my_work.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import SGDRegressor as sgd\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.ensemble import AdaBoostRegressor,RandomForestRegressor ,GradientBoostingRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "from joblib import cpu_count\n",
    "\n",
    "n_jobs = cpu_count()\n",
    "\n",
    "print(n_jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'uci_id': 1, 'name': 'Abalone', 'repository_url': 'https://archive.ics.uci.edu/dataset/1/abalone', 'data_url': 'https://archive.ics.uci.edu/static/public/1/data.csv', 'abstract': 'Predict the age of abalone from physical measurements', 'area': 'Biology', 'tasks': ['Classification', 'Regression'], 'characteristics': ['Tabular'], 'num_instances': 4177, 'num_features': 8, 'feature_types': ['Categorical', 'Integer', 'Real'], 'demographics': [], 'target_col': ['Rings'], 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1994, 'last_updated': 'Mon Aug 28 2023', 'dataset_doi': '10.24432/C55C7W', 'creators': ['Warwick Nash', 'Tracy Sellers', 'Simon Talbot', 'Andrew Cawthorn', 'Wes Ford'], 'intro_paper': None, 'additional_info': {'summary': 'Predicting the age of abalone from physical measurements.  The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task.  Other measurements, which are easier to obtain, are used to predict the age.  Further information, such as weather patterns and location (hence food availability) may be required to solve the problem.\\r\\n\\r\\nFrom the original data examples with missing values were removed (the majority having the predicted value missing), and the ranges of the continuous values have been scaled for use with an ANN (by dividing by 200).', 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': 'Given is the attribute name, attribute type, the measurement unit and a brief description.  The number of rings is the value to predict: either as a continuous value or as a classification problem.\\r\\n\\r\\nName / Data Type / Measurement Unit / Description\\r\\n-----------------------------\\r\\nSex / nominal / -- / M, F, and I (infant)\\r\\nLength / continuous / mm / Longest shell measurement\\r\\nDiameter\\t/ continuous / mm / perpendicular to length\\r\\nHeight / continuous / mm / with meat in shell\\r\\nWhole weight / continuous / grams / whole abalone\\r\\nShucked weight / continuous\\t / grams / weight of meat\\r\\nViscera weight / continuous / grams / gut weight (after bleeding)\\r\\nShell weight / continuous / grams / after being dried\\r\\nRings / integer / -- / +1.5 gives the age in years\\r\\n\\r\\nThe readme file contains attribute statistics.', 'citation': None}}\n",
      "             name     role         type demographic  \\\n",
      "0             Sex  Feature  Categorical        None   \n",
      "1          Length  Feature   Continuous        None   \n",
      "2        Diameter  Feature   Continuous        None   \n",
      "3          Height  Feature   Continuous        None   \n",
      "4    Whole_weight  Feature   Continuous        None   \n",
      "5  Shucked_weight  Feature   Continuous        None   \n",
      "6  Viscera_weight  Feature   Continuous        None   \n",
      "7    Shell_weight  Feature   Continuous        None   \n",
      "8           Rings   Target      Integer        None   \n",
      "\n",
      "                   description  units missing_values  \n",
      "0         M, F, and I (infant)   None             no  \n",
      "1    Longest shell measurement     mm             no  \n",
      "2      perpendicular to length     mm             no  \n",
      "3           with meat in shell     mm             no  \n",
      "4                whole abalone  grams             no  \n",
      "5               weight of meat  grams             no  \n",
      "6  gut weight (after bleeding)  grams             no  \n",
      "7            after being dried  grams             no  \n",
      "8  +1.5 gives the age in years   None             no  \n"
     ]
    }
   ],
   "source": [
    "from ucimlrepo import fetch_ucirepo \n",
    "  \n",
    "# fetch dataset \n",
    "abalone = fetch_ucirepo(id=1) \n",
    "  \n",
    "# data (as pandas dataframes) \n",
    "X = abalone.data.features \n",
    "y = abalone.data.targets \n",
    "  \n",
    "# metadata \n",
    "print(abalone.metadata) \n",
    "  \n",
    "# variable information \n",
    "print(abalone.variables) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Rings\n",
      "0        15\n",
      "1         7\n",
      "2         9\n",
      "3        10\n",
      "4         7\n",
      "...     ...\n",
      "4172     11\n",
      "4173     10\n",
      "4174      9\n",
      "4175     10\n",
      "4176     12\n",
      "\n",
      "[4177 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Sex  Length  Diameter  Height  Whole_weight  Shucked_weight  \\\n",
      "0      M   0.455     0.365   0.095        0.5140          0.2245   \n",
      "1      M   0.350     0.265   0.090        0.2255          0.0995   \n",
      "2      F   0.530     0.420   0.135        0.6770          0.2565   \n",
      "3      M   0.440     0.365   0.125        0.5160          0.2155   \n",
      "4      I   0.330     0.255   0.080        0.2050          0.0895   \n",
      "...   ..     ...       ...     ...           ...             ...   \n",
      "4172   F   0.565     0.450   0.165        0.8870          0.3700   \n",
      "4173   M   0.590     0.440   0.135        0.9660          0.4390   \n",
      "4174   M   0.600     0.475   0.205        1.1760          0.5255   \n",
      "4175   F   0.625     0.485   0.150        1.0945          0.5310   \n",
      "4176   M   0.710     0.555   0.195        1.9485          0.9455   \n",
      "\n",
      "      Viscera_weight  Shell_weight  \n",
      "0             0.1010        0.1500  \n",
      "1             0.0485        0.0700  \n",
      "2             0.1415        0.2100  \n",
      "3             0.1140        0.1550  \n",
      "4             0.0395        0.0550  \n",
      "...              ...           ...  \n",
      "4172          0.2390        0.2490  \n",
      "4173          0.2145        0.2605  \n",
      "4174          0.2875        0.3080  \n",
      "4175          0.2610        0.2960  \n",
      "4176          0.3765        0.4950  \n",
      "\n",
      "[4177 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(X)\n",
    "Y = np.array(y)\n",
    "feat = X.columns[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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xO9MxKJquLvNt5Vw9eLLrpKEhtZhob79wobO6T3U/TsqQSt7p1D8xH4UsF/2hU/HaOe0SANLHQ/EEnH6PvrC/bw8Yzc3upgO8jhgBwIva291Nl6ntC0VoPWSrTtrbM3s9xnsLANmXbvzOVd/d1JSb/bpZBqcxNZPHmmre6ZaJmI9C5PXz2uvlB4Bc4qF4Ak7mik0mHeBltbXupgO8jhgBwIsqK91Nl6ntC0VoPWSrTiorM3s9xnsLANmXbvzOVd89YUJu9utmGZzG1Ewea6p5p1smYj4KkdfPa6+XHwByiTnFE2BOcSCIOcWBcMwpnlnUAZAZ9hx669dHH8nC7TnFY+0nkt8vdXcXzuga8eYUd1on6eyTOcVzh/hFHQCZkG78znQMiiZ0Tuts7tfm9pziieo+n+cUT7b+izXmE7+Kow5y0R86xZziAJAa5hR3idOHGDwQRzEoLZXmzImfZs4cHoijeBAjAHiR3y8tWGB+jxzJwv57/vz0b7TE20/kPn2+4DVGIYyuEaseE9W9zyfV1cXPu64umDbePp1ctyXDzXMDAJC8dOO3k+2jrYuVzok5c6S+fZ1dD7gttE769k0+JoZuX1rqrO5T2Y/TMqSSd6r1T8xHoXPaH2ab/ZnIybU+ACA1PBR3INF/jOXbf5QBmTR3rtTQ0PMCzO83y+fOzU25gFwhRgDwovp6adEiqaoqfHkgYJbX12d2P9H2OXdu4rS2AQOkEoefZIYONa9EqqvNA+fIa5xoN6USiVePiep+6dL411pLlzp/72Jdt7l9TACA7Eg3fsfbfvFi80oUsxcvjh5bQkXeH4i13+pqky4QiF9uv1+aNCm5B0GRdRLvXkZdXc8yRG7vtO5j7SeeWPWQat7p1j8xH8UgUX+YbDtOV3V1/M9EtEsAcAfDpydhxQrp2GPNAw6fT/r3v/n2H4rXgQPSwoVSc7MZRmz2bL4hjuLmdowohiHLEqEOgMzr6pIaG6X2djM33bRpmbn5E7qfESPMss2bo+8zWtqNG6UtW6Thw80NomnTTDr7WqSmxvS5W7dGz19ylqffH/0aRwouGzvW7GvTJun1102/P26cWbZtm/N6TFT3ia61knnvnB7Tli3BOqmoiP8+ITbiF3UAZFq68Tve9k5jdmhsGT3aLFu3Lv79gVj7jVw+ebL0wAM9Y2BkPLvqKumhh3peC8Srk1jx1WmdOk0XuZ9Zs8x1Q7x6TTXv0Hpwo/6LOeYTv4qvDuKd/3Zba2oy6To7pd27zbqhQ6Xf/17au9dMAVVdbdpfRYUZ3nzfPukTnzCfGXw+M/3BscdKw4ZJzz5rpjYYOFCaMUMaMyb+Z6Jib5cA4ITT+MVDcQAA8hDxizoAAHgT8Ys6AAB4E/GLOgAAeBNzigMAAAAAAAAAAAAAil6vXBfAS159VZo6Nfj38uXSlCm5Kw+QSx0d0llnmeHRRo+WXnhBKi/PdamA3PnnP80wf7bXX5dOPDF35QFQXDI1vF6ifO3169dHH4o80fpE+xw2zExP0dISfzjQZKZ12bvXzBG4cqX5feJEafz44PDnQ4eafa5Z43wI0tBt7CFk16wxQyVOnmyGU3Q6vLm9fXOz9M47Up8+0oQJ0rnnSjt2BPfV3Gz2XVERXv503v9Uh1V1e8hZAPCiTPZxqeTtdIj0VPvtTA6f7nTY8K99Tbr9djO08YQJ0rx5Ut++8evFvg5oajJDrtfUSG1tPY8hVt6h20+YIN19t/TII+HTkSSKx7t2SZdeGtxm6lRzrRRZd6lMW+fGeUi8RjGxz/fWVnPvf8MGqazMtNHPfMZM5dDUJH38sRnyfNcuaft2c40+cKB0xhnSr34lrV4dbJ922rIy89ln504zjLokffKT0uDB5tp++3bzKimRTjnFvJx8RqJdAkAarALX0dFhSbI6OjrSysfMFhj9BRSb2trobaG2NtclA3IjEzHCrfjlZdQB4MzixZYVCIT3PYGAWZ7JfKOtD03X0BB/fbTyxctTsiy/3+QbqqHBLE+UzrIsq64ufp/tdJ+JypnMMUcrf7qvVN7/WO93tPcx0XkQbf+ZOk/zEfGLOkDxyWQfl0re8bZxo99OFLucxk4nccbmNF7W1cWul1SuA0JfI0emH48nTXJ23ZHM9U0650om8vAy4ldx1UEq1/SZfA0d6vwzUjG1SwBwwmn8UpbKkzNuBHInQQsoFrEeiNsvHoyj2GQqRhTTB9FYqAMgscWLLcvn69nv+HzmleqNkkT5NjREX5/MK7J8sfYZ7WXfEG5ocJbOstK/EW7nlUw5Ex1zovKnU7fJvP/JHlOi8yBy/5k6T/MV8Ys6QHHJZB+XSt7xtnEaN1LJw83YGVmeZONltAfj6V4HuBGPEz0QT7Ze3T4Piy1eR0P8Kp46SOeaPtMvJ5+RiqldAoATTuOXz7IsK/vfT88ep5OrxxI5ZHosDKWOYtDRIQ0alDjdjh0MpY7iEDlkeiypDKWebvwqBNQBEF9Xlxlys60t+nqfTwoEzNDjyQytlyhfKTjUaTpCyycl3mfk/nfsMNcl8crh90t79pg0/fqlV16/3wx9ePjhzssZKfSY7TKlW49O9hXv/XfyfscS7zyw979qlRny1e3zNJ8Rv6gDFI9MxeJU806nT3fabycjndhpl2flSjNEcbLxcs+e4FDqe/emfx2QKvs4Vqxwdj/FCfv6xh6q2Y3zMJPnspcQv4qjDtLpK7MhEDBTMUm0SwBwymn8KslimTzJyQPxZNIBXnbWWe6mA7zOyQPxZNIBQDIaG+PfyLEsMzdeY6O7+UruPMgNLZ+TfUbu/9JLE5ejq8vMxdnQkF5Z7bwaGtK7eRZ6zAsXZu6BeOS+4km27kPFK7+9/4ULM3OeAkA+yFQsTjXvdPp0p/12MtKJnXZ5GhpSi5ehsd+N64BU2cfh5n0S+/rG5sZ5mMlzGcg36fSV2dDW5uwzEu0SAJLXK9cFAOAd69a5mw4AAKSuvd3ddKmmT1eq+2tudp6uqSm1fURyK5/2dufld2Nf6axPl9PjzPZ5BwBuyFQsTjVvN/pSt+NTurEz1e1Dt3MrfqfD7fskoe+TG+dhJs9lIN944TxOpoxeOB4AyBd8UxyAY6NHu5sOAACkrrLS3XSppk9XZWVq+6ytdZ5uwoTk84/GrXwqK52X3419pbM+XU6PM9vnHQC4IVOxONW83ehL3Y5P6cbOVLcP3c6t+J0Ot++ThL5PbpyHmTyXgXzjhfM4mc9IXjgeAMgXzCmeAHOKA0HMKQ6EY07xzKIOgPjsufDWrzdD50VKd07xWPlKJr/u7tjrnYg2p3i8fUbuP5dzijstZ6R8nlM8lWOKdx5Ezk3r9nmaz4hf1AGKR6Zicap5p9OnO+23k5FO7GRO8dhizSmeznmYyXPZS4hfxVEHXptTvNjbJQA4wZziLnH6oJsH4igG5eWJ/2u8tpYH4igeTh90J/tAHACc8PulBQvM7z5f+Dr77/nzk79Bkihfn0+aMyfp4kZlly/ePqOZM0caMCBxOebMMTeM+/aV6urSK+ucOSafZMoZjX3MpaXu1WOkZN7/ZOveThd6HsQ7/0pLM3OeAkA+yFQsTjVvJ9skys9Jv+1UqrEztDx9+yYfL+vqgg/EJXeuA1IRehzl5dKkSe7ka1/f2Nw4DzN5LgP5xj7fU72ez7QFCxJ/RqJdAkCKrALX0dFhSbI6OjrSysf8P1b0F1Bsamujt4Xa2lyXDMiNTMQIt+KXl1EHgDOLF1tWIBDe91RXm+WZzDfa+tB0DQ3x10crX7w8Jcvy+02+oRoazPJE6SzLsurq4vfZTveZqJzJHHO08qf7SuX9j/V+R3sfE50H0fafqfM0HxG/qAMUn0z2cankHW8bN/rtRLHLaex0EmdsTuNlXV3seknlOiD0NXJk+vF40iRn1x3JXN+kc65kIg8vI34VVx2kck2fydfQoc4/IxVTuwQAJ5zGL4ZPT0LkUOoMmY5i1tEhnXWWtG6dmRvrhRf4hjiKW+RQ6qkMmR6qGIYsS4Q6AJzr6pIaG6X2djOn3LRp7nxjIFG+9vr166UtW6Thw6WqqmC6ROsT7XPYMDPcaEuLGY1m9uzwb0bZDhyQFi6Umpvjp5PMEKoNDWY41r17pYkTpfHjzc9t26ShQ80+16yJn1doOUO3secMXbPGfINj8mSpujr+MYeW396+uVl65x2pTx8zF+q555oh4+19NTebfVdUhJc/nfc/1vvt9DxIdP5l6jzNN8Qv6gDFKZN9XCp5x9vGjX47Wuxat8557Ewmztgi4/3XvibdfrvU1GRi5bx54d8Qj8a+DmhqkmpqzKutrecxxMo7dPsJE6S775YeecSUaexYZ/F41y7p0kuD20ydaq6VIusumesbJ++ZU8USr6MhfhVfHdjne2urufe/YYNUVmba6Gc+Iz3wgGnvH39spoPYtUvavt1cow8cKJ1xhvSrX0mrVwfbp522rMx89tm5U9q0yaz75CelwYPNtf327eZVUiKdcop5OfmMVGztEgCccBq/eCgOAEAeIn5RBwAAbyJ+UQcAAG8iflEHAABvYk5xAAAAAAAAAAAAAEDR46E4AAAAAAAAAAAAAKBg9cp1Abzk2Welurrg3888Y+b/AIrRli1mvmR7ftB//tP8BIrVCy9IZ58d/Pv556WzzspdeQAgV0LnuxsxwizbsEF6/XXJssycmBMnSlu3BufDk6LPz23Pn3ngQPjcm1dfbebrXLpU2r1bOvzw4FyfieYxt+cC/fBDqX9/acYMU47IOcFXr5Y2bjTr7Dm7t25NPM95tP2PGBHcZswYk665OZj/uHFm2dq1wflIt2wJbl9RYdZv3hysn9Wrg3OWDx9uPqs0N0efUzXaHIShdZ5oXsJoxxRaJqd5pjsXInMpAkBP6c43bsfq0P7cyXzeV10lPfSQ+bumJhgnQ/OLjOmh83TX1pp7ah99JJWXm3l77dj64IPSnXf2nM878lgnTzbbRZZhyBBzz86Oiz/8ofTmmz2PNzSmBwLmZ0tLz1gaut9Bg0zMXb06/pzikfUaWtZY84NHHt/UqdLy5YnnYA9NF1rnkfOkz5plrseSyS+Vud+BfNXVJS1bZl7d3aavqKgwnxWmTjXnt71u6FAz9/ezz0qdneYzhGQ+z3R2mp9795p2tmaN6cu6u82rtNRse+utpk/61a/M3OG7d0u9e5v5yL/4RfP7oEHmmn7HDlM+u8+oqTH7S9R+AQDJYU5xh3y+2OsKuwaBngYNkjo6ei4vLzcXcUCxyUSMYB4v6gDwoiVLpBtukNranG8zdKj5uW1b+vufNMncKIq2/0DA3EB644309xPK75fmzJHmzk3t+DOlrs7800C0MkWr80BAWrBAqq8Pz8fpMSXKM1o+sfYZTbrbZxPxizoAsiWVvjFRvx5t+1tuke67zzywyZV4MT5T6uqkr341M7E99PpBiv6+2A+ibYGAdPHF0uOPx0/nlNP8YqXL1zicDuJX4dbBkiXBf6yNpqTEPND2ikJsfwCQDqfxi4fiDsR72GEr7FoEgmI9ELfxYBzFJlMxolA/iCaDOgC8ZckSaebM4r0urqsz3yTJp+OfNMl8M85Jmex4tmhR8OZauu+pnefNN0s//nHPfKLtM5pY5XC6fbYRv6gDIBtS6Rud9OuR299yi/nWNDKjoUE66SRvXkPlaxxOB/GrMOtgyRLpvPNyXQp3FWL7A4B08FD8/0k3kEcOmR4LQ6mjGGzZEhwCLJ7NmxlKHcUhcsj0WFIZSr0QP4gmizoAvKOrywwZmg/fkEbqfL7g8LGSO++pz2e+eRPrG2yh+4w2BGSicyvR9rlA/KIOgExLpW9MJlbb269cKQ0cmNtviBc6v98M37x+fa5Lkpp8jMPpIH4VXh10dZnpi7zaxuIptPYHAOlwGr9KslgmT3LyQDyZdICXnXiiu+kAr3PyQDyZdADgVY2NPBAvBJYltbaa99Ot99Sy4j9MCd1nNInKkWh7AChEqfSNyfTr9vYNDTwQz7SuLm8/rCMOI981Nnq7jcVD+wOA5PXKdQEAeMeWLe6mAwAAhaG9PdclgJty8X7G2qfTsnAOAigmqfSNqfSTTU3Jb4PiRBxGviqGc7MYjhEA3MI3xQE45nRIdIZOBwCguFRW5roEcFNlZfbf01j7c1oOzkEAxSSVvjGVfnLChOS3QXEiDiNfFcO5WQzHCABu4aF4As884246wMv++U930wFe9/zz7qYDAK+aNs3MZ+fz5bokSIfPJ1VXm/fTrffU54s/x2HoPqNJVI5E2wNAIUqlb0ymX7e3nzePeWozze+Xqqq8ew1FHEa+mzbNtLFCRPsDgOTxUDyBc891Nx3gZcOHS+Xl8dOUl/NNcRSPs85yNx0AeJXfLy1YYH736k3ddNXV5d+xT5pkyuT0AYgkzZ9v3s/Q9zRVdp5z5kQvR+Q+o4l3bjnZHgAKUSp9o9NYHbp9376mDy92mYzvc+ZIP/tZ5veTCcRheIHfH2xjhYT2BwCp4aG4A5aV3nqgkOzYEfvBeHm5WQ8UE2IEABj19dKiRcl/E2PoUPNyw6RJ5ltw0VRXm/Vu8/ulhgZp6VJz/LH2n211dWb0nmjvSbQ6DwRM2vr64DL7PXVyTPHynDs3ejmi7TOaWOeW0+0BoBCl0jc6idWR28+da+Jcrh+6xIvxmVJXJy1enJlvmdrXD3Pnxn5fIuu8utpsE1kPqb43TvOLlY44DK+orzdtOd5njhKPPSWh/QFAanyWVdi36zs7O1VeXq6Ojg6VlZWlldezz5oLYtszz/ANcRSvLVukE080P4cPNzdd+YY4itkLL0hnnx38+/nn0/uGuJvxy6uoA8CburqkxkapvV0aMcIs27BBev11849CtbXSxInS1q1m/jt7uD97m6FDpRUrpDVrTNrZs6UDB6RLL5Wam6WxY6Wrr5a2bTMPonfvlg4/3Azx2rdvcP/r1wevU6qqzH78fmnvXnNj98MPpf79pRkzTDnsfY4ebcqzerW0caNZN358sMzDhpm0LS3B8pWW9jz+0P2PGBHcZswYk665OZj/uHFm2dq15vgmTjTb2ttXVJj1mzcH62f1avMNkcmTTZpnnzV5TpgQrIto70m0OreXxbqpHu2YQsvkNM9o5UjmRn6622cL8Ys6ALIplb4xWqwO7c+jbX/ggLRwoYk1tbXSVVdJDz1k/q6pCcbJ0PwiY/rXvibdfrvU1GT+Pvdc6aOPzD/YP/BAMLY++KB0550mXWhcizzWyZPNdpFlGDLE3LOz4+IPfyi9+WbP4w2N6YGA+dnS0jOWhu530CATc1evNunuvlt65JHgNcrEieYaJbJeQ8sa7foh2ns5daq0fHnP9zZeutA6t69p1q0z+5w1y1yPJZNfvHT5GofTQfwq7Dro6pKWLTOv7m7TV1RUmM8KU6ea89teN3SoNHiwae+dneYzhGQ+z3R2mp9795p2tmaN6cu6u82rtNRse+utpk/61a+k7dvN55bevaU+faQvftH8PmiQuabfscOUz+4zamrM/hK1XwCA4TR+Jf1QfNy4cXrjjTc0NOJfq3bs2KFPf/rTWr16dWolzpBCDuQAgMKVifhFDAcAIPOI4cRwAIA3EcOJ4QAAb3Iav5IeGGTNmjXq6urqsXz//v1av359stkBAIAsIYYDAOBNxHAAALyJGA4AQP7o5TThs88+e+j3P/3pTyoPmVS4q6tLL730ksaOHetq4fLN449Ll1wS/Puxx6SLL85deYBcYvh0IFw+xwhiOJD/Mj0cpT1keKxhSGMNNZ7usNtOj7u1NTi8+rhxwaFH4w2lvmqVGW71yitNme10oUOX2uXr6goO+WoPJdrSYo6hosLkLYUPXx66/YEDwfqrrTVTKm3eHL3M0fYZOkxqtGMOBKRXXw2Wf8oUs94u3+jRZhjTtWvNUIqzZpn3K9rw8bbIYW7t/cdanqzQfKLVmZvDphczYjjgHU77ulT6xMg4HjocuNPhrdMtX7zh06PFuWhDs59wghlOOHL49Mh0ocN328MX2zHc5zPxMN6w6JHXD6FlDY1ZoUOhjxljYvK6ddGnIrF1dJhpstatM+V54QVzPRJZdwMGmPK1tpp0Tz8t/fa3pgxVVeb47PI9+qhJH+892rUrOJVM6DbxxIv52Rp2v5gRw/NPvM82a9cGr63toctXrpT+8x/z+8CB0qRJ0r590rvvSvv3m6HSx4+X3n7b9NN+vzRypEm7fbvpGzZvNvvt29f0P/36mbbfr59Zf/LJpl9bvtz0L+PGmWv9bdvMZ4WuLpPv5MlSdTVtDQDSZjnk8/ksn89nlZSUHPrdfpWWllqHH3649dxzzznNLms6OjosSVZHR0da+ZhL4+gvoNiUl0dvC+XluS4ZkBuZiBFuxS/LIoYD+W7xYssKBML7jkDALHdDXV30/mnSpJ77Dd1/Q0PP9UOHmpcbZY123Jl4DRhgWSUlqW/fp487+/T7zXuRyWOuqzN129Bg9he5/0mToi9vaEjuvYuWf6xzItPndz4ihhPDUXyc9nWp9Imx4riTuG3nnW756uri9/tux7lE+8rWy46rttra6Olqa9O/rqmtjf0eTZoUfZtJk2KfN7GuBRoaUjsPEx1focR2YnjhxvBo53C0zzb5/iqUtgYAbnMav5KeU7ympkZvvPGGhg0b5vbz+YxwYx4Uny9xmuRqEfCuQYPMfy7GUl4u7diRrdIAuZepGJGJebyKMYYD+W7JEmnmzJ79hN23LFok1dennv+MGdIzz6S+vROplDXWcSN9tbXmG2HJamiQ5s5NnO6WW8y35+Kxz4mbb5Z+/OPMnd/5ihhODEdxcRrLU4n56cZxny92rHVavmJXV2e+PTp+fGrxNR3x3j/bpElm5L5QTmJ1tH1J0c9DJ+dGocR2YnhhxvBC6998Pu+3NQBwm9P4lfRD8VD79u1Tnz59Ut08K9IN5JHD4caST8PkApmyZUtwiKx4Nm9mKHUUh0zGiEx/EC2GGA7ku64uM4ReW1v09T6fGVa7pSW1IfL27jXD8mVDMmVNdNzIDb9f2rMn/lDqBw6YcyrKtJg9+HxSSUnstOme3/mMGE4MR/FwGstXrTL/tJRMzM9GHHdavmJnD7Wcr3buDA6lnkysjhTtPEzmuq0QYjsxvPBieCF+9iiEtgYAbnMav0qSzbi7u1vf//73VVVVpQEDBmj16tWSpO985zt6+OGHUy9xnnLysCOZdICXnXiiu+kAr/NajCi2GA7ku8bG+DdnLMvMQ9nYmFr+DQ2pbZeKZMqa6LiRG/Yc6PEsXOj8JrtlxU+b7vldbIjhQH5yGssXLkw+5mcjjjstX7E77rhclyC+Sy8N/p5MrI4U7TxM5rqN2B4dMTy3CvGzB20NAFKX9EPxH/zgB/rlL3+puXPnqjTkawTHHHOMHnroIVcLByC/bNnibjoA2UUMB/JLe7u76SI1NaW2XTqclDXV40HmJRoWNhPDxnI+OEMMB/KT0z7Maf8Zml8243i2hwX3mnyfIi70/XPjvQw9D1OJ08T2cMTw3Crk87GQjw0AMiXph+K//vWv9eCDD+rLX/6y/CHjcxx77LH64IMPXC0cgPzidEh0hk4H8hMxHMgvTofhTHW4zgkTUtsuHU7Kms/Djxa72tr01qeC88EZYjiQn5z2YU77z9D8shnHM9G/F5JBg3JdgvhC3z833svQ8zCVOE1sD0cMz61CPh8L+dgAIFOSfii+fv16jR8/vsfy7u5uHTx40JVC5ZPHHnM3HeBl//ynu+kAr/NajCi2GA7ku2nTzFxwPl/09T6fVF1t0qVi3rzUy5asZMpqHzfyi98vzZ4dP83s2c7nLfT54qdN9/wuNsRwID85jeWzZycf87MRx52Wr9i9/XauSxDfo48Gf08mVkeKdh4mOscTbQ9ieK4lcw57BW0NAFKX9EPxo446So1RJqxYtGiRPvWpT7lSqHxy8cXupgO8bPhwqbw8fprycr4pjuLhtRhRbDEcyHd+v7Rggfk98iaN/ff8+anf2OzbV6qrS7l4jiVbVvu4C+nGVD5J9Rtic+ZIISN6RlVaatIlYr+3c+aY3zNxfhcbYjiQn5zG8tLS5GO+G3E8dF+plq/Y1dVJFRXO4qvbdeckv0mTpAEDgn8nG6sj/448D+Od4062BzE815yew15DWwOA1CT9UPyOO+7Qddddpx/96Efq7u7WkiVL9PWvf11333237rjjjkyUMecsK731QCHZsSP2g/Hy8vyfawtwm5diRDHGcCDf1ddLixZJVVXhywMBs7y+Pr38ly6NfUN90qTY39iurpYaGnquHzrUvNItq33c2fjG+MCBUknSn3qC+vZNfpsBA3ru0+8370Umj7muTlq1yrx3kTfJ/H7znkdb3tAgzZ3rbB9z50bPP5R9Tsydm9nzu5gQw4H85TSWpxLz48XxULHidiAgLV5sXqmWr7ralCHRwxc341y+POipqzPvgWTia6wH47W10es4GbW1sd+/SZOibzNpUvSR+mLFajvmOzkfQsU6N5xuX+yI4bkX6xyO9tkm31VX09YAIB0+y0r+dn1jY6Puuusu/fvf/9auXbv06U9/WnfccYe+8IUvZKKMaens7FR5ebk6OjpUVlaWVl6PPy5dcknw78cey59v/wHZtmWLdOKJ5ufw4eaDGN8QRzFzO0a4Gb9CFWsMB/JdV5fU2Ci1t5u54aZNc/eG8N695iZoU5OZo3TePPOw197v+vXBmF5VFdx/tHJJ7pXVzr+1VXr9dfOPROPGSRMnStu2mZtUK1ZIa9aYm8WzZ0sHDkiXXmpuTpeXS1deacpspxs7Nri9Xb6uLmnhQqm5WRo92uy7pcUcQ+i3v9aujb79gQPB+qutNTfJN2+OXuZo+7TLXloa/ZgDAenVV4PlnzLFrLfLN3q0tHy5KV9NjTRrlnm/li6Vdu+WDj88+J7aDhyIvv9Yy5MVmk+0Ogs9JzJ9fucbYjgxHMXJaV+XSp8YGcd/+EPpzTd75hEv73TLFxk/rrpKeuih2HGuvV0aMcLkuXmzyeuEE6Rbbw3G03PPlT76qGe6qVNN3GtvlwYPlp59NhjDfT4TDyPrITSPyOuH0LKGxqxBg0zeq1dLY8aYmLxuXfi1UqSODumss0y60aOlF14IfnEg9NgHDDDla2016Z5+Wvrtb00ZqqrM8dnle/RRkz7ee7Rrl7n+sevb3iaeeDE/lfMw3ntbKLGdGF7YMTzeZ5u1a4PX1jU1ZvnKldJ//mN+HzjQ/CPKvn3Su+9K+/dL3d3S+PFmioW9e00bGDnSpN2+3fQNmzeb/fbta/qffv1M2+/Xz6w/+WTTry1fbvqXcePMtf62beazQleXyXfy5OCQ6YXQ1gDAbU7jV0oPxb2kkAM5AKBwEb+oAwCANxG/qAMAgDcRv6gDAIA3OY1faQwkCAAAAAAAAAAAAABAfuuV7AaDBw+Wz+frsdzn86lPnz4aP368Lr/8cn3ta19zpYD5ZMEC6cYbg3/Pny/dcEOuSgPk1rp10tFHS3v2mCF/3n03OBwpUIxuv126557g39/6lnT33bkrTzTFHMOBQuXW0JcbN0YfPt0ervXDD6X+/aUZM8zwovZ6eyjP0KHM7euB0OFLV60y+6isNNuvXm2GDbSHJy0tDZapTx/pO98JDh1+0UXm98ihzltazFCDkydLo0YF9zlsmNnnypXm+qRvXzMMoWWZYRFHjzZlsbefOtUMPx5v+PPqapN+9ergcK/t7dLDD5thDgMBk/emTaZOx4yRNmwIDm8qBYc8tYdHb2sLln/YsPDhW+3j27vXlGvcOLPMHqpVMnVqH9+ECT2HoA19TysqgvUTbRj8IUOkZ54xeUZ7n4tt+PN8RAwHvC3deG3HtpaWnsNgx5rOIt6Q5KHDi0dOgfGpT0mXXx7M7+qrpZ07Tbz59rfN9qNGmWHDb7ghPLZt2GDW2VOC2MMA79hh8p48WXrggZ5lLSuTHnwweHz33y9de23PocIjh5G/+27pkUfCp0cJjZXr1pntL7pI+vznTflGjDDXLBs2mPh91FFmiPPQ4dwjh3ofPdrE3LY2c5101FHm98gy1NSYY9q6NXzY93hDvdsxuLnZ1FdNTbA8ocO+hx5TvGlLQs+H0PJEngOFOvx5PiKG505k3xutH/zwQ7Osu1vq7DT3Obu7TR87cKBpc5YlffxxMN9Bg6TTTzf9oD2Mene3aUN2upIScz29fr108KBZV1ERbOP9+5s2b/exdjvcu7fn56uaGtooALjGStJ9991nDR061PrKV75i/exnP7N+9rOfWV/5ylesYcOGWXfffbd11VVXWb1797YefPDBhHm98sor1tlnn21VVlZakqynn346bP1ll11mSQp7nX766UmVt6Ojw5JkdXR0JLVdJBP+or+AYtOrV/S20KtXrksG5EYmYoRb8StUscZwoFAtXmxZgUB4nxMImOXJbBPtFQhY1qRJ8dfX1ibOx+mrTx/38nLjNWCAZZWU5L4cmXoNHWpeTs6Dhobkz7NiRwwnhgOhMhGv/X7TPzc0mN+d9P1O0+Xrq7w892XI15d9PiU6H+KtI7YbxPDCieHR+tF87wcPO4w2CgCpchq/kv6m+N///nf94Ac/0DXXXBO2/IEHHtCf//xnLV68WJ/85Cf1s5/9TF//+tfj5rV7924de+yxuuKKK1RfXx81zRlnnKFHHnnk0N+9e/dOtshpi/LPfD3WW1Z2ygLk2mGHhf93ZKiPPzbrDx7MbpmAXPJSjCjGGA4UqiVLpJkze/Yv69eb5YsWSZFNM9Y20bS1mVe89W7at8/d/NK1a1euS5BZ27Y5S9fWZr7JHyneeYbMIIYD3pSpeN3VFb1/jqerK7n0+aajI9clyF/r10vnnZc4XbxzgNieOcTw7IvVj+Z7PxjvfmpbG20UANzgs6zkbtUPGDBAb7/9tsaPHx+2fNWqVTruuOO0a9cuNTc365Of/KR2797tvCA+n55++mnNmDHj0LLLL79cO3bs0NKlS5MpYhink6vHEjlkeiwMpY5isG5dcEjPeOyhSYFCFzlkeiypDKWebvyKpthiOFCourrMsJmxHkz7fGY40JaW4BB7ibYBkhXtPINBDCeGAxLxGt5DbCeGS96P4YXej9rTOhVrGwWAWJzGr5JkMx4yZIiee+65Hsufe+45DRkyRJL5r7WBAwcmm3VUy5Yt04gRI3TEEUfo2muv1bYEX2vYv3+/Ojs7w17pcPJAPJl0gJcdfbS76QCvc/JAPJl0mVZsMRwoVI2N8W/yWJaZp66x0fk2QLKinWfIHGI44D3Ea3gNsT0ziOHZVej9KG0UANKT9PDp3/nOd3Tttdfq5Zdf1oknnihJeuONN/T73/9ev/jFLyRJL774oqZPn5524c444wzV19erpqZGzc3N+ta3vqUzzzxTr776qvwx/h3q3nvv1Z133pn2vgH0tGePu+kAZBcxHCgM7e3Jp3O6DZAszq3sIIYD3kO8hldxHrqLGJ5dxXD+FsMxAkCmJD18uiT94x//0M9//nOtXLlSknTEEUfo+uuv19SpU1MvSJQhXyKtXr1atbW1+stf/qLPfe5zUdPs379f+/fvP/R3Z2enqqurUx7yJdFcsaHyZc5YIFMGDnQ2x+aAAdLOnZkvD5BrmYwRmRqyrJhiOFColi2TTj01cbqXX5ZOOSW5bYBkhZ5nMIjhxHBAIl7Du4o5thPDvR/Di6EfLeY2CgCxOI3hSX1T/ODBg5o1a5a+853v6PHHH0+7kMkaN26chg0bplWrVsUM5L1791bv3r1d2+f8+c7nFAcK3bvvOptT/N13M18WIB9861vO5xTPtWKM4UChmjbNzPe4fn30f7ix54OcNs35NkCyop1nyAxiOOBNxGt4DbHdfcTw7Cv0frS6mjYKAOlIak7xww47TIsXL85UWRJqa2vTtm3bVFlZmbV93nCDu+kALxs9WuqV4F9pevUy6YBicPfd7qbLpGKM4UCh8vulBQvM75EjVth/z59v0jnZBkhWrPMMmUEMB7yJeI1sceNcIbZnBjE8+wq5H/X5aKMAkK6kHopL0owZM7R06VJXdr5r1y69/fbbevvttyVJLS0tevvtt7Vu3Trt2rVLDQ0Neu2117RmzRq99NJLqqur0/jx43X66ae7sn+nEv1XWSH+1xkQy8GDsR+M9+pl1gPFxEsxohhjOFCo6uulRYukqqrw5YGAWV5f73ybaKqrpUmT4q+vrU2uzPH06eNeXm4YMEAqSfqTkncMHWpeiVRXSw0N5rwKFe88Q2YQwwFvylS89vtN/9zQ4PzhiNcfopSX57oE+SsQkBYvTnw+xFtHbM8cYnj2xepH870fLC2Nva66mjYKAG5Iek7xH/zgB/rJT36iz33uczr++OPVv3//sPXf/OY3Hee1bNkynRplko/LLrtM999/v2bMmKG33npLO3bs0KhRo/SFL3xB3//+9zVy5EjH+3BzLpgFC8KHUp8/n2+Io3itWycdfbS0Z4/Ur58ZMp1viKOY3X57+FDq3/pWet8Qz8RcZsUcw4FC1dUlNTZK7e1SZaUZSi/RzZ7QbUaMMMs2bpS2bJGGDzc3j+x89u41N1g//FDq31+aMcNMpWKv37VLuvRSadUqc7P6yiuD1wObN5sHrytWmPUbN5oyjhkjrV4trVkjTZggzZtnbgDZZerTR/rOd8zvFRXSRReZ3+2/7YfxLS3m2xKTJ0ujRgX3OWyY2efKleb6pG9fadw4809Ka9ea8m3cGNx+6lTp8MOliROlbduC9djVJS1cKDU3m5tQLS2m3LW10rnnmvI8/LDU0WFuJK9dK23aZOp0zBhpwwaT9tFHTdkuvdTkNXasNGWK1NYWLP+wYdKzz5r87alqWlpM/U+caMovmesvu35XrQoe34QJpkwffRT9Pa2oCNaPfXxSsM6HDJGeecbkGe19TuU8K2bEcGI4ECndeG3HtpYWE1tmzw4+PDlwIBivxo4NxjM7Hth9/9Sp0vLlJj87Pq9ZE75NZaX0qU9Jl18ezO/qq6WdO028+fa3zfajRkkvvGDuiYXGtg0bzLpXXzV5jxsnzZol7dhh8p48WXrggZ5lLSuTHnwweHz33y9de61JZ8fSAQOC1yVNTSb23X239MgjJp0dH0Nj5bp1ZvuLLpI+/3lTvhEjzDXLhg0mfh91lNTaGozvH31kynrCCdKtt5p9jR5tYm5bm7lOOuoo83tkGWpqzDFt3SoNHmxie3OzSffDH0pvvhl+/bV5czAGNzeb+qqpCZbnqqukhx7qeXyR71no+RR6PoSWJ/IciDw/iO0GMbywYnhk3xutH/zwQ7Osu1vq7DT3Obu7TR87cKBpc5YlffxxMN9Bg6TTTzf94P79Jn13t2lDdrqSEnM9vX69+QKR32+uye023r+/afN2H2u3w717e36+qqmhjQJAIk7jV9IPxWtqamJn5vNp9erVyWSXcYUUyAEAxSMT8YsYDgBA5hHDieEAAG8ihhPDAQDe5DR+JZgduKeWlpa0CgYAAHKDGA4AgDcRwwEA8CZiOAAA+aOAZ8oDAAAAAAAAAAAAABS7pL8pLkltbW169tlntW7dOh04cCBs3X333edKwfLRTTdJoYc3Z470k5/krjxALr33npmbqrvbzJOzYoWZVwsoVl6JEcUaw4F8kun5mUPnkrTnHfX7e84jHjr/dugcpVLPuUk3bZJef93MpzduXHAey9A5w1esMPONjh8fnCvPnv/ygw+kv/7VzLE3ZoyZf6+8PHqZQ+fMDASCc3nbc12PGBGcozPa/Nfbt0vTp5v5+0pLpbPPlo480uRpzykumblBFy82c/yVl0vXXGO2rayUnn/e7P/jj831zfjxwe3tOb/XrOlZJntOcMsy5d60SRo50tRHe7vJs7o6mNfu3aY+J0wwadraguVbvTpYp7W1Jl1ra3B+0M2bg3OGDxkS3P+4cWZO1O3bo88pHm0O0QMHes4dHwj0TBfrPLXfv5Urg3PHH354+Jy3kaK1Ayk3c5d7Zc50YjiQH9LtM0K3jzfvd2iMdjo3dOhc4ZFzj4fud9AgM7f36tWmv1+zxsSsUaNMvH73XZMudF7s2lqpri4YK0PnBw8ETF72fu10/fqZecjtucb/+Efp3nuDc4LPm2fiXOS1y1e/aubRtefFvvpqM9evZZk5yvfsMXkvX27mHl+3zsz1fcwxwTm3JVOeCROku+6S7rjD7LeqSnrttWCMPvFEc6xVVdL775vrh9GjzXH/5z9mXeic52PGmPrfsKHnvN8jRkhPPGFiYUWFdPHFZj/x5oGvrjZ119Ji9rtxo7lGqK2VHn5Y+vWvw6/pSkt7zq1u12My55fTcyXdczyTcdUr8VsihudKtM9Fke3w/fdNu+jqMtfmBw+a37duNfc8E+nVK3y+cUny+czP4cNNu16zxswbfthhpn/cvdu0wZdeMssHDzbn72GHmT5AMvOZH3usacdvvGH6vwkTen5eizwuAEACVpL+8pe/WP369bOOOeYYq1evXtZxxx1nDRo0yCovL7dOPfXUZLPLuI6ODkuS1dHRkVY+JvREfwHFhvYAhMtEm3ArfoUq1hgO5JPFiy0rEAjvJwIBs9wNDQ2W5feH519SYlkDBsTvq+yXz2deTtKm+6qtjV3mVF6BgGWVl2en7IX06tPHef1GO0/jvX9+v1nvpB0MHWpeTvbppky0SWI4MRyFK90+I9r2kf2mkz45mXQNDYn3m8tXba071wH5/rLfi3Sue/x+U1/R1tXVpXd+xTtX0j3HMxHPM7UfYnjhxPBobc2NdpjrV7TPa7GuuQGgmDiNXz7LsqxkHqKfeOKJOvPMM3XnnXdq4MCB+ve//60RI0boy1/+ss444wxde+21bj+3T4vTydXjsf+7K57kahHwLtoDEC5TbcKN+BWpGGM4kE+WLJFmzuzZJ9j9yKJFUn196vnfcov5ppCXlJdLHR25LgWciHaeOj3nGhqkuXPN77HagdN9uilTbZIYTgxHYUq3z0im/wPySbrnuNvxPJP7IYYXRgz34uciN4RecwNAsXEav5J+KD5w4EC9/fbbqq2t1eDBg/X3v/9dRx99tP7973+rrq5Oa9asSbfsrko3kEcOhxtLvg6TC7jpvfeko49OnO7ddxlKHcUhkzEiEx9Eiy2GA/mkq8sM5dnWFn29zxccKjyVYR8PHDDDiHZ1pVVMIK7Q87Sry/k55/eboW79/vjtINE+3RwSNZNtkhhODEfhSbfPSLQ9kO/SPcfdiueZ3g8x3PsxvJg/F9nX3AylDqAYOY1fJclm3L9//0Nzn1RWVqq5ufnQuq1bt6ZQ1PzmdFoXpn9BMZg40d10gNd5LUYUWwwH8kljY/wb4ZZl5otubEwt/4ULi/PGD7Ir9DxN5pzr6jLpE7WDRPt0U6bbpNuI4UBupdtnpNL/Afkk3XPcrbjqtfgtEcOzrZg/F9nX3ACA2Bw/FL/rrru0e/dunXTSSfr73/8uSfriF7+om266SXfffbeuuOIKnXTSSRkrKIDc6+52Nx2A7CCGA7nX3u5uukgh99aAjGtvT/6ca25O/fy29+mmTLdJtxDDgfyQbp+R674EcEu653i6bcEr8VsihudKsX8uKvbjB4BEHD8Uv/POO7V7927dd999mjx58qFln/vc5/Tkk09q7NixevjhhzNWUAC5V+Kwx3CaDkB2EMOB3KusdDddpNra1LYDUlFZmfw5V1ub+vlt79NNmW6TbiGGA/kh3T4j130J4JZ0z/F024JX4rdEDM+VYv9cVOzHDwCJOJ5TvKSkRBs3btSIESMyXSZXMac44B7mFAfCeWVO8WKN4UA+sec/XL/eDOsYiTnF4QVuzSkeqx0k2mcm5hTPRJskhhPDUXjS7TMSbQ/ku3TPcbfnFM/Ufojh3o/hxfy5iDnFARSzjMwp7vP50i6Y1zh9iMEDcRQDpw+6eSCOYuGlGFGMMRzIJ36/tGCB+T2yOdp/z5+f+k3C0lLzDzheU16e6xLAqcjzNJlzbs4ckz5eO3CyTzdluk26iRgO5F66fUay/V8m0JUUBzfeZ7fPcTfjqpfit0QMzwWvfi5yg33NDQCILamH4ocffriGDBkS91WIEv0XL//li2JCewDCeaVNFGsMB/JJfb20aJFUVRW+PBAwy+vr08t/7lypoaHnTcCSEmnAAGd5+HzZu2leWyvt2BG9zKmoruYheyr69HGWLtp5Guucs/n9Zv3cucFlsdrB0KHmlWifbsp0m3QLMRzID+n2GbG2D+U0HiaTrqFBWrw4/n5zqbY2fx5gZpL9XqRz3eP3xx4aua4u8fscb7/xzpV0z3G346pX4rdEDM+VWNeobrTDXIv2eS3aNTcAILqkhk+fP3++yhPcabrssstcKZhb3BzyJXKYXIZMRzF77z1p4kSpu9vcbF+xgm+Io7i5HSPcHrat2GM4kE+6uqTGRqm93cx3OG2auzdlDhyQFi6UmpvNjdPZs03+9j7tERw3b5aGDTMxvKUlmFYKbj92rIn3mzZJr79u/tFn3DizbNs28xBzxQpp1Srzs29fafx4qaZGam2VRo82+X3wgfTXv0offyyNGSO98EL4A+zQMtvbrFsXHP5y9Wqpf39pxgxT/mefNWntZWPGBOtx+3Zp+nQzrGZpqXT22dKRR5o8164N5v/uu+bG78GDpizXXGO2rayUnn/e7P/jj831zfjxwe3HjDG/r1nTs0yrV5v1lmXKvWmTNHKkqY/2dpNndXUwr927TX1OmGDStLUFy7d6dbBOa2tNutZWk27iRPP+bdkiDR8uDRkS3P+4cdK555pjsddXVATf89D33z7/DhwwN9I+/DB4TIFAz3SxzlP7/Vu5Utq40aQ//HBzPsX6tkq0diBltm3E4nabJIYTw1HY0u0zQreP7JOnTpWWLzfrQmO03fdv3dozXWgedlxesyYY1+1+OHS/gwZJDzxg4kYgYNJv2iSNGmXi9bvvmnSDBwdjbm2tefBqx8rJk00ezc0mj9Wrg/u10/XrJ33729KGDSbvP/5RuvdeqanJxL5580yci7x2+epXpSuvNH/X1EhXXy11dpr4OmuWGR64Xz9TB9dea+JrVZV0zDHm97FjzTGvWWP2c9dd0h13mP1WVUmvvRaM0SeeaI61qkp6/31z/TB6tDnu//zHrCsrkx580LwXY8aY+t+wwZT1qqukhx4yZR0xQnriCRMLKyqkiy82+4l8L0KPt7ra1F1Li9nvxo3mGqG2Vnr4YenXvw6/pistlfbuNXE7sh6TOb+cnivpnuOZjOeZ2A8xvLBieLTPRZHt8P33Tbvo6jLX5gcPmt+3bjX3PBPp1ct8ZghlP7QePty06zVrpJ07pcMOM/3j7t2mDb70klk+eLA5fw87zPQBkjRwoHTssaYdv/GG6f8mTOj5eS3yuACgWDmNX8wpDgBAHmIuM2I4AMCbiOHEcACANxHDieEAAG9yfU5x5kABAMCbiOEAAHgTMRwAAG8ihgMAkH96OU3o8AvlBe2008ywJrbPfU76y19yVx4gl/74R+nMM4N//+EP0hln5K48QK6deqq0bFnw71NOkV5+OVelCUcMB4x8HAoyUZ5Oh5gOXRY6HKY93OrGjdGHP7eHRf3ww+AwovZQ3q2twaHCW1rMMICTJ5ttHnzQ5G8PIdraavZdUWGGAX3+eTMUeCBghi9fu9YMSyiZvKdONcOTVlWZ5atWmTL262e2P/ts8/eoUdKrr5qhAffsMdvW1pp9rV1r8ps61QyTuny5WVZVJX30kSnT9u1S794mnylTTJns4cU3b5aWLjXDF1ZWmmubjg4zRKo9fOlhh0mf/7wpkz0c6ogR5rjb2oLDn1dXmz5/2zYzTOJ995n50h9+2OQ5YoQZqnXfPlOeI480+7fzP+KInkPSf/ihOSbLMsdnWeaYqqvNcbW1meEaP/c5M8S7PayuPSTuhx+a9L16mTq55x5TXvvLShs2BM+Jqirznq1da4anHTjQ7PPss01d2+eBPZzr7NlmyMlLLw0Os3/11Wb4x2jnZLRh+qMN8Zit4Va9hBiOYhSrL8hVHxFvv07LFGtI63jDW5eXm/68pcX0/T6fGRq8tla6/34zbHhkHxwv7xNOkG691Qy5HTpM99ixwbhcWyt9+cvSl74UnPLj1lulXbtMvLjySmn/fhPL3n7b3Adobg5OPdLWZq4lNm0ysaq83MSLnTtNfHrlFZOHPdz5UUeZbUKHNI8cZj00BkUOcf7YY9Ill5j8KitNGe3rB8nEutGjpSeflK67zpS1osJM67J9u7mmOfpoky4QMMuibRMImPpvbTX7ef99k3b4cOnvf5eeeio4FLo97UvkMb3wgnTDDSbdyJHSv/9tYm55uXT66SbduHHBa7DIodlHjTJ13Npq9tvcbOL6wIHmvVi9Ornza+BAU8etreb4zjknOCR8KkMxxxumulgRw7PL7mfXrw9OIVRV1XOqoMMOM1P+dHSY/vqII0wfUlJi2lEm+Xym39mzx7SPzk6zvHdv6bzzpAEDgsOnl5WZa+1TTjHttrU1eO1uD6Ne7G0MAFLhePh0r3JryJd4/9xX2DUI9ER7AMJlok0wZBl1AHctWWJuRLa1BZcFAtKCBVJ9fea3TyXPaOuHDjU/t22LvwzIpUTnpN8vzZkjzZ0bXJaJNpYrxC/qAKmL1RdcfLH0+OPZ7yPi9U2Ss34rWh6x2A//gUxwen5Fi9Px3HKL+YfA0LyTzSNfEL+8WQfx+tk+fcw/pnqVzxf9vpJX2xgAZIrrc4p7lRuB3MloN4Vdi0AQ7QEIl6k24cUPom6jDuCWJUukmTN7tkW7/S5aFP+Gerrbp5LnzTdLP/4xMRWFraHB3MjLRBvLJeIXdYDUxOoLYsl0HxGvb4pVxsgyJXtMQD6x43Q8t9wizZuXXh75hPjlvToo9n7Wa20MADKFh+L/T7qBPHLI9FgYSh3FIHLI9FgYSh3FInLI9FhSGUrdax9EM4E6gBu6usywoLG+neXzmW91tbTEHu40ne1TLVNJCd8UQ+Hz+82Quocf7m4byzXiF3WA5CWKjbFkqo9ItTyhZVq1ygwjnUoeQD7w+4PDPEdz4ICZ+ibeNWuiPPIN8ctbdZBOX10ovNbGACBTnMavkiyWyZOcPBBPJh3gZU4eiCeTDvA6Jw/Ek0kHwH2NjfFvktjzNDc2Zmb7VPPkgTiKQVeX+XaL220MgPckio2xZKqPSLU8oWVauLC4H9TA+7q6zHkcy8KFia9ZE+UBpCOdvrpQ0MYAIDk8FAcAAEDBam9PL12626ebFih0TU3O0tFugMKWbht3u49wI7/m5vTzAHIt3nns9BynLSBTuD40aGMA4BwPxQEAAFCwKivTS5fu9ummBQrdhAnO0tFugMKWbht3u49wI7/a2vTzAHIt3nns9BynLSBTuD40aGMA4BwPxRP43OfcTQd42R/+4G46wOtOOcXddADcN22amdfT54u+3ueTqqtNukxsn2qeXpk7GUiH3y/Nm+d+GwPgPYliYyyZ6iNSLU9omWbPTj0PIB/4/eY8jmX27MTXrInyANJh99XFjDYGAMnhoXgCf/mLu+kALzvjDHfTAV738svupgPgPr9fWrDA/B55U9r+e/782Df00t0+1TznzDG/cyMdhWzOHKlvX/fbGADviRcbY8lkH+EkVsdbN3++VFqa/DEB+WTOHHMex1JaatKkkweQDruvLuY+ljYGAMnhobgDlpXeeqCQ0B6AcLQJIP/V10uLFklVVeHLAwGzvL4+s9unkufcudHXDx1qXomWAbmU6Jz0+6WGBnOeS5lpYwC8J1ZfUF1t+ozIbwNmuo+I1zctXmxeifqtWHnEwj8AIZOcnl+RcTqeuXNN2si8k8kDSIfdz8b6xnjfvtktj9tiPfCnjQFAanyWVdi36zs7O1VeXq6Ojg6VlZWllddpp0kvvRT8+3Of4xviKF5//KN05pnBv//wB74hjuJ26qnSsmXBv085Jb1viLsZv7yKOoDburqkxkapvd3MPzdtWnI3n9PdPpU8o62X4i8bOlRasUJas0aqqZEmTpQ2bpRef938o864cWbZtm3S4MHSs89KH34obdokjRxp5niuqZFaW6UxY0zeLS3mhszkyWabBx80+Y8ZI02datK2t0sVFebhxfPPS21t5ubU2WdLa9dKy5ebvGpqzDbr15uHBMuXS6tWmTL262e2P/ts8/eoUdKrr0rNzdKePWbb2lqzr7VrTX5Tp0pjx5p81q41eX70kSnT9u1S794mnylTTJnGjZPOPVfavFlaulTavdvU4x//KHV0SGVl0ujRZv+HHSZ9/vOmTE88YZaNGGGOu63NbDtunFn/8sumTocPl+67T9qxQ3r4YZPniBHSa69J+/aZ8hx5pNm/nf8RRwTfE/v9+/BDc0yWZY7PsswxVVeb42prk3r1Mp9JjjrKbL91qzRokPTAA2b71laTpqpKuuceU94RI0y9bdgQPCeqqsx7tnat1NkpDRxo9nn22aau7fNg7VpT/7NnSwcOSJdeat6bsWOlq6+Wdu6Mfk4OG2aOqaUluH20b7Rkoo3lAvGLOkB6YvUFueoj4u3XaZlC09n98ObN4b9XVpqYtny5SVdebvrzlhbT9/t80rp1ph+9/37p2mt79sHx8j7hBOnWW6WmpmCcW7vWbG/H5dpa6ctflr70JbOv6mqzza5dJl5ceaW0f7+JZW+/be4DNDeb/O3YP3KkuabYvt0cw4EDpmyDBkmvvGLyWLfOxJ6jjjLbVFWZOLlpk4nZf/yjdO+9pqyhMaiqSnr/fVPW0aOlxx6TLrnE5FdZacpoXz9IJtaNHi09+aR03XWmrBUV0gcfmPINHiwdfbRJFwiYZdG2sYfBb201+3n/fZN2+HDp73+XnnrKpKuuNu/X6tU9j+mFF6QbbjDpRo6U/v1vE3PLy6XTTzfpxo0LXoPV1kpXXSU99JDZZtQoU8etrWa/zc0mrg8caN6L1auTO78GDjR13Npqju+cc8yxx4vT8Rw4IC1caMqVah75gPjl3Tqw+9n166UtW0w7qaoy/fKBA+YB8ocfmuvvlSvNNbrfb67D162TSkpMO8okn8/0O3v2mPbR2WmW9+4tnXeeNGCA6cMk85nk0kvNvaXly01bta/dJ0zwbhsDgExxGr94KA4AQB4iflEHAABvIn5RBwAAbyJ+UQcAAG9yGr8YPh0AAAAAAAAAAAAAULB65boAXhIImCFYbFVVZpgnoBi98ooZwse2bJk0fXquSgPk3uGHm+H9bBMmmKG5ABSOVIZtjTaMX0WFWWcPZRk6pF9Tk+k/5s0z899F237YMOmZZ8zwlHba0tLoZbOHsvzww+Dw5kccERxub9cuMyzfqlVm+M4rrzTXvJFDXYcOlW0Pf24PkfrCC8GhvE87zfSHkhmGcPRo8/v775uhRQ8cMEMBWpbZd1mZmX5l/XpTHyNHmmFS333XDOFZUWGGXN20yZT3nHPMMa9ebcq8b590zDFmmT38eL9+0o9+ZIYYHDRIuvFGk6895PvIkdKbb5p8u7vNMIWVlWY4wy1bzD4vusik/etfpY8/Ntf9Q4aY46ysNMOubt1qhlk8/nhp/HhT5vXrzfDnH3xghpeVTB0GAtI//iEdPGjqado0c3yrVpm6GDDAHMPmzWb/9hCtmzaZ3/v3N8u3bg3W0bp1weHWa2pMuezhaN9/32z/8cdmqPbaWum994LDzb7wginbWWcF36dbbzX52UPKjx1r0qxdGxxm1x5Gf8oUc0xPPGHKWFEhff/70t69wfN0yBAzNP/q1cGhb9esMccyY4Yprz3Mf7RhVkOHYR071ry/W7b0HA7Ti8OrA3Cf0xgdrW/Zti3+sOjxhqB2Orx7R0ewzw0EpP/6LzN8bmTeodvs3Ru8NrCn/tixo+dQ6GPGmL55zZrwdJFTV4QOiz5ihIlBu3ebfnnYMDP1SOQw39XVpiytrabfXbPGlHXUKNPHX3qpyc+OXfaxDx1qYuaYMSZutbaaOHf33dIjj5i8Q4dFj5ym4+yzpU99ytRR375SXZ2Jgf37mzhlWWZ///iHmU+3udmUb/t2U4bhw83x7dljhgl/8UVT/zt2BK89Nm0yx714sTkvSkvNsMY7d5rtX3nF5N3UZI73tdfMsQ8YYI7Z9swz0uWXm+369DHXFvv2md8PP9ycX5HDsVdWSm+9ZYZOLiuTjjvOxNnRo01d2vV1113SHXeYMtTUmFdbW3B6GTuGhg6nX1srPfqoKef27eY+zYYNZpsXXzSxu7nZHOP//b+mXocNk+bPN+dcZaWZLueBB4LXffYULaHna+h0PclMjZJK+wGciHeuDRpk+qyVK6V33jF9cklJ8Bzbt89co9vX79nk95t+7uSTTZ/7/vvm7yOOMJ/z/P6eUxP4/bQXAEgVw6c75PPFXlfYNQj0RHsAwmWiTTBkGXWA/LJkiblBHPoPkYGAtGCBVF/vfJto+vQxN2IiTZpkbnQ4+SfMyDwCAfOw9vnnzQ2iSH6/uQG6aVPivIFs8PulOXPMA4hbbjHzskc7d0MlaoO5QvyiDpBdTmN0or7F3kaKH7/tB3ih2118sfT449HLcMst5mGGE/Y2v/61edgKpKpXL/PPcdkSGsdt0dpmsu0n2zGe+OXNOnByrhWKkhLzT8C7dgWX5es1MQBkE3OK/z9uBPJ4DztshV2LQBDtAQiXqTbhxQ+ibqMOkC+WLJFmzuzZlu32v2hRzxsQsbYBEN+kSdIbbzhP7/NFb4O5RPyiDpA9TmP0LbeYb9zF4/O5G7dTyc/tMgDZ1tBgHoyney0c7zo7k4hf3qsDPnflrr0AQD5hTnGXBALupgO87JVX3E0HeJ09RLBb6QDkn64u862DaDdZ7GU33hj+LYR42wCIL5kH4rbINgigODiN0Xv3mm+IJ+J23E4lP64d4HX33WfaXLrXwrGus4FQfO4yaC8A4BwPxRMInUPcjXSAl4XOIe5GOsDrQucQdyMdgPzT2Bh/+HLLMvM+NjY63waAe6K1QQDFwWmMbmjgIQGQLV1dps25cS1MjEcifO4Kor0AgDM8FAcAAABiaG9PPp3TbQC4h3YHFB+n7Z5/UAWyy+02R4xHLJwbPVEnABAfD8UBAACAGCork0/ndBsA7qHdAcXHabufMCGz5QAQzu02R4xHLJwbPVEnABAfD8UTqKpyNx3gZcuWuZsO8DqnH/a5EQd417RpUiAg+XzR1/t8UnW1Sed0GwDuidYGARQHpzF63jzJ789u2YBi5febNufGtTAxHonwuSuI9gIAzvBQPAGn85IwfwmKwfTp7qYDvO7DD91NByD/+P3SggXm98ibLfbf8+eH32wP3QZAciZNSn6byDYIoDg4jdF9+0pz5iTOLzQPNx6wpJIHD3bgdXPmmDYXq206Fes6GwgVLw4UE9oLADjHQ3EHLCu99UAhoT0A4WgTQOGrr5cWLeo5MlAgYJbX18feJhBInH+fPtGXT5rkbPtoeVRXS3V1sW+K+P3SyJHO8gaywe+XGhqkf/7T/HRyQ6+6OnYbBFAcnMbouXMT9y2BgLR4sXnFGw0wMo/qapN3ZMy286utdX489jZ1dc63AaLp1Su7+7Pj+Ny55u9YbTOZ9kOMhxNOz7VCUVIiDRgQvoz2AgDO+SyrsG/Xd3Z2qry8XB0dHSorK0srr0BAWr8++HdVFd8QR/F65RXplFOCfy9bxjfEUdwOP1xqagr+PWFCet8QdzN+eRV1gHzT1SU1Nkrt7WautmnTEt9ssbdZv17askUaPlyqqDDrNm8O5nPggLkh2NRk+o9588y3bKJtP2yY9MwzUnNzMG1pafSyHTggLVxo+qP2drPvI46QZs822+zaJV16qbRqlVReLl15pbnmXbFCamkxN/Jnzzb5XHqp2WdNjTR1qrkOHjlSeuEF83uvXtJpp5n+UJLWrZNGjza/v/++9NRTJp+yMvMPQ7t2md/POMMc3+bNJr8xY6R335U2bDDl3blT2rTJlPecc8wxr15tyrxvn3TMMWbZxInStm1Sv37Sj34ktbZKgwZJN95o8n3wQWnNGvP7m2+afLu7zU2lykrzDYstW8w+L7rIpP3rX6WPPzbX/UOGmOOsrJQ++EDautXclDr+eGn8eFPm9euloUPN+v37zbHX1po6/cc/pIMHTT1Nm2aOb9UqUxcDBphj2LzZ7N/nM+XftMn83r+/Wb51a7CO1q0zxzt0qHlPKivNsqoqU9+trabsRx5pyvDee6Z8o0eb90ySzjor+D7deqvJb+lSafduaexYk2btWrN+40ZzTvh80pQp5pieeMKUsaJC+v73pb17g+fpkCHSs8+a92rMGJPXmjXmWGbMMOVdscIss8+z0tJg27HP3eZmU5aJE03edv5VVc7aYC4Qv6gDZJ/TGB2tb9m2rec2ofmNGGGW2XF76lRp+fKe+4pXho6OYJ8bCEj/9V/Snj098w7dZu/e4LXBuHHSuedKO3aYdCecYPrtpibTx/p8pj8NTTdsWHg8//KXpS99yZRhxAgTg3bvNv3ysGHSRx9Jo0aZGHHDDaaOqqtNWVpbTb+7Zo0p66hRpo+/9FKTnx277GMfOtTEzDFjTNxqbTVx7u67pUceMXnbsWHt2vDfa2uls8+WPvUpU0d9+5p/Eti61ZR16VITO30+E1vnzjX5VVVJ27ebMgwfbo5vzx5p4EDpxRdN/e/YEbz22LTJHPfixea8KC2VBg821wfDh5t7HnPnmjoeNUp67TVz7AMGmGO2PfOMdPnlZrs+fcy1xb595vfDDzfn1+jR0pNPStddZ8paWSm99ZbU2WnKc9xxJs6OHm3q0q6vu+6S7rjDlKGmxrza2kx5Xn01GEPvv1+69lqTd22t9Oijppzbt5v7NBs2mG1efNHE7uZmc4z/9/+aeh02zHzDdO9eU7bJk6UHHghe902caNKFnq9Dh8aP47HaZirtJ5uIX96tg3jn2qBBps9auVJ65x3TJ5eUBM+xffvMNbp9/Z5Nfr/p504+2fS5779v/j7iiOD0H3bcstua358f7QUA8onT+MVDcQAA8hDxizoAAHgT8Ys6AAB4E/GLOgAAeJPT+MXw6QAAAAAAAAAAAACAgsVDcQAAAAAAAAAAAABAweqV6wJ4ic/Xc1lhDz4PxDZrlpmb03b11WbOKaBYESOA/BFrHtBo83ZKsecMdTIfabQ5FKPtf+PG6HOKJypTtDnHa2vN3JrbtwfnC21uNv3Q5Mlm7k57jkd7Tu9Vq8yc1337mjlUN24MzhVdWWnmG920yZTn8MPN/JGtraasL7xg0trzXo4ebeb63LgxOM/k5s3B4+vXT5ozx/w9bJh06qmmLG++aebZHDLEpNm61eR/0klmX/Zc0716SddcY+YB7dPHzIG5ZYuZq7OkxGx/xhlmm/feM+krK80+du40+ZSUmPJ2dZkyBAJm/s/OTrN+zx4zZ6DPZ+YDr6019blhg5lz8N13TVltffqYOdd37QqWf906M/embfRos37wYFNfH39slvt80mGHmffQqZKS8P3H4/ebffbpY+aDHTDA1G1XV3D9wIHm2Lu7zd+jR5u0Pp+Zx3TsWDNX6oYNpm769TN1OnasmYd85Egzd+mePaa+L7rIvPe/+IWZk9GeD3XDBjM/7bhx5mWfZ+PGmWvH7duD58mAAWbu97Y2Mw/sWWeZczBRm7LbRFdX/HYIIL/Fmz84lXm/nc6pGm+e7ljbx5untn9/05e1tpq+9YUXTLwI3U/kXNqhxxQ5L/PXvibdfrvZLhAwcW79etNvfvCBmfe2Xz/p9ddNOju/qVNNutD+MLLcn/ykOV57DvCOjuA80WVlJt3o0dJjj0mXXGLSjRpl4l5bm8nj7bfNdoMGSX/9q3ThhabvHzzYpDlwQOrd21ynfPSROb5nnzX10a+ftGyZNHOmiQWDBpm4bcedCRPM9cTo0eZew2c+Y9b362fi6K5d5jpmxw7zvvh8Zj7wCy+UDh4Mzsu7b5/5efCgKU+/fmYe7XPOif75bMQIE7sGDDBxyPaLX5jrkXh8PnMf5Oqr46eTpIcekq66Kvq64cNNPQwebOrZnl/85z+Xpkwx1yx+f/BapH9/Uw/d3aZuXn3VzAO/bl34udK7t0lz8GDP86aqyrxHGzaEz3MfOad46PzlNTUmnnd0mLI++6xJM2GC9MMfmmu9ZNpjrGt25khGLNH6fcksW7/e9C1Dh5r+tbxcevppc47362f6WvvzRP/+Jq+dO3N7PLH07Wv63muuMe2uqoo2AQBuYU5xh6I97LAVdg0CPdEegHCZaBPM40UdIDVLlpibgm1tidMOHWp+btsWfX0gIC1YINXXB5fdcot0333BB46SuTkxZ440d25y+3dapj59zI1NoFgkalMDBpgb96H/OBC6Ta4Rv6gDxBetXdsx97XXesbZUHY6KXYeoXE71IwZ0jPPxC5XtO2jldXvj10+yTx82bMn9vpM8/vNP6/961+pX48A6UrUHhNdMyfaPlOIX/lbB9HOmUSf5wpJrtoEAHiF0/jFQ3EH4j3ssBV2LQJBtAcgXKbaRL5+EM0m6gDJWrLEfPvIrThkt+9Fi8zNh1tukebNi52+rs58Y4Y4CLgjlTbV0JD7B+PEL+oAscWK1T6fs7YeL11k3A6V6IF4tO3dvq4Aikm89uikbcXbPpOIX/lZB/THhs+X/TYBAF7BQ/H/J91A7uRhh62waxLoOWR6LAyljmKRyRiRjx9Es406QDK6uszwoW5/I8rnM/+Vv3KlGYI63jfDAOSe32++oZnLodSJX9QBostUrA5lx+2WluAws/bQ3clsv2qVGYqcb1oDqYvWHpPpB6Jtn2nEr/yrg2zEDq/IRZsAAK9wGr9KslgmAB7n5IF4MukAAHBLY2NmbpRYlpkntKGBB+KAF9hzjQPIP5mK1aHsuN3YGFzW0JD89gsX8gAGSFe09phMPxBtexSfbMQOr6BNAED6eCgOAAAAz2tvz2z+TU2ZzR+Ae5qbc10CANFkOlbH2lcqMZx+BHBPaHtMpR/IZt+B/MP73xN1AgCp46E4AAAAPK+yMrP5T5iQ2fwBuKe2NtclABBNpmN1rH2lEsPpRwD3hLbHVPqBbPYdyD+8/z1RJwCQOh6KA3Ds6qvdTQcAgFumTTPzq/l87ubr80nV1dK8eczbBuRCsm3a75dmz85MWQCkJ1OxOpQdt6dNCy6bNy/57WfPznxZgUIXrT0m0w9E2x7FJxuxwytoEwCQPh6KJ2BZ7qYDvOyBB9xNB3gdMQLIH36/tGCB+d2tGyZ2PvPnS337SnPmxE9fV2e24YYN4I66OvMzmTY1Z45UWpqZ8gBIT7xY7bSdh6aLlcf8+eH/yNa3b7A/cZL3/PmmH3H7ugIoJrHao9Nr9ljbo/hk4nOel9EmACA9PBR3INHDDB52oJjQHoBwtAkgf9TXS4sWSVVVztIPHWpesQQCJr/6evP33LlSQ0PPmxB+v1m+dGly+3dapr59U88P8KJEbWrgQKmkJPo2c+dmrZgAUhArVgcC0uLF0eNstHSLF0fPIzRuh1q6NPGD8cjtY5U10cOIfv3ir880v98cayCQ23KguMVrj06u2eNtj+IT65xJ9HmukFRX0yYAwA0+yyrs2/WdnZ0qLy9XR0eHysrK0sor2n+jFXbtAbHNmiU9+GDw76uv5hviKG5uxwg345dXUQdIVVeX1NgotbdLI0aYZZs3h/9eWRkcdi5aWnt9tBvfBw5ICxdKzc1mztHZs8O/mRpt/xs3Slu2SMOHSxUVzstkLztwwDwoaGoy+6yrk7Zvl4YNk1asMGXx+aTJk6WRI82yNWuk0aNNfqtWSe++ax6wjx1ryrN2rVlfWSm1tEibNpnyHH64VFMjtbaasr7wgknb2SmVlZltzj7b5FFTI02caMpvH1+/fubbulu2mPKdeqopy5tvSt3d0pAhJs3WrSb/k04y++rfX5oxQ+rVS7rmGmnPHqlPH2nUKJPX/8/encdHVd3/H39PhiwkZGFPQsIWcEPcFUEptMWtLlCK4lKLW9Wqrf5QqH5dcEMtWIVWsVqttH5VWjEu1RarfgVx36BFcWEJsoVFhEAAAyT398enk8kkM5klM5nt9Xw85gG5c+6dc8/cc983czPn7NhhN0K7dJFOPtnWWbrUypeU2Gvs2GHbyciw+tbXWx3KyqTVq21ZXp5tu67O2mzAAGvTb7+V1q+XioqsrRoavO9pTo5UWCjV1nrrv3q1tHu3t0zv3vZ8587WXvv22XKXS8rMtPcwVBkZvq/fGrfbXjMnR9q6VerUydq2vt77fH6+7XtDg/3cu7eVdbmkww6zY+K992z/d+2y/SspseXr1tkx9e679lyHDtLZZ9t7/4c/SDU1dlwcdpitv3On1L+/PTzHWf/+du347bfe46RTJ+k3v5HWrrUPOE891Y7BYH3K0yfq61vvh/FEftEGCM5fv/ZkbtOc7dvXcmbLlpblWttGILt3e/O0f3/pjDOkbdtaX7/56wwbJr3zjv2cl2fnsjVr7Nz68suWF01fZ+BAaepU6fHHW+5T167ezK6okC68ULrxRluvrMxybt06O29+8YX03Xd2jn7/fSvn2d6wYVau6fmweb0POcT2d/Vqy9+aGsuLHj3sPF5dbfvw1FPSuedaudJSy721a20bixfbekVF0v/9nzR+vJ37O3e2Mnv2SNnZdp2ydavt34svWnvk5krz50vjxlkWFBVZbntyZ+BAu57o3ds+azj+eHs+N9dytLbWrmO2bbP3xeWyP44YP17au9feu44drY06drRle/bY+nPmSKef7v/3sx49LLs6dbIc8vjDH+x6pDUul30OEsoUco8+Kl1yif/nune3dujc2dp5wwZrhwcekIYOtWsWt9t7LZKXZ+3Q0GBt8+670tVX23vW9FjJzrYye/e2PG569bL3aP16O9Z+/WurQ0mJXU8+/LCVKy217a9aZdl/2WV2DHTubO/tihX23t1zj13rhdMfA12zh7p+LJBfid0G/s77ki1bt87OLV272vm1sFB67jk7xnNz7Vzr+X0iL8+2tWNHfPcnkI4d7dx7+eXW73r1il+fAIBkEWp+cVMcAIAERH7RBgCA5ER+0QYAgOREftEGAIDkFGp+MXw6AAAAAAAAAAAAACBldYh3BZIJw6cDXiNHSgsWeH8eMcKGQgPSFRkBxF4kw6QG428odMm7rKzMhhavqrJhLDdssOEjCwuliy+2ISVfeME7dOT06VbP88+3ZX362JCmq1fbkH0dO9rr9O9vQ636e82SEumll2wY0vJyG85y61abD3XnTlv22Wfe4S5PP913+wMGeIeD9QxFuXq19NhjNtxlv37eYVZLS20I2BUrbN9yc23Zjh02fGXPnjb09po19rzj2FCc2dk2xGZ+vvTQQzY091132fY9r7l5s60/dKi139Kl9n716SMNGmR1KiuzYbvXrrVtSFYmO9uG/czJsceOHTb0p2TPZWT4Dl2emWl1q69vee717NO6dTb8aFaWDau4eXN4Q5pnZnqHRI/n+d0zl3bz4dX79rU27NzZ3qtdu7zDww8YYGXWrLHja/VqK+NySYceasfUvHn2/tXX23tYVmbb27zZhtk9+2wbCv31160dSkvtec8wp1On2s/vv2/tU1FhwwN7huaVAg+JGqxvx6LvA2gfgfpvqP26aTnPlCFVVS2HGu/Tx8p//bXvNCMlJdLhh0sXXNByqPHmQ7M3Lde7t537PFkdaHtNy/XubedVf3VoOnS5J7vXr7fz78qVtk8DB0qTJ9vv1p6pJu691zsE+E032TqlpTZs9fnne4fL/vxzy7jsbCtbW2sZ3bmz7V9pqeX1iBGWHxkZNlx4XZ2Vr6/35vrHH3uvbZpeB3XpYvPZ7ttnmfjcc7aPnuk6Dj3U9rdLF+nTT21508zKzJSeeMLyJJjZs62NPTzDobtc3usBya49bryx9Vx2u+0aqOn2gglnnQcekK66qvUyLpddx40f752+pWmOe6ZN6dDB3oNdu2x4+XfflX77WzvGe/Wy99kznL7nPc/Nlf7zHzvuqqttKPxHHvFeu3qOz4EDpRtusClo1q+34/Kuu+zasvlQ6E2H+w80RUmw6YSAWPJ3/NXX27QVX3xhUyHk50sffmjnrHCmJmpPns+RMjLsPH3CCXb+37LFlnftaufh/fajj8UTv4sAKcaJowULFjinnXaaU1JS4khynnvuOZ/nGxoanJtvvtkpLi52cnJynB/+8IfOV199FdZr1NTUOJKcmpqaNtXVLrH9P4B0Q38AfMWiT0Qrv2IlmTIcqeHZZx2nrMy3f5WV2fJITZrkOG637zZdLnu01q+j/YjHa/LgEc9H074brG/Hou/HWqLnFxmO9hKo/06aFFq/9rc+Dx482v/hdlu/9fB3Dd28TLJK9Pwiw/0ff+nwSJU+lmyS8XcRIF2Fml9xHT59586dOvTQQ/Xggw/6fX7atGn63e9+pz/84Q96//33lZeXp5NOOknfffddu9bT37f/wnkeSCX0B8BXuvaJZMlwpIbKSmncOPs2cVPr1tnyysrwtzl5svdb3U15ftVtT/F4TSCePH138uTW+3aw5yPp+yDD0T4CZffatZa/wfp1oPUBtL/6euu3kycHvoZuWgaxk+4ZHuj4Swf0sfYXi88hAMSfy3ES4yM4l8ul5557TmPGjJEkOY6j0tJSXXvttbruuuskSTU1NerZs6dmz56ts0MZb0mhT64euF6hl02MlgRip/mQ6YEwlDrSRSwzoq351Z4SNcORGurrbXjTQB+Ku1zeoT1DHcLMMwxpOn6YAiQKl8uGimytH3qGWA60frh9v70kU36R4YiFYNkdiKdfL19uw+FyQxxILIGmcGnK7bbh35N1mOdkyq90y3B+hzPJ3seSRSw+hwAQW6HmV1y/Kd6aqqoqbdiwQaNGjWpcVlhYqCFDhujdd98NuF5dXZ22b9/u8wAQHaHcEA+nHIDURIYjmhYubP1DccexeRIXLgx9m7Nm8WEKEG+OE7wftvZ8JH0fwZHhiIZg2R2Ip1/PmsUNcSARNTQEn5e5vt76MNpfqmc4v8MZ+lj7iMXnEAASQ8LeFN+wYYMkqWfPnj7Le/bs2ficP3fffbcKCwsbH+Xl5TGtJwAA8EWGI5qqq6NbTpJWrIisLgASTzh9H8GR4YiGtvZLchpIbvTh+Ej1DOe48qItYi8Wn0MASAwJe1M8UjfccINqamoaH2vWrIl3lQAAQAjIcPhTUhLdcpINyQogNYTT9xE7ZDiaamu/JKeB5EYfTi7JkuEcV160RezF4nMIAIkhYW+KFxcXS5I2btzos3zjxo2Nz/mTnZ2tgoICnweA6BgxIrrlAKQmMhzRNHy4zdXlcvl/3uWSysutXKiuuIJ5v4B4c7mC90O3O7p9H8GR4YiGYNkdiKdfX3FFZOsDiK2MDO+84oG43daH0f5SPcP5Hc7Qx9pHLD6HAJAYEvameL9+/VRcXKzXX3+9cdn27dv1/vvva+jQoe1WD8eJbjkgmc2fH91yQLIjI/xLlAxHanC7pZkz7f/NfyH1/DxjRngfkGRlSRMnRqV6ACLg6bsTJ9r//fVtl8vbT6PV9xEcGY5oaC27A2nar7Oywl8fQOxde609WjNxovVhtL9Uz3B+hzP0sfYRi88hACSGuN4Ur62t1eLFi7V48WJJUlVVlRYvXqzVq1fL5XLpmmuu0Z133qkXX3xRS5Ys0c9+9jOVlpZqzJgx7VrPYDcz0u1mB9Ib/QHwla59IlkyHKlh7Fhp7lypVy/f5WVltnzs2PC3OW2aNGlSy19i/d2gi7V4vCYQT56+O21a63072POR9H2Q4WgfgbK7vNzyt6zMd3nzfh1ofQDtz+22fjttWuBr6KZlEDvpnuGBjr90QB9rf7H4HAJA/LkcJ34f18+fP1/f//73WyyfMGGCZs+eLcdxNGXKFD3yyCPatm2bjj/+eM2aNUv77bdfyK+xfft2FRYWqqamps3Dv/j7sDJVb3YAwYwcKS1Y4P15xAi+IY70Fu2MiGZ+xUKyZThSQ329tHChVF1tc3cNH972D0T27JFmzZJWrLC52TxD0XmWlZVJVVX26N1b2rBBWrVKKiyULr5YKi2VXnjByg4cKE2fbvU8/3xb1qePNGyYtHq1tGSJ1LGjvU7//tKaNf5fs6REeuklae1au3nw619LW7dKzz8v7dxpyz77TFq/3up3+um+2x8wQDrjDGnbNqlHD9v26tXSY49JNTVSv35Wp3XrrP7vvGOvu2GDlJtry3bskDZtknr2lPLzra4bNth5LTvbHrt22XMPPSRt3y7ddZdt3/Oamzfb+kOHWvstXWrvV58+0qBBVqeyMum992xft2+39dxu2/5330k5OfbYsUPau9eez862oTt37/a+j5mZVrf6+pbnXs8+rVsn1dXZNyu6drX67dkT+rGSmSnt22f/j+fvAJ5hSxsafJf37Wtt2LmzvVe7dlk2DRhgD8nex5ISa/sNG+z5Qw+1Y2rePHv/6uvtPSwrs+1t3iwVF0tnny19/bX0+uvWDqWl9vymTbbNqVPt5/fft/apqJAGD5a++cZ7THjKNu+7wfp2LPp+LCV6fpHhaE+B+m+o/bppuW7dLOuqquwcc+GF0o03SsuWWbZIdp6qqJBGj5a+/da2ffjh0gUXWNb17evNwL597Ty1ZUvLcr1727nPk9WBtte0XO/edl71V4dDDrFs9mTf6adbjpeXSytX2j4NHChNnmy/W2/eLHXvLt17r2VVbq500022Tmmp9OKLdq2xerWV+/xzy7jsbCtbW2sZ3bmz7V9pqeX1iBGWHxkZUqdOtk5urrWzJ9c//th7bdP0OqhLF7sJsG+fZeJzz9k+1tdb2x96qO1vly7Sp5/a8qaZlZkpPfGE5Ukws2dbG3t07GjXAS6X93pAsmuPG29sPZfdbrsGarq9YMJZ54EHpKuuar2My2XXcePHW5u7XL45npFhP3foYO/Brl1SUZH07rvSb39rx3ivXvY+r1vn+57n5kr/+Y8dd9XVUkGB9Mgj3mtXz/E5cKB0ww3SySfbcVRSYu23c6cdJy++6L2enTpVevxx3+vj5t9M9XcNnQrfXk30/CLDjb/jr77ebhp/8YW0caP1pQ8/tHOWp48lGs/nSBkZdp4+4QQ7/2/ZYsu7drXz8H77pU4fS0bJ9rsIkK5Cza+43hRvD8kQ5AAANEd+0QYAgOREftEGAIDkRH7RBgCA5BRqfiXsnOIAAAAAAAAAAAAAALQVN8UBAAAAAAAAAAAAACmrQ7wrkEyYUxzwoj8AvugTQPQEm390zRrvvMUDB7acXy3Y+uvWeefqLC62dTZtsrkbPfMp5uVJY8ZYmYcftnkZ+/Sx8qtW2XzWgwbZ3OArV9oyz3ymK1Z45+MeMMDm8F61yuZwLimxcp75Ofv3tzlGv/3WW6eCAumee2w/e/WSDjzQ5olcudLq1b+/zdO9fr09/8470vLltv2OHW3+yIMO8s5J/tln3teXbL5Kz1zPPXvac1u3Wvu43fYanTrZ/NJFRTZfZU2NzeHpmetyxw7vnKLDhtm8fStW2LkwL88eW7faPHgjR9ocsIsW+X+/u3Sx+c+bzvOXnW316dDB5hZvLiur5XzgsZwr0OWK7TndM6+rZ85Dt7v1+c5797b3pK7O2q+83OYDXbfOjrEvvrD3t6HB5sw94ACbx7VTJzt2m8632KOHHWPTp9vreuZnbD4/7xVX2POevtV0nnBP31m50vrk9Ol2LDbHfHxA8oq0/9bW2tzXnnlfPeei5gLNT9z0dfPzLR9Xr7bz4Ny50p13eucUd7ks0/r2tXP211/bOemee6SPPrJtdO1qmbRqle982c3nW+7ZUzrnHDvH9eghPf20nTNLS+11Tz7ZcrtbN5vb/JtvbHuea4K+fW2/Vq1qOVd4166W0+vW2fl7yxZ7zd69bR7rESMsFwsKpMMOs/zu3Fl66y3vfOBPPWX182STZ87tnBxry717rZxnvm9JmjPH3ou9e+29Kyy0+aTz8y13t22z65DZs6Uf/CB4ps6aJf3yl76v4ZGVZZnWoYNvns2aZe9tMM3nFA/k+uvt/Q3m1lvtEY4//EG6/PLg5f73f6Vrr7X2y8uzazrJjseCAruOKSqS5s2z42bbNt953PPypKOPtuOrb1/vNV5FhR1bN95ox7jnmnHbNnvPHnnEjq9w+tUll0iPPtp6PyOfEUuxONY817ZLlkiffOI99xx0kM0pnmify2T89yuKublW19xcy4UJE+wc0KsXfRAAYok5xUPk72aHR2q3INAS/QHwFYs+wTxetEG6qqyUrr7abuh6lJXZB79PP+273MPtliZOlKZNi2x9IF0UFtrN9EhkZNiHdrW1oZUfPVp6/nnvz4H65syZ0tixkdUpUZFftEGqibT/HnOM3ZBo7uijpQ8+8P48ebJ0332+N1fdbum006SPPya7gVCE0q+aC9TPUjWfQ0F+xa4NYnEtOGaM9MILUaleQknnPggAkQo1v7gpHoLWbnZ4pHYrAl70B8BXrPoEv4zTBumoslIaNy7yHBk92r6tSg4BicFzYzxQ3/Zk6Ny5qfWhH/lFG6SSSPtvoBviHp4beJMn2+gSANouWv0qVfM5FORXbNogFteCqXpD3MPlSs8+CACR4qb4f7U1yEO52eGR2i0J0B+A5mLZJ/hlnDZIN/X1Nlwc3wYDUsuOHTY8e6C+7XJ5hzBOlWEiyS/aIFUEy+ZA/be21oZ3DmbLFhuavLVvsgIIT7T6VSrmcyjIr+i3QaRZ0prdu20Eo1RXXp5+fRAAIhVqfmW0Y50AAAAAvxYu5IY4kIrOP7/1vu040po1dg4AkFiCZXOg/nv++aFtf8QIbogD0RatfkU+I1oizZLWTJrU9nolA/ogAEQfN8UBAAAQd9XV8a4BgFhYsSK0cpwDgMQTar9sXi7Ufr9+fXj1ARBctPsV+Yy2ijRLWrNsWWR1SUb0QQCILm6KAwAAIO5KSuJdAwCxUFERWjnOAUDiCbVfNi8Xar8vLQ2vPgCCi3a/Ip/RVpFmSWsGDoysLsmIPggA0cVNcQAAAMTd8OE2l5zLFe+aAIimJ55ovW+7XDZf4vDh7VsvAMEFy+ZA/feJJ0Lb/oIFzJMKRFu0+hX5jGiJNEtaM316dOqW6OiDABB93BQPwnGiWw5IZvQHwBd9Aoget1uaOdP+H+mN8dGjbV1urAOJYfRoqVOnwH3b8/OMGdwYAxJRa9ncWv/t1Ek6+ujWt3300VKXLtLEiVGpKgBFr1+Rz4imSLOkNR072nVmKnO56IMAEAvcFA9BsJsZ3OxAOqE/AL7oE0D0jB0rzZ0r9erlu7y8XJo0yb5h4I/bbc8//3xk6wPporAw8nUzMuxGV6hGj7Y+KQXu22Vltnzs2MjrBSC2Iu2/H3wQ+Mb40Ufb85I0bZpldPMP/d1uO4+Q3UBoQulXzQXqZ+Qzoi0W14LPP5+6N8bLy+mDABArLsdJ7Y/rt2/frsLCQtXU1KigoKBN2/L3raPUbj0gMPoD4CvafSKa+ZWsaIP0VV8vLVwoVVfbHGrDh9uHdp7la9ZI779vfWzgQOmKK6SsrNDXX7dO2rxZ6t5dKi62dTZtkoqKpBdflFaskPLypDFjrMzDD0tVVVKfPlZ+1Spp925p0CCpf39p5Upb1qePbWvFCttez57SgAFSv372/IYNVp8+fWx7VVW2/hlnSN9+661TQYF0zz22n716SQceKH39tb1OXp6tM3SotH69Pf/OO9Ly5bb9jh2l3r2lgw6S1q61D1Q++8z7+pLUoYM0eLD0zTdWx1WrpK1brX3cbnuNTp2kmhprk7o6+//evbZufr60Y4e0b5+UmSkNGyZt3Gj77XLZ+nl5ts2uXaWRI6UlS6RFi/y/3126SNu2SQ0N3mXZ2VafDh2k775ruU5WlrRnj++yjAzfbUSTyxXb65xOnayd9+2z/XC7W+5fU71723tSV2ftV14ude5sx3ZJifTFF/b+NjRIfftKBxxgwxl36mTH7qRJVmbjRqlHDzvGpk+31501y95Lz/H89dc2P/AVV9jznr7Vo4c937TvrFxpfXL6dDsWmwvUN1MN+UUbpKJI+29trXT++XZeqajwnoua27PHe/7xnHOysnxfNz/f8nH1ajsPzp0r3XmntGyZnbNcLsu0vn3tnP3113ZOuuce6aOPbBtdu1omrVplN2M8eTxwoHTDDdLJJ1u+9uwpnXOOneN69JCeftrOmaWl9ronn2y53a2bdOGFds4tK/NeE/Tta/u1apVte/JkacQIW6drV8vpdevs/L1li71m797SY49ZuW3b7HrgsMMsvzt3lt56y87rGRnSU09Z/TzZ1LGj5XROjrXl3r1Wrr7e28Zz5th7sXevvXeFhdLOndau2dn2mt27S7NnSz/4QfBMnTVL+uUvfV/DIyvLMq1DB988mzXL3ttgZs+WLrggeLnrr7f3N5hbb7VHOP7wB+nyy4OX+9//la691tovL8+u6SQ7HgsK7DqmqEiaN8+Om23bpNxca7ddu2ydo4+246tvX+81XkWFHVs33mjHuOeacds2e88eecSOr3D61SWXSI8+2no/S+V8DgX5Fds2iMWx5rm2XbJE+uQT77nnoIOkDz9MvM8qM/77FcXcXKtrbq7lwoQJdg7o1Su9+yAARCrU/OKmOAAACYj8og0AAMmJ/KINAADJifyiDQAAySnU/GL4dAAAAAAAAAAAAABAyuoQ7wokk/79bVgtj379bGgsIB0xfDrgiz4B+BdsiLxwhtDzDAH5+ec2JHjHjjY8uWcoSX/re4bTW7bMhok84wwbutQz/Hr//jaU+JYt/oeAzsry1q+gwIaqXL7cynftasO1fvutd9jTl1+21z31VBvetbjYzg/r1tnweAceaEOlvvKKDX+dn2/D5K1fb9vJyfEOse4ZVlWyYS7XrbPhyj3DVm/ebENnb9pkw9M2NNj6XbtK27fbvufmWr03b7ahtt1uG16ze3d7vU6drJ5NNR8qvHNn256/4VED6dDB6pOZaa8bTZmZNvSrx4AB1obvvmv77O81MzO9Q5Ln5Nhw4mvXWtt5eIbY9/zrkZdn7+PatbZ+VpYNa7h5sw0j79n+tdfa+9C5s/TAA9Zmkh13FRXe46S01N4jzxC7GRn2flx0kW3TM/zuihXe4c33289+91izxjuseVWVvVdDhlj9liyxZU2HQ21NoKGKQ8VQq0Bqanpu6NvXm5FNp0woKbGpM955p+V0Cs3PB03PFa1NtRDK6wYbPt1x7LmmQ6kPHCjddJM0bpw917On/evJ4C5dbOjz0lLL8Kuv9tbBM4x1QYH0u9/ZUNe5ubbfv/iFbad7d9uf2lrbl4ICuyYpKvJmcX6+DW992mnedu7WzZ4rKrIs3rzZ6jBzpvTDH3rL9ehhQ5x36mSZ4HHvvdJ114X+vmZmSr//fWjDgT/6qA2zHUq5a66xdsnKstfYvdtydvfultcNGRnSX/8qjR/f+tDsLpf05JPSeed5r0fcbvu/y+W73Weekc491ztcvON4y3mmDMnPt/f2xBOtrp7rgM2bLbP/8x/v9gYPtvev+fHVdOjy7Gzp5pttWPvSUhsW/e67rVy/fvZYu9b+9UxX07zPNB3Gv3m5QP0nkqwlqxFPsTz+PNtetky64w47P7rd1vf37vX9XaG95eVZXWprvUOle871J5wgHXecTTPVvTtDpQNIX/G8RmH49BD5u9nhkdotCLREfwB8xaJPMGQZbZAKKivtw+W1a73LysrsA9+xY4M/39TkydJ99wW/Mdt0/TFjpBdeaNs+5OT4n9MaSGRutzRxojRtmv/n/fWnYOs0FU7fTUfkF22QrELNWqnlHxA15TkfSC3PFc2NHm1/+BPq6wLJqrU+01Rr/SecrCWrI0N+RacNYnn8+dt2MqNfAkg3scoI5hT/r2gEeWs3OzxSuxUBL/oD4CtWfYJfxmmDZFdZad/Kan78e/rMddfZt5wCPT93rvdiePJk+zZZKDzrH3WU9OGHkdUdSBWTJrW8yR2sP/lbp6lgfbtp301X5BdtkIzCydpgmo84AiB0rfWfULOWrI4c+dX2Nojl8Rdo28nO5aJfAkgPscwIbor/V1uDvPmQ6YEwlDrSQSg3/zxS+8wCmFj2CX4Zpw2SWX29DTva2l/vt/ZtGZfL/kq0qsrK5Oby7TEgEm63d1hbyYYnDtafmq/TVLC+3bTvpvMwkOQXbZBsQjk3AEgMwbKWrG4b8qttbRDL4y+U3zGTWXk5/RJAaov1NUqo+ZUR/qbTSyg3xMMpBwAAkOoWLgz+YUVrH7w7js2dvHChzS/Kh/RAZOrrrQ95hNKfmq/TVLC+3bTvAkgeZC2QPIJlLVmNeIrl8RfK75jJjH4JINUlyjUKN8UBAAAQVdXV0dvOihXR2RaQrpr2oVD7U6ByofbtaJ0DALQPshZIPoGylqxGPMXy+EuHYzYd9hFA+kqUaxRuigMAACCqSkqit52KiuhsC0hXTftQqP0pULlQ+3a0zgEA2gdZCySfQFlLViOeYnn8pcMxmw77CCB9Jco1CnOKB8Gc4oAXc4oDvphTPLZog+TlmSdo3brAx77bLTU0+H+eOcWB6IjVnOKB+jbzlBryizZINswpDiSPUOcUJ6sjQ35FZ07xWBx/zCkOAMkt1tcozCkeJaHe6OaGONJBqDf1uCGOdEGfAPxzu6WZM+3/zf94xOWyx8SJgZ+XpBkzbDtZWd6yofBs/+ijI6o6kFImTvS9uR1Kf2q+TlPB+rbk7bsAkke4WRtM0/NDOH9ECqD1/hNK1pLViKdYHn+ebadirrhc9EsAqS9RrlG4KR6CYDczuNmBdEJ/AHzRJwD/xo6V5s6VevXyXV5WZsunTWv9+bFjvcumTZMmTQrtwtiz/gcfSKNHt30/cnLavg2gvbnd1memTWv5XKD+1No6TQXr2037LoDkEU7WSq2XKyuTnn3WHs3PFc2NHh3e6wLJKtRjvLX+E2rWktWIp1gef55tl5W1rY6JpLycfgkgfSTCNQrDp4eh+VDqDJmOdObvLzNT+2wCtC7afYJh22iDVFFfLy1cKFVX27xAw4f7figY7Pmm9uyRZs2SPv9c+uwzqWNHacAA6YwzpG3b/K+/e7d92L5smc2ZesYZ0ubN0vvvWx/t318aPFjaskUqKpJefNGu7wYOlKZPt2/PeepXUCA98oi0fLmV79pV6tNH+vZbaf16qXdv6eWX7XVPPVVavVoqLrbzw7p10r590oEH2gcfr7wi1dRI+fk2fNT69badnBzbjz59bJ3yctveihX2c2am1KOHLdu8WerSRdq0SaqtteHoc3KsXtu3277n5lq9N2+W6uqsbTp1krp3t9fr1Mnq2ZTL5Xv+6tzZthfOsLodOlh9MjPtdaMpM1Pau9f784AB1obvvmv77O81MzNt3/fssTY64AAbenHTJm8Zt9v20fOvR16evY9r19r6WVn2C9zmzdKOHd7tX3utvQ+dO0sPPGBtJtlxV1HhPU5KS+09WrlSWrVKysiw9+Oii2ybZWX23IoV0saN9n7vt5/97rFmjR0bkv1e4nJJQ4ZY/ZYssWUVFdIVVwT+treHpz+tWBH6Ok2F03fTDflFGySzpueGvn29GenJnk2brM8PGya9846dA5o/1/R80PRc4S9nO3YM/XXz86V77rHc6t3bPji7807L+D59LLtWr7b/u1x2jh04ULrpJmncOHuuZ0/715PBXbpI33xj5+aXX5auvtpbh6FD7bxdUCD97nc2tURuru33L35h2+ne3fanttb2paDArkmKirxZnJ8vPfGEdNpp3nbu1s2eKyqyLN682eowc6b0wx96y/XoIe3caWU2bvQuv/de6brrQn9fMzOl3/9euvzy4GUffVS65JLQyl1zjXfKjcxM26ecHPu3+XVDRob0179K48fbNUIgLpf05JPSeed5r0fcbvu/y+W73Weekc49164LMjKsjKdcp07e9n/5ZenEE62unuuAzZsts//zH+/2Bg+296/58dW/v/d6MztbuvlmacMGe8/mzZPuvtvK9etnj7Vr7d/Bg+34at5nuna13F61qmW5QP0nkqwlq8NHfkWvDWJ5/Hm2vWyZdMcddn50u63v793r+7tCe8vLs7rU1tp5SfKe6084QTruOGnrVsuPXr3olwDSUywyItT84qY4AAAJiPyiDQAAyYn8og0AAMmJ/KINAADJiTnFAQAAAAAAAAAAAABpr0O8K5BMpk+XJk/2/uyZcwtIRwyfDvg67TTvsMmSDZ380kvxqw8QTFuGKmrrMEf+hjTfutU7PPmKFTZUaL9+Nmx0//42bLRnONWNG73Dn1dUeIec7NzZhmVdulT64gsbwru8XDroIBvGsqzMnlu92oY0zc+3YS89PxcU2NB2mzdbzh16qLT//t5hMD3DYtfUeIcq79zZhnfdu9dez+2WvvvOu6/Z2TYkrCcjMzOlwkJrg7o671DfnmFEe/a0ITt37LChYrt2tdfevdued7utnt99Z+vl5Fgd163zDgXuclm9du6052tqfNu/Y0db7hkGtq7O/i9Z3bp2tWG4PXXOzrYyGRnW3mVl0ptv2nHgcnnbf/NmawPPUODffGP/nzPHjpUdO2x41S5dbHs7dth70Lu3DUHqeQ9KS224wU2bbP2CAlt/7157LwcMsNetqrLhCceMsXIPP2zL+va1IUpXr7b1iott+FxPPT1D8W7YYHXu3t0enmFMe/e251eutDIlJd7jbMuW4Me8p3+sW+fdfnGxPdd0aGEpuYc0ZUhWoP2E2t+i0S+bDmPes6f09NN2LiwutiFq6+ps20OG2Hl3xQrvtA+rVtn5cvRomzKipEQ65BDL+dWr7WfH8W7P5bK69u4tPfWUDYO9erXlwKBBln8FBdJzz3mnt/AMO1tRYZ/RHH+8DWndsaP965GdbbmRmyu98IJ08sne4XQzMix3MzJsqPWaGnvNhx6SRozwZnJRkXc48KZZWllpdfVMS9Kxo5XLy7OM2rTJsmb5csvi/Hy7vjnzTO828vK81wGejPds+5ZbbNj2oiLfKfs805tkZNiQ3ePHW5bm5tpz333nHbZ3+3b7f22t7Xd2tq1z2mneIcTr6+25zEzL/poay6x582y4+fXrbT/69LH/Fxfb9deuXbZPCxZYO6xfb8fKscda9vXv753yo6zMsrmqyrJ46lTp8cftuGk6bHhrw/M3PZZra6Xzz/dO/fHEEzYksWT190xf06uX9/qv+VD9gUSj/7RnXwVSnb/pN1assHPbiSdaVn3zjZ03WpuSIRays6WjjrL/DxrknXK1+blu2TLvdEfl5fR1AEgEDJ8eIn83AD1SuwWBlugPgK9Y9AmGLKMNYqmy0ubMXLvWu6yszOaxHDs2dutKdgPzhRciqjaQMAId8/76hz9du9q/W7YE32Yiaut5INWRX7RBNIXa36LRLydPlu67r+Vc0EB7c7t9j8OyMrsJtWJFy7JHH21/hOHvuaZGj5aef97/c9HoP+3ZVxE75FditEGo19TJhr4OALHDnOL/FY0gb+1mh0dqtyLgRX8AfMWqTyTCL6LxRhvERmWlffun+XHpOZbnzg38S3pb1pW4IY7U4e+YD9Q/2rLNRNTW80A6IL9og2gJtb9Fo19OnmzfpgVSmb8b49HoP+3ZVxFb5Ff826Ct19SJzuWirwNALHBT/L/aGuTNh0wPhKHUkQ5CufnnkdpnFsA0HzI9kEiGUo/3L6KJgDaIvvp6G1o60F/cu1zeYS6bD+vWlnUlGxo0N7cttQcSS9NjXmq9f0SyzUQcWrGt54F0QX7RBtEQan9bvtyGkW5Lv9yzxzKab4gjHeza5R1KPRq51p59FbFHfsW3DYL1p1RAXweA2Ag1vzLasU5JKZQb4uGUAwCkjlBuiIdTDoi1hQtb/4DBcWz+x4ULo7uuxB8PIvU0PeaD9Y9ItpmI2noeABC6UPvbrFlt75ezZnFDHOmj6TVpNHKtPfsqkOqidU2dyOjrABBfHeJdAQAAALSP6urIy7VlXUlatiy09YFkE2rfiPc2o6Gt5wEAoQu1HwWbRzmU7YW6DSAVNL0mjUautWdfBVJdOh3/6bSvAJBI+KY4AABAmigpibxcW9aVpIEDQ1sfSDYlJaH3j3C2mYjaeh4AELpQ+1FFRdu3F+o2gFTQ9Jo0GrnWnn0VSHXpdPyn074CQCLhpngQ06ZFtxwAIHWcemp0ywGxNny4zV/mcvl/3uWSysutXDTXlaTp0yOrM5Comh7znv4RzW0moraeBwCELtT+dsUVbe+XV1zBvKZIH02vSaORa+3ZV4FUF6w/pQL6OgDEFzfFgwh1/kvmyUQ6cJzolgOS3UsvRbccEGtutzRzpv2/+QcNnp9nzPD/wXhb1pWkjh2l0aMjqTWQeJof857+0ZYP8ELpR/HW1vMAgNCF2t+ystreL7OypIkT21pjIPGNHm3XpB7RyLX27KtAqmutP6US+joAxA83xUMQ7AYfNwCRTugPgC/6BJLN2LHS3LlSr16+y8vKbPnYsbFZV5Kef54b40gN/o55T/8I5RvjXbvaI9g2E1FbzwMAQhdqf4tGv5w2zf7Ynw/pkQiaH4fl5YGHHz/66NCGJh892q5Fm4tG/2nPvgqkukD9JBWUl9PXASDeXI6T2h/Xb9++XYWFhaqpqVFBQUGbtjV9ujR5svdnzy+NQDry9xebqX02AVp32mnSyy97fz711LZ9Qzya+ZWsaIPYqq+XFi6UqqttPrPhw0P/ILwt60rS7t12DbVsmX2IecYZ0tatUkGB9Mgj0ooV0s6dUr9+0n77Sf37S2vWSH37SoMHSxs3Su+/b7lTUWHLvvlG6txZevFFaelS6YsvpA4d7IOHgw6S1q61DxyXLpVWr5a2b5fy86XSUu/PBQVSUZG0ebPl3KGHSvvvb/VYu9a2/8ADUk2N1NAg5eTYsi1bpL177fXcbum777z7mp0t7dnjzcjMTKm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",
      "text/plain": [
       "<Figure size 2000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "X['Sex']= le.fit_transform(X['Sex'])\n",
    "\n",
    "\n",
    "ig, axs = plt.subplots(2, 4, figsize=(20, 10))  # 2 rows, 4 columns\n",
    "axs = axs.ravel()  # Flatten the 2D array of axes\n",
    "for i in range(8):\n",
    "    axs[i].scatter(X.iloc[:, i], Y, color='blue')\n",
    "    axs[i].set_title(f'Feature {i+1} vs Target')\n",
    "    axs[i].set_xlabel(f'Feature {i+1}')\n",
    "    axs[i].set_ylabel('Target')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex              -0.034627\n",
      "Length            0.556720\n",
      "Diameter          0.574660\n",
      "Height            0.557467\n",
      "Whole_weight      0.540390\n",
      "Shucked_weight    0.420884\n",
      "Viscera_weight    0.503819\n",
      "Shell_weight      0.627574\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Y = Y.flatten()\n",
    "\n",
    "y = pd.Series(Y)\n",
    "\n",
    "# Calculate correlation coefficients\n",
    "correlations = X.corrwith(pd.Series(y, name='Target'))\n",
    "\n",
    "# Display the correlation coefficients\n",
    "print(correlations)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first split , remove outliers and then standardize and then normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df):\n",
    "    Q1 = df.quantile(0.25)\n",
    "    Q3 = df.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    df_out = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
    "    return df_out,df_out.index\n",
    "\n",
    "X_train,t1 = remove_outliers(X_train)\n",
    "X_test,t2 = remove_outliers(X_test)\n",
    "\n",
    "y_train = y_train[t1]\n",
    "y_test = y_test[t2]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train_standardized = scaler.fit_transform(X_train)\n",
    "X_test_standardized = scaler.transform(X_test)\n",
    "\n",
    "# Normalize features\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train_standardized)\n",
    "X_test  = scaler.transform(X_test_standardized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.7579765165552095\n"
     ]
    }
   ],
   "source": [
    "model1 = linear_model.LinearRegression()\n",
    "reg = model1.fit(X_train,y_train)\n",
    "\n",
    "y_pred = reg.predict(X_test)\n",
    "\n",
    "\n",
    "print(metrics.mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SGD Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sgdregressor__alpha': 1.0, 'sgdregressor__epsilon': 0.1, 'sgdregressor__l1_ratio': 0.4, 'sgdregressor__loss': 'squared_error', 'sgdregressor__penalty': None}\n",
      "4.810725703554004\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Define the model\n",
    "model2 = linear_model.SGDRegressor(max_iter=1000)\n",
    "\n",
    "# Create a pipeline\n",
    "pipeline = make_pipeline(scaler, model2)\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'sgdregressor__loss': ['squared_error', 'huber', 'epsilon_insensitive'],\n",
    "    'sgdregressor__penalty': ['l2', 'l1', 'elasticnet',None],\n",
    "    'sgdregressor__alpha': [0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0],\n",
    "    'sgdregressor__l1_ratio': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
    "    'sgdregressor__epsilon': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "}\n",
    "\n",
    "# Perform GridSearchCV for selecting parameters\n",
    "m2_search_param = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=16)\n",
    "\n",
    "# Fit the model\n",
    "reg2 = m2_search_param.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters\n",
    "print(reg2.best_params_)\n",
    "\n",
    "# Predict and calculate MSE\n",
    "y_pred2 = reg2.predict(X_test) \n",
    "print(metrics.mean_squared_error(y_test, y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MLP Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'solver': 'adam'}\n",
      "4.385469735124744\n"
     ]
    }
   ],
   "source": [
    "model3 = MLPRegressor(max_iter=1000)  \n",
    "param_grid2 = {\n",
    "    'hidden_layer_sizes': [(100,), (200,), (100, 100), (200, 200)],\n",
    "    'activation': ['relu', 'tanh', 'logistic'],\n",
    "    'solver': ['adam', 'sgd'],\n",
    "    'alpha': [0.0001, 0.001, 0.01],\n",
    "    'learning_rate': ['constant','adaptive']\n",
    "}\n",
    "\n",
    "# Print the best parameters\n",
    "# Use GridSearchCV to get reg3\n",
    "reg3 = GridSearchCV(model3, param_grid=param_grid2, cv=5, scoring='neg_mean_squared_error', n_jobs=16)\n",
    "reg3.fit(X_train, y_train)  \n",
    "\n",
    "\n",
    "print(reg3.best_params_)  # Access the best_params_ attribute\n",
    "\n",
    "y_pred3 = reg3.predict(X_test)\n",
    "print(metrics.mean_squared_error(y_test, y_pred3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gradient Boost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.01, 'max_depth': 5, 'max_features': 'sqrt', 'min_samples_split': 10, 'n_estimators': 600}\n",
      "4.7514735606207035\n"
     ]
    }
   ],
   "source": [
    "# Define the model\n",
    "model_gb = GradientBoostingRegressor()\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid_gb = {\n",
    "    'n_estimators': [300,500,600,800,1000],\n",
    "    'learning_rate': [0.01,0.03,0.05,0.07,0.095,0.1 ],\n",
    "    'max_depth': [4, 5 ,8],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'max_features': ['sqrt', 'log2', None],\n",
    "}\n",
    "\n",
    "# Grid search\n",
    "reg_gb = GridSearchCV(model_gb, param_grid=param_grid_gb, cv=5, scoring='neg_mean_squared_error', n_jobs=16)\n",
    "reg_gb.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters\n",
    "print(reg_gb.best_params_)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_pred_gb = reg_gb.predict(X_test)\n",
    "print(metrics.mean_squared_error(y_test, y_pred_gb))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ADABOOST REGRESSOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.03, 'loss': 'exponential', 'n_estimators': 100}\n",
      "5.64333108734749\n"
     ]
    }
   ],
   "source": [
    "# Define the model\n",
    "model_ab = AdaBoostRegressor()\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid_ab = {\n",
    "    'n_estimators': [100, 200,400,600],\n",
    "    'learning_rate': [0.01, 0.03,0.08,0.06,0.1,0.3,0.5],\n",
    "    'loss' : ['linear', 'square', 'exponential'],\n",
    "}\n",
    "\n",
    "# Grid search\n",
    "reg_ab = GridSearchCV(model_ab, param_grid=param_grid_ab, cv=5, scoring='neg_mean_squared_error', n_jobs=16)\n",
    "reg_ab.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters\n",
    "print(reg_ab.best_params_)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_pred_ab = reg_ab.predict(X_test)\n",
    "print(metrics.mean_squared_error(y_test, y_pred_ab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomForest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 30, 'max_features': 'log2', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "4.692778016522817\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics\n",
    "\n",
    "# Define the model\n",
    "model_rf = RandomForestRegressor()\n",
    "\n",
    "# Define the corrected parameter grid\n",
    "param_grid_rf = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_features': ['sqrt', 'log2', None],  # Add None as a valid option\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# Grid search\n",
    "reg_rf = GridSearchCV(model_rf, param_grid=param_grid_rf, cv=5, scoring='neg_mean_squared_error', n_jobs=16)\n",
    "reg_rf.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters\n",
    "print(reg_rf.best_params_)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_pred_rf = reg_rf.predict(X_test)\n",
    "print(metrics.mean_squared_error(y_test, y_pred_rf))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:3.06905\n",
      "[1]\tvalidation_0-rmse:3.01747\n",
      "[2]\tvalidation_0-rmse:2.96827\n",
      "[3]\tvalidation_0-rmse:2.91631\n",
      "[4]\tvalidation_0-rmse:2.86837\n",
      "[5]\tvalidation_0-rmse:2.82577\n",
      "[6]\tvalidation_0-rmse:2.78411\n",
      "[7]\tvalidation_0-rmse:2.74054\n",
      "[8]\tvalidation_0-rmse:2.70625\n",
      "[9]\tvalidation_0-rmse:2.67798\n",
      "[10]\tvalidation_0-rmse:2.64581\n",
      "[11]\tvalidation_0-rmse:2.61687\n",
      "[12]\tvalidation_0-rmse:2.58449\n",
      "[13]\tvalidation_0-rmse:2.55627\n",
      "[14]\tvalidation_0-rmse:2.52965\n",
      "[15]\tvalidation_0-rmse:2.50223\n",
      "[16]\tvalidation_0-rmse:2.47831\n",
      "[17]\tvalidation_0-rmse:2.45681\n",
      "[18]\tvalidation_0-rmse:2.43933\n",
      "[19]\tvalidation_0-rmse:2.42116\n",
      "[20]\tvalidation_0-rmse:2.40561\n",
      "[21]\tvalidation_0-rmse:2.38720\n",
      "[22]\tvalidation_0-rmse:2.37099\n",
      "[23]\tvalidation_0-rmse:2.35311\n",
      "[24]\tvalidation_0-rmse:2.34126\n",
      "[25]\tvalidation_0-rmse:2.32603\n",
      "[26]\tvalidation_0-rmse:2.31278\n",
      "[27]\tvalidation_0-rmse:2.29926\n",
      "[28]\tvalidation_0-rmse:2.29169\n",
      "[29]\tvalidation_0-rmse:2.27880\n",
      "[30]\tvalidation_0-rmse:2.27146\n",
      "[31]\tvalidation_0-rmse:2.26175\n",
      "[32]\tvalidation_0-rmse:2.25412\n",
      "[33]\tvalidation_0-rmse:2.24566\n",
      "[34]\tvalidation_0-rmse:2.23683\n",
      "[35]\tvalidation_0-rmse:2.23168\n",
      "[36]\tvalidation_0-rmse:2.22518\n",
      "[37]\tvalidation_0-rmse:2.22057\n",
      "[38]\tvalidation_0-rmse:2.21422\n",
      "[39]\tvalidation_0-rmse:2.21023\n",
      "[40]\tvalidation_0-rmse:2.20584\n",
      "[41]\tvalidation_0-rmse:2.19814\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Dell\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[42]\tvalidation_0-rmse:2.19222\n",
      "[43]\tvalidation_0-rmse:2.18746\n",
      "[44]\tvalidation_0-rmse:2.18381\n",
      "[45]\tvalidation_0-rmse:2.18129\n",
      "[46]\tvalidation_0-rmse:2.17897\n",
      "[47]\tvalidation_0-rmse:2.17646\n",
      "[48]\tvalidation_0-rmse:2.17176\n",
      "[49]\tvalidation_0-rmse:2.16835\n",
      "[50]\tvalidation_0-rmse:2.16597\n",
      "[51]\tvalidation_0-rmse:2.16422\n",
      "[52]\tvalidation_0-rmse:2.16222\n",
      "[53]\tvalidation_0-rmse:2.15871\n",
      "[54]\tvalidation_0-rmse:2.15911\n",
      "[55]\tvalidation_0-rmse:2.15783\n",
      "[56]\tvalidation_0-rmse:2.15379\n",
      "[57]\tvalidation_0-rmse:2.15259\n",
      "[58]\tvalidation_0-rmse:2.15120\n",
      "[59]\tvalidation_0-rmse:2.14886\n",
      "[60]\tvalidation_0-rmse:2.14811\n",
      "[61]\tvalidation_0-rmse:2.14777\n",
      "[62]\tvalidation_0-rmse:2.14442\n",
      "[63]\tvalidation_0-rmse:2.14284\n",
      "[64]\tvalidation_0-rmse:2.14422\n",
      "[65]\tvalidation_0-rmse:2.14361\n",
      "[66]\tvalidation_0-rmse:2.14358\n",
      "[67]\tvalidation_0-rmse:2.14182\n",
      "[68]\tvalidation_0-rmse:2.14331\n",
      "[69]\tvalidation_0-rmse:2.14214\n",
      "[70]\tvalidation_0-rmse:2.14103\n",
      "[71]\tvalidation_0-rmse:2.14238\n",
      "[72]\tvalidation_0-rmse:2.14329\n",
      "[73]\tvalidation_0-rmse:2.14266\n",
      "[74]\tvalidation_0-rmse:2.14487\n",
      "[75]\tvalidation_0-rmse:2.14232\n",
      "[76]\tvalidation_0-rmse:2.14080\n",
      "[77]\tvalidation_0-rmse:2.14061\n",
      "[78]\tvalidation_0-rmse:2.13817\n",
      "[79]\tvalidation_0-rmse:2.13786\n",
      "[80]\tvalidation_0-rmse:2.13889\n",
      "[81]\tvalidation_0-rmse:2.13838\n",
      "[82]\tvalidation_0-rmse:2.13888\n",
      "[83]\tvalidation_0-rmse:2.13745\n",
      "[84]\tvalidation_0-rmse:2.13565\n",
      "[85]\tvalidation_0-rmse:2.13607\n",
      "[86]\tvalidation_0-rmse:2.13503\n",
      "[87]\tvalidation_0-rmse:2.13544\n",
      "[88]\tvalidation_0-rmse:2.13652\n",
      "[89]\tvalidation_0-rmse:2.13610\n",
      "[90]\tvalidation_0-rmse:2.13717\n",
      "[91]\tvalidation_0-rmse:2.13668\n",
      "[92]\tvalidation_0-rmse:2.13634\n",
      "[93]\tvalidation_0-rmse:2.13604\n",
      "[94]\tvalidation_0-rmse:2.13472\n",
      "[95]\tvalidation_0-rmse:2.13492\n",
      "[96]\tvalidation_0-rmse:2.13489\n",
      "[97]\tvalidation_0-rmse:2.13617\n",
      "[98]\tvalidation_0-rmse:2.13769\n",
      "[99]\tvalidation_0-rmse:2.13757\n",
      "{'colsample_bytree': 0.9, 'gamma': 0.2, 'learning_rate': 0.04, 'max_depth': 6, 'n_estimators': 100, 'reg_lambda': 0.5, 'subsample': 0.5}\n",
      "4.557031682029127\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the model\n",
    "model_xgb = XGBRegressor(n_jobs=16)\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid_xgb = {\n",
    "    'n_estimators': [100, 300, 500, 600],\n",
    "    'learning_rate': [0.01, 0.04, 0.07, 0.1, 0.2 , 0.5],\n",
    "    'max_depth': [3, 4, 5, 6, 7],\n",
    "    'subsample': [0.5, 0.8, 0.9, 1.0],\n",
    "    'colsample_bytree': [0.8, 0.9, 1.0],\n",
    "    'gamma': [0, 0.1, 0.2, 0.3, 0.4],\n",
    "    'reg_lambda': [0.5, 1.0, 1.5, 2.0]\n",
    "}\n",
    "\n",
    "# Grid search\n",
    "reg_xgb = GridSearchCV(model_xgb, param_grid=param_grid_xgb, cv=5, scoring='neg_mean_squared_error', n_jobs=16)\n",
    "reg_xgb.fit(X_train, y_train, early_stopping_rounds=10, eval_set=[(X_test, y_test)])\n",
    "\n",
    "# Print the best parameters\n",
    "print(reg_xgb.best_params_)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_pred_xgb = reg_xgb.predict(X_test)\n",
    "print(metrics.mean_squared_error(y_test, y_pred_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus after runnning all models , MLP Regressor is the winnner . "
   ]
  }
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