data row15 col14\" >0.872534</td>\n",
       "      <td id=\"T_bacdd_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row15_col16\" class=\"data row15 col16\" >0.490083</td>\n",
       "      <td id=\"T_bacdd_row15_col17\" class=\"data row15 col17\" >-0.288999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row16\" class=\"row_heading level0 row16\" >ShapeFactor4</th>\n",
       "      <td id=\"T_bacdd_row16_col0\" class=\"data row16 col0\" >-0.001742</td>\n",
       "      <td id=\"T_bacdd_row16_col1\" class=\"data row16 col1\" >-0.365265</td>\n",
       "      <td id=\"T_bacdd_row16_col2\" class=\"data row16 col2\" >-0.439272</td>\n",
       "      <td id=\"T_bacdd_row16_col3\" class=\"data row16 col3\" >-0.491591</td>\n",
       "      <td id=\"T_bacdd_row16_col4\" class=\"data row16 col4\" >-0.273554</td>\n",
       "      <td id=\"T_bacdd_row16_col5\" class=\"data row16 col5\" >-0.456479</td>\n",
       "      <td id=\"T_bacdd_row16_col6\" class=\"data row16 col6\" >-0.453895</td>\n",
       "      <td id=\"T_bacdd_row16_col7\" class=\"data row16 col7\" >-0.371420</td>\n",
       "      <td id=\"T_bacdd_row16_col8\" class=\"data row16 col8\" >-0.402038</td>\n",
       "      <td id=\"T_bacdd_row16_col9\" class=\"data row16 col9\" >0.142275</td>\n",
       "      <td id=\"T_bacdd_row16_col10\" class=\"data row16 col10\" >0.695040</td>\n",
       "      <td id=\"T_bacdd_row16_col11\" class=\"data row16 col11\" >0.479108</td>\n",
       "      <td id=\"T_bacdd_row16_col12\" class=\"data row16 col12\" >0.490530</td>\n",
       "      <td id=\"T_bacdd_row16_col13\" class=\"data row16 col13\" >0.257126</td>\n",
       "      <td id=\"T_bacdd_row16_col14\" class=\"data row16 col14\" >0.537707</td>\n",
       "      <td id=\"T_bacdd_row16_col15\" class=\"data row16 col15\" >0.490083</td>\n",
       "      <td id=\"T_bacdd_row16_col16\" class=\"data row16 col16\" >1.000000</td>\n",
       "      <td id=\"T_bacdd_row16_col17\" class=\"data row16 col17\" >-0.365705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bacdd_level0_row17\" class=\"row_heading level0 row17\" >y</th>\n",
       "      <td id=\"T_bacdd_row17_col0\" class=\"data row17 col0\" >0.012671</td>\n",
       "      <td id=\"T_bacdd_row17_col1\" class=\"data row17 col1\" >0.770679</td>\n",
       "      <td id=\"T_bacdd_row17_col2\" class=\"data row17 col2\" >0.856849</td>\n",
       "      <td id=\"T_bacdd_row17_col3\" class=\"data row17 col3\" >0.814923</td>\n",
       "      <td id=\"T_bacdd_row17_col4\" class=\"data row17 col4\" >0.784747</td>\n",
       "      <td id=\"T_bacdd_row17_col5\" class=\"data row17 col5\" >0.307983</td>\n",
       "      <td id=\"T_bacdd_row17_col6\" class=\"data row17 col6\" >0.238923</td>\n",
       "      <td id=\"T_bacdd_row17_col7\" class=\"data row17 col7\" >0.772137</td>\n",
       "      <td id=\"T_bacdd_row17_col8\" class=\"data row17 col8\" >0.834189</td>\n",
       "      <td id=\"T_bacdd_row17_col9\" class=\"data row17 col9\" >-0.012577</td>\n",
       "      <td id=\"T_bacdd_row17_col10\" class=\"data row17 col10\" >-0.328793</td>\n",
       "      <td id=\"T_bacdd_row17_col11\" class=\"data row17 col11\" >-0.555520</td>\n",
       "      <td id=\"T_bacdd_row17_col12\" class=\"data row17 col12\" >-0.297618</td>\n",
       "      <td id=\"T_bacdd_row17_col13\" class=\"data row17 col13\" >-0.819143</td>\n",
       "      <td id=\"T_bacdd_row17_col14\" class=\"data row17 col14\" >-0.637964</td>\n",
       "      <td id=\"T_bacdd_row17_col15\" class=\"data row17 col15\" >-0.288999</td>\n",
       "      <td id=\"T_bacdd_row17_col16\" class=\"data row17 col16\" >-0.365705</td>\n",
       "      <td id=\"T_bacdd_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x135a58fa0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation=data_encoded.corr()\n",
    "correlation.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(data):\n",
    "    z_scores = stats. zscore(data[skewed_columns]) \n",
    "    abs_z_scores = np. abs(z_scores)\n",
    "    filtered_entries = (abs_z_scores < 3). all(axis=1)\n",
    "    new_df = data[filtered_entries]\n",
    "    return new_df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new_features=data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new_features['Equivalent Spherical Volume']=(4/3)*np.pi*np.sqrt(data_new_features['Area']/np.pi)**3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y                              1.000000\n",
       "Perimeter                      0.856849\n",
       "EquivDiameter                  0.834189\n",
       "MajorAxisLength                0.814923\n",
       "MinorAxisLength                0.784747\n",
       "ConvexArea                     0.772137\n",
       "Area                           0.770679\n",
       "Equivalent Spherical Volume    0.690892\n",
       "AspectRation                   0.307983\n",
       "Eccentricity                   0.238923\n",
       "ID                             0.012671\n",
       "Extent                        -0.012577\n",
       "ShapeFactor3                  -0.288999\n",
       "Compactness                   -0.297618\n",
       "Solidity                      -0.328793\n",
       "ShapeFactor4                  -0.365705\n",
       "roundness                     -0.555520\n",
       "ShapeFactor2                  -0.637964\n",
       "ShapeFactor1                  -0.819143\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new_features.corr()['y'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "Decision_tree_importance(data_new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scale=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1360b8100>]"
      ]
     },
     "execution_count": 31,
     "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": [
    "from sklearn.decomposition import PCA\n",
    "data_new_features=scale.fit_transform(data_new_features)\n",
    "pca=PCA()\n",
    "pca.fit(data_new_features)\n",
    "exp_var=pca.explained_variance_ratio_\n",
    "exp_cumsum=np.cumsum(exp_var)\n",
    "plt.plot(exp_cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(n_components=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_prep (data,test=False):\n",
    "    if test==True:\n",
    "        id_col=data['ID']\n",
    "        data_scaled=scale.transform(data)\n",
    "        data_scaled=pca.transform(data_scaled)\n",
    "        data_scaled=pd.DataFrame(data_scaled)\n",
    "        return data_scaled, id_col\n",
    "    else :\n",
    "        new_df=remove_outliers(data)\n",
    "        labels=new_df['y']\n",
    "        new_df=new_df.drop('y',axis=1)\n",
    "        data_scaled=scale.fit_transform(new_df)\n",
    "        data_scaled=pca.fit_transform(data_scaled)\n",
    "        data_scaled=pd.DataFrame(data_scaled)\n",
    "        return data_scaled,labels\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data=data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=final_data['y']\n",
    "X=final_data.drop('y',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_val,y_train,y_val=train_test_split(X,y,test_size=0.2,random_state=42,stratify=data['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['y']=y_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=encode_target(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_to_model,labels=data_prep(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "model = LGBMClassifier(max_depth=6,n_estimators=200,\n",
    "                       learning_rate=0.05,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(learning_rate=0.05, max_depth=6, n_estimators=200,\n",
       "               random_state=42)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data_to_model,labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val,_=data_prep(X_val,test=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=model.predict(X_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,f1_score,classification_report,confusion_matrix,plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.912782648823258"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_val,predictions,average='micro')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test_to_model,ID=data_prep(data_test,test=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarmarie/miniforge3/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAEGCAYAAADxD4m3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA1R0lEQVR4nO3deXxU1dnA8d8zk8kKCYQECAEEFUFUFMsi2loQX5e6oK1a7KYtbxVLa2trVaqvS31Fu7iLtdSlboi4FawgWCt1eRUFREVkiRC2hCUJWUggmcw87x9zAyEkw8TMnZkMz5fPfDJzc+c+994Jz5x7zj3niKpijDHJyBPvHTDGGLdYgjPGJC1LcMaYpGUJzhiTtCzBGWOSVkq8d6C5vFyvDujni0vstau6xSUugPr9cYuNtaIfUvZQS4PWS0e2cea4LC2vCES07tJP6xeo6lkdidcRCZXgBvTz8eGCfnGJfc7J58clLkBgy9a4xVZ/Q9xim9hbrG92eBtlFQEWL+gb0bq+gi/zwv1eRIqBGiAANKrqCBHJBZ4HBgDFwCWqutNZfyowyVn/alVdEG77dolqjGknJaDBiB4RGqeqJ6jqCOf1DcCbqjoIeNN5jYgMBSYCxwBnAQ+LiDfchi3BGWPaRYEgGtHjK5oAPOk8fxK4oNnyWapar6rrgSJgVLgNWYIzxrRbMMJ/EVBgoYgsFZErnGW9VLUUwPnZ01leCGxq9t7NzrI2JVQdnDEm8SmKP/LLzzwRWdLs9QxVndHs9SmqWiIiPYE3RGRVmG211jgStphoCc4Y0y4KBCK//CxrVrd24LZUS5yf20XkFUKXnNtEpEBVS0WkANjurL4ZaN4K2RcoCRfcLlGNMe0WjTo4EckSka5Nz4EzgBXAXOAyZ7XLgDnO87nARBFJE5GBwCDgw3AxrARnjGkXBQLRuX+yF/CKiEAoF81U1ddF5CNgtohMAjYCFwOo6uciMhtYCTQCU1Q17A15luCMMe0WcQ1cGKq6Dji+leXlwPg23nMHcEekMSzBGWPaRdH21MHFlSU4Y0y7qIK/c+S3zpPgfjRqKBldAng84E1RHnp9DW+/msPTd/dm09p0Hpi3hqOO3713/VkP9uT153rg9ShX/e8WRoytidq+eDzKfY+/TfmOdG777Wiu//1S+vbfBUBWVz+1NT5+cfk3oxavLRN+vJWzLy1DRJn/XD7/eLy36zEBfn3PRkafXkNlWQpXnjY4JjGbGzG2msm3l+D1KPOfy2X2Q70sdkwJgVbv2Eg8riY4ETkLuB/wAo+q6l0d2d4fXygip8e+OsUBQ/Zw86PFPHD9/v1XN6xJY9Gc7sx4axUV23zc8N0jeOzdL/CG7dQRufMvWcem4q5kZoU6yf/h5q/t/d2kX3xO3S73Bww47Kg6zr60jF+efzR+v4c7nlrDh//uRklxuuuxFz6fy9wn8vjt/ZsOvnKUeTzKlGlbmDrxcMpKfTw4by0fLMhh41r3j/tQjd2SAsFOUoJz7TYRp4/YdOBsYChwqdOXLGr6D6qn35H1Byx/f0EOYyfsJDVN6d2/gT4D6ln9cWZUYvbI383Ik7ez4NX+rfxW+cZpJfznjT5RiRVO/yP3sOrjLOr3eAkGhM8Wd+XkM3e6HhdgxeIu1OyMT+F/8PA6SopT2boxjUa/h0VzujHmzCqLHWMBpxR3sEe8uXkf3CigSFXXqWoDMItQX7KvRpTfXXoEU848innP9Ai7almpj/w++4YgyivwU741OqWqK371OU9MP5rWbuQ+5oQKKivSKNncJSqxwilek8Gxo2ro2q2RtPQAI8dVkt8n+UcG6dHbz46S1L2vy0p95BXEZripQzV2S6EbfTtHgnPza7i1fmOjW67k9D+7AqB/Ydu7c++ctfTo3UhlWQo3TDyCfkfu4biTaltfubXicxTO9ciTt1G1M5Wi1d04bnjZAb//5ulb+M+/wnaNi5pNRRm88EgBdz67mt21HtatzCTQGP8/KLdJK4cYqyHtDtXYB8QF/No5+gi4meAi6jfm9EubATDi+PQ2P7IevRsB6JbXyClnVbHq48w2E1xeHz87SvaV2MpKffTo1fFvu6HDKhj99W2MGPMvUlODZGT5ufaWZfz5thPxeIOcPLaUX/741A7HidSC5/NZ8Hw+AJf/djNlW1MP8o7OL1Q631dSjWbp3GJHRhECnaQTlJt72e5+Y23ZU+ehbpdn7/Ol/+nKgCF72lz/pDOqWTSnOw31wtaNqWxZn8bg4XVfJfR+nnzkaC674L/4yXdO5w83n8inS/P4820nAjB8RBmbN3ShfEdGh+NEKqdHKGnn96nnlLN2smhObsxix8vq5ZkUDmygV796UnxBxk6o5IOFORY7xoIqET3izc0S3EfAIKfP2BZCA9V976tsaOeOFG6bNBCAQCOMu7CSkeNqeG9+Dg/fVEhVeQr/88PDOeKY3Ux7bh0DBu/h1PMquWLsELxe5efTNketBbUtp56+hf+8EZvL0yb/80gRXbs3EvAL028+jF3Vsan4v+HhDQwbs4uc3EaeWbKSp+/uxYLnwteLRkswIEy/sZBpM9fh8cLCWblsWBOblsRDNXZLTXVwnYG4ObO9iHwLuI/QbSKPO90s2jTi+HS1Ictjy4YsP7Qs1jep1ooOZachw9L1b3MjG7L81IFfLg03mojbXP3KV9V5wDw3YxhjYis0om/nqIPrND0ZjDGJQVVoUJfrfKLEEpwxpt2CnaQOzhKcMaZdQo0MdolqjElKQsBu9DXGJCNrZDDGJLVAAtzEGwlLcMaYdlEEv3aO1NE59tIYkzCskcEYk7QUsUvUr2LtqhzOGX1uXGJ7n4lfl6XGcWFnPnOVpMT3T0AD8Tv2uI03lASskcEYk5RUsdtEjDHJKdTIYF21jDFJyhoZjDFJSUmMwSwjYQnOGNNuVoIzxiSl0LyoluCMMUkpMaYEjIQlOGNMu4SmDbRWVGNMElIVu0Q1xiQvu9HXGJOUQuPBWR2cqzwe5b6/v0v5jnRu+81IfnDlak76xjZUhcqdqdz7++OpKOv4vJFar/ivrgC/QgA830wj5SddAQi8VEvglTrwCp6T0ki5qita2kjDj8qQ/qFTK0N9+H7jzgS9T76/gt21HoIBIdAo/OKcIa7EaU1WdiO/+uMGBhy1G1Xh3t8exhfLurge15cW5O6XivClBfF64Z3Xcnj67gLX4zYZMbaaybeX4PUo85/LZfZDvQ6J2PuzEX0RkceBc4HtqnpstLd//nfXs6m4C5lZjQC89MzhPPPXwQCcd8l6Lp20lul/OK7jgVLBd293JNODNir+n1cQHN0A9UrgvXp8j+chqYLu3NdpXApTSH0sr+OxI3DdxUdRvTP231OTb93E0kU53DH5CFJ8QdIygjGJ668XrrvkCPbUefGmKPe8spaP3spm1bIs12N7PMqUaVuYOvFwykp9PDhvLR8syGHjWvcnYI5n7JZCt4lErwQnIl5gCbBFVc8VkVzgeWAAUAxcoqo7nXWnApOAAHC1qi4It2030/DfgbPc2HCPnrsZecp2FszZN0n07lrf3ufpGYGoDRQhIkimc5oagUYFgcCc3aR8LwtJDX3Q0r1ztCpFQ2aXAMeN2sXrs0Kz2Tf6PdRWxyrJCnvqQuc6JUXx+jRmg4IMHl5HSXEqWzem0ej3sGhON8acWZX0sVtq6osaySNCvwS+aPb6BuBNVR0EvOm8RkSGAhOBYwjlloed5Ngm1xKcqr4NVLix7SuuWckTDx2NtvgW+dHkVfx97puMPXMLz8w4KmrxNKA0TCqj4YLteEak4Rmaim5uJPhpAw2Ty2m4upzgF/5965cGQutfXU7wExeHYVKYNnMtD837grO/X+ZenBZ696+nqiKF39y9gYfmreRXfygmLSN2wx55PMrDC1fx/Kcr+Pjtrqz+2P3SG0CP3n52lKTufV1W6iOvwB/mHckRuzVBPBE9DkZE+gLnAI82WzwBeNJ5/iRwQbPls1S1XlXXA0XAqHDbj/uFtIhcISJLRGRJQ2D3Qdcfeco2qipSKVp1YL3WU48M4fLzx7NoQSHnXbwhevvoFVIfyyP1hXyCX/gJrvOHCsg1iu8vuaRc1RX/rZWoKvTwkjo7n9TH8kiZko3/9kq01p3Lt2suPIqfn300N/7wSM6/bAfHjq5xJU5L3hTlyGPr+OfT+fz8W0PZs9vLd3+2NSaxAYJB4WdnDOH7I4YyeHgdhw0++N9NNEgrV2WxKj3GM3ZrcQMqET2AvKb/387jihabuw+4Dmj+n6SXqpaGYmkp0NNZXghsarbeZmdZm+Ke4FR1hqqOUNURqd6Mg64/9PidjD51O4+/8m+u/9+PGTaijGtv/Xi/dRYt6MPJ40qjvq/S1YNneCrBDxsg34Pn1DREBM/RqaEzWaVIqiA5odPqGexDCr3oJndKNxXbQt/oVeU+3ns9hyEn1LkSp6Wy0lTKSlNZvTxUcnpnXjeOPDY2sZurrU7hk//rwsixsUnsZaU+8vvsK5HnFfgp3+oL847kiN2aoEpED6Cs6f+385jRtA0RaaqjXxph2NYq/sKm+bgnuPZ68uEhXHbeeH5y4Wn84abhfLokjz/fOpw+/Wr3rnPSN7axeUN0WvS0MojWhL5ctF4JLqlH+nvxfj2d4LLQH1xwU2OolTVHQusHQudcSxrRzQGkT/Tr59IyAmRkBfY+/9qpNRSvjk2F884dPnaUptL38D0ADD+lho1rD/7lFA05uY1kZYcallLTg5z4jRo2fZkWk9irl2dSOLCBXv3qSfEFGTuhkg8WutNCnkixWwqNJuKJ6HEQpwDni0gxMAs4TUSeAbaJSAGA83O7s/5moF+z9/cFSsIF6LS3ibR0+ZRVFPbfhQaF7VszotOCCmh5gMZpVaECtIJnbDrek9NRv9L4hyoaLi+DFPD9LgcRIfBJPYHHd4EX8EDKr7OR7Oh/j3TPb+SWR9cB4PUqb/2jO0sWxe4P/uGb+3HdA+vx+ZTSjancc+2AmMTN7eXn2vs24vEoHg+8/Wo3Fv8rNscdDAjTbyxk2sx1eLywcFYuG9bE5kslnrFbCnXV6vjftKpOBaYCiMhY4FpV/YGI/Am4DLjL+TnHectcYKaI3AP0AQYBH4aLIerShbyIPAeMBfKAbcAtqvpYuPfkpPXSk3t/z5X9ORjvM41xiQtQP25H3GKLJ743bNqcDLG1WN+kWis69KHnD83TC586J6J1/zbyqaWqOuJg6zVLcOeKSA9gNtAf2AhcrKoVzno3Aj8hdE/Dr1R1frjtulaCU9VL3dq2MSa+ot2TQVUXAYuc5+XA+DbWuwO4I9LtJs0lqjEmNppaUTsDS3DGmHaz0USMMUnJ5mQwxiQtBRqtBGeMSVZ2iWqMSU5ql6jGmCRlA14aY5KaleCMMUkp2gNeuimhEpw2+GncEv1RQCLROC4uYQHYMGto3GIfdslncYsdd62NQRQrnbibmCI0Bq2RwRiTpKwOzhiTnNQuUY0xScrq4IwxSc0SnDEmKSlCwBoZjDHJyhoZjDFJSa2RwRiTzFrOSZyoLMEZY9rJOtsbY5KYleCMMUlJFQJBS3Ax4/EoD85bRflWHzdffmRMYz/5/gp213oIBoRAo/CLc4ZEb+MNQXrfsg5pVAgodSflUHVJLzLfryLnhW34ttSzddoRNByRCUBqUR09/rpl79srL+7J7lHuzBk6Ymw1k28vwetR5j+Xy+yHerkSJ5Fi5/dp4Lf3b6R7vh8NCvOe7cE/HsuPSWyI7zlv6ZBvRRWRfsBTQG9C0ybPUNX73Yh1waTtbCpKJ7NLfObYvO7io6je6cKp9AnbbhmIpnuhUel985fsPqErDf3S2HHtYfSYsWW/1f390im960jwCt6dfgp+u5bNX8sGb3T/GD0eZcq0LUydeDhlpT4enLeWDxbksHGt+xMRxzN2oFGYcVsfilZkkpEV4KHX17Ds7a5Jf9wtKZ3nEtXNu/Uagd+o6tHAScAUEYn6sBl5BQ2MGl/N/Jl50d50/ImEkhsggVApDoHGvuk09kk7YHVN8+xNZuJX10bLGDy8jpLiVLZuTKPR72HRnG6MObPKlViJFLtiu4+iFaHS8u5aL5vWppHX2x+T2PE87gOFGhkiecSbmxM/lwKlzvMaEfkCKARWRjPO5Fs38+gdhXErvaEwbeZaUHjt2XzmPxvlRBtUCq4vImVrAzVn5tIwKDPs6qlr6+jxl82k7PBT9ou+US+9AfTo7WdHSere12WlPoacWBf1OIkWu7lefes54tjdrPo4/OcRLYly3E06y2hPMamDE5EBwHBgcSu/uwK4AiCd9v2xjB5fRWVZCkWfZTJsTE0U9rT9rrnwKCq2pZLTw89dzxWxqSiNFYu7Ri+ARyj90yCkNkDPP2/At3EP/v5tX5Y0DMqk9J6jSNm8h7zpm9l9QldIjW5BvbWCYaz+4OMZu0l6ZoD/+Vsxj9xSSN0ub0xiJsJx7x87/qWzSLjeoUxEugAvAb9S1eqWv1fVGao6QlVH+DjwsiucoSN3cdIZVTz5/gqmTl/P8afUcN0D66O055Gp2Bb6Vq0q9/He6zkMOcGdb1XN8rJnaBYZyyNL5I1909F0D6mb9kR9X8pKfeT3adj7Oq/AT/lWX9TjJFpsAG+K8j9/K+bfr3TnvfndYhY33sfdXKgV1RPRI95c3QMR8RFKbs+q6svR3v4TdxXyg5HHcdmYY7lzykA+ea8rf7x6YLTDtCktI0BGVmDv86+dWkPx6uhV+nqqG5Ha0PalIUj6Z7vwF7b9JZCyvSFUTwd4dzSQUlJPY35qm+t/VauXZ1I4sIFe/epJ8QUZO6GSDxa601qbSLFB+fXdG9lUlMbLM3rGKGZIfI/7QKqRPeLNzVZUAR4DvlDVe9yKE0/d8xu55dF1AHi9ylv/6M6SRdH7o/Pu9JM3fXOoDVqVujE57P5aNhkfVpH7eAne6gA979pAw4B0tt84kLRVteT/Ywd4BfVAxaQ+BLOj/xEHA8L0GwuZNnMdHi8snJXLhjWxac2LZ+xjRtZy+kU7WbcynYcXrgLgibv68NG/s12PHc/jbk1nuUQVdSnNisjXgXeAzwj9FwX4narOa+s92ZKro71nuLI/iczmZIiTQ3BOhsX6JtVa0aEDTz+yUAf88cqI1l39nVuWquqIjsTrCDdbUd+FTnI3oDGmXRLg6jMiSdGTwRgTQwpqXbWMMcmqs9TBWYIzxrRbIrSQRqLNBCciDxLmUltVr3Zlj4wxCS1afVFFJB14G0gjlIteVNVbRCQXeB4YABQDl6jqTuc9U4FJQAC4WlUXhIsRrgS3pKMHYIxJQgpE5xK1HjhNVXc598y+KyLzgW8Db6rqXSJyA3ADcL3Tl30icAzQB/iXiBylqm3202wzwanqk81fi0iWqtZ2/JiMMZ1dNC5RNXSP2i7npc95KDABGOssfxJYBFzvLJ+lqvXAehEpAkYB77cV46A9GURkjIisBL5wXh8vIg9/heMxxiQFQYORPYA8EVnS7HHFflsS8YrIcmA78IaqLgZ6OYN1NA3a0dRtpBDY1Oztm51lbYqkkeE+4ExgrhPwExE5NYL3GWOSVeQluLJwN/o6l5cniEg34BUROTbMtlq7Lg67JxH1RVXVTS0WxWlsImNM3GmokSGSR8SbVK0kdCl6FrBNRAoAnJ/bndU2A/2ava0vUBJuu5GU4DaJyMmAikgqcDXO5aorNHjwdVzgyciIS1yIb3epNY+MiltsgKN+tjR+wYP2Pf2VRaEOTkTyAb+qVopIBnA68AdCV4uXAXc5P+c4b5kLzBSRewg1MgwCPgwXI5IENxm4n9C17hZgATCl3UdjjEkiUWlFLQCeFBEvoavJ2ar6TxF5H5gtIpOAjcDFAKr6uYjMJjRobiMwJVwLKkSQ4FS1DPh+x47DGJNUonChpaqfEhoIt+XycmB8G++5A7gj0hiRtKIeLiKvisgOEdkuInNE5PBIAxhjkkzTfXCRPOIskkaGmcBsQsXJPsALwHNu7pQxJrF1lgEvI0lwoqpPq2qj83iGzjNaijHGDRrhI87C9UXNdZ6+5XSXmEVol78LvBaDfTPGJKoEuPyMRLhGhqWEElrTkTQfwlOB293aKWNMYpMEKJ1FIlxf1NjN3mKM6TxUIJkGvHS6TwwF9s5yoapPubVTxpgE19lLcE1E5BZCPfuHAvOAs4F3AUtwxhyqOkmCi6QV9SJCN91tVdUfA8dDO2doNsYkl87eitrMblUNikijiGQT6viaMDf6+tKC3P1SEb60IF4vvPNaDk/fXeBavGvuLGLUaTupLPdx1bdOAOD7V2/irEu2UVURmmn8ybv789F/uru2D01GjK1m8u0leD3K/Odymf1Qr6htO6Wint5/X4e32g8iVH09n8rxvSn4WxG+bXsA8NY1EshMYeNNoQEgUjfX0evZ9Xj2BEFg49RjUJ87c4t7PMqD81ZRvtXHzZcf6UqM1rh5zhM59n6iN+Cl6yJJcEucoUz+RqhldRcH6eAKbQ9H/NV3tXX+euG6S45gT50Xb4pyzytr+eitbFYty4p2KADeeLknc5/pzbV/Ktpv+T+e6MNLj/VxJWZrPB5lyrQtTJ14OGWlPh6ct5YPFuSwcW10JgNWr7Djov7U989C9gQ4bNoK6o7OofSn+5JJ3osbCWZ4Qy8CSsETX1L64yNo6JuJZ5cf9br3n+CCSdvZVJROZpfYdZh3+5wnauzWdJZW1IN+varqz1S1UlUfAf4LuMy5VD2YpuGIjwdOAM4SkZM6tLetEvbUhf6TpaQoXp+6egf1io+yqamM/1w9g4fXUVKcytaNaTT6PSya040xZ1ZFbfuBnFTq+4e+JDTdS0PvDFIqG/atoErXpRXUjOgBQNbKKuoLM2nomwlAsIsPPO4kuLyCBkaNr2b+zDxXtt8Wt895osZuVWe/RBWRE8P9TlWXhdtwmOGIo87jUR56fTV9BjTw6t/zWP2xO6W3cM774VbGX7iDtZ9l8bc7B7Cr2t0k2KO3nx0lqXtfl5X6GHJinSuxUsrqSdtUx56BXfYuyyiqIdA1BX+vUAnCt30PCBQ+sApvTSM1I3qw80x3qgom37qZR+8ojGnpDWJ7zhMpdms6Swku3P/Cu8P8ToHTDrZxZxiUpcCRwHRnOOKW61wBXAGQTubBNtmqYFD42RlDyMpu5JbHijls8G42rI7d+G6vPduL5x7qiyr86JpN/HRqMfdOdbdeSFopHLlRcpU9AfrMWMuOS/rvuxwFun5UQc3IHvvWCygZRTVsmHoMmuqh772r2HNYJruH5ER1f0aPr6KyLIWizzIZNqYmqts+mFid80SL3arOXgenquM6uvHWhiNW1RUt1pkBzADIltwOfWS11Sl88n9dGDm2JqYJrrJ83zfr/Od7ctvfVrkes6zUR36ffZeMeQV+yrf6ohskEKTPjLVUj+rBruG5zZYrXT6uYOPv9o0u3dg9lbpB2aFLU6D22G6kb6yLeoIbOnIXJ51RxcjTVpCaFiSza4DrHljPH692/770mJzzBIx9gAS5/IyEO01cLbQYjjiqcnIbycpuBCA1PciJ36hh05exvYule/6+P7yTz6hgw5qvVhJtj9XLMykc2ECvfvWk+IKMnVDJBwujmExU6f3Uehp6Z1B5+v6XmpmrqmjonUFj932JvXZoDmlb6pCGAASUjLU1NBRE/0vmibsK+cHI47hszLHcOWUgn7zXNSbJDWJwzhM0dqs6ex1cR4UZjjiqcnv5ufa+jXg8iscDb7/ajcX/cu+Dv/7eNQwbXU1290aefncpT9/fl2Gjqzn86FpQYduWNB64yf27aIIBYfqNhUybuQ6PFxbOymXDmui1qKV/uYvsxeXUF2bQ/39Dhe7yCX2pPa7bAZenAMGsFHae3pv+d64Egdpjcqg9rlvU9icRuH3OEzV2ayQ+Mwu0m6hLF/IiMozQnIbNhyP+fbj3ZEuujvac7sr+HEw852QI1sWvstjmZDi0LNY3qdaKDlWgpfXrp31/eU1E66777W+WhptVy22RdNUSQkOWH66qvxeR/kBvVQ17L1xbwxEbYzo30c7TihpJHdzDwBjgUud1DTDdtT0yxiS+TjJkeSR1cKNV9UQR+RhAVXc60wcaYw5VnaQEF0mC8zv3synsbTzoJFWMxhg3dJZL1EgS3APAK0BPEbmD0OgiN7m6V8aYxKWdpxU1knlRnxWRpYSGTBLgAlV1b2Z7Y0ziS5YSnNNqWge82nyZqm50c8eMMQksWRIcoRm0miafSQcGAquBY1zcL2NMAkuaOjhVPa75a2eUkSvbWN0YYxJGu7tqqeoyERnpxs4YYzqJZCnBicivm730ACcCO1zbI2NMYkumVlSga7PnjYTq5F5yZ3eI2yBXwT31cYkbb3HtCwqsvyN+fWGPuP2TuMWOZ//jqEiGEpxzg28XVf1tjPbHGJPghCRoZBCRFFVtDDd0uTHmENXZExyhmbNOBJaLyFzgBaC26Zeq+rLL+2aMSURJNppILlBOaA6Gc4HznJ/GmENVMMJHGCLST0TeEpEvRORzEfmlszxXRN4QkbXOz+7N3jNVRIpEZLWInHmw3QxXguvptKCuYN+Nvk06Sf42xrghSiW4RuA3zq1nXYGlIvIGcDnwpqreJSI3ADcA14vIUGAioU4GfYB/ichRztwvrQpXgvMCXZxH12bPmx7GmENVFOZkUNXSpulHVbUG+AIoBCYQGg0c5+cFzvMJwCxVrVfV9UARELYZPlwJrvRgQ4wbYw5BLkwoIyIDCI0AvhjopaqlEEqCItLTWa0Q+KDZ2zY7y9oULsHFfzhOY0xCasclap6ILGn2eoYzVei+bYl0IXRv7a9UtVpamwTWWbWVZWH3JFyCGx/ujcaYQ1jkCa4s3KQzIuIjlNyebXZnxjYRKXBKbwXAdmf5ZqBfs7f3BUrCBW+zDk5VKyLZe2PMoUeCkT3CbiNUVHsM+EJV72n2q7nAZc7zy4A5zZZPFJE0ERkIDCJ0O1ubXJsXNZZGjK1m8u0leD3K/Odymf1Qr5jF9niUB+etonyrj5svPzJmcSG+xw3uHnvvzF388ev/Jj+jjiDC82uO5qkvhvHLEz5kfL9iFKF8TwY3vDuO7buz8HkC/H7M2xzbYweqwv9+eDIfbgtbPRORa+4sYtRpO6ks93HVt07Y73ffmVTCf0/dwHdHjqB6p/uzzMf7894renVwpwA/BD4TkeXOst8BdwGzRWQSsBG4GEBVPxeR2cBKQi2wU8K1oEIMEpzT3WsJsEVVo37/nMejTJm2hakTD6es1MeD89bywYIcNq6NzaS4F0zazqaidDK7xHaOzXgfN7h77AEV7loyhpUV+WSlNPDyuS/xXklfHv38BO5fHmo4++GQz5hy/FJu+eBULhkUGmT6vLmXkJu+m0dPf43v/PM7aAerkt94uSdzn+nNtX8q2m95XkE9w79eybYtsZl/KRE+7yZCdCroVfXdMJtqtYpMVe8A7og0RiQ3+nbULwk1/7pi8PA6SopT2boxjUa/h0VzujHmzCq3wu0nr6CBUeOrmT8zLybxmovncYP7x75jdxYrK/IBqG1M5cuq7vTKrKXWvy+hZKb49xYkjuy2k/dLQyW2ij0Z1DSkcVze9pabbbcVH2VTU3lgOeDKG4t57A+HxWxqvHh/3geIwm0iseBqghORvsA5wKNuxejR28+Okn1/9GWlPvIK/G6F28/kWzfz6B2FcRkAJZ7HDbE99sKsaobmlvFJWeiS7Jrhi/nPRU9z3uFruf/j0NCEqyp6ML5fMV4J0rdLNcf02EHvzNpwm/3KRo+voGxrKutXZbmy/dbE+/NuqWny54M94s3tEtx9wHWE6bQhIleIyBIRWeKn/UMWtdaiHIv/dKPHV1FZlkLRZ5nuB2tFvI4bYnvsmSl+Hhy3kGkfnby39Hbvx6P55os/5NV1g/jhkBUAvFg0hK11Wbx87kv8buT/8fH2XgRcKF2lpQeYeNUWnr6v38FXjqJ4ft6t6iQlONfq4ETkXGC7qi4VkbFtrefcEzMDIFty231Kykp95Pdp2Ps6r8BP+Vb3K3yHjtzFSWdUMfK0FaSmBcnsGuC6B9bzx6sHuh4b4nfcELtjT5EAD45dwKvrBrFw4+EH/P7V9YOYMX4eD3wykoB6uPOjU/b+btbZr1BcnRPV/QEo6L+H3v328PA/PwUgr3c9D875lF99+zh2lrlXHxfPz/sASTbg5Vd1CnC+iHyL0GQ12SLyjKr+IJpBVi/PpHBgA7361VO+1cfYCZXcNeWwaIZo1RN3FfLEXaE6n2Fjarjoym0xS24Qv+OGWB27Mu2U//BlVXeeWHn83qWHda1kQ003AMb3K2ZdVagfdrrXjwjsbvRxcsEmAurhy6rcKO8TFK/J4tLR+0bs//uiZVx94XGut6LG8/NuVQKUziLhWoJT1anAVACnBHdttJMbQDAgTL+xkGkz1+HxwsJZuWxYE/uWpVhL9uP+Ws+tXHDEGlZV5DLnvBcAuGfZKC4atIqB2ZUEVSip7cotH3wDgB7pu3nsv15DVdhWl8Vv3zktKvtx/b1rGDa6muzujTz97lKevr8vC1+I/e0ZifZ5J0L9WiREY3Ah3yzBhb1NJFtydbTEqQOFxxufuADB2N5isp94Hjc2ZHmsLdY3qdaKDlVOZvbsp4Mv+vXBVwSW/+XXS8P1ZHBbTG70VdVFwKJYxDLGuK+zlOCSoieDMSaGlIMOZpkoLMEZY9olKSadMcaYNlmCM8YkK4nrXcaRswRnjGmfBOmlEAlLcMaYdrM6OGNM0rKuWsaY5GUlOGNMUkqQoZAiYQnOGNN+luBMxA7VfrDAEbd9HLfYJZNPjFvs3vf9X9xid5Td6GuMSWoS7BwZzhKcMaZ97D44Y0wys9tEjDHJy0pwxphkZY0MxpjkpMR5Sq/IWYIzxrSb1cEZY5KS3QdnjEleqnaJaoxJXlaCi6ERY6uZfHsJXo8y/7lcZj8Uu3krPR7lwXmrKN/q4+bLj4xZ3HjHjuU5v+YP6xg1bieV5T6uOnsYAJNu2Mjo8Ttp9AulG9K557rDqa2Jzp/zbWe9xalHFFNRl8F3npgIwFH5Zdx0xttkpvopqerK1H+eTm1DKjnpe7j7ggUc03s7c1cM4c5/fSMq+9CaeP6dH6CTJDiPmxsXkWIR+UxElovIEjdieDzKlGlbuOn7A/np2MGMm1BJ/0F73AjVqgsmbWdTUXwm4I1X7Fif8zdezOOmHw/Zb9nH72Yz+axh/Oxbw9hSnM53f1YStXhzVgzmqhf3n8L3lrMWcf/bJ3HRE9/l32sHcvmo5QA0BLxMf2cU9yw6OWrxWxPvv/OWRCN7xJurCc4xTlVPcGvy18HD6ygpTmXrxjQa/R4WzenGmDOr3Ah1gLyCBkaNr2b+zLyYxEuU2LE+5ys+yqamcv/S2bJ3uxEMhOYvXvVxF/J6N0Qt3rLNfajenbbfsgG5lSzdVADA+8X9GH/UOgB2+318vKWA+kZ3B0yI59/5ARQIaGSPOItFgnNVj95+dpSk7n1dVuojr8Afk9iTb93Mo3cUxqW+NZ6x43nOW3PGxTv4aFE3V2MUleUy9sjiULzBX9I7e5er8VpKtHNuJbgQBRaKyFIRuaK1FUTkChFZIiJL/NS3O4BIK0FjcGJHj6+isiyFos8y3Q+WQLEhfue8NRN/toVAo/DWnB6uxrll/jgmDl/Bcz96gczUBvyB2JYNEumc7w0eySPO3G5kOEVVS0SkJ/CGiKxS1bebr6CqM4AZANmS2+4zUlbqI7/PvsuTvAI/5Vt9Hdztgxs6chcnnVHFyNNWkJoWJLNrgOseWM8frx6Y1LEhfue8pdO/vYNRp1Uy9QdDCN2d5Z7iiu5MfuE8AA7rXsmpR2x0NV5LiXLOm0SrdCYijwPnAttV9VhnWS7wPDAAKAYuUdWdzu+mApOAAHC1qi4It31Xv4ZUtcT5uR14BRgV7Rirl2dSOLCBXv3qSfEFGTuhkg8W5kQ7zAGeuKuQH4w8jsvGHMudUwbyyXtdY5Zg4hkb4nfOm/vaqZVcfGUJt11xFPV73B8wNDezDgBB+emYpbywfKjrMZtLhHO+l7bjcXB/B85qsewG4E1VHQS86bxGRIYCE4FjnPc8LCJhP3zXSnAikgV4VLXGeX4G8PtoxwkGhOk3FjJt5jo8Xlg4K5cNa+LTqnmoiPU5v/7+IoaNria7eyNPv7eMp+/vy3cnl+BLVe54ahUAq5Z34aGbopPk7zrvDUb0K6Fbxh4WXvUUf3l3JBmpfiYOXwHAm2sO5x+f7WvVnXflM3RJbcDnDTBu0Homzz6XdeW5UdmXJon0dy6ARKkBQVXfFpEBLRZPAMY6z58EFgHXO8tnqWo9sF5EiggVmt5vc1/VpetkETmcUKkNQol0pqreEe492ZKro2W8K/tzUPEcNjye4jxkuSc9fl9Gh+KQ5Yv1Taq1okPX89nZfXXkiCkRrfvvt363AShrtmiGUy21l5Pg/tnsErVSVbs1+/1OVe0uIg8BH6jqM87yx4D5qvpiW/FdK8Gp6jrgeLe2b4yJk/aN6FsWxVvEWkvMYfek098mYoyJtQhbUL/61eE2ESkAcH5ud5ZvBvo1W68vEPYOb0twxph2c/k+uLnAZc7zy4A5zZZPFJE0ERkIDAI+DLehpOiLaoyJsSjV3YvIc4QaFPJEZDNwC3AXMFtEJgEbgYtDIfVzEZkNrAQagSmqGrYS2RKcMaZ9NKqtqJe28atWWxudhsqwjZXNWYIzxrRf/DspRMQSnDGm3SQBumFFwhKcMab9LMEZY5KSAjbpjDEmGQlql6jGmCQW7BxFOEtwTeLcJ/NQFdwTv2G349UfFICThsUn7qdROGa7RDXGJDO7RDXGJC9LcMaY5JQYw5FHwhKcMaZ9mmbV6gQswRlj2s3q4IwxycsSnDEmKSkQtARnjElK1shgjElmluCMMUlJgUDn6MqQFHMyjBhbzaPvrOKJ977gkp9vs9gWO2liX3juSmbcN5e/3jeXG655B59vX5fCiyZ8zoKXnya7a6y7uyloMLJHnLma4ESkm4i8KCKrROQLERkT7RgejzJl2hZu+v5Afjp2MOMmVNJ/UGw+cIttsd2M3SO3jgvOWcXPr/sWV/7qfLweZezXiwHI71HL8GGlbNuR5Vr8sNydVStq3C7B3Q+8rqpDCM2R+kW0AwweXkdJcSpbN6bR6PewaE43xpxZFe0wFttixyW216ukpQbweIKkpTVSXpEBwJU/WcJjT58YnxzS1IoaySPOXEtwIpINnAo8BqCqDapaGe04PXr72VGSuvd1WamPvAJ/tMNYbIsd89jlFZm8OGcoT//1ZZ577EVq63ws+6QPJ43cRFl5JuuKc12LfVBWguNwYAfwhIh8LCKPisgB5WkRuUJElojIEj/17Q4ircx1HavzarEttpuxu2TVM2bUJi676kK+998XkZ7WyOljv+TS73zGU7OOdy9wJCzBkQKcCPxFVYcDtcANLVdS1RmqOkJVR/hIa3eQslIf+X0a9r7OK/BTvtX31ffaYlvsBIk9fNhWtm7rQlV1OoGAh/cW9+eMcV/Su9cu/nLPP3nykZfJ71HH9D+/Rvduu13bjwOoQiAQ2SPO3Exwm4HNqrrYef0ioYQXVauXZ1I4sIFe/epJ8QUZO6GSDxbmRDuMxbbYMY+9vSyTo48qIy21EVBOOG4r7y7uz3d/fAmXTf42l03+NjvKM5ly7TnsrMxwbT9a1UlKcK7dB6eqW0Vkk4gMVtXVhCZyXRntOMGAMP3GQqbNXIfHCwtn5bJhTXq0w1hsix3z2KvX5vPO+4cx/c+vEQgKRetymb9wkGvx2iUBklckRF3cURE5AXgUSAXWAT9W1Z1trZ8tuTpaWp3Q2pjkEqchyxd/+gjVu7a0UpsYuRxfvp7c7TsRrft62V+XquqIjsTrCFd7MqjqciBuB2eMcYGCJsBNvJGwrlrGmPbrJF21LMEZY9pH1aYNNMYksU7SyGAJzhjTbmolOGNMckqMe9wiYQnOGNM+NmS5MSZZKaAJ0A0rEkkx4KUxJoY0egNeishZIrJaRIpE5IC+6h1lJThjTLtpFC5RRcQLTAf+i1Df9Y9EZK6qRq1Lp5XgjDHtF50S3CigSFXXqWoDMAuYEM3ddLUvanuJyA5gw1d8ex5QFsXdsdgWOxljH6aq+R3ZARF53dmPSKQDzcd1n6GqM5ztXAScpar/7bz+ITBaVX/ekf1rLqEuUTty4kVkSbw69Vpsi30oxG6iqmdFaVOtdfqPaonLLlGNMfGyGejX7HVfoCSaASzBGWPi5SNgkIgMFJFUYCIwN5oBEuoStYNmWGyLbbE7D1VtFJGfAwsAL/C4qn4ezRgJ1chgjDHRZJeoxpikZQnOGJO0kiLBud3dI0zcx0Vku4isiFXMZrH7ichbIvKFiHwuIr+MYex0EflQRD5xYt8Wq9jN9sHrzLf7zxjHLRaRz0RkuYgsiXHsbiLyooiscj73MbGM3xl1+jo4p7vHGpp19wAujWZ3jzCxTwV2AU+p6rFux2sRuwAoUNVlItIVWApcEKPjFiBLVXeJiA94F/ilqn7gduxm+/BrQvN9ZKvquTGMWwyMUNWY3+grIk8C76jqo06rY6aqVsZ6PzqTZCjBud7doy2q+jZQEYtYrcQuVdVlzvMa4AugMEaxVVV3OS99ziNm35Qi0hc4h9CMbYcEEckGTgUeA1DVBktuB5cMCa4Q2NTs9WZi9B89UYjIAGA4sPggq0YzpldElgPbgTeaTfAdC/cB1wHxGFZWgYUislRErohh3MOBHcATzqX5oyKSFcP4nVIyJDjXu3skMhHpArwE/EpVq2MVV1UDqnoCobvPR4lITC7RReRcYLuqLo1FvFacoqonAmcDU5xqilhIAU4E/qKqw4FaIGb1zZ1VMiQ417t7JCqn/usl4FlVfTke++BcJi0CotU/8WBOAc536sJmAaeJyDMxio2qljg/twOvEKoiiYXNwOZmJeUXCSU8E0YyJDjXu3skIqei/zHgC1W9J8ax80Wkm/M8AzgdWBWL2Ko6VVX7quoAQp/1v1X1B7GILSJZToMOzuXhGUBMWtBVdSuwSUQGO4vGA643KHV2nb6rViy6e7RFRJ4DxgJ5IrIZuEVVH4tFbEIlmR8Cnzl1YQC/U9V5MYhdADzptGB7gNmqGtPbNeKkF/BK6LuFFGCmqr4ew/i/AJ51vsjXAT+OYexOqdPfJmKMMW1JhktUY4xplSU4Y0zSsgRnjElaluCMMUnLEpwxJmlZgutERCTgjGKxQkReEJHMDmzr786sRjjdfoaGWXesiJz8FWIUi8gBsy+1tbzFOrvC/b6V9W8VkWvbu48muVmC61x2q+oJzsglDcDk5r907ktrN1X974OMQjIWaHeCMybeLMF1Xu8ARzqlq7dEZCahm369IvInEflIRD4VkSsh1PNBRB4SkZUi8hrQs2lDIrJIREY4z88SkWXOWG9vOh35JwPXOKXHbzg9GV5yYnwkIqc47+0hIgudzuB/pfV+wvsRkX84Hdc/b9l5XUTudvblTRHJd5YdISKvO+95R0SGROVsmqTU6XsyHIpEJIVQZ++mu+hHAceq6nonSVSp6kgRSQPeE5GFhEYbGQwcR+iO/JXA4y22mw/8DTjV2VauqlaIyCPALlX9s7PeTOBeVX1XRPoT6kVyNHAL8K6q/l5EzgEiGW3jJ06MDOAjEXlJVcuBLGCZqv5GRG52tv1zQpOuTFbVtSIyGngYOO0rnEZzCLAE17lkNOuW9Q6hvqgnAx+q6npn+RnAsKb6NSAHGERoLLHnVDUAlIjIv1vZ/knA203bUtW2xro7HRjqdFkCyHb6aJ4KfNt572sisjOCY7paRC50nvdz9rWc0FBIzzvLnwFedkZOORl4oVnstAhimEOUJbjOZbczRNFezn/02uaLgF+o6oIW632Lgw8jJRGsA6GqjTGquruVfYm475+IjCWULMeoap2ILALS21hdnbiVLc+BMW2xOrjkswC4yhlKCRE5yhn54m1golNHVwCMa+W97wPfFJGBzntzneU1QNdm6y0kdLmIs94JztO3ge87y84Guh9kX3OAnU5yG0KoBNnEAzSVQr9H6NK3GlgvIhc7MUREjj9IDHMIswSXfB4lVL+2TEKT4fyVUEn9FWAt8BnwF+A/Ld+oqjsI1Zu9LCKfsO8S8VXgwqZGBuBqYITTiLGSfa25twGnisgyQpfKGw+yr68DKSLyKXA70HxOh1rgGBFZSqiO7ffO8u8Dk5z9+5wYDU9vOicbTcQYk7SsBGeMSVqW4IwxScsSnDEmaVmCM8YkLUtwxpikZQnOGJO0LMEZY5LW/wPhlha/C6lveQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,f1_score,classification_report,confusion_matrix,plot_confusion_matrix\n",
    "plot_confusion_matrix(model,X_val,y_val);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 5, ..., 3, 0, 1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions=model.predict(data_test_to_model)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.156392</td>\n",
       "      <td>-3.313845</td>\n",
       "      <td>10.280887</td>\n",
       "      <td>-1.518740</td>\n",
       "      <td>2.293267</td>\n",
       "      <td>4.431227</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.416419</td>\n",
       "      <td>-1.136787</td>\n",
       "      <td>0.293735</td>\n",
       "      <td>-1.746563</td>\n",
       "      <td>0.425362</td>\n",
       "      <td>-0.408536</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.716612</td>\n",
       "      <td>2.209204</td>\n",
       "      <td>2.429434</td>\n",
       "      <td>-1.693204</td>\n",
       "      <td>0.132507</td>\n",
       "      <td>-1.320355</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.184385</td>\n",
       "      <td>-1.958459</td>\n",
       "      <td>0.464651</td>\n",
       "      <td>-1.779952</td>\n",
       "      <td>-0.530553</td>\n",
       "      <td>-0.109291</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.911954</td>\n",
       "      <td>7.749260</td>\n",
       "      <td>-1.300149</td>\n",
       "      <td>-1.665028</td>\n",
       "      <td>0.202942</td>\n",
       "      <td>0.816565</td>\n",
       "      <td>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2704</th>\n",
       "      <td>4.298869</td>\n",
       "      <td>-0.321515</td>\n",
       "      <td>-0.186749</td>\n",
       "      <td>-2.562767</td>\n",
       "      <td>0.633965</td>\n",
       "      <td>0.217972</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2705</th>\n",
       "      <td>-3.666540</td>\n",
       "      <td>2.848061</td>\n",
       "      <td>-0.248227</td>\n",
       "      <td>-2.627324</td>\n",
       "      <td>-0.358599</td>\n",
       "      <td>0.618002</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2706</th>\n",
       "      <td>8.354228</td>\n",
       "      <td>-3.595534</td>\n",
       "      <td>10.845687</td>\n",
       "      <td>-2.362128</td>\n",
       "      <td>2.603451</td>\n",
       "      <td>5.354068</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2707</th>\n",
       "      <td>-1.898482</td>\n",
       "      <td>-1.117783</td>\n",
       "      <td>0.773054</td>\n",
       "      <td>-2.617754</td>\n",
       "      <td>-0.318100</td>\n",
       "      <td>-0.470480</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>-0.123981</td>\n",
       "      <td>0.687582</td>\n",
       "      <td>-1.091316</td>\n",
       "      <td>-2.594970</td>\n",
       "      <td>0.675310</td>\n",
       "      <td>-0.093935</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2709 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1          2         3         4         5  y\n",
       "0     7.156392 -3.313845  10.280887 -1.518740  2.293267  4.431227  3\n",
       "1    -3.416419 -1.136787   0.293735 -1.746563  0.425362 -0.408536  0\n",
       "2     0.716612  2.209204   2.429434 -1.693204  0.132507 -1.320355  5\n",
       "3    -4.184385 -1.958459   0.464651 -1.779952 -0.530553 -0.109291  0\n",
       "4     8.911954  7.749260  -1.300149 -1.665028  0.202942  0.816565  6\n",
       "...        ...       ...        ...       ...       ...       ... ..\n",
       "2704  4.298869 -0.321515  -0.186749 -2.562767  0.633965  0.217972  4\n",
       "2705 -3.666540  2.848061  -0.248227 -2.627324 -0.358599  0.618002  2\n",
       "2706  8.354228 -3.595534  10.845687 -2.362128  2.603451  5.354068  3\n",
       "2707 -1.898482 -1.117783   0.773054 -2.617754 -0.318100 -0.470480  0\n",
       "2708 -0.123981  0.687582  -1.091316 -2.594970  0.675310 -0.093935  1\n",
       "\n",
       "[2709 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test_to_model['y']=predictions\n",
    "data_test_to_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.156392</td>\n",
       "      <td>-3.313845</td>\n",
       "      <td>10.280887</td>\n",
       "      <td>-1.518740</td>\n",
       "      <td>2.293267</td>\n",
       "      <td>4.431227</td>\n",
       "      <td>HOROZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.416419</td>\n",
       "      <td>-1.136787</td>\n",
       "      <td>0.293735</td>\n",
       "      <td>-1.746563</td>\n",
       "      <td>0.425362</td>\n",
       "      <td>-0.408536</td>\n",
       "      <td>DERMASON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.716612</td>\n",
       "      <td>2.209204</td>\n",
       "      <td>2.429434</td>\n",
       "      <td>-1.693204</td>\n",
       "      <td>0.132507</td>\n",
       "      <td>-1.320355</td>\n",
       "      <td>BARBUNYA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.184385</td>\n",
       "      <td>-1.958459</td>\n",
       "      <td>0.464651</td>\n",
       "      <td>-1.779952</td>\n",
       "      <td>-0.530553</td>\n",
       "      <td>-0.109291</td>\n",
       "      <td>DERMASON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.911954</td>\n",
       "      <td>7.749260</td>\n",
       "      <td>-1.300149</td>\n",
       "      <td>-1.665028</td>\n",
       "      <td>0.202942</td>\n",
       "      <td>0.816565</td>\n",
       "      <td>BOMBAY</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2704</th>\n",
       "      <td>4.298869</td>\n",
       "      <td>-0.321515</td>\n",
       "      <td>-0.186749</td>\n",
       "      <td>-2.562767</td>\n",
       "      <td>0.633965</td>\n",
       "      <td>0.217972</td>\n",
       "      <td>CALI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2705</th>\n",
       "      <td>-3.666540</td>\n",
       "      <td>2.848061</td>\n",
       "      <td>-0.248227</td>\n",
       "      <td>-2.627324</td>\n",
       "      <td>-0.358599</td>\n",
       "      <td>0.618002</td>\n",
       "      <td>SEKER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2706</th>\n",
       "      <td>8.354228</td>\n",
       "      <td>-3.595534</td>\n",
       "      <td>10.845687</td>\n",
       "      <td>-2.362128</td>\n",
       "      <td>2.603451</td>\n",
       "      <td>5.354068</td>\n",
       "      <td>HOROZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2707</th>\n",
       "      <td>-1.898482</td>\n",
       "      <td>-1.117783</td>\n",
       "      <td>0.773054</td>\n",
       "      <td>-2.617754</td>\n",
       "      <td>-0.318100</td>\n",
       "      <td>-0.470480</td>\n",
       "      <td>DERMASON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>-0.123981</td>\n",
       "      <td>0.687582</td>\n",
       "      <td>-1.091316</td>\n",
       "      <td>-2.594970</td>\n",
       "      <td>0.675310</td>\n",
       "      <td>-0.093935</td>\n",
       "      <td>SIRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2709 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1          2         3         4         5         y\n",
       "0     7.156392 -3.313845  10.280887 -1.518740  2.293267  4.431227     HOROZ\n",
       "1    -3.416419 -1.136787   0.293735 -1.746563  0.425362 -0.408536  DERMASON\n",
       "2     0.716612  2.209204   2.429434 -1.693204  0.132507 -1.320355  BARBUNYA\n",
       "3    -4.184385 -1.958459   0.464651 -1.779952 -0.530553 -0.109291  DERMASON\n",
       "4     8.911954  7.749260  -1.300149 -1.665028  0.202942  0.816565    BOMBAY\n",
       "...        ...       ...        ...       ...       ...       ...       ...\n",
       "2704  4.298869 -0.321515  -0.186749 -2.562767  0.633965  0.217972      CALI\n",
       "2705 -3.666540  2.848061  -0.248227 -2.627324 -0.358599  0.618002     SEKER\n",
       "2706  8.354228 -3.595534  10.845687 -2.362128  2.603451  5.354068     HOROZ\n",
       "2707 -1.898482 -1.117783   0.773054 -2.617754 -0.318100 -0.470480  DERMASON\n",
       "2708 -0.123981  0.687582  -1.091316 -2.594970  0.675310 -0.093935      SIRA\n",
       "\n",
       "[2709 rows x 7 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=decode_target(data_test_to_model)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=pd.concat([ID,s['y']],axis=1)"
   ]
  },
  {
   "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>ID</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10834</td>\n",
       "      <td>HOROZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10835</td>\n",
       "      <td>DERMASON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10836</td>\n",
       "      <td>BARBUNYA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10837</td>\n",
       "      <td>DERMASON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10838</td>\n",
       "      <td>BOMBAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2704</th>\n",
       "      <td>13538</td>\n",
       "      <td>CALI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2705</th>\n",
       "      <td>13539</td>\n",
       "      <td>SEKER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2706</th>\n",
       "      <td>13540</td>\n",
       "      <td>HOROZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2707</th>\n",
       "      <td>13541</td>\n",
       "      <td>DERMASON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>13542</td>\n",
       "      <td>SIRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2709 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID         y\n",
       "0     10834     HOROZ\n",
       "1     10835  DERMASON\n",
       "2     10836  BARBUNYA\n",
       "3     10837  DERMASON\n",
       "4     10838    BOMBAY\n",
       "...     ...       ...\n",
       "2704  13538      CALI\n",
       "2705  13539     SEKER\n",
       "2706  13540     HOROZ\n",
       "2707  13541  DERMASON\n",
       "2708  13542      SIRA\n",
       "\n",
       "[2709 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}