Activity-3-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afc6ca90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Data\n",
      "     Unnamed: 0  album_release_year  danceability    energy       key  \\\n",
      "0      0.000000                 1.0      0.745098  0.715505  0.636364   \n",
      "1      0.007752                 1.0      0.771242  0.701280  0.636364   \n",
      "2      0.015504                 1.0      0.588235  0.911369  0.818182   \n",
      "3      0.023256                 1.0      0.910131  0.656418  1.000000   \n",
      "4      0.031008                 1.0      0.750000  0.948572  0.727273   \n",
      "..          ...                 ...           ...       ...       ...   \n",
      "125    0.968992                 0.0      0.526144  0.658606  0.090909   \n",
      "126    0.976744                 0.0      0.637255  0.775687  0.454545   \n",
      "127    0.984496                 0.0      0.553922  0.600613  0.818182   \n",
      "128    0.992248                 0.0      0.220588  0.122442  0.636364   \n",
      "129    1.000000                 0.0      0.857843  0.435387  0.636364   \n",
      "\n",
      "     loudness  mode  speechiness  acousticness  instrumentalness  liveness  \\\n",
      "0    0.852596   1.0     0.015640      0.110518          0.043142  0.368016   \n",
      "1    0.818922   1.0     0.014890      0.307734          0.000084  0.255029   \n",
      "2    0.935720   0.0     0.006535      0.000659          0.326991  0.241371   \n",
      "3    0.869591   1.0     0.015854      0.050649          0.553097  0.012789   \n",
      "4    0.906294   1.0     0.009534      0.003821          0.004155  0.000000   \n",
      "..        ...   ...          ...           ...               ...       ...   \n",
      "125  0.818843   0.0     0.015426      0.149760          0.000000  0.145766   \n",
      "126  0.960622   1.0     0.050241      0.147748          0.000041  0.060715   \n",
      "127  0.839260   1.0     0.006749      0.166865          0.000000  0.073752   \n",
      "128  0.702675   0.0     0.004285      0.981888          0.001173  0.078719   \n",
      "129  0.867034   1.0     0.031709      0.103475          0.000178  0.118450   \n",
      "\n",
      "      valence     tempo  time_signature  disc_number  duration_ms  explicit  \\\n",
      "0    0.319936  0.732581            0.75          0.0     0.513457       0.0   \n",
      "1    0.564033  0.732254            0.75          0.0     0.568029       0.0   \n",
      "2    0.620799  0.355212            0.75          0.0     0.427626       0.0   \n",
      "3    0.707084  0.388904            0.75          0.0     0.549589       0.0   \n",
      "4    0.869437  0.489930            0.75          0.0     0.419404       0.0   \n",
      "..        ...       ...             ...          ...          ...       ...   \n",
      "125  0.259764  0.798533            0.50          0.0     0.725762       0.0   \n",
      "126  0.403951  0.716157            0.75          0.0     0.716327       0.0   \n",
      "127  0.132607  0.699215            0.75          0.0     0.751258       0.0   \n",
      "128  0.121253  0.367634            0.75          0.0     0.597077       0.0   \n",
      "129  0.051998  0.715697            0.75          0.0     0.926022       0.0   \n",
      "\n",
      "     is_local  track_number  \n",
      "0         0.0      0.000000  \n",
      "1         0.0      0.066667  \n",
      "2         0.0      0.133333  \n",
      "3         0.0      0.200000  \n",
      "4         0.0      0.266667  \n",
      "..        ...           ...  \n",
      "125       0.0      0.600000  \n",
      "126       0.0      0.666667  \n",
      "127       0.0      0.733333  \n",
      "128       0.0      0.800000  \n",
      "129       0.0      0.866667  \n",
      "\n",
      "[130 rows x 19 columns]\n"
     ]
    }
   ],
   "source": [
    "#1928152 Animesh Pathak\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12.0, 9.0)\n",
    "df1 = pd.read_csv('C:\\\\Users\\\\KIIT\\\\Downloads\\\\twentyscience.csv')\n",
    "#Kaggle Dataset https://www.kaggle.com/federicoseijo/twenty-one-pilots-discography/version/1\n",
    "df1.describe()\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df_norm = pd.DataFrame(scaler.fit_transform(df1), columns=df1.columns)\n",
    "print(\"Normal Data\")\n",
    "print(df_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f0cbe06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0            0\n",
       "album_release_year    0\n",
       "danceability          0\n",
       "energy                0\n",
       "key                   0\n",
       "loudness              0\n",
       "mode                  0\n",
       "speechiness           0\n",
       "acousticness          0\n",
       "instrumentalness      0\n",
       "liveness              0\n",
       "valence               0\n",
       "tempo                 0\n",
       "time_signature        0\n",
       "disc_number           0\n",
       "duration_ms           0\n",
       "explicit              0\n",
       "is_local              0\n",
       "track_number          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18a195a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVMkRLkiRJhQzRkiRJUiFDtCRJklTIEC1JkiQVmjBER8SsiHjB2m48Ig6KiKsjYnlEfHicMftGxKURcUVE/Gxt30uSJElqlwlDdGauAf5lbTYcEbOBE4CDgfnA4RExf8yYTYETgUMycxfgNWvzXpIkSVI7tdLO8YOI+IuIiMJt7wUsz8xrMvNh4Ezg0DFj3gB8MzOvB8jMWwvfQ5IkSWq7OS2MeS/wRGBVRDwIBJCZufEkr9sauGHE85XA88aMeQawXkT8FNgIOC4zT2+lcEmSJKlTJg3RmbnRWm672cx1Nnn/PYH9gQ2BX0bERZn5f6M2FLEAWAAwb968tSxHkiRJmhqtzEQTEZsBOwIbDC/LzPMnedlKYNsRz7cBbmwy5vbMvA+4LyLOB3YDRoXozFwMLAYYHBwcG8QlSZKktpq0Jzoi/go4H/g+8PeNPz/ewrYvBnaMiO0iYn3g9cDZY8Z8G/iziJgTEX1U7R5XtV6+JEmS1H6tnFj4LuC5wIrMfDHwHOC2yV6UmauAt1OF7quAr2bmFRFxTEQc0xhzFXAecBnwv8DJmXn5Wn0SSZIkqU1aaed4MDMfjAgi4gmZ+duIeGYrG8/Mc4Fzxyw7aczzzwCfabliSZIkqcNaCdErG9dzPgv4YUTcyeN7myVJkqQZo5Wrc/x548ePR8RPgE2oWjAkSZKkGanVq3PsA+yYmV+OiLlU14C+ttbKJEmSpC7VytU5/g74EPCRxqL1gCV1FiVJkiR1s1auzvHnwCHAfQCZeSPV3QUlSZKkGamVEP1wZiaNuw1GxBPrLUmSJEnqbuOG6IjYuPHjVyPii8CmEfFW4EfAl9pRnCRJktSNJjqx8JKI+GhmfjYiXgLcDTwT+Fhm/rA95UmSJEndZ6IQvR/wuYh4C3BsZn6gTTVJkiRJXW3cEJ2ZK4A/j4iDgAsi4mJgzYj1h7ShPkmSJKnrTHid6MbtvT8I/Bw4gREhWpIkSZqpxg3REfFJqkvbvS8zv9e+kiRJkqTuNtFM9Gpgj8x8sF3FSJIkSb1gop7oj7azEEmSJKlXtHKzFUmSJEkjGKIlSZKkQpOG6KgcGREfazyfFxF71V+aJEmS1J1amYk+EXg+cHjj+T1Ul7uTJEmSZqQJrxPd8LzM3CMiLgHIzDsjYv2a65IkSZK6Visz0Y9ExGwgASJiLt50RZIkSTNYKyH688C3gC0jYhFwAfBPtVYlSZIkdbFJ2zkycygilgH7AwG8KjOvqr0ySZIkqUu1cnWOHYBrM/ME4HLgJRGxad2FSZIkSd2qlXaObwCrI+LpwMnAdsB/1VqVJEmS1MVaCdFrMnMVcBhwXGa+B9iq3rIkSZKk7tXq1TkOB94EnNNYtl59JUmSJEndrZUQfTTVzVYWZea1EbEdsKTesiRJkqTu1crVOa4E3jni+bXAJ+ssSpIkSepmk4boiNgR+GdgPrDB8PLM3L7GuiRJkqSu1Uo7x5eBfwdWAS8GTgf+s86iJEmSpG7WSojeMDP/G4jMXJGZHwf2q7csSZIkqXtN2s4BPBgRs4DfRcTbgT8AW9ZbliRJktS9WpmJfjfQR3Vy4Z7AkcBRNdYkSZIkdbVWrs5xMUBEZGYeXX9JkiRJUnebdCY6Ip4fEVcCVzWe7xYRJ9ZemSRJktSlWmnn+BxwIHAHQGb+GnhhjTVJkiRphjvlFIiADTaYfGwntBKiycwbxixaXUMtkiRNmaGhIQYGBpg1axYDAwMMDQ11xbbUHdyn3eWhh+Cww6rQPPx4y1seW/fAA52tr5lWrs5xQ0S8AMiIWJ/qBMOr6i1LkqS1NzQ0xIIFC7j//vsBWLFiBQsWLADgiCOO6Ni21B3cp533u9/B/PmwatXE43bcES64ADbcsD11lYjMnHhAxBbAccABQAA/AN6VmXfUX97jDQ4O5tKlSzvx1pKkHjEwMMCKFSset7y/v5/rrruuY9tSd3Cftt/pp8NRLVzb7T3vgc9+Fma11CtRv4hYlpmDTddNFqK7jSFakjSZWbNm0ey/bxHBmjVrOrYtdQf3ab0efhiOPBK+9rXJx553Hhx4YP01ra2JQvS47RwR8QVg3ISdme+cgtokSZpy8+bNazrTOG/evI5uS93BfTq1fv97ePazJ+9b3m47uPBCeOpT21NX3SaaLF8KLJvgIUlSV1q0aBF9fX2jlvX19bFo0aKObkvdwX26bs48c/QJgE9/evMA/Y53wOrVkFk9rrlm+gRomGAmOjNPG/k8Ip6YmffVX5IkSetm+OSwj370o1x//fXMmzePRYsWrdVJY1O5LXUH92nrHnkEjj4aWrl4yXe+A694Rf01dYtWTix8PvAfwJMyc15E7Ab8dWYe244Cx7InWpIkqR7XXQe77gr33DPxuKc9DS6+uPpzOpuoJ9qbrUiSJM1QX//66NaM7bZrHqAXLqwuRzfcmvGHP0z/AD2ZVq4TTWbeEBEjF3mzFUmSpB6yahW89a1w6qmTjz3rLDj00Lor6m3ebEWSJGkauuEGeM5z4I5J7uwxdy4sWwbbbtueuqaLVto5jgHeBmwNrAR2BzrSDy1JkqTmzjprdGvGvHnNA/TRR1cnDA63Ztx6qwF6bUw6E52ZtwOPnq4aEZtRhWivAyNJktQBq1dXfcpf+tLkY7/2NXj1q+uvaaaZ6GYr2wJ/CzwN+BZwBvAPwJsaP0uSJKkNfvIT2G+/ycdtuilccgkMDNRdkSZq5zgduBH4AvAs4CKqlo5dM/NdbahNkiRpRnrpS0e3ZowXoN/4xuo228OtGXfeaYBul4naOTbPzI83fv5+RNwCPDczH6q/LEmSpJnhkUdg/fVbG7vddtWd/9R5E55YGBGbRcTmEbE5cDPQN+K5JEmSCp122uhZ5okC9JlnPjbLPHzrbHWHiWaiNwGWASMvEP2rxp8JbF9XUZIkSdPFhhvCgw+2Nvauu2DjjeutR1Nj3BCdmQNtrEOSJKnnrVoF663X+vjM+mpRvVq5TrQkSZKaOPfc0a0ZEwXo//f/RrdmGKB7W0u3/ZYkSRJstRXcfHNrY2+6CZ761HrrUecYoiVJkppYvRrmFCQlZ5ZnlpbaORpX6dg1IvYYftRdmCRJUjv9+MejWzMmCtDvfa+tGTPdpP++iohPAH8J/J7qqhw0/mzhvjmSJEndaY89qrv7teKGG2CbbeqtR72llf9J8Vpgh8x8uO5iJEmS6rBmDcye3fp4Z5Y1mVbaOS4HNq25DkmSpCnzi1+Mbs2YKED/zd/YmqFyrcxE/zNwSURcDjx6y+/MPKS2qiRJkgqUtGb8/vewvbeM0zpqJUSfBnwK+A2wpt5yJEmSJpYJswrudOHMsurQyiF4e2Z+PjN/kpk/G37UXpkkSRJw8cWjWzMmCtCHH25rhtqjlZnoZRHxz8DZjG7n+FVtVUmSpBlr333hZy1O1111Fey0U63lSE21EqKf0/hz7xHLvMSdJElaZ7ZmqFdNGqIz88XtKESSJE1/v/oV7Llna2Nf/nI455x665HWVis3W/lYs+WZ+Q9TX44kSZpOXvQiOP/81sZedhk8+9n11iNNlVbaOe4b8fMGwCuAq+opR5Ik9bKI1sfamqFe1ko7x7+MfB4Rn6U6yVCSJM1gl1xSXZ+5FXvsAcuW1VuP1E4FrfyP6gO8RLkkSTPM4ODoS81NFKB/8pPRl5kzQGu6aaUn+jdUV+MAmA3MBeyHliRpmitpzVizpmy81Ota6Yl+xYifVwG3ZOaqmuqRJEkdsHQpPPe5rY3dYAN44IF665G63aTtHJm5AngycChwGOB5s5Ik9bgttxzdmjFRgD7rrNGtGQZoqfVL3L0G+GZj0akR8bXM/MdaK5MkSVOmpNVi9eqyG6BIM1Er7RyHA8/JzAcBIuKTwK8AQ7QkSV3o8svLrrfspeakcq38O/M6qutDD3sC8PtaqpEkScV22ml0a8ZEAfrkk0e3ZhigpbUz7kx0RHyB6qocDwFXRMQPG89fAlzQnvIkSdJYJa0ZjzwCc1r5/86Sikz012pp489lwLdGLP9pbdVIkqRRfvtb2Hnn1sc7syy1x7ghOjNPW9eNR8RBwHFU15c+OTM/Oc645wIXAa/LzK+v6/tKktSrdtwRli9vbeznPgfvelet5Ugax0TtHCNvsjJqFZCZuetEG46I2cAJVO0fK4GLI+LszLyyybhPAd8vrF2SpJ5X0prx0EOw/vr11SKpdRO1c7xignWt2AtYnpnXAETEmVTXmr5yzLh3AN8AWrzEuyRJvenaa2H77Vsfb2uG1L3GvTpHZq5o9gC2AT7Ywra3Bm4Y8XxlY9mjImJr4M+Bk8pLlySpux144OirZkwUoD/yEa+aIfWSls7XjYjdgTcArwWu5bEbr0z4sibLxv5K+BzwocxcHRP8/6yIWAAsAJg3b14Lby1JUvuVtGbcdx/09dVXi6R6TdQT/Qzg9VQ3W7kD+AoQmfniFre9Eth2xPNtgBvHjBkEzmwE6C2Al0XEqsw8a+SgzFwMLAYYHBz03+aSpI67+WbYaqvWxzuzLE0vE91s5bfA/sArM3OfzPwCsLpg2xcDO0bEdhGxPlUgP3vkgMzcLjMHMnMA+Dpw7NgALUlSN3jd60a3ZkwUoD/8YVszpOluonaOv6AKvj+JiPOAM2neotFUZq6KiLdTXXVjNnBKZl4REcc01tsHLUnqWiWtGXffDRttVF8tkrpP5CT/PI6IJwKvomrr2A84DfhWZv6g9uqaGBwczKVLl04+UJKkFt11F2y6aevjnVmWZoaIWJaZg83WTdTOAUBm3peZQ5n5Cqq+5kuBD09tiZIktc8nPjG6NWOiAL1woa0Zkh6vpatzDMvMPwJfbDwkSeoJJa0Zf/wjbLZZfbVImh6KQrQkSd3u3nvL+pOdWZa0NiZt55AkqZudfPLo1oyJAvRHP2prhqSp4Uy0JKmn9PXBAw+0NvaOO2DzzeutR9LMZIiWJHWtRx6B9ddvfbwzy5LaxXYOSVLX+J//Gd2aMVGAPu44WzMkdY4z0ZKkjjnqKDj99NbG/ulPsMkmtZYjSS0zREuS2mL1apjT4n91dtsNLr201nIkaZ3YziFJqsUll4xuzZgoQH/ta6PbMgzQkrqdM9GSpCmxcCGcdFJrY23NkNTrDNGSpGJr1sDs2a2N3WEHWL683nokqd1s55AkTeryy0e3ZkwUoIeGRrdmGKAlTUfOREuSHue44+Dd725trDc0kTQTGaIlaYbLhKc+FW69dfKxT3kK3Hxz/TVJUreznUOSZpgVK0a3ZsyaNX6A/s53RrdmGKAlqWKIlqRp7qSTRofmgYHxx9555+jQ/IpXtK1MSeoptnNI0jSSCdtvD9ddN/nY/feHH/2o9pIkaVpyJlqSetjKlY9vzRgvQH/jG6NnmQ3QkrT2DNGS1ENOOWV0aN522/HH3nbb6NB82GHtq1OSpjvbOSSpS2XCs54FV145+djnPx8uvLD+miRJFWeiJa2VoaEhBgYGmDVrFgMDAwwNDXW6pJ53882Pb80YL0CfccboWebpGqCHj7OIYM6cOURE0+NtXY/Hbjiex6uh3bWNfb9jjz12wufD9Rx77LGP7qM5c+Zw7LHH1lpniVa+w3Z/z7vssgsR8ehjl112ael1BxxwwKjXHXDAAbXW2UndfEwBkJk99dhzzz1TUmctWbIk+/r6Enj00dfXl0uWLOl0aT3lP/9zZAye+HHLLZ2utv2aHWfNjrd1PR674Xger4aFCxe2tbaJvvPxHn19fbn//vs3Xbdw4cJa6lzXzzT2O2z3MTB//vym39f8+fMnfN143/P+++9fS52dtHDhwq44poClOU4mjWp97xgcHMylS5d2ugxpRhsYGGDFihWPW97f3891rVwWYoZ65zvhC1+YfNzuu8Mll9ReTtcb7zgbNny8revx2A3H83g1zJ49m9WrVz9ueV21Tfadl5o9ezarVq2asu2tjVb2b7uPgYgYd91EuWxtX9eL5syZ0/TYb/cxFRHLMnOw6bpe+9IN0VLnzZo1q+kv7IhgzZo1Haio+9x1F2yzDdx77+RjTzsN3vSm+mvqNeMdZ8OGj7d1PR674Xie7LOOVVdtpXW0otM5o5X92+5jwBA9uW75rBOFaHuiJRWbN29e0fKZ4Oc/H93PvOmmzQP0+uvDHXeMbtYwQDc32fE0vH5dj8duOJ7He6/Zs2cXja+rjrU1Xv3t1Mr+7YZjQKONd+x0wzE1zBAtqdiiRYvo6+sbtayvr49FixZ1qKL2O+640aH5hS9sPm7BAliz5rHA/NBDsPnm7a21VzU7zoaNPN7W9XjshuN5vBoWLFjQ1tom+s7H09fXx/7779903YIFC6airHXSyv5t9zEwf/78ouXDxvuex1vey8Y7drrhmHrUeM3S3frwxEKpOyxZsiT7+/szIrK/v39an1R4772Z++3X2gmA3/tep6udXoaPMyBnz56dQNPjbV2Px244nserod21jX2/hQsXTvh8uJ6FCxc+uo9mz57dFScVDmvlO2z39zz25MLJTiocNvbkwul4UuGwbjim8MRCSWrdZZfBbrtNPm6PPeC//7tq3ZAkTT/2REvSBE48cXRrxngB+u//fnRrxrJlBmhJmqm8Y6GkGeWBB6rbX5933uRjzz8f/uzP6q9JktR7DNGSprUrr4RWbgT27GfDT34CT35y/TVJknqf7RySppUvfWl0a8Z4AfojHxndmnHZZQZoSVLrnImW1LMefBBe+1r4zncmH/vjH8OLX1x/TZKkmcEQLalnXH017LxzNXM8kZ13hp/9DObObU9dkqSZx3YOSV3rtNNGt2bstFPzAP2BD4xuzbjySgO0JKlezkRL6goPPwyHHw7f/ObkY3/wA3jJS+qvSZKk8RiiJXXE8uUwfz488sjE47bbDn75S3jKU9pTlyRJrbCdQ1JbDA2Nbs3YccfmAfrd74bVqx9rzbjmGgO0JKn7OBMtaco98ggcdRScccbkY889Fw4+uP6aJEmaSoZoSevs2mth113h3nsnHrfttnDRRfC0p7WnLkmS6mI7h6RiV1wBRx75WGvG9ts3D9ALF8KqVY+1Zlx/vQFakjQ9OBMtaUJr1sC3vw2f/nQ1izyRb38bDjmkPXVJktRJhmhJo9x1F5x0EnzqU3Dnnc3HbLEFfOhDsGABbLxxe+uTJKkbGKKlGe6qq+Azn4Evf3n8MfvsAx/8ILz85TDLJjBJkgzR0kyyZg2cc041y3zhheOPe/Ob4f3vr26fLUmSHs8QLU1jd98NX/xi1c98++3Nx2y+eTXL/Nd/DZtu2tbyJEnqWYZoaRq5+mr47Gfh5JPHH7P33lU/8yGH2JohSdLaMkRLPWrNGvje96rWjJ//fPxxf/mXVWvGLru0rTRJkqY9Q7TUI+65B770pao145Zbmo/ZdNOqNeOYY2CzzdpaniRJM4ohWupSv/td1ZqxePH4Y/baqwrNr3oVzJ7dttIkSZrxDNFSF8iE886rZpl/+tPxxx15JHzgA9UttiVJUucYoqUOuPde+I//qELzjTc2H/OkJ1UnAB57bHUFDUmS1D0M0VIb/P738K//CieeOP6YwcGqNeOww2zNkCSp2xmipSmWCT/8YXXVjB//ePxxb3hD1Zqx++5tK02SJE0RQ7S0ju67D045pWrNWLmy+Zi+vmqW+W1vgy22aG99kiRp6hmipULXXlu1Zhx//Phjdt+9Cs2veQ3M8W+ZJEnTjv95lyaQWbVkfPrT8IMfjD/uda+rQvMee7SvNkmS1DmGaGmEBx6AL3+56me+/vrmYzbY4LHWjC23bG99kiSpOxiiNaOtWFG1Znz+8+OP2XXXKjS/9rWw3nrtq02SJHUvQ7RmjMzqRiaf/nR1Y5PxvPrV1VUz9tqrbaVJkqQeY4jWtPXgg3DaaVVovuaa5mPmzKluaPKOd8BTntLe+iRJUu8yRGvauOEG+NznqvaM8TzrWVVrxuteB+uv37bSJEnSNGOIVk/KhAsuqE4A/O53xx932GFVa8bee7evNkmSNP0ZotUTHnoITj+9as1Yvrz5mFmzqlnmd74TttqqvfVJkqSZxRCtrvSHP1StGZ/97Phjdtqp6md+wxtszZAkSe1liFZX+MUvqlnms88ef8yhh1YzzS94QfvqkiRJasYQrbZ7+GEYGqr6ma++evxxw60ZW2/dvtokSZJaYYhW7W66CY47rgrN43nGM6rQfOSR8IQntK82SZKktWGI1pT75S+r1oyzzhp/zCteUfUz77NP28qSJEmaMoZorZOHH4YzzqhC85VXjj/ufe+Dd78bttmmbaVJkiTVxhCtIrfcAp//fBWaV61qPmaHHapZ5je+ETbYoL31SZIktYMhWhO6+OKql/kb3xh/zMteVvUzv/CFENG+2iRJkjrFEK1HPfIIfOUrVWi+/PLxx7373dWjv79dlUmSJHUXQ/QMduut8IUvVK0ZDz/cfMzAQNWacdRRsOGGbS1PkiSpaxmiZ5Bly6rA/NWvjj/mwAOr0LzvvrZmSJIkjccQPU2tWgVf+1oVmi+9dPxx73wnvOc91YyzJEmSWmOIniZuvx1OOKHqZ37ggeZj5s2rTgA8+mjo62tvfZIkSdOJIbpHXXJJNct85pnjjznggCo0H3CArRmSJElTyRDdA1atqi4x9+lPw69+Nf64t70N3vte2H779tUmSZI0E82qc+MRcVBEXB0RyyPiw03WHxERlzUeF0bEbnXW0yvuuAM+8QnYaKNqBnm99eD1rx8doLfeurrpyb33Qmb1OP54A7QkSVI71DYTHRGzgROAlwArgYsj4uzMHHlz6GuBF2XmnRFxMLAYeF5dNXWryy6Dz3wGliwZf8x++1WtGS99qa0ZkiRJnVZnO8dewPLMvAYgIs4EDgUeDdGZeeGI8RcB29RYT1dYvRrOOqs6AfDii8cfd8wx8L73wdOf3rbSJEmS1KI6Q/TWwA0jnq9k4lnmtwDfq7GejrjzTjjxxKqf+e67m4956lOrWea/+quqhUOSJEndrc4Q3azpIJsOjHgxVYjeZ5z1C4AFAPPmzZuq+mpx+eVVa8bpp48/5kUvqm5octBBtmZIkiT1ojpD9Epg2xHPtwFuHDsoInYFTgYOzsw7mm0oMxdT9UszODjYNIh3wpo18O1vV7PMF100/rgFC6rWjGc8o321SZIkqT51huiLgR0jYjvgD8DrgTeMHBAR84BvAm/MzP+rsZYp8ac/wRe/WPUz33ln8zFbblm1Zrz1rbDxxm0tT5IkSW1SW4jOzFUR8Xbg+8Bs4JTMvCIijmmsPwn4GPBk4MSo+hpWZeZgXTWtrUcegfXXb75un32q0Pzyl8OsWi8YKEmSpG4RmV3THdGSwcHBXLp0aVvfM7MKyxdeCG9+M7z//bDzzm0tQZIkSW0WEcvGm+D1joUtiIBf/KLTVUiSJKlb2IAgSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVKhWkN0RBwUEVdHxPKI+HCT9RERn2+svywi9qizHkmSJGkq1BaiI2I2cAJwMDAfODwi5o8ZdjCwY+OxAPj3uupZF0NDQwwMDDBr1iwGBgYYGhrqdEmSush0/h0xnT+b6uNxo5lgTo3b3gtYnpnXAETEmcChwJUjxhwKnJ6ZCVwUEZtGxFaZeVONdRUZGhpiwYIF3H///QCsWLGCBQsWAHDEEUd0sjRJXWA6/46Yzp9N9fG40UwRVX6tYcMRrwYOysy/ajx/I/C8zHz7iDHnAJ/MzAsaz/8b+FBmLh1vu4ODg7l06birp9zAwAArVqx43PL+/n6uu+66ttUhqTtN598R0/mzqT4eN5pOImJZZg42W1dnT3Q0WTY2sbcyhohYEBFLI2LpbbfdNiXFter6668vWi5pZpnOvyOm82dTfTxuNFPUGaJXAtuOeL4NcONajCEzF2fmYGYOzp07d8oLnci8efOKlkuaWabz74jp/NlUH48bzRR1huiLgR0jYruIWB94PXD2mDFnA29qXKVjb+CubuqHBli0aBF9fX2jlvX19bFo0aIOVSSpm0zn3xHT+bOpPh43milqC9GZuQp4O/B94Crgq5l5RUQcExHHNIadC1wDLAe+BBxbVz1r64gjjmDx4sX09/cTEfT397N48WJPjpAETO/fEdP5s6k+HjeaKWo7sbAu7T6xUJIkSTNTp04slCRJkqYlQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVKhyMxO11AkIm4DVnTo7bcAbu/Qe6s93Mczg/t5ZnA/zwzu5+mvk/u4PzPnNlvRcyG6kyJiaWYOdroO1cd9PDO4n2cG9/PM4H6e/rp1H9vOIUmSJBUyREuSJEmFDNFlFne6ANXOfTwzuJ9nBvfzzOB+nv66ch/bEy1JkiQVciZakiRJKmSIHiMiDoqIqyNieUR8uMn6iIjPN9ZfFhF7dKJOrZsW9vMRjf17WURcGBG7daJOrZvJ9vOIcc+NiNUR8ep21qd118o+joh9I+LSiLgiIn7W7hq17lr4nb1JRHwnIn7d2M9Hd6JOrb2IOCUibo2Iy8dZ33X5yxA9QkTMBk4ADgbmA4dHxPwxww4Gdmw8FgD/3tYitc5a3M/XAi/KzF2BT9Cl/VgaX4v7eXjcp4Dvt7dCratW9nFEbAqcCBySmbsAr2l3nVo3Lf5dfhtwZWbuBuwL/EtErN/WQrWuTgUOmmB91+UvQ/RoewHLM/OazHwYOBM4dMyYQ4HTs3IRsGlEbNXuQrVOJt3PmXlhZt7ZeHoRsE2ba9S6a+XvM8A7gG8At7azOE2JVvbxG4BvZub1AJnpfu49reznBDaKiACeBPwRWNXeMrUuMvN8qv02nq7LX4bo0bYGbhjxfGVjWekYdbfSffgW4Hu1VqQ6TLqfI2Jr4M+Bk9pYl6ZOK3+XnwFsFhE/jYhlEfGmtlWnqdLKfj4e2Bm4EfgN8K7MXNOe8tQmXZe/5nTyzbtQNFk29vIlrYxRd2t5H0bEi6lC9D61VqQ6tLKfPwd8KDNXVxNY6jGt7OM5wJ7A/sCGwC8j4qLM/L+6i9OUaWU/HwhcCuwH7AD8MCJ+npl311yb2qfr8pcherSVwLYjnm9D9a/a0jHqbi3tw4jYFTgZODgz72hTbZo6reznQeDMRoDeAnhZRKzKzLPaUqHWVau/s2/PzPuA+yLifGA3wBDdO1rZz0cDn8zqur3LI+JaYCfgf9tTotqg6/KX7RyjXQzsGBHbNU5IeD1w9pgxZwNvapwlujdwV2be1O5CtU4m3c8RMQ/4JvBGZ6x61qT7OTO3y8yBzBwAvg4ca4DuKa38zv428GcRMSci+oDnAVe1uU6tm1b28/VU/7eBiHgK8EzgmrZWqbp1Xf5yJnqEzFwVEW+nOkt/NnBKZl4REcc01p8EnAu8DFgO3E/1r1/1kBb388eAJwMnNmYpV2XmYKdqVrkW97N6WCv7ODOviojzgMuANcDJmdn0ElrqTi3+Xf4EcGpE/Ibqf/t/KDNv71jRKhYRZ1BdWWWLiFgJ/B2wHnRv/vKOhZIkSVIh2zkkSZKkQoZoSZIkqZAhWpIkSSpkiJYkSZIKGaIlSZKkQoZoSeqQiFgdEZdGxBUR8euIeG9EtOX3ckR8PCLe32T50yLi642f942Icxo/HxIRH278/KqImN+OOiWpW3mdaEnqnAcyc3eAiNgS+C9gE6rro3ZEZt4IvLrJ8rN57AYXrwLOAa5sX2WS1F2ciZakLpCZtwILgLc37sg1EBE/j4hfNR4vGB4bER+MiN80Zq8/2Vi2Q0ScFxHLGq/bqbH8lRHxPxFxSUT8qHE3t2G7RcSPI+J3EfHWxviBiHjczUgi4i8j4vhGHYcAn2nMou8QEb8aMW7HiFhWy5ckSV3EmWhJ6hKZeU2jnWNL4FbgJZn5YETsCJwBDEbEwVQzwc/LzPsjYvPGyxcDx2Tm7yLiecCJwH7ABcDemZkR8VfAB4H3NV6zK7A38ETgkoj4bgs1XhgRZwPnZOZw28ddEbF7Zl5KdRexU9f5y5CkLmeIlqTuEo0/1wOOj4jdgdXAMxrLDwC+nJn3A2TmHyPiScALgK81blMP8ITGn9sAX4mIrYD1gWtHvNe3M/MB4IGI+AmwF3DpWtR8MnB0RLwXeF1jO5I0rRmiJalLRMT2VIH5Vqq+6FuA3aha7x4cHgbkmJfOAv403F89xheAf83MsyNiX+DjI9aN3c7Y5636RqPeHwPLMvOOtdyOJPUMe6IlqQtExFzgJOD4zEyqEwxvysw1wBuB2Y2hPwDeHBF9jddtnpl3A9dGxGsayyIidmuM3wT4Q+Pno8a87aERsUFEPBnYF7i4xXLvATYafpKZDwLfB/4d+HKL25CknmaIlqTO2XD4EnfAj6gC8t831p0IHBURF1G1ctwHkJnnUV0lY2lEXAoMX6buCOAtEfFr4Arg0Mbyj1O1efwcuH3M+/8v8F3gIuATjStztOJM4AONkxV3aCwboprJ/kGL25CknhbVhIckSWuvcc3pTTLzbztdiyS1gz3RkqR1EhHfAnaguhqIJM0IzkRLkiRJheyJliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRC/x8/nnNWwUTp+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.2414859185800835 R2_SCORE:  0.0463387171307752\n"
     ]
    }
   ],
   "source": [
    "X = df_norm.iloc[:, 2:3].values\n",
    "Y = df_norm.iloc[:, 1].values \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, Y_train)\n",
    "\n",
    "def linear():\n",
    "    plt.scatter(X_test, Y_test, color='black')\n",
    "    plt.plot(X_test, lin_reg.predict(X_test), color='blue')\n",
    "    plt.title('(Linear Regression)')\n",
    "    plt.xlabel('Daceability')\n",
    "    plt.ylabel('Album Release Year')\n",
    "    plt.show()\n",
    "    return\n",
    "linear()\n",
    "\n",
    "y_train_predict = lin_reg.predict(X_train)\n",
    "rmse=(np.sqrt(mean_squared_error(Y_train,y_train_predict)))\n",
    "r2=r2_score(Y_train,y_train_predict)\n",
    "print('RMSE :',rmse,'R2_SCORE: ',r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d38e8958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yhLv3MbPeAO7+cOKYE4hgfUSR+61HzHZDhPhn3b3ct35acCkiIiIi2VDtFlzmgsK3iIiIiGRDdVxwKSIiIiJSpyh8i4iIiIhkicK3iIiIiEiWKHyLiIiIiGSJwreIiIiISJYofIuIiIiIZInCt4iIiIhIlih8i4iIiIhkicK3iIiIiEiWKHyLiIiIiGSJwreIiIiISJYofIuIiIiIZInCt4iIiIhIlih8i4iIiIhkicK3iIiIiEiWKHyLiIiIiGSJwreIiIiISJYofIuIiIiIZInCt4iIiIhIlih8i4iIiIhkicK3iIiIiEiWKHyLiIiIiGSJwreIiIiISJYofIuIiIiIZInCt4iIiIhIlih8i4iIiIhkicK3iIiIiEiWKHyLiIiIiGSJwreIiIiISJYofIuIiIiIZInCt4iIiIhIlih8i4iIiIhkicK3iIiIiEiWKHyLiIiIiGSJwreIiIiISJYofIuIiIiIZInCt4iIiIhIlih8i4iIiIhkicK3iIiIiEiWKHyLiIiIiGSJwreIiIiISJYofIuIiIiIZInCt4iIiIhIlih8i4iIiIhkicK3iIiIiEiWKHyLiIiIiGSJwreIiIiISJbkNHyb2d5mNt7MJpnZZUlu39nM/jCzEYnL1aneV0RERESkuslZ+Daz+sADQE+gC3CkmXVJcuhn7r554nJ9Be8rIiJSIQMGDKBjx47Uq1ePjh07MmDAgGpxLqk+9LpKVTTI4WNvDUxy958AzOx54ADg+wzfV0REJKkBAwbQq1cvFi5cCMCUKVPo1asXAEcffXTOziW5s3w5jB8Pq64KbdvCs88OoFev/7BoUVdg3v+/ru5wzDF6XaV85u65eWCzQ4C93f2UxNfHAtu4+1lFjtkZeBmYBkwHLnL3sancN5kePXr40KFDM/F0RESkFujYsSNTpkxZ4foOHTowefLknJ1LsmfqVHjxRXjhBUg9MrwIHEarVnDwwbD77rDLLtC6dQYHKtWemQ1z9x4lr8/lzLclua7kO4HhQAd3/8vM9gFeAzqleN94ELNeQC+A9u3bV3qwIiJS+02dOrVC12frXFI2d7BkyaCExYth2jT45Ze4TJkCH34IH39c1REcBsDcudC3b1wKHH10XP75T2jatKqPI7VBLsP3NKBdka/bErPb/8/d/yzy+Ttm9qCZtU7lvkXu1xfoCzHznZ6hi4hIbdS+ffuks9WVmbxJ57mkuAUL4NNP4YMP4P334YAD4JprYMaMmLkuCNdFL1OnwuzZFX+sffeFbt3g1Vdh3Dho0OBHli8/DviaiCKdgM1o1Ogkli7ddIX7DxgQl6JOPRWOOgq6dIE2bVJ74yC1Ry7D9xCgk5mtC/wKHAEcVfQAM1sTmOnubmZbEwtE5wLzy7uviIhIRfXp06dYnTZAkyZN6NOnT07PVdctXgxvvglPPQXvvLPi7aNHw003QX5+5R+jXj3YYgvYbrvCS8eOMGhQhOVff4WLLoIuXYZw1lkjWLgwH5gCTKFJky/p27c7Rx+9KX/+Cb16RdlKaR59NC4FGjWCgw6CDTeETp3issEG0KqVgnltlLPw7e7Lzews4D2gPvBEop67d+L2h4FDgNPNbDmwCDjCo0g96X1z8kRERKTWKFgIeeWVVzJ16lTat29Pnz59KrVAMp3nqu3mzYNnnoHXX4ePPqrYfddcE9q3h7XXhvnzo6xk2rQI7OXZeusI2dtvD1ttFWUhZnH54w/497/htdfi3AMHwjbbgNkR5Ocb1113Lb/8MpV27dpy3XXXcsghR7J0KTRuDP37R5g+7bRYsHnHHXH+Tz+F//0Pvvyy+DiWLk0e1ps3LwzjnTrBuuvCyy/DXnvFG4JGjSr2vZLqIWcLLnNBCy5FRERyZ/lymDgRRo6EV16JICqV99prUXIj1VN1XHApIiIitdTcuTBqVFy++w769cv1iGqfpUtzPQKpDIVvERERqbSCPtgFQXvo0FgEWVGNGsGee0LnzhEqhw2Db7+N86eiRQvYYQfYcceol65fHxo0iI8Fl6Jfl/z8jTfgiiviXCeeCFddFSUkpd2nfv3U67Hz86NW/V//Krxun32iPObHH+Hvvwuvr18/as0bNYqFon/9Vfp5DzsMLrgAbr5ZJSg1icpOREREJCVz5kTJSNEZ7ZEjK36evfaKXti77RadRKZNizrogsuwYaXfd401okZ7222jXrt7d2jSpPLPafZsOOus6O29xRbw5JMxpopavjw6qkyaVHj58cfCj0uWrHif9u2jrnyDDQov7dtDw4Zxuzs8/TRcfHGMs00bWGml+H4ls/fe8aZh221jAanklspOREREJCXLlsVsdtGgPXJktPKrjK23hp49I2xvuSV8/32E7FtuiUWIv/1W+n179CjegaRDh/R0AHGPwH3WWfDnn3DjjXDJJYXBN5mlS2Hy5OIBu+Dy88/FZ+kbN44w3blztCtcf/3CgD1mDJx8cjzv9u3h9NNXDMtjxsAZZ8Bnn8XzHjy4+JuCefPguedi/AUGDoxLgf33jxaMW25ZpW+VpJlmvkVEROqwWbMKw3VB0P7++6rVE3foAIccEmF7442jFWDBrPann5Z+v9at4R//KAza3btHiE23336LYPvqq9GF5MknYZNN4rZFi+Cnn5IH7KlTi7czXHXV4rPWRS9rrVX2m4TZs+GUU6LcZffdo43iOutEmcl118Fdd0GzZnDbbVEGU9pM9tKl0fnk6aejLKZ169LfzOy9N/TpozCeLaXNfCt8i4iI1AFLl8IPPxSfyR41quxZ54o47TTYddcoC5kwoTBo//RT6ffZaqsI2QUlJOma1S6Ne7QBPPnkmN3feuuow548ubBEpGRJR8uWpQfs1q2Tjzc/P8pMUrncfz+8/Xby8Z51FqyySvnnWLwYvv469e/Dt9/G914yS+EbhW8REakbZs5csWRk3LgInOly1FERmldZJTag+fJLePfd0o9v1izCebpntd2j3KNkIP3tt5jBHzs2LoMGpX7O1VeHdu2gbduoJy8IuKkG6lQXiebK0qVll9dIeqjmW0REpJZZsiRmswuCdsHHWbPS/1g9e0YJycorR7B9/3149tmy77PuulHisOWWMSNeEGI/+yzun2qYLe3yxx/pfY5msaBxyZLoNDJrVnxd8lLQ6SQ/v/ANTV5efJ7L4L3FFrEo9pdf4uuJE2OGXqoXzXyLiIhUc+4ReEvWZo8bVxj2VlopZpKXLo3wmJeX2zGnokGD5OG24NKoUSwsLK/lXkkrrxw13KutVrhjZr16UXLSpUucd8kSWLgwarwXLozLX3/FLpm//174sejnBR+TdS4pqlGj9PbgXm212F2z4NK9e7yZKa0O/Msvo5ymQYMoaemxwtyrZIPKTlD4FhGR6m/x4gjVRYP2yJExo1mgbdsojSgI2gWzwOmeCS7PP/4BO+8cW5+XFaJLu9SrF28Spk0r3pqvaKu+hQsLH69Bg5hN32CDKAtp3RpatYpL8+bRL3v+/Ajst90GCxYU3nfzzeP78/vv8bGs+FOvXpyvRYu4FHzevHncryCo//xz1LeXF8bLc+ihhcF6ww0jvJ99drxZ2Hbb+Lj++hU75/jxscBy9uzYSbRnz6qNUSpO4RuFbxERqT7cYfr0FRdA/vBD4ax148aw6abRjm7ZssKgXXRhYP360VmjtN7PZWnYMGqaywvt//53Ya32lltWvFZ7+XKYMqV4sJ44MfqET5+e2jhbt47uIn/9FQF60aLUHrtJkyjHKBmii35s0SKeU8EM+Pz5Mbavv47FiZWdxd5sswjP22wTr+Mbb8C998ZrecUV0dpw5ZVLv//zz0Pv3vHzcN99cPzxFVuQ+ttvsZnPqFHw6KPRNUWyR+EbhW8REcmNxYtj0V/JoD13buEx7dtHH+dOnQq7Zfz+OwwfHrOrBTbYIGZBP/ggM/XFXbvCLrsUdiFp3770wLdsWWEpxsyZMdYhQ2KXy/HjqzYOswjHyYJyshDdrFn0vb7//phVv+++aOXnHn81+PXXuPzyS4zzm2+iBWJlrLRS4Uz1VlvFa9K2bendT776Knp5jxwZs9H335/6TPbUqXDccfDJJ9G+8eGHY6Y/VQsWxP0GDYLrr4f//CezHWWkkMI3Ct8iIpJZ7hHwSnYamTCh+Gz2ZptF0N544yilWLw4ZoK//TY2Vyk4du21ox3e2mvDgw9mZsx77QUbbRSX9daLsZRW6zx9etm7T5ZntdWi/nijjQqDdGlherXVyt+l8e+/Y0yffBK9rtNh9dWLz1a3axeXVq0qHlrnzoXLLoPHHotwfs89cNBBFT9PXh7ccUcE5zZtoF+/6A2eqmXL4o3I00/H9+nBB+PnTjJL4RuFbxERSZ9Fi2I2u2jQHjUq6o0LdOwYM8ndukWQa9o0bh82LIL2d99F2IUInVttFUHv228rPytbEQ0apGf23CzG3qNH4WWDDSrfSjAvL2bSf/01wvWvv8YM8LBhMWNdtJa7Ijp3jlDdo0fUjrdtG5fKBOuy5OfHpjmXXBJvXM4/P3aaXGWVqp13+HA4+ugoTbrgArjpppiFT4V7bD3fpw/st1+UtDRtWrXxSNkUvlH4FhGRinOPUoWSnUYmTCjc7bBp05jNLgjam20Wm7NMmBBBuqAUo6C2unHjqNNeaaWohS66qDBTOnSANdeMkJ+fH7Oyc+dGSUZZnUTWWqv41ugFl/XXjxnqinCP4FxQAlIQrqdOje9PVf6Lbts2ZpW7dInPC/p0t2yZ3TKLUaOixOTLL2HHHWOWebPN0nf+hQvh4ovjvF27RrvHgt05U/Hww3DmmfFm6c03YyZdMkPhG4VvEREp28KFUfZRMmjPn194zLrrRsAuCNpdu0a98bBhEbILLunaObIiNtggulr07Bmt6ObOjYD744/FO4n8/nvx+7Vrl3wHx/XWS322dtkymDGjcKa64FIwYz1xYuWeU7NmMVu95ZbxBmKtteDFFyN0tmwZtd1HHpn7OuYFC+Daa6O0pEULuP32qNUur3Smst5+G046Kd7Q3XZb7IaZ6mO9/joccUS8ORk4sOKdVCQ1Ct8ofIuISHCPUFhyc5qJEwtb0K2ySmFtdtHSkXr1ChcWFlzK2kI9U/bdNxZnrr561EcXtOsruBSdza5XL0pgkgXsddctu+OGe4T1krPVv/4as/bDhkU7u8ro0KGwvV5BCUjBjHWLFisG6jFjInAOGQIHHwwPPBBvMnLJHV56Cc47L9589OoV5SAtW2b+sWfNiu/H229H7f6TT8abk1R89VX0Aq9XL+6v7ebTT+EbhW8Rkbro778jtJUM2n/+WXjM+usXn8nu1i3C6vLlcWxByP7kk9wEbYiA3KZNXJYsiZnsgnpxiHZ8661XWBJSNGB36BC9o0tavDiCdMnZ6unTI1gPGVL5mvBu3aK3dsGCxaLhunnzis1UL1sGt94a3TqaN4/QfeihlRtXOk2aFDPO770Xz/Whh2KxZja5RynJhRdGW8XHHoMDD0ztvgW9wGfNil7g++yT0aHWOQrfKHyLiNRm7jB58oqb0/z4Y+Fs9qqrRrguGrQ33TSuz8uLMDJkCAweDAMG5PTpJLXyysnrrws2nalfP47Lzy/eXq9kuJ4yJTbyqewYtt46xlFytrpt24oH6/KMGBH9qUeMiFKJe+/NfZ3y4sVwyy1xWWkluPHGqPPOZQeRH36IxZjDh0dnk7vuSq1kqGgv8L59YyZd0kPhG4VvEZHaYsGC5LXZBV0wzCKQlgzaHTvGbe4RQN98M9q2VaV9XrqtskrycL3BBlFSsGhR8pnqgs8nT47yh8pYc83i7fVKhutmzbJXW710aYTam2+ObiQPP5z6jG4mvfdeLFj88ceoNb/jjtRLPTJt6dLoqnLrrfHmaMCAeKNUnqK9wK+7Lrqi5LqGvjZQ+EbhW0SkpsnPjy28S25O8+OPhcc0a1YYsguC9iabFM765edHF41+/aLPcVmdPbLtoIMi7K6/ftRer7pqlHmUFq4nT658Z5ROneLNR0GQLhmuV1ut+gSuoUNjtnvMGDj2WLj77uzUUJdl2rRoGfjSS1Gj/uCDsNtuuR1TaT75JL5v06fHItDLLy/8q0hp1As8/UoL3/q2iohItfDnn9HbumjQHj26MCybRYDccks44YTC2eyCHRiXLYsa3G++gSuvjB0gU1GvXmHLQIiQ0qpV1MGmS/v2sOuuMf7GjaNsoSBYDxxYGK4rq6C9XtFQXTRcN2uWvueSSYsXR1i8/faYTX7rrVhYmkvLlkVHlWuuiTdGN94IF12Uen/tXNhpp/gdOuOMmMUeOBCeeSbe4JWmYcPoTd6uXfQCnzFDvcAzRTPfIiKSVfn5sWix5C6QP/9ceEzz5isugNxkk1hQtnBh1GaPGxeb1Dz3XGrBtUmT2GRl1qzC481il8l11oGPPkrfdu1t2kTQLqi7/vvvyp2nadNYLJmsBKTg42qrpWfMufbVV1Fv/MMPcPLJUc6R6zcNX3wRtdyjR8ebgPvuKzvAVkcDBkQId49t7Y89tvy/cBT0Au/RI94A5brGvqZS2QkK3yIi2fbHHxFcigbt0aMLw2i9evEn/JJBu23buO+4cYWX0aOj3rY8TZpEoF5llSjTmDKl8LbOnSNQrLNOzIznstZ7tdVKLwEp+Ly2BOuyLFwYs7N33RXP/dFHYc89czumOXPg0kvhiSdiTPfeCwccUH3Kcipq8uToOf7ZZ3DYYRGuW7Qo+z7qBV51Ct8ofIuIZEpeXtRhl+w0UjT4tmix4uY0m2wS5Sbff18Ysr//Pi7lbVLTuDFstFH8+b9evfgzedHZ8/XXL9zqfO21o4PJU09l5OmvoFmz0gN1u3YR/utCsC7PZ5/FbPekSTHDfOutUfeeK/n58PjjcNll8XN5wQVw9dW1o/QiLy++v9dcEwtrn34adtml7PuoF3jVKHyj8C0ikg7z56+4AHLMmMKFgPXrxwxz0QWQm20W//kXnckuCNwld1tMpkuX6FNdr15c5s+PwFagY8fCoN2jR2y80rdvlAmkW/PmZc9Wt22b2wBZE/z9dywCvP/+eO0ef7z8IJhpI0bEG4Cvv4Z//jMWHFZk2/aaYujQaEk4cWLUrt9wQ9n16+oFXnkK3yh8i4hURF5e/AddMmhPnVp4TKtWhbPYBTPZK60UNd1FQ/YPP6TWpaNevZixbtgwPjeL1npFe3W3bVs8aHfvHgv1br01wlxVbbhhjKG0cJ3qduuS3EcfRU335Mlw9tmxG2QuZ5b//DNmt++7L36e77gDjjmm5paYpOLvv2NTnkceic2Bnn02SrVKo17glaNuJyIiUqp58wpDdkHQHjOmcAfF+vWjxGPHHSNkb7hhbLYyf35hyB40KML6smWF523VKnZjLC14t2sX5zGLy/LlEbQLuo+suWb8qfvYYyNkd+8e1/3yS2xwsvfepT+nCy6AO+8s+3mfcUa0+ysoBVGwzpwFC+CSS6LeuFMn+PTT+HnKFXd48cVoH/jbb9C7d3T5KK8WujZo2jReh332iTdCW24J//1v/D4ke9Ox5prRvvCQQ+L4adPUC7wqNPMtIlKHLF8eAblkp5Fp0wqPad06ZrMLtlhv1CiCSsFs9vffR211wX8fBbPVnTrFbPlff8Xlzz9jljwvL45r0ybqtAuCtnt0AinoMNKmTfEZ7YJabYiwffPNsX13aa68MoJUq1bx9fTpEaiLatcOZs8uvi37aqtFmUzBZcMN42OnTrF4U6pu0KDoHV3QK/v663P7vZ0wIbp5vP9+vKF76KG6W8/822/RU33gwAjjTzwRZVvJqBd4xZQ2842715lL9+7dXURyq3///t6hQwc3M+/QoYP3798/10OqtebMcf/wQ/e77nI/8UT37t3dV17ZPWKve4MG7ptt5n700e4XXeR++eXuV1/tfvbZ7rvv7r722oXHgnujRnH8YYfFcddf737JJe69erlvu23xc6+2mvuaa7qvtVacZ5114v4Ft7ds6b7nnu5XXOH+yivuU6a45+cXjn3KFPfevYs/fsnLf/7jPndu8ude8HMGeP369R34/5+3vDz3yZPd33vP/bjjvvVVV33KYZDXrz9thcdo3959jz3czzrL/d574z6TJ7vn5RV/nFz/PJc2jmyPr+TjnXTSBd606fOJn7eJftBBtyYdz+mnn/7/r1P9+vX99NNPz8j4Fi50v+qq+Fls1sz9/vvdly+v2HNK9j3M9ve5S5cuDvz/pUuXLuXeZ7fddit2n9122+3/b8vPd7/vvvgdbtPG/Y03Sj9Pfr77lVfG78d++7n/9Vc6nlH6ZetnqizAUE+SR3MeiLN5UfgWya3+/ft7kyZNiv0H0KRJEwXwKlq2zH3MGPdnn3W/9FL3nj0j7BYNkauvHoH6sMMibJ9wgvvxx7vvuGME4aLHrrKK+1ZbuR93nPvNN7u/9pr7Bx+4v/BCnH/XXSO4FBy/0kruLVrEeVq1cm/dOq4ruL1Zs7jPJZe4v/ii+08/FQ/a7hFoTzut7LB91VWlh+2ikv2cJft5S3Zc48atvE+ft/yFF+LNxdFHu/fo4b7qqsXH0rixe/v287x+/ZccbnA4xmErb9x4zaz/PJf2e3X66adn9fdtxXHs4/CLwzKHmxxWSvp6lAyFBZd0h6W333Zfd914/Y45xn3GjMo8pxW/h9n+d61k8E4lgJf2PS4awN3j35Fu3eJ7dNppZQfrhx5yr1fPfeut3WfNStOTS5PTTz89Kz9T5SktfKvsRESypmPHjkwp2nsuoUOHDkyePDn7A6qB5swp3spv1KgoA1myJG5v2DDKJlZZJT4vuMybFyUjRTd7adUquohsvHFcCj5feeXoiDBkSOFl5sy4T/36xWu0zeKxCx5/lVXiz/hFS0fWWy9KU4qaPDkW2j36aOnP9Zpr4NxzK16DW9rPWYGCn7eK/Dy6x/dg/PjCy0MPfcCiRe2A9Si6hKpevdnsuGObYiUsnTvH5iwNG1bsuaSitOdRv3598gpqforI1O9b4ThaAHcDxwGjgROBijdUr1+/PsvTsOvRL7/Ez9Grr8a6hQcfTL2zSio/I9n+d83KKLQuLdNV5D5LlsB//hMLTzfcMDbp6d49+X2ray/wBg0aJP3ZT9fPVKrU7QSFb5Fcq1evXtL/HMyM/KL7ewvLlkXAK1mbPWNG4TEtWkQQLhqy8/Mj2C5dWnjcOuskD9lt2kRt9rBhxYN20V7ZDRoUD9r5+YULKps0gS22KB60N9xwxaANcc6bboLHHiv9OV97bYSk5s2r8I2j9J+zAgU/b1X9eSy8f0MigHdOXDZihx1OYsKEqC8v0KBBhJOSobxz53gtKrt4rbznW1Kmft9iHPsDDwGtgZuAPsCyMu9XlqpklGXL4O674brr4uf26qtjEW6jRqmfI5WfkWz/u5bp8F3gww9jY56ZM6NG/5JL4s13SdWxF3hlnm+GxqFuJyKSW+3bt086Q9S+ffscjKb6mDVrxc1pvv++eNeQAg0aRHho0CB2gCzokV2vXsysdukCBx5YGLI32qhwM5clS+L8Q4bAk0/Gx3HjCjuLFFUQoAsmiVZeOVqSFW3vt9FGZS+2co8Zxk8+SX7+a66Bc86petguqbSfs6K3l3Vcqj+PhfdfBoxPXGLG8/PPoxfbvHmxuK/ojPmECbFTZ8FfCyC+B8lCeadO8b2vzPMtbeY7E79vc+ZA48avsXDh/sB3QE9gZJXOWT9Z0kvRZ59Fz+6xY2H//eGee2LxcEWl8jNSW/9d23XX+Peid2+44oqY2X76aejQofhx220HX3wRnYd23rl69AIv7We/Kj9TaZWsFqW2XlTzLZJbdb3me8kS95Ej3Z95JhY47rmn+xprlF3nXPLSsKH7Jpu4H3poLHp87rk456JFxR9r+fKo33zySfczzoga7qILHsu6NGoUdc69e7s/9pj7iBHuS5dW/Pnm5RWes3599xtucJ8/Py3fyjJVpea7Ij+PVbn/8uVR+/7uu+533+1++unuu+3m3rZt8dfCzL1jx/hZOfvsWCA4eLD71KnFF33mquY7Pz/WArRp416//nJv2PBahwZJv/elvR7prPmeOTPWMoB7hw7ur79etedX12q+k8nPd+/XL9Y9NGvmPmBA8uNmzHDfYov4XX/88Uo+uTSp7jXfOQ/E2bwofIvkXnXpDpFpM2ZEZ4zbb4/FXV27RnBONWQ3bRrdSY491v2mm9xffdV9/PhYXFlSfn4EuRdeiFC/006xaDKVx2nQIP7DPPVU90cecR82LN4k1HRldTtJdlxlfx4z8fO8YIH78OHxxuraa92POip+Fkq+pk2axOK4ww5zP/DAUd6q1TkOPbxdu02y0u3kt9/c//3vGEuPHu6jR6/4eKeffnqZX6er20lenvvDD8fC34YNo3NPurpw1IVuJ6n46Sf37beP1/uoo9x//33FY/78M94ogvt11624sDqbqnO3E9V8i4hUwZIlUbpRchfIWbNSu3/LlsXrsAs+b9s2ee00RA1mQX32t9/G4sg5c8p/rHr1YgfKojXaXbuWX9Yg1YN71PyXLGEZPz5q6ouWD625ZvHylYJLx45V78vsHjsinnNOLOC97rrYLTFX/Z6HD48Sk2+/jTKnBx4oe7dGqbzly6Pf/nXXxVqSZ56Bf/6z+DHqBV5ICy5R+BaRynOPzShKLoD84YfCuuiyrL128pBd3kK7P/6IBZHfflsYuH/5pfzHM4vHKFqjvfnm2jSmtlqyJHYGTRbM584tPK5hw8JFnyUvrVqVv+hz+vSoAX7zzaj1feKJqP3PhT/+iF0WH3ggfo/uuAOOOkq7LmbDN9/AMcfEz9yll0YYL7qQ1T1emz59YL/94PnnY1fNukbhG4VvEUnN4sUxm10yaJc3u2wWix5LhuyNN4ZmzVJ73BEjigft8eMLb19//Zj1/uuvFe/bujXsvjtsvXWE7S220FbpEubOXTGQjx8PkyYV74rTokXyUL7++rDSSvDUU7E75dKlEarOOSd594tMc4fnnovOJbNmxZboN96Y/oW7Ura//oLzzoPHH4839/37r/hG7OGHYyfRHj3grbfiTVJdovCNwreIFOceM3lFy0VGjYrZ7CQL5f9fgwbRlaJkyO7cObZPr6hLL4XBg2H06MJZ9LXWipZdBZcePWJm8oQToF+/5OepVy+Oad06/pMr79K6dWb6TkvNkJcXbSmTBfPp00u/3wUXwL77xu/AOutkd6b5hx8izH34YfxePPRQ6T2oJTtefTXKTBYtgjvvhNNOK/4zUV17gWeDwjcK3yJ12eLF0XasZNAu+if5kpo0iZmckv2x118/vaH18MOjJV1B0N566wg1pVm6NGbhZ88ufkl23ezZ8RxL+6e+efPkoby0wF6ZNxdS8yxYECH84ovh448Lr2/cOEJWgaZNV2yPuOGGcVl11fSNZ+HCmG2//fZ4zJtvjnri6tI5rq6bPj0mBgYPjp7fjz0Gq69eeHt17AWeDQrfKHyL1AXu8OuvK+4COX588n7WEAE02SY07duXvuixJsnLi3CfLJiXFuBLq2Nv2rT82fSiX6+6qmpwa6LJk2M284MPot/zY49FSVXB71fJmfLx4+M+RSPF2muvWMKy4Yax6LMiofmtt+Dss+P8xx0Ht90Ga6yR3ucrVZefD/fdF3/Ja9Ys9hIo2u97/PjoBT5rVvXoBZ4NCt8ofIvUNosWwZgxK3YaKdh4pqS11ipeh10QstdYQwGxKPdYzJZKWC+4LF6c/FwrrZR6GUybNvFGqDa84amp8vOjlOPSS+N1+O9/Y4Y5ld+PxYujjrxkKB8/vvjvZKNGsMEGK4bygkWfBaZMiR1PX389flcffBB22in9z1nSa/RoOPro+HjmmfFmqWCh92+/RegeNQr69oWTTsrtWDNN4RuFb5Gayj06fJTcBXLixOSz2R07Jp/J1oKszHCPlnNllb6UvCxYkPxc9etHAEs1rLdqVXfbmKXbpEkx2/3JJ7DXXhGO0rFJo3v8XCSbLf/xx+I7ubZqBeutF4uNCxx/PNx/vxYQ1ySLF8eumHfdFf/2DhgQi8AhfvcPOQQGDYouKVddVXsnPxS+UfgWqQn+/jtqs4vWZY8aBfPnFz+uQYOYPSsZsjt3Vju9mmDx4orVrc+bV/q5WrZMrV694LLSStl7njVBXl6UC1xxRcxK33VX1O9mIxAtXx49ygtCed++xTv8FKhXL8peSs6Ud+4cf9GqreGtphs8OH6WZs+OjjQXXRSvZV3pBV5a+K6FT1VEagL3+LNyyQWQEycWrxtt3Dj+gy0Zstdfv3hfWalZVl45uh+0bZva8cuXx8LR8mbUJ02KxV1z5pTesWbVVVOvWW/TJurca2u4Gz8+/vT/5ZfRj/nhh8te7JtuDRpAp06w2mrRC3r8+PjL1X33wT/+EaG85Gz5Rx8VX/S56qqFYbxoKN9ww7rZW7o62WOP+He9V68oZXr33Qjc7dpF68p27WIh7YwZdasXuGa+RSTrhg6NP2sXnc1s1iz5JjQdOqgGWCouPz/+WlKRuvWiPa+LWnnl1MtgWreO8qbqHtaXL4+2cFdfHX8puvfeqNPN9rjz8uCRR2LWfeHCCGiXX172X6/y82HatOS15VOnFn/z3rZt8tny9u3VKSWb3GMB5jnnRKeohx+OLk9Qu3uBq+wEhW+R6mLatKjhbNu2MGSvuWb1DyxSe7lHLWqqNeuzZ0eJVDINGlRskWnLltkNgmPHwoknRl31QQfFn/zXXDN7j19g6NDYFn7oUNhtt9ipsnPnqp1z0aL460fJUD5+fCwiLrDSSjHjXjKUd+4cmw1JZkyaFDtjfvMNHHts/D+w2mqxqPbII+OvLrWpF3i1DN9mtjdwD1AfeMzdbylx+9HApYkv/wJOd/eRidsmAwuAPGB5sidXksK3iIiky6JFqdesz5694rqFAmaFi0xTCe2tW1eu5GrZsug8cf31EXgeeAAOPTT7b3rnz4crr4yuKmusETXmhx+e2XG4R4u7ZLPlP/1UvLVmmzbJQ/l666nULR2WLYtSkxtuiL9APPMM7Lhj7ewFXu3Ct5nVByYAewDTgCHAke7+fZFjtgfGufvvZtYTuNbdt0ncNhno4e7lbPhcSOFbRERyZdmy1GbWC46ZO7f03vTNmlWsbn3ixJjt/u67CLr33Zf9P++7R9eLCy+M53jWWfFGoFmz7I6jpGXLYtFnstnyWbMKj6tfPwJ4yVDeubPalVbGV1/FLPjkyVFqdM018TrsvTfMnFk7eoFXx/C9HRGm90p8fTmAu99cyvEtgDHuvk7i68kofIuISC2Vlxf9sVMtg5kzp3jbvtL07Fl+cF9ttfSGye+/hzPOiDaG22wTs94Freeqs/nzk8+WT5xYvLf9aqslny3v1Endl8qyYEHUgT/1VMx0DxgQC2j33TcW4j/yCJx8cq5HWXnVsdvJOsAvRb6eBmxTxvEnA+8W+dqBQWbmwCPu3jfZncysF9ALoH06GpaKiIhkQf36EYZbt461EeUp2BypYOZ84MCYWS6w774R6GfNirrv2bOLdw0pqlGjitWtt2iRfGH0339Hi7n//jdC1SOPRIu5mrKIunlz2HrruBSVnx97D5QM5Z99FgGyqHbtVpwp79w5rq8p34dMWXXVWIi5777REWXzzeHuu+Hjj6Mk6pRTYo3Q1VfXrr8s5DJ8J/s2Jp2GN7NdiPC9Y5Grd3D36Wa2OjDYzH5w909XOGGE8r4QM99VH7aIiEj1YxZhceWV4Yknor577bUj8O63X/L7FGyOVF7d+s8/x8c//0x+noLNkYoG9q++iq3oIWZ/H3ooFlfPmhXH1eS+zvXqRSemDh1gzz2L37ZwYcyMlwzmTz9dfHOplVcuXPRZ8pLrUpxsO+QQ2Hbb6Aneqxe8806E8ssvh2uvjQD+0EM1+2emqFw+jWlAuyJftwWmlzzIzLoCjwE93X1uwfXuPj3xcZaZvQpsDawQvkVEROqKb76J2u5x46J/9x13lL2za9OmcenYMbXzL1lSft360KFRXlLUwoVwxBHFr2vRIrV69YLLyitX5DuRO02aQLducSnKPWqZS4bykSPh1VeL96VfffXkoXzddaNVX23Utm3sennXXdF6csstI4C3axd/PZkxA154oXb0As9lzXcDYsHlbsCvxILLo9x9bJFj2gMfAse5+5dFrm8K1HP3BYnPBwPXu/vAsh5TNd8iIlIbLVoU23TfdVe0a3v00eiln01LlkTYv/HGmBm+7rro3/znnxWrWy9tc6RVVkmtE0zB56usUnNKFZYuja4rJYP5hAnxfSnQoEEs+kwWzNu0qTnPtzwjR8JRR8VagXPOied8wQXQvXv0Al999VyPMDXVbsElgJntA9xNtBp8wt37mFlvAHd/2MweAw4GpiTustzde5jZesCriesaAM+6e5/yHk/hW0REapvPP49Z7okT4bTTotxktdWyO4YPP4wFlePHw8EHx5uAdu3Kv19JBZsjVaTf+pIlyc+10kqp16y3aVN9N0eaN2/FRZ8TJsTrXfS5N2+evBPLBhvETsE1zaJFcNllsQHUJptEl56bb45SqoED43lVd5UK32ZWD9i26KxzTabwLSIitcXff8ef5++7L2qPH38cdt01u2P47bdoHfjsszE7ef/90U0lW9zhr7/KbtlY8vLXX8nP1aBBYb/1VC6tWuV2l8y8vNjRs2QoHz8+aqQLmEU/7ZKhfMMNo9Sjui/6fO+9qAWfNw/23x8++CBeq5rQC7zSM99m9pW7b5exkWWRwreIiNQGH38cLdh++gnOPhtuuinKLLIlLy8WwF15ZbTcu+yyuNSEGdbFi1MvgZk9O9o9JmMWu5OmWrPepk32Nun566/iiz6LzpwXffPRpMmKiz4LZs+z/deTssyeDaeeGjthtmsXtfMNGsCLL0anlOqqKuH7OmAU8IrX8L3oFb5FRKQmW7AALr00gu8GG0RXk3/8I7tj+Pbb2BZ++HDYY4/YKbNTp+yOIZuWLYsNj1Itgylrc6TVVqvY5kjpXlzoHgsXk4Xyn38uPu4110weytddNzddR9zhscfgvPNiAS/EXx6qcy/wqoTvBUBTYDmwmGgR6O5ejd4TpUbhW0REaqrBg2P2b+pUOP/82J47mxu4/P57tH7r2zeC2d1352Z7+uouPz9KJCpSt17a5kiNG6deBtO6dbQorOzrsWQJ/Phj8mA+d27hcQ0bwvrrJw/mrVtn/udhwoTYGXPIkMLrrr22evYCr5YLLrNN4VtERGqaP/6Aiy6KWb+NNorZ7u2yWAzqHj2qL744QuU550TYqU5lCTWZe/KOMGWF94KZ35IaNqzY5kgtW6ZW8z13bvJQPmlSdGop0KJF8lC+wQbpbRW5bFl00+lTpNXGKadUv17gVQrfia3dOwH//61LtqFNdafwLSIiNck778SmIzNmRPi99trs9rseMya6mHz2WQT+hx5asX+1ZN/ChanXrM+eHW/gkqlXr3CRaSqhvXXr4n3G8/Jg8uQVQ/n48TC9yM4tZtFLvmQo79w5WmNWdsb6889jFnxKoifeXnvByy9Xn17gVSk7OQU4l9gEZwSwLfCVu2d5TXXVKXyLiEhN8PvvUdv69NPRZu3JJ7Pb2eGvv2Jr+rvuihnu226LzXuqe2cMSW7p0oqVwcybFzPyyTRvnlrNeuPG8XOcrCPL338Xnq9p08IwXjSUb7hhbD9fnj/+iEXHzzwTX7dsGZtMVYde4FUJ36OBrYCv3X1zM9sIuM7dD8/MUDNH4VtERKq711+H3r0jBF1+OfznP9GzOhvc4bXX4Nxz4ZdfYiHbLbdEsJK6Iy8vAnhFNkdavjz5uZo2XTGkL1sW5583LwL63LnJu8qsvfaKobxz55hFL9nm8YUXiu+iOnFi7nuBlxa+U6mMWezui80MM1vJ3X8ws84ZGKOIiEidNWdO1FM/91yUdrzzDmyxRfYev6Bt4TvvQNeu8PzzsP322Xt8qT7q1y8My6lwj82RyqtZ/+03GD06Pl+8uPzzTp8el48+Sj7GHXeEbbaJtRCdO8N330V51OLF0YHn3Xdh770r9NSzIpXwPc3MmgOvAYPN7Hdgepn3EBERkZS99FJsxf7771HucdllxWtrM2nJkigruemmWKx2550RwqvTwjWp3sxisWWLFjFLXR73KD1JtWZ99uxos1lUXh588klcStOzZ/z16Kabqvb80q1C3U7MbCegGTDQ3ZeWd3x1o7ITERGpTmbOjND98svQvXvUdm+2WfYe//33Y0HlxInRNvCuu2IBnEh1s3hx8tn0GTOi7eAXXxTvvFJUrhr7VaXsBDPbEejk7k+aWRtgHeDnNI9RRESkTnCP8pJzzokZvZtvjnaC2Zptnj4dLrgg6mQ32AAGDoxOESLV1corQ9u2cUnFsmWxcVB1WHhZUrnrls3sGuBS4PLEVQ2B/pkclIiISG01YwYceCAcfXTUpY4YEWUm2Qjey5fDPfdEjexrr0Wv5NGjFbyl9mnYMEpgmjfP9UhWlMqv+kHAFsBwAHefbmYpNH8RERGRAgWb1Zx3XvwJ/Y47oqtIya4NmfL117Et/IgREbbvvz/33SBE6qJUOnYu9SgMdwAzqyaty0VERGqGadNg333hhBOipnvUqCj7yEbwnjs3NurZbruokX3ppegCoeAtkhulhm8zK9g49kUzewRobmanAu8Dj2ZjcCIiIjWZOzz6aGyU88kncO+98PHHUW6Safn5sYCzYEv6Cy+MzUcOPrjyOwqKSNWVVXbynZld6e7/NbM9gD+BzsDV7j44O8MTERGpmSZPhlNPjY4iu+wCjz0G662XncceNSq6mHzxBeywQ2wLn80uKiJSurLKTnYFDjezwcBkd7/Y3S9S8BYRESldfj48+GCE3a+/juD7/vvZCd4LFsQM95Zbwg8/xIz3p58qeItUJ6XOfLv7FOAgM9sb+NzMhgD5RW7fPwvjExERqTF+/BFOOSVKS/bcM0pO2rfP/OO6R6/w886DX3+NGu+bboJWrTL/2CJSMWV2O0lsI38J8BnwAEXCt4iIiIT8fLjvPrjiimhx9vjjcOKJ2amtnjQpdqQcOBA23zwWVG67beYfV0Qqp9TwbWa3APsDF7r7u9kbkoiISM0xYQKcdFLUV++7LzzySHZ2iVy8GG69NTboadQo+nefcYa2hRep7sr6Fc0DtnT3xdkajIiISE2RlxfbsV91FTRuHD28jzkmO7PdgwbFtvSTJsERR0TP8LXXzvzjikjVlbrg0t2vVPAWERFZ0fffw/bbw8UXw957w9ixcOyxmQ/ev/4Khx0Wm+TUqweDB8c29QreIjVHKpvsiIiICLE9+003wRZbxOLK556DV16BtdbK/OPeeWf07H7zTbjhhmgnuPvumX1cEUk/VYaJiIikYNSoWEQ5fDgcemhsz7766pl/3C++iFruUaNgn31iYWe2+oWLSPqVO/Nt4RgzuzrxdXsz2zrzQxMREcm9pUvh2muhe/fYJv6ll+DFFzMfvOfMgZNPhh13hN9/jxn2t95S8Bap6VIpO3kQ2A44MvH1AqLtoIiISK02fDhstRVcdx0cfnjUeh98cGYfMz8/dsPs3DkWcV5ySTzuQQdpW3iR2iCV8L2Nu58JLAZw99+BRhkdlYiISA4tWQJXXglbbw2zZ8Prr0P//pnftGbkyJjpPvVU2GQT+O67aCe4yiqZfVwRyZ5UwvcyM6sPOICZtUGb7YiISC317bexPftNN8Fxx8Ws8/4Z3tP5zz/h/PPjcSdNgn794JNPYNNNM/u4IpJ9qYTve4FXgdXNrA/wOXBTRkclIiKSZYsWRYnHdttFGH73XXjiCWjePHOP6R714xtvHJvk9OoF48dH6FeJiUjtVG63E3cfYGbDgN0AAw5093EZH5mIiEiWfPlldDKZMCEC8O23w2qrZfYxJ0yAs86KXt1bbgmvvhplLiJSu6XS7WR94Gd3fwAYA+xhZs0zPTAREZFM+/vvKPfYccfoavL++7E9fCaD96JFcPXVsNlm8M030Trw228VvEXqilTKTl4G8sxsA+AxYF3g2YyOSkREJMM++QS6dYO7744+2qNHw267ZfYx33036rhvuCF6hY8fH7Pf9etn9nFFpPpIJXznu/ty4N/APe5+PpDhvbxEREQy46+/4MwzYeed4+uPP44NczLZUeSXX6JF4T77QKNG8OGH0T1lzTUz95giUj2l2u3kSOA44K3EdQ0zNyQREZHMeP/9mHl+6KEoNxk1CnbaKXOPt2wZ/Pe/saDy3Xejg8rIkbDLLpl7TBGp3lIJ3ycSm+z0cfefzWxdoH9mhyUiIpI+f/wRCyn32ANWXhk+/xzuvBOaNMncY37+eSykvPhi2HXXaFl4+eUx8y0idVe54dvdv3f3c9z9ucTXP7v7LZkfmoiISNUNHBiz3Y8/Hq0Ev/sOtt8+c483e3Z0TvnHP6Jl4euvwxtvQMeOmXtMEak5Uul20snMXjKz783sp4JLNgYnIiJSWb//HiG4Z8/oXvLVV7FbZOPGmXm8/PzolNK5c9RzX3ZZdjboEZGaJZWykyeBh4DlwC7A08AzmRyUiIhIVbz5ZmzP/swzsU388OGZbeX33XexOU/v3tFBZeRIuPlmaNo0c48pIjVTKuG7sbt/AJi7T3H3a4FdMzssERGRips7F44+Omab27SJ/tk33ggrrZSZx/vjDzjnHOjRAyZPjrD/4YfQpUtmHk9Ear5yd7gEFptZPWCimZ0F/AqsntlhiYiIVMzLL0e/7nnz4NprM7u40R2efx4uuABmzozHvfHGzG5FLyK1Qyrh+zygCXAOcANRenJ8BsckIiKSslmzom/3Sy9Fd5HBg6Fr18w93vjx8XgffBAz3m++GR9FRFJRbvh29yEAZubufmLmhyQiIlK+gtnns8+GBQuih/bFF0ODVKaVKmHhwniM226LFoUPPhjtC7U7pYhURCrdTrYzs++BcYmvu5nZgxkfmYiISClmzICDDoKjjoINNogFj5dfnrng/dZbsYCzTx844oiY/T79dAVvEam4VBZc3g3sBcwFcPeRwD8zOCYREZGk3OHppyMIv/de7B75xReZW+A4dWqE/H/9K1oUfvxxPP4aa2Tm8USk9kslfOPuv5S4Ki8DYxERESnVtGmw335w/PERvkeOhAsvzMzs89Kl0RN8441h0CC45RYYMSKzW9GLSN2Qyh/ofjGz7QE3s0bEwstxmR2WiIhIcIcnnojOIsuXwz33wFlnQb2Upo8q7pNPonvJ99/DgQfC3XdDhw6ZeSwRqXtS+aerN3AmsA4wDdg88bWIiEhGTZkCe+8Np5wSnUxGjYq+2pkI3jNnwnHHwc47x+LKN9+EV19V8BaR9Cr3ny93n+PuR7v7Gu6+ursf4+5z0/HgZra3mY03s0lmdlmS283M7k3cPsrMtkz1viIiUnPl58NDD8Gmm8KXX0ZnkQ8+gPXXT/9j5eXFY220UXRPufJKGDs2SlxERNKt1PBtZvclgm/SS1Uf2MzqAw8APYEuwJFmVnLJTE+gU+LSi9jmPtX75tyAAQPo2LEj9erVo2PHjgwYMCDXQxKRaqY2/ztR2ef200+w++5R+rHddjBmTHQWycRs97Bh8RhnnFE4s37jjdFKUHKjNv9OiEDZNd9DM/zYWwOT3P0nADN7HjgA+L7IMQcAT7u7A1+bWXMzWwvomMJ9c2rAgAH06tWLhQsXAjBlyhR69eoFwNFHH53LoYlINVGb/52ozHPLz4f77y9sGfjoo3DyyWCW/vHNnw//+U/MqK+xBjz7bLQQzMRjSepq8++ESAGLXJvCgWZN3f3vtD2w2SHA3u5+SuLrY4Ft3P2sIse8Bdzi7p8nvv4AuJQI32XeN5kePXr40KGZfk8ROnbsyJQpU1a4vkOHDkyePDkrYxCR6q02/ztR0ec2cSKcdBJ8/jnssw888gi0bZv+cbnDgAHRJWXOnNip8oYboFmz9D+WVFxt/p2QusfMhrn7Cvvf5nKTnWTzCyXfCZR2TCr3jROY9TKzoWY2dPbs2RUcYuVNnTq1QteLSN1Tm/+dSPW55eXBHXfEdvBjxkC/frGhTSaC97hxsOuucOyx0LEjDBkC996r4F2d1ObfCZECudxkZxrQrsjXbYHpKR6Tyn0BcPe+7t7D3Xu0adOmyoNOVfv27St0vYjUPbX534lUntu4cbDjjnDRRbDXXtHa77jj0l/68fffUcrSrVv0Bn/4Yfjqq6jxluqlNv9OiBTI5SY7Q4BOZrZuon/4EcAbJY55Azgu0fVkW+APd5+R4n1zqk+fPjQpsWKnSZMm9OnTJ0cjEpHqpjb/O1HWc1u+HG6+GTbfPMpNnn02WvqttVb6x/HGG7Ehzy23wNFHww8/wGmnZa5HuFRNbf6dEPl/7l7mBXgJ2B4YDjQCLgKeL+9+qVyAfYAJwI/AlYnregO9E58b0dXkR2A00KOs+5Z36d69u2dT//79vUOHDm5m3qFDB+/fv39WH19Eqr/a/O9Esuc2apR79+7u4H7IIe6//ZaZx/75Z/d//SseZ5NN3D/9NDOPI+lXm38npG4BhnqSPFrugkszaw3cA+yeCMODgHPcfV6a8n/WZHPBpYiIFFq2LGa7b7wRmjePLiOHHJL+x1m6NGrIb7ghZrevvRbOPRcaNkz/Y4mIlKW0BZflbi/v7nOA/+/vY2YtgDMA/Q1IRETK9d13cOKJUW995JGxyLF16/Q/zocfRveSH36Af/87toVv167cu4mIZFVZm+y0M7O+ZvaWmZ1sZk3M7L/AeGD17A1RRERqoiVLopf2VlvBrFnw2mtR353u4P3bb3DMMbDbbjHz/fbb8PLLCt4iUj2VNfP9NPAJ8DKwN/A1MBbo6u6/ZWFsIiJSQw0ZErPdY8fC8cfDXXdBixbpfYyCbeGvvBIWL4arroquJo0bp/dxRETSqazw3dLdr018/p6ZzQS2cvclmR+WiIjURIsXwzXXwH//C2uvDe+8Az17pv9xvv02tpwfPjy2on/gAdhww/Q/johIupXZbMnMWphZSzNrCfwGNCnytYiIyP/78stoH3jbbbEt/Jgx6Q/ev/8eoXvbbWHGDHjhBRg0SMFbRGqOsma+mwHDKL6b5PDERwfWy9SgRESk5li4MGq7774b2reHwYNjNjqd3OGZZ2JDnrlzo4PJddfBaqul93FERDKt1PDt7h2zOA4REamBPv00ZrknTYIzzojNbFZdNb2PMXZsnPvTT2G77WKme/PN0/sYIiLZoj2+RESkwv76C84+G3baCfLz4aOPou46ncH7r7/gkksiaI8ZA48+Cp9/ruAtIjVbuX2+RUREivrgAzjlFJgyJco/+vSBpk3Td373aEt47rnwyy9w0klw662Z6Q0uIpJtmvkWEZGU/PknnHZa1HM3agSffRZ13ukM3j/9BP/6V2yS07x5zHQ//riCt4jUHinNfCd2tWxX9Hh3H176PUREpDZ57z049VT49ddY9Hj99entp71kCdx+e8yiN2gAd94ZZS0N9PdZEallyv1nzcxuAE4AfiS6nJD4uGvmhiUiItXB/PlwwQXw5JPQpUu0E9xmm/Q+xvvvx7bwEybAoYdG8G7bNr2PISJSXaQyp3AYsL67L830YEREpPp4660oM5k5E664Aq6+GlZaKX3nnzEjgv3zz8P668O778Lee6fv/CIi1VEqNd9jgOYZHoeIiFQTc+fCMcdE7XWrVvDNN1EOkq7gvXw53HsvdO4Mr74K114b3UwUvEWkLkhl5vtm4DszGwP8/9by7r5/xkYlIiI58cor0VN77tzYJv6KK2JxZbp8/XXsUDliBOy1F9x/P2ywQfrOLyJS3aUSvvsBtwKjgfzMDkdERHJh9mw46yx48UXYYotYYNmtW/rOP28eXH559Opee2343//g4IPBrPz7iojUJqmE7znufm/GRyIiIlnnHoH7rLOileCNN8bGNg0bpuf8+fnQr1+c8/ffo8b7mmvSvwumiEhNkUr4HmZmNwNvULzsRK0GRURqsN9+ixKTV1+FrbeGJ56ATTZJ3/lHj47zf/45bL89PPQQdO2avvOLiNREqYTvLRIfty1ynVoNiojUUO7Qv3/sILlwIdx2G5x/fvp6ai9YANddFxvwNG8eof7446GetnUTESk/fLv7LtkYiIiIZN6vv0Lv3tFGcPvtIxh37pyec7vHgs1zz43HOfVUuPnm6JgiIiIhlU12rk52vbtfn/7hiIhIJrjHRjkXXABLl8as9FlnQf366Tn/pEmxI+XAgbFQ83//g+22S8+5RURqk1T+CPh3kUse0BPomMExiYhIGk2dCj17wsknw+abRy32ueemJ3gvXhwlJptuCl98EaF+6FAFbxGR0qRSdnJH0a/N7L/E4ksREanG3KFvX7j44ug68sADUXKSrtrrQYNiW/hJk+Dww2Nb+LXXTs+5RURqq8r8E9wEWC/dAxERkfT5+WfYffcI29tsEztInnFGeoL3r79G2N5rr+jTPWhQbBGv4C0iUr5Uar5HE91NAOoDbQDVe4uIVEP5+fDgg3DZZRG0+/aFU05Jz2Y2y5fDfffB1VfH5zfcELPq6dp2XkSkLkilsdR+RT5fDsx09+UZGo+IiFTSxIlR1/3ZZ7D33hG827VLz7m//DK2hR81KurH778f1tPfQEVEKqzcP0C6+xSgFXAA8G9gs0wPSkREUpeXF/XW3brFYsonn4R33klP8J47N2bOd9ghtoh/+WV4+20FbxGRyio3fCdaDfYjAnhr4Ckz+0+mByYiIuX74Qf4xz/gwgujxnvsWDjhhKqXmeTnw+OPRw/wfv2ivGTcOPj3v9NTwiIiUlelUnZyJLCFuy8GMLNbgOHAjZkcmIiIlG75cvjvf+Haa6FpUxgwAI48Mj3BeOTIKDH56qsI9g8+GK0ERUSk6lJZ9z4ZWLnI1ysBP2ZkNCIiUq4xY6KP9uWXw377wfffw1FHVT14L1gQm/B07x714089BZ98ouAtIpJOpc58m9l9RJeTJcBYMxuc+HoP4PPsDE9ERAosWwa33BJdRpo3hxdfhEMPrfp53WNHyvPPhxkz4LTToE8faNmy6ucWEZHiyio7GZr4OAx4tcj1H2dsNCIiktSIEXDiifHxiCPg3nuhTZuqn3fixNhmftAg2GILeOWV6AsuIiKZUWr4dvd+2RyIiIisaOlSuPFGuPlmaNUKXn0VDjyw6uddtChm0W+5BVZeOfp3n356eracFxGR0pVVdlJ0c51iNwHu7l0zNioREWHo0JjtHjMGjjsO7rorPaUgAwfGbPePP0at+H//C2utVfXziohI+coqO9mvjNtERCRDFi+OLia33x6h+K23YN99q37eX36Juu6XX44Wgh98ALvuWvXziohI6soqO5mS7Hoz2wE4CjgzU4MSEamrvvoKTjop+neffDLccQc0a1a1cy5bBvfcE4E+Ly8WU154obaFFxHJhVT6fGNmmxOB+zDgZ+CVDI5JRKTOWbgQrroqSkvatYP33oM996z6eT//PGq5x4yJtoT33gvrrlv184qISOWUVfO9IXAEscnOXOAFwNx9lyyNTUSkTvjss5jtnjQpgvKtt8Kqq1btnLNnw6WXxlbz7dvDa6/B/vtrd0oRkVwra5OdH4DdgH+5+47ufh+Ql51hiYjUfn//DeecAzvtFOUgH34Yu0lWJXjn50PfvlHT/cwzcNllsQnPAQcoeIuIVAdlhe+Dgd+Aj8zsUTPbjeh0IiIiVfTRR7DZZnD//XD22TB6NOxSxb8rfvcdbL99bJLTtWtsE3/zzbH9vIiIVA+lhm93f9XdDwc2IjbWOR9Yw8weMrM0VCKKiNQ9CxZEacmuu0KDBvDpp7EYsioB+c8/4dxzoUcP+PlnePrpCPdduqRv3CIikh5lzXwD4O5/u/sAd98PaAuMAC7L9MBERGqbQYNg002jLOTCC2O3yh13rPz53OH552GjjWKTnN69o0vKsceqxEREpLoqN3wX5e7z3P0Rd1dnWBGRFM2fH20D99oLmjSBL76IjW2aNKn8OcePhz32gCOPhLXXhm+/hQcegBYt0jZsERHJgAqFbxERqZi33oJNNoF+/WLx43ffwbbbVv58ixZFS8KuXWMHzAcegG++iZITERGp/lLq8y0iIhUzb17UYffvH6Umr79e9YD89tuxOPPnn6O05PbbYY010jNeERHJDs18i4ik2auvxmLH55+Hq6+GYcOqFrynToWDDopNclZeORZTPv20greISE2k8C0ikiazZ8MRR8C//w1rrQVDhsB110GjRpU737JlcNttsPHGsePlLbfEIs2dd07nqEVEJJtUdiIiUkXu8L//wVlnxeLKG26I3SUbNqz8OT/9NFoSFmyQc8890KFD2oYsIiI5kpOZbzNraWaDzWxi4uMK6/PNrJ2ZfWRm48xsrJmdW+S2a83sVzMbkbjsk91nICISZs6EQw6Bww+Hjh1h+HD4z38qH7xnzYLjj49dLxcuhDfeiK3hFbxFRGqHXJWdXAZ84O6dgA9I3jd8OXChu28MbAucaWZFt4y4y903T1zeyfyQRUQKucOAAVHb/fbbcOut8OWXsbiyMvLy4KGHYlv4556DK66AsWPhX/9K77hFRCS3chW+DwD6JT7vBxxY8gB3n+HuwxOfLwDGAetka4AiIqWZPj1KQY45JsLyiBFwySWxY2VlDBsG220HZ5wBW2wBo0ZBnz5V6wMuIiLVU67C9xruPgMiZAOrl3WwmXUEtgC+KXL1WWY2ysyeSFa2UuS+vcxsqJkNnT17dhqGLiJ1lTs8+WTMdr//Ptx5J3z2WewwWRnz50frwK23jo4mAwbABx9U/nwiIlL9ZSx8m9n7ZjYmyeWACp5nFeBl4Dx3/zNx9UPA+sDmwAzgjtLu7+593b2Hu/do06ZN5Z6MiNR5v/wC++wDJ50E3brF7PT550P9+hU/V0HJykYbwYMPwplnxo6VRx2lbeFFRGq7jHU7cffdS7vNzGaa2VruPsPM1gJmlXJcQyJ4D3D3V4qce2aRYx4F3krfyEVECrnDo4/CRRdBfj7cd1+Uh9Sr5NTFuHERtj/6CLbaKurFu3dP75hFRKT6ylXZyRvA8YnPjwdeL3mAmRnwODDO3e8scdtaRb48CBiToXGKSB02eTLssQecdloE5dGjo51gZYL3woWxiLJbt9hi/qGH4KuvFLxFROqaXIXvW4A9zGwisEfia8xsbTMr6FyyA3AssGuSloK3mdloMxsF7AKcn+Xxi0gtlp8PDzwQnUu+/RYeeSRqvNddt3Lne+ONqBO/+eYoLRk/Hnr3rlzJioiI1Gw52WTH3ecCuyW5fjqwT+Lzz4Gk1Y/ufmxGBygiddakSXDKKfDJJ7DXXtC3L7RvX7lzTZ4M554b4XuTTeKc//xnWocrIiI1jLaXFxEh+mzfdRd07RqtA594At59t3LBe+nS2Aq+oCvKbbdFqYmCt4iIaHt5Eanzfvghuph89RXstx88/DCsU8ldBT76KBZk/vAD/PvfEegrO3MuIiK1j2a+RaTOWr48ZqU33zzC8jPPRIlIZYL3b7/Fpju77gpLlkQXk5dfVvAWEZHiNPMtInXSmDEx2z1kCBx0UPTbXnPNip8nLy9myq+8EhYtgquugssvh8aN0z9mERGp+TTzLSJ1yrJlcOONsOWW8PPP8MILMUNdmeA9ZAhss020H9xqq9h45/rrFbxFRKR0Ct8iUmeMHBlh+aqroh77++/hsMMqvqvk779HXfc228D06fD88zBoEHTunJlxi4hI7aHwLSK13tKlcM010KNHhOVXXonA3KZNxc7jDk8/HSH7kUfgnHOiVvzww7UtvIiIpEY13yJSqw0bBieeGLtTHntsdB9p1ari5xk7Nma7P/0Utt02Zro33zztwxURkVpOM98iUistXhzbuW+zDcydC2++GbPWFQ3ef/8Nl10WQXv06Nh054svFLxFRKRyNPMtIrXON9/EbPe4cdHR5I47oHnzip3DHV5/PUpLfvklznPLLRUvVRERESlKM98iUmssWgQXXQTbbw9//QUDB8Ljj1c8eP/8M/zrX9GCsFkz+OyzOI+Ct4iIVJXCt4jUCp9/Dt26xSz3qadGH++99qrYOZYsgT59Ylv4Tz6Jcw0fDjvumJkxi4hI3aOyExGp0f7+O2q777sPOnSADz6IXSYr6oMPYkHlhAlwyCGxMLNt2/SPV0RE6jbNfItIjfXxx9C1K9x7b2x0M3p0xYP3jBlw1FGw++6xW+W778L//qfgLSIimaHwLSI1zoIFMUu9yy5Qr160/7v3XlhlldTPsXx53GejjWKHy2uuifC+996ZG7eIiIjKTkSkRhk8OGq6p06FCy6AG26AJk0qdo5vvoHTT4fvvoM994T774dOnTIzXhERkaI08y0iNcIff0To3nNPaNw4em3fcUfFgve8eXDaabDddjBzJrz4YnREUfAWEZFsUfgWkWrvnXdgk03giSfg0ktjxnq77VK/vzs89VRsC//443D++bEt/KGHalt4ERHJLoVvEam25s2D44+HffeNXt1ffx0b3ay8curnGD0a/vnP2HRnww1ju/k77oBVV83YsEVEREql8C0i1dLrr8ds94AB8J//RGjeaqvU7//XX3DxxbDFFrHT5eOPx2Y53bplbswiIiLl0YJLEalW5syBs8+G55+PoPzOOxGgU+UOr7wC550H06bBKafEbHmrVhkbsoiISMo08y0i1cb//he7S778Mlx/PQwZUrHg/eOPUaJyyCERtr/8Eh59VMFbRESqD818i0jOzZwJZ54ZobtHj9htcrPNUr//4sVw221w003QsGHsTnnWWdBA/8KJiEg1o/+aRCRn3OG55+Ccc6JG+5Zb4MILKxaaBw+O4D5xIhx+eCymXGedzI1ZRESkKlR2IiI5MWMGHHggHH109Nn+7rtoI5hq8J4+PcL2nntGiH/vvagTV/AWEZHqTOFbRLLKHfr1i9ruQYNipvrzz2HjjVO7//LlcPfdsS38669Hbfjo0RHCRUREqjuVnYhI1kybBr16wbvvwj/+Ee3/KrK75Jdfxrbwo0ZBz55w332w/vqZG6+IiEi6aeZbRDLOPbqObLIJfPIJ3HsvfPxx6sF77tzYWn6HHWLjnZdfhrffVvAWEZGaR+FbRDJq8uQoCenVC7p3jxKRs8+Gein865OfH7PjnTvDk0/CRRfFhjn//re2hRcRkZpJ4VtEMiI/Hx58MFoGfv01PPQQvP8+rLdeavcfNSpKU045JerBv/sObr8dVlkls+MWERHJJIVvEUm7H3+E3XaLFoDbbw9jx0Lv3qnNdi9YABdcAFtuCRMmxIz3p59WrO+3iIhIdaXwLSJpk5cH99wDXbvGTPXjj8PAgdC+ffn3dY8dLjfaKLqZnHIKjB8PJ5ygEhMREak91O1ERNJi/Hg46aToSLLvvvDII6n33J44MXakHDQotpN/+WXYdtvMjldERCQXNPMtIlWSlxe12JtvHoshn34a3nwzteC9eDFce21hXfi998K33yp4i4hI7aWZbxGptO+/hxNPjMB84IGxwHKttVK778CBMdv9449w5JGx2U6q9xUREampNPMtIhW2bBncdFOUiPz4Izz3HLzySmrhedo0OPTQ2CSnfv3ogPLsswreIiJSN2jmW0QqZNSomO0ePjxC9P33w+qrl3+/ZcuirOSaa6JU5cYbo2/3SitlfswiIiLVhcK3iKRk6dKY7e7TB1q2hJdegoMPTu2+X3wR28KPHh2LMe+7D9ZdN7PjFRERqY5UdiIi5Ro+HLbaCq67Dg4/PGq9Uwnes2dHB5Qdd4T58+HVV2MxpoK3iIjUVQrfIlKqJUvgyith661hzhx44w3o3x9atSr7fvn50LdvbAv/zDNw6aXRCeXAA9WzW0RE6jaVnYhIUt9+G7XdBR1N7rwTmjcv/37ffRclJt98AzvtFB1QunTJ+HBFRERqBM18i0gxixbBJZfAdtvBn3/Cu+/CE0+UH7z//BPOOw969ICffop+3x99pOAtIiJSlGa+ReT/ffllzHJPmAC9esXmOautVvZ93OGFF+CCC+C336B371iU2aJFdsYsIiJSk2jmW0T4+284//xYGLl0afTefuSR8oP3hAmw556xSc7aa0epyYMPKniLiIiURuFbpI775BPo1g3uvhvOOCPaAe62W9n3WbQIrroqtoUfMiR6fX/zTXREERERkdIpfIvUUX/9BWeeCTvvHF9//HGE6FVWKft+77wDm2wSm+Qcdhj88EOcp379TI9YRESk5lP4FqmD3n8fNt0UHnooyk1GjYrOJGWZOhX+/e/YJGelleDDD6ON4JprZmfMIiIitUFOwreZtTSzwWY2MfExaYWomU02s9FmNsLMhlb0/iJS3B9/wKmnwh57wMorw+efRwvBJk1Kv8+yZbHwcuONYeBAuPlmGDkSdtkle+MWERGpLXI1830Z8IG7dwI+SHxdml3cfXN371HJ+4sI0TJw002jbeAll0Q/7u23L/s+n34KW2wRx+++e/T8vuwyaNQoO2MWERGpbXIVvg8A+iU+7wccmOX7i9QZv/8OJ5wA++wT3Uu++gpuvRUaNy79PrNmxX122ilqw19/PS4dO2Zp0CIiIrVUrsL3Gu4+AyDxcfVSjnNgkJkNM7Nelbg/ZtbLzIaa2dDZs2enafgiNcMbb8TiyP79Y5v44cNjq/jS5OXBww/HtvDPPguXXw5jx8L++2dvzCIiIrVZxjbZMbP3gWRLsa6swGl2cPfpZrY6MNjMfnD3TysyDnfvC/QF6NGjh1fkviI11dy5cM45EaC7doW33oIttyz7PsOHx7bw334b9dwPPBB13iIiIpI+GQvf7r57abeZ2UwzW8vdZ5jZWsCsUs4xPfFxlpm9CmwNfAqkdH+Ruujll6Nf97x5cO21MXtdVo32/PnRs/vBB6FNm5glP+ooMMvWiEVEROqOXJWdvAEcn/j8eOD1kgeYWVMzW7Xgc2BPYEyq9xepa2bNgkMPhUMOgXbtYNgwuOaa0oO3OwwYABttFMH7jDOiZ/fRRyt4i4iIZEquwvctwB5mNhHYI/E1Zra2mb2TOGYN4HMzGwl8C7zt7gPLur9IXeQOzz0HXbpEjfdNN8HXX0e5SWl++CF2sTzmGGjfPkpN7rsPmjfP2rBFRETqpIyVnZTF3ecCK2xgnSgz2Sfx+U9At4rcX6SumTEj6rRffx222SbaCHbpUvrxCxdCnz7Rt7tp09hk59RTtTuliIhItuQkfItI1bjH7pLnnQeLFsF//xuflxWi33wTzj4bpkyB446D226DNdbI1ohFREQEtL28SI0zbRrstx8cf3y0ERw5Ei68sPTgPWUKHHBAtAts2hQ+/hj69VPwFhERyQWFb5Eawh0efzwC98cfwz33wCefwIYbJj9+6VK45ZZoF/j++7GxzogRsXGOiIiI5IbKTkRqgClToFcvGDQIdt4ZHnsM1l+/9OM//ji6l4wbBwcdBHffHQsrRUREJLc08y1SjeXnx6LITTeFL7+MloAffFB68J45E449NjbJWbw4Ntd55RUFbxERkepCM98i1dRPP8Epp8BHH8Eee8Cjj0KHDsmPzcuDRx6BK66Ijib/+U9srtOkSXbHLCIiImVT+BapZvLz4f77Izw3aBCh++STS9/4ZuhQ6N07NtXZbbfYFr5z5+yOWURERFKjshORamTCBPjnP+Hcc6O2e+zYmP1OFrx//x3OPBO23hp+/TU22hk8WMFbRESkOlP4FqkG8vKiV3e3bhG4+/WLeu22bVc8tqDH90YbwcMPR+/uH36AI47QtvAiIiLVncpORHLs++/hpJPgm2+iH/dDD8Faa5V+7BlnRIvBbbaBgQNhiy2yO14RERGpPM18i+TI8uVw880RnidNgmefhVdfTR68//4bLrssZsZHjYrFlV9+qeAtIiJS02jmWyQHRo+GE0+MRZKHHBILLJPtOOkOr78eNeBTp8Z9br0V2rTJ/phFRESk6jTzLZJFy5bB9ddD9+4Rpv/3v7gkC94//xxbwh90EKy2Gnz2GTzxhIK3iIhITaaZb5Es+e67mLkeORKOOiq2h2/desXjliyBO+6AG2+EevViIeY550DDhtkfs4iIiKSXZr5FMmzJktj0ZqutYNasKCMZMCB58P7gg6jrvvJK2Hff6GJy4YUK3iIiIrWFwrdIBg0ZEiUmffrAMcdEG8H991/xuBkzYjZ8992jNOWdd6IcJVmrQREREam5FL5FMmDxYrj0Uth2W/jjjwjTTz0FLVoUPy4vD+67L3p2v/wyXH01jBkDPXvmZNgiIiKSYar5FkmzL7+Mvt3jx8Opp8Ltt0OzZise9803cPrpUQu+xx6xLXynTtkfr4iIiGSPZr5F0mThQrjgAthxx5j5HjwY+vZdMXjPmwe9e8N228HMmfDii/DeewreIiIidYHCt0gafPppLJS8666YzR49Ouq3i3KPbeM32ggeewzOOw/GjYNDD9W28CIiInWFwrdIFfz1F5x9Nuy0E+Tnw0cfRfnIqqsWP27MmDjmhBNggw1ic50774z+3SIiIlJ3KHyLVNIHH8Bmm0XYPvfc2PZ9552LH/PXX3DxxbD55tHp5LHH4PPPY5ZcRERE6h6Fb5EK+vNPOO20KCtp1Ch2nrz7bmjatPAYd3jlFdh449gk54QTYgHmySfHxjkiIiJSNykGiFTAwIGwySYxg33RRTBiBOywQ/FjfvoJ9tsPDj4YWraEL76I45NtqiMiIiJ1i8K3SAp+/z22hu/ZM+q0v/wyWgg2blx4zJIlcMMNEc4//TRquocNg+23z924RUREpHpRn2+Rcrz5ZpSZzJoFV1wRG+GstFLxYwYPhjPPhIkT4bDDInivs05uxisiIiLVl2a+RUoxd25sCb///lEy8s03sU180eA9fToccQTsuWfUeb/3HrzwgoK3iIiIJKfwLZLEK69E+cgLL8A118DQodC9e+Hty5fDPfdEz+7XXoPrrove3nvumbMhi4iISA2gshORImbPhrPOil0nt9giZrJLtgX86qvYSGfkSNh7b7j/flh//dyMV0RERGoWzXyLECUjL7wAXbrETHafPlFmUjR4z50Lp54aCyjnzIGXXoJ33lHwFhERkdQpfEud99tv0RbwiCNgvfVg+PBYWNmwYdyenw9PPAGdO8OTT8KFF8a28AcfrG3hRUREpGIUvqXOcodnnonZ7nfegdtui57cm2xSeMyoUfDPf8bmOBttBN99F5vmlNw+XkRERCQVCt9SJ/36a3QxOe642IVy5MjYBr5BYhXEggUxw73llrEz5ZNPRu/uzTbL7bhFRESkZlP4ljrFPUpINtkEPvggtoX/9NMoKSm4/aWXIpDfeWfMeP/wQ2wPr23hRUREpKoUJ6TOmDo1dqg8+WTYfPNoDXjuuVC/ftw+aVLcfuih0KZNdDV55BFo1SqnwxYREZFaROFbar38/AjRm24Kn38ODzwAH35Y2KVk8WK49tq4/csvo3/3kCGw7bY5HbaIiIjUQurzLbXaTz/BKafARx/B7rvDo49Cx46Ft7/3XmwL/+OP0e3kzjthrbVyNlwRERGp5TTzLbVSfj7cd18skBw6FPr2hUGDCoP3tGlRXrL33lF2MngwPPecgreIiIhklsK31DoTJ8LOO8M550SbwLFjY3McM1i2LGa3N94Y3noLbrgh2gnuvnuuRy0iIiJ1gcpOpNbIy4t67SuvhJVXjvaAxx9fuBHOF1/EtvCjR8M++8TM+Hrr5XbMIiIiUrdo5ltqhXHjYMcdozf3HnvEbPcJJ0TwnjMnOpzsuCPMnw+vvhqz3greIiIikm0K31KjLV8Ot9wCW2wBEybAgAHw+uuw9tpR9/3YY9HD++mn4ZJL4Pvv4cADtS28iIiI5IbKTqTGGjMGTjwxFlQefHC0EFxjjbhtxIgoMfn666j7fvDB4tvGi4iIiOSCZr6lxlm2LBZKbrklTJkCL74Yu1KusQb8+Secdx507x7tA/v1g48/VvAWERGR6kEz31KjjBgRs90jRsCRR8YCyzZtYlv4F1+E88+H336D006Dm26CFi1yPWIRERGRQpr5lhph6VK4+mrYaqsI16+9Bs8+G8F7wgTYc8/YJGettaLU5KGHFLxFRESk+lH4lmpv6NAoI7nhBjjqqOhkcsABsGhRBPLNNoNvv4X774+PW2+d6xGLiIiIJKfwLdXW4sVw2WWwzTbw++/RHrBfP2jZEt59FzbdNAL5oYfC+PGxTXz9+rketYiIiEjpchK+zaylmQ02s4mJjysUCJhZZzMbUeTyp5mdl7jtWjP7tcht+2T9SUhGffVVtA+89dao8R47FvbdF375JTqb7LMPNGoEH34I/fvDmmvmesQiIiIi5cvVzPdlwAfu3gn4IPF1Me4+3t03d/fNge7AQuDVIofcVXC7u7+TjUFL5i1cGBvl7LBDfP7ee9Gru0kTuP322Bb+3XdjMeXIkbDLLrkesYiIiEjqctXt5ABg58Tn/YCPgUvLOH434Ed3n5LZYUkuffYZnHQSTJoUPbpvvRVWXTWuP/30mP3+17/g3nuhY8dcj1ZERESk4nI1872Gu88ASHxcvZzjjwCeK3HdWWY2ysyeSFa2UsDMepnZUDMbOnv27KqNWjLir7/g7LNjM5y8vCglefDBWFB5wglx/YIFsXPlG28oeIuIiEjNlbHwbWbvm9mYJJcDKnieRsD+wP+KXP0QsD6wOTADuKO0+7t7X3fv4e492rRpU/EnIhn14YfQtWvsTnnOOTB6NOy0EzzyCGy0UWwXf9llsS38/vvnerQiIiIiVZOxshN3372028xsppmt5e4zzGwtYFYZp+oJDHf3mUXO/f+fm9mjwFvpGLNkz59/wiWXRMju1Ak+/RR23BGGD48Sk2+/hZ13jlDepUuuRysiIiKSHrkqO3kDOD7x+fHA62UceyQlSk4Sgb3AQcCYtI5OMuq996JN4KOPxuLKESOiV/c558QmOpMnRweTDz9U8BYREZHaJVfh+xZgDzObCOyR+BozW9vM/r9ziZk1Sdz+Son732Zmo81sFLALcH52hi1VMX8+nHwy7L03NG0KX3wRHUxeey1KTO6/P2a9x4+Ho48Gs1yPWERERCS9ctLtxN3nEh1MSl4/HdinyNcLgVZJjjs2owOUtHvrLTjtNJg5M2q4r7kmZrh33z1muHv0gDffjI8iIiIitZV2uJSMmjcPjj02WgS2bAlffw1XXRU7U3btCsOGRWeTr79W8BYREZHaT+FbMubVV6Nm+/nn4eqrI2j/9htssklsknPEEVFicvrp2hZeRERE6oZcbbIjtdjs2dG3+4UXYPPNYeBAaNECDjssenV36QIffxwtBUVERETqEs18S9q4w4svxsz2K6/AjTfGosr33ovAPXhw7Fr53XcK3iIiIlI3aeZb0mLmTDjjjAjdW20FTz4ZM+A9esC4cXDggXD33dChQ65HKiIiIpI7mvmWKnGPXSi7dIG3346Z7ddeg1tugV12iS3i33wz6r8VvEVERKSuU/iWSps+HQ44AI45Bjp3jt0pV1klgvgLL8CVV8LYsbDffrkeqYiIiEj1oLITqTB3eOopOP98WLoU7rwTdtgBjj8ehg6FXXeN9oGdO+d6pCIiIiLVi2a+pUKmToV99oGTToJu3eDTT2HSJNh2W5g2DZ59Ft5/X8FbREREJBnNfEtK3KFvX7j4YsjPh3vvhWbNYN99Yc6caC14/fVxnYiIiIgkp5lvKdfPP8c28L17RyeTF1+El1+OMpOOHWHIELjnHgVvERERkfJo5ltKlZ8ftduXXQb16sFdd8UOlQccAKuuCo88AqecEreJiIiISPkUviWpSZPg5JOjpnuvvaJjye23R833CSdES8HVV8/1KEVERERqFs1ZSjF5eTHD3bUrjBwJV18NDRtGTfdqq0UYf/JJBW8RERGRytDMt/y/H36ILiZffQV77gmdOsVsd7168fHccyOIi4iIiEjlaOZbWL4cbrsNNt+8MIBPmQIPPAA9e8b28BddpOAtIiIiUlWa+a7jxoyJsD1kCGy3HTRuDE88AeutF9vF77NPrkcoIiIiUnto5ruOWrYMbrwRttwyFlf+4x+xFfznn0ed95gxCt4iIiIi6aaZ7zpo5Eg48UT47jtYd90oO/nss+jl/cADsOGGuR6hiIiISO2kme86ZOlSuOYa6NEjZrnXWAMmT47w/cILMGiQgreIiIhIJmnmu44YNixmu0ePjq/r14fZs6ODyXXXRRtBEREREckszXzXcosXwxVXwDbbFAZvgK23jkB+110K3iIiIiLZopnvWuybb2K2e9y4wutatozdKU86SdvCi4iIiGSb4lcttGhR9OXefvviwfvkk2H8eDjlFAVvERERkVzQzHct8/nnMas9cWLhdZttBg89BDvskLtxiYiIiIhmvmuNv/+OxZP//Gdh8F5lFbjzThg+XMFbREREpDrQzHct8NFHUVLy88+F1x16aATvtm1zNy4RERERKU4z3zXYggVw+umw666FwXuDDWDgQHjxRQVvERERkepGM9811KBBcOqpMHVqfL3SSnD55XDppbDyyrkdm4iIiIgkp/Bdw/zxB1x4ITz+eOF1e+0F998fs94iIiIiUn0pfNcg77wDvXrBr7/G1+usA3ffDQcfDGY5HZqIiIiIpEA13zXAvHlw/PGw774RvOvXj9nvcePgkEMUvEVERERqCs18V3Ovvw69e8Nvv8XX228fPbu7ds3tuERERESk4jTzXU3NmQNHHgkHHhjBu1UreOIJ+OwzBW8RERGRmkrhuxr63/+gSxd4/vn4+tRTY1v4E0/UtvAiIiIiNZnKTqqRmTPhzDPh5Zfj627d4OGHYdttczsuEREREUkPzaNWA+7w7LOwySYRvFddNbqYDB2q4C0iIiJSm2jmO8dmzIgFlW+8EV8fcQTccQesvXZuxyUiIiIi6afwnSPu8PTTcN55MH8+dOoEDzwAe+yR65GJiIiISKao7CQHfvklenafcAIsXgw33ACjRyt4i4iIiNR2mvnOInd47LHYIGfBAujZM7aFX2+9XI9MRERERLJB4TtLJk+OloHvvw9t20K/ftHDW7tTioiIiNQdKjvJMHd48EHYdFP4+GO4+OLYFv6ggxS8RUREROoazXxn2NdfR+/uf/yjMISLiIiISN2k8J1hW28Nw4bBFltopltERESkrlP4zrD69WHLLXM9ChERERGpDlTzLSIiIiKSJTkJ32Z2qJmNNbN8M+tRxnF7m9l4M5tkZpcVub6lmQ02s4mJjy2yM3IRERERkcrL1cz3GODfwKelHWBm9YEHgJ5AF+BIM+uSuPky4AN37wR8kPhaRERERKRay0n4dvdx7j6+nMO2Bia5+0/uvhR4HjggcdsBQL/E5/2AAzMyUBERERGRNKrONd/rAL8U+Xpa4jqANdx9BkDi4+qlncTMepnZUDMbOnv27IwNVkRERESkPBnrdmJm7wNrJrnpSnd/PZVTJLnOKzoOd+8L9AXo0aNHhe8vIiIiIpIuGQvf7r57FU8xDWhX5Ou2wPTE5zPNbC13n2FmawGzqvhYIiIiIiIZV53LToYAncxsXTNrBBwBvJG47Q3g+MTnxwOpzKSLiIiIiORUrloNHmRm04DtgLfN7L3E9Wub2TsA7r4cOAt4DxgHvOjuYxOnuAXYw8wmAnskvhYRERERqdbMve6UQffo0cOHDh2a62GIiIiISC1nZsPcfYX9bKpz2YmIiIiISK2i8C0iIiIikiUK3yIiIiIiWaLwLSIiIiKSJQrfIiIiIiJZovAtIiIiIpIlCt8iIiIiIlmi8C0iIiIikiUK3yIiIiIiWaLwLSIiIiKSJXVqe3kzmw1MycFDtwbm5OBxJbv0OtcNep3rBr3OtZ9e47ohl69zB3dvU/LKOhW+c8XMhrp7j1yPQzJLr3PdoNe5btDrXPvpNa4bquPrrLITEREREZEsUfgWEREREckShe/s6JvrAUhW6HWuG/Q61w16nWs/vcZ1Q7V7nVXzLSIiIiKSJZr5FhERERHJEoXvNDKzvc1svJlNMrPLktxuZnZv4vZRZrZlLsYpVZPC63x04vUdZWZfmlm3XIxTqqa817nIcVuZWZ6ZHZLN8UnVpfIam9nOZjbCzMaa2SfZHqNUXQr/ZjczszfNbGTidT4xF+OUyjOzJ8xslpmNKeX2apW/FL7TxMzqAw8APYEuwJFm1qXEYT2BTolLL+ChrA5SqizF1/lnYCd37wrcQDWsN5Oypfg6Fxx3K/BedkcoVZXKa2xmzYEHgf3dfRPg0GyPU6omxd/lM4Hv3b0bsDNwh5k1yupApaqeAvYu4/Zqlb8UvtNna2CSu//k7kuB54EDShxzAPC0h6+B5ma2VrYHKlVS7uvs7l+6+++JL78G2mZ5jFJ1qfw+A5wNvAzMyubgJC1SeY2PAl5x96kA7q7XueZJ5XV2YFUzM2AVYB6wPLvDlKpw90+J16001Sp/KXynzzrAL0W+npa4rqLHSPVW0dfwZODdjI5IMqHc19nM1gEOAh7O4rgkfVL5Xd4QaGFmH5vZMDM7Lmujk3RJ5XW+H9gYmA6MBs519/zsDE+ypFrlrwa5euBayJJcV7KVTCrHSPWW8mtoZrsQ4XvHjI5IMiGV1/lu4FJ3z4sJM6lhUnmNGwDdgd2AxsBXZva1u0/I9OAkbVJ5nfcCRgC7AusDg83sM3f/M8Njk+ypVvlL4Tt9pgHtinzdlngXXdFjpHpL6TU0s67AY0BPd5+bpbFJ+qTyOvcAnk8E79bAPma23N1fy8oIpapS/Td7jrv/DfxtZp8C3QCF75ojldf5ROAWj97Lk8zsZ2Aj4NvsDFGyoFrlL5WdpM8QoJOZrZtYqHEE8EaJY94Ajkusut0W+MPdZ2R7oFIl5b7OZtYeeAU4VjNkNVa5r7O7r+vuHd29I/AScIaCd42Syr/ZrwP/MLMGZtYE2AYYl+VxStWk8jpPJf66gZmtAXQGfsrqKCXTqlX+0sx3mrj7cjM7i+h6UB94wt3HmlnvxO0PA+8A+wCTgIXEu22pQVJ8na8GWgEPJmZFl7t7j1yNWSouxddZarBUXmN3H2dmA4FRQD7wmLsnbWUm1VOKv8s3AE+Z2WiiPOFSd5+Ts0FLhZnZc0SnmtZmNg24BmgI1TN/aYdLEREREZEsUdmJiIiIiEiWKHyLiIiIiGSJwreIiIiISJYofIuIiIiIZInCt4iIiIhIlih8i4jUQGaWZ2YjzGysmY00swvMLCv/ppvZtWZ2UZLr1zazlxKf72xmbyU+39/MLkt8fqCZdcnGOEVEqiP1+RYRqZkWufvmAGa2OvAs0Izob5sT7j4dOCTJ9W9QuLHJgcBbwPfZG5mISPWhmW8RkRrO3WcBvYCzEju4dTSzz8xseOKyfcGxZnaJmY1OzJbfkrhufTMbaGbDEvfbKHH9v8zsGzP7zszeT+z+V6CbmX1oZhPN7NTE8R3NbIVNaMzsBDO7PzGO/YHbE7P265vZ8CLHdTKzYRn5JomIVBOa+RYRqQXc/adE2cnqwCxgD3dfbGadgOeAHmbWk5h53sbdF5pZy8Td+wK93X2imW0DPAjsCnwObOvubmanAJcAFybu0xXYFmgKfGdmb6cwxi/N7A3gLXcvKE/5w8w2d/cRxK5zT1X5myEiUo0pfIuI1B6W+NgQuN/MNgfygA0T1+8OPOnuCwHcfZ6ZrQJsD/zPrODurJT42BZ4wczWAhoBPxd5rNfdfRGwyMw+ArYGRlRizI8BJ5rZBcDhifOIiNRaCt8iIrWAma1HBO1ZRN33TKAbUV64uOAwwEvctR4wv6B+vIT7gDvd/Q0z2xm4tshtJc9T8utUvZwY74fAMHefW8nziIjUCKr5FhGp4cysDfAwcL+7O7Hwcoa75wPHAvUThw4CTjKzJon7tXT3P4GfzezQxHVmZt0SxzcDfk18fnyJhz3AzFY2s1bAzsCQFIe7AFi14At3Xwy8BzwEPJniOUREaiyFbxGRmqlxQatB4H0iWF+XuO1B4Hgz+5ooOfkbwN0HEl1HhprZCKCgXeDRwMlmNhIYCxyQuP5aohzlM2BOicf/Fngb+Bq4IdHpJBXPAxcnFnGun7huADFzPijFc4iI1FgWkyQiIiK5kegZ3szdr8r1WEREMk013yIikjNm9iqwPtFdRUSk1tPMt4iIiIhIlqjmW0REREQkSxS+RURERESyROFbRERERCRLFL5FRERERLJE4VtEREREJEsUvkVEREREsuT/AKT/PQAcIEuIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.23221562692591352 R2_SCORE:  0.11815265272779696\n"
     ]
    }
   ],
   "source": [
    "degree=4\n",
    "poly_reg = PolynomialFeatures(degree)\n",
    "X_poly = poly_reg.fit_transform(X_train)\n",
    "pol_reg = LinearRegression()\n",
    "pol_reg.fit(X_poly, Y_train)\n",
    "\n",
    "def polymonial():\n",
    "    plt.scatter(X_test, Y_test, color='black')\n",
    "    plt.plot(X_test, pol_reg.predict(poly_reg.fit_transform(X_test)), color='blue')\n",
    "    plt.title('(Polynomial Regression)')\n",
    "    plt.xlabel('Daceability')\n",
    "    plt.ylabel('Album Release Year')\n",
    "    plt.show()\n",
    "    return\n",
    "polymonial()\n",
    "\n",
    "y_train_predict1 = pol_reg.predict(X_poly)\n",
    "rmse1=(np.sqrt(mean_squared_error(Y_train,y_train_predict1)))\n",
    "r2_1=r2_score(Y_train,y_train_predict1)\n",
    "print('RMSE :',rmse1,'R2_SCORE: ',r2_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c00819e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.2773500981126146 R2_SCORE:  -0.08333333333333282\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_norm.iloc[:, 0:1].values\n",
    "Y = df_norm.iloc[:, 1].values \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)\n",
    "\n",
    "model = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
    "model.fit(X_train, Y_train.astype(int))\n",
    "\n",
    "p_model = model.predict(X_test)\n",
    "score = model.score(X_test, Y_test.astype(int))\n",
    "cm = metrics.confusion_matrix(Y_test.astype(int), p_model)\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm, annot=True, linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(score)\n",
    "plt.title(all_sample_title, size = 15);\n",
    "\n",
    "y_train_predict2 = model.predict(X_train)\n",
    "rmse2=(np.sqrt(mean_squared_error(Y_train.astype(int),y_train_predict2)))\n",
    "r2_2=r2_score(Y_train.astype(int),y_train_predict2)\n",
    "print('RMSE :',rmse2,'R2_SCORE: ',r2_2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}