All types Of Feature Transformation.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "### Transformation of Features\n",
    "\n",
    "Why Transformation of Features Are Required?\n",
    "\n",
    "1. Linear Regression---Gradient Descent ----Global Minima\n",
    "2. Algorithms like KNN,K Means,Hierarichal Clustering--- Eucledian Distance\n",
    "\n",
    "Every Point has some vectors and Directiom\n",
    "\n",
    "Deep Learning Techniques(Standardization, Scaling)\n",
    "1.ANN--->GLobal Minima, Gradient\n",
    "2.CNN\n",
    "3.RNN\n",
    "\n",
    "0-255 pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Types Of Transformation\n",
    "1. Normalization And Standardization\n",
    "2. Scaling to Minimum And Maximum values\n",
    "3. Scaling To Median And Quantiles\n",
    "4. Guassian Transformation\n",
    "5. Logarithmic Transformation\n",
    "6. Reciprocal Trnasformation\n",
    "7. Square Root Transformation\n",
    "8. Exponential Trnasformation\n",
    "9. Box Cox Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Standardization(Why standardization?)\n",
    "Standardization comes into picture when features of input data set have large differences between their ranges, or simply when they are measured in different measurement units (e.g., Pounds, Meters, Miles … etc).\n",
    "\n",
    "We try to bring all the variables or features to a similar scale. standarisation means centering the variable at zero.\n",
    "z=(x-x_mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass   Age     Fare\n",
       "0         0       3  22.0   7.2500\n",
       "1         1       1  38.0  71.2833\n",
       "2         1       3  26.0   7.9250\n",
       "3         1       1  35.0  53.1000\n",
       "4         0       3  35.0   8.0500"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('titanic.csv', usecols=['Pclass','Age','Fare','Survived'])\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].fillna(df.Age.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Age         0\n",
       "Fare        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Independent and dependent features\n",
    "X=df.iloc[:,1:]\n",
    "y=df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass   Age     Fare\n",
       "0         3  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         3  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         3  35.0   8.0500\n",
       "..      ...   ...      ...\n",
       "886       2  27.0  13.0000\n",
       "887       1  19.0  30.0000\n",
       "888       3  28.0  23.4500\n",
       "889       1  26.0  30.0000\n",
       "890       3  32.0   7.7500\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>41.5792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10.5167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>120.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>77.2875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>596 rows × 3 columns</p>\n",
       "</div>"
      ],
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       "     Pclass   Age      Fare\n",
       "6         1  54.0   51.8625\n",
       "718       3  28.0   15.5000\n",
       "685       2  25.0   41.5792\n",
       "73        3  26.0   14.4542\n",
       "882       3  22.0   10.5167\n",
       "..      ...   ...       ...\n",
       "106       3  21.0    7.6500\n",
       "270       1  28.0   31.0000\n",
       "860       3  41.0   14.1083\n",
       "435       1  14.0  120.0000\n",
       "102       1  21.0   77.2875\n",
       "\n",
       "[596 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### standarisation: We use the Standardscaler from sklearn library\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "### fit vs fit_transform\n",
    "X_train_scaled=scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled=scaler.transform(X_test)"
   ]
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       "       [ 0.80576177, -1.02129548,  0.15114801],\n",
       "       [-0.41002054, -0.01728892, -0.4162854 ],\n",
       "       [-1.62580285,  0.83225509,  1.99439528],\n",
       "       [ 0.80576177, -0.09452019, -0.46999109],\n",
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       "       [ 0.80576177, -0.09452019, -0.46772233],\n",
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       "       [ 0.80576177, -0.09452019, -0.4672363 ],\n",
       "       [ 0.80576177, -0.09452019, -0.32402243],\n",
       "       [-0.41002054, -0.09452019, -0.62041562],\n",
       "       [ 0.80576177, -1.87083949, -0.37788948],\n",
       "       [-1.62580285,  1.52733655,  0.48634343],\n",
       "       [ 0.80576177, -1.09852675, -0.3394119 ],\n",
       "       [-1.62580285, -0.09452019,  1.53526365],\n",
       "       [ 0.80576177, -0.01728892, -0.46926205],\n",
       "       [ 0.80576177,  0.98671764, -0.47363627],\n",
       "       [ 0.80576177, -0.86683293, -0.2743663 ],\n",
       "       [ 0.80576177, -0.63513911, -0.45646406],\n",
       "       [-0.41002054,  1.14118018, -0.11495032],\n",
       "       [-0.41002054, -0.09452019, -0.36768297],\n",
       "       [-0.41002054,  0.60056127, -0.11495032],\n",
       "       [ 0.80576177,  0.75502382, -0.05419727],\n",
       "       [-0.41002054,  0.36886745,  0.01141601],\n",
       "       [-0.41002054,  0.05994235, -0.36768297],\n",
       "       [-0.41002054,  2.8402682 , -0.4162854 ],\n",
       "       [-1.62580285, -0.09452019,  2.22809133],\n",
       "       [ 0.80576177,  1.06394891, -0.46391578],\n",
       "       [ 0.80576177, -0.09452019, -0.45103614],\n",
       "       [ 0.80576177, -0.40344529, -0.47468997],\n",
       "       [ 0.80576177, -0.86683293, -0.46885768],\n",
       "       [ 0.80576177, -0.09452019, -0.15091612],\n",
       "       [ 0.80576177, -0.09452019, -0.46391578],\n",
       "       [ 0.80576177, -0.09452019, -0.48335676],\n",
       "       [ 0.80576177, -0.09452019,  0.47791772],\n",
       "       [ 0.80576177, -0.09452019, -0.46901904],\n",
       "       [-0.41002054,  0.52332999, -0.11495032],\n",
       "       [ 0.80576177,  0.2144049 , -0.31908053],\n",
       "       [ 0.80576177, -0.44206093, -0.47987294],\n",
       "       [-1.62580285, -0.09452019, -0.10425778],\n",
       "       [-0.41002054,  0.75502382, -0.36768297],\n",
       "       [-1.62580285, -0.09452019,  0.9770647 ],\n",
       "       [ 0.80576177, -0.09452019, -0.45597804],\n",
       "       [-1.62580285,  2.76303693,  0.58452035],\n",
       "       [-0.41002054, -0.48067656, -0.4162854 ],\n",
       "       [-1.62580285, -0.09452019,  0.38784185],\n",
       "       [ 0.80576177, -0.55790784, -0.46691358],\n",
       "       [ 0.80576177,  0.44609872, -0.48335676],\n",
       "       [-1.62580285, -0.48067656,  0.61133139],\n",
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       "       [-1.62580285,  0.52332999, -0.10741694],\n",
       "       [ 0.80576177, -1.56191439, -0.32402243],\n",
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       "       [ 0.80576177,  0.67779254, -0.01021207],\n",
       "       [-0.41002054, -0.9440642 , -0.4162854 ],\n",
       "       [ 0.80576177, -1.94807076, -0.29575137],\n",
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       "       [-1.62580285, -0.40344529,  4.19162962],\n",
       "       [-1.62580285,  1.21841146, -0.10425778],\n",
       "       [-0.41002054, -0.01728892, -0.08149629],\n",
       "       [ 0.80576177, -0.09452019, -0.46691358],\n",
       "       [-1.62580285, -0.09452019,  0.06973893],\n",
       "       [-0.41002054,  0.98671764, -0.36768297],\n",
       "       [ 0.80576177,  0.52332999, -0.15091612],\n",
       "       [-0.41002054,  0.29163617, -0.38177767],\n",
       "       [ 0.80576177, -0.9440642 , -0.48327511],\n",
       "       [ 0.80576177, -0.01728892, -0.43572637],\n",
       "       [-0.41002054,  1.60456783, -0.11495032],\n",
       "       [-1.62580285,  0.44609872,  1.12927197],\n",
       "       [-1.62580285,  0.67779254,  3.80289181],\n",
       "       [-0.41002054,  0.36886745, -0.36768297],\n",
       "       [-1.62580285, -0.9440642 ,  0.48771985],\n",
       "       [ 0.80576177, -1.40745185, -0.01239918],\n",
       "       [ 0.80576177,  2.45411184, -0.49915255],\n",
       "       [ 0.80576177,  0.05994235, -0.45200819],\n",
       "       [-0.41002054, -1.71637694, -0.11009007],\n",
       "       [ 0.80576177,  2.60857438, -0.43402529],\n",
       "       [ 0.80576177, -0.71237038, -0.47987294],\n",
       "       [ 0.80576177, -0.09452019, -0.18574851],\n",
       "       [ 0.80576177, -0.01728892, -0.43605104],\n",
       "       [-1.62580285,  0.52332999,  1.71250117],\n",
       "       [ 0.80576177, -0.09452019, -0.3393322 ],\n",
       "       [ 0.80576177,  1.60456783, -0.46391578],\n",
       "       [-1.62580285, -0.17175147,  3.49135022],\n",
       "       [ 0.80576177,  0.05994235, -0.47946856],\n",
       "       [-1.62580285,  0.29163617,  1.12927197],\n",
       "       [ 0.80576177, -0.09452019, -0.4702341 ],\n",
       "       [ 0.80576177, -0.09452019, -0.12531819],\n",
       "       [ 0.80576177, -2.10253331, -0.21069711],\n",
       "       [-0.41002054, -0.32621401, -0.03718642],\n",
       "       [-1.62580285,  1.6817991 ,  0.57285576],\n",
       "       [-0.41002054, -0.32621401, -0.11495032],\n",
       "       [ 0.80576177, -0.09452019, -0.47946856],\n",
       "       [ 0.80576177, -0.09452019, -0.46691358],\n",
       "       [-0.41002054, -0.40344529, -0.36768297],\n",
       "       [ 0.80576177, -0.86683293, -0.22746495],\n",
       "       [-1.62580285, -0.09452019, -0.02746594],\n",
       "       [ 0.80576177, -0.32621401, -0.48335676],\n",
       "       [-0.41002054, -0.40344529, -0.33852151],\n",
       "       [ 0.80576177, -0.55790784, -0.47420395],\n",
       "       [-1.62580285, -2.18594308,  2.32586387],\n",
       "       [-0.41002054, -0.40344529, -0.11495032],\n",
       "       [ 0.80576177, -0.24898274, -0.46634591],\n",
       "       [-0.41002054,  0.36886745, -0.21215518],\n",
       "       [-1.62580285, -0.63513911,  4.48040973],\n",
       "       [ 0.80576177, -0.09452019, -0.46974808],\n",
       "       [ 0.80576177, -0.09452019, -0.47987294],\n",
       "       [ 0.80576177, -0.09452019, -0.45200819],\n",
       "       [ 0.80576177, -0.55790784, -0.46926205],\n",
       "       [-1.62580285,  2.53134311,  0.93486224],\n",
       "       [ 0.80576177, -0.86683293, -0.42908339],\n",
       "       [-1.62580285,  0.98671764,  3.80289181],\n",
       "       [-0.41002054,  2.14518674, -0.3803196 ],\n",
       "       [ 0.80576177, -0.78960166, -0.62041562],\n",
       "       [-0.41002054,  0.98671764, -0.11495032],\n",
       "       [ 0.80576177, -0.09452019, -0.47995459],\n",
       "       [ 0.80576177, -0.71237038, -0.45200819],\n",
       "       [ 0.80576177,  0.40748308, -0.49526435],\n",
       "       [ 0.80576177, -0.09452019, -0.43572637],\n",
       "       [ 0.80576177, -0.09452019, -0.46391578],\n",
       "       [ 0.80576177, -0.48067656, -0.46691358],\n",
       "       [ 0.80576177, -0.55790784, -0.47946856],\n",
       "       [-1.62580285,  1.75903037, -0.02746594],\n",
       "       [ 0.80576177,  0.05994235, -0.37788948],\n",
       "       [-0.41002054,  0.56194563, -0.11495032],\n",
       "       [ 0.80576177, -0.09452019, -0.46974808],\n",
       "       [ 0.80576177, -0.55790784, -0.47987294],\n",
       "       [-1.62580285,  1.60456783,  1.97787045],\n",
       "       [ 0.80576177, -0.17175147, -0.48481483],\n",
       "       [ 0.80576177, -0.55790784, -0.46391578],\n",
       "       [-1.62580285,  1.6817991 ,  0.8951696 ],\n",
       "       [-0.41002054,  0.52332999, -0.4162854 ],\n",
       "       [-0.41002054, -0.01728892, -0.11495032],\n",
       "       [-1.62580285, -0.55790784,  2.32586387],\n",
       "       [-1.62580285,  1.60456783,  1.44858996],\n",
       "       [-0.41002054,  0.83225509, -0.36768297],\n",
       "       [ 0.80576177, -0.17175147, -0.45200819],\n",
       "       [-1.62580285, -0.09452019, -0.62041562],\n",
       "       [-1.62580285,  2.76303693, -0.10425778],\n",
       "       [ 0.80576177, -0.9440642 , -0.45200819],\n",
       "       [-1.62580285, -0.09452019, -0.10425778],\n",
       "       [-1.62580285, -1.94807076,  0.9709894 ],\n",
       "       [ 0.80576177, -0.09452019, -0.46691358],\n",
       "       [ 0.80576177, -2.17976458, -0.40397343],\n",
       "       [-0.41002054,  0.52332999, -0.08092861],\n",
       "       [-0.41002054,  1.06394891, -0.11009007],\n",
       "       [-1.62580285,  0.67779254,  2.36304473],\n",
       "       [-0.41002054,  0.13717363, -0.11009007],\n",
       "       [ 0.80576177, -0.01728892, -0.46391578],\n",
       "       [ 0.80576177, -0.86683293, -0.45905554],\n",
       "       [-0.41002054, -0.09452019, -0.32782897],\n",
       "       [-1.62580285,  0.75502382,  1.53526365],\n",
       "       [-0.41002054,  0.75502382, -0.36768297],\n",
       "       [ 0.80576177, -0.24898274, -0.45200819],\n",
       "       [ 0.80576177, -0.71237038, -0.48335676],\n",
       "       [-1.62580285, -0.09452019,  1.97787045],\n",
       "       [ 0.80576177,  1.52733655, -0.62041562],\n",
       "       [-0.41002054, -0.48067656, -0.32791062],\n",
       "       [ 0.80576177, -2.17976458,  0.15114801],\n",
       "       [ 0.80576177, -0.09452019, -0.4672363 ],\n",
       "       [ 0.80576177, -0.09452019, -0.1645248 ],\n",
       "       [-0.41002054, -2.02530203, -0.11495032],\n",
       "       [ 0.80576177, -1.94807076, -0.40397343],\n",
       "       [ 0.80576177, -0.78960166, -0.47169217],\n",
       "       [ 0.80576177,  0.87087073, -0.46974808],\n",
       "       [ 0.80576177, -2.17976458, -0.31438165],\n",
       "       [ 0.80576177, -0.09452019, -0.32402243],\n",
       "       [ 0.80576177, -0.63513911, -0.46634591]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Model Building\n",
    "## fit() for training and predict for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classification=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled=df_scaled.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.78927234,  0.82737724, -0.56573646, -0.50244517],\n",
       "       [ 1.2669898 , -1.56610693,  0.66386103,  0.78684529],\n",
       "       [ 1.2669898 ,  0.82737724, -0.25833709, -0.48885426],\n",
       "       ...,\n",
       "       [-0.78927234,  0.82737724, -0.1046374 , -0.17626324],\n",
       "       [ 1.2669898 , -1.56610693, -0.25833709, -0.04438104],\n",
       "       [-0.78927234,  0.82737724,  0.20276197, -0.49237783]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.78927234,  0.82737724, -0.56573646, -0.50244517],\n",
       "       [ 1.2669898 , -1.56610693,  0.66386103,  0.78684529],\n",
       "       [ 1.2669898 ,  0.82737724, -0.25833709, -0.48885426],\n",
       "       ...,\n",
       "       [-0.78927234,  0.82737724, -0.1046374 , -0.17626324],\n",
       "       [ 1.2669898 , -1.56610693, -0.25833709, -0.04438104],\n",
       "       [-0.78927234,  0.82737724,  0.20276197, -0.49237783]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([216.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 184.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 491.]),\n",
       " array([-1.56610693, -1.44643272, -1.32675851, -1.2070843 , -1.08741009,\n",
       "        -0.96773588, -0.84806167, -0.72838747, -0.60871326, -0.48903905,\n",
       "        -0.36936484, -0.24969063, -0.13001642, -0.01034222,  0.10933199,\n",
       "         0.2290062 ,  0.34868041,  0.46835462,  0.58802883,  0.70770304,\n",
       "         0.82737724]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_scaled[:,1],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 40.,  14.,  15.,  31.,  79.,  98., 262.,  84.,  73.,  45.,  35.,\n",
       "         35.,  29.,  16.,  13.,  11.,   4.,   5.,   1.,   1.]),\n",
       " array([-2.22415608, -1.91837055, -1.61258503, -1.3067995 , -1.00101397,\n",
       "        -0.69522845, -0.38944292, -0.08365739,  0.22212813,  0.52791366,\n",
       "         0.83369919,  1.13948471,  1.44527024,  1.75105577,  2.05684129,\n",
       "         2.36262682,  2.66841235,  2.97419787,  3.2799834 ,  3.58576892,\n",
       "         3.89155445]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_scaled[:,2],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([562., 170.,  67.,  39.,  15.,  16.,   2.,   0.,   9.,   2.,   6.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.]),\n",
       " array([-0.64842165, -0.13264224,  0.38313716,  0.89891657,  1.41469598,\n",
       "         1.93047539,  2.4462548 ,  2.96203421,  3.47781362,  3.99359303,\n",
       "         4.50937244,  5.02515184,  5.54093125,  6.05671066,  6.57249007,\n",
       "         7.08826948,  7.60404889,  8.1198283 ,  8.63560771,  9.15138712,\n",
       "         9.66716653]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD4CAYAAADmWv3KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOZ0lEQVR4nO3dXYhc932H8ecbybUTpyESXglVkrsqiDZywHZZVLeGkkZprdYh8o1BgQRRBLpRW6cEgpSb0AuBCiWkF3VBOG4W4toIx8HCKWnEJiYUiu31S2tLsrCwXWkr1do4pEl64VTOrxd7XMb2vsy+jMb79/OBMGf+e87sb5D86OzZmUmqCklSez4w7AEkSYNh4CWpUQZekhpl4CWpUQZekhq1dtgDANxwww01Ojo67DEkaVV5+umnf1RVI3N9/T0R+NHRUSYnJ4c9hiStKkn+Y76ve4lGkhpl4CWpUQZekhpl4CWpUQZekhpl4CWpUQZekhpl4CWpUQZekhr1nngn63KNHvrOko999eidKziJJL13eAYvSY0y8JLUKAMvSY0y8JLUKAMvSY0y8JLUKAMvSY0y8JLUKAMvSY0y8JLUKAMvSY0y8JLUKAMvSY0y8JLUKAMvSY0y8JLUKAMvSY0y8JLUqL4Cn+TVJM8neS7JZLe2PsnJJC91t+t69j+c5FySs0nuGNTwkqS5LeYM/g+q6paqGuvuHwImqmo7MNHdJ8kOYC9wE7AbuDfJmhWcWZLUh+VcotkDjHfb48BdPesPVdUbVfUKcA7YuYzvI0lagn4DX8D3kjyd5EC3trGqLgF0txu69c3AhZ5jp7o1SdJVtLbP/W6vqotJNgAnk7w4z76ZZa3etdPMPxQHAG688cY+x5Ak9auvM/iqutjdXga+zcwll9eSbALobi93u08BW3sO3wJcnOUxj1XVWFWNjYyMLP0ZSJJmtWDgk1yf5Fff2gb+CHgBOAHs63bbBzzabZ8A9ia5Nsk2YDvw5EoPLkmaXz+XaDYC307y1v7/WFXfTfIUcDzJfuA8cDdAVZ1Kchw4DVwBDlbVmwOZXpI0pwUDX1UvAzfPsv46sGuOY44AR5Y9nSRpyXwnqyQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqP6DnySNUmeTfJYd399kpNJXupu1/XsezjJuSRnk9wxiMElSfNbzBn8PcCZnvuHgImq2g5MdPdJsgPYC9wE7AbuTbJmZcaVJPWrr8An2QLcCdzXs7wHGO+2x4G7etYfqqo3quoV4Bywc2XGlST1q98z+K8BXwJ+2bO2saouAXS3G7r1zcCFnv2murW3SXIgyWSSyenp6UUPLkma34KBT/Jp4HJVPd3nY2aWtXrXQtWxqhqrqrGRkZE+H1qS1K+1fexzO/CZJH8CXAd8JMk3gdeSbKqqS0k2AZe7/aeArT3HbwEuruTQkqSFLXgGX1WHq2pLVY0y88vT71fV54ATwL5ut33Ao932CWBvkmuTbAO2A0+u+OSSpHn1cwY/l6PA8ST7gfPA3QBVdSrJceA0cAU4WFVvLntSSdKiLCrwVfU48Hi3/Tqwa479jgBHljmbJGkZfCerJDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSoxYMfJLrkjyZ5N+SnEryV936+iQnk7zU3a7rOeZwknNJzia5Y5BPQJI0u37O4N8APllVNwO3ALuT3AYcAiaqajsw0d0nyQ5gL3ATsBu4N8maQQwvSZrbgoGvGT/v7l7T/a+APcB4tz4O3NVt7wEeqqo3quoV4Bywc0WnliQtqK9r8EnWJHkOuAycrKongI1VdQmgu93Q7b4ZuNBz+FS39s7HPJBkMsnk9PT0cp6DJGkWfQW+qt6sqluALcDOJB+fZ/fM9hCzPOaxqhqrqrGRkZH+ppUk9W1Rr6Kpqp8AjzNzbf21JJsAutvL3W5TwNaew7YAF5c9qSRpUfp5Fc1Iko922x8EPgW8CJwA9nW77QMe7bZPAHuTXJtkG7AdeHKlB5ckzW9tH/tsAsa7V8J8ADheVY8l+VfgeJL9wHngboCqOpXkOHAauAIcrKo3BzO+JGkuCwa+qv4duHWW9deBXXMccwQ4suzpJElL5jtZJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGmXgJalRBl6SGrVg4JNsTfKDJGeSnEpyT7e+PsnJJC91t+t6jjmc5FySs0nuGOQTkCTNrp8z+CvAF6vqY8BtwMEkO4BDwERVbQcmuvt0X9sL3ATsBu5NsmYQw0uS5rZg4KvqUlU9023/DDgDbAb2AOPdbuPAXd32HuChqnqjql4BzgE7V3pwSdL8FnUNPskocCvwBLCxqi7BzD8CwIZut83AhZ7Dprq1dz7WgSSTSSanp6cXP7kkaV59Bz7Jh4FvAV+oqp/Ot+ssa/WuhapjVTVWVWMjIyP9jiFJ6lNfgU9yDTNxf6CqHumWX0uyqfv6JuBytz4FbO05fAtwcWXGlST1q59X0QT4OnCmqr7a86UTwL5uex/waM/63iTXJtkGbAeeXLmRJUn9WNvHPrcDnweeT/Jct/Zl4ChwPMl+4DxwN0BVnUpyHDjNzCtwDlbVmys+uSRpXgsGvqr+hdmvqwPsmuOYI8CRZcwlSVom38kqSY0y8JLUKAMvSY0y8JLUKAMvSY3q52WSTRs99J0lH/vq0TtXcBJJWlmewUtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDXKwEtSowy8JDVqwcAnuT/J5SQv9KytT3IyyUvd7bqerx1Oci7J2SR3DGpwSdL8+jmD/waw+x1rh4CJqtoOTHT3SbID2Avc1B1zb5I1KzatJKlvCwa+qn4I/Pgdy3uA8W57HLirZ/2hqnqjql4BzgE7V2hWSdIiLPUa/MaqugTQ3W7o1jcDF3r2m+rW3iXJgSSTSSanp6eXOIYkaS4r/UvWzLJWs+1YVceqaqyqxkZGRlZ4DEnSUgP/WpJNAN3t5W59Ctjas98W4OLSx5MkLdVSA38C2Ndt7wMe7Vnfm+TaJNuA7cCTyxtRkrQUaxfaIcmDwCeAG5JMAV8BjgLHk+wHzgN3A1TVqSTHgdPAFeBgVb05oNklSfNYMPBV9dk5vrRrjv2PAEeWM5Qkafl8J6skNcrAS1KjDLwkNcrAS1KjDLwkNcrAS1KjFnyZpOY2eug7Sz721aN3ruAkkvRunsFLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqMMvCQ1ysBLUqN8J+uQ+C5YSYPmGbwkNcrAS1KjDLwkNcrAS1KjDLwkNcrAS1KjDLwkNcrAS1KjfKPTKrScN0ktl2+yklYPz+AlqVGewWtVWO5PLf7kofejgZ3BJ9md5GySc0kODer7SJJmN5Az+CRrgL8D/hCYAp5KcqKqTg/i+0mD5AfDabUa1CWancC5qnoZIMlDwB7AwK9yqzV2w/zFtNr3Xv3vYlCB3wxc6Lk/BfxO7w5JDgAHurs/T3J2QLMs1g3Aj4Y9xFVyVZ9r/vpqfadZDeXPdUjP2b/Dq8gi/o7M9lx/fb4DBhX4zLJWb7tTdQw4NqDvv2RJJqtqbNhzXA0+1zb5XNu0lOc6qF+yTgFbe+5vAS4O6HtJkmYxqMA/BWxPsi3JrwB7gRMD+l6SpFkM5BJNVV1J8mfAPwNrgPur6tQgvtcAvOcuGw2Qz7VNPtc2Lfq5pqoW3kuStOr4UQWS1CgDL0mNMvCd98tHKyTZmuQHSc4kOZXknmHPNGhJ1iR5Nsljw55lkJJ8NMnDSV7s/nx/d9gzDUqSv+z+/r6Q5MEk1w17ppWS5P4kl5O80LO2PsnJJC91t+v6eSwDz9s+WuGPgR3AZ5PsGO5UA3MF+GJVfQy4DTjY8HN9yz3AmWEPcRX8LfDdqvot4GYafc5JNgN/AYxV1ceZeSHH3uFOtaK+Aex+x9ohYKKqtgMT3f0FGfgZ///RClX1C+Ctj1ZoTlVdqqpnuu2fMROBzcOdanCSbAHuBO4b9iyDlOQjwO8DXweoql9U1U+GO9VArQU+mGQt8CEaep9NVf0Q+PE7lvcA4932OHBXP49l4GfM9tEKzUbvLUlGgVuBJ4Y7yUB9DfgS8MthDzJgvwFMA//QXY66L8n1wx5qEKrqP4G/Ac4Dl4D/rqrvDXeqgdtYVZdg5iQN2NDPQQZ+xoIfrdCaJB8GvgV8oap+Oux5BiHJp4HLVfX0sGe5CtYCvw38fVXdCvwPff4Yv9p015/3ANuAXwOuT/K54U713mTgZ7yvPlohyTXMxP2Bqnpk2PMM0O3AZ5K8ysxlt08m+eZwRxqYKWCqqt76aexhZoLfok8Br1TVdFX9L/AI8HtDnmnQXkuyCaC7vdzPQQZ+xvvmoxWShJnrtGeq6qvDnmeQqupwVW2pqlFm/ky/X1VNnulV1X8BF5L8Zre0i3Y/nvs8cFuSD3V/n3fR6C+Ue5wA9nXb+4BH+znI/8s+Vv1HKyzW7cDngeeTPNetfbmq/mmIM2ll/DnwQHeS8jLwp0OeZyCq6okkDwPPMPOqsGdp6CMLkjwIfAK4IckU8BXgKHA8yX5m/oG7u6/H8qMKJKlNXqKRpEYZeElqlIGXpEYZeElqlIGXpEYZeElqlIGXpEb9H3MPjXItyI32AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_scaled[:,3],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([562., 170.,  67.,  39.,  15.,  16.,   2.,   0.,   9.,   2.,   6.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.]),\n",
       " array([  0.     ,  25.61646,  51.23292,  76.84938, 102.46584, 128.0823 ,\n",
       "        153.69876, 179.31522, 204.93168, 230.54814, 256.1646 , 281.78106,\n",
       "        307.39752, 333.01398, 358.63044, 384.2469 , 409.86336, 435.47982,\n",
       "        461.09628, 486.71274, 512.3292 ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Fare'],bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Min Max Scaling  (### CNN)---Deep Learning Techniques\n",
    "Min Max Scaling scales the values between 0 to 1.\n",
    "X_scaled = (X - X.min / (X.max - X.min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.673285</td>\n",
       "      <td>0.101229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.346569</td>\n",
       "      <td>0.030254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.308872</td>\n",
       "      <td>0.081157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.321438</td>\n",
       "      <td>0.028213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>0.020527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0         1         2\n",
       "0  0.0  0.673285  0.101229\n",
       "1  1.0  0.346569  0.030254\n",
       "2  0.5  0.308872  0.081157\n",
       "3  1.0  0.321438  0.028213\n",
       "4  1.0  0.271174  0.020527"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max=MinMaxScaler()\n",
    "df_minmax=pd.DataFrame(min_max.fit_transform(X_train))\n",
    "df_minmax.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e3c6d5ebb0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_minmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([216.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 184.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 491.]),\n",
       " array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "        0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_minmax['Pclass'],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 40.,  14.,  15.,  31.,  79.,  98., 262.,  84.,  73.,  45.,  35.,\n",
       "         35.,  29.,  16.,  13.,  11.,   4.,   5.,   1.,   1.]),\n",
       " array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "        0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_minmax['Age'],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([562., 170.,  67.,  39.,  15.,  16.,   2.,   0.,   9.,   2.,   6.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.]),\n",
       " array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "        0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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08BHg1yc42KHf9/YDwE8lWZvkOxg8YfWJFa5zlPr0fIbBbyok2Qj8IPDlFa1yZY08vybizL0WeJxBkl/r9r+fwScnbgdmgf9h8JN/YvXs+XeB7wHu685kL9YEP1GvZ89N6dNzVT2R5JPAI8A3gQ9U1bwfqZsEPb/Ovw98KMmjDC5ZvKuqJvZRwEk+DLwRWJ9kDngP8FIYX375+AFJatCkXJaRJF0Fw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ16P8AaCrkNxX1Ai0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_minmax['Fare'],bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Robust Scaler\n",
    "Robust Scaler are robust to outliers.It is used to scale the feature to median and quantiles\n",
    "Scaling using median and quantiles consists of substracting the median to all the observations, and then dividing by the interquantile difference. The interquantile difference is the difference between the 75th and 25th quantile:\n",
    "\n",
    "IQR = 75th quantile - 25th quantile\n",
    "\n",
    "X_scaled = (X - X.median) / IQR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0,1,2,3,4,5,6,7,8,9,10\n",
    "\n",
    "9-90 percentile---90% of all values in this group is less than 9\n",
    "1-10 precentile---10% of all values in this group is less than 1\n",
    "4-40%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.602069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.230769</td>\n",
       "      <td>1.161670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.153846</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.461538</td>\n",
       "      <td>-0.168630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0         1         2\n",
       "0 -2.0  2.000000  1.602069\n",
       "1  0.0  0.000000  0.044788\n",
       "2 -1.0 -0.230769  1.161670\n",
       "3  0.0 -0.153846  0.000000\n",
       "4  0.0 -0.461538 -0.168630"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "scaler=RobustScaler()\n",
    "df_robust_scaler=pd.DataFrame(scaler.fit_transform(X_train))\n",
    "df_robust_scaler.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  0.00000000e+00,  3.39014989e-02],\n",
       "       [-1.00000000e+00,  2.30769231e-01, -1.69344754e-01],\n",
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       "       [-2.00000000e+00, -1.53846154e-01,  2.75785011e+00],\n",
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       "       [ 0.00000000e+00, -9.23076923e-01, -2.87117773e-01],\n",
       "       [-2.00000000e+00, -6.92307692e-01,  5.06599572e-01],\n",
       "       [-2.00000000e+00,  6.92307692e-01,  1.65506638e+00],\n",
       "       [ 0.00000000e+00,  1.23076923e+00, -2.74269807e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  4.71627409e-01],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -3.09601713e-01],\n",
       "       [-1.00000000e+00,  6.15384615e-01, -6.22783726e-02],\n",
       "       [-2.00000000e+00, -9.23076923e-01,  1.06834261e+00],\n",
       "       [-2.00000000e+00,  1.07692308e+00,  1.63169165e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.83725910e-01],\n",
       "       [-1.00000000e+00, -7.69230769e-02, -6.22783726e-02],\n",
       "       [-2.00000000e+00,  1.46153846e+00,  1.60795717e+00],\n",
       "       [ 0.00000000e+00, -3.07692308e-01, -2.80873662e-01],\n",
       "       [-2.00000000e+00,  4.61538462e-01,  5.18021413e-01],\n",
       "       [ 0.00000000e+00, -6.92307692e-01, -2.82655246e-01],\n",
       "       [ 0.00000000e+00, -6.15384615e-01, -2.23948608e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.96145610e-03],\n",
       "       [ 0.00000000e+00, -1.38461538e+00,  5.75837259e-01],\n",
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       "       [ 0.00000000e+00, -4.61538462e-01, -2.87117773e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  6.95571734e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.95683084e-01],\n",
       "       [ 0.00000000e+00, -7.69230769e-01, -2.74269807e-01],\n",
       "       [ 0.00000000e+00, -7.69230769e-02,  0.00000000e+00],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  9.01319058e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.87117773e-01],\n",
       "       [-2.00000000e+00,  1.53846154e-01,  7.08599572e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.80873662e-01],\n",
       "       [-1.00000000e+00, -5.38461538e-01,  2.52872805e+00],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -3.17276231e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.74269807e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.87117773e-01],\n",
       "       [ 0.00000000e+00,  1.30769231e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00, -9.23076923e-01,  8.53139186e-01],\n",
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       "       [ 0.00000000e+00, -3.84615385e-01, -2.23948608e-01],\n",
       "       [ 0.00000000e+00, -3.07692308e-01, -3.17096360e-01],\n",
       "       [-2.00000000e+00,  2.30769231e+00,  4.23215418e+00],\n",
       "       [ 0.00000000e+00, -1.76923077e+00,  2.05743041e-01],\n",
       "       [-2.00000000e+00,  1.84615385e+00,  3.38525910e+00],\n",
       "       [ 0.00000000e+00,  9.23076923e-01,  5.75837259e-01],\n",
       "       [-2.00000000e+00, -1.30769231e+00,  4.52016274e+00],\n",
       "       [ 0.00000000e+00,  2.84615385e+00, -2.87117773e-01],\n",
       "       [-2.00000000e+00, -7.69230769e-01,  2.79211135e+00],\n",
       "       [-1.00000000e+00,  3.07692308e-01, -1.69344754e-01],\n",
       "       [-2.00000000e+00,  1.69230769e+00,  9.98143897e+00],\n",
       "       [-1.00000000e+00,  5.38461538e-01,  2.80334047e-01],\n",
       "       [ 0.00000000e+00, -6.92307692e-01, -2.81584582e-01],\n",
       "       [ 0.00000000e+00, -5.38461538e-01, -2.86047109e-01],\n",
       "       [-1.00000000e+00, -1.15384615e+00,  2.16094218e-01],\n",
       "       [-1.00000000e+00,  0.00000000e+00,  4.94466809e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  4.91254818e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.87117773e-01],\n",
       "       [-1.00000000e+00,  2.23076923e+00, -1.69344754e-01],\n",
       "       [-1.00000000e+00, -5.38461538e-01,  2.52872805e+00],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.81764454e-01],\n",
       "       [ 0.00000000e+00, -8.46153846e-01, -3.09421842e-01],\n",
       "       [-2.00000000e+00,  1.84615385e+00,  2.73286938e+00],\n",
       "       [-1.00000000e+00, -1.53846154e-01,  4.94466809e-01],\n",
       "       [-2.00000000e+00, -7.69230769e-01,  1.06175931e+01],\n",
       "       [-2.00000000e+00, -1.53846154e-01,  6.65773019e-01],\n",
       "       [-2.00000000e+00,  8.46153846e-01,  2.94235974e+00],\n",
       "       [ 0.00000000e+00, -3.84615385e-01, -2.82655246e-01],\n",
       "       [ 0.00000000e+00,  6.15384615e-01,  4.69293362e-02],\n",
       "       [ 0.00000000e+00,  1.92307692e-01, -2.87117773e-01],\n",
       "       [-2.00000000e+00, -4.61538462e-01,  1.50089079e+00],\n",
       "       [-1.00000000e+00,  7.69230769e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00,  1.00000000e+00,  2.46608137e-01],\n",
       "       [ 0.00000000e+00, -9.23076923e-01,  1.08065525e+00],\n",
       "       [-1.00000000e+00,  7.69230769e-02, -1.69344754e-01],\n",
       "       [-2.00000000e+00,  9.23076923e-01,  5.14114775e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.87653105e-01],\n",
       "       [-2.00000000e+00,  2.15384615e+00,  6.95571734e-01],\n",
       "       [-2.00000000e+00,  3.30769231e+00,  1.50107066e+00],\n",
       "       [-2.00000000e+00,  2.30769231e-01,  4.23215418e+00],\n",
       "       [-2.00000000e+00,  1.53846154e-01,  1.81905782e+00],\n",
       "       [-2.00000000e+00,  7.69230769e-01, -6.19023555e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.08531049e-01],\n",
       "       [ 0.00000000e+00, -1.46153846e+00,  2.59991435e-01],\n",
       "       [ 0.00000000e+00, -1.46153846e+00,  7.20376874e-01],\n",
       "       [-2.00000000e+00,  2.53846154e+00,  7.65164882e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.88008565e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.80873662e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.82655246e-01],\n",
       "       [-2.00000000e+00,  1.53846154e+00,  1.60795717e+00],\n",
       "       [ 0.00000000e+00,  1.30769231e+00, -2.74269807e-01],\n",
       "       [-1.00000000e+00,  6.15384615e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00, -7.69230769e-02, -2.80873662e-01],\n",
       "       [-2.00000000e+00,  2.76923077e+00,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  5.38461538e-01, -3.13884368e-01],\n",
       "       [-2.00000000e+00, -6.92307692e-01,  3.28158458e+00],\n",
       "       [ 0.00000000e+00,  7.69230769e-01, -3.17096360e-01],\n",
       "       [ 0.00000000e+00,  1.92307692e-01, -2.74269807e-01],\n",
       "       [ 0.00000000e+00, -2.30769231e-01, -2.86047109e-01],\n",
       "       [-1.00000000e+00,  9.23076923e-01,  1.05121199e+00],\n",
       "       [-2.00000000e+00,  1.46153846e+00,  1.02979872e+00],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -2.12171306e-01],\n",
       "       [-2.00000000e+00,  9.23076923e-01,  7.08599572e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  1.60795717e+00],\n",
       "       [ 0.00000000e+00, -5.76923077e-01, -3.08531049e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  3.19700214e+00],\n",
       "       [-2.00000000e+00,  6.15384615e-01,  5.06779443e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  8.87901499e+00],\n",
       "       [ 0.00000000e+00, -6.15384615e-01, -1.97361884e-01],\n",
       "       [-2.00000000e+00,  3.30769231e+00,  8.65096360e-01],\n",
       "       [-1.00000000e+00,  6.15384615e-01, -6.76316916e-02],\n",
       "       [-1.00000000e+00, -4.61538462e-01,  6.22946467e-01],\n",
       "       [ 0.00000000e+00,  1.53846154e+00,  8.53139186e-01],\n",
       "       [-1.00000000e+00, -3.84615385e-01, -1.26518201e-01],\n",
       "       [-2.00000000e+00,  3.07692308e-01,  2.64828694e+00],\n",
       "       [-2.00000000e+00,  2.69230769e+00,  2.71966167e+00],\n",
       "       [-1.00000000e+00, -2.09000000e+00,  6.22946467e-01],\n",
       "       [ 0.00000000e+00, -2.30769231e-01, -3.08531049e-01],\n",
       "       [-2.00000000e+00,  5.38461538e-01,  5.18021413e-01],\n",
       "       [-2.00000000e+00,  1.61538462e+00,  4.91434690e-01],\n",
       "       [-2.00000000e+00, -3.07692308e-01,  2.77283940e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.75588865e-04],\n",
       "       [ 0.00000000e+00, -5.38461538e-01, -2.74269807e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.81584582e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  3.39014989e-02],\n",
       "       [-1.00000000e+00,  0.00000000e+00, -6.19023555e-01],\n",
       "       [ 0.00000000e+00, -1.76923077e+00, -8.47623126e-02],\n",
       "       [-2.00000000e+00,  1.61538462e+00,  1.81905782e+00],\n",
       "       [ 0.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  4.12972591e+00],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.86047109e-01],\n",
       "       [ 0.00000000e+00,  1.07692308e+00, -2.95683084e-01],\n",
       "       [ 0.00000000e+00, -7.69230769e-01,  1.43289079e-01],\n",
       "       [ 0.00000000e+00, -5.38461538e-01, -2.57854390e-01],\n",
       "       [-1.00000000e+00,  1.23076923e+00,  4.94466809e-01],\n",
       "       [-1.00000000e+00,  0.00000000e+00, -6.22783726e-02],\n",
       "       [-1.00000000e+00,  6.92307692e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  8.46153846e-01,  6.28299786e-01],\n",
       "       [-1.00000000e+00,  4.61538462e-01,  7.72839400e-01],\n",
       "       [-1.00000000e+00,  1.53846154e-01, -6.22783726e-02],\n",
       "       [-1.00000000e+00,  2.92307692e+00, -1.69344754e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  5.65595717e+00],\n",
       "       [ 0.00000000e+00,  1.15384615e+00, -2.74269807e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.45897216e-01],\n",
       "       [ 0.00000000e+00, -3.07692308e-01, -2.98004283e-01],\n",
       "       [ 0.00000000e+00, -7.69230769e-01, -2.85156317e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  4.15237687e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.74269807e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.17096360e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.80049679e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.85511777e-01],\n",
       "       [-1.00000000e+00,  6.15384615e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  3.07692308e-01,  4.47880086e-02],\n",
       "       [ 0.00000000e+00, -3.46153846e-01, -3.09421842e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  5.18021413e-01],\n",
       "       [-1.00000000e+00,  8.46153846e-01, -6.22783726e-02],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  2.90006852e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.56783726e-01],\n",
       "       [-2.00000000e+00,  2.84615385e+00,  2.03533191e+00],\n",
       "       [-1.00000000e+00, -3.84615385e-01, -1.69344754e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  1.60206852e+00],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -2.80873662e-01],\n",
       "       [ 0.00000000e+00,  5.38461538e-01, -3.17096360e-01],\n",
       "       [-2.00000000e+00, -3.84615385e-01,  2.09439400e+00],\n",
       "       [-1.00000000e+00, -7.69230769e-01, -6.22783726e-02],\n",
       "       [-2.00000000e+00,  6.15384615e-01,  5.11062099e-01],\n",
       "       [ 0.00000000e+00, -1.46153846e+00,  3.39014989e-02],\n",
       "       [ 0.00000000e+00, -8.46153846e-01, -3.13884368e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.09601713e-01],\n",
       "       [ 0.00000000e+00,  7.69230769e-01,  7.25194861e-01],\n",
       "       [-1.00000000e+00, -8.46153846e-01, -1.69344754e-01],\n",
       "       [ 0.00000000e+00, -1.84615385e+00,  9.61798715e-02],\n",
       "       [-2.00000000e+00,  1.34615385e+00,  6.01533191e-01],\n",
       "       [-1.00000000e+00, -3.84615385e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00,  3.07692308e-01, -2.79623126e-01],\n",
       "       [ 0.00000000e+00, -1.53846154e-01, -2.82655246e-01],\n",
       "       [ 0.00000000e+00, -1.69230769e+00,  7.20376874e-01],\n",
       "       [-2.00000000e+00, -3.07692308e-01,  9.98143897e+00],\n",
       "       [-2.00000000e+00,  1.30769231e+00,  5.18021413e-01],\n",
       "       [-1.00000000e+00,  7.69230769e-02,  5.68162741e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.80873662e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  9.01319058e-01],\n",
       "       [-1.00000000e+00,  1.07692308e+00, -6.22783726e-02],\n",
       "       [ 0.00000000e+00,  6.15384615e-01,  4.15237687e-01],\n",
       "       [-1.00000000e+00,  3.84615385e-01, -9.33276231e-02],\n",
       "       [ 0.00000000e+00, -8.46153846e-01, -3.16916488e-01],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.12171306e-01],\n",
       "       [-1.00000000e+00,  1.69230769e+00,  4.94466809e-01],\n",
       "       [-2.00000000e+00,  5.38461538e-01,  3.23536617e+00],\n",
       "       [-2.00000000e+00,  7.69230769e-01,  9.12508779e+00],\n",
       "       [-1.00000000e+00,  4.61538462e-01, -6.22783726e-02],\n",
       "       [-2.00000000e+00, -8.46153846e-01,  1.82208994e+00],\n",
       "       [ 0.00000000e+00, -1.30769231e+00,  7.20376874e-01],\n",
       "       [ 0.00000000e+00,  2.53846154e+00, -3.51892934e-01],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -2.48038544e-01],\n",
       "       [-1.00000000e+00, -1.61538462e+00,  5.05173448e-01],\n",
       "       [ 0.00000000e+00,  2.69230769e+00, -2.08423983e-01],\n",
       "       [ 0.00000000e+00, -6.15384615e-01, -3.09421842e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  3.38505353e-01],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.12886510e-01],\n",
       "       [-2.00000000e+00,  6.15384615e-01,  4.52016274e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.75588865e-04],\n",
       "       [ 0.00000000e+00,  1.69230769e+00, -2.74269807e-01],\n",
       "       [-2.00000000e+00, -7.69230769e-02,  8.43879229e+00],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -3.08531049e-01],\n",
       "       [-2.00000000e+00,  3.84615385e-01,  3.23536617e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.88188437e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  4.71627409e-01],\n",
       "       [ 0.00000000e+00, -2.00000000e+00,  2.83546039e-01],\n",
       "       [-1.00000000e+00, -2.30769231e-01,  6.65773019e-01],\n",
       "       [-2.00000000e+00,  1.76923077e+00,  2.00963597e+00],\n",
       "       [-1.00000000e+00, -2.30769231e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.08531049e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.80873662e-01],\n",
       "       [-1.00000000e+00, -3.07692308e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00, -7.69230769e-01,  2.46608137e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  6.87186296e-01],\n",
       "       [ 0.00000000e+00, -2.30769231e-01, -3.17096360e-01],\n",
       "       [-1.00000000e+00, -3.07692308e-01,  1.96145610e-03],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -2.96933619e-01],\n",
       "       [-2.00000000e+00, -2.08307692e+00,  5.87134047e+00],\n",
       "       [-1.00000000e+00, -3.07692308e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00, -1.53846154e-01, -2.79623126e-01],\n",
       "       [-1.00000000e+00,  4.61538462e-01,  2.80334047e-01],\n",
       "       [-2.00000000e+00, -5.38461538e-01,  1.06175931e+01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.87117773e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.09421842e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.48038544e-01],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -2.86047109e-01],\n",
       "       [-2.00000000e+00,  2.61538462e+00,  2.80710064e+00],\n",
       "       [ 0.00000000e+00, -7.69230769e-01, -1.97537473e-01],\n",
       "       [-2.00000000e+00,  1.07692308e+00,  9.12508779e+00],\n",
       "       [-1.00000000e+00,  2.23076923e+00, -9.01156317e-02],\n",
       "       [ 0.00000000e+00, -6.92307692e-01, -6.19023555e-01],\n",
       "       [-1.00000000e+00,  1.07692308e+00,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.09601713e-01],\n",
       "       [ 0.00000000e+00, -6.15384615e-01, -2.48038544e-01],\n",
       "       [ 0.00000000e+00,  5.00000000e-01, -3.43327623e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.12171306e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.74269807e-01],\n",
       "       [ 0.00000000e+00, -3.84615385e-01, -2.80873662e-01],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -3.08531049e-01],\n",
       "       [-2.00000000e+00,  1.84615385e+00,  6.87186296e-01],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -8.47623126e-02],\n",
       "       [-1.00000000e+00,  6.53846154e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.87117773e-01],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -3.09421842e-01],\n",
       "       [-2.00000000e+00,  1.69230769e+00,  5.10474518e+00],\n",
       "       [ 0.00000000e+00, -7.69230769e-02, -3.20308351e-01],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -2.74269807e-01],\n",
       "       [-2.00000000e+00,  1.76923077e+00,  2.71966167e+00],\n",
       "       [-1.00000000e+00,  6.15384615e-01, -1.69344754e-01],\n",
       "       [-1.00000000e+00,  7.69230769e-02,  4.94466809e-01],\n",
       "       [-2.00000000e+00, -4.61538462e-01,  5.87134047e+00],\n",
       "       [-2.00000000e+00,  1.69230769e+00,  3.93879229e+00],\n",
       "       [-1.00000000e+00,  9.23076923e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00, -7.69230769e-02, -2.48038544e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00, -6.19023555e-01],\n",
       "       [-2.00000000e+00,  2.84615385e+00,  5.18021413e-01],\n",
       "       [ 0.00000000e+00, -8.46153846e-01, -2.48038544e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  5.18021413e-01],\n",
       "       [-2.00000000e+00, -1.84615385e+00,  2.88668522e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.80873662e-01],\n",
       "       [ 0.00000000e+00, -2.07692308e+00, -1.42222698e-01],\n",
       "       [-1.00000000e+00,  6.15384615e-01,  5.69413276e-01],\n",
       "       [-1.00000000e+00,  1.15384615e+00,  5.05173448e-01],\n",
       "       [-2.00000000e+00,  7.69230769e-01,  5.95324625e+00],\n",
       "       [-1.00000000e+00,  2.30769231e-01,  5.05173448e-01],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.74269807e-01],\n",
       "       [ 0.00000000e+00, -7.69230769e-01, -2.63563169e-01],\n",
       "       [-1.00000000e+00,  0.00000000e+00,  2.55160600e-02],\n",
       "       [-2.00000000e+00,  8.46153846e-01,  4.12972591e+00],\n",
       "       [-1.00000000e+00,  8.46153846e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00, -1.53846154e-01, -2.48038544e-01],\n",
       "       [ 0.00000000e+00, -6.15384615e-01, -3.17096360e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  5.10474518e+00],\n",
       "       [ 0.00000000e+00,  1.61538462e+00, -6.19023555e-01],\n",
       "       [-1.00000000e+00, -3.84615385e-01,  2.53361884e-02],\n",
       "       [ 0.00000000e+00, -2.07692308e+00,  1.08065525e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.81584582e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  3.85259101e-01],\n",
       "       [-1.00000000e+00, -1.92307692e+00,  4.94466809e-01],\n",
       "       [ 0.00000000e+00, -1.84615385e+00, -1.42222698e-01],\n",
       "       [ 0.00000000e+00, -6.92307692e-01, -2.91400428e-01],\n",
       "       [ 0.00000000e+00,  9.61538462e-01, -2.87117773e-01],\n",
       "       [ 0.00000000e+00, -2.07692308e+00,  5.51391863e-02],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  3.39014989e-02],\n",
       "       [ 0.00000000e+00, -5.38461538e-01, -2.79623126e-01]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e3c4e20100>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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kRzjeZwH8TkYcAXAMwLwJqh8RERERaUQ2g+I3ANQKIWYP3Tz3MQA7ErY5AWAZAAghSgGcD+DohNaSiIiIiKY9Q7YOLKUMCiHuAvBHAHoAP5NSNgsh7hha/wiABwD8QgjxDiLDLb4ipezKVp2JiIiIaHrKWlAMAFLKZwE8m7Dskbi/TwO4bqLrRURERETawhntiIiIiEjzGBQTERERkeYxKCYiIiIizWNQTERERESax6CYiIiIiDSPQTERERERaR6DYiIiIiLSPAbFRERERKR5DIqJiIiISPMYFBMRERGR5jEoJiIiIiLNY1BMRERERJrHoJiIiIiINI9BMRERERFpHoNiIiIiItI8BsVEREREpHkMiomIiIhI8xgUExEREZHmMSgmIiIiIs0znMvOQogBADLVeillzrmUT0REREQ0Ec4pKJZSOgFACLEZQBuAXwIQAD4JwDnS/kKI5QC2AtAD+ImU8psq21wN4D8AGAF0SSk/eC51JiIiIiJKdE5BcZwPSykvjXv+n0KIvwL4VqodhBB6ANsAXAvgJIA3hBA7pJT747bJA/BDAMullCeEECUZqi8RERERUUymxhSHhBCfFELohRA6IcQnAYRG2OcSAEeklEellH4AjwNYlbDNJwD8Tkp5AgCklB0Zqi9pVMXMKgghRvWomFmV7WoTERHROMtUT/EnEBkGsRWRMcb/N7RsOBUA3o97fhLApQnbzAVgFEK8jMhwjK1SysfUChNCrAWwFgCqqhjEkLrTJ9/HrT/aM6p9nvj85WM6FtskTTZskzTZsE3SZJKRnmIp5XEp5SopZZGUslhKuVpKeXyE3YRaUQnPDQAuAvD3AD4M4D4hxNwUddgupWyQUjYUFxeP9iUQZRzbJE02bJM02bBN0mSSkaBYCDFXCLFbCNE09HyhEOJrI+x2EsDMuOeVAE6rbPOclNIlpewC8AqARZmoMxERERFRVKbGFP8YwFcBBABASrkPwMdG2OcNALVCiNlCCNPQ9jsStnkGwJVCCIMQwobI8IoDGaozERERERGAzI0ptkkpXxdCMSIiONwOUsqgEOIuAH9EJCXbz6SUzUKIO4bWPyKlPCCEeA7APgBhRNK2NWWozkREREREADIXFHcJIc7D0JhgIcTNAFpH2klK+SyAZxOWPZLw/NsAvp2hehIRERERJclUUHwngO0A5gkhTgE4hsgEHkREREREk16mguIWKeU1Qgg7AJ2UciBD5RIRERERjbtM3Wh3TAixHcBlAAYzVCYRERER0YTIVFB8PoAXERlGcUwI8QMhxBUZKpuIiIiIaFxlavIOj5TySSnljQAuBJAD4H8zUTYRERER0XjLVE8xhBAfFEL8EMBbACwA1mSqbCIiIiKi8ZSRG+2EEMcAvA3gSQBfklK6MlEuEREREdFEyFT2iUVSyv4MlUVERERENKHOKSgWQnxZSvktAP8uhJCJ66WU686lfCIiIiKiiXCuPcUHhv5tPNeKEBERERFlyzkFxVLKnUN/7pNS/i0D9SEiIiIimnCZyj6xRQhxUAjxgBCiLkNlEhERERFNiEzlKf4QgKsBdALYLoR4RwjxtUyUTUREREQ03jKWp1hK2SalfBjAHYikZ9uQqbKJiIiIiMZTRoJiIcR8IcT9QogmAD8AsAdAZSbKJiIiIiIab5nKU/xzAL8GcJ2U8nSGyiQiIiIimhDnHBQLIfQA3pNSbs1AfYiIiIiIJtw5D5+QUoYAFAohTBmoDxERERHRhMvU8IkWAP8nhNgBwBVdKKXckqHyiYiIiIjGTaaC4tNDDx0AZ4bKJCIiIiKaEBkJiqWUm8aynxBiOYCtAPQAfiKl/GaK7S4G8BqAW6WUvx1zRYmIiIiIVGQkKBZC/AmATFwupVw6zD56ANsAXAvgJIA3hBA7pJT7VbZ7CMAfM1FXIiIiIqJEmRo+8cW4vy0AbgIQHGGfSwAckVIeBQAhxOMAVgHYn7DdFwA8BeDizFSViIiIiEgpU8Mn3kxY9H9CiP8dYbcKAO/HPT8J4NL4DYQQFQA+CmApRgiKhRBrAawFgKqqqjRqTTS+2CZpsmGbpMmGbZImk0zNaFcQ9ygaGitcNtJuKssSh2D8B4CvDKV9G5aUcruUskFK2VBcXJxexYnGEdskTTZskzTZsE3SZJKp4RNv4mxAGwRwHMDnRtjnJICZcc8rEclgEa8BwONCCAAoAvARIURQSvn0OdaXiIiIiCjmnILioawQ70spZw89/zQi44mPI3lscKI3ANQKIWYDOAXgYwA+Eb9BtNyhsn8BYBcDYiIiIiLKtHMdPvEjAH4AEEJcBeAbAB4F0Adg+3A7SimDAO5CJKvEAQBPSimbhRB3CCHuOMd6ERERERGl7VyHT+illGeG/r4VwHYp5VMAnhJCvD3SzlLKZwE8m7DskRTbfubcqkpEREREpO5ce4r1QohoYL0MwEtx6zI1XpmIiIiIaFyda+D6awD/K4ToAuAB8GcAEELMQWQIBRERERHRpHdOQbGU8t+FELsBlAN4XkoZzUChQ2TSDSIiIiKiSe+chzhIKV9TWXboXMslIiIiIpooGZm8g4iIiIhoKmNQTERERESax6CYiIiIiDSPQTERERERaR6DYiIiIiLSPAbFRERERKR5DIqJiIiISPMYFBMRERGR5jEoJiIiIiLNY1BMRERERJrHoJiIiIiINI9BMRERERFpHoNiIiIiItI8BsVEREREpHkMiomIiIhI8xgUExEREZHmZTUoFkIsF0K8K4Q4IoT4V5X1nxRC7Bt67BFCLMpGPYmIiIhoestaUCyE0APYBuB6ABcA+LgQ4oKEzY4B+KCUciGABwBsn9haEhEREZEWZLOn+BIAR6SUR6WUfgCPA1gVv4GUco+Usmfo6WsAKie4jkRERESkAdkMiisAvB/3/OTQslQ+B+APqVYKIdYKIRqFEI2dnZ0ZqiLR2LFN0mTDNkmTDdskTSbZDIqFyjKpuqEQH0IkKP5KqsKklNullA1Syobi4uIMVZFo7NgmabJhm6TJhm2SJhNDFo99EsDMuOeVAE4nbiSEWAjgJwCul1J2T1DdiIiIiEhDstlT/AaAWiHEbCGECcDHAOyI30AIUQXgdwA+JaU8lIU6EhEREZEGZK2nWEoZFELcBeCPAPQAfialbBZC3DG0/hEAGwAUAvihEAIAglLKhmzVmYiIiIimp2wOn4CU8lkAzyYseyTu79sB3D7R9SIiIiIibeGMdkRERESkeQyKiYiIiEjzGBQTERERkeYxKCYiIiIizWNQTERERESax6CYiIiIiDSPQTERERERaR6DYiIiIiLSPAbFRERERKR5DIqJiIiISPMYFBMRERGR5jEoJiIiIiLNY1BMRERERJrHoJiIiIiINI9BMRERERFpHoNiIiIiItI8BsVEREREpHkMiomIiIhI8xgUExEREZHmMSgmIiIiIs0zZPPgQojlALYC0AP4iZTymwnrxdD6jwBwA/iMlPKtCa8o0Tjo9XhxqM2F9n4fSnPMmFtmR57Vku1qZR3PS2oTcW6CwTCaW/sw6AvAqNejY8CHshwzdEKgrd+LUqcFdWU5ONnnwbFuF+xmPawGPQZ9AZiNBnQN+lDkMMPrD8JiMqDPE0CBzYQFM3IBAO+c7kNbvxfnFdvQ7w2hxx1Avs2IAW8ATosRLn8QTrMR3S4fbEY97GYDBrwBWE0GDPoCcJgj2+RZjHD5Qxj0BVFgN8HjD8JqMiAYCsKgN6BjwIcSpxmV+Xq09obgD0kMeIMotJvQNeiD1aSH02yAEMCAL4RBbxDFThMGfEHkWowIhsPwBUIwG8+WlWPVYdATRuegHw6LAXaTHgO+AKxGAzoHfSh1miEh4faFYDUZ0O3yo9BuQp8ngFyrEaFwCAadHt0uP3JtBpj1erT2eVGWY8H5pTYcbHOhc9CPHIsBNpMenmAQRp0eZ1x+FNhN6PMGUGg3IRiS6Br0o9BhgicQhNVogNsfQKHdAgHgVJ8H+TYT+r0B5FqMMBt1cJiNmFVoh04nzr7Pp/twqs+DQocZ/mAQuVYzLijLgU4ncLzbhfZ+L0pzLIr9Jlqvx4ujHW4U2HXoGgyjc8AHh9mAPJsB4bBEx6AfTosBLl8QTosRZr1A19B5D4WBjgEfnBYDcqwG2IwCZ9yh2OfHbtLjWLcbJU4zcq16nHEFUZpjQVW+De/3uNHW70W/JwCn1YDyHCuqh85DOCyTzk8wGMa+obZdnmPBghm5MJn0ab/O+DLLcy2QMlJ3lz+I6gI7ZhepH7sy14oD7f1o7fOiPNeKuvIcGAzs7xyrrAXFQgg9gG0ArgVwEsAbQogdUsr9cZtdD6B26HEpgP8c+pdoSuv1ePF8Uyc27GiCNxCGxajD5pX1uK6+WNMBIM9LahNxboLBMJ7eewqPv96Cm5ZUYdOuZuTbTLjtA9XYuvuw4rjbXj6Mlm4PLEYd7r1+HjyBML734qHYNuuW1uKJxhO4taEKTzSewBeW1sJk0OHLv92Hj9SVwnVeEba9fAS3NlTh4ZciZVcXWvFPV8/BP+54K1bOvdfPg8mgx/07zy77xkcX4HD7ILa8cCi23x1XzcEjrzQryovWtTLfjK/+vgmfumwW1j3+t9i69ctqMSPPgl++ehyNLX1D29fhVI8HP/7zUdWy4l/3PdfORbHDjK/+XlnfVOfin66eg980nogda/2yWjz2agt63H5sXlmPJxtb4upxASQENu5ojpXzxevOx4A3iK/+7p3Yso0r6vDUW4exdF4Znmjcj49dXBUrM/64uw+0YvWFVVheV4ZwWOLpvafwtaebFOVsfeswPn35bOiEDv/ym7dj67asWYzldWUTHhj3erx45d1uVOab8cZxF+57Jq6+N9Thkf89Ensv4l+r1SBwvNuNb//x3dj2X/rw+Sh2mPHlp/bFlm1aWYfn3mnFq8fOxP4+2u3CF687H6193lj7ir5XtaUOXF1bgucPtOOeJ8+en+9//EL0ugOK+m1eVY/VC2ekFRiHwxLPNbfhniffRr7NhH/8YA1c/pDiM7dlzWJcN79UcezqQivu/FAtNsQd98HV9Vi9qIKB8Rhl86xdAuCIlPKolNIP4HEAqxK2WQXgMRnxGoA8IUT5RFeUKNMOtbliwQ0AeANhbNjRhENtrizXLLt4XlKbiHPT3NqHrz3dhNsur8GmXZFg7MYllbEv5/jjrlhYEXve5fLHgsDosodfOowVCyti/37t6SYc6RiENxDGzRdXYcOO5tj66H4rFlbEgsD4su/fqVx2rNsVC1ii+23alVxetK5C6LBiYQW+8/y7inVbdx/Ge50u3HZ5Tdz2zegY8KUsK/51b3nhEI51u5Lqm+pcbNzRrDjW1t2HceOSyljZ8etsJmPSufjO8+/iWJfyeJt2RcqMHiO+zPjjfvKy2bjnybdxvNsVe5/VyjncMRgLiKProvtNtENtLhzpHEQwJGIBZ6y+O5sV70X8a82zm2MBcXT9t//4Lo50DiqWbdzRjM9cMVvx94qFFTjcMahoX9H3at/JPjS39sWC0ui6d071JdVvwzNN2He6L63XebzbFSvzxiWV6HL5kz5z9zz5dtKxVyysiAXE0e2+9nQTmlvTOy4ly2ZQXAHg/bjnJ4eWjXYbAIAQYq0QolEI0djZ2ZnRihKNxXBtsr3fF7uQRXkDYbT3+yayipMOz0tqmTg3I10nW/u88AbC8PiCsWMJAdXjirhOw7BMvU38v2EZWdc14FMsP1u/5HLUyk5clnicxHp0DfpSrgtLwOMPJi1L53XHv6Z0z0XisaLlJa5zxb0Hwx0vul/i+U48bq87AG8gjI4Bb+x9VisnVf07BrwYDyNdJ8MSaB9Qr2/iexF9rT2uQNrnrtcdUPwtROr3MCyheu5Sbd/en945a+8/W+Zwx088dqo22tY3Pu+VFmQzKFb7HUaOYZvIQim3SykbpJQNxcXF51w5onM1XJsszTHDYlR+/CxGHUpzzBNZxUmH5yW1TJybka6T5blWWIw62MwGxbHUjivjrsR6kXqb+H+jv74XO8++FrX94qmVnep4qcorcphTrtMJwGoyJC1L53XHbztS3aLnIPFY0fIS19kthpT1TVxmNRmSznficfNsRliMOpQ4LbH3Wa2cVPUvcY7P8KWRrpN6AZTmWNJ6L6KvNd9uTPvc5dmMSX+nOgc6AZTnJtcl1falOemds8TXl6o8tWOrPS/L1fZQs3ORzaD4JICZcc8rAZwewzZEU87cMjs2r6xXfJFvXlmPuWX2LNcsu3heUpuIc1NXnoMHV9fj0T1HsXFFHSxGHZ568yTWL6tNOu6ufadizwvtJtx9zVzFNuuW1mLXvlOxfx9cXY85JQ5YjDr85o0T2LyyDjv3RtZH99u59xQ2raxTlFNoN+H+G5TLZhXacc+1cxX7bVyRXF60rlKGsXPvKXzxuvMV69Yvq8V5xXY8tudo3PZ1KHGaU5YV/7rvuXYuZhfak+qb6lxsWlmnONb6ZbX43VsnY2XHr3P7Aknn4ovXnY/ZRcrjbVwRKTN6jPgy44/7q9eOYcuaxZhVaI+9z2rlzClx4Lu3LFasi+430eaW2XFesQMGncQDqxLqe0Od4r2If629Lh++9GHle/2lD5+POcUOxbJNK+vwi78cU/y9c+8pzClxKNpX9L1aWJmLuvJcbFmjPD/1FblJ9du8qh4Lh24uHcmsQnuszKfePIlCuynpM7dlzeKkY+/cewqbE4774Op61JWnd1xKJqRU7Xgd/wMLYQBwCMAyAKcAvAHgE1LK5rht/h7AXYhkn7gUwMNSyktGKruhoUE2NjamOi5u/dGetOv5xOcvR7bOEWXeaN9/YMQ2kNadJ2ptklkW1PG8pJbmuRlzmwTOZp9w+QIwDGWfKB3KPtHe70WJ04L6uOwTNpMeNqMeLn8AJkNc9olAEBZjJPtEvs0UCxDSzT5xxuWHxaiDw2yIZXhw+QKwm41w+4PIsRjh9ofg8gWRr5J9onPAh+LE7BO+IAptJnQP+mEx6eA0G6CLZp/wRTJTuIbKjs8+ES0rln3C5YfDHMk+MegLwDKUdaPYYQbE2ewT0awR/d4AcixGhGUIOqFHj8uPHKsRZoMOrX2RLALz4rJPOIcyW0SzT0TPUb83gIKh7BPdQ2WfzT4RycKhg8DpPi/yhrbPtUR6Te3DZZ+wmxEIhZBjMeKC8txY9omOgcj7nYHsE+d0nVRknxj0wWEyIM9qQFhGsk84zJHX7zQbYTYMZZ+wmRCSQMegD06zYeicxmWfcJphN4+cfWLAE4TdoseM3OTsE/HnJ5p9IpoVYuEYs090DESykUSzT7j9QVSpZJ+IHjuafaKtz4uyXAvqynN5k116VNtk1rJPSCmDQoi7APwRkZRsP5NSNgsh7hha/wiAZxEJiI8gkpLts9mqL1Gm5VktuGQ2g71EPC+pTcS5MRh0WDQzf8Tt5licmFPqHHX5F80qGEu1zsmMvAk/5Jg0ZOi9ra8ceRuDQYdFVflYBPX3uqbYgZpiR0bqcy7yrBYsqY6cl1lFmS+/riJPdfnsYgdmp3j9Op1IOj8mkx4N59C21cpUO77adotm5mPRzKRNaQyymqdYSvksIoFv/LJH4v6WAO6c6HoRERERkbawj52IiIiINC9rY4rHkxCiE0BLitVFALomsDrDYV3UTaa6AMPXp0tKuXykAtgmx4R1SW2822S6x5rMWO+JNZFtcqTjTYRsHl/Lrz1Tx1dtk9MyKB6OEKJRStmQ7XoArEsqk6kuwPjXZzK9XtZF3WSqCzCx9Zlsrz1drPfEmuh6Z/s8ZfP4Wn7t4318Dp8gIiIiIs1jUExEREREmqfFoHh7tisQh3VRN5nqAox/fSbT62Vd1E2mugATW5/J9trTxXpPrImud7bPUzaPr+XXPq7H19yYYiIiIiKiRFrsKSYiIiIiUmBQTERERESaNy2D4uXLl0sAfPAxEY+0sE3yMYGPtLBN8jGBj7SwTfIxgQ9V0zIo7uqainnPaTpjm6TJhm2SJhu2Scq2aRkUExERERGNBoNiIiIiItI8BsVEREREpHmGbB5cCLEcwFYAegA/kVJ+M2G9GFr/EQBuAJ+RUr41lmP1erw41OZCe78PpTlmzC2zI89qOcdXQNMZ2wxpjd8fwjun+9Ax4EOB3Qi9TqC934fKfCv8wTDa+30oyTEjFA5Br9Oja9CHQocZZr0Ogz4/TAYDugZ9KHKY4Q0EYTEa0OcJIN9mhEmvw4A3CKNBh0AoBJNej/YBH4odJpj0OvR7/bCajOj1BFBgM+KMKwCLUQeH2YBAOAgBPQa8ATgtRrj9QeRajHD5Qxj0BVFgN2HAF4DTbESxQ4/OwVDsczsjX4/jnQG4/EEU2o2AFOgc9MNhMSDHYkAgFIInIOHyBVHkMKHHHUCu1Qi9LgyjXg9vQMbKEiIMIXWx/e0mPXrcfjgtRvR7A8ixGOEPheA0GxEIhdE56EOh3YxBbwC5VhN0OsCo16HfE0CfJ4jSHDPqy3NhMulj70E4LHG824X2fi9KcyyYVWiHTieSllfl23Cix636vMRpgV4HtPYpy0iU7rFS7T8RvN4gmtr6UZmnw4meEDr6fSh0mpBvM2DQG4q9Xz3uSDuzmfRo6Xaj2GmGXicRCgvFNTz+ml5X5oDdas7K66LJKWtBsRBCD2AbgGsBnATwhhBih5Ryf9xm1wOoHXpcCuA/h/4dlV6PF883dWLDjiZ4A2FYjDpsXlmP6+qLGeSQKrYZ0hq/P4Rn9p3Gfc80Id9mwm0fqMbW3Ycxt8SBj19ajU07m2OfhU0r6/CbxhNobOmDxajDvdfPgycQxvdePBTbZt3SWjzReAKfuKQaVqMOX//DQXgDYTRU5+KWhips3HG2vHuunQuzXoef7dmPT1xSjXVx5dx7/TxYTQbc98zZz+I3ProAh9oHseUF5fHefr8b18wvx4a4sjevrEO+TWDbS8fxycuqFcddv6wWZbkW/MeLh9DS7YHFqMPd18zFf7/egns/Mh99bm/SNWDby4dj265fVgubUY+f7dmPWxuq8ETjCdxz7Vyc7vViwzPNSefijg/OgV5IbNixP7bugVX1WLVwBkwmPcJhieea23DPk2/H1m9ZsxjXzS/F8wfaY8urC634wtJafO3pJtXn0bo99moLetx+bFmzGMvryhSBbbrHii5P3H8ieL1B7GxqxZVznHjl8IDivdi0sg4/fPlI7L2IP8e//msLTAaBNQ1VsbbQUJ2LNQ3VSe/n39eXMDCmmGwOn7gEwBEp5VEppR/A4wBWJWyzCsBjMuI1AHlCiPLRHuhQmyv2QQAAbyCMDTuacKjNdY4vgaYrthnSmn2n+2KB541LKrF192F4A2HcftV5sYAYiHwWNu5oxm2X18Sed7n8sYA4uuzhlw5jxcIKfO/FQ+hy+WPrbru8JhaYRrfd8sIhdLv9se3j13W5/LF6RZcd63bFAuL4433ystmxICi6fMOOZuRabbj9qvOSjrt192Ec63JhxcKK2LLvvXgIKxZWIBCUqteA+G237j4cq3f09b7X6YoFxInnYtPOZthMRsW6+55pwr7TfQCA492uWDAaXX/Pk2+jubVPsXzFwopYAKz2PFq3G5dUxso43q28dqV7rFT7T4R3WiNt8sSZUNJ7sXFHs+K9iD/Ht191Hm67vEbRFiLPk9/P5rbBCX9dNHllMyiuAPB+3POTQ8tGuw0AQAixVgjRKIRo7OzsVKxr7/fFPghR3kDkp0AiNZloM8O1SaKoiplVEEKM6lExs2pMxxquTbb1e2NtXgjE/vb4gqqfBY8/GHsellDdJlpOOC4raKrywlJ53OHKTnW8HldA/XM74B3xuIn1dqXYPnHb+HoLMfK5cMWdt1j9+r0AgPa49yB+fWufcnnieVI7b/F19QbC6BjwKtane6xU+2fK8G3SF3v/0nkvoufB4w8mvd+p3n/GARQvm2OK1X6HSUyonM42kYVSbgewHQAaGhoU25TmmGEx6hQfCItRh9Ic/mRC6jLRZoZrk0RRp0++j1t/tGdU+zzx+cvHdKzh2mR5jkXR5qN/28wG1c+C1XT260MvoLqNlJF/4391T1WebiigTFynVnaq4xXYjeqfW6cF7fClPG4oLlaK1ttuUa+nlFDdP7rOoBv+XNhNyq/dyHUlMiSrNOE9iK4vz1VfPtLzaF0tRh1KnMphX6mPZVVdnrh/pgzXJsuGrsOp6pr4XkTPsdVkgIDynKRqd4wDKF42e4pPApgZ97wSwOkxbDOiuWV2bF5ZD4sx8nKjY4nmltlHWxRpBNsMac2CGbl4YFWkzT/15kmsX1YLi1GHH7/yHjbeUKf4LGxaWYfH9hyNPS+0m3D3NXMV26xbWotd+07h7mvmoshuiq17dM9RbFqpLO+ea+ei0GbCzr2nksoptJti9Youm1Voxz3XJh/vv147hs0JZW9eWYc+jxs/fuW9pOOuX1aL2UV27Np3Krbs7mvmYte+UzDqheo1IH7b9ctqUWgzYde+U7HXW1Nsx+ZVdarnYuMNdXD7A4p1D6yqx8IZuQCAWYV2bFmzWLF+y5rFqCvPVSzfufcUHlxdn/J5tG6/e+tkrIxZhcprV+pj5aguT9x/Iiwoj7TJqnx90nuxaWWd4r2IP8c/eeU9PLrnqKItRJ4nv591ZY4Jf100eQkps9OBJYQwADgEYBmAUwDeAPAJKWVz3DZ/D+AuRLJPXArgYSnlJSOV3dDQIBsbGxXLmEmARivNNpPWnSdqbZIIAIQQY+opHubaPeY2mZR9Qgi0D8RlnxjwocRpRkiGoBd6dA/6UGA3w2wYXfaJYCgEo16PjgEfimLZJwKwmoa2txpxxj189okcixFufwguXxD5dhMGfQE4hsk+4faFUOAwRLJPuPxwmM9mn/AOZZ8osJvQ64lmn5Aw6nXwBiQ6hl63ToQBqYvtbzfp0ePxw2k2xurmD4XgNBkRCIfRNehHwVDdciwm6HWAQafDgDeAPm8QJU4zFqTIPtExEMkikZgRIro8mm1C7XmxI5J9oq1fWUaidI+VgewTY26TSdknBnwotJuQZzPA5Quh1x1ATkL2iRNn3ChyMPsEDUu1TWZt+ISUMiiEuAvAHxFJyfYzKWWzEOKOofWPAHgWkYD4CCIp2T471uPlWS24ZDaDYEof2wxpjcmkx0WzCrJdjXM2u1j5vDIvK9UYE51OoKbYgZpix4jLR3o+q2j4XtDRHCtbLBYDGobaZFleevvUV6TekNd0Gk5W8xRLKZ9FJPCNX/ZI3N8SwJ0TXS8iIiIi0hbOaEdEREREmsegmIiIiIg0j0ExEREREWkeg2IiIiIi0jwGxURERESkeQyKiYiIiEjzGBQTERERkeYxKCYiIiIizWNQTERERESax6CYiIiIiDSPQTERERERaR6DYiIiIiLSPAbFRERERKR5DIqJiIiISPMYFBMRERGR5jEoJiIiIiLNY1BMRERERJrHoJiIiIiINI9BMRERERFpHoNiIiIiItI8BsVEREREpHkMiomIiIhI8xgUExEREZHmMSgmIiIiIs1jUExEREREmsegmIiIiIg0j0ExEREREWmeIRsHFUIUAHgCwCwAxwGskVL2qGx3HMAAgBCAoJSyYeJqSURERERaka2e4n8FsFtKWQtg99DzVD4kpVzMgJiIiIiIxku2guJVAB4d+vtRAKuzVA8iIiIioqwFxaVSylYAGPq3JMV2EsDzQog3hRBrhytQCLFWCNEohGjs7OzMcHWJRo9tkiYbtkmabNgmaTIZt6BYCPGiEKJJ5bFqFMX8nZRyCYDrAdwphLgq1YZSyu1SygYpZUNxcfE515/oXLFN0mTDNkmTDdskTSbjdqOdlPKaVOuEEO1CiHIpZasQohxAR4oyTg/92yGE+D2ASwC8Mi4VJiIiIiLNytbwiR0APj3096cBPJO4gRDCLoRwRv8GcB2ApgmrIRERERFpRraC4m8CuFYIcRjAtUPPIYSYIYR4dmibUgB/EULsBfA6gP+RUj6XldoSERER0bSWlTzFUspuAMtUlp8G8JGhv48CWDTBVSMiIiIiDeKMdkRERESkeQyKiYiIiEjzGBQTERERkeYxKCYiIiIizWNQTERERESax6CYiIiIiDSPQTERERERaR6DYiIiIiLSPAbFRERERKR5DIqJiIiISPMYFBMRERGR5jEoJiIiIiLNY1BMRERERJrHoJiIiIiINI9BMRERERFpHoNiIiIiItI8BsVEREREpHkMiomIiIhI89IKioUQ5wkhzEN/Xy2EWCeEyBvXmhERERERTZB0e4qfAhASQswB8FMAswH897jVioiIiIhoAqUbFIellEEAHwXwH1LKuwGUj1+1iIiIiIgmTrpBcUAI8XEAnwawa2iZcXyqREREREQ0sdINij8L4AMA/l1KeUwIMRvAf41ftYiIiIiIJo4hnY2klPsBrAMAIUQ+AKeU8pvjWTEiIiIioomSbvaJl4UQOUKIAgB7AfxcCLFlfKtGRERERDQx0h0+kSul7AdwI4CfSykvAnDN+FWLiIiIiGjipBsUG4QQ5QDW4OyNdkRERERE00JaY4oBbAbwRwB/kVK+IYSoAXB4rAcVQtwC4H4A8wFcIqVsTLHdcgBbAegB/ORcxjH3erw41OZCe78PpTlmzC2zI89qGWtxpAFsM6Q1fn8ITa19aO/3wWExIMdsQJfLh1yrEVICXYN+lDjNMBoEMPTcYTHApNehY8CL0hwr6spzYDAo+1vCYYnj3S6093thMxngD4VQ4jSj1xVAa78XhXYTTAbAFwQ6+n0oyTHD7Q8ix2KEXgCn+rwocphh0EkEwwI97gBKHCa4/CEMeIModppgN+sw4A2je9CHQocZPe4A8m1GOC16tPf7YDMZYmUHwyHYTUb4g2GcGdrO5QvAbjbC5Q/CbjJg0BtAidMCTyCE9uh+oaH9QmG0DV0XzHqBQBjocfuRZzWizxNArtUIm0kPlz8EnRA44/KhNMeK2iIbmtsG0Nbvw4xcCww6gdZ+L8pzletKc8wozzOj1G7FgfZ+tPZFtsmzGdAx4INJr4PbH0JpjgVV+Tac6HGjvd+L0hwLZhXaodOJpPM+mnWTidvjR1PbANr7fbi42oYTPSF09PtQ5DTBaTagxx2ItQFvMIhwWMBm0sOo12FOkQ12qxkAEAyG0TzUtvNsxlj7WFCWA6uVybQoIt0b7X4D4Ddxz48CuOkcjtuEyFCMH6XaQAihB7ANwLUATgJ4QwixY+imv1Hp9XjxfFMnNuxogjcQhsWow+aV9biuvphBDqlimyGt8ftDeGbfadz3zNk2v35ZLQpsRvR7g/jq796JLd+0sg4/fPkIWro9se0ee7UFPW4/Hlxdj9WLKmKBcTgs8VxzG+558u2zn6UbLsCRDhc27WyO+3zV4cnGE2hs6YPFqMM3ProA73W68O0/vpu0zQ0LK3C0cxBbdx+GNxBGdaEVd15dq/i8rltaiycaT+Cfrp6DQrsJt/3sdUU5DksQDz13ELc2VOHhlw7H1m1cUYdHXmlGTaEdyxeUY+OOs3V86KYFOORy4ZvPHUx5LqLH/dzfzYYnEMb3XjykuIZse/lw0nkzGURS/R9cXQ+ToQ9f/u0+xbH8wTD+/dkDiu2+/9LZMresWYzldWUAkHTe01k3mQJjt8ePXU3t2LCjCb/63BK8cnhAcY42razDb+LazP031OG3b57AsvllKMu1oK3PiyvOK4DZaMTTe0/ha08nt487r67FDfVlDIwJQPo32lmEEHcKIX4ohPhZ9DHWg0opD0gp3x1hs0sAHJFSHpVS+gE8DmDVWI53qM0V+yABgDcQxoYdTTjU5hpLcaQBbDOkNftO98UCYiDS5rfuPoxTfV4c63Iplm/c0YwVCysU2924pBLeQBhfe7oJza19sXKPd7tiwVd0e5vZGAuIo8s27GjGbZfXxJ4f6z4bECdu0+32xwJiAFixsCLp8/rwS4exYmEFNu5ohsmgSyonHI7sFw2Io+s27Yq8ts9cMTsWEEfXvdd5NiBOdS6ix+1y+WMB8dnjNqmeN7X6f+3pJhzpGEw6VuegL2m7+DLvefJtHO92qZ73dNZNJk1tZ4PgUNiQdI42JrSZ+3dGnm/dfRjHulwIhCSa2wbR3NoXC4ij20bfpw07mvBOW3/WXiNNLumOKf4lgDIAHwbwvwAqAQyMV6WGVAB4P+75yaFlqoQQa4UQjUKIxs7OTsW69v6zF5EobyCM9n5fBqtL00km2sxwbZIoG4Zrk239XtU2H5ZAWCJpuRDqz72BMNr6vLF17SrlunxB1WN5/MHY87CE+ja+YNI6IdS3jS7vcQeS1rn8wWH363EFktalqpPauRjNtqnqoXbe03kvOga8quc9nXUTLd3v7vYB9TrHt5no8+h5cvmDaO/3obVPfd/oeWcsQFHpBsVzpJT3AXBJKR8F8PcAFgy3gxDiRSFEk8oj3d5etd9wpMqyyAopt0spG6SUDcXFxYp1pTlmWIzKl2ox6lCaY06zKqQ1mWgzw7VJomwYrk2W51hU27xOAIm/qFuMOkip/txi1KEs9+wQo1KVcu0Wg+qxrKazI/r0Aqrb2MyGlOvU6mgx6pBvMyatsw8dK9V+BXZj0rpUx1U7F6PZNlU91M57Ou9FidOiet7TWTfR0v3uTlXn+DYTfR49T3aTITI+O9c6bPtgLEBRaU/zPPRvrxCiHkAugFnD7SClvEZKWa/yeCbNY54EMDPueSWA02nuqzC3zI7NK+tjH4ro2K65ZfaxFEcawDZDWrNgRi4eWKVs8+uX1aIi14LZRXbF8k0r67Br3ynFdr9762RsjGtdeW6s3FmFdmxZs1ixv9sbwMYb6hI+X3V4bM/R2PNZhXZ86cPnJ23z6J6jKLCZsH5ZbWzdzr2nkj6v65bWYte+U7FxuInl6HSR/dYtrVWs27gi8tp+/pdj2LRSWceaYjv+dfm8Yc9F9LiFdhPuvmZu0jVE7byp1f/B1fWYU+JIOlaxw5y0XXyZW9YsxqxCu+p5T2fdZFJf5oydF70IJp2jTQlt5v4bIs/XL6vF7CI7jHqBujIH6spz8OBq9faxeWU9FpTlZO010uQipEzZ+Xp2IyFuB/AUgIUAfg7AAWCDlPKRczq4EC8D+KJa9gkhhAHAIQDLAJwC8AaAT0gpm0cqt6GhQTY2KotkJgEarTTbTFp3pai1SSIAEELg1h/tGdU+T3z+cgxz7R5zm4xlnxjwwWk2wGE24IzbhxxLJPtEt8uPIocZZoOIPbebo9knIp+TuvLcEbJP6BEIhVGcKvvEgA8lTmX2idN9XhTGZZ/odQdQ5DDBPZR9oshhgsMylH3C5UOh3YxedwB5idknhspWyz7h9gdgM6XIPhHdLxSCzWREIBSOLYtmn+h1+5FrNaLPG0CuJZJ9wh0IQUDgjMuP0hwzaovssQwT5bkWGGPZJyyKdaVOM8rzz2afaOvzoizHgjy7EZ0DPhhVsk90DHhR4lTPMDHadeNgzG1SNfvEgA9FjoTsEw4TfKEQQmHAatLDlCL7REe/D7k2Y6x9MPuEZqm2ybSC4kwTQnwUwPcBFAPoBfC2lPLDQogZiKRe+8jQdh8B8B+IpGT7mZTy39MpnwEITSAGxXROJlNQTDRO2CZpslFtk8OmZBNC3DPceinlmKZ6llL+HsDvVZafBvCRuOfPAnh2LMcgIiIiIkrXSHmKnRNSCyIiIiKiLBo2KJZSbpqoihARERERZUu6k3c8KoTIi3uefy6TdxARERERTSbppmRbKKXsjT6RUvYAuHBcakRERERENMHSDYp1Qoj86BMhRAFGHo9MRERERDQlpBvYfhfAq0KI3yAyq9waAGmlRyMiIiIimuzSCoqllI8JIRoBLEUkt9uNUsr941ozIiIiIqIJMlKeYguAOwDMAfAOgEeklMGJqBiRVlTMrMLpk++Pap8ZlTNx6v0T41QjIiIi7Rmpp/hRAAEAfwZwPYD5AP55nOtEpCmnT74/phnNiIiIKHNGCoovkFIuAAAhxE8BvD7+VSIiIiIimlgjZZ8IRP/gsAkiIiIimq5G6ileJIToH/pbALAOPRcApJQyZ1xrR0REREQ0AUaa5lk/URUhIiIiIsqWdCfvICIiIiKathgUExEREZHmMSgmIiIiIs1jUExEREREmsegmIiyqmJmFYQQo3pUzKzKdrWJiGiaGSklGxHRuOKMfkRENBmwp5iIiIiINI9BMRERERFpHoNiIiIiItI8BsVEREREpHkMiomIiIhI8xgUExEREZHmMSgmIiIiIs1jUExEREREmsegmIiIiIg0LytBsRDiFiFEsxAiLIRoGGa740KId4QQbwshGieyjkRERESkHdma5rkJwI0AfpTGth+SUnaNc32IiIiISMOyEhRLKQ8AgBAiG4cnIiIiIlKY7GOKJYDnhRBvCiHWDrehEGKtEKJRCNHY2dk5QdUjSo1tkiYbtkmabNgmaTIZt6BYCPGiEKJJ5bFqFMX8nZRyCYDrAdwphLgq1YZSyu1SygYpZUNxcfE515/oXLFN0mTDNkmTDdskTSbjNnxCSnlNBso4PfRvhxDi9wAuAfDKuZZLRERERBRv0g6fEELYhRDO6N8ArkPkBj0iIiIioozKVkq2jwohTgL4AID/EUL8cWj5DCHEs0OblQL4ixBiL4DXAfyPlPK5bNSXiIiIiKa3bGWf+D2A36ssPw3gI0N/HwWwaIKrRkREREQaNGmHTxARERERTRQGxURERESkeQyKiYiIiEjzGBQTERERkeYxKCYiIiIizWNQTERERESax6CYiIiIiDSPQTERERERaR6DYiIiIiLSPAbFRERERKR5DIqJiIiISPMYFBMRERGR5jEoJiIiIiLNY1BMRERERJrHoJiIiIiINI9BMRERERFpHoNiIiIiItI8BsVEREREpHkMiomIiIhI8xgUExEREZHmMSgmIiIiIs1jUExEREREmsegmIiIiIg0j0ExEREREWkeg2IiIiIi0jwGxURERESkeQyKiYiIiEjzDNk4qBDi2wBuAOAH8B6Az0ope1W2Ww5gKwA9gJ9IKb851mP2erw41OZCe78PpTlmzC2zI89qGWtxpAFsM6RF4bDE8W4X2vu9sJkM8IdCKHaYEQoDHQNelOZYMKvQDp1OpF1OdB8AON7tQrfLB6tRj153AC5/EAU2EwZ8ATjMRvhDIZj0enQM+FCWY4bFoMeJHjeKHGb0eQLItRph1EsEQwLtA5HPpg4CfR4/HBYjbGYBt0+ivd+HslwzzAYdulw+OExGdA74UJlnhT8URlt/pPw8mx497hDc/iBsJgM6+n2oyLNAAjjj9iPHEtmv2GmGLxREnsUETyCMHncA+TYj/MEQzAY9AuEQjDo9ugb9KLAboRcCXS4/CuymWL37vAEU203wByW6XD7Fa9ILICSBXncAJU4TAiEZu/bMK7PjwNC1qCrfimBYon3AiyKHGcFQCAa9Hl2DPpQ4zTDqdDjd50WRwwSXP4B8mwV15TkwGHSq709bnxdmgw59Xj8K7erbjvZ9HqltjIbHE8CBjgEM+ILwBkKYXWjHnBIndDqBXo8XJ7o8cPlD6Hb5I+3FqEdLtxvVBTZ4AiG0DZ3DsAzBqDNAL4Autx8lDjNCUsIfDOOMKwCnxQCHSY8utx/FDjNsJoEedwjdg36UOE0ISyi+C96N+24ocerh9ocxONTuKvIsMOgFTvd6UZ5rTeucer1BvNPaF2mXuWaU55pwqscf+RzkmrGgLBcWiwHBYBjNrX1o7YuUfV6RBfvj6lJf5oTNasrY+Z+K/P4Q9p3uQ1u/F+U5FiyYkQuTSZ/WvlkJigG8AOCrUsqgEOIhAF8F8JX4DYQQegDbAFwL4CSAN4QQO6SU+0d7sF6PF883dWLDjiZ4A2FYjDpsXlmP6+qLGeSQKrYZ0qJwWOK55jbc8+TbsXb/1eXz4AuFseWFQ7FlW9YsxvK6spTBj1o5W9Yshskg8MCu/bj9ihoM+oLYuvtwbP26pbV46WAbbmmowsYdzbHlG2+ow6//2oJDHYOxbdY0VGHDjmbk20y47QPVinI2r6zHtpcPo6XbA4tRh3uvnwdPIIzvvXgo5faH2npwXkkeNu16K7bN42+cwK0NVXj4pbPb3n9DHQasITz03EHFuupCK+76UC3ue6ZJ9RjrltbiicYT+MQl1Wg1evD1PxxMWnfHVXPw1FsnsOaimeh2+bFpZ3PSa/IHpaLs6kIr7rhqDjbtSn2+nmjcjy8srcXqRRWxwEzt/Um17Wjby0htYzQ8ngBeeLcDp3o9ivP53VsW4e9qC/DXoz044wooztX9N9Th9aNduLimSLF808o67D7QioZZRShzmnAyGMaAN6hoa+uX1cJhNuCBXftx59W12PbyYeRajPj4pdVJ78eTjS1obOmDxajDt29eCG8gnPL9f3B1/bDn1OsNYsc7rQnfN3V4svFE7BibV9ZjRX0pnt3fjq89HdluzUXlaJhVnPQ9taK+VLOBsd8fwtP7TmPDM3HnZFU9Vi+ckVZgnJXhE1LK56WUwaGnrwGoVNnsEgBHpJRHpZR+AI8DWDWW4x1qc8UaDQB4A2Fs2NGEQ22usRRHGsA2Q1p0vNsVC3CASLvvdvtjAXF02T1Pvo3j3ak/C2rl3PPk29h3sg8rFlagc9AXCxqi6x9+6TBuu7wmFqREl2/a2YzbrzpPsc2GoW1uXFKZVM6GHU1YsbAi9rzL5cf3Xjw07PbX1FXEAsvoNisWVsSC3ui29+9sRjiMpHUrFlbgvqEvYbVjPPxSpLzvvXgIXS6/6rpNu5px2+U1sJmNsQAs8TUllh3db7jztWJhBb72dBOaW/uGfX9SbTva9jJS2xiNd9r6caRzMOl8/stv9uJQmwuBoEw6V/fvbMbNF1clLd+4oxmfvGw2trxwCHl2M8JhJLW1rbsPo3PQhxULK2Ln/ParzlN9P267vCb2/HDH4LDv/0jn9J3WPpXvm2bFMTbsaEJT20AsIAaA1UuqVL+nmtoGMnL+p6J9p/tiATEwdE6eacK+0+m16ckwpvgfAPxBZXkFgPfjnp8cWqZKCLFWCNEohGjs7OxUrGvv98VOUJQ3EEZ7v2/MlabpLRNtZrg2SZQNI7XJ9n5vUrsPS6h+FjoGvCmPo1aONxBGWAJCpC7T4wuqL/cHFX9HtxFCvRwR10kZf6xU23cOeJO2SbWtyx9MWhf/fLg6Rc9BqnUefxCuFOdAiOSyUx0r/nxFt2nrO/t+pXp/1LYdTqpyhmsbiUb67k7VVtr7fSnPVfeg+vW71x2ANxAZLpFq32gbjZ6PkdokkF4bG+6ctqX4vok/RvQ1x2/XNcDYJlFbijbZ3p9emxy3oFgI8aIQoknlsSpum38DEATwK7UiVJZJlWWRFVJul1I2SCkbiouLFetKc8ywGJUv1WLUoTTHPJqXRBqSiTYzXJskyoaR2mRpjiWp3esFVD8LJc7Uw4jUyrEYdYj+op6qTJvZoLrcajKc3cak3EZtexn3TZF4rFSvRW0btW3tcXVJXDdSneLPgdo6q8kAu0X9HERf03DHjT6PP1/Rsstyz75fqd4ftW2Hk6qc4dpGopG+u1O1ldIcc8pzVehQv37n2YywGHUosBtT7qsTiJ0HKTFimwTSa2PDndOyFN838ceIvub47YqdjG0Sladok6U56bXJcQuKpZTXSCnrVR7PAIAQ4tMAVgD4pJRSLdg9CWBm3PNKAKfHUpe5ZXZsXlmvuNBtXlmPuWX2sRRHGsA2Q1o0q9COLWsWK9p9gc2Ee66dq1i2Zc3i2I1z6ZazZc1iLKzMxc69p1DkMGP9slrF+nVLa/HonqPYtLJOsXzjDXX4ySvvKbbZPLTNU2+eTCpn88p67Np3Kva80G7C3dfMHXb7F5pPYeMKZZk7957CuqXKbe+/oQ46HZLW7dx7Cg+sqk95jHVLa7Fr3yncfc1cFNlNqus2rqjDY3uOwu0NYOMNdaqvKbHsnXvP1jvV+dq17xQeXF2PuvLcYd+fVNuOtr2M1DZGY0FZDs4rdiSdz+/esghzy+ww6kXSubr/hjr89o0TScs3razDr147hnuunYtelw86gaS2tn5ZLYodZuzadyp2zn/8ynuq78dje47Gns8pcQz7/o90TheU56p839QpjrF5ZT3qy5x4cPXZ7X7/1gnV76n6MmdGzv9UtGBGLjavSjgnq+qxcEZ6bVqox6PjayirxBYAH5RSqv6uLIQwADgEYBmAUwDeAPAJKWXzSOU3NDTIxsZGxTJmEqDRSrPNpHU3iVqbjBUgBG790Z5R1e2Jz1+ObHx2xwNff8Zf/zm1SWX2CT0CoTCKhrJPdA56UeIcXfaJjoGz+wCRcahnXD5YUmSfCIRCMOr16ByIZFOwGvV4v8eNQpXsEx0DPpQMZZ/o9/phNydkn8gxw2TQ4YzbB/tQ9omKPCsCQ9knSnPMyE/MPjHgw4yhXr1Y9olBH4odZvhDQeSYTfAGIz/F59mMCATDMBl0w2af6PcGkGMxot8bQKEtklkiln3CG0CuJS77hCeAEsdQ9okBH0qdyuwTM/OtCMVlnwiFQ9DrItknih1mmPQ6tPZ7UWg3weUPIt9mQl15bsrsE+39Xhj1Ogx4/Siwm1W3He37rNI2xtwm47NP+AJhzCq0pcw+Ueo0w2qKZJ+oKrDBG5d9QsoQ9DoDDALodvtR5DAjPJR9oscdgMNsgN2kxxm3H0V2E2xmXXL2iaH3Qy37hMcfxkBC9onWXi/Kci1pndNo9olomeV5kewTnUMZVhaUK7NPtPVFyj6vyMrsEwmi2SeiGVEWqmefUG2T2co+8QMAZgAviMjgr9eklHcIIWYgknrtI0OZKe4C8EdEUrL9LJ2AOJU8qwWXzGYQTOljmyEt0ukEaoodqCl2JK07ryR52WjLSVX2cOor80a1/XQ0mmvRojS2Ge59Ho1MlZOK1WrEkuoC1XV5VgvyZiafl/qKvHGpS7x03o+FaikEUrBYDLh4dqFiWWV+8nYGgw6LZuZjUdzv6PyeUjKZ9GiYpd5mRpKVoFhKOSfF8tMAPhL3/FkAz05UvYiIiIhImyZD9gkiIiIioqzKypji8SaE6ATQkmJ1EYCuCazOcFgXdZOpLsDw9emSUi4fqQC2yTFhXVIb7zaZ7rEmM9Z7Yk1kmxzpeBMhm8fX8mvP1PFV2+S0DIqHI4RolFI2ZLseAOuSymSqCzD+9ZlMr5d1UTeZ6gJMbH0m22tPF+s9sSa63tk+T9k8vpZf+3gfn8MniIiIiEjzGBQTERERkeZpMSjenu0KxGFd1E2mugDjX5/J9HpZF3WTqS7AxNZnsr32dLHeE2ui653t85TN42v5tY/r8TU3ppiIiIiIKJEWe4qJiIiIiBQYFBMRERGR5jEoJiIiIiLNm5ZB8fLlyyUAPviYiEda2Cb5mMBHWtgm+ZjAR1rYJvmYwIeqaRkUd3VNxcmAaDpjm6TJhm2SJhu2Scq2aRkUExERERGNBoNiIiIiItI8Q7YrkC4hhB5AI4BTUsoV2a4PTX/hsMTxbhfa+70ozbFgVqEdOp2YMuUTEU13vI5SJk2ZoBjAegAHAORkuyI0/YXDEs81t+GeJ9+GNxCGxajDljWLsbyuLCMX3PEun4houuN1lDJtSgyfEEJUAvh7AD/Jdl1IG453u2IXWgDwBsK458m3cbzbNSXKJ9Iiv9+Pv/71r4qH3+/PdrVonPA6Spk2VXqK/wPAlwE4U20ghFgLYC0AVFVVTUytaNpq7/fGLrRR3kAYHQNe1BQ70ipjuDaZifKJRmu6Xyf/9re/4QvbnkFuRQ0AoO/UUXz/TuDSSy/Ncs0olXNpk7yOUqZN+p5iIcQKAB1SyjeH205KuV1K2SClbCguLp6g2tF0VZpjgcWo/HhYjDqUOC1plzFcm8xE+USjpYXrZG5FDQpn16Fwdl0sOKbJ61zaJK+jlGmTPigG8HcAVgohjgN4HMBSIcR/ZbdKNN3NKrRjy5rFsQtudKzarEL7lCifiGi643WUMm3SD5+QUn4VwFcBQAhxNYAvSin/XzbrRNOfTiewvK4M89ZdiY4BL0qcmb2rebzLJyKa7ngdpUyb9EExUbbodAI1xY5xG5s23uUTEU13vI5SJk2poFhK+TKAl7NcDSIiIiKaZqbCmGIiIiIionHFoJiIiIiING9KDZ84F5wKkoimOl7HSKvY9mkiaCIo5lSQRDTV8TpGWsW2TxNFE8MnOBUkEU11vI6RVrHt00TRRFA83FSQRERTAa9jpFVs+zRRNBEUcypIIprqeB0jrWLbp4miiaB4VqEd371FORXkd2/hVJCUXcFgGHvf78FzTa3Y+34vgsHwyDuRZnFKW9Kqkdp+OCxxtHMQr77XhaOdgwgGw4rn4bDMZvVpCtHMjXZhGcbaq2oQloBOAGEZRjgsOUifsiIYDOPpvafwtaebYjeOPLi6HqsXVcBg0MT/VWmUdDqB6+aX4om1l6G1z4vyXAvqynN5DaNpb7i2n3gTXnWhFV9YWqu4tvKmPEqXJoLi/a19eHTPMdx2eQ08viBsZgMe3XMU1QU2LJyZn+3qkQY1t/bh+y8dxueuqIEYuk5//6XDqC1xYJHG2yRTL6kLhyWeP9DOO/BJc6Jt/6HnDmDFwgrsb+1HvyeID9QU4kSPW3ET3oqFFbGAGDh7U968dVdiVqGd1xYaliaC4j6vHzctqcKXf7s39mWycUUd+r2BbFeNNKrb5cOtDVV4+KXDsTa5bmktzrh82a5aVjH1Umqp7sCft+5K1BQ7slw7ovFzvNuFh547kHTNfOimhZiRZ1HchCcEVG/KO+Py4WDbAK8tNCxN/E5r0huwaVez4stk065mGPX6LNeMtCrHYopd3IFIm3z4pcNwWkxZrll2MfVSarwDn7Sqvd+LFQsrkq6ZX3lqH0x6nepNeInPjXodry00Ik0Exd0un+qXSbfGe+Uoe/zBsGqbDIS0fbMdA7/UeAc+aVVpjgV6nXoPsNsfUtyEt3PvKTy4uj7ppjy3P8RrC41IE8MnKvJssBh1ig+ExahDRa41i7UiLSvLtai2ydIcbQc40cAv8bww8Dt7B37iz7/MPkHT3axCOy6uLkh5zbx0diHmrbsSHQNelDgtqMq3YUlVfux5dCwxry00Ek30FNeV5yT9z/HB1fWom5Gb5ZqRVjG9ljqel9R0OoHldWV4dt2VeHztpXh23ZUcD0maoNMJfKCmEA/dtFD12qDTCdQUO3BZTRFqih0wGHSK5zqd4LWF0jLpe4qFEBYArwAwI1Lf30opN46mDINBh9WLKlBb4kBbnxdlQ+lcmPqKsiUa4MT3bvBOaJ6XkUS//HljHWmNwaDDDQtnYEFF7piuDby2UDomfVAMwAdgqZRyUAhhBPAXIcQfpJSvjaYQg0GHRTPzsWjm+FSSaLQY4KjjeSEiNed6beC1hUYy6YNiKaUEMDj01Dj0GPX0NMx9SjQ18LNKRGp4baDxNumDYgAQQugBvAlgDoBtUsq/qmyzFsBaAKiqqlKsC4clXjvWiWAIOOMKIBAKo63fjctmF/MDReNmuDYJRGa1a27tG5qhyYq68hzND+lhnuLhnWtQMFKbJJpo6bbJYDCMPUe70dhyBmEZyTJx34oLMLvQgY6B4T8PDKYpXVMiKJZShgAsFkLkAfi9EKJeStmUsM12ANsBoKGhQdGTfLLXhffPeLFxR3Psi3bTyjpU5rtQVcCfUWh8DNcmOc2zOk5QkVom/sMwXJskyoZ02mQ4LPE/Ta34ylP7Ym3/q8vn4WinC3f999+G/TzwP9o0GlMiKI6SUvYKIV4GsBxA0wibx7T1+vDDl48optT94ctHMLtwEYNiygpO86xuuDzFWg+K+R8G0qrj3a5YQAwMzTPg9uOZt08prqEPPXcA88qcis/DdP/csBc8syZ9UCyEKAYQGAqIrQCuAfDQaMro8wZUp9Tt83GaZ8qOHrf6NM89bn+2q5ZVzFOcGv/DQFql1vZtJr3qNfSMy6f4PEznzw17wTNvKvxOWw7gT0KIfQDeAPCClHLXaArItRpVp9TNtRgzX1uaNoLBMPa+34Pnmlqx9/1eBIOZm23OblJvk3bTpP9/6rhiLtHUSnMsqC604s4PzcFdSyOP6kIr/8NA055a259VYFe9hhr1uqR9E2eCrC60wmrU49X3unC0cxDh8NQcSZSqF5xTV4/dpP8GllLuA3DhuZTR4/ar/k+x182eYlI33mN+z6Rokz0ab5PMJZpaVb4NX1ham9Qmq/Jt2a4a0bhSa/sP3bQw5bTP8RJngqwutOILS2tx6/bXpnzv6nTuBc+WSR8UZ0JZjhXVhVasWFgRG3u0c+8plOaYs1sxmrSaW/vw+Ost+NbNi+DxBWEzG/DonqMZG/NbZDejoToXt11eoyi/wG7KQO2nBzk1O2/GzYkedywoACJffl97uglLqvL5BUjTWrTt59tMuHFJJYQA9DqBhupcXFpTnPC9rvzlJPE/2lajHrduf01R1rtt/big3IlZRVPrc8ThZpmniaD4/GIH7ry6Fht2nP1f5uaV9Ti/2JntqtEk1e/146YlVfjyb/fG2szGFXUY8GZmzK/FpMOaBmX5m1fWwWqaCiOaxg/HyKXGXiHSqvZ+L/JtJnzqsmrFGOLNK+ux7eXDaOn2RJ6vqkdlrjVp//hJO159r0u1rOpCO6oKptavUom94Bxudu408Q38bucAtr0cudP/rqVzcPuVNdj28mG82zmQ7arRJGUxGLBpV7OiV27TrmaYDZn5f6TbF8KGHcryN+xohtsXGmHP6Y1j5FJTGxvJXiHSgtIcC25pqEwaQ7xhRxNWLKxAea4Fn7uiBid73Nh7avj7P1KVde/v35ly15loL/iz667E42svxbPrrmQHwjnSRE8x7/Sn0ep2qY/57XZlps10DvpUy+8c9GWk/KmKvaGpVeXb8ODqeo4pJs2pyrfhvGKH6rXBadEren23v3J02Ps/ZhXaMbfEOW2uM5y6OrM0ERTbTEa8dLAtaXxowyzt5oOl4ZXnWlXHapXnZqZXrshhVi2/yKHtce7Ru8wTx/+zNzQyrjI+t7WUkdzWHFNM092JHjdaez1J18zqQivml+fgzZYe3H5lDV55twNXzi3BiTNu7D3Ziwur8pN6TXU6gfllTo7FJVWaCIo9gaDq+FBPIJjtqtEkdX6xA5tX1o/bOPRgKISNK+piQzSibTIU1vbwCWZYSK2934uWbg+2/emIYvlU7N0iGo32fi8efbUF65bWxnqEqwutuPPqWnz+l28qrqGPvHIELd0ebH/laMoZ7k72ubHxhjps2tnM6wwpaCIothoN2LTrraTxoY/9wyVZrhlNVvHj0KO9cttePozzyzKTfcKg1+ORV44oyn/klSP49s2LMlD7qavljHqGhQtn5uO8Em0HfiVO9TvNix3s3aLprcRpQY/bj1++1hK7Zs4rdeKLQx1dwNnv9c9dUYNtfzqScua6490uvNnSq5gNj7+6UJQmguLOAfXxm10aH79JqbX2qffKtfV5sWjmuZffNehTLV/rbbLljEv1s3rijEvzQbFeB6xfVoutu8/eG7F+WS30mrhdmrQsvu1v+9MRWIw6PLCqXvVaIYTyeeIvKe39XoQl0vrVJZ0plKPbdLt8MOl1cPtDnG55CtNEUFzkNKn2sBQyJyylUJZiTHFiDswxl58iv2Spxse02U0G1fNi0/hMf0DkP2qPvdqi6N167NUWXFiVN+XyqxKNRrTtf/vmRXi3fQBSRgJYtWtFfH5ztXHCpTkW6AVGHFOcTnrI6DYPPXcg6WZ+ppKcmjTxTWM16PGNGxfgWJcLYQnoBTCryA6rUZ/tqtEkpRdIGnO28YY66DN0gdMJkaLXT9sX0NIcM7729/PRMeCLfVaLnWZOtIPIl3mP26/o3eLNQaQF0bbf4/ZhXqkTLl8QuTYjvnHjAnz1d+8kjSkGUk8RP6vQjgWVuUnX38RtU6WHjB+OEd3mc1fUJKV4Uxu6QZOfJoLibrcP3YN+bH/laOwD8MXrzke+zZjtqtEk1XLGjV//dWhGO38QVpMBP3nlPXz+g+dhQWXeOZf/fo9HtdevMt+GxVXazYoyI8cKs0Gv+Kw+sKoeM3KSE/JrDRP1k1bNKrTjR59agtO9vtg44uhkHV/58PnodgcgJfDr11uwYmEFFlbkoLbUqTqEQacTWHp+KeYUO7CkKh9ufxBVBXbMLlJum056yOg2QmDapHjTOk0ExQ6TEd95/l3F/+K+8/y7+CVvtKMUynMsMBmGLpASEABMBpGx4ROlOeaz5QMQIlq+tntED7T3475nlDfa3fdME+aWZuYGx6kscbraEifHLZI26HQCeRYTPr9TecP8hmea8J2bF2HTrgMoz7XgxiWV0Osiw9+G+2zodAKzihyoKrDHxgwLAcU+6UyhHD+hDlO8TQ+aCIrPpJiIoccdyFKNaLK7oNSJNQ3VCdMw16OuNDMp2eaX2VWnHp9fpu1ev9Y+9d6ZTN3gOF3Ej5sk0oLWFD23ISlRXWhVjOlNlY4t3khjhtP5ZSa6zUPPHVCki+OvOFOXJoLiArv6jXYcPkGpHOoajAWswNkpRTOVku1gm0u1/NlFl+Di2drtXUg1aUpZhiZNmcrSufGHaDoKhyVyrUbVa4PFEBlGEc1XDKQ3pnekMcPp/DIT26bMiTMuH55YexmzT0xxkz4oFkLMBPAYgDIAYQDbpZRbR1NGIBzCppV12Ljj7E1Tm1bWIaDxiRIotVQ9lq0Z6rFs71dPE9g+oO2UbPNLnfjWzQtxpGMwdqPdeSUOzC/NyXbVsi6dG3+IpqPj3S589/mDigmPqgut2LCiDr5gCKGQRL7NhNY+b2wfbyCMlhSp0sJhifc6B0ccB5zOFMqcZnl6mfRBMYAggH+RUr4lhHACeFMI8YKUcn+6BVgNBghIfOfmRXD5g7CbDHD7A7AapsLLp2wozVGfhrnEmZkxv2W56uWXZqj8qard5YE/GFbcaPfg6nq0uzyYadL2l046N/4QTUft/V40tvTBaW7F9k81oNftQyAM3Pnfbymy9zz2akssMLYYdTjYFknfFj+s4bu3LEZYhnGkY5DjgCnJpE/7LqVslVK+NfT3AIADACpGU4bLH8KGHftx16//hq889Q7u+vXfsGHHfrj87CmmFGQkWXz8TRTrl9UiUz+GGXQCG2+oU5S/8YY6GPXa/rmttcenOqNda4+2e9AB5U09UfwSJy2Itv0LKvKw9peNeLfdhX/7/TuK68TW3YdxS0MlgMjn4p5r56Km2AFvMITbr6xBea4F3kAY//Kbt3G4YxBPNp7EuqXKazzHAdOU6ioVQswCcCGAv6qsWwtgLQBUVVUp1g14g6o9LAPe4HhVlaa40ykmSqgqtOHCNMsYrk12DPihT/z1whdAx6A/w69kamlPMfuk1oeVAJlJyTZcmyTKhnTaZLTtH2zrHzYFWkWuFd+6aQFmF9mx/3Q/1j/+t9hnZd3SWvzytUhPclhGhsjFTxt96ewC/N15RRwHrHFTJigWQjgAPAXgn6WU/YnrpZTbAWwHgIaGBsW92cVOExqqc3Hb5TXw+IKwmQ14dM9RFDs4ox2pK3Gqp0wrcaQ/vGG4NplvM2L7K0dw2+U1gAQkgCfffB9fXj4/My9giipLMWylTOOp6oDMpGQbrk0SZUM6bTLa9qsKrLAa9Ti/1Kl6nTjV58FFVfkwG3T4xnMHFT3JD790GJ+7ogY//ctR5Jj1uPNDc2JTQu/cewrX15WOOKUzTX9TIigWQhgRCYh/JaX83Wj3n1dqV02vNU/j6a8oNZNeh3+6ek7SzZkmQ2ZGHOkEcNOSKkWb3LiiDhofPYG6Mic2r6xPSlVXV5aZVHhTHW/qIS1r6fZg6+7DyLeZVGcELckxA0Ji0BdS7UnW64Dvf/xC9LoD2PLi2WvMN25cgIPtg7EhGczsol2TPigWQggAPwVwQEq5ZSxlHGx3q6a/qim+FA2zOB6PkvlDYfzw5SOxn9YA4IcvH8F3bl6UkfJDEnjkFWX5j7xyBN/OUPlT1eEuF7a9fFgxbGXby4eHUuHxlx2/P4R9p/vQ1u9FeY4FC2bkwmTidPU0/b3XOYh/+c3bsSxAj73agnuuqcWcEie8wciQiv/80xEc6hjEE2s/oNqTfMV5RZGbd/9nv+La29bnjQXYADO7aNmkD4oB/B2ATwF4Rwjx9tCye6WUz6ZbQKq7ttv7vSn2IK3r8wYUyeCjY9L6vJmZ8KXPM77lT1WtfV60dHuw7U9HkpZrffIOvz+Ep/edxoZn4nrRV9Vj9cIZDIxpWvP7Q9jf2p/0PR4MA/8Ul4Fi3dJadL7WgkAolDT+fv2yWqx/4m189vLqpGvvA6vqmdmFAEyBoFhK+Rfg3G76L3aaUV1oxYqFFYoxRMUaT39FqeVYjLGLJnB2TNpjGZoaPNc6vuVPVcUO9c9q0SjGck9X+073xQJi4Ow0tzVFdjTMKshy7YjGz77TfTjaOai4Npxf6sSXhoafAWevoWuvqkGB3YwlVQU4/wtX4mjXIHyBME72ugEA5Xm2pP1O9rhVe5YFBI52DiaNLw6HZdbGH2fz2Fow6YPiTAjLkOr40LBkSjZSl3JqcFdmskN0D6pnWTjj0naWBadVhzuvnoMNcZ/VzSvrkGud9Nkjx13HQOo8xUTTWceAF3862IE7PjgHm3ZGrg3rls1R/TycV+xAVb4NAPBu+4Cit3jd0lqc6vEk7fdk40k8sLoe9z3dpOhZ/ucn3kaP268YX5zNmSU5q+X408Q3jU7oYwExEPngbNzRDJ3gT46kLjo1eDyLUYd8e2bGtRY6zKrlF9i13SPa6w7FAmIgOv6/GT1u/ge2OEWbKWYvOk1j4bBEkcOMq+eVxAJiAAhLqH4ezHodTvS4YzNA5ttMuPNDc3D7lTXwBUOoLrIl7dfj9qOm0I4n1l6Gn3+mAWuvqsEf3mnFjUsqcfuVNXi3rR8nzrgApJ5Z8ni3a9zPRTaPrRWa6Cnudvkxt8SB2686L5aS7cevvIczGer1o+knGArhoZsW4L1OV2y64ZpiO4KhTAVnYXzrpgU4Elf+ecV2RGYy1672fh/ybSbcuKQyNnziqTdPor1f2z3oAKDXCWy5ZRHCEnD5grBbDNABMGg9ZQlNW9Ge0YeeO4B/ue58xc1xzSd78Y2PLsCxbuU1utBpxPHuQeRaTPjidXORazXhZI8bfzrYgavnlUBKiX//6AJFpol7rp2Lu379N/S4/fjOzYvwm8aT+NRl1Ypxx9WFdlQV2LM6syRntRx/mgiKZ+Ra8PFLlSnZNt5Qh/JcZp4gdXaTEU39A4rphu+5di5mFWQmjZ9Jp0dbvy+p/OjPflo1M9+C2z5QnZRqqTKPn1WrSYc+bzDWWxa9js02auIHP9Kg+N5elzeIn/7l7PXyB5+4EEc7XUnX0PY+LyQErEYPvvP8IXgDYVQXWnHHVXOwaVdz7Pn31izG4Y5BBMNhmPWRz5A3EMbhjgHc0lCZdM/Hvb9/B4tn5sVm18vG9NDZPLZWaCIodgdCeOR/E9Jf/e8RfPOmhdmtGE1a/b4gfvXXFkWb+dVfW3BBeU5Gyh/whVTLn5+h8qcqnRBJqZG27j6Mv1t7WZZrln0DHvXr2Ldv0nYaP5q+oj2jNy6pxCOvvKdo++EwsOWFQ4prxZYXDuHbNy/Cl367F/dcUxvb/vxSJ77z/NnJPFq6Pbj7ybfxuStqsO1PR2Ax6mJ/P9l4El+9fl7KHtlLZhWe88ySY5WJWS1peJoIige9QdX0V4Oc5plS8AdDqm3GF8zM8Al/SL18f8aGZ0xNbSl+Hmzr90Hr/4XtT5EmsN+n7TR+NH1Fe0adFn1S29+8sg75NhNa+87eaOoNhOH2BZFvM8FuMWLLi8rPSnSa5+i20QA7/u8etx+ziuwpe2QzMbPkWGXz2Fqhid/dUqW/yrUas1wzmqxSpWRzWjLTZlKVn2PWdpsstKe6AZETd6S6juVkqE0STTbRntHKPFtS29+woxm3NFQqtrcYdegc9OGWhko8sGt/0mflxiVnt68utGJuqRN3LZ2D9cvmwG7Sx3peLyjLwZY1i2PXosQe2ejMkpfVFKGm2DGhQWk2j60Fmugp7k6RXos32lEq/mBYtc0Egpm5ES5Vm+zWeJsMSYl1S2uTekMlZLarlnVdTONHGhPtGX3xQLtq268uONujazHqcPc1c/Hfr7fgC0trU07zDEQC4js+OEdxn9HXP7oAz62/ElUFdvbIapgmguLCofRaiT+FsPeJUinPs6q2mfI8a0bKL2CbVGU26PHSwTZ86+ZF8PiDsJkMeHTPUfzdnMJsVy3rSlLcZFPs4E02NH3pdCLlDWZ2kx53fWgOwhKYVWRHW68bm1fVw+MPqW5/UXU+7lo6B3XlObg7IbXZvb9/B//zhStjgW+0R3YsWR04wcbUpYnhEx5/EOuW1ip+Clm3tBYeP8cUk7oyhwmbV9Yr2szmlfUoc2QmaA2HQ9i4ok5R/sYVdQiHtT2mWAiJWxqq8OXf7sVXnnoHX/rtXtzSUAV+nwDFTiM2r6xLaJN1KM7h8Ama3vLthqS2v25pLb7x3AHodQK/fv0EvvzbvXBaTPj+7kPo6PfigVX1SdfX7/7xXfzgpSNoVpky2hsI40BbP8Lhc/tVKppG7iMP/xkf//Ff8ZGH/4znmtvOuVyaGJroKbaaDKq9Tw3V+dmuGk1STW0D2Pby4djdy1IC214+jFlFNlwy+9x7LXVCj0deOaIo/5FXmEnAH5T44cvKDAs/fPkIvnvL4qzWazJo7fXjUFsvfvHZS9A59JPuC82nhvKnZrt2ROPndK8Xh9p68dhnL8Ff3utCKAz88rUWAIAnEMK9H5kPAHh0z1Hcc9087H2/Fzv2nsJ/fe5SdA36YDPpcbh9EFedX4LOQT/0Aqo9yZDAO6f6sKAid8w9u6km2Ji37krmEp4CNBEUA2GsGep9ir9zFULbEyVQau39PrR0e7DtT0cUyzsGMjN+s3NQvfxOjY8P7ff61TMseLU91hoAAuEQasvy8Jmfv66Yrj6g8V8XaPoLhcOoLcvDX97rwsO7I9fM8lxL0gQb65bWomvQh4eee3doT4mwBNb+8k1F3vMLZjjx9Y8uwL1xE3isX1aLf3/2QNK0zqPFCTamNk0MnwB0qlPHaubl06iV5qhnQShxZmZK3WInp+xV4zSnyPqh8awcAGDS62O96HctjUxb+8OXj8Ck53T1NL0Zh9p+bYkT65bNwV1L5+CTl1alzCpVnmvBumVz0OMO4N22fuTbTLFttu4+jMo8O5ZU5WHtVTV46MYFWHtVDR57NZKu7VynTo6Of46XzgQb4bDE0c5BvPpeF452DnK4RZZkrKdYCFEK4OsAZkgprxdCXADgA1LKn2bqGGPVmeKu7a5BbffKUWrBcEh1GuZghnrlguEQNq+si/1nLfrrRabKn6p6PQHVaZ77PMzF2+8N4PYratA56Iu1yduvqMGAl+eGprd+T6Ttt/a6UVvihMcfREmORTVP8ZH2waRZMe9bcQGeeP0E9p2KjCXuHIxMwjGvLAcH2/pjvc/x5Yy1Z3csE2xExyEn7nNBuROtfbxZbyJlcvjELwD8HMC/DT0/BOAJAFkPikuHeuWSEnFrvFeOUks1zXN1hmYOMhsM2PbykYQxy0ewReNjZ0ucZtVpnrXegw4AhTYTDrcPKtrk+mW1KLBpO2MJTX9FdhNO93oQksCX4oZBrl9WG+vhBSLf6wsqc3Hbz15X9CA/sGs/tqxZjAd27UeP26+YhKMizxr7TEWdy9TJY0nnlmoc8tqravDw7iOxIHmsQzoofZkMiouklE8KIb4KAFLKoBDinLu9hBA/A7ACQIeUsn4sZQRDEvdePw9dLn+sh6XQbkJQ8ucJUjfgDapOIfqT2xoyUn53ijHFWs9T7Bv6eTNxmueLqnhT7KAvhMffOKG4CfHxN06gviI3uxUjGmcD/hC6XH5F8Bq9NqxfVouHnns3FiSHpVT9ZfhgWz9uaajEvLIcxSQcCypyx9SzO1zKtdGmc0s1Djk6goI3602cTAbFLiFEIRDJsi+EuAxAXwbK/QWAHwB4bKwFuANBeAJhRQ/L3dfMhZsp2SiFAW9Q9SI1kKGpwS1GveqvF4lj0bSmY0B9qFMHhzohEFafGjyg8anBafob9AURllC9NpTlWvCv15+PAW8Ij73aknJeglAYWFiRi2svKE0KYEfTs5tqqMO59OKmysMc32/Hm/UmRia/ge8BsAPAeUKI/0MkiP3CuRYqpXwFwJlzKcNpNuJ7Lyp7/b734iHevEMpFTtNqjdLFGUoT7HDbMD6Zcrc2euX1cJh1khCmBRS3eBYmqEbHKcy3oRIWhQOSxQ7TLE0avEsRh2Odbkw4A1h25+OoMfth9NixH0rLkjKabxr3ymU5ZpVA9fRTJ2caqjDiTOuMd8oFx2HnPh98Lu3Tipe61iHdFD6MvYNLKV8SwjxQQDnAxAA3pVSTtgdIEKItQDWAkBVVZViXaob7TrZ+0QpOC16PLCqHvc90xTrDXhgVT1yrOnf6T9cmzTpBSryLFh7VQ3CEtAJoCLPApNe2+PFHGa96g2IDgszLHRk4Do2XJskyobh2mS0V1avA4ocZty34gI8sGu/4peSX77WgpsuqoxN0PHN5w7AH5RYe1UNqvJtsJsN+OZzB/CFpbWoKz/3oUZqQx3ybSa8daJXkeJtNL3Hib3VxQ4LjnUPoscdGU6XzpAOyoxMZp+4MWHRXCFEH4B3pJQdmTpOKlLK7QC2A0BDQ4Piv2hFDjOqC61YsbAiNhZv595TKLKz94nU9XlCeGH/afzoUxeh1xVAnt2IX712DNWFc9IuY7g26Q6E8ZfDHVi9pApdAz4UO834/VsnUJZbndkXMsUM+kJoPN6Fn33mYnQN+lDsiJyX2UX8MijOwHVsuDZJlA3Dtcnj3S489NwBfOumRWjpcmHRzLxYR4KUkQk8TAaBK+cUYWFFLk71uuEPSrT2eWM3qP38Mxfj6x9dgBm5VkWAGg5LnDjjQnu/Dy5/ENUFdswuOjtsItW4YZvJkDTU4ZaGylhADIxtDHDiOOTZRXY8O4qb9SgzMvlb7ecAfADAn4aeXw3gNUSC481Syl9m8FijkmPV4c6r5yT1PuXYtD1+k1Lr9QSweGYhPh+X9H3d0tqMpQbzhYK4sKoI//CLN2Llb1xRB39I2+PcA6EQLpqlPC+bVtZx3CwAIcKq1zGdjpMQ0fTU7fLh1oYqfPrnryPfZsI/flAPq1Efuxm3utCKO66ag0/HTWhz9zVz8Ys9x2M5h99s6cF3nj+k6L0FgJfebcfh9kFFppv49anGDftDIaxbWqsY2z8z35bxCTtGe7MeZUYmo8IwgPlSypuklDcBuACAD8ClAL6SweOMWr8nrDp5R7+HXyakLs+qPn4z15qZ8ZtmvQGbdinb5KZdzTDptT2m2KDXY2PCZ3XjjmYYOEEFpFSfhCgs+Z97mp5Meh2eaIxkXPnnZbXodvnxh3da8a2bF2Hdsjn4l+vmJV1Hv/fiIdy4pBJAZNiBJ6H39p1TvXjj+BnsO9mXlOkmuv5Yl/q44ePdLhTazbE63bV0Dj53RQ26B31jmrBjOJzMIzsy+Q08S0rZHve8A8BcKeUZIcSYu9eEEL9GpNe5SAhxEsDG0U4IkuqO9s4MTdlL00+3y6/aZs5kKGVaZ6o2qfFx7h39KbJP8LOKdp4b0hh/KKyaceV0rxsP7z6Cu5bOUf1MCAHFmOP4dbsPdsCg06XMZrH7YAfmljhT9vxeMqsQX1k+X9GL/KNPLcGDq+vxtafP3oPy4Op6VOXbxvS6xyPDBaUnk0Hxn4UQuwD8Zuj5TQBeEULYAfSOtVAp5cfPtWIlKSbvKOYd7ZRCqrQ+BfbMZJ8oTtUmNT5JRWmu+nlh9omzmTmSJxnguaHpyaTXqf5i9+2bFykyNSR+Ji6bXYAP1BTg3t+/o5jxLpqarbbEjiMdAylTtx1OsS5+0o/4FG5SAhueaVZMxvT9lw5jSVX+mIY/pMpwwTzF4y+Tv7vdiciMdouHHq8DkFJKl5TyQxk8zqj5AkGsW6pMf7VuaS18AW2P36TUPP4g7r5mrqLN3H3NXHgylNu63xtQbZP9Gp+y12rQY9PKOsV52bSyDlYjh09YjAKbV9Yrzk3kOXuOaHpy+0OqPbbHulxYv6wWO/eeSrqOPrCqHkAY75zswz9cPlux7sHV9XCY9QiHw6gtdSSlbrv7mrn43Vsn8WTjSXz9owti66oLrXjk/12EIx0D2Pt+L4LBSJ2ieYQ7BryxyZh+8NIRbPvTEbR0e9Ax4MVYpJrMY6zlac25DD3JZEo2KYR4D5ExxGsAHAPwVKbKPxdmoz42Bij6v7gnGk+gYdbCbFeNJqlChwlWo06RMs1q1KEwQz3FhXaTapv83prFGSl/quoY8OK5d1ojWT/cAeTZjPjFX46hOEP5oaeyQCiEXJsB37l5EVz+IOwmA4wGgUCI90bQ9FTiVJ/Uon5GDqwmPYocRuTZzPjppxsw4A1CB4GOAQ/+30/3xYLc9ctq4fKHYDHoYNILbN19GPk2Ez77d7Pwq7+24HNX1ECvA+aX5eA/Xz6C1j4vLEYdllTl4dl1V6Lb5cOJMx7c8V9nb7revKoeT77RgsaWPliMOvz4Uw0pe5bHItVkHsxTPLJzHXpyzkGxEGIugI8B+DiAbgBPABDZ7h2OZ9Dp8P9dWYOOAR/CEjDogP/vyhoYdLxBhdR5/GF8/Q8Hky5Kv/yHSzJSvpTAxy6uUtz5vH5ZbUbKnsocFiOOdrvw+rGeWNqxo90uOCycoCIU1uHrzx5ISsn27ZsXZbdiRONErwPWL6uNBbK3NFRidpEd/lAYR04OwGLU4/2ePtSWOCEE0OvyorU/ct9H9Ka7b9+8CO+2D0CvE/jyU5G0aTcuqcSWFyITem370xEAkev7d29ZhEPtA1hQmYuqgkgKtAFvAP+WkG5twzNN+NbNi9DY8jd4A2F87Zl38NBNC/GVp/alPVX0cKKTeYxm6mmKiKbxi3Y4AcBDzx3AvDJnWkNPMtFTfBDAnwHcIKU8AgBCiLszUG7G9HkDMOl1immeN6+s0/xP1ZRaqglfujJ0I9zpXi8ee7VF0VP82KstmFlgw5KMHGFqcvmDuOOqObE7yqOp6lyckh19ngA+cUl1bHbOaE9YptIEEk02rX2R6+TPPtOAE90e3L/z7HXhnmvnItdmxE//7xhauj2x4UT7T52dFsEbCOPd9gH85M9H8dCNC2PXdCHUb7I70BbZdkvcL3bR1G6J28YPpWvp9mBGniVjeYVHO/U0nRVN45d4c2a3y5dWUJyJrtKbALQB+JMQ4sdCiGWIzGg3aThMBtVURnaTttNfUWpFDvXphgszdCNcSY4ZPW6/Ygxaj9uv+Zum7Cb1VHX8rEbSBKpNV5+XoTSBRJNNaY4FPW4/jDpdLCAGIm1/ywuHcLrXixULK2LLNuxowkcvmhnb32LUQQ5lmTjaNai4pqtd36PbRtOvAUB5rlV1W2vcNcli1MGk16U9VXQ6RjP1NJ0lIFRvzhRphqXnHBRLKX8vpbwVwDwALwO4G0CpEOI/hRDXnWv5mdA1qJ5eqztD6bVo+ul1q98I15uhXjm3P4iNK5Q3lG1cUQe3xntEXb6A6mfV7Wdv6HinCSSabKLDCLpStP0CmwlOi0GxzDt0DY1es3/31kkAwJONJ/Hg6siNqk+9eRLrlyVf36Pbxt/UVleeE9svuu2mlXV4bM9Rxb4c2z85nHGp/8qb7nUykzfauQD8CsCvhBAFAG4B8K8Ans/UMcbKaTGgoToXt11eA48vCJvZgEf3HIXDzN4nUpdnM+Lt97vxo09dhB5XAAV2I/7rtWNomJWfkfJtJgPe62jHLz57CTqHfh57ofkUziupyEj5U5XdbFS9wcRmYm+ow2xQnebZzusYTVPRYQRvHO/Gvdefj/I8G9y+ILpcPjzZ+D7sZgPK8s7efGYx6lBZYMO6ZXNQPyMXh9oHcNNFkYk8du49hXllztgQh+j3/4w8C/RCh6NdLtx0USWeevPk0K92kXINBh1WL6pAbYkDbX1eFDrM2P7KYVxaU4zLziuO3SS9vL5s4k8QJSnNsaqn9cxJ71fYcbmaSinPAPjR0CPrnGY9bmmowpd/u1cxdazTzDRPpE6vk7hmfrlimufNK+ug12VmVqF8mx5zy/LwmbjpSTevrEO+Tdttsmeohz5xPFiPmz3FFqPAHR+cg01x4yo33lDHlGw07XUN+hGSwJcSv8OtevS5Iz2A0THFXYMe/KbxJGZea8MP/nREkTFibrETJpMeNcUOhMMSbf1etHS78e0/vqu44bky36qYeMNg0GHRzHwsmhnJbrD6wireBDdJRXv2EydSqSvPTWt/TXQx+EISP3z5iOJuxB++fIR3bVNKobDAtoQ2sy2DbabHHVId5/5YhrJbTFX5NqNqqjp+VgEpBR75X2WbfOR/j+A7PDc0jR3vduHd9gE88/appO/wT39gFmpLnfjPTy6B3WxAa68LRQ47bmmoxH3PNCHfZsKNSyohBHCqx41TfR5UF9pxrMuFljMu5FiMsYAYiFyHt+4+jPXLanGix616YxZvgpvcEnv2y3ItqCvPhcGQ3mhhTQTFvZ6A6t2ImRofStNPX4o2k6k7/VNO2duv7Sl7zUbg3uvnIxCScPmCsFsMWFgxH2aOnsCAT71NDvh4HaPpq73fC5tJr9r2K/KtACJZa3Q6AZc/jM4BPxqq8/HO7ALccnEVDrb1IyyBp98+hZkFNpw448Ln/+steANhrFumPk20yx9Cx4AXswrtON7tQnu/F6U5Z4Pf6E1wqbIZhMNSdT+aGPE9+6PeN/PVmXzyrUbVuxG13itHqeWlaDOZylNcnmI647I0xz1NVwahR58nEOtFjw4rqRz68tMyh5nXMdKe0hwLKvNs+OLQ0AngbNv/+Wcuxmd/8QbybSbc9oHqWN736kIrvrC0VjHEYd3SWnz/pcP41+XzkW8zobXPi7BUnyZaJ4Bih2VMk0Cc6+QRlF2amL2i161+R3sfxylSCj3uAPJtJtz5oTm4a2nkkW8zoTdDbcagE0l3P69fVguDXtsXzUGf+rCSQV8oyzXLvq5xzp1NNBnNKrRDQj2vcFufF/k2E776kfnwBEK4/coalOdasGJhRWxMaXTbh186jBULK3CgrR83LoncfPfUmyeTsgzdt+ICXFVbBJc/gINt/bEyE1O1pXK82xULiKPHTmc/mhw00VNsNupU/zdoNmri/wQ0Bk6LXtHzEA1aHZbM3Ah3KsXkHVUFNiyuysghpqTOAfXAr5OBHwrt6r8uFNq1/esCTW86nUCJ06Ta9oucZnzqsmrFTfTrltYiGJaq1xG9DgiFERuX3NrnxRONJ7D11gvhDgRxrMuFJ14/gesXlCuu/euW1uKXr7Wgtc+LjgHvsJNAtPerT/Yx0n40OWgiKrSa9Kq9claTtu/0p9QMOl3sogicvQEjU1ODFzvVJ+8o1vjkHSU56pOmaH1SEwAIhNVzWwdD7EWn6c0VCKrmjYeUqkOKZhfZVa8j88pysGvfKURHMViMOnzs4ioc7RrEV3/3Dh7efQRXzi1JuvY//NJh3LikcuhaZMFwSnMsKa5hw+8XDksc7RzEq+914WjnIMLhzGQ6otHRRE+xDkBZrgVrr6pBWAI6EXmuif8R0JiM9zTPg3711GMujU9SEZ3UJHGaZ61PagIAQurw1Fsn8K2bF8HjD8JqMuCxPUfxz9ecn+2qEY0ri0GvmpWmunCe6nW6tdeNzavqseGZs2m57ltxAX72l/fwiUuqISDx/Y8vRoHdhC/+Zh9uuqhyxCmg9TqklXotOuHIaFK2cRzy5DElgmIhxHIAWwHoAfxESvnNUe6PxZUOVORa0T4QuRu02KFHj5sz0JC6IodZdcKXTE3z7DAZ8dLBtliAYzNFym+YNT8j5U9VnNQkNZtJjwdWX4ABj0T7gERpjhkPrL4AXk5oR9PYoMcLk16Hhz+2EKGQDgP+ILyBEK6ZXwS3P4yHblqAHIsBnQM+dAz6oRfARdUFMOiBX91+KToHfLCZ9LCa9PinD9bAYDDgjCuAPJsJeVYjPnt5Nc4vz8G6ZXMQloDDrFcdqnHNvBLUV+RBpxOK7BIlTgv0ushQjGimidGmbEs1Drnos5dg0B9EdYEds4uYwWIiTPqgWAihB7ANwLUATgJ4QwixQ0q5P90yZuTq8MrhAWzYcfZ/jZtX1uOqWud4VZumuFyrDmsSJnzZvLIOudbM/L5gMkjV8s0Gbf9kVpmvPqlJZT6HOpXzOkYaM+jx4tmmTvS73SjJteNUrxdbdx9OyjYRncjmlXc7cKhjEBV5VjzZeAK3NFRh49CNuw3VuVjTUIUNO85ecx9YVQ+HxaCYpOnua+bi3uvn4et/OKi4n6Rj6FdCtV7d9ctq8dirLehx+2M9vMOlbEuUahzyX97rwsO7j7DneAJNhREElwA4IqU8KqX0A3gcwKrRFHCiJxT7IgGid7Q34cQZjsUjdX2esGoWhD5PZn5d8AeFavm+oLYveCd7QrFJU+5aOge3X1mDbS8fwckeflZPnOF1jLRlf5sLG3Y0YeHMIhzpdOHxNyJDKO79yPykcb+bdjbj9qvOi11Lb7u8JhYQA8Btl9ckXXPve6YJbXE5472BML734iHUVeRi/bJa3LV0Dj53RQ0ee7UFd/3333C826Xaq7t1d2TM8VgzTaQahxwa+rphBouJM+l7igFUAHg/7vlJAJcmbiSEWAtgLQBUVSlv3+/gHe00SinbzED6bWa4Nplq8o52jU/e0eNOMdEO0yeiPUWbbM9QmyTKhnSuk+0Dygk8br+yRvWz4Bm69yD6d/w2Hl9QdZ/E+9mi1/mHnns3qa4dA15IqT7mOJrRYiyZJqrybUlTE0czXsQfgxksxt9UCIrVus6SfmOWUm4HsB0AGhoaFOtLnOqpjLR+pz+lVuI0o7rQihULK2IXu517T42qzQzXJktz1Msv1fjkHfk2TlCRSmmO+nVsNG1muDZJlA0jXScjbdwCAaGYwCP6WSjPteDGJZXQ6yI30JfnWtDj9sNmMig+LzazQfXzkzgawWLUoTzXorptNIOE2jopk7dL14keN77/0mF87ooaVBVYYTcZ8O3nD6K1z6t6fBo/U2H4xEkA8ZP1VQI4PZoCgiH1dC7BEO9oJ3XeYBD/+ME5+OlfjuIHLx3BT/58FP/4wTnwBTPTZmxmgTuvVpZ/59VzYDNre/hEd4qsH90ubfegA0CxQ48HVtUrrmMPrKpHsYPjrWl6uqDMjs0r6yERBOKyQkQn3agutOJTl1Xjp385iod3H8Hnf/kmbvtANTasmI9H9xxVpDB8dM9RbF6Z/Pkpi0sDaTHq8NBNCzG/NAdb1ixWLI9mkIhml0hM8frnQx1Yt2wOvnPzIkiJUaVUa+/3oqXbg21/OoL/ePEwugd9+NjFVarHp/E1FXqK3wBQK4SYDeAUgI8B+MRoCjAZDKrpXC6ZvWg86kvTgNVgwP0731L0WN6/szlj0zy7fVJ1TLHWe0RLnOo9NMUO9pD0uMPwBUOK1JK+YAg97jBmZ7tyROPAZjZjwUwHPD4Jgy4cuza09nnxy9da8G8fmZ80/fPW3YfxyP+7CJfPKcavXz87QZJOAIV2PR77h0vQ2udFns2IX/zlGA62D2LtVTWozLOi5YwHW154F0a9DtfNL8WzKTJIxGeXKHZYYNBHxgXf+/t3xpRSLTqmOPraHnnlKD57eTV++ukG+INhVDH7xISZ9EGxlDIohLgLwB8RScn2Myll82jKCITC+NjFVUmzkwVC4ZF3Jk1KNTV4psa29nvUy+/3aHvsrE4A65fVJn1WNT77NQDA5Q/hwf85kPQfhh/f1pDFWhGNn+PdLnx022vY/qmL8I0/HFDkdu9x+xEIh1WvowfbBlCaY8GhjkHsO9UfyypxqteH+TYTgmGpyDhhNerxvRcPx4Yr3PPk23h23ZUpM0jodEKx7mjnYCwgjtbhniffxryhMkaSmNu4x+1HZYEdH6gpSgqE49PBleZYUJVvw4ked+z5SOnfaHiTPigGACnlswCeHev+fZ6g6pS6NUUXZLCWNJ3YTOq5KjM1C6ItRS5Mm8ZnWTzVl3r66wuzXbksc6W4Ucjt4zAwmp6iqcrc/hBauj345WvKa4PVqH4drSqw4aHnDuBzV9RArwPml+UgEAyi1xNE4/HeWBYLvQ5YVJmHrS8eUozfHe1Nbec6tbNOJ9LKbZyYDq660IovLK1V3KDH1G3nZkoExeeqyGGKTakbZTHqUGg3ZbFWNJlFpwZP7LHMVNBqMaiXbzFqOyguiZv+Ooo3xUYUOUyqAQCvYzRdRYcVFDsi435b+7yxa4PFqMM919QmzQz67x9dgIeeOxAboxvd9js3L0KXy43trxyFNxBWrFt7VQ32neqPHTd6U1tir2yqXtj44Q+JZaQrsfdZTWI6uBULK2IBMTD6HmpKpomg2OUL4O5r5uJ7Lx5SJOjW+pS6lFrnoB82o14xftNm1KNrMDPTh50epkdUyzwppr/2cJpnDPI6RhoTHVZgNiIpZdkXrzsfRp3Ar/Yci/X6Lq7MwxlX5Ka1eN5AGC5/EOEU6dSq8m2xoDba21qVb0t76uWxTO08Fok90qmmpG7vZ+q2sdJEUGw1GfHC/takKXUvrNL2lLqUWr7NiK8/uz+WMi0UBn625xi+fXNmbs5kj6g6q8moelNsps77VGbjdYw0JjqsoLHlDM4M+rB+WS0KbCbYzAac6nXjuaY2/Mt183CkYwAXVefjf/aexM0N1aq9tnaTAXqhnk6trd8bC6yXzSvBgoq8lFMvz1t3JWYV2pN6kK+bX4on1l6G1j4vynOtqCvPyfgQhlQ90hyGlzmaCIpd/iBuWqKcUnfjijq42ftEKYXxT1fPic2IZDHqsGllHYTIzM2ZRr3E5pV1sQwU0emMjXptp47l5B2p8TpGWqTTCbT3+7BrXys+fkk1Nu5sVrT/x/YcxbUXlOP7uw/hpouq8KXf7k36ten+G+oQCAVRaDclDVv70ofPx0/+fCw2RfOCiryhY6qPE27v9+Jg24CiV/gHn7gQ/qBMq1f5XCT2SO/cewobV9Rh065mxfWSSQTGThNBsd1kwKZdyvRam3Yx/RUNR4fdB1rxo09dhF5XAHl2I3712jHMLpqTkdIDIYEnG09Eev18QdjMkV6/Ly/Xdq9fvs2Ilw62JfWGNszS9nkBeB0j7SrNMeP6BeWx4A9Qtv8+TwDrl52P/++XjfAGwrEb8vQ6YE6JE999/iA+dnEV7CY9Fs3MxfZPXQSXL4RcqwEmgw7fvnkBynNtirRnqXplbSY9PvuLNxT12HeyLzZWObpsPMb2Jt6QZzXqse1Ph5K+R5bXl2XsmFqjiaB40Kee/srlY+8Tqev3BrB4ZqEibc+6pbXo92amzfR5Alg6r0zR67duaS36NJ6SzaCTWNOg7A3dvLIOBp22e9ABYMCrfh0byFCbnMz8fj/+9re/JS2/8MILYTLxRsPpzmYSqMy3qrb/My4f+t0BnOw727Mbf0PeXUvnoKXbA5c/hIeeexf/ev35CIeh6El+cHU9LputTH+WapywP5ScBi7VWOXxmJY5/oa8YDCM6+pmKK6XD66uR1W+tu9NOReaCIodZqP6GCOzMYu1osksx6Ic2wpkdmxrrpXTGasJhgUnNUnBaVG/jjkt0/869re//Q1f2PYMcitqYsv6Th3F9+8ELr300izWjCaC2y9h0OtU238wBJzs8yIsU0+/HP/vjDxbLIgEIteYrz3dhNoSBxbNzI/tmypN2vFuV9JxUo1VHu9pmU/0uJOyT3zt6SYsqcrnjXZjNBWmeT5nHn8Q999wdrrH6BgjT4Bj8UjdoC+Af7h8NvRDnxC9AP7h8tkYzNCvC12DPswtceDhj1+Ih25cgO9//ELMLXGge1Db0xl3pZrmWePnBQAkQqrTPEuEslyziZFbUYPC2XWxR3yATNNXMBiGyx+AxSjw4Gpl+9+4og7uQAAXzszDrEIb/uPWxagutAIAqgut2LJmMaxGHb63ZjH+erQT65fV4niXS/Ua09bnw6vvdeFo52BsiuZor+xlNUWoKXZApxOq0zwvqMxNOS30eBouPzKNjSZ6ip0WI1q63Yr0Wv5gCE72FFMKeVYT3g0MxsaJRfMI51kz81NtVYENH7+0WnnT1A11mKnxn70q8tTH8c3I5TTPRp0BHr9LcR3z+IMw6sb3i5coW8JhiT1Hu5FrMeG9Thd+8KfDsbHC88py8PTfTuC6uhn4x1+9FbuOblhxAfSQMBoNiqEPD920EDVFdrSccateY5pb+/Dw7iMj3iSXqgcZwIiTb2RaiVP9elns4PVyrDQRFA/6gvj6Hw4mNZztn7ooi7WiyWzQF4zdoQxE/ve9dffhjLUZjz+ETTsTbhrZ2YxfanyYQCgMfOnD5+Pbf3xXcXd4iEOKMcDrGGnM8W4XGlvOoKE6H/c905Q06ca3bl6UNBRi86792P6pBqwduukuuvwrT+3DrruugFEvcN+KC/DArv2KQPrx10/Etr3nybdxwforEZZQnbgj1UQbI02+MRrpTByi1wH3XDsXW144m7v8nmvnxn7hpNHTRFDsCYRUf2JIXEYU5Q8mtw9vIAx/MDNtpn1AfZhAx4C2hwn0ewMosJkUvaEFNlPGbnCcynwq16zpeh1LvLGuqakJMsz/GWlNe39krLAvxfXY41ef+jzVsILOQR+CIeD3b72PLWsW42BbP0Jh4EevvIdbG6rQOehHa58X+TYT3jrRi3t//864plhLJXE651TH7xz0wazXKa6XZr0OXYM+/P/t3Xl8VNXdP/DPmX1JZrIvBJIQCVsCQYiIFqyCWmoRUFHbWq1Kf7TPU4VHn1pbraKI+rhUK1VbbbVW2yq4A7UUC1i1rhFZEkESIwlLdsLMJLPPnN8fs2QmcydMkjtb7vf9evEimeXcMzdnznzn3O85pzyPcopHQhJBcZ5eLXiJIYe2RyVRGLTCk5oMWnFSbgoyhduk1Dfv0KkU+MlfdkecF5poB+Tohdtkjm7spYENnlh3bM97yKqkEXGpKTRosGXvMZxTmSfY9ifk6ARv1yjlwduLjRpcOns85DJAJZfhwX8ewJKZJWEbcwC+1ShWzq/AE7uacHnteDy241DYROsHth3A1KLMhExgG2rjkNDjq+Qy3L8t8urRxlXz4l7HsUoSg+xmhwt3Lw2faHf30ipYaEk2EkWv1bfdcGibEXMTCafHI9gmXR5pTJqKptNMI+jRnLD6tnkObTM3nT8ZvWN0Gb/QiXUZBSUjKsPrcaO+vh4ff/xx2D+nU5zt2kl8lefqceviaThpi+yP71gyHeBe3LFketjtP7twCv78QTPWL69GWa4WV88rwzPvN2PDjiZc9czHuLK2FJkauWA/w/yrSMwoMeLK2lI8834zHt/ZhD++14wra0vR05+YfijWCXRWp/BVcKtT2p8joyGJkWK9SoEn32kI2zr2yXeaaOtYElW2Lr7bDavkcrwc2LwjZJMKqW/eUWBQo7bMiGvOrghbjL5A4iPoAJClVeJvn7SEtcm/fdJC/dgQLB2t+M1hO4qaBlIvaCm39BG6zfPg/vjpd7/CQytqUJGnw++umo0uiwOnFWSAc45xRg2KsjR49IpZ+P4fP45Y+vKhFTWCI8znVOZhxewSdPc5BZfM/MvKxLSZaBuHDF7iLdrjCg000W6kJBEUW+wutPTYggn6obcTIoTDI7jNs1jLX/U5hDfvEGvJt3SlVkBw8w61JHqqodmcwltg22ib5yFlFpUjd2JVsqtBRkgmY1Hb/ol+J+7/xwG09NhQlquN6LPXLatGtk6FNtPACKvd5UVLT3/EVtCrF1billf24tbF06CUsSgbhSTmCkO0jUMGL/EW6+NI7FL6o4YxdjmAuwBMAzCXc143knKkvOg9GRmGkJHcOGzDnKGmzTuEON0Q3LxD6qtyAIBWFbIFdkibrC3PPvWTCUlj0dq+3Z2Py+dMwMPbD2HJzJJgQAz4+44367HqnAps2DEwIKZRyjCvIhcAx19Wnol3G7vg8QIvfNSCNpMdN2/ag7+uPDPKCGz8rlgNXm3iwmmFeOsUS7xFWx4uEZMBx6qUDooB1AO4FMBToykkkB86+Fthr0j5oWTs6XcKj+RaneK0mRP9TsGRiN4EjUSkqp4o56VH4ucFAFweDy4fNIpOeehECmwud0TbX7ukCi9+0oJrzipHsVEDxoS3Wp6Ypw8GuIF+HOCoLc/Fh191hwXMged4wbF+eXVwtziNUoZ7L5kBp8eL5q4+0QPPoVabONXEvmjLw5GRSemgmHN+AAAYG13ji3d+KBl79Kr4juTm6lW0IoqAYqNW8LwUU44clHJ5xEjYWtoCm0iAVqnA2s27w9r+3VsbsOqcCrT2WnHp7PEAhLda7u134pErZqGpsw8Otxcb61qxuLoIQPSc3Fy9GqePz0ZlQQY6zA6oFDKs3VyPlh5bXJZni3W1CRJ/Y2b1CcbYKsZYHWOsrqurK+y+TLUcPz13UnAm6TPvN+On505CplqepNqSVNcVZR3hrmGsgjBUm3S4PYKrWzjc0h71y9IpsHbQluxrL65Clp5SneLdJglJhljaZLQra6XZOrxcdxSMAVv2HovoO9YsqoTD7QswHW4vnnm/GbcunhbMuRXasjmQk6tQyFAzIRuTCjLwk798hpYeW/C4N2/ag8M9/aKdA9quOXUkfaSYMfYvAEUCd93OOX8z1nI4508DeBoAamtrw1Z5/6rLio++6saz156Bnj4HcjPUeOXTVujUCkwblzWa6pMxKj/aOsIZseeUDdUmlXK54NWLOWXSvnrRaXFADo6HV9Sg3+mGXqWA1eFCl8WBslxpj5jkZoz+6sJQbZKQZIilTeZFafvtZjt6rU6cUZ6NOaVZePTtQ2F96vMftuCyOeNhd3kxs8SAt1YviNiZ7lQ5uUMFrGKN4sa62gSJv6QHxZzz8+N9jCKjGm81dOC1PW3B2zRKGb53Zlm8D03SlFbhG6EMbMUcGLHUKsW5uFJoUOO7Z5QGt5IOjGrEcyJHOlDJZbhzyxe0GL0ArVIu2CZ1KrriRcY2rUqONYsqw/rL1QsrsbGuFasXVuLON+vxswun4lBnH/YdMwefp1HKwLnv/8pC4Y03TpWTm4iAlVaRSB1JD4oTobrIiHVLq3Hn5oGk+XVLqzGj2JjsqpEUdfSkHS9+3BJcR1irUuCP736F/z5vEmZMGH35pTl6VBZmhG3PWVmYgdIcaXeCtBh9dH12t2CbvHXx1GRXLe0N3lY64PTTT4dKJe08/1Rgtrnx/Ie+NbrVChnK8/Q4ftKKn104Ffe9dQBtJjuOnbRGDZxHE2AmImClVSRSR0oHxYyxSwD8FkA+gL8zxvZwzr813HI0GgW+U12A8ry56DA7UGhQo6ooAxpNSr98kkQFmWqY7C582W4JbvNpsruQN4z0iaHIZAznVhYgP0ONNpMdxUYtqooNku8EaTH66DI0Shzq7MPqFweCN41Shgzqx0Zt8LbSAG3ykUoKDRr0Wp1hew1olDI8tKIGP5hXCqWMoTRXj/HZWlwwrRBdfQ4o5TKY7U5s+O7pqCo2jrhvlckYLpxWiI2r5sW1r6ZVJFJDSvemnPPXAbw+2nKcTg/+3tCJO98MGSleVo3lM8dBRZceiQClgglu3qFWiNMRer0c2w90CC7BI+XAmC4jRscYx91LqyLa5CgX5yF+gW2lSeopz9Xjvktm4LbX94elm9331gH0Wp1Ys6gSd2/5Ar1W30oTKgXDDX/7XJS+lfpqaUnpoFgs+46b8MSuxmACPgA8sasRFXl61JbnJLdyJCVxDsHlr8TKbaUleITRZcToHC6v4IYyt0p0a3Cvx436+vqw2+rr68G9NH9wrJHJGGaXZmHVORUoMWpxzGTD8x+2+EduNbC5PPifRZU4ctKGB7YdwLJZJaL1rdRXS4skguJeq1Nwi8heK20IQITFO7c1ETOa0xVdRhR20ia8ocxJmzQ3IbJ0tOI3h+0oahoIgo/teQ9ZlXOSWCsSL6U5ekwtMuBguzm44UaxUYOr55VFfLbLBs2HHk3fSn21tEgiKDZqwzfvAGjzDjK0eOe20hI8ZLjGaj8mNMkt1hHfzKLysJQH0/Fm0etHUkPgKlJZrhYA4OXAlMJMPLz9YMQmS49deTp+et6k4Ptky95jI+5bqa+WFkkExXanW3Ck2O5yJ7tqJEWNN2pxz7Jq3BGSh37PsmqMN2pFKZ9yZ6Nzu71oaDOFTWpRKMbMPkMjZneNzX5MaJJbKoz4CqVn0GoUyeX1chxos+Dpd5vD3gMvfORLpQACWz1zbN13LLgD3frl1SjN1o3omNRXS4skgmKtShHXLXvJ2NPYZYHN6Q5bMs3mdKOxy4KqkqxRl0+5s8Lcbi/e2HsMv3pj4MvI+uXVWF5TIvnAWKscu/3Y4EluqTDiOzg9g1ajSL6GNlOwbwAG3gMr51cEV6YIrE18ZW1pMFj+1Rv1mF2aPaJ0B+qrpUUSQXFPlC0iT/RTTjERdsLqwn3/OBhxyewP19SKdgzKnY0k9KH3qzfqUVmQgZoJ2UmuXXJRP5Z4g9MzSHK1mYTze+X+78uBVSmYjIUFy6PNAaa+WjokERRnqBWCOUEZakm8fDICVqc7ykS79L5UneqiTWrpMDuSVKPUQf2YOGjVivRVbNQKvgcmFWTihoWTgls7/8+iSthd3mBOMeUAk1hJojc1aBW4+YLJeOTtQ8FLsjdfMBmZtOg9iSJPr0ZtmRHXnF0RtvxVrl7a2zDHW65ejbJcLZbMLAmbJJOjpzxOo064HzNqqR8bDlq1In1NK8zEvZfMwO0h6xXffMFkHOu1AgAYA1QKBovDFbbFM+UAk1hJojc12ZwoyFSH5YcWZKphttNlRyJMqWC4vLY0bPmru5dWQSXS5h1EmEwG/OScSbh768AGFWuXVAUvj0pZv8OFIkN4P1ZkUKPfKe6SbEKrQYg1wWw0K02IiVatSD9eL8e/vuzEhh2HsHJ+BeQyYNb4LFgcLtz66kCQvPbiKsgZx72XVKPL4sCfr5uLM8pzKAeYxEQSQbFWqcQvXtsdccnlLytpwgQR5nRzPPlOU9jyV0++04RfXz4rqfUa62SMBQNiwJc6cfdW8TZNSWdyJsfP/R/+ARqlDC+IPNFu8GoQYk4wS9WVJkZKKMgHaJWKeDjc048Hth0IXkXycuDLDgse2xE++fTuLQ1YdU4FVHIZHt/VhLdWL6CAmMRMEkGx0+0VzFN0ebxRnkGkzmQT3vCFri7EV78jvpumpLNoE+164zDRLp5bHqfiShMjJRTk0yoV8dHT74jok+9ZVi34nvBywOnxUtoEGTZJBMVFRo1gnqJYGzGQsSdTE/+NErxejsM9/egw21FooGV+AECvVgi+V3UqeXIrlgJy9CrBPPcsyrdOqnh+gSADVHJZRJ/c2+8Q7C9kDDinMh+zxmdF7VOp/yVCJBEUj8vU4KfnVuLOzQNrn65bWo1xNBuVRGF1ugRHim0i5W96vRzbGtojFoRfXFUk6Y6ZMY7/PncS1m5uCMvllvApCfJ4PYJ57h5Oo+hk7HN6vBF98s8unIKbL5gcmVMMjv99eQ9uXTxNsE+l/pdEI4npK/Xt5mBADPgur9y5uR717eYk14ykKp1KKbhRglalFKX8wz39wQ45UP7Nm/bgcE+/KOWnK6ebBwNiwHde1m5ugMNNy2XJZXLBcyNnNIqeCIGl3D7++OPgP6FJgkKPczop7Wq0VHJZRJ/88PYv8VVXf0ROcWefEy09tqh9KvW/JJqUHilmjD0E4GIATgBfAbiOc35yuOV0WoTXPu202MWoJhmDOi0OwTbTZRFnvdxo6/GOZoH5saArynnv7qN1ijvNwuemU6Q2SYYW61JutBNefFidwvMNBi9cYnd5kZ+pxg0LJ+HVz46iwxzZp8aj/6V0jLEhpYNiAG8D+CXn3M0YewDALwHcOtxCigwawQW/KaeYRFOQqRZsM/mZ4qxTXJAp3CbzM6TdJvMyVILnJZfyZlFoEG6ThSK1SXJqsS7lRjvhia8gQ7j9D447NUoZWk/Y8Mz7zVizqFJwP4LCKDHBSDf4oHSMsSOlg2LO+faQXz8CsGIk5Xg4x5pFlcGlWwJbQXo5XZIlwvRqOdYvrw5uOaxRyrB+eTX0anEuVctlENyIQerr8TIG3H/pDHzd3Q8vB+QMKM/TByfRSJlK4cuXvHtLyBrOF1dBpZB4o0lxQjvoAbRs23DZ3J6IPtPX/lkwwNUoZbhjyXRY7C5k61R4bEcjasuy4fXysOC0PFePR66YFRHEjnSlisBycaGTAB/YdgBTizIlfeUvHaV0UDzI9QA2RruTMbYKwCoAKC0tDbuvw+zA8x+2BBtsYCvICTm6uFaYpK8uiwOZajkeXlGDfqcbepUCSjnQPYxL1UO1ya4+B9RyWdhGDGq5DN19DpTnSbcTNdlccLrCJ445XR6Y7eJuUJGOjvba8OLHLXhwRQ1sTje0KgX++O5X+PE3T0PNhOyYyhiqTZL4EEq7EEqpiOemKaks1jbZabHD4+V49IpZkDEgU6tE/dGT8AL4+bemIDdDja+6+vD4zib0Wp1YvbASL3zUgveautHV5wwbtZXJGBZXFWHq6gXotNhRkDm6dAeh5eJWL6zEiX4HBcVpJulBMWPsXwCKBO66nXP+pv8xtwNwA/hrtHI4508DeBoAamtrw4aACzLV6LU68cSupuBtvksldNmRCMvSqvBhW0/E1YWzKnJjLmOoNqmSy3D/toMRl++kvklFrl6NQx19ePrd5rDzPqXIkOyqJV2hQY1DnX1Y/eJA4DTc9Imh2mQ0sY500kYW0Q1OpxA6p/X19fj9O03IGn8aAOnkIsfSJr1eDpPVjcd2NCJbp8I1Z5WF9c13LJmOR97+Ei09tuBzNuxsxKpzKuDxAjdv2oOpqxeEBagyGUNFfoYoQavQJMANOxsl35+no6QHxZzz84e6nzH2QwBLACzifGT5Di6PB2uXVEVsHev20FJGRJjF7o7YKemxHY2oLjGKUn60SSNS36TC4hA+7zNEOu/pzO0VTgPzxDkNLNaRTtrIInZDTdqjXORIh3v68cvXfcuuXTp7fEQfcc/WL7ByfkXYwJfd5UVptg6/9qdbxHMSM/XnY0fSg+KhMMYWwzex7pucc+tIy8lQq/Dq7kNhlx2f/6AZdyyhzocI63O4BTu5PrtblPKjTfSQ+uTPPrvwebc4xDnv6ay33yWYBpaIHbtiHek0FE+koC5GsU7aI+GrRTAGwT5i8HwMjVKGdrMdbSb7qCbRxYL687EjpYNiAI8DUAN4m/my1z/inP9kuIVUFRvw3bllYYver19ejapiGn0iwvIzhVdByM8Q5zKw2BM9xopCI62wEE2hIXXSwGJdnoyISyhFxeXy5dsrlQNrqI+1lJXBQadQHzFrQlbYhLs1iyrx/IctCelbqT8fO1I6KOacTxKjHIVChuU1JagsyEC7yY4iowZVxUYoaNY2iWJ6YSbuWVaNO94cWH3inmXVmF6UKUr5Yk/0GCuqi4yC572avsCiulj43MwYl5xzQyOd8RVL3jHg+zIiz8xF0WnTAYzNlJXQoPPVz45GpBHdu3wG3vuyAz//1hSMy9IiU6OAXqXArAlZCVkzmPrzsSOlg2IxKRQy1EzIRs2EZNeEpAOdVoXvVBWiLFeHDrMDhQY1qosyodOKN/oi5kSPsUKlkmPZzHGYmKcPLoI/c5wRKhXt2kbnRlpizTs2HW+Gwlg07LSVdJoYGRp0dpjtyFArUDPeCJPdjYIMNfINKpRka5IajFJ/PjZIJigmZLh0WhXmTox9tQkiDpVKjtrynGRXIyXRuZGWkYzGx7paSLpNjDxV0FmaQ8EoGT0KigkhhMTF4ACtvr4efPC+vERUsa4WAgDGkgqaGElICAqKCSGExMXgAI0m4yXGSLeZFhplpvWoiZRQUEwIISSM6djAZfq+zmOQ2+zo0etGdltmeAqSpf3wsMsaaR1GXfck1kHM55mONaO+PjzPtr6+PuzvDABt+z/E+o/6kD3OFxj3d7fh59+7ANXV1WHPe/DFt6HPKw7e1t/dhufvvSkl0y4IGQ42wv0wUhpjrAtAS5S78wB0J7A6Q6G6CEulugBD16ebc774VAVQmxwRqkt08W6TsR4rlVG9EyuRbfJUx0uEZB5fyq9drOMLtskxGRQPhTFWxzmvTXY9AKpLNKlUFyD+9Uml10t1EZZKdQESW59Ue+2xononVqLrnezzlMzjS/m1x/v4tFAvIYQQQgiRPAqKCSGEEEKI5EkxKH462RUIQXURlkp1AeJfn1R6vVQXYalUFyCx9Um11x4rqndiJbreyT5PyTy+lF97XI8vuZxiQgghhBBCBpPiSDEhhBBCCCFhKCgmhBBCCCGSNyaD4sWLF3MA9I/+JeJfTKhN0r8E/osJtUn6l8B/MaE2Sf8S+E/QmAyKu7vTcd1zMpZRmySphtokSTXUJkmyjcmgmBBCCCGEkOGgoJgQQgghhEieItkVICRVOZ0e7DtuQrvZjmKDBjPGGaFSydOmfELIqbndXjS0mdBhtiNXr4YXHLl6Ncpz9ZDJWLKrRwhJIAqKCRHgdHrwxr7juPPNethdXmiUMqxbVo3lM8eJErjGu3xCyKm53V68sfcYfvXGwPtw9cJKbKxrxa2Lp2FxVREFxoRICKVPECJg33FTMGAFALvLizvfrMe+46a0KJ8QcmoNbaZgQAz43ocbdjZiycwS3LxpDw739Ce5hoSQRKKgmBAB7WZ78IMywO7yosNsT4vyCSGn1mYSfh8y5vu/00Lvx1TBOYfJZALtwkviiYJiQgQUGzTQKMPfHhqlDIUGTVqUTwg5tWKjVvB9yLnv/4JMej+mCrPZjCsf2QKz2ZzsqpAxjIJiQgTMGGfEumXVwQ/MQM7vzHHGtCifEHJqVcUGrF8e/j5cvbASW/cdwyNXzEJ5rj7JNSShlFr6e5D4ool2hAhQqeRYPnMcKvL06DDbUWjQYKaIq0PEu3xCyKkpFDIsrylBZUEGOswO5OhV4OBYXF1Eq08QIkEUFBMShUolR215TtqWTwg5NYVChpoJ2cmuBiEkBVD6BCGEEEIIkTwKigkhhBBCiORRUEwIIYQQQiQvZYJixtgExtguxtgBxlgDY2yN//YcxtjbjLFG//+U/EUIIYQQQkSVMkExADeA/+WcTwMwD8BPGWPTAfwCwA7OeSWAHf7fCSGEEEIIEU3KBMWc8zbO+W7/zxYABwCUAFgG4M/+h/0ZwPKkVJAQQgghhIxZKRMUh2KMlQM4HcDHAAo5522AL3AGUBDlOasYY3WMsbqurq6E1ZWQaKhNklRDbZKkGmqTJJWkXFDMGMsA8CqA/+Gcx7yfI+f8ac55Lee8Nj8/P34VJCRG1CZJqqE2SVINtUmSSlIqKGaMKeELiP/KOX/Nf3MHY6zYf38xgM5k1Y8QQgghhIxNKRMUM8YYgGcAHOCcPxJy12YAP/T//EMAbya6boQQQgghZGxLpW2evwHgagD7GWN7/LfdBuD/AGxijK0E0Arg8uRUjxBCCCGEjFUpExRzzt8HwKLcvSiRdSGEEEIIIdKSMukThBBCCCGEJAsFxYQQQgghRPIoKCaEEEIIIZJHQTEhhBBCCJE8CooJIYQQQojkUVBMCCGEEEIkj4JiQgghhBAieRQUE0IIIYQQyaOgmBBCCCGESB4FxYQQQgghRPIoKCaEEEIIIZJHQTEhhBBCCJE8CooJIYQQQojkUVBMCCGEEEIkj4JiQgghhBAieRQUE0IIIYQQyaOgmBBCCCGESB4FxYQQQgghRPIoKCaEEEIIIZJHQTEhhBBCCJE8CooJIYQQQojkUVBMCCGEEEIkj4JiQgghhBAieSkTFDPGnmWMdTLG6kNuu4sxdowxtsf/76Jk1pEQQgghhIxNKRMUA3gOwGKB2x/lnM/y/3srwXUihBBCCCESkDJBMef8XQAnkl0PQgghhBAiPSkTFA/hBsbYPn96RXa0BzHGVjHG6hhjdV1dXYmsHyGCqE2SVENtkqQaapMklaR6UPw7AKcBmAWgDcCvoz2Qc/4057yWc16bn5+foOoREh21SZJqqE2SVENtkqSSlA6KOecdnHMP59wL4A8A5ia7ToQQQgghZOxJ6aCYMVYc8uslAOqjPZYQQgghhJCRUiS7AgGMsRcBnAsgjzF2FMBaAOcyxmYB4AAOA/hxsupHCCGEEELGrpQJijnn3xO4+ZmEV4QQQgghhEhOSqdPEEIIIYQQkggUFBNCCCGEEMmjoJgQQgghhEgeBcWEEEIIIUTyKCgmhBBCCCGSR0ExIYQQQgiRPAqKCSGEEEKI5FFQTAghhBBCJI+CYkIIIYQQInkUFBNCCCGEEMmjoJgQQgghhEgeBcWEEEIIIUTyKCgmhBBCCCGSR0ExIYQQQgiRPAqKCSGEEEKI5FFQTAghhBBCJC9uQTFjbD5j7Dr/z/mMsYnxOhYhhBBCCCGjEZegmDG2FsCtAH7pv0kJ4C/xOBYhhBBCCCGjFa+R4ksALAXQDwCc8+MAMuN0LEIIIYQQQkZFEadynZxzzhjjAMAY08fpODFzu71oaDOhzWRHsVGLqmIDFApKqSbR2e1u7G8zod3sQJFBjRnFRmg04r1lrDYn6tst6DA7UGhQo7ooEzqtSrTyCUknXi/H4Z5+dJjt0KkUcHo8yM9Qw+MFOi12FBo0KM3WobXXip5+B1RyGaxODwoNGpTn6iGTsWS/hKQLPYd0XggZvngFxZsYY08ByGKM/T8A1wP4Q5yOdUputxdv7D2GX71RD7vLC41ShvXLq7G8poQCYyLIbndj8/423Ll5oM2sW1qNpTOKRQmMrTYnttZ3RJS/pLqQAmMiOV4vx7aGdty8aU/w/fDLxVPh8HjxyNuHwvrtlz5pwcKpRdiwszF4+yNXzMLiqiJJB4BC55DOCyHDI3pEyBhjADYCeAXAqwCmALiTc/5bsY8Vq4Y2UzAgBgC7y4tfvVGPhjZTsqpEUtz+NlMwYAV8bebOzfXYL1KbqW+3CJZf324RpXxC0snhnv5gMAf43g89VmcwIA7c9qs36nHN2RXBgDhw+82b9uBwT3/S6p8KhM4hnRdChkf0kWJ/2sQbnPM5AN4Wu/yRaDPZgx1FgN3lRbvJjpoJSaoUSWntZodgm+kwO0QpvyPO5ROSTjrMkX20l0PwPWJzugVv77TYUZGfEfe6piqhc0jnhZDhiVfuwEeMsTPiVPawFRu10CjDX6pGKUORUZOkGpFUV2RQC7aZQoNalPIL41w+Iemk0KCJeD/IGQTfIzqVQvD2gszU78+9Xo7mrj58+FU3mrv64PVy0coWOofpcl4ISRXxCorPA/AhY+wrxtg+xth+xti+oZ7AGHuWMdbJGKsPuS2HMfY2Y6zR/3/2SCozrTAT65ZVBzsMjVKGdcuqMa3QMJLiiARUFxmwbumgNrO0GtVF4rSZ6UV6wfKnFyV9TiohCVeeq8cjV8wKez/k6FS4+YLJYbetX16NLXuPYO2SqojbS7N1Sat/LAI5vxdteA/f+8PHuGjDe9jW0C5aYCx0Dh+5YhbKc6lPISRW8Zpo9+0RPOc5AI8DeD7ktl8A2ME5/z/G2C/8v9863IKPmmx4YlcjVs6vAGMA58ATuxpRW5ZNl5WIoLY+B/514DieunoOTlpdyNIp8dePvkZteTYqtMpRl/9Vtw11h7vw7LVnoKfPgdwMNd7Y3YopRRmomUAjO0RaZDKGxVVFmLp6gX/1CTlcHi/yMtRYNLUQXX12FGT6Vp+oLMjA6pc+D+vPf7uzEbNLU7s/j5bzO3X1AlHqHXoOOy2+80WrTxAyPHEJijnnLQDAGCsAENMnPOf8XcZY+aCblwE41//znwG8gxEExR1mO1p6bHhiV1PY7ZRrRaLpMNux/YtubP+iO+z26+eL02baTHZs+qwNmz5rC7t90bRiynMnkiSTMVTkZwi+v04rGLjN6vSkZX+eiJzfoc4hIeTU4rWj3VLGWCOArwH8G8BhAP8YQVGFnPM2APD/XzDEMVcxxuoYY3VdXV3hhVCuFRkmMdrMUG2S8txJMgzVJtNFuvbn6VrveBsLbZKMHfHKKb4HwDwAhzjnEwEsAvCfOB0LAMA5f5pzXss5r83Pzw+7j3KtyHCJ0WaGapNVxQasX14dkRdZVWwU70UQMshQbTJdpGt/nq71jrex0CbJ2BGvnGIX57yHMSZjjMk457sYYw+MoJwOxlgx57yNMVYMoHMklZHJGC6cVoiNq+aF7WhHuVYkGpmMYWFlPl64fm7YjnZitRmFQoaLpheiNEcXtqMdbSZDpCrW3dhOlTsrxq5uQ5Ux0vIp55eQ1BevoPgkYywDwLsA/soY6wTgHkE5mwH8EMD/+f9/cySV8Xo5th/ooJ1+SMycTg8217fhzjdDdpxbVo3lM8dBpZKPuny73S24o51YO+YRkk6GuxtbtNxZMXZ1G6oMAKMqn3J+CUltog5LMcZK/T8uA2AFcBOAbQC+AnDxKZ77IoAPAUxhjB1ljK2ELxi+wJ+ffIH/92GjnX7IcO07bgoGxIB/x7k367HvuDg72sV7xzxC0olYfbQY5QxVBn2WEDK2iT0k9QaA2ZzzfsbYq5zzy+BbNeKUOOffi3LXotFWKtqs3w5zas9WJsnTPkSbEad82tGOkACxVmYQo68fqi48yi57qb7yBSEkNmInMIZeP6oQuewRi7YDkk6Ey+BkbCqOMlO80CDOTPFoO9oV0I52RILEWplBjL5+qLrQChKEjG1ijxTzKD8nldPjweqFldiwszGYB7Z6YSVcHu+pn0wkacY4Ix777iy43Bz9Djf0GgWUcoaZ48RZHUKjlOE3V9bA7UGwfIUscltbQsaiwZPVSrN1eOSKWRG5usNZmcHr5TDbnVi9sBIb61qxZGYJ5DJgWpEBnMf+cRRYJSJaXQL3ZetUuLx2PCYXZIJz3/Fpjgoh6U3soLiGMWaGb8RY6/8Z/t855zwp+yrn6NTYWNcatgPSxrpWfMs/cYIQISabO2KinViMWiUOtllw5+aGkIl2VZhWPPrd8ghJZdEmsl04rRBvjXBlhkCZX7ab8Z+mLqw65zTcs/WLYPkPXDYTM8fHFrSeapWIxVVFmL5mAXa3nsRtr++nyduEjCGiDktxzuWccwPnPJNzrvD/HPg9KQExAMhlwHfPKMUz7zfj8Z1NeOb9Znz3jFLIaVCORBHviXadZkcwIA6Wv7kBnZRTTMa4aJPVWnutqMjPwLyKPFTkZwwruAyUuanuKK6fPxAQB8q/9dV9w5oMF1glQqguMhmDlyMYEIe+BppwR0h6k8TaT20mO57/sCVspPj5D1twemkWyvNocgSJRBPtCImPeGx3HCizzWRHU2df3CfDJWLLZkJI4kkiKC40aNBrdeKJXU3B22hyBBlKYKJd6AefmBPtivwT7SLLp4l2ZGwrjPLeGk1/HFqm0+MVvfyhjhevYxBCEk8SCQS0vSYZrhnjjFi3LHwb5nXLqkWbaDej2Ih1SweVv7QaM2ibZzLGxaM/Di3z1c+OYs2iyrj29/SZQsjYJImRYtpekwyXSiXH8pnjUJGnD86QnznOKMpudgCg0SiwdEYxJuYNbPM8o9hIu9mRMS8e/fHgMosMGlw4vQhdffHp7+kzhZCxSTKfwLS9JhkulUqO2vKcuJWv0ShwxsTcuJVPSKqKR38sVOZpBfHr7+kzhZCxRxLpE4QQQgghhAyFgmJCCCGEECJ5FBQTQgghhBDJo6CYEEIIIYRIHgXFhBBCCCFE8iSz+gQhw+V2e9HQZkKbyY5ioxZVxQYoFOJ9j4x3+YQQH6+X43BPf3B5xXgsn5aIYxBC4ouCYkIEuN1evLH3GH71Rj3sLt8OWeuXV2N5TYkogWu8yyeE+Hi9HNsa2nHzpj3B99ojV8zC4qoi0YLWRByDEBJ/9OlLiICGNlMwYAUAu8uLX71Rj4Y2U1qUTwjxOdzTHwxWAd977eZNe3C4pz+tjkEIiT/JBMVeL0dzVx8+/KobzV198Hp5sqtEUlibyR78gAuwu7xoN9nTonxCiE+HWfi91mmJ/l4b/HnhdnuH/PwYyTGGgz6/fDjnMJlMMJlM4Fya54DElyTSJ7xejp1fdmDfURO8HJAzYMZ4IxZOKaRLW0TQOKMWGqUs7INOo5ShyKgRp/ws4fKLRSqfkJEKzY3VqRRwejzI1auTmiMrlK8LIKYc3kKDRvC9VpAp/F4bnApRlqvFjQsrw1KdBqdGDPcYw33tlJrh43ZY8ZPnP4VcIcNf/mshjEZjsqtExhhJjBS39PSjsaMPT7/bjMd3NuGpd5vR2NGHFrq0RaJQKYG1F1dBo/S9RTRKGdZeXAW1UpwPIRkTLl9qH3IktQQCsIs2vIfv/eFjXPn0h/j0615c99wn2NbQnpQRysF1umjDe9jW0I6dX3ZE3Ob18ohR1dJsHR65YlbYe+2RK2YFA+vBBqdCLJlZEpHqNDg1ojxXP6xjDAelZoRTavVQamlrbRIfkhgpPm6y4bEdjWGdymM7GjFzvBETad96IqDL4sLv/92ElfMrwBjAOfD7fzdh/fIZmFo0+vJ7+6OXT0iyCAVgG3Y2YuX8Cty8aQ+mrl6AigT3mdGCwlXnVETcNuXGBfiywxIxqnrhtEK8tXoBOi12FGQOvTLE4FQIxhA1NSJwLmQyhsVVRZga4zGGI1pqRofZnvC/BSFjnSSCYovdLdip9Nk9SaoRSXV9djdaemx4YldT2O39Drc45TuEy++zi1M+ISMRLQALBIahgWCy6zR40Nru8qL1hHAA/ZY/mI+l7tFSIU6VGiGTsZiPMRw6lULw+DqVXNTjEEIkkj4RyN8M5cvfVCepRiTV5WaoBNtMjl4lSvn5mcLl52eIUz4hIxEICENplDJwLl6OrFh1GjwI6wsUFSOeVPfp4R7sPdKLnn4HHrhsZvCYW/Yew/rl1WGpEfddMgMyhoSkkzg9HqxeWBl2/NULK+HyeE/xTELIcElipHhqQSbWLavGnW8OTJRYt6waUwsNya4aSVH5GXKsX14dsY5wfoY4ozMFBuHyC4w0+kOSJ5AbG5p+sHphJTbWtYqWIztcpdk6wfdKlk4ZHEENpEkUGtQoy9ViycwSMH/QvGXvsVNOqntg2wFcWVuKDTsbg5Prnr66Fko5Q0GmBkdP9mPNokrkZ6jR2mvFQ//8Er1W57AnvI1kg49cvRob61rDUq021rVicbUIeVyEkDBpERQzxg4DsADwAHBzzmuH8/yjJhue2NUY1qk8sasRtWXZlJNFBJlsHFaHG6vOqYCX+ybGWR1umGzijAz1WLywuzxh5dtdHvRYvCjNEeUQhAxbaG6sb/UJOVweLxZXFyVt9YnWXit+uzO8//7tzkb8+bq5EXnCXi+PWCli/fJqlGbrBMsO5CuvnF8RDIgBoKXHhlUv1OGt1QsAAD9+YTdWzq/AYzvqw0aih5NnPdJVJMpz9bh18bSI5yXjC0qq4ZzDbDYDAAwGAxijicpkdNIiKPY7j3PePZIndpjtgvmbyciPI+nBYnfjvn8cjMjj+8PVw/o+Fr18hxvr/34govynr54jSvmEjFS8cmNHKlr/3W62Y15FXlg9D/f0C26KM7tUeAAkkK881GQ6znHKx8RyrqJNGDxVUB3PSXzpzmw24we/2wkAtEQbEYUkcoqj5aQlIz+OpIc+h/DkzH6nOBPhrE6PYPlWJ03+JCTUcPrv4W6iEVp2tGPE8phYjGaDj8AXlcCXAAqIByi1GbREGxFNuowUcwDbGWMcwFOc86cHP4AxtgrAKgAoLS0Nu08oT44uP5GhjMvSorbMiGvOroDN4YZOrcCfP2hGsSH2L1JDtclxxijl0+YdJI6GapNiG82GG6GG038PdxONQNkPbDuAdRdPh06tRL/DDb1GgUyNPHiMwGNWL6wMplkM93Mknht8pLNEtklCToWlw1aJjLFxnPPjjLECAG8DuJFz/m60x9fW1vK6urqw29xuLxraTGgz2VFs1KKq2ACFQhID5WQErDYnttZ34M7NIZMzl1ZjSXUhdNqwFSJiGrIZ3CaHUT4hwzWiNimmaPmzKgXDDX/7fNg7swUC7BP9DijlMlidHsGgOtpxL5xWiNZeq2Aw7vVyHOntx8df94ZNxl6/vBrLa0qgUMhiPv5IzslwXv9wvkykmFG3SZPJhCsf3QJ1Zh7kCjn+dO1cAMB1z30CAPjTtXMpfYIMh2CbTIuRYs75cf//nYyx1wHMBRA1KB7M6+V4p7EzuM3zgTYzuvrstM0ziaqh3RIMWAHfZc47N9djYp4OZ0zMTfnyCUmm4Wy4MX3NAng5hgz4ZDKG8lw9DrZHbswRGlTKZAwXTivExlXz/AMgGkwrNGD7gY6oz5PJGE5aXcGAOFC3X71Rj8kFGcjQKIN1m12aM6LPjEBQm61TYuOqs+DyeJAT49bZtM0zIYmT8kExY0wPQMY5t/h/vhDAuuGU0XpiYJvnQKeyZlElJuVnoDyPcpFIpA6zQ3gXKYtDlPI7LcLld4pUPiHJFOuGG9k6FXa3nsRtr+8P9s33XTIDs0uzUJoTHjDGMlHN6+VhAXBZrhZ3L61Gc1cffrSgAq9+dhRtJnvE89pMwvU93GPFz1/dN6pgVCiove+SGcjLiG2d/JFO0COEDF865A8UAnifMbYXwCcA/s453zacAtrNDsFtnjvMFIAQYQUGtfCkmhg/yE4lL1O4/Fg/KAlJZbFuuHF57fhgQAz4+ubbXt+P1z4/hm0N7WGbY8QyUS00gCw2anBlbSl+8pfP8PD2Q/jje824el4Zio2aiOcVG4U3eGrq6osIRg/39A/rXAgFtdFeo5DRTNAjhAxPygfFnPNmznmN/18V5/ze4ZZhtrkEOxWTzSVaPcnYYnO6sXZJVdis87VLqmBzibP6hMMlXL7DTds8k/QXmMAW2r4fuWIWZo43ht02qSAj6ojy4AA0llUoQgPIS2ePD1t72O7yYsPORlw6e3zE86qKDRG71t2zrBov1x2NqNtwg9GhRs1jCbJp9SRCEifl0yfEkKGRC8761atp9zAiTKWQ49XdrXhwRQ1sTje0KgWe/6AZ/3vhVFHK1ygVeHV3Y0T5P188TZTyCUmmaGvrAgjbcKO7zyHYNwfWBg5dAziWVShCV3iItq6wXIaI5ykUMiyvKUFlQQbaTXYUGTUwaJTotTrDnj+SYDTaqhNCr1EIrZ5ESOJIIig2aJS4+YLJeOTtQ8FO5eYLJsOoVSa7aiRFZekUuGpeOZo6LfByQM6Aq+aVI0srzltGr5LjmrMnhpV/zdkToVfRFzUyNkTbBCT0NhkD1iyqDKa3BbaVfuGjlogANJZNLEIDSACCweiiqQWYUZIVkResUMhQMyEbNRN8v3u9XJRgNNrW2dvq27B60SRYnR40d/VFnXRHm3cQkjiSCIqnFhrwdXd/2Ja6xUYNphYakl01kqImZmegqTP8sqZCxjAxR5yJLRNzdGjuDi+f+W8nRCpKc/SoLMzAmkWVyM9Qo7XXihc+akGv1Yn7LpkBGfMFp6GrSwSC6mjLlAVWn+g0O/C7q2bjri0NaOmxBZdZm1ZoCHteabYuYrk2wJcLnJ+pwsZV84Zcgu1Uy6UF6rRp1Twc7rGiqasP2+rb8O0ZxWFfBoaaxJdquwwSMlZJIiiWyRgMWgUmF2Si3+mGXqVAplZO37RJVI3dFhzttUWsWNLYbcGM8dmjLv+rnn7B8r/q6ceM8bROMRn7AsGkXqXA2aflgXMvKgsyMKUoE1+2W/DQP79Er9UpGCwOtR7x4OXX7lgyHRa7Cxa7B7/d2YiSLC0+bO6BlwMZKjlKsrW45ZXwFSaE1lM+c2KuYEB8quXSQlfEyNapcHntePzsW1Ow6oXPaEWJEeKcw2QywWCggS0iLkkExYd7+vHjF3ZHXEZ7izogEkWv1YWXPm3FyvkVYP7PwZc+bUX1OHE64XiXT4iYxN48IlowOb04E1f+4aOwvlooWPy6W3iZso2r5kXcfs/WL7ByfgWe2NWEYqMGzd39EV9Gs3Wq4LJsoespFxs1uHT2eBxsN6MkS4sZJcaYlokrWTUvmKIR+pg2kx0bdjRh9aJJUVeUGGoUnPi47VasfPodPLPq3GRXhYwxkgiK2012nDUxB9fOn4jefhdy9Er86f2v0WEeeoIDkS4v57iytjRsS9fVCyvhEWkHyHiXT4hY4rF5hFAw+cC2A3joshr8aEEFAODVz3wrP1w6ezwOdVgAIJjacKDNLBhUHjtpE7w98MXz8trxuGfrFxHLc66cX4HXdh/FpbPHgzFgSmEmZpYY8J2Z4/Dov3xzUZ5+tznidQ9eWWIgiLbAbHPjrIpcwcdUFmQK5jtrlXJ8ergHx0/acWuU9ZEHB8yh6R86lQJOjwe5MW4Mks6UGppoSMQniaA4W6/A4hnF+LH/cpVGKcPdS6uQpZPEyycjoFMpBJdzev76uWlRPiFiicfmEUKB4pW1pbjmT58E++hfLp4Ku9sbDEoDweG0okw0dloigsqyXC2ydaqoKz1olDJMzNMLBs1qhQxXzysL+5L66BWzcJPA655y4wKcVuB73aErSxQbNRFlPHjZTFTk67F60SR4OfDul51YXF2Mh7cfxOqFlWGPXbe0GvdsbcCZFfl45v1mwfNdnqsP+4JSlqvFjQsr8as3BranXr2wEhvrWnHr4mm06x0hw5Ty6xSLwWL3YO3mhrBOZu3mBljsniTXjKSq7j7hHee6+8TZ8CXe5aczm82FT77uwZa9x/HJ1z2w0XriSRWPzSMGr70rtKZwj9UZDIgDt928aQ+Om2zYdbATj1wxC6sXTcINCyehtsyI/z53En7x2j6sXlgZtt7wHUumY3KhHivnV8DqcAuu+VuRp484/oF24dHo1hMDE2RD12MWeg0/f3Uftn/RgQ07mvDH95rxk3MnYcPORrT02PDCRy1YOb8CqxdNwoMravDEO404syI/6lJynRZ7RNrIkpklwYA48LgNOxuxZGbJKddA9no5mrv68OFX3Wju6jvlJiKESIEkhkq7+xyC6RMUgJBooo04ZevEmQQXrfwskcpPVzabC1vq23Hn5vqwEbSLq4ugpSUU0W9zoKG9Dx1mBwoNalQVZUCvje8uiNHW2R3N5hGDlymTyyIDQS8XDg4dbg+WnV4Sls7x5Pdn47//5ps30tRhxrPXnoHuPgcKMzV49bMW5Bv02LrvGP7n/MkRS8D9YvFUcHDB4wu9bp1q4GMzsFxayap5ONhuibpJR+DngyGBdpvJjid2NQEAblg4CS09tmCaR7TzfbinP+z2aAF04PZoayDHIyWGkLFAEkFxaY5OMH2ilJa/IlFolDKsvbgKd29pCLaZtRdXRYwyjZROJRcsX6+U9jrF+9vNeOKdxrAJiE+804jyPB3mTsxNbuWSrN/mwN/rOyO+MHynuiCugXE8No8YvPauUiYLTn4LkDPh4NCgUeGhf34eNjq65+hJ2F1enDc5D2eelofrn/s07BxVl2Tgm5V5uOZPnyBbpwq2LxkDJhVm4KTVFXGsLXuP4Y4l04M5yIFJeYWG8HMtkzHMKMmC2eYWrO/kwkzcsHASAECtkA2Z3sE58NruoxGpFYHz3WUR3uwkWnnRvrjEIyWGkLFAEkFxv0M4feLP11H+JhGmlMuQrVPg4RU1wWX8FHLf7WKwu92C5Ts80t7mudfqEpyAeNJKKRQN7X3BgBjw9WN3bq5Hed5czJ0Yv6A4XptHhK69++nhnohAMEenivjiuHphpWA6R2BU99r5E4ODH8DAOXruurmwOd3BFSACI7QA8Kdra2HUKiKO//25ZTBqFWHr21cWZqA0J/LLgEzGcFZFLh68bCZ+HjJBbu3FVfj19oPBdZJv+/ZU3HVxFe4a9Jo21rXi7qVVePKdJrSZ7NhY14pH/WkZZbn64PkuNKjDRrq37D0meI421rXigctmoqffdzV08N9rqJQYCoqJlEkiKO60COdvdlkofYIIs9hdeG33EVw1byI8Xo5MrQJ//ehrXPeNClHK93qBnQfasXx2KXgfR06GCm/sbsUlsyeIUn66ytYpaQJiFB1m4X6swxz/fiwem0cEVlHo6XeAgcHLOR5aUYNjJ62w2D149oOvcdXcUjy4ogZNnRZ4vMALH7XggctmCI7q3nvJDPT2u6KcIzvGGTXBCW+vfnYUbSY7NEoZSnP0YAz41Rv1wRFkzoG/fdKC2y6ajkkFmWjt6ceCyjzBnfBCV4MoyPQFrf1OD6qKDfi/bQfQ0mML1uO+fxzEn68/A2sWVYIxhunFmbC5vHjsytPh9Lpxy4VT0e9wQ69RQKeWYcGkgrDjBTY7CQ3Ux2Wp8fcbfV9YdCo5nB4vqkuMuOPN/cFgfHBqRDxSYggZCyQRFBca1MIdgCG+uXgkfdlcHpw/tQgWmxt2lwcWO8P5U4tgd4szOTNLp8T8yYX4yL+JgLyzD/MnFyJL4nmzJ63CQY2JRoqj9mODL+eng0BO6wPbDkRcGbjp/MkwauT42YVTYdQqcLDNjJfrfMulXTZnPJRyFpEX/N0zSlGRp4XLEy0fV42rn/WlTlxzVhluv2gaZDJfbv+ELC2Om21Ys2gybnt9f7DMO5ZMR4fZhn6HB+W5emSoI9+bQrm5qxdW+paTmzM+GBAH2F1e/KepB4UGDSx2F259dT96rU5sW7MAX7TZ8bNX9oalTAwmkzEsnFKIiryMiFH7wIoYzV19uOiZ94ZMjYhHSgwhY4EkguJcvRwPXjYTTV19vgCEAaflZyBHL+38TRJdnl4Nk82NQ52WYJspz9MjVydOAOJ0cdidbt8ui/6RIavDBadb2jPAo45gpWHgJ7bJRXqsW1odkVM8uSj9AplATuvK+RURVwYe/dchrDqnAje++Dk0Shnuv2QG/uubFbjvHwdhd3lRkDEdepU8bLQ0V6/EwfZ+fN7SjXVLq3Dn5oawc/TJV5244bxJKDJqcOSEFfe+dQC9VifWLKpET78Tx3ut8AJ49IpZ8HIOzoETVgcUMhke23EwLHA8f0oBDnSY0WayIz9DjQe2HYi4snHrt6agoiBDcGTa4wXu2foFVp1TgavnlWFbfRs6zQ4cbDfjRwsqgo+NluN7qlH7WFIj4pUSQ0i6k0RQbLZ50dXnCNvF6JZvTYHFpk121UiKcni8aDfZI3a+Gp8tTptxeb3wcBY2MrT24iq4vN5TP3kMqyo2YP3y6rB1V9cvr0ZVsTHZVUu6Q+392FTXggdX1MDmdEOrUuD5D5r9kxDFu+w90t3UhtpUIlAO4AuID3VY8KMFFVDJZYIBXOiKDb98fT/WLKoMpjZYXR6U5+qQo1cPpBoo5fjvv+3GyvkV2FTXGnaOtu49ggumj8Mdb4av5fvCRy14bEcjVp3jS4nasKMp+D78/b+bsGRmScR6wQ9sOwCby4PbX98f3LL5hvMq0Wmx4y8ftaLNZEe2TgW9Whk2sTuQ5/uTcybhxU9agq9xY10rVp1zWtj6zIG6tZnsI9pgKtbUiEBwXZ6rx+Gefnz8dQ/tnkckTxJBscXhxkP//DKsc3von1/i6avnJLlmJFXZnJ7g5VlgYOcrsdqMy8ODk2MC5d+9pQF/uvYMUcpPVwqFDMtrSlBZkIF2kx1FRg2qio1QKCSxpPqQOswO1LWYUNfyecTtYhnpUl1Cz1u/vBq/9a/Jq1HK8Pj3T4fTzcMeE5hMJrR6QoDd5UV+hhqP7fAFtWW5Wvz0vErc+Wb4F6fAUmSDz9FPz5sUDIgD5W3Y2Rjc+tk76Fh3b2kIBuCDA/YlM0uCAfHgjToCwezlteMjJkRu2NmIB1fU4NfbD2LJzBIc6uwD577yBu+wF6jbM+83Q6ca/tXM4aRGjJWl2TjnMJlMMBgMYCx96k1SjySCYpvLIzgaYXdKe1SORBfvNtPT7xQs/0S/U5Ty05nD5YLD7YXTw+Fwe+FwuaBQUPpEInKKQ5fqGtiy2Iz8DBW0KjksdnfEqK9ve2F5xBJfgYlrT+xqgt3lxb6jJry551gw4MxQy9FmsuH+S2fgcHc/NtUdDaY0PP9hS9hr7OpzBJ83pTATt/ivsASOdeSENWzDjrCl3QTWQA4E0BqlDDIGeLwDWzQHjnHspDVst7pLZ4/HxDwdHlpRA4WMRex2t2Gnb9T5tPwMweMd6rCgpccGuQxhAbTQYwOPcXki+5tTjeQPJzVirCzN5rZbsfLpd/DKLctgNNJVJTJykgiK8zOEN0rIzZT2pCYSXX6GcACSmyHO5hpF0SZ/Zko7+Ou3OfD+Vyfg8nBYHW50WoDefifmn5YT900qUp1cxgXyZasgl4mXh97uv/x/1ZmlKDRocLTXik11R/H0u83BYLXX6sQjV8yCSsFww98+R7ZOhdsvmoYfLfClIQRyYgOBZ4BOJQ9OqgtMeHvQfwVPo5Rh3bJqTC7Qo7vfiV6r78uhRinDfZfMgMXmCl65Wb1oUkQguanuKNYtrcamuhasXVKFu7cOnKNZ47ME32syBqxZVAm9So5XPjsaMfJ70/mT8cvFU/HsB19HTAa8Z1m1YDA7o8SIDI1iyLWDpxUZsG7rF+i1OnFGWY7gY6vGGdHc1Qez3Y3mrr5gUBvryG6sq4Wk89JsnHOYzebg70pN+uXWk9QjiWuSDAy3fGtK2EjCLd+aAhnoMgsRJme+Ge6hbWbNokooRLqkqJKHbwYSyGVUSzxNoKnbCqvLg0OdFhw5aUNjhwVWlwdN3dZkVy3pPF4WzJd94LIZeHBFDTbVtcLjFa8f06vkuOasMjy+qwm3vLIPT73bjKvnlSFbp8JjOxpx6ezxwdxau9OL/z53Eu5cMh0PbT+Ix3f6tjK+el4Zio2aiDSI8Vm6YGB56ezxEelJd75Zj8DA6KpzKnDDwklYs6gSZTk63L/tYPCxgTWJQ/VanThpdeDni6fh1d3+c3TpDDy0ogbb6o9h3bLqsPfaPcuqcU5lHuZPykORUYtV55wmOOHP6vLg/y6dGXHf0V5rRB00Shn2HzPh/r8fwN1Lw9/bdyyZDqNGjtu+PRUTcrR49MoavLV6Ac6qyA1uEx147C8XT0VLTz8e29GI65+rw0Ub3sO2hvbgCLHQyO7u1l643cO/ijV4u+1AHdJhaTa3w4obn3sfHpFWBCIEkMhI8QmrE/l6JZ6+eg5O+Ld5PtnvwAkrXaomwtrMdnzS3IOnrp4TtjX4xDw9akQov7XXhk+bu33b0VocyM9U4+VPW2HQKDBzQrYIR0hPzjhPcExnXRbHwOokHGAAnG4u6nb1fU53RLC6YWcj1iyqRJ/Dg9JsLW751mTk61XBlVlaevpxx5Lp+OK4BU6PFxvrWnH7RdOgVDAc67XhhoWTIGeALCSNIdr2xB0WOx7Y5su7VStkKM/VY59/t7qAVz87GrbTXFmuFrcunoamzj443b6A++chE1jvvWQGbA4XHlpRg6+7++Fwe/H4rkbcf8lM/O/Lu9HSYxMcfba7vJg53gCPl0eMgm+qOyq4293zH7bgmrPK4HB58JsrZqHP6UbrCSse39mEXqsTN50/GSetTshlvpHqlhNW5GeqsHHVPFidHuhVcjjcXlz97CcRgW/JqnnBEfjB9Xy3sQuHe/qxvKZkWPn3pdk6wYmtpdmx7/Y60omZYlBoaFdaIi5JBMW5ehW6+5z4+eBtnvXiXAonY0+RQYVvVYdvDX7XxVUoEil/szxPh7kV4dvR3nVxFcpypd3J25we7DjQ7ls9wOGGTq3Anz9oRtU4Q7KrlnRlOTp878yysIBv7cVVmDCMAGYoXi+Hxe6OCLqydSoYtMqwdYHXXlyFN/ccC06iW7OoEi9+0opeqxP3XlKNLL0Svf1OmGwuvPqZL1f46atrw1IFoq4nPK8cD28fSKtYu2Q6ynK1wTV/20x29Ntd+NVFU1FRkIFjvfawdII1iyqRrVMFA8jbX9+PlfMrsP6tg1i9sBIv7vYFth993YM1iyZDr2bI1KhRkqWFTqXAa58dwfSSLBQbVDh+0hG2asVN50/G3/cdx7lTC6BWyPDw5TXI1iogk8nwxXEzHrxsBhweL5o7+6BTy3H/tgNYMrME35tbiol5erSdtEKtlOOk1YXPj5wMy6V+6urZcFq9aI+S0rDjYCe8HCjL1WLJzJJgasqWvcfg8fo2HynL0SEvQ412sx06lQJOjwe5enXUQLW114rf7mwM27TktzsbMbs0O6b0ibEyUY+QAEkExU43x5PvNAXf+ADw5DtNeOgyMcb8yFjk8iC4FSvg+1C6a0sDXhBpZzWbwxPX8tOVTMaxYk5pWOB318VVEGl37bRmc3nw+3+H92O//3cTHl4hTj92uKcfbSZbRLB6ee34iBUSAis0BCbRPbbDF1i9tvsoOs0O3P565PJnd7y5Pzi6+u6XnRHbE9+9tAqMsWBAHDzW1i/w6BWzghPbNEoZJhVmoMvixEfNJ4JXFQKPf2xHI566eg7aT9qhUyvwh3e/Co5Mh67sYNAo8NiOQ7hx4WSseWngy+m6pdVwut0oyNRi9cbPI1IqfnPlLPzPxj3BUer/PncS1obked90/mRolXIcbDMLbkxy4Lg5uOZy4Pxsq29Dd58TLT1WlBi1WLNoEjbV+YJ3AMH1jd871ImfnDMpLGd67ZKq4DJv7zV1Q6uUB3O/A0vB3bp4mmCg2mG2o6XHFrbtNYCYc4rHykQ9QgIk8VFjcbhwZW0pnnm/OZj3dmVtKSxO2iWLCOuIsjV4p0hbg0crv0PiW49r5ArBLwtqOW2002sV7sd6beL0Yx1mO/YfOYnfXTUbqxdNwg0LJ6EsV4uKPOHVFEIn0QV+F8oV3rCzEbddNA1ON0eGWo6HVtTgpgsmBwP8GxZOwsr5FXjynSY0dfYJHqvf6cbK+RVYvWgS/nB1LTweYO3mBni5cBrGZy29uPW1/bjllb343pllKPBPkA2s7LBmUSUm5OixZGYJfvXG/vDc5s31yNKp4eFcsOzQOi6ZWRIMiAP3P/qvQ+jud6I4JId68H2Dz89/nTsJnWbfWvq3vrYfT73bjGvOGsjNXr2wEq/tPooFkwuCAXHg+XdvbcCCyQXBwDk093vDzkYsmVmCmzftweGe/oi/+WhzioeaqEdIOkqLoJgxtpgx9iVjrIkx9ovhPl+nUkR0Tht2NkKnlMRAORmBXL1K8MMiR6SUm2jl50o8paerT3ipuq4+yv/P0ikF+7EsjTir6JRka3B2ZT7+66+7sWGHL+hedc5pyNErBdtq6CQ6jVIGvUqOqUWZ+NECX6BbbNQE69nYacE1Z5Xh+Ek7bnllL5xub1gKwGu7j6Klx4aCTHXU94VCBuTqVDDZnOh3DiyZKPT4wIS9wKh2XoY6eN+kgkw8/2ELnG4vtErhzUM4AKNOIVh2ach6v9Fyo70c+Lq7P+p9g29ze7nguuj3Lq/GU1fPwcY638Yg0Y4XWMLttd1Hw76wBH6OFqgG1jQOneg3nO2e03miHiFCUj4qZIzJATwB4AIARwF8yhjbzDn/ItYy+gTy5OwuL/rsblHrSsYOlZxFXN5de3EVVHJx8uQy1HLB8vUjWKx/LMnUKARzJjM1Kd9VxZ3J6hLsx0wijRR3mZ3BCVeBsu/Z+gVuPr8S915SjZYea3DL84p8PR55+xAAXxB027enwubyBtcPDk2b6LU6gyOYN5w3Cdk6Fcx2d3C3uNAd3wIrNYROYLtjyXR82WEBAEzI0SBTo8JnLb3QKGV49bOjWL2wUnATjdBzZHG4g/fd79/iuctiR215Nh64bAZ0Kl+axb5jZmiUMjR2WqBRGiPKvun8yTBo5Pjt906H1eGGUacMy3cOnI+phZloOdEfdSm4UBqlDFlapeDf1ub2oK3bhhsXVuLICStUCplgmZMKMnH/WweCW0kHvrCELgUnFKiOdrvn4WwUQkg6SIdPmrkAmjjnzQDAGHsJwDIAMQfF+QaV4Adtfqa0R+VIdIwBSjnw8Ioa9Dvd0KsUsLvdEGuzpAm5Whw32cPKVyoYSvOkvcpClk4RkaN599IqZGnToauKr0ytXPgLg0jnpjNKSo8XvnkZoSuC/OzCKbhqbily9GqY7S6cVpCBlX+uixjFXnVOBTQKObbVt2Hl/ArkZ6hx20XT8PD2g7jhvEnIz1DDqFNCxhjuWjodciZDSZYGz18/F10WB7J1KjR3WfDWvjacO7UAFocXWrUXOTol1iyqxGM7GvHCRy1Ys6gS47K0kDOGh7YfDObiAv7R3Rwdnvz+bDAZcM+yKrg8HHq1HPuOnMSHX/XgsjkT8KMFFdAq5XB6PDjSa4PJ6sTGutZgDrdGIUO2ToEDbZawSYf3LKvG47sGdu5bvbASz33QjB+fMwn3Lp+B1hMDk+l8+cYDgW0wz7jNJBjsahQymO1unLS5IWdAWa4u4sv0Pcuq0X7SisvmjIec+a5C/e7fzWFfNu67ZAZkzDcxLhDwer0cX3f3o+VEP/QqBYoMGpTmDKyHHMuKEqMNqglJNenwSVMC4EjI70cBnDn4QYyxVQBWAUBpaWnYfRqFDD/55qSIUbnBl30ICZDLZJDLZMFlp2QMmJinh5zF3maGapNZWg3OrMjGofZ+dJiBAoMak4v0yNJK+7Kj3cUjcjTXbm7AX1ZGvOUlZ5xRiZ+eWxncQjgwKWycMfZufMg2qRPedKIsRx+xe9vD27/EyvkVeORfvl3rPv76hGBAXWLU4q8ft2BxdXFwxPW2b0/B9+eW4dF/HQq+jpsvmAy1XIZnP/gi4r7bvj0V3z+zLJhrHlhh4pPmHjxyeQ1cXo5snRKrXvhMcPvldUur0NZrwUNvN+O6b5TjkbcPhZVzyZzxeGj7wWBQu/biKvzri3b874VT8d0zSsM2DTl60h4xse+ON+vx3HVnoO5wL2wuL7bVt2FxdTFufOnz4HHuXDIdeRlqONwedFscuOG8SbC7vb5AlXM8/2FLMMgPPGf98moc67VFLE+oV8mxcn4FtEoZzqrIRbvZjjveDF8Z5Lqzy1BRkInjJ61YNqsEFpsLP/zTJ8EJdwAiVo1Ys6gSlYUZOLeyANsPdMS8okSsG4WMpE0SkmjpEBUKfeWM2MKJc/4057yWc16bn58fdl93nysYEAMDeWbdfTTRjgg7aXPhl6/tx4YdTXh8ZxM27GjCL1/bD5M99jYzVJv0ejk+aOrFNc9+ghtf/BzXPPsJPmjqhXdwwqHERBut7JL4BEQA6DB7ggExMDAprMMc++YFQ7XJTLVScMOawLFCheaqMia8oYZGKcMxkw0LJheE5UIXZ+mCQW+grEfePoQeqxNLZpZE3Nfd74yYfPnSp604v6oIN7+8F2te2oO6ll7YXV60mex44aOW4AS+h1bU4Il3mjA+x4BLZ48PBsSBch7b0Yimzj5cPmdC8La7tzTgmrMr0HrCiuc/HCirxKiNOrGvtccKGWPByXCDc7/Xbf0Ce4+acMsr+9Dd78LD2w8F+5V+pwdtJjue/7AFT35/dnDiYaZGGVylIrS+2Xo1Xtt9FDLG8O/GLvzvy3sjPttMdg9u+NtudJid2LCjCff7134OTLgTWjXisR2N2HfUhIY2k+CKEkIT9cQwVJsUoWyYTCaYTCZwzgXvG3w7kbZ0CIqPApgQ8vt4AMeHU0CfI0pOsYNyiokwW8hEngC7ywubc/i7RgmJtpRRvD540oVBIzy5iXKKo09C7BZpEqLF4Q4LAlfOr8DzH7ZAq5JHnWgX+H/L3mNYv3xGWEC9fnk18vQqlGZrw+o91AQ0oYlkQoHokpklYcvEhQblbSY7ntjlmyh4sN2Clh4bOizRJ6l5uW9b99DbnG4PiowaXF47Hoz5Nu04ctIGORMO/rX+ydyXzh4f9TihXyIGn0fAtytfn9ODx3c24YldTTBZhf/eVocbl84ejw07G6MG6YOPNXjCXbRVI7wcUTcISYcVJUKDYK/XiyNHjuAHv9uJH/xuZ9iW0ABgNptx5SNbIm4n0pYOQfGnACoZYxMZYyoA3wWweTgFGLTCH7QG+qAlUUSbBZ+XIU4eettJm+AHT9tJW5RnSINRqxAcrTRQTnHcvzAUGjTotTrxxK6mYGDWa3Xi666+iC3JVy+sxNZ9x4L/37p4GpbOKMbGVfPw1A9mY+OqeVg6YxzyMjU47l/7OMDp8Qq+jsCV+cH3CQWicll4MBiYcDe4jq/tPgqNUoZC/ySzaMfVqRVht2XpVPjxC58FV+G4el4ZPvqqC5MKMiPa580XTMbRXmtwFYhoxwl8iQh9nWsWDdRx3bJqPP9Bc/C+bJ3wCjVdfY6w13+qLyyDbyvI1ERdNULGgGKjNuVWlAgEu6diNpuDQfDRo0dx3RPbIFNqodQKp3YotTQhkIRL+U8azrmbMXYDgH8CkAN4lnPeMJwysrQK3HzB5LBcspsvmAwjfdCSKLK0MsHtT7N14nyPVEaZRa4cxhatY5FKwTAhR4dV51QEc7kn5OigVtDEnbwMRUTe6ZpFlcjLEKcfE1pJ4I4l02Gxu6D0ePCn686AxeZGjl4FDo5vTMqFy+PF4uqi4OSqmgnZqAm5rre4qgjTizNRlqvHba/71gPesvcY7llWHbZT3EBO8de46fzJYTnFuXpVxOOnFxvC3j9tJjs21rXisStPh93twVddfcGVL9YtrYJOzfDqZ0cjJqkFcnTbTloB+N6D914yA/cMWgt4w85GPLiiBlv2HsFV8yb6Jsg63DhhdUItl+H3//FNbJtckAmFnGH98hnB9Y9DJ7ytvbgKFpsTNyychCytAhNy9Ljp/EpU5Gdg5jgj5pRm40C7GYc6LPjtzsaIz621F1fBZHOiPC9zyNU3Nta1BredDr0tdGWIwX/rQE5xVbEh5VaUMJvN/gA3emDOOYfZbA4LgJVaae8QSoaPjcV8mtraWl5XVxf8/ZOvu9BtceFgx8CkqamFmcjLVGLuRHFzmMjY8OnhHni9Hni5DN19DuRlqCFjXshkcpxRnhv60JiitcFt8p8N7fiqsy/iw+y0ggx8yz8RRoq27D2OXQfaseKMUvT0OZCbocYrn7bivGlFuLhmXLKrl1SHu0/iy3YbGtrMwX6sqtiAKUValOdlhT50RG0SQHDVgU6LHfkZGshlQLt59KsKhK5moFPJoZQzONxedFmc0KrkUMiALrMDRUYtTHYXsrVKnLC6oFHKkKFWwOV1g0EOs80Fg0YJq8uNE/2uYKAdWIWhPE8HlZzD4WLosNhRmKkB5270uzhcbiAnQwGb04t2kx1alQJtJ63QqRWw2N2wuTw4vTQbDBzX/qku4jXcsHASHt/ZhNu+PQVOD0eRUYPWE1a87F9Z4p5l1ajI08PqdEGjkoNzhpNWFwxaBfYcOQmL3YP3DnWGTTrUKGX49eWz8O3qorBVIQJ/gyKDBh4v0NUX/vcoMmjwRZsFN2/ag2ydCpfXjseUokyMN2rh8nqRrVNDLvN9WdCp5HB5vMgZtN1zYPWJ1hP90KkUKDSoI1afEHFFiRG3SQAwmUy48tEt8Li90GUXwOuyw97nS3sI/d1tt8JQXA65Qo7fXDIFq559F+rMPMgVcvzp2rkwGAzBdAnOOa7+/S785b8Wwmg0jua1kfQk2CYlMlQqwztftmP57IEP2jd2t2JFbVmyK0ZSVK5ejeue+yS4/NXB9j5s3XcMf7pWnG2Ys3XKsOWeOAc21rXiIZG27E1XhQY13mrowGt72oK3aZQyfPdMeq92WTyQMTfmn5YXDPhMNiu6LB6U54lzDKGVBMrzRr9d71ArFLjdXjS0maBReQAGOFwedHk42k029FhdkDFgec04gDF4OUdephoerwqP79yLB1fUwOZ0Q6tSYOveI/jRgklY/dK+iPftQytqMHeq78tsaIBelqOFl3N0WpxQyBmcbi+OnrAKXsUJpB8UZenQ1GnB1KJMnD4hG2eflouCTA1Ks3Vo7bXC5fUiVz8QSDZ39eE3/xqYeNfV58Sqcypw+oQslOXqIwJOoXN1WkHk36M0R3/KpdCG+tvJZAynFWSElR3L3yuVKTRDjwwH0isA4InvzwneHhhlNhgMYGKtu0nSkiSC4slFetSW5+P658L3t59cRPlERFh5rh63Lp4Wt0uIM4oMgstrzSgyiFJ+uqouysS6pdUR56W6KDPZVUu6GcVGbN5vxY0vfRJ2bhaclt6jXAqFDDUTsgH4AtZtDe3BJeCC77u8jGCgFnjc9fNPi3h/TivSC76vpof09UIBX+C4gZHXwWkqgfSD1Qsr8evtB3Hr4mmYXmwMBpYBQoHk4LSUXqsTU4sM+ObkglGNvqZr4JoIgSBXiFKbEbw/dGLeqmfexcabLw4bNaZgWXokkT4BACdtdv+asA4U0pqwJAYxXkIc8WVBm82F/e3mYJucUWSAVivOlr3pzGpzor7dEjwv1UWZ0Glpox0AsNvd2N9mGmgzxUZoIifajepSdbLFeuk+2uP6bHZ8EdLXTy/SIyOGvl4obaHT4ks/cHq8UMllgmkIYr6mMSwh6ROhv/d1t0Oh0UGXXQCZXIbfXDIFAHDTG4fgsvUH71dq9PC6bZAptHjxxvPDgmKTyYQrH9kSESwDqRMwp0o90pCU0yd8myXMnUhBMIldvEditFol5k7MPfUDJUanVdF5iUKjUeCMMX5uYn3fRXtcxgj7+lOlLYwGjeomXmgqhdtuxbW//QcAwFBcHna/UquH1yWH2+WGyWQKBpeBUeTAChWDg8/Akm6BgDl0hQyj0TiiAHXwMWIJeAfXQ+pCrxKM5IuCtKe6E0IIIWTMU2h0Q+Ycux1WrHz6nWBAFVjxwuP2Bn+/8pEtwXSLQMAcCIaPHDmCy+7fhMsf3hyWmjGcq/GDj3HkyJGItZSFyqWl5QaELss3kjWoJTNSTAghhJD05LJZ4fV44bL1w+u2wW23+m8X73eFRhcc7Q3877L3h62RfPToUfzs5T1w2a2QKTVhv3s9XsiUCAa11z+xDc/+dHHMI7iB4ww+RujxB5drMpngsvXHtI6zFIz2PIzJnGLGWBeAlih35wHoTmB1hkJ1EZZKdQGGrk8353zxqQqgNjkiVJfo4t0mYz1WKqN6J1Yi2+SpjpcIyTy+lF+7WMcXbJNjMigeCmOsjnNem+x6AFSXaFKpLkD865NKr5fqIiyV6gIktj6p9tpjRfVOrETXO9nnKZnHl/Jrj/fxKaeYEEIIIYRIHgXFhBBCCCFE8qQYFD+d7AqEoLoIS6W6APGvTyq9XqqLsFSqC5DY+qTaa48V1TuxEl3vZJ+nZB5fyq89rseXXE4xIYQQQgghg0lxpJgQQgghhJAwFBQTQgghhBDJGzNBMWNsMWPsS8ZYE2PsFwL3M8bYBv/9+xhjs2N9bhzqcpW/DvsYYx8wxmpC7jvMGNvPGNvDGIvcBD4+9TmXMWbyH3MPY+zOWJ8bh7rcElKPesaYhzGW479PtHPDGHuWMdbJGKuPcn/c2gtj7CHG2EF/ua8zxrKiPE7Ucx/lGJczxhoYY17GWNQlbuLRLkdRl0SclxzG2NuMsUb//9lRHhe38zKaPk3kesTUXlNFItqH2BhjExhjuxhjB/zvgTXJrtNwMMbkjLHPGWNbE3CspP59E9EXDjpexGdVrP1THI9/F2PsWMhn9UVxOrbg+yKur59znvb/AMgBfAWgAoAKwF4A0wc95iIA/wDAAMwD8HGsz41DXc4GkO3/+duBuvh/PwwgL8Hn5lwAW0fyXLHrMujxFwPYGY9zA+AcALMB1Ee5P27tBcCFABT+nx8A8EC8z/0QdZkGYAqAdwDUDvE4UdvlSOuSwPPyIIBf+H/+hdDfKJ7nJcb3rWAbjUNdTtleU+VfotpHHOpdDGC2/+dMAIfSod4h9b8ZwN+EPkfG2t83EX3hoONFfFbF2j/F8fh3AfhZAl674Psinq9/rIwUzwXQxDlv5pw7AbwEYNmgxywD8Dz3+QhAFmOsOMbniloXzvkHnPNe/68fARg/iuONuj5xeq4Y5X0PwIujOF5UnPN3AZwY4iFxay+c8+2cc7f/12h/f7HPfbS6HOCcfyl2uSMRY10Scl78Zf7Z//OfASyPwzGGMpo+TVQxttdUkaj2ISrOeRvnfLf/ZwuAAwBKklur2DDGxgP4DoA/JuBwafn3HY0on1UJ659i+KyMmyHeF3F7/WMlKC4BcCTk96OI7FCiPSaW54pdl1Ar4RvtCeAAtjPGPmOMrRpFPYZbn7MYY3sZY/9gjFUN87li1wWMMR2AxQBeDblZ7HMzlES1l+sR/vc/1fGTJZHnfiiJOi+FnPM2wNcxAyiI8rh4nZfR9GnxFK29popUe98MG2OsHMDpAD5OclVi9RsAPwfgTcCxUuHvmwp9Yaz9Uzzd4E+pejae6RsBg94XcXv9CrEKSjImcNvgteaiPSaW54pdF98DGTsPvqB4fsjN3+CcH2eMFQB4mzF20P9NLZ712Q2gjHPe588NegNAZYzPFbsuARcD+A/nPPQbqtjnZiijai+MsX8BKBJ47O2c8zf9j7kdgBvAX4dx/GGLpS4xEOXci1CXhJyXYRQTrzY5mj5t+AcbfXtNFWL3WQnFGMuAbyDgfzjn5mTX51QYY0sAdHLOP2OMnZuIQwrclui/byI/h1LV7wDcA9+5vwfAr+H7whwXg98XjAk1A3GMlaD4KIAJIb+PB3A8xseoYniu2HUBY2wmfJebvs057wnczjk/7v+/kzH2OnyXi0bzhjtlfUI7X875W4yxJxljebG+FjHrEuK7GJQ6EYdzM5RRtRfO+flDFc4Y+yGAJQAWcX9iVIzHH7ZT1SXGMkQ59yLUJSHnhTHWwRgr5py3+VMSOqOUEa82OZo+bdhEaK+pQuw+K2EYY0r4Pvj/yjl/Ldn1idE3ACz1D6ZoABgYY3/hnP8gTsdL+t83wZ9D0cTUP8UL57wj8DNj7A8A4jbBMsr7In6vfzQJyanyD77gvhnARAwk31cNesx3ED4p5ZNYnxuHupQCaAJw9qDb9QAyQ37+AMDiBJybIgxs5DIXQKv/PCX83PgfZ4Qvh0kf53NTjugT7eLWXuBLC/kCQP5oz5WI76F3EH1ym+jnfhR1Sch5AfAQwidyPJjI8xLj+1awjcbhXJyyvabKv0S/b0SsNwPwPIDfJLsuo3gN5yL+E+2S+vdNdF8Yctywz6pY+qc4H7845OebALwUp+MKvi/i+foT0pAS8Q++mdiH4JuZerv/tp8A+EnIyX3Cf//+0A9doefGuS5/BNALYI//X53/9gr/m3wvgAYx6hJjfW7wH28vfBNpzh7qufGsi//3awe/ycQ+N/CNQrcBcME3+rAyUe0Fvi9ER0L+/r/33z4OwFvxOvdR6nKJ//U7AHQA+OfgusSrXY6kLgk8L7kAdgBo9P+fk+jzEsP7NmobFbkegu01Vf8lon3Eoc7z4bsUvS/kPF+U7HoN8zWcizgHxcn++yaqLxx0TKHPKsH+KYHHf8Hf5+wDsBkhQbLIxxZ8X8Tz9dM2z4QQQgghRPLGyuoThBBCCCGEjBgFxYQQQgghRPIoKCaEEEIIIZJHQTEhhBBCCJE8CooJIYQQQojkUVCcphhjtzPGGvzbLO5hjJ0pQplLGWO/EKl+fWKUQ9IfY8zjb6P1jLGX/Vt4R3vsXYyxnyWyfoSEYoxdwhjjjLGpya4LkbaQvjPwrzzZdRrrKChOQ4yxs+DbXWo253wmgPMRvh/8UM+Nuosh53wz5/z/xKklIUE2zvksznk1ACd8a+0Skqq+B+B9+HbVJCSZAn1n4N/hUz2B+VBsN0J04tJTMYBuzrkDADjn3dy3F/th//bMYIzVMsbe8f98F2PsacbYdgDPM8Y+ZoxVBQpjjL3DGJvDGLuWMfY4Y8zoL0vmv1/HGDvCGFMyxk5jjG1jjH3GGHsvMJrCGJvIGPuQMfYpY+yeBJ8Pkj7eAzAJABhj1/ivdOxljL0w+IGMsf/nb097GWOvBkaYGWOX+0ed9zLG3vXfVsUY+8Q/mrKPMVaZ0FdFxgTGWAZ8WxevhD8oZozJGGNP+q/MbWWMvcUYW+G/bw5j7N/+/vCf/i1nCYkLxlgGY2wHY2w3Y2w/Y2yZ//ZyxtgBxtiTAHYDmMAYu8Xff+5jjN2d3JqnDwqK09N2+Br9IX9n/c0YnjMHwDLO+fcBvATgCgDwd+LjOOefBR7IOTfBt2NPoNyL4dtdzAXgaQA3cs7nAPgZgCf9j3kMwO8452cAaB/1KyRjjv8qxbcB7Pd/KbsdwELOeQ2ANQJPeY1zfob//gPwBSoAcCeAb/lvX+q/7ScAHuOczwJQC9/OS4QM13IA2zjnhwCcYIzNBnApfNvczgDwIwBnAQBjTAngtwBW+PvDZwHcm4Q6k7FLG5I68ToAO4BLOOezAZwH4NeMMeZ/7BQAz3POT/f/XAlgLoBZAOYwxs5JfPXTT9RL6SR1cc77GGNzACyA742xMYZc4M2cc5v/500A3gawFr7g+GWBx28EcCWAXfCNmDzpH0U5G8DLA+9DqP3/fwPAZf6fXwDwwHBfFxmztIyxPf6f3wPwDIAfA3iFc94NAJzzEwLPq2aMrQeQBSADwD/9t/8HwHOMsU0AXvPf9iGA2xlj4+ELphvj8ULImPc9AL/x//yS/3clgJc5514A7YyxXf77pwCoBvC2vz+Uw7cdLiFisfm/6AMIfhG7zx/gegGUACj0393COf/I//OF/n+f+3/PgC9IfjcRlU5nFBSnKc65B8A7AN5hjO0H8EMAbgyM/msGPaU/5LnHGGM9jLGZ8AW+PxY4xGYA9zPGcuAbZd4JQA/gZOibdHC1RvZqyBhnG9xm/KMbp2ovzwFYzjnfyxi7FsC5AMA5/wnzTSz9DoA9jLFZnPO/McY+9t/2T8bYjzjnO8V9GWQsY4zlAlgI35cxDl+QywG8Hu0pABo452clqIqEXAUgH8AczrmLMXYYA5/1/SGPYwDu55w/leD6pT1Kn0hDjLEpg3ImZwFoAXAYvgAWGBi1jeYlAD8HYOSc7x98J+e8D8An8KVFbOWcezjnZgBfM8Yu99eDMcZq/E/5DwYmplw17BdFpGYHgCv8gQj8X74GywTQ5h8dCbYpxthpnPOPOed3AuiGL5WoAkAz53wDfF/oZsb9FZCxZgV8l5/LOOflnPMJAL6Gr41d5s8tLoT/yxmALwHkM9/EZ/jnXFQJFUyISIwAOv0B8XkAyqI87p8Arvdf3QVjrIQxVpCoSqYzCorTUwaAPzPGvmCM7QMwHcBdAO4G8Bhj7D0AnlOU8Qp8QeymIR6zEcAP/P8HXAVgJWNsL4AGAMv8t68B8FPG2KfwvXEJiYpz3gBf/uW//W3pEYGH3QHgY/hSfQ6G3P6Qf5JJPXyXA/fCd8Wj3p+mMRXA83GsPhmbvofIUeFXAYyDL0e9HsBT8LVJE+fcCV8g/YC/De+BL72MkHj5K4BaxlgdfJ/FB4UexDnfDuBvAD70X0l+Bb5BBnIKjHO64k0IIYREwxjL8M/lyIXvCto3OOc0oZiQMYZyigkhhJChbWWMZQFQAbiHAmJCxiYaKSaEEEIIIZJHOcWEEEIIIUTyKCgmhBBCCCGSR0ExIYQQQgiRPAqKCSGEEEKI5FFQTAghhBBCJO//A5yrI7vi/L9lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(df_robust_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e3bfc1f280>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 40.,  14.,  15.,  31.,  79.,  98., 262.,  84.,  73.,  45.,  35.,\n",
       "         35.,  29.,  16.,  13.,  11.,   4.,   5.,   1.,   1.]),\n",
       " array([-2.12153846, -1.81546154, -1.50938462, -1.20330769, -0.89723077,\n",
       "        -0.59115385, -0.28507692,  0.021     ,  0.32707692,  0.63315385,\n",
       "         0.93923077,  1.24530769,  1.55138462,  1.85746154,  2.16353846,\n",
       "         2.46961538,  2.77569231,  3.08176923,  3.38784615,  3.69392308,\n",
       "         4.        ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_robust_scaler['Age'],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([562., 170.,  67.,  39.,  15.,  16.,   2.,   0.,   9.,   2.,   6.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.]),\n",
       " array([-0.62600478,  0.48343237,  1.59286952,  2.70230667,  3.81174382,\n",
       "         4.92118096,  6.03061811,  7.14005526,  8.24949241,  9.35892956,\n",
       "        10.46836671, 11.57780386, 12.68724101, 13.79667816, 14.90611531,\n",
       "        16.01555246, 17.12498961, 18.23442675, 19.3438639 , 20.45330105,\n",
       "        21.5627382 ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_robust_scaler['Fare'],bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Guassian Transformation\n",
    "\n",
    "Some machine learning algorithms like linear and logistic assume that the features are normally distributed\n",
    "-Accuracy\n",
    "-Performance\n",
    "- logarithmic transformation\n",
    "- reciprocal transformation\n",
    "- square root transformation\n",
    "- exponential transformation (more general, you can use any exponent)\n",
    "- boxcox transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "### fillnan\n",
    "df['Age']=df['Age'].fillna(df['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Age         0\n",
       "Fare        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stat\n",
    "import pylab "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### If you want to check whether feature is guassian or normal distributed\n",
    "#### Q-Q plot\n",
    "def plot_data(df,feature):\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.subplot(1,2,1)\n",
    "    df[feature].hist()\n",
    "    plt.subplot(1,2,2)\n",
    "    stat.probplot(df[feature],dist='norm',plot=pylab)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(df,'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logarithmic Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df['Age_log']=np.log(df['Age'])\n",
    "plot_data(df,'Age_log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reciprocal Trnasformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age_reciprocal']=1/df.Age\n",
    "plot_data(df,'Age_reciprocal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Square Root Transformation\n",
    "df['Age_sqaure']=df.Age**(1/2)\n",
    "plot_data(df,'Age_sqaure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Exponential Transdormation\n",
    "df['Age_exponential']=df.Age**(1/1.2)\n",
    "plot_data(df,'Age_exponential')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BoxCOx Transformation\n",
    "The Box-Cox transformation is defined as: \n",
    "\n",
    "T(Y)=(Y exp(λ)−1)/λ\n",
    "\n",
    "where Y is the response variable and λ is the transformation parameter. λ varies from -5 to 5. In the transformation, all values of λ  are considered and the optimal value for a given variable is selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age_Boxcox'],parameters=stat.boxcox(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7964531473656952\n"
     ]
    }
   ],
   "source": [
    "print(parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(df,'Age_Boxcox')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(df,'Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Fare\n",
    "df['Fare_log']=np.log1p(df['Fare'])\n",
    "plot_data(df,'Fare_log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Fare_Boxcox'],parameters=stat.boxcox(df['Fare']+1)\n",
    "plot_data(df,'Fare_Boxcox')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}