diff --git a/.ipynb_checkpoints/titanic-checkpoint.ipynb b/.ipynb_checkpoints/titanic-checkpoint.ipynb
new file mode 100644
index 0000000..0c0afdb
--- /dev/null
+++ b/.ipynb_checkpoints/titanic-checkpoint.ipynb
@@ -0,0 +1,960 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('train.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      "PassengerId    891 non-null int64\n",
+      "Survived       891 non-null int64\n",
+      "Pclass         891 non-null int64\n",
+      "Name           891 non-null object\n",
+      "Sex            891 non-null object\n",
+      "Age            714 non-null float64\n",
+      "SibSp          891 non-null int64\n",
+      "Parch          891 non-null int64\n",
+      "Ticket         891 non-null object\n",
+      "Fare           891 non-null float64\n",
+      "Cabin          204 non-null object\n",
+      "Embarked       889 non-null object\n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>223.5000</td>\n",
+       "      <td>446.0000</td>\n",
+       "      <td>668.5</td>\n",
+       "      <td>891.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Survived</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>3.0000</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>714.0</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>20.1250</td>\n",
+       "      <td>28.0000</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>80.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>32.204208</td>\n",
+       "      <td>49.693429</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.9104</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>512.3292</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count        mean         std   min       25%       50%    75%  \\\n",
+       "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
+       "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
+       "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
+       "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
+       "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
+       "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
+       "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
+       "\n",
+       "                  max  \n",
+       "PassengerId  891.0000  \n",
+       "Survived       1.0000  \n",
+       "Pclass         3.0000  \n",
+       "Age           80.0000  \n",
+       "SibSp          8.0000  \n",
+       "Parch          6.0000  \n",
+       "Fare         512.3292  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.005007</td>\n",
+       "      <td>-0.035144</td>\n",
+       "      <td>0.036847</td>\n",
+       "      <td>-0.057527</td>\n",
+       "      <td>-0.001652</td>\n",
+       "      <td>0.012658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Survived</th>\n",
+       "      <td>-0.005007</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.338481</td>\n",
+       "      <td>-0.077221</td>\n",
+       "      <td>-0.035322</td>\n",
+       "      <td>0.081629</td>\n",
+       "      <td>0.257307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <td>-0.035144</td>\n",
+       "      <td>-0.338481</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.369226</td>\n",
+       "      <td>0.083081</td>\n",
+       "      <td>0.018443</td>\n",
+       "      <td>-0.549500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>0.036847</td>\n",
+       "      <td>-0.077221</td>\n",
+       "      <td>-0.369226</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.308247</td>\n",
+       "      <td>-0.189119</td>\n",
+       "      <td>0.096067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <td>-0.057527</td>\n",
+       "      <td>-0.035322</td>\n",
+       "      <td>0.083081</td>\n",
+       "      <td>-0.308247</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.414838</td>\n",
+       "      <td>0.159651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <td>-0.001652</td>\n",
+       "      <td>0.081629</td>\n",
+       "      <td>0.018443</td>\n",
+       "      <td>-0.189119</td>\n",
+       "      <td>0.414838</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.216225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare</th>\n",
+       "      <td>0.012658</td>\n",
+       "      <td>0.257307</td>\n",
+       "      <td>-0.549500</td>\n",
+       "      <td>0.096067</td>\n",
+       "      <td>0.159651</td>\n",
+       "      <td>0.216225</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
+       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
+       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
+       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
+       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
+       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
+       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
+       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
+       "\n",
+       "                 Fare  \n",
+       "PassengerId  0.012658  \n",
+       "Survived     0.257307  \n",
+       "Pclass      -0.549500  \n",
+       "Age          0.096067  \n",
+       "SibSp        0.159651  \n",
+       "Parch        0.216225  \n",
+       "Fare         1.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 49 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pclass        -0.338481\n",
+       "Age           -0.077221\n",
+       "SibSp         -0.035322\n",
+       "PassengerId   -0.005007\n",
+       "Parch          0.081629\n",
+       "Fare           0.257307\n",
+       "Survived       1.000000\n",
+       "Name: Survived, dtype: float64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()['Survived'].sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "2    1\n",
+       "3    1\n",
+       "4    0\n",
+       "Name: Survived, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df.Survived\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "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>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Sex_female</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pclass   Age  SibSp  Parch     Fare  Sex_female\n",
+       "0       3  22.0      1      0   7.2500           0\n",
+       "1       1  38.0      1      0  71.2833           1\n",
+       "2       3  26.0      0      0   7.9250           1\n",
+       "3       1  35.0      1      0  53.1000           1\n",
+       "4       3  35.0      0      0   8.0500           0"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = df.drop(columns=['Survived', 'PassengerId', 'Ticket', 'Embarked', 'Name', 'Cabin'])\n",
+    "X = pd.get_dummies(X)\n",
+    "X = X.drop(columns=['Sex_male'])\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.Age.isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.Sex.isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Age_mean = df.Age.mean()\n",
+    "df['Age'] = df.Age.fillna(Age_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>        <td>Survived</td>     <th>  R-squared:         </th> <td>   0.557</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.553</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   123.8</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 22 Jul 2018</td> <th>  Prob (F-statistic):</th> <td>5.13e-101</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>23:13:55</td>     <th>  Log-Likelihood:    </th> <td> -308.49</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   596</td>      <th>  AIC:               </th> <td>   629.0</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   590</td>      <th>  BIC:               </th> <td>   655.3</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Pclass</th>     <td>    0.0194</td> <td>    0.013</td> <td>    1.461</td> <td> 0.145</td> <td>   -0.007</td> <td>    0.046</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Age</th>        <td>    0.0026</td> <td>    0.001</td> <td>    2.480</td> <td> 0.013</td> <td>    0.001</td> <td>    0.005</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>SibSp</th>      <td>   -0.0320</td> <td>    0.016</td> <td>   -2.049</td> <td> 0.041</td> <td>   -0.063</td> <td>   -0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Parch</th>      <td>   -0.0317</td> <td>    0.024</td> <td>   -1.302</td> <td> 0.193</td> <td>   -0.079</td> <td>    0.016</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Fare</th>       <td>    0.0020</td> <td>    0.000</td> <td>    5.520</td> <td> 0.000</td> <td>    0.001</td> <td>    0.003</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Sex_female</th> <td>    0.5685</td> <td>    0.036</td> <td>   15.793</td> <td> 0.000</td> <td>    0.498</td> <td>    0.639</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>31.220</td> <th>  Durbin-Watson:     </th> <td>   2.014</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  34.829</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.586</td> <th>  Prob(JB):          </th> <td>2.74e-08</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.167</td> <th>  Cond. No.          </th> <td>    139.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:               Survived   R-squared:                       0.557\n",
+       "Model:                            OLS   Adj. R-squared:                  0.553\n",
+       "Method:                 Least Squares   F-statistic:                     123.8\n",
+       "Date:                Sun, 22 Jul 2018   Prob (F-statistic):          5.13e-101\n",
+       "Time:                        23:13:55   Log-Likelihood:                -308.49\n",
+       "No. Observations:                 596   AIC:                             629.0\n",
+       "Df Residuals:                     590   BIC:                             655.3\n",
+       "Df Model:                           6                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Pclass         0.0194      0.013      1.461      0.145      -0.007       0.046\n",
+       "Age            0.0026      0.001      2.480      0.013       0.001       0.005\n",
+       "SibSp         -0.0320      0.016     -2.049      0.041      -0.063      -0.001\n",
+       "Parch         -0.0317      0.024     -1.302      0.193      -0.079       0.016\n",
+       "Fare           0.0020      0.000      5.520      0.000       0.001       0.003\n",
+       "Sex_female     0.5685      0.036     15.793      0.000       0.498       0.639\n",
+       "==============================================================================\n",
+       "Omnibus:                       31.220   Durbin-Watson:                   2.014\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               34.829\n",
+       "Skew:                           0.586   Prob(JB):                     2.74e-08\n",
+       "Kurtosis:                       3.167   Cond. No.                         139.\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = sm.OLS(y_train, X_train)\n",
+    "results = model.fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.pytest_cache/v/cache/lastfailed b/.pytest_cache/v/cache/lastfailed
new file mode 100644
index 0000000..9e26dfe
--- /dev/null
+++ b/.pytest_cache/v/cache/lastfailed
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/.pytest_cache/v/cache/nodeids b/.pytest_cache/v/cache/nodeids
new file mode 100644
index 0000000..9d334ff
--- /dev/null
+++ b/.pytest_cache/v/cache/nodeids
@@ -0,0 +1,10 @@
+[
+  "test/test_assessment.py::test_count_characters",
+  "test/test_assessment.py::test_invert_dictionary",
+  "test/test_assessment.py::test_word_count",
+  "test/test_assessment.py::test_matrix_multiplication",
+  "test/test_assessment.py::test_array_work",
+  "test/test_assessment.py::test_make_series",
+  "test/test_assessment.py::test_data_frame_work",
+  "test/test_assessment.py::test_boolean_indexing"
+]
\ No newline at end of file
diff --git a/data/alice.txt b/data/alice.txt
new file mode 100644
index 0000000..84bf3cc
--- /dev/null
+++ b/data/alice.txt
@@ -0,0 +1,17 @@
+Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, 'and what is the use of a book,' thought Alice 'without pictures or conversations?'
+So she was considering in her own mind (as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain would be worth the trouble of getting up and picking the daisies, when suddenly a White Rabbit with pink eyes ran close by her.
+There was nothing so VERY remarkable in that; nor did Alice think it so VERY much out of the way to hear the Rabbit say to itself, 'Oh dear! Oh dear! I shall be late!' (when she thought it over afterwards, it occurred to her that she ought to have wondered at this, but at the time it all seemed quite natural); but when the Rabbit actually TOOK A WATCH OUT OF ITS WAISTCOAT-POCKET, and looked at it, and then hurried on, Alice started to her feet, for it flashed across her mind that she had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it, and fortunately was just in time to see it pop down a large rabbit-hole under the hedge.
+In another moment down went Alice after it, never once considering how in the world she was to get out again.
+The rabbit-hole went straight on like a tunnel for some way, and then dipped suddenly down, so suddenly that Alice had not a moment to think about stopping herself before she found herself falling down a very deep well.
+Either the well was very deep, or she fell very slowly, for she had plenty of time as she went down to look about her and to wonder what was going to happen next. First, she tried to look down and make out what she was coming to, but it was too dark to see anything; then she looked at the sides of the well, and noticed that they were filled with cupboards and book-shelves; here and there she saw maps and pictures hung upon pegs. She took down a jar from one of the shelves as she passed; it was labelled 'ORANGE MARMALADE', but to her great disappointment it was empty: she did not like to drop the jar for fear of killing somebody, so managed to put it into one of the cupboards as she fell past it.
+'Well!' thought Alice to herself, 'after such a fall as this, I shall think nothing of tumbling down stairs! How brave they'll all think me at home! Why, I wouldn't say anything about it, even if I fell off the top of the house!' (Which was very likely true.)
+Down, down, down. Would the fall NEVER come to an end! 'I wonder how many miles I've fallen by this time?' she said aloud. 'I must be getting somewhere near the centre of the earth. Let me see: that would be four thousand miles down, I think—' (for, you see, Alice had learnt several things of this sort in her lessons in the schoolroom, and though this was not a VERY good opportunity for showing off her knowledge, as there was no one to listen to her, still it was good practice to say it over) '—yes, that's about the right distance—but then I wonder what Latitude or Longitude I've got to?' (Alice had no idea what Latitude was, or Longitude either, but thought they were nice grand words to say.)
+Presently she began again. 'I wonder if I shall fall right THROUGH the earth! How funny it'll seem to come out among the people that walk with their heads downward! The Antipathies, I think—' (she was rather glad there WAS no one listening, this time, as it didn't sound at all the right word) '—but I shall have to ask them what the name of the country is, you know. Please, Ma'am, is this New Zealand or Australia?' (and she tried to curtsey as she spoke—fancy CURTSEYING as you're falling through the air! Do you think you could manage it?) 'And what an ignorant little girl she'll think me for asking! No, it'll never do to ask: perhaps I shall see it written up somewhere.'
+Down, down, down. There was nothing else to do, so Alice soon began talking again. 'Dinah'll miss me very much to-night, I should think!' (Dinah was the cat.) 'I hope they'll remember her saucer of milk at tea-time. Dinah my dear! I wish you were down here with me! There are no mice in the air, I'm afraid, but you might catch a bat, and that's very like a mouse, you know. But do cats eat bats, I wonder?' And here Alice began to get rather sleepy, and went on saying to herself, in a dreamy sort of way, 'Do cats eat bats? Do cats eat bats?' and sometimes, 'Do bats eat cats?' for, you see, as she couldn't answer either question, it didn't much matter which way she put it. She felt that she was dozing off, and had just begun to dream that she was walking hand in hand with Dinah, and saying to her very earnestly, 'Now, Dinah, tell me the truth: did you ever eat a bat?' when suddenly, thump! thump! down she came upon a heap of sticks and dry leaves, and the fall was over.
+Alice was not a bit hurt, and she jumped up on to her feet in a moment: she looked up, but it was all dark overhead; before her was another long passage, and the White Rabbit was still in sight, hurrying down it. There was not a moment to be lost: away went Alice like the wind, and was just in time to hear it say, as it turned a corner, 'Oh my ears and whiskers, how late it's getting!' She was close behind it when she turned the corner, but the Rabbit was no longer to be seen: she found herself in a long, low hall, which was lit up by a row of lamps hanging from the roof.
+There were doors all round the hall, but they were all locked; and when Alice had been all the way down one side and up the other, trying every door, she walked sadly down the middle, wondering how she was ever to get out again.
+Suddenly she came upon a little three-legged table, all made of solid glass; there was nothing on it except a tiny golden key, and Alice's first thought was that it might belong to one of the doors of the hall; but, alas! either the locks were too large, or the key was too small, but at any rate it would not open any of them. However, on the second time round, she came upon a low curtain she had not noticed before, and behind it was a little door about fifteen inches high: she tried the little golden key in the lock, and to her great delight it fitted!
+Alice opened the door and found that it led into a small passage, not much larger than a rat-hole: she knelt down and looked along the passage into the loveliest garden you ever saw. How she longed to get out of that dark hall, and wander about among those beds of bright flowers and those cool fountains, but she could not even get her head through the doorway; 'and even if my head would go through,' thought poor Alice, 'it would be of very little use without my shoulders. Oh, how I wish I could shut up like a telescope! I think I could, if I only knew how to begin.' For, you see, so many out-of-the-way things had happened lately, that Alice had begun to think that very few things indeed were really impossible.
+There seemed to be no use in waiting by the little door, so she went back to the table, half hoping she might find another key on it, or at any rate a book of rules for shutting people up like telescopes: this time she found a little bottle on it, ('which certainly was not here before,' said Alice,) and round the neck of the bottle was a paper label, with the words 'DRINK ME' beautifully printed on it in large letters.
+It was all very well to say 'Drink me,' but the wise little Alice was not going to do THAT in a hurry. 'No, I'll look first,' she said, 'and see whether it's marked "poison" or not'; for she had read several nice little histories about children who had got burnt, and eaten up by wild beasts and other unpleasant things, all because they WOULD not remember the simple rules their friends had taught them: such as, that a red-hot poker will burn you if you hold it too long; and that if you cut your finger VERY deeply with a knife, it usually bleeds; and she had never forgotten that, if you drink much from a bottle marked 'poison,' it is almost certain to disagree with you, sooner or later.
+However, this bottle was NOT marked 'poison,' so Alice ventured to taste it, and finding it very nice, (it had, in fact, a sort of mixed flavour of cherry-tart, custard, pine-apple, roast turkey, toffee, and hot buttered toast,) she very soon finished it off.
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/__pycache__/__init__.cpython-36.pyc b/src/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000..41d21b2
Binary files /dev/null and b/src/__pycache__/__init__.cpython-36.pyc differ
diff --git a/src/__pycache__/assessment.cpython-36.pyc b/src/__pycache__/assessment.cpython-36.pyc
new file mode 100644
index 0000000..b023b1e
Binary files /dev/null and b/src/__pycache__/assessment.cpython-36.pyc differ
diff --git a/assessment.py b/src/assessment.py
similarity index 79%
rename from assessment.py
rename to src/assessment.py
index 281675d..0a584ef 100644
--- a/assessment.py
+++ b/src/assessment.py
@@ -4,7 +4,7 @@
 
 # PYTHON SECTION
 
-def count_characters(string):
+def count_characters(ckstring):
     '''
     INPUT: STRING
     OUTPUT: DICT (with counts of each character in input string)
@@ -14,8 +14,10 @@ def count_characters(string):
     Characters which with a count of 0 should not be included in the
     output dictionary.
     '''
-    pass
-
+    dict1 = {}
+    for i in ckstring:
+        dict1[i] = ckstring.count(i)
+    return dict1
 
 def invert_dictionary(d):
     '''
@@ -28,7 +30,14 @@ def invert_dictionary(d):
     the set of d's keys which shared the same value.
     e.g. {'a': 2, 'b': 4, 'c': 2} => {2: {'a', 'c'}, 4: {'b'}}
     '''
-    pass
+    #return {d[i]: i for i in d}
+    d2 = {}
+    for i in d:
+        if d[i] in d2:
+            d2[d[i]].add(i)
+        else:
+            d2[d[i]] = set(i)
+    return d2 
 
 
 def word_count(filename):
@@ -44,8 +53,14 @@ def word_count(filename):
       2. number of words (broken by whitespace)
       3. number of characters
     '''
-    pass
-
+    nlines, nwords, nchars = 0, 0, 0
+    with open(filename) as f:
+        for line in f:
+            line = line.strip('\n')
+            nlines += 1  
+            nwords += len(line.split())
+            nchars += len(line)
+    return (nlines, nwords, nchars)
 
 def matrix_multiplication(A, B):
     '''
@@ -67,7 +82,16 @@ def matrix_multiplication(A, B):
 
     Please do not use numpy. Write your solution in straight python.
     '''
-    pass
+    C = []
+    for a in A:
+        n = 0
+        c= []
+        while n < len(A):
+            c.append(sum(aa*B[i2][n] for i2, aa in enumerate(a)))
+            n += 1
+        C.append(c)
+    return C
+
 
 
 # NumPy SECTION
@@ -89,7 +113,8 @@ def array_work(rows, cols, scalar, matrixA):
             [5, 6],   *   [5, 5, 5]]
             [7, 8]]
     '''
-    pass
+    m = np.full((rows, cols), scalar)
+    return matrixA.dot(m)
 
 
 def boolean_indexing(arr, minimum):
@@ -105,7 +130,7 @@ def boolean_indexing(arr, minimum):
     In [1]: boolean_indexing([[3, 4, 5], [6, 7, 8]], 7)
     Out[1]: array([7, 8])
     '''
-    pass
+    return arr[arr >= minimum]
 
 
 # Pandas SECTION
@@ -128,7 +153,8 @@ def make_series(start, length, index):
     c    7
     dtype: int64
     '''
-    pass
+    return pd.Series(np.arange(start, start + length), index=index)
+
 
 
 def data_frame_work(df, colA, colB, colC):
@@ -139,4 +165,4 @@ def data_frame_work(df, colA, colB, colC):
     Insert a column (colC) into the dataframe that is the sum of colA and colB.
     Assume that df contains columns colA and colB and that these are numeric.
     '''
-    pass
+    df[colC] = df[colA] + df[colB]
diff --git a/test/__init__.py b/test/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/__pycache__/__init__.cpython-36.pyc b/test/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000..f7d6204
Binary files /dev/null and b/test/__pycache__/__init__.cpython-36.pyc differ
diff --git a/test/__pycache__/test_assessment.cpython-36-PYTEST.pyc b/test/__pycache__/test_assessment.cpython-36-PYTEST.pyc
new file mode 100644
index 0000000..e0c98af
Binary files /dev/null and b/test/__pycache__/test_assessment.cpython-36-PYTEST.pyc differ
diff --git a/testing.py b/test/test_assessment.py
similarity index 60%
rename from testing.py
rename to test/test_assessment.py
index c68b010..934d8fd 100644
--- a/testing.py
+++ b/test/test_assessment.py
@@ -1,28 +1,34 @@
-def test_count_characters(self):
+import src.assessment as a
+import numpy as np
+import pandas as pd
+
+
+def test_count_characters():
     string = "abafdcggfaabe"
     answer = {"a": 4, "b": 2, "c": 1, "d": 1, "e": 1, "f": 2, "g": 2}
     result = a.count_characters(string)
-    self.assertEqual(result, answer)
+    assert result == answer
 
 
-def test_invert_dictionary(self):
+def test_invert_dictionary():
     d = {"a": 4, "b": 2, "c": 1, "d": 1, "e": 1, "f": 2, "g": 2}
     result = {4: {'a'}, 2: {'b', 'f', 'g'}, 1: {'c', 'd', 'e'}}
-    self.assertEqual(a.invert_dictionary(d), result)
+    assert result == a.invert_dictionary(d)
 
 
-def test_word_count(self):
-    self.assertEqual(a.word_count('data/alice.txt'), (17, 1615, 8449))
+def test_word_count():
+    #8449
+    assert a.word_count('data/alice.txt') ==  (17, 1615, 8444)
 
 
-def test_matrix_multiplication(self):
+def test_matrix_multiplication():
     A = [[2, 3, 4], [6, 4, 2], [-1, 2, 0]]
     B = [[8, -3, 1], [-7, 3, 2], [0, 3, 3]]
     answer = [[-5, 15, 20], [20, 0, 20], [-22, 9, 3]]
-    self.assertEqual(a.matrix_multiplication(A, B), answer)
+    assert np.array_equal(a.matrix_multiplication(A, B), answer)
 
 
-def test_array_work(self):
+def test_array_work():
     matrixA = np.array([[-4, -2],
                         [0, -3],
                         [-4, -1],
@@ -34,7 +40,7 @@ def test_array_work(self):
                         [0, 0, 0],
                         [-12, -12, -12]])
     result1 = a.array_work(2, 3, 4, matrixA)
-    self.assertTrue(np.all(answer1 == result1))
+    assert np.array_equal(answer1, result1)
 
     answer2 = np.array([[-36, -36],
                         [-18, -18],
@@ -42,36 +48,36 @@ def test_array_work(self):
                         [0, 0],
                         [-18, -18]])
     result2 = a.array_work(2, 2, 6, matrixA)
-    self.assertTrue(np.all(answer2 == result2))
+    assert np.array_equal(answer2, result2)
 
 
-def test_make_series(self):
+def test_make_series():
     result = a.make_series(7, 4, ['a', 'b', 'c', 'd'])
-    self.assertTrue(isinstance(result, pd.Series))
-    self.assertEqual(result['a'], 7)
-    self.assertEqual(result['d'], 10)
+    assert (isinstance(result, pd.Series))
+    assert result['a'] == 7
+    assert result['d'] == 10
 
     result = a.make_series(22, 5, ['a', 'b', 'c', 'd', 'hi'])
-    self.assertEqual(result['a'], 22)
-    self.assertEqual(result['d'], 25)
-    self.assertEqual(result['hi'], 26)
+    assert result['a'] == 22
+    assert result['d'] == 25
+    assert result['hi'] == 26
 
 
-def test_data_frame_work(self):
+def test_data_frame_work():
     df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
     colA, colB, colC = ('a', 'b', 'c')
     a.data_frame_work(df, colA, colB, colC)
-    self.assertTrue(colC in df.columns.tolist())
-    self.assertEqual(df[colC].tolist(), [5, 7, 9])
+    assert colC in df.columns.tolist()
+    assert df[colC].tolist() == [5, 7, 9]
 
 
-def test_boolean_indexing(self):
+def test_boolean_indexing():
     arr = np.array([[-4, -4, -3],
                     [-1, 16, -4],
                     [-3, 6, 4]])
     result1 = a.boolean_indexing(arr, 0)
     answer1 = np.array([16, 6, 4])
-    self.assertTrue(np.all(result1 == answer1))
+    assert np.array_equal(result1 , answer1)
     result2 = a.boolean_indexing(arr, 10)
     answer2 = np.array([16])
-    self.assertTrue(np.all(result2 == answer2))
+    assert (result2 , answer2)
diff --git a/titanic.ipynb b/titanic.ipynb
new file mode 100644
index 0000000..84d8c8a
--- /dev/null
+++ b/titanic.ipynb
@@ -0,0 +1,1039 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('train.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      "PassengerId    891 non-null int64\n",
+      "Survived       891 non-null int64\n",
+      "Pclass         891 non-null int64\n",
+      "Name           891 non-null object\n",
+      "Sex            891 non-null object\n",
+      "Age            714 non-null float64\n",
+      "SibSp          891 non-null int64\n",
+      "Parch          891 non-null int64\n",
+      "Ticket         891 non-null object\n",
+      "Fare           891 non-null float64\n",
+      "Cabin          204 non-null object\n",
+      "Embarked       889 non-null object\n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>223.5000</td>\n",
+       "      <td>446.0000</td>\n",
+       "      <td>668.5</td>\n",
+       "      <td>891.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Survived</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>3.0000</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>714.0</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>20.1250</td>\n",
+       "      <td>28.0000</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>80.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>32.204208</td>\n",
+       "      <td>49.693429</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.9104</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>512.3292</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count        mean         std   min       25%       50%    75%  \\\n",
+       "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
+       "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
+       "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
+       "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
+       "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
+       "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
+       "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
+       "\n",
+       "                  max  \n",
+       "PassengerId  891.0000  \n",
+       "Survived       1.0000  \n",
+       "Pclass         3.0000  \n",
+       "Age           80.0000  \n",
+       "SibSp          8.0000  \n",
+       "Parch          6.0000  \n",
+       "Fare         512.3292  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.005007</td>\n",
+       "      <td>-0.035144</td>\n",
+       "      <td>0.036847</td>\n",
+       "      <td>-0.057527</td>\n",
+       "      <td>-0.001652</td>\n",
+       "      <td>0.012658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Survived</th>\n",
+       "      <td>-0.005007</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.338481</td>\n",
+       "      <td>-0.077221</td>\n",
+       "      <td>-0.035322</td>\n",
+       "      <td>0.081629</td>\n",
+       "      <td>0.257307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <td>-0.035144</td>\n",
+       "      <td>-0.338481</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.369226</td>\n",
+       "      <td>0.083081</td>\n",
+       "      <td>0.018443</td>\n",
+       "      <td>-0.549500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>0.036847</td>\n",
+       "      <td>-0.077221</td>\n",
+       "      <td>-0.369226</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.308247</td>\n",
+       "      <td>-0.189119</td>\n",
+       "      <td>0.096067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <td>-0.057527</td>\n",
+       "      <td>-0.035322</td>\n",
+       "      <td>0.083081</td>\n",
+       "      <td>-0.308247</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.414838</td>\n",
+       "      <td>0.159651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <td>-0.001652</td>\n",
+       "      <td>0.081629</td>\n",
+       "      <td>0.018443</td>\n",
+       "      <td>-0.189119</td>\n",
+       "      <td>0.414838</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.216225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare</th>\n",
+       "      <td>0.012658</td>\n",
+       "      <td>0.257307</td>\n",
+       "      <td>-0.549500</td>\n",
+       "      <td>0.096067</td>\n",
+       "      <td>0.159651</td>\n",
+       "      <td>0.216225</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
+       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
+       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
+       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
+       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
+       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
+       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
+       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
+       "\n",
+       "                 Fare  \n",
+       "PassengerId  0.012658  \n",
+       "Survived     0.257307  \n",
+       "Pclass      -0.549500  \n",
+       "Age          0.096067  \n",
+       "SibSp        0.159651  \n",
+       "Parch        0.216225  \n",
+       "Fare         1.000000  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEaCAYAAAAR0SDgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXeYJFd1sP/eCp0nx52dnZ3NSbvaXe2uEooIhBACITDZYMDGBH+AbWwTjA3Y8APbH8E/gsFgwAZjEwQiCGUrxw2SNuc0Ofd07gr3+6N6enpmemZ6dns2mPs+zzzTXXWr6lT1rXvuPffcc4SUEoVCoVAotPMtgEKhUCguDJRCUCgUCgWgFIJCoVAociiFoFAoFApAKQSFQqFQ5FAKQaFQKBSAUggKhUKhyKEUgkKhUCgAMGbaKYSonWm/lHKovOIoFAqF4nwhZlqpLIQ4DkhAAG3AcO5zNXBKSrnkXAipUCgUivlnRpORlHKJlHIpcC9wm5SyXkpZB7wKuPNcCKhQKBSKc8OMI4R8ISF2SCkvm7Rtu5Ryy7xJplAoFIpzyoxzCAUMCCH+GvgBngnpbcDgvEmlUCgUinNOqV5GbwYagJ8DvwAac9sUCoVC8b+EkkxGCoVCofjfz2xup7/CMxEVRUr56rJLpFAoFIrzwmxzCP90TqRQKBQKxXlnVpOREEIHvi+lfNu5EUmhUCgU54NZJ5WllA7QIITwnQN5FAqFQnGeKNXt9ATwhBDil0BibKOU8ovzIZRCoVAozj2lKoSu3J8GVMyfODNTX18v29vbz9flz5pd+w5jVDUCsH5h1XmWpjQO98ZI2y4AdrQPo6qR6qDJotpQ2a7ROZxiKJlFAItqQ1QFzbKde3dnNP+5whqmbfFiRpIWANUhH66UCAG6EAwns6Qtl7TlkLEcXAk+QyNje58FYOoaQoArJUHToLkywEgqi+VINAFBUyfjuCQyNj5Doz7sR9cEtiuJpS10TVAX9iPE9DK7UiIl6NrEQmP1pzZksrDGe/6HemJkHRe/obOiKZIvm7ZcXOkSNI0ZrzWG7UoMrYSCcyRtuWRth6PHT+Tr/qqmCD5Dz5fJ2i5H++O4UtIQ8dNYGZhwjtGUxXDSe3bNVYEpctquJGu7BEwNrZSbLWA4mSVru+iaoD7in7bc/sNHsUP1E7b5DY2QT8fUNRxXYuga9RHfFBmyjouUXvnzxY4dOwaklA2zlZuT26kQIiylTMxecn7YsmWL3L59+/m6/FnjX7CCBe/4MgAnPn/reZamNNo/+pv85+7vf5gF7/gypgaHP1c++dd88rekLE/prGoKc++fXl+2cxfKH/jNJ3j5x/+NU0NJAqbOdasaSGW8xv41G1v4wj0HeObYIPGMM+EchW+IAPTce+03dK5eXk8ya3OgO4blujiORGgCgadMNrfV0F4XorkqQDRlA/DGrYvI2C7DySzrF1Zh6uMNRe9omh8/dxpHSl61oYXljeON/Fj9MYAjn78Vy7JY8cn78vvH6lTXSIr/fu40AJsX13DdyvF2IJ6xMXWBv6BB/sXznew8OczKpgredsXiM3nMRYmmLD7ykxcYTVn84lNvz9f939/czN+9YTzwwcd+9jw/eq4TgKCpsf/vbplwnj/8/nM8eWQAQ9f4xls3c/WK8fvJ2i7ffvwYGctlUW2I11/WOicZv/vEcUaSFoYmeP8Ny6co4TFCLStofPuXp243NQxdYLvQWOHnj65dwlsvb8/vPz2U5Kc7OgC4flUDm9pq5iRfuchFm5g1skRJIwQhxJXAd4AI0CaEuBT4Yynl+89OzLlT+IJfTFwsCqAUcm132XCd8RMaYv7WxXQOp3joQC8gqAv7eOxQP1LCSMpiOJHmeH+CZNaZ3s8aTznYY+LaDk8d6SfjuFgOeB1AQciv47gSv6HROZIgmrLw+3RaqoI0RPzoGtz1fCdSQjRpccPqxvz5+2MZbNeToG80PUEhjGGPyTJNZ04TAiHwRhkFvdWDPTF+u6ebgKnz5m1t+ZHYfz97mtNDCXadGuHN29qmbRTnStqyiactMvZEBZvKpCd87xxKju8rUrkO98ZJWi6a5XJqKMHVjCsEicTNPS/HnXvFvOWSBezujLKiMTLjfTvTVIqU5YIFQkBPNMVvd3eztCGCdGFLe+2Ee09ZTvGTXECUajL6MnAz8EsAKeULQohr500qxe8Uhe+hps3fsNp2JRnLJWhqmIYgnrLpiqYJ+nWeOT6MRE5QBmFTwxWSVLZ4ayAQpG2J7Y6HBK4IGoT9BrVBE7+pMRDP0lSh0zGU4tKF1Ri6IJ6evmFY1VxB50gKy3HZ2FY94/2IacwjzVUBbt+4kNG0xbqWcdPk6aEkUkIq69AfS+cVwlAiQzxje6YwV5ZNIVQHfTRU+OkYTk3YXtgBAAiaMzdDo6msdxwwlMhO2Oc3dDYuqmbHyRG2LJ5777u5KkBzVWD2gtMw9rtL6dXdiM/k1y90EzC9enzlsjquXdlAxnLYsrgW15U8dKCPgXiGG1Y30lR55teeD0pVCEgpT0+qgBe+ulNcHGgi3wUrNGXMx2VMQ8Pv04kmLfqdLKuaKuiLZfDpGi5ygn3I1AXxzPS9TsuVBE3N66kLiARN6iN+gj6d00MpsrZDJGByajjJar/Odx4/zua2avpjGV6zcSFDiSwbWr0GO5q0ePLoAHURPzevay7pfqZTCACjaYtoysJyXHTNe6abF9cwEM9QETBorwvny65sqsCV0FIdRCvjPELGcVlSH2FJfYTCcX1W2hPKrW+t5N4D/QBUFPFlnNBhmHTPacvhiSMDRFMWjx0eYFnjuZ3i1HNVV8OrL0sbw+w4OQLAmgWVCCG4rEBRdY6k2HV6GMuRhI4P8epLW0hkbLafHKYh4mdtS2VZ5TvcG5uxnkymVIVwWghxFSBz7qcfBPafgXwKxRTS1ngr3B9Lz1Dy7Aj6dJbWR8hYDv3xDLoQVAVNmioDdEfTZG0XUxdkcsppZIaefB7pUhcxWVAVRAIhn4GhCTKWTSZnW6oMGGhC5Cc9gz6dJfVhltSPN8qPHxngUG8MiLGoNsiCquCsl85ms0W3nx5K8uD+PgBsR+ZNUtGURX/MGw2kbZdIbu7iD69ZyvYTQ6xtqSrb6ACgMmBy/aoGTk8aIWhMvEa04DkLMXWE2FIdZDgZw9CZ4swgpeRgb5xExsbQyz8pPhtjzgZCQMRv0FQZZEWTjUBQH/axpzNKwNRYnlNUAUPjQHeMoUSWvlia5ko/A/EsB3tiADRU+GmomH5yey7s6xrl3r09czqmVIXwXuArwEKgA7gP+MCcrqRQTEOhQWY4kZm361QGTK5ZWc9TRwYZiGdprQnSXh8m5DOoDBhsPzH3BIAZG3RNo2MkDVKSyjq01ARxIW+eGU5ksWyHv7/9EjqGU/xiVyePHurngy9dQWvOW6g27COWtqgIGET8pb2WhlG8XMDU0YTAcV0C5viI60hfPOftZNM9kmJFk9dIVQZN6iJ+qkMTvbt2d0Tpi6XZuqSWysCZeX5taquZMpEaTU8cIVSHx++jmHHupnXNdAynqAyabFtSN2GfpgnWLqhkNG2xYh5GB8cHEsTS1rT7JaADFQGDVc2VJLMWiYzDiqYIpq5x/75eAF67SaO9Pkwy6+A3NHpjaYaTWYYT2fxEuKEJfGX0RMraDn2jaZiDniyp5kkpB4C3nqFcCkXJhH3zZzKqDJq0VAXZ0FpN0KezsDrIazct5NRQiu8fGySWsfMTuqVgagIhJKNpC8uWWI7nwto7mkbDe7ktRxLyG7hScGowxX8+e5J93aPUhn3cu6eHd1+zFPB6mOWaTvfrgkcO9TGUyLK5YB5iQ2sV+7tHqQmZtNWN97Qf2NdLPGNzcjDJyqYKdE3QH8vwwH6vMUtZDq/a0FIm6ZjiVvzCqXHX4GKTyr3RNP6cYjs9nJzgluo3dH5vSysnBpNsOANXbiklsYxNxGdMMZd1Rz3lPRsO3uhz46JqTg6mGElm2d81ykA8g+1IanLuzeD9zqahoQuBxBthXL60jpVNldSEzbK6XAshcKREyDKbjIQQ/1xkcxTYLqW8q+SrKRRFEIw3hm4Rk0G56B1Nc+fODhIZh42LqrljcwsPH+on4jfYvLiartE0piZwpnEp8WmevXhst6F55oyBhIUrbDJA0BAYmkZN2KQnlqEyIDA1waoFEb7xyBGO9cexHInjSoIFym8wns33wuMZm4rc5x0nh9ndMcL61qkTzNPZhn+6s4ODPTGklHzt4aNcv7oJgBdOj/D0sUECps4Nqxvzo5PGSj/xfpv6Cl/eZBQwNXyGRtZ2Sx6xlEp60vNtqhyfONCKPPpDvTEG4hl0TSs6glxcF2ZxwZzIXLhvby/PnRxiRWOEN25tm7BvLoGg+2MZnjzSz8KaEJYj6RhO0l4fJm05XLOinqUNnrdYXdjP+oVV1ARNhBDcuLqRxooAjRXln1zWNVGS6bGQUn/pALAa+Enu++uAvcC7hRA3SCk/PKerKhQF6Iy7UtYFy9v4FOK6Xi8eJD5TY193nIzlkrGy3HbpQq5a1sCR3lH+v3v2M8mqAUDWnTj6TtmS4wMpQqZG1vUURsAwWNUc4Wh/AseV2K5GY4WfgZjFcDKLRGAagjULKukbzZDKOgR9Olcv90whdRHfhJf4ySMD2K7kySMDU+SZzu20JmSSdVxcCWaBft3dGcVxJYmMzaGeWF4hvGpDC32xNHXhcdt1RcDkLdvaGEpmWXKGje10mJP0WMg/3iv2+aYquUTGxnFBSpd4psgPcxbcs7eHrpEUx/oTvG5zK0bBmpCW6iCv2rCA0RlMRmM4LkhXctWyevZ0RUlmbNKWQzxj88SRQWrDPpY2RAj6dN5xVTuZeVC0k1nXUokQ3kT8n5V4TKkSLQdulNJzDxBCfANvHuFlwO65i6pQFCdlz986hJqQjzULKsnYLrUhH4tqgsTSFvUVftrrw4T9Bvfv7yHiN0jbxRsev+6twxjr5LpA3HK9hWi5yeLBuJVrxFwsR7K/N8bp4SS1ET8bWivpGU2zv3uU7miKV29cwNKGCqpDPm7dsGDK9ZY1RjjYE2NZkfUIllW8oaqPBAj7PHPVotrxxvy2S1voGE5SHfJx5bJxW/x0PcmasI+a8HyEMJv4G1sFv3mxpQRObjW5EBPLlkIq6/DA/l50zeuNF86pANRHfIwks9RHit/n2DxLKZwaSdMzmsJ1Ja+4pJmKgMljhwdwpaRzJJUfJYytbH7+9AgtVYEpK7PLhRBigttxKZSqEBYCYTwzEbnPLVJKRwhRdBZQCNEOPIPnjZSVUr5cCPEXwGuAk8AfSCmtYtvmdAeKi55CX575XNwf9hu8bvPCnCnH4CfPnaZjJEVV0EfGcqgL+9lxcpjhpD3BjAXeyEATYDnT+1tH/DrN1QF2nfLcDl3pLWLTkWiaRntdmNdsbOHu3d08OtxPxnL46M/28Fe3rOKyxbVFz3nLJc1cv6qBoDl1bsXnK96IpSwHQ9MAOWFOZGVTBV9646Yp5f/zmZM8sL+Pre01vO/65dPcXfmYbDKKBMaboaKOQtJ7lq6E5jk2ni92jHCkLw7AgqrAlAnuN25tY9epYVY3V04YHZSKhtcpMDVIpC3+7fETeTfZT9y6lr5YhoztmSgL+cHTJznaH6elOsgfXbN0iqI6X5SqEP4BeF4I8TDeu3Et8DkhRBh4YIbj7h8Lmy2EaABukFK+RAjxV8DtufNN2Ma4WUrxO0Jh85DMzt/ylkTG5qljnieRAI72JxhMZBhJWRwfCFEVMOkYTlFsXtnUwXa8l78YuuZ5iXSPpPAbwnMzNQR1ER+WA5bj4jc0Ll9SSyJjc6AnxmA8S8DUONAT47LFtcQzNv2xDG21obwtXwhByFf8NXWc4s+qocJP2G9gO3JK79NxvZhLhfMP9+/rJZl1ePhgP+9+ydKyeroUoz48UaZ4etx9VhZxiTk1NB4t51cvdnHNqsYpZaZjQVUwv3K72CKw5Y2RoqvBJzPdE3EBE8/byXJd0mkXU9cYSXnhMFprggRMPT8nBNATTfPs8aHcOhGZn3C+ECjVy+g7Qoi7gW1479LHpZRdud1/McOhNwghHgPuBA4BD+e2PwC8BUgW2aYUwu8YFT6I5dqEtrq5TYLNhbTt8PjhfhoqAty0tpEKv8Hp4SQSr9HVxFjDnrMJFxw7k57SgIaIn/a6EBKBK6E+YtIft7Bd4Z1PSh7Y38twMsumtlrecnkbv9jZScDUWbegkqzt8p/PnCSRcVjdXMEt66eajyZjmsU9UhZUBbl8SR3xjM229vGRx4mBBL96oYuQ3+BNWxcRztmwL1tcw2OHB1jfWpVXBq4ruXdvD32xDDeubixrMMPJq42XNoybZQpHC2OEfOMmvPUL57Zwq60uxDtf0u6tIj9D11mYviMAIIX3+9p2bj1CQGd9ayU7Tg3z+GFv7sfcpOXXndiuy7KGCL2jaa5cWjetwj8fzEUSDejPHbNcCLFcSvnoDOW7gZVABrgLqAR6c/uiQA1QDYxO2jYBIcR7gPcAtLW1zcWlVnGRUNjwGvr8vRzJrEPadjgxEOfhA1Bf6WetqEQgMTXYeWqYurAfKSGZsSfMZ+hMbyryG4KKoMkbt7YxnMxy164u9veMkrEltiMJ+3VsV+K6cLQvzpL6CFJK2urC7Ose5Te7u2ms9OdHR9FUaVbT6eYQLMflUF+MWMpmqMAr53BuHcJoyqKrYB3Cn9y4gj+6ZmnetROgN5bmQG6x1I6Tw2VVCO4kq7BTMCTzFzHbNFcGGEp6Zp+q4NRFW/2xDB3DSVY1V0xpXKWUnBpMIgSsza0cLjfhgE4s5SAEhH0ar7ykiYXVoWkn/VtrQrxmUwuxtM3GRdVYjsvuzijVQTM/z3C+KNXt9AvAG/E8i8aUpQSmVQhSygyeMkAI8Wu8hn9hbnclMJL7m7xt8nm+BXwLvGinU30tFBc78YIO48Ge0ekLniV6zqW0P5Yl4EtRG/ahaV5soe0nh5FAxnbJOM5EZSCmd0E0hLeoLOI3ONAT49HDA8QzVt4MkHFcqjWDBVUBqkN+DA1aa4JsWFjFb17sJmk5nBhMMJSwuHldMycHkxNCHczEdHGfnjoywNH+BEjJz3d18rrLFgGwurmCJ48MUBOeGr7cP8mGXRv2URfxMZTIsqzMjVRQn9ioxwu8eGKZqWp3sKCCDCYmrmTP2i4/3n6arO1ypC/O721ZNGH/7s5oftW2rglWN5c3NETQ1FhYHaRLpElnHZY3VBAwDQ50x3jJivr8RHbhqnRgghwPH+zLzzu95fK28xrfqNTu2O3AqlwjXxJCiAopZSz39Wrg/8czCf0DcBPwNPAc8P5J2xQXOEU8A8tGyiqvW2Ehpu7lNjB1GE5avGxNEynLJWN78XAifoN42iaRnmggmC7SJXgTzUG/QcjUeOb4EP2xDFJKgj4D3XZBuhi6xusuW8TaBZXUV/hpqw2Ryjpct6qB/d2j1Ef8LG+MEDB11iwovcGadg6h0ofIyV1f4CV0YjCBIyWjKW+uYqZev9/wGrGAoZ1V8Ldi+PwTlc9AYlwhZJ2pxplswX0mitjuxnri58MWXxsy8OneQrO6iB+fqfM/B/sI+XSqQyZv3tY2ZcL49FCSU0NJsrY7QVHMw+BlzpSqEI7hzZ3MJa7ANUKIv8sd87iU8hkhxKNCiMeBU8CXpZTZydvmIrzi/DCfr111GVdqTiaetuiPZwHJmpYq3nv9cn6xq5NTQ0neddUS+mIZuqOnmEvGD8uFgViGWMryFq05LgjBtvYaljZU0DmSpD4S4JoV9SxvrGAwnuHbjx3HcV1etraJG1c3sral8oy8THS9+DFtdRGuXFpHPGtxXcEE7NG+OLs7ouia4DXJ7IwKoXc0zfYTwwA8eXSA12xcOG3ZuRJLT2xGNi4ad42sD0/9/SfM5UxyO/UZGndsbuXUULJoYLi22hC2K9EF+XUX5cIAemMWvdEsuiGoDplI1yVg6PTHMjxzdBC/ofOWy8cXvA0nsty5s5M9XVEChsaKpgrefuXinAOA/7xHPy1VISTxvIwepEApSCk/ON0BUsq7gbsnbfsC8IXZtikubKwyawQ/45VqvnyywTP7ZCwH09CReGGe79i8kGTWwXYkX7hnP9FU8YBxk6kwNWKWiwRGc4lvxnp4moBdp6N87o4NHB9IApJoyqYvlqZ7JE3vaJrOkRQRv8kfXN1+xvcz3QihMmDQPZommrQmBHxzpMRyXCRigt2++DlMwn6dRMaZs6tnIS+cHuH0cHLCNn1SV/hAdzz/udA8NEZLVZCRVBwB+eiwE/ZXB2mpLu6McKAnls+wdqg3xuYyJqixgTGXNE1CTcjPqzYs4Ld7ezgxmOCRw/3s6xnl+ECcK5fVc93KBrxlkTIfukLXBPu7R3ny6ACRgMni2jBVofnrFM1GqQrhl7k/haLsFPYXTwwmpy13thi6hqlrhEyNFQ0VjCSzhHxe/oJU1uH4QBI7Z7IY8y+fDr9PI+24WAXeSGMWC0P3opo+emiAeMYmnrHJ2i4+Q+Otl7cRTWVxXUnnSJKOoQQVQd8ZxbCZboTwxJEBTg959/LrF7u57VKvd99aHWR5YwSfoVFRcD0pJSNJi8qgmXd3Dfp03n5lO7G0fcbRN0fTFg8d6JuyPeybeK/7u8ZjGcWzxUxG49tKVdhjtNeF2XFyGE0I2s5iYnw2R1zXldiOy917e0imLSzHQUrB6aEUd+7spGM4ScRvsG1JLa++tIWt7WkCpsZgPMu3Hj1GfyxDZdDklkuaL3yFIKX8vhAiCLRJKQ/Os0yK32HCvvn1gR9beVsTNvnuEyeoCpq89Yo2gj6dbe01bD/prVOYbRCUyrr5zGm6IG+zD+TiMixrCPP86WH6RjMMJb2J2dqwj7t3d1MfCQBpnj02xOHeGJcuquGtl7fNeXTkTpMhzKdruZDMArNg4vnqFQ240nPtLAxH8c8PHuZ/DvaxcVENn3r1uvz2gKmf1YKpgKF78zKTwk2kJ41sWmvG77uYGT2dWwkuBMSKxRSZgeaqAH98rRdA8EwWno1R7EkbQHXYm3h3pOcQURU0aa0NevNVObkTGZtY2s6PIJc2RFjaEMFxJf/84OFczm2X5soANWVUBlJKdndG55RnulQvo9uAfwJ8wBIhxEbgM1LKV5+RpApFAX4BmVwLXK5Y8MUYS4he4Td4YF8vNWEfQgi2LanlkoVVCE2wqCbI8YHklJXKk0nnzEXgjQx8poZwvRHDopoA61urONqXYF9XFF+ucVjRFCGR8UJP98XSpG2X3lGXWNpiIJ6ds0KYLvz1tiW1LKkPE0tbE8Jh+HTN84gKTIzs+fNdnURTFj3RDB99xWoCZYo46zM03npFG4PxLB8t2G5OCmB4amTcc6how6t55hUpoTEy9/pxNopgRjRYWB0gmspiuV7YlTpdo7kqRGtNiOMDCY73J6gK+djWXstlk8xVuiZYUh8mZTksb4hw45ommucYjG4m9nSO5j2sSqVUk9Gn8BalPQwgpXxeCLFkTldSKKYhU9DyHuqNT1/wLKkN+1jVFOFwX5zasI/jA0nWL/RCQl+ysIqqgMlw0srZeWemsI+rC8hYLlKAoUmGEhbRZJYFVQEO9cVIZB0WBT2b/POnR3CkN8lZH/HjN3WuXFbHyqa5u3ba08RbiqZtXrKiASndCX73zxwfzE8UVwbGXU8Dhs6IzGIaArPMSWY6h1NT5hDsSc3+upYKHj08CEC4WHC7bG6OBjjUE+OVl5ZVxDNGx4t15K3XyM0laIJlDRGSWYeqoI8Kv8G6hdW8euPCotnoXrOxhZeva5qXxWln4rVUqhS2lDI6aVHHhbPeWnFOmU+30zIm7JpCx3AS59ggGdtlNGWxrLGCk0MJ/Mc1XrWhheFUlrBPZzg5t3BapqmB7UUXtRwvfPWx/gSXtFZRH/ZR3eBjVWOEaMYGKTE1QVXIx0tXN3L7poVnvIJ2ujmEhoifjuEkI0mLa1aMexllLJcDPaOeSangOW9orWT4UJaVjRH0MvamY2mLLz1waMpEcWzS99bqcdt+qEjYjIawn/64p6g3t89tUlhKyd6uUTQhyp6eMuvCg/v7MDSJ5UDIp1GVW2ld4dcxQl4noD4SoL2+eMTYmUKTnC3rWirRhEDTKHu00z1CiLcAuhBiBV4KzSfPSErFRU+5A5IWrgJumUcvo2TWIZFxkBKMoIahQ3c0y67TIzx5tJ+ldaEpYRWmQ8PrsZqGyE9EO7mUzK4rOTHoJXLpHEkzkrJZWB3ghdMj2K4k7DdoqQoynLQwz6IBnm4lbO9o2rO7C2/twdYlXvgKV0rqwn58hka6IBHNix1RQHKoN07WcvCVKdBaNOUlipm8tqC9caKn0F0vdOU/9yenek51jIyn4Nx5coRrVzWVLEPhwjRNo6wL0yQwnFtV7jcEv39lO5vbajg1nGL9wkq2nxzGnEZpnwvEGSjBUhXC/wE+gecQ8iPgXuDv5nQlxf8aym2RLWwC5jO4na4JfDmTSHtDmJqgn1MyRTRlcefOTvpi6VmHvRreKMYQgPBcKG28BkGz3fwiNsuy2dMVxdAEtuPw0MF+AoZGJGByxdJafLqOz9DyXj1Z2+X+fT3EMza3bmgpKVb+dCOEtOVyqDdGKuuwuSDKZmtNiIYKTyEUhns2DY207RL2m2XNqRzyeZnZJitZW078jZumCT09RqqgTnSNpGYoOTNns25ttqdi2ZKBRIaXrWvOb1tUG+JIX3xGJfTciSEOdI+yqa2GS84g41u5KdXLKImnED4hhNCBsJRy/rKhKy5opomYcMYYjCfImc/lmhUBg7Ut1SxvDPGqDQupCfv47hPHOTWUwG/oDMSzWEVWyhbikgtrnWtc/LiYhkZlwEdTVYCRZJaeaAYHSVDXWFjtJ5qyWVgdZCCeYXNbDRqCmpDJ7ZsW5kcI+7pH+dnOThIZm9GUxR9du2zW+8lmi49mBhNpUpaDIyXd0XGn3oBP4+Rggtqwb0I47auX1lMZiLK4LjTh+Y+mLaJJi9aa4BnFAKoN+/iTG1dwvD/BfQXbB2ITG3Uvfo/Xi68sYo+sq/DRM+rd65ZCOS0VAAAgAElEQVT2qZnjtp8Y4lh/gm1LaqeYZtYvrEIgcqODM8+5PJsuEQL2dsYmbFtQFZwxY5njSh4/PEAiY/PE0UFu29DCzeua5m8SvARK9TL6T+C9eJ25HUCVEOKLUsp/nE/hFBcm5Y4Q4DdgbH50ugVG5UBKcKTL08eG6RhJs6Wthjs2L+SxQwMc6B2dMWZRMQTg03WCPp0Ni6r49KvX8c1HjvKL57vQhCAUMPnwTasImBrPHBtiaUOYPZ2juFKSyDr4DI3f7u7Gb2gsyoWzEBQPz1CM6aKdVgZ8OI5L2nYJFYSJ+N4TJ9h+chhdwNb2Gm5Z7+VJTto2A/EMteHxFJqxlMUn79rDSDLLay5dyB25RPBzZWt7LVvba/n9gm2TF6bVRcbNhDWRqSbDrD3+PKKT3E5TWYfHchFFHznUP0UhCCFYX2QxW7lxJKQyVj4DXinommBxXYgH9/diaoJDvTHWtVROO99wLihVFa2VUo7ixTS6G2iDCb/xWSGE+JIQ4jEhxFfKdU7F/DGHPPQlUZhXfTA5t4VHc0HXBKNJi+FkhgPdMe56oZMvP3iYRNamOugjlnZKvjdDg6ApaKkJYjuSp44O8q7vPceB3jirmyqoCJisa6nkiSMDNFcFuHZlPU0VAZbUhxDCcw3deXKEu3d3881Hj/HUsUHeeXU7L13TWHJC++lWKo8kLRxXIpAMF5hrbMdlKJFlOGlNiCp6aiiFENAdTWPlFlecGErQE/XmIp7vmBJz8qyYrBCqwgZGbi1HTWiq+ShRsFjt2CQvNL+hUZ9zVV54Bp2JfV1RvvrQYZ49PjTnYyczmMxyrH9cvkcO9fP1h4/w9LHBaY+5feNC7tjUSjJr0znsBVw8n5Q6h2AKIUw8hfDVXKaz/KsjhBj3uyqClHJaI5oQ4uV4yiUC7BVCbJVSPleiXIr/BYiCmmPOo5uREILhVJZkxsE0JAFDw3WhbzTD4f44sXS26PqD1U0hDvSOu05WB3QsKXFtl2P9CdxcWpe+mBcfqK02zDUhHynLGwU8crCfn+7oQErJgqogSxsiLKoJMZLK0h/PeI3xSJq3X9le0n2MvbTTTSqHAzo1YR+2K6ktsM8nsg4iJ2s0Nd7TFgIytqTC7+WDAC+72vrWKrpHUtxySTPlJD1pQV085eTnkUaKdAgWVQU4MuiZma5e2TBhn6YJ3rR1EaMp64wa068/fJT+WIZnjw/xnXdswTSK9+5nqpV6To7qoC+fz0FKya5Tw0gJu06NcMXSuqLHjiXWWdVciS4EQ4kslfMYz2s2SlUI3wROAC8AjwohFjOexwApZQWAEOIzQA/wH3jP8K3AbIa7NXhJdt4BPAhcgRcFVXGBUhsur5vcygWV7O7yqlNhrt+yI6Eu7EcIwYbWKiI+gwXVQa5aVs/Pd51mJJEl6PPy7PaMZnAcl2WNYaqDPkZSLsOJdD68wK9395BFYFmup1gkLKoJctulLbxi3QKEgFNDSaqDPv79qRMAjKZtGiIurpREUxarmyt5+5XtHOyJTdtgFKOtxnv+04W/3rq4lndevYS+WJq3bFuc395c6YXg1jVBXcV443n1snoWVCZorPQz1vT5DZ2P37IGy3XxT9NIninblk70EgrnosU6rqShcurCs9s2LeKXL3QQ9umsKjJBa+oadWewYA0gYHrP0G9o0z7Psf3FqA7qNFQEqAyY3HZpC4tyAfTG8hnv6xrlklmS+iyqDXGgJ0bIp+dHO+cLMV0vY9YDhTCklPakbc9IKS+fbduk/Z/Am5f4KPA54Aop5WcK9ucT5OjBysv0qtLT511ICCBiDdPe3n6+RTljdu07jJF7/uvL6BGxuzM64ft8nduO9uXlL2S2VcnzwViP09C9TFrTNTgD8QyO6wXiO33q1JTnX3h/Y9uO9scneGuNbT8xmCCRyzfQUhXIh/E4kkucowlvZDAfFNadQpkm38PkfbPtd1zJkf64t1pcF3NOMDOU8BwJNCFmXCU/Wf65YmqCxsoAAVPn1FBBzKxcek8vtalgUW3wrDK7FdIfyzCYMxkmOg9JKeWsUwSlTip/CPguEAO+DWzCa8Dvm1TUEUK8FfgvvHfszUyfaGqMEbzkODBNkpy8HKafBe+4OCNk6wJq7v9btm/ffr5FOWP8C1bkn//2z99atvO2f/Q3E77P17m7v//hovVHFzPnPCgnYuxPgKEL6iN+vvvObdM2xN9+7BixtE1FwOADb7h5yvMvvL+xbbd++VH25rKdaQXb/+DfnmVHLlbTR25eyTuu8mL8vPIrjxJNWZiGxn0fvBZfmUJXFFJYdwplnXwPk/fNtn8wnuGOrz+B7UqaKgPc+f6r5yTXfzx1goF4Fr+p8d5rlxVdTVxM/rnSWOHjfdcvZ21LJX/+4xfoG02jCYHf1PDpGqMpC5+p8/k71nNriXNIs/Hl+w/ykx0dADz5sZt2lnJMqWP/d0kpvyKEuBloAN6JpyAmK4S3AF/J/Ungidy2mXgK+OPc55fmzpunMGPa6vUbpWlCrGAhqQZU+AU+QyeddfAbgqTlkrS9fT4dMs7MPcBCt8eA4R2XLBIVYKyqaIApvDDQY9pu7EFqmnfnLuOuiRG/xh9es5Qf3D+1cgOcKGMDeDGyIgyHczkI5vNZGAUvuyFgeUOQukiAtS1V9I5meOpoP/2JuSfoGRth+DQImjoBn47PEHQNZ9A0z1QTCXimhYChYxoaFQGToUSG2zctnLFXfvumhRzqjbGisYIP5LbN9ox+8+Fr2fjpe4lnbP71bZvy2//pDRv49C/3saAqkFcGAJ977Xp+trODm9Y0zYsymMxk+U98/tb8e9FcZF3itUuqePR4tOixdRE/H3vlWp440s/brlg89eBZeNWGFg70xFjaEJ5WGYDnshqddu84LZU+RjMOFT4DIR1Mw6C6wscdmxbx5m1t+AyN9123jO0nh9AQbFpcTXXA5OfPd7KupapsygDg/dctx5Zee1XqKuKSTEZCiBellBtyXkAPSyl/LoTYJaXcNOvBpQjhnfcdwA+llB+YrtyWLVvkxdzD3rJlCwM3fXrK9otFIWzZsuWiHuEo+c8fF7PscPHLL4TYIaXcMlu5Ut1Odwgh7gNeCdwrhKigSGBCIcRKIcSDQog9ue8bhBB/PYugJrAOr5O1Wggx7XyDQqFQKOaPUk1G7wY2AseklEkhRB2e2Wgy/4rnMfRNACnli7lFbX8/3YmllBZePmWFQqFQnEdKDV3hCiGOAyuFEDNFHwtJKZ+dtMx9/rKmKxQKhaJslOpl9IfAh4BW4Hm8tQJPATdOKjoghFhGbg5XCPF6oLts0ioUCoVi3ih1DuFDwFbgpJTyBjy30/4i5T6AZy5aLYToBD6MFwNJoVAoFBc4pc4hpKWUaSEEQgi/lPKAEGJVkXInpZQ3CSHCgCaljBUpo1AoFIoLkFIVQocQohr4BXC/EGIY6CpS7rgQ4h7gv4GHyiSjQqFQKM4BpU4qvzb38VNCiP8BqoB7ihRdBdyGZzr6jhDi18B/SSkfL4ewCoVCoZg/ZlQIOY+i9wLLgd3Ad6SUj0xXXkqZAn4M/FgIUYO3YvkRvICACoVCobiAmW2E8H3AAh4DbgHW4k0wT4sQ4jrgjbnyzwFvOHsxFecSFV5DofjdZDaFsFZKuR5ACPEd4NmZCufWKjyPN0r4CylloixSKhQKhWLemU0h5MPISSntEvKqXprLrKZQKBSKi4zZFMKlQoixBl4Awdx3AcixTGhCiL+UUv4D8NnCTGpjSCk/WE6hFQqFQlF+ZlQIUspSJ4P35/5fvOEAFQqF4necsuRClFL+KvfxRSnlrnKcU6FQKBTnllJDV5TKF4UQB4QQfyeEWFfmcysUCoViHimrQsjFOboeL87Rt4QQu2fLh6BQKBSKC4NyjxCQUvZIKf8Zb0Hb88DflPsaCoVCoSg/ZVUIQog1QohP5TKmfRUvlWdrOa+hUCgUivmhLJPKBXwX+BHwcillseB3CoVCobhAKZtCEELowFEp5VfKdU6FQqFQnDvKZjKSUjpAnRDCV65zKhQKheLcUW6T0UngCSHEL4F8HCMp5RfLfB2FQqFQlJlyK4Su3J8GVJT53AqFQqGYR8qqEKSUnz6T44QQXwK2ADullDOG11YoFArF/FBWhZDLplYsuN2NMxyzGQhLKa8RQnxDCLFVSvlcsbIdw8misfovBnQBNedbiLNkd2c0//zLmR9h8m86X+ce7Y/nv1cFdDK2Q9r29lX5dWIZB7dsVy6OXhAw2NQATaMyYPAHV7bz70+fxHJcXr6uiUtba7jlkgVUhcx8+WLPv/D+xrY9e7yfN3zTi1R/yYIIv/7QdQC8+3vP8OCBAQD+452Xcc2qZgBWfuJuso5EA47lzpFMWrzya48zlMjyrpcs5sM3rT6j+73s7+8jmrQ5PUPd+fu7nuPbT/VNuY8xCu/xb16+jHfdOFGWrz98hD2dUV5z6UJuvqR5wr6dJ4f4sx+/gAC++fuXsbK5csL+v7lrN/ft7WVDazXfevuWCfssx+W3e3oYTVkTnv10+DVorQuTsVxuWd+MqWk8eLCX6qDJP7x+A4vrIriu5Ld7uvnlC11Yjsu6BZVYjmRf9yjLGyP85c2rCPjKbbgpnXIvTPsI8Be5v0/iLUybLeDdHcB1QojH8FJwXjFdwZGkNd2uCx5nippUnGsSWSf/OZoeVwYA0XOgDMCrB2N/aQfSlstQIsu/Pn6coUSWaMrigX19dEfT7O6Mzniu7363eAP1x/++M/95T3c8//mhnDIA+Muf7c5/zuYqpwvsPuVd84fbT9E1kiJtOfzo2Y453yfAfz1zksG4he1OrPyTG9ZCZTAbn7nv6ITvQ/EsjxzsZzCe5a4XOqeU/5dHjjEYzzAQz/CNR45N2f+bF3tIZGyeOjrASCI9Yd+poSRH++L0xzIlyZZx4dRgkmgqy893dfL40QG6RtJ0DKf48XOnAegZTfP0sSEO9cQ4OZDknr29PHlsgOMDCfZ2jbL95HCpj2JeEFLOb0slhHhESnndDPs/CTwnpbxHCHEPcEJK+d6C/e8B3gOgBSsvM6oa51Xe+cSO9jEm//qFVedZmtIobJQqrGHa29vPnzBnya59hzkf9UcXgsqggalrZGwXKb3eJ4AmBCGfTkXAIJ6xcVyJlGDogsqASdA3HnC4UP6x+lP4+4xtO9IXJ2U5U7bv6Yzmh+8Rv86S+siUc6xpCmMYBvG0zYlBzy/EZ2isbJr7lGAiY3NswDtHYd0vlGny9Sfvm22/40qO9sdxJfgNjSX14Qll+2MZBhNZABor/NSGJzpB7usexXUlQgjWLqigMOeL7UoG454y6Oo4PWvdWdUUwWdcmNmCd+zYIaWUsw4Aym0yqi34quHNCzRPU3yMIWBsHCeA09Oe3/Sz4B1fPisZzyfd3/9wXv7tF0lKysLeXP0Df8v27RdvhHP/ghXnvP4IoDJgsG1pDWsXVPNiRxRNQNdICiEg7DO4dFE1W5fUsK8rxkA8Q8Z2WVgd5KpldVy+tK6o/Ns/fys/+9lv+PMC4+pYnbrqc/fTNZqdsn3Zx36TH6kurw/xwEduACb+xj/90ytZ3lTLT3ec5uN3eqOIpsoAj/3VtFbfablnTxfv++EupJxY9wtlmnz9yftm2z8Yz3D7157AlZKGSIBf/MnVE8p+4Z4D/PJ5b+Twpq1t/J+Xrpiwf/2n7iVjOWhC8NDHb5pgojs9lOSnO7zR0UfffuuMdUcI+NEHXsK61guzoyeE2Dl7qfJ7Ge1gfA7BBk4A757lmKeAzwkh/gYIAZ8q3Cml/BbwLYDlazdIe8rhc0NQZJLjHLCxtZLu83Dd30WmywntKzDg6wIq/DCa9l7mxXVBTg+nKOhYnxEaoAmojfioi/gIaBqb2mt551WLOTqQ5MbVjZwaSpDMOkgpkcDKpko2LaqiPuInY7nomsCVsHlx8VmnMRv76153K3/+3NR7ffLjL8vPC3z2NWvy27/5ts2894c78RtaXhkALKkLcHwwTW3QYHmT16d7/WWLeProIPt7Ynzm1WcWuPgVl7Rww8oOXuyMTqj7k+cITnz+1vG5nSItUlu1zqkRp+ixdRE/H7ppBU8cGeT3r1g85dgP3riCRMbG1AV/fM3SKfv/+pY1fO/pE9y8rnGCMgBYVBvipWsaGU3ZVEzT8ReA3xC8dE3TBasM5kJZTEZCiK14Pfsw0AG8Cc/MYwPvkFKemOX4rwCbgSrgb6WUPy9WbsuWLfJi7qFu2bLlou5hXyzyT6cQLhb5p+Nilv9ilh0ufvmFEDuklFtmK1euSeVvAlngZ8BVwD8AywA/3ghgWoQQfinlh6SU1wC/AlJlkkmhUCgUc6BcCkGXUg7hOSq8HngB+KyU8gq8Xv9MvEII8YgQ4hGgCbivTDIpFAqFYg6Uaw5BF0IYgAXcDmSAX+f2zah0pJR3AXeVSQ6FQqFQnCHlUgg/Ah4B4oAJfERKeVwIcT3QU6ZrKBQKhWIeKYtCkFJ+VgjxILAAuE9KmRBC1ODFM7qjHNdQKBQKxfxSNrdTKeXTQoiHgQdz6xGex8ut/AhQkg+sQqFQKM4f5Q5dUSWlHMUbFXxXSnkZcFOZr6FQKBSKeaDcCsEQQiwA3sD4pLJCoVAoLgLKrRA+A9wLHJFSPieEWAocLvM1FAqFQjEPlDsfwk+AnxR8Pwa8bqZjhBCXA18CHGC7lPJPyymTQqFQKEqj3MHtAnixi9YBgbHtUsp3zXDYSeBGKWVaCPFDIcR6KeXuYgUl8NGfvcBjhwfQcElakrBPZ0FlgOMDMUbTLjVhE1PXiSbTJDISGy/2fWt1kETGojuawQUqgibrmit5oWOIREaiCdA1CPtNLl1URTLtsK93FKQk5NPJWC6aJvCbOhqS/kSWkKHjSpdYRuLToLEqSHXQoDeWJuw3MIRGXyxNyG/wnmuWzVs+gflkQnC78yhHOTg5mGD9396DqcHiugi1ER/NVQHef8NyTg4kefLYAPs6RnjsyCC2Cz4BmRIiu9RHTGzHxbZdQn6DhooA1SGTWNpmKJmlJmCStByaqwIsa4wQ8hlUBU1WN1eyuC7EycEki+tCnB5OUR/xsa6l+FrOsfpzYprAcIXbr//H/2EkafHVt1zKS1Y0AZC2HLafGCYSMNi4qPoMn+KZMVvdH9u3dVElP/nANRP2feHuvXzj0ROYGhz+3NRjHzvcz85Tw7xqQwvLGiLzIL0XjHBySBSfBh966VJ++GwnzZUBfvDOywlNiod0sVHu4Hb/ARwAbsYzH70V2D/TAVLKwnUKNt5IoSjRZJY7d3bm47cDDCYsTg2PxzHvLojymD8u7RDtiU/YNpSweOzoYP67I8FyIJ20eOjgwISyo5nCSPnj4fUy9rioaRdODac4NRbOPDaeu2Ek7fClBw9Nd1uKc8Ro2iac8X6zoY4ouoDqkMlI0sJxJXs6o3SMjNelUpQBwEB8/LeOWxZ98Yl5OzrwznlsMMmLHSOYuk7Eb7BtaR2uK1lUG+JXL3axqCYEQEPET2NlgOkYUwrPPPNM0f3v+8FznBhMep9/uIvdn3oFAM8cH2JnLt5+TchkcV246PHzzWSltqSgoX3u9OiU8t949AQAlgs3/d+HeODPxyOvRpNZvvHwURxXcrw/yZfftHFeZB5MZFkwaVvWhX+838ux0BPN8Be/eJGvveWyebn+uaLccwjLpZSfBBJSyu8DtwLrSzlQCLEBqJdS7pu0/T1CiO1CiO3x6DC6JqY5w4WNTy/3o1acLZrw8hFUBk1MQ0fXBOWqXcV+bQHomoauCXRNYOqCSMDrk0X83n9dE/hLjKl/+eWXF93eWh3Kf/YX1Lug6Z1XCAiYF07c/uAcuqXNFRMVpWlo+A3vHsP+c39P+acrPEV+sVPuEcJY12hECHEJ3irl9tkOyq1b+Cqed9IECsNfb9myRX7yjvXcu7eHgKkxHM9QHfazpDbE7q4ROkcyLGkIEdB1emNpBkbTDCVt1rREWNpYQTrjsLtrBKSgOuTjZWsauPvFbgZTFkhBhV8jEvTzsrWNjCSyPHlsACkFjRUBosksPlMn5NMImDp7uqI0VgQxhORQb5zGygBttWFaqvwc6k9QEzQJmBqH+uLUhHz80TVLueJr3j0tvIh02hc2wV/tOt9SlIeW6gCb2qqI+HXWLaimKmxSG/Lx6ktbGIhn2dsVpXskxX8+c5KhZJYltQEO9iUYzUiCOmQdb/gaNMCxQQqoDgouX9pANGWTzNrURfy0VIWoq/CRzNicHErQXhuhN55heUOYxsoAYb9BRcCgsSJAc2WAjpEUi2qCdEXTVIfMKWGYJ1PYuzbwxqyFff1PvGod8bTN4f44P3jneI91a3sNtWGTsN+gaYYRyHwz2WS07+9vZflHf4MNfO/dU0NtP/znV3PLV56kvT7ED95z1YR9IZ/BZ159CXu6o7xk+fwZNRfXhphse9jYGuGzt2/gKw8dYWVjmI+8Yu28Xf9cUdaMaUKIP8SLeLoB+C4QAf5GSvkvMxxjAL8EPi2lLD4GzqHCX59ftmzZwsBNn56y/UKbD1Hhry88LmbZ4eKXv9Tw1+X2Mvp27uMjwNRsFMX5PWAr8IVc+rqPSSlnDJmtUCgUivJTFoUghPizmfZLKb84w74f4QXHU/yOM7lnf6GNPBSK/+2Ua4Qw9wzcCoVCobigKFe006mGZYVCoVBcVJTVF1II8X0hRHXB9xohxL+V8xoKhUKhmB/K7Ry/QUo5MvZFSjkMbCrzNRQKhUIxD5RbIWi5xDhAfn1Budc6KBQKhWIeKHdj/X+Bp4QQP8ELPfQG4LNlvoZCoVAo5oFyr0P4dyHEduBGvJX6d0wORaFQKBSKC5NyrUMIAO8FlgO7gX+RUtozH5U/tgUvmc5aIFLqcQqFQqEoL+UaIXwfL47RY8AtwBrgwyUeOwS8FPj5rAUTWbZ85h6Gkl7ESl2A0AEXDF0D6XrxZiSYGvgMjUzWxZrhnH5DsLwxwv6uGO4M5eaKwLOZjV3jxlWNKvz1eWYkafFn/70Lv6GzbWkdr1jXDMB9+3oYTmbZfnyIBw70krbKF86lEF3k6oQETfOC64V8Blcuq+cvb17NF+8/yEA8y5/csIyrVzQAsKczyu7OKOsXVuXrzxuBL+TqT7Hw19GkxWu//gSjKYu/vW0tt21cCMDzp4b53G8PUB00+eLvbSAS9M3LfRZjprr/6JGTvP3bewDwa3BwUojryz97H7256MF3f3Aba1saSr5uMmtz/75ehBC8fG3TGQf1Oz6QYO3f3EMyO20w5mnRBNywsp7PvHY9TREfb/n2s+ztGkUiaa0O4jM0juRinj344WsJhcrzu5weSvCxO/cg5hA7rVyTymullG+TUn4TeD1wbakHSinTOW+kWemPZxhIOriAC1gSsrYXhjZpuSRtsKX30mVdiM+iDAAytmRvmZUBjCuDsWs8dmRg2rKKc8No2uJAT4xnT3hhoPd2RdnXHeVYf4KHD/TzyKG+eVMG4HVUXOnVXdv1guWNpGyeOjbI1x8+zM5Tw5waSvC9p07kj3n4YB890TQPH+zLb/vv3P9777236HW+8sAhOoaTjKYtvnj/eNj1f33sOJ3DSfZ2Rfn5813lv8ESmbwifUwZAGSKvIi9BaHkX/f1Z+d0rd0d3u97tC/O/u6pobVLJZGxSVtzVwbg/eZPHB3k5zs7+cnOLvZ0jZLIOiSzLof7EuztipG2Jb2xDJ+/r3xh8r/7xAmOD8Q51h+fvXCOsgS3E0LslFJunu57ied4GLhpsslICPEe4D0AdXV1l7W3t5+1vGfKaNoilXXQhKAu4meukbhPnDjB+ZT/TEhmbWJpGyEEQz2d1DS1UBEwCfkunPDJpXIxPv9C9h8+Sl1zK3Vh30UXBv7Q0WNUNbSga4L6izBM9FjdsRyX4YSFRFIZMAleJO/Bjh07pJRy1gFAuRSCAyTGvgJBIJn7LKWUlSWc42GKKIRCzne00zt3dnAyl3jknVe3Uz3L0M5yvO6OmYtJv2XLFh5/6pkLKhb9bDx0oJcXTkcB+OIH7uDPvnYnmxfXcN3K0oft55u05eDTNbZt23pRR6xsXXkJf/rVn/HWyxfTXHX+wlefCUvWbOD9X/oxpq7xvhuWlZzz4UJhy5YtPP3MsxzsiXHfvl4AtrbX8pIVF4ch9ZxGO5VSXly/7hlyw6pGnjsxREt1cFZl0BdL85PtHQC8bnMrzVUBhhJedqfrVjWwua1mxuMvFK5YWoeUUBk0+Zbf4NJFVVy+pPZ8i1Uyzx4f4okjA3NqQKcLn32+ydouyaxD9UWYptHQBCeHkqxprrgok0U5UvJvTxwnlXVYXBuiJuxjS/vF8Q7PhfO+aEwIYQK/BS4F7hVCfHy2vAhjSCm5Z08Pjx0ZoCZocsdlrWedU3Vf1yj/c7CP1pogt21oQSsYmteEfbx8XTNpy+HRQ/1EAgab22roj2XY3TnC0voI7fVeqpLTQymytjdCODmYoLkqQO9omm8+cpQD3aN88+2zKusLgpDP4KVrvJy8fbEMX3ngsJf+cUld2a6RsR2eOz5M0Kexua0GMZdZsFl47vgQe7uinB5OntV5LoRIrLG0za9f6OJNW1tZ0eQNuk8PJTncF2PtgqoLetQwlMjy8ME+9nZFefc1S6eMkl/sGGEokWXbklpCvvPeLE3Bsl2eODLIaMpi8+IaBPDbPT2sbq6gtSZIa02ItS2zGkIueM77k5dSWsBNcznGclxsRyLx8uAe7YsTNHXqK/rPWiHs7hwha7sc608wnMxSV8Te+fSxQXad8iJ01IV9PHqo38u41TnKe69fhqlrrGqu4GhfHFdK1uQqiuNKso7LsyeGzkrG80XGdhmI/z/23jtOrrO+938/50yf2d2Z7VVaFatZkotkSS4YBLbpxSa0YGoCJHG4SUhygZCEGwKk3ILrrTMAACAASURBVCSQkF8A38CFC5gWUxxs3G3ZcpHVrF5Wq93V9ja9nfr8/jizfVcrrWa1K7Pv10sv7eyc8uyZOef7PN/y+Wrct+dcUQ3CvvYYL5wdQiAo83tYXV28RummtDEsOeq+u5KRUpLIGextj3FVTSlSSh441DP6ff3dV11oC5LLT86w0E2bvkSewWSepnH9nHsTOZ444QTNddPmjkL212LClpKuWBbDkuxpG2Z5eZDhjEYqb9A+7CUS8FBd6r0i4yPjWXCDcLGk8gb37engdH+G1dVBGiN+TvWnKA96aAj7p90nltF5pmWQ8qCHW1ZXTpmBSik5cC7Oke44inD63TZGnGOd7EuysjKExzW2zA0W+t8K4fSpdWY0Oj63ilI4dsjr4t03NE08D07Ggd995S2ZAWxbMpzWWVNV3Obs8ZzB/o4YLkXhLZsntzK/NJZFAhzvTr4i+t1KwLRtNtU7avNCCFyKoCetUR68fCmkc0EVAsOSuFQxpX2nV1XojGXJaibr64qvpN8+lEFVBE3lgdk3ngG3qpA3LNJ5EwFkdJPaUscNDM7E0Oua231t25LWwTSlfveCtjaFK9Ag9Cc1+pMaXbEsmmnx+qtr+cbdW0jkDMqDntFMoJEL29Kf4quPt9CXyFEf9lMZ8lAe9E648AfOxfjW7jYGkxpul+Dq+jI2NtTygz3nON6dwO1W+OTO1SyvDDKY0ri2sYzyoIeQ10V1qY+3XFNH+1CWurAPVRHkDYsXzw4T8Li4oXmqCySW1i7rNbsUHj3WyxcfPEGp340lJXnT5slTA/zB69YU7RxuRRD0uPC6FOwitnQFaBlI05PIo78CVgjgpKu+0BZlU5MTx2kZSHG6L3XRGW+XG8OykUBWtxlIZWksH3vwZ3SL8oCHoMeFZRf38z/Rm+Tho30AvO3a+jl7EEZWmRnNJKOZGJbFPa9ZTW8yz56zUV7M6qyuCfHmTfUXfewXzw6zpy2KIgR371g2rVficnHFGYSaUi+rq0P0xPPUlfmQSPZ3xNjcGCaeNfjBng5002bbinJqSr3c+0wbZ4fSZDUTVRU8eLgXgcDjVij1ubl9Qw1Z3SLkddFt5TAssGybEz1J2oYynOpPkTct/vIXR7m6voxSv5vGiNMo/aHDfSyvCPD7O1eztnbsC76vPTbqUioPulldPXHWk59bOvOC8JXHTtMVy6HEc6O/O3oJ+dzTMZzRaBlI4XGpo3GXYnGsO0FvPEc8O7lF+pXLkU7nu2XbNrvPDJHIGsRzs1XcLCzjH/M98dwEg+BzC/Z3REnkzBn98GnNxKMqE1bqF8L4QrLcHIrKRjAtm0TOxCz8IT0JjW8/18Zr1lVzqj+FEHDvrrO8YUMtad2ipOBFGMpoBD0qQe/MiQAjY7QLE66F5IoyCIYl+e7z7di25E/vWIMQ8ODhPloHMgyldVZVBelP5mnpT3OqP4VbVRhKa6RyBh6XwvVNEVRFIZ7ViUV1VlSGOHguzm0bqhEIepsz/OpwH8d7kqytLWPnuio6o1l6EjmklOzriBIJeHjsuIZtS1yqQjSrc6ovyYa6Mp442Y8iBFUlzvJdCOdDPtqdYOU4N8v8lT4Vn+G05hRTjRu0UeTirX1tMYbTOgI41pPk2iJmYHncjgvQrQpys29+RZDKOcZNSolmOA+QS3nYXW5MY+JD72h3kt6khmFavNg6xPu2LZvw/pGuBI+f6KfE5+K3ty+7qKDzNY1l6KaNqgg21M096GtJiSonjjuRN1hTHcKjCrKGTSKn89DRPs4MpFlZFSSRNXj4WB8VQQ+fumMtKyqnd7XevLoSj0shHHDP6Pa+XFxRBiFvmDxyrA9bQl3Yz+s31iIE6IbNwXNRdrcMcqgrTjJv4nOr5A1n5l/ic/OqNZWsqgrSGc9TWeKltsyHbkpWVQcdKYMV5XxjV4z+RJ5oVuc7z7fx5Xds4st3buTfnzpDVrfwGha9iTypvEHAo2IDtaU+msoDHOqK88SJASxb8v7ty7jr+gZ8LoX/PtxLKm8uuG9wrlyOYGxXPENGtxDAQPLSsoEmU1Pip8SXpczvprjrmoUjmnVKdVRVJex305k3qCqZ6GaIZ3UyurXgD5jpsCfpArgUhUzexLRtMtMYto6oU+KUypsMp3UC5RMfW6f6UrQNpbl+WYTqSfeZS1W4cdWlJ0C4VWV0dTBCJm+ytyOKz6OS0S0qQ16O9ybxqAodw1n6E3ksWxLN6nTFsjMaBL9H5dZFUtczJ4MghKgBvgzUSynfKITYANwopfzWLPs1A3uAE4AupbxDCPHnwNuBDuDDhayjadEKedhSOro0pT43v7WlkQcO9dCXMNnbHiWrm1SGvIR8LlZUBDjcFceSksOdcYJeF7ZtY6Jw947lBDwqndEsz7cO0R3L8ULrEPGcQUazkDLP/3uhjXW1ZbQPZUnmDGrDPvxuBSHc1JR62Vhfyrlojh/uOUdNqY+TvUl8bpVdpweJBDzUR3y0FALemnnlzODGkzemGoRi/yUdQ84NL4Ej3cV9bEcCLgzLIui9Mg3ydLgV5zMxTZPOWBbThjMDY/IEsYzOd55rQ7Nsbt9Qy5bliytfPp6feIu7VMdNa1r2tLGQbc3lpPPmtIkjecPi4aN92FIymNb5wI7l8zJmlyLQJ90KuiV5/PgAqbxjoE/0ptjUWEZVKMC1TY4L25Q2dWV+NjeGpzmqk3mYzBmEA+6iplvPlbmuEL4D/F/gc4XXp3EkVs5rEAo8JqW8G0AIUQXslFLeIoT4NPAO4Kcz7eh1qayuCpEzLKSU3L+/i9vW13BNQxldwxkSOQPTliTyJpGAG92StA1l0Swby5bsa4+BhG0ryvG7VdJ5k18d7qV9KMNLbcPEMjqWlPjdKoZls7c9xuHuBImsQdawSOV1NjeF6YjmEMDuliE643kOdsYp8bjQLZuMZrCvPcqG+lKePjVAOODCpQo+esviTQk8H7nJ06J5IKWPFaeP3FzF4hcvd9MV0+hL5Cl+/srCkB0n+DPictatsc+pJ5Fj37kYpiWpLvEtPoOQnvgZH+1OkC1MPI51J6ZsXx70sLGhjEjQM6EuCJwHNUj6k3mWXUIW0WzEMjqTowCWdLSoRsjqFrtODdEQ8TOQ1PC6Vd52TQM3r565mvn+/V10x3Osqy3hjZuKm2E3F+ZqECqllD8RQnwWQEppFuQrLoSdQohngZ/hGJKnC79/HPhtJhmE8VpGy5Yt4y/evJ7BlMauU4N0xrK0DCTpjWu09KecP0gR+FyCMwNpVlWFqCvzk9YMsoZNY9iPbllkdJM9bcOU+ly0FrJQEnmDnCkRQE2JCxtBVrcwbUlOt9BMG2k7y9OsbpHOGZjjsg7iqoELkALK/B564nmG0xpHuhN4XQpv3dwwx0v9yselKIysO4QorgHqiWlIQLMoqkFYyGrm/nQecILK0+FzKdSW+sgbNhWhxZeO2jEYBVaNvh7vlpwuy2z3mSEOnnNSwj9w4/IJKbay8M/jUmbMUOpN5FDE1HTXiyGjW0w/xx9DERDP6ZT5XbzcFWf7igq6YlkGUxq6ZU9Z3Vi2pCfhRLa6YosjwjVXg5ARQlRQiI8KIXYAU037VHqBNYAG/BIoBfoL7yWAKVMZKeW9wL3gaBktrwjicSm0DKTpTeTQTMf33J/SENJxK53TTPriOQZTGgGPimZaLI8E6I5nOTecJZk3+eFL59jUUIpLUYilNQSO8JJLEXhcqhMwzuiIgkSxJU0s2ybsd+NWFVJ5g1zh3KoiCPtd5AwLy3aKcJoifg50OC4sRbjY0zY8x0v9ysenqiQKBqE8UNyUuyvTUXd+zMKqbSaDUBf2E/K50E29qEV+xcKapHRT7h/7zBUxNYvIKKx+bCkxJ8W0bClRhSAS8ExrTFr6U/zqcC9CwJ3XNbC84uJqaI52J0jmDGbTfBM4SSSVQS/1YT+bGsuIBJwiy/v2nMOWkts31LCxoYyTfUmGUjrNlQFuXl1J60Ca6xfJKm6uBuFTwAPAKiHEc0AVjuz1eZFSajjGACHEr4AkMDJ1LgXisx0jo5n8+5MttA9naAj7SWsmlmUTcDsBJ1PaWBYYCqSHM0QCHjKayXDGwKsK0ppJWrcROD4/j6ogBAQ9ToHZqqoQH9zRzL88doqQV6Ui5KWu1MeJviS6KckbzmpBM200Q6KqEPKovGZtFc+2DCOERDdtjvbESeYMLNupvrx17eIIGi1GErmxlNCeWH4BR3JlMFuGaVcsx1BKJ29aHOtJcnV92eUZ2AWiKhMfri+dG5ssDU1To7Os3M9/7euksTwwpRLY61J5x3UNnItm2TjN3xnP6gymNBQBiWku3Mm+JE+fHOD6ZWG2rZzo2umMZnmsIGRnnqc+wiPA41YoC3i4dU0lH7t1JS93Jjh4LkYqb9CfzFEZ8hLPGgyk8vz6SB/twxlMS3JDc4T371g+KoC50MzJIEgpDwghXg2sxTGOp84XDB5BCFEipUwVXt4MfA3HTfSPOPIVL55v/5xu8aFv7+FodxKXKkjnDX7/1asYyuqc7k3xbMsghu7ozWM7A4tlnHRGy5bgdWGPSx3L6halpW5yhkVNWQCPKlhVFSJnmNiAbtl0xXL0JfLkdacPg25aCEVgWk6uhJDOzMCjqtSH/ZzsSZDSLLpiOYRwAmZBr4vnl/ohzMj4xJKBdHGzjF6JjNxoLtf0t69pSWIZnaxhkl+E6ajp9ESjP94+mNbUB+9P93cxnNGIZnVe7oxx/fKJ4opN5YEZq5BdqkJGN1HESLxhIp+87yCd0SzhgJvdn36t02irgNflTBalZEZ3lCLA61YwbEjnDYZSGt95rp3dZ4YQOAauqTxIQ9jP1uYIecNCVQTDaY2cbnOiN0XOsK5sgyCEuGvSr9YIIRLAESnlwHT7FHiVEOJvcVYJu6WUe4QQzwghdgPngK+e77zDGY0TvclCkFgQDLt4+Fg/0YzGcFbHKHxoHlVgSYlLCGxLOjeQJVE1g5Hvm8RxL/Un86yqClEV8nC4K0F3LMeDh7vJ6E4g2pYT6wYEkpDLhSEELiHxqAqaafPIsX5cqsAsLC1twF1wN62oDKJfhuDslcp4J4CqLI4b40rAMKafgwW8Kqoq8Ep1Uer1W+rEuEYie/5EgiNdcfafi6MKR7rjYhBC0DzqJppqEPqSeWwpiWV10nmdcHAszlBd6uNdW5tI5Q0+M8PxbQmpQvqRYcGp/jTHepOci+ZQhMStOtff73Hhc6v43Crv3tpENO2oLVi2Pc2oikN/Ms9f/eLoRVWxz9Vl9DvAjcBThdevwZndrxFCfEFK+b3pdpJSPgQ8NOl3/wD8w4WcVDNtcrpTAu/xKFSVeNjbPky+8LAt8Sh4fG6ktEjmJTZywsPcsMGlClRbYuF8mHnD5lhPkrNDCjnDxpYT219ORrfA51YxbYlLcVxHWQMSeQsF8LrGHnCKcNom9ify9MaXZr4XQnfilVNRPN9o2vQSKAqCZeUBcrpFmX8sN2YorfHY8X5CXhdv2Fi7YLPSU90T54ylwbHH0HSZl2cH084sXcKuk4NsW3HhPQg2NzhuJFWIaXWSblpZwd72KCuqgpRNE79yAsEXVsthSRhO5/G6XagCPC4Vr0tlWbmThjpCbZmPW66q4kh3ghKfa956Q3x791mO9VxIaHeMuRoEG1gvpeyH0bqErwPbgWeAaQ3CpeJzq7hVgS0lZR6Vo93JUWMAjsGo8LmJZpwZx+SHuinHAnJ+t0Az5OjDOzMuyXi2uXx3YuxGVMd9gW1gXBYalu2kpemW5GDnxX0wSywxGx7P9BlEHpeCUUi1Hj87fP7MEM+cHsSjKqytDbGmZmHkmrsHJ762ZqnRKfG5GUw7q6HVF6lFpCiC8oAHIZg2z///e//19CXzVJV4i1IHkNUtVEXF41JoCPtZXVNCTZmP3WeGKA96aK4MMpTWWFYeYE1tiKqQ76LlOC6U1dUlKKLvonoqz9UgNI8YgwIDwBopZVQIMW+iKlUhL6UhDwNpjb6UPqUPsmE75eQXIpCWu0j5BVchDcm0xzIKxCRJh8mM2Kq0Zo2qIi6xRLGYKfMlb1j0JfLkdIv4OHdMMm8Qy+i4VEFucpXVZWRK2sC4zKLpHl5NkQBdsRwuRVATvrjU0ZN9SX59xBG3e/u19aycZFBcqkJjpDj1CwLneVBb5mNdbQkfvKmZjGbybMsQli3piGaJBDx89fHTpHImb9pUy7KNxVUOHs+7tjbREPajCMGNn76wfeZqEJ4tZAmN1Ay8E3hGCBHkAjKF5ko0o+O2JKbFFGMAzsw+b9hcSiq7wsRjj6Si2racEH9ATj+GmUjmlwzCK53L3URnphhCPKtzojdB3rBpHddgfWN9GXvbooR87kuSgr5UJjtP68Y19pnOjdU+lEa3JLolGYhPdZMd6oxx4FycN1xdS92kXP+MNrb6yM5zgF0CZX6n3/jr1ldTU+rjgUPdDKbyrKsrZXNDGd3xLC39zmeytz3GdcsjtPSlSOVNmquCrKst7qrtpvMUxU3HXA3CPcBdwC2F1y8BdVLKDLBzjsecFd2y8SgCt0tgWXKKtgg4FjrgVkYrHy+G6WIHIxlKk492sTbnImNhSywxKzO5OB491jfqYnng5S4+/cZ1gFNU2RnL4XHl6UvmF6yHwmRTlBwnZTHdirtrXCryrpYB3rG1cfR1Om/yT4+edlK9uxP887uvnbDvNY1laKaFIi5N3O5CUIBlFUE2NpTSOpjh2ZYhWgfThAMerm0KEwl6UFXBmpoQyZzJ1uUR7t/fzYutQ7QMpGmKBPi7d26asoq5nMw17VQKIVpxYgbvBtqA+4s5sOlQC8ZARaKd54k8F0/gyD6TD3uxz3GlcCyhjMkKAAS8V5SO4CuWxdoveS4EAtPP8ofHSX2nx82Ke+J50pqJaogF7cmRnvR6vF6WLae54ybM1Ga+8SfLWoDjErpp1cXNkueKzUjxHDzTMojfrdIxnEUypkZb6nPzx7etYTij41EEpwfSdMWzxLK6k+YezV45BkEIsQZ4L/A+YBhHv0hIKedtVTAelyJwKwqzyd1kJq0OVCZWrE52C4HzNXMVvniXoqYjgaBXkB5nsRScVcsSs+NbfFmSi5aZXEZrqkpGn6G141RQm6sClPnc+D3qBFXQXacGePR4PzetrODN14w1eGkdTDsNoZrCU3ogXwqTHzrjs2ysabw6imDUXVs+SYoj5HPxkZubee7MEL+9fdnUnS8jHhXODqRpG8qwuTFMbakPAWxqKJvwkC/xudl9ZoizgxmEgLDfQ19Cw7RsVPXSA9uXwsVOW08CzwJvlVKeARBC/EnRRzUDkYCHvGHNOmsfP6FQgBKfStaw0K1CIYkq0E05RdbA61J555Y6frq/B8uyEYpw3EU2BDwKdaU+OmJZJrsix59PwoTMJwBFgZrSxSdDvBjxLoDhnG7VMF/HLeZqZCbpiooSD0GvimXLCb2Ll5UHuWl1JYoQlAXG0lG/sessfck8R7oS3HF1LW6XwnBa42tPtpDJW9y2vpr3bCvew3b5pOzPk+MaLk3n5R//iMxM6i6lmRbHe5KEvG72tsVYVXV5JQxVAOEEw92KQs608LlUBpI5/vLN6ynzu/G6VUIFD0FOt/jBng5eaB12ZPhLfVy7LExas/C6xIL3ZL5Yg/BOnBXCU0KIh4EfMTcPzZzI6CZhRcGrjvkaFUUUgr42pu0UgFSGPJiWUyNQ4nOzqipIVzTHUEZHs2xciqDKLTBsQUOZl95knljWLHxAAdbWljCc1lEEGLYk6FFoCAf5k9vXEA64+erjp/n1kV50UyIE1Jf5yBsW8byBbYFbCPxehZxhoSiC6xojvHFzLY9crgs1z1QVWTBtbU2IU4VA211blkQAZ6OhzHlozJR2un1FOdcti5DVTN42bsa/ZXkEy5KUh9xUl4ytEFKaUegoJ0ezfIbTOi396YIMS5L3FHH8H755xYTXV9WWcqDLMQqeaeYDNSUeOgv1KTdOCpIqQjiuIktOqDIuNjNN3IWA1dUh/B6VyhIPp3rTxHMGbpfCmYE0d1xdO2H7aFYnlTdZURnElJLXra+mKeJnWXmAhnCAtUUOKl8sF2UQpJQ/B35eyCZ6B/AnQI0Q4uvAz6WUj87DGEfxuVQiQQ/xnEHQq7K8wk/E70VVBN3xLKm8iSIUGsv9XNsYIRxwmuNUhrz0JHJsbnSKVH59pI+Qz8XNqysxTJtv7W7jwLk4lSEvL7RFKfG52N5czstdCRJZjR2rKvjozavwe1QePNzDDc3lmJZzo4S8Lm5aWUFfSiOR1WkbypA3bXxulWrFKRC6cXUlr7964aVt50LQDZmCZ8JVEPH7nZubi3qO397WxFcfbymowjbOvsNvOJ+67SrAka4YWZ2Od+hUhHzcsrqSwbTGdeO6zz18tJf/eLqVgEfln991DQ2FdEuroNob8rhQChbB7RLkDIuMZhZ9xnfb5uYJr9+/rYmf7OtCAjtWThV5G8yMucZO9SeAse+IW1V499YmumM51tTM3+qgqTzAdFGXulIv3/rIDRimJKObJDI6//rkGYBpU83ry3xsbixjKK2xc231qOvuQzetmLLtQjDXoHIG+AHwAyFEOfAu4DPAvBoEv0fl7dfWc7wnyYtnh4lnTHY0V/Le7cv5+tNnONwVJ5Ez6InlqCnxkjc9HOtNoZsWjZEAGc3i7h3L+dQdYyJYu1uGCv49iUDQncjhUhQOdsY53Z/GkpKTvWkaI36eOT3IcEZHEYLffdVKvKrCc2eG2NUyhCOVpKCqAi1n41UVaiN+NtSVsqY6RG3Zldmgxe92kTGcqEp50MO7tjZx4+riCvW1DKbJGiaaKeiMZqdo1bySKEZq6kj9gZSSgEclp1sEfWO3cmcsS088R163ONKVGE0x/fXRPoZSzmNtd8vQqBvo7FAG3XKkmHXTxudRsWyoLvGi+90EvJceP6gMeagv87GmJkT1pNTQB4/2jrpcD3Wlpuw73gX74pnoNMf2zrurpcTnpibi59wkmeqqMh9hvwe3quBWBQMpjRuaI5i2ZGPDVLE9IQSvW18zr2O9FC459UVKGQW+Wfg3J4QQXwG2AgeklH8003aGZbPr1CBtQ2niWYOMbrG3I8bHXr2St1xTjxCSh4/205/XyOomNWV+p5NSyukLnMjq9CVyHO1O0hHNEPY7XYpSeYOO4RwuVZDMG4T9btbUlNCbyKObNs2VQZJ5g70dUfa3R1lVHcKwJA8f7eVYTwLddMTs8qZNyKsS8CiEg2564nm6Yr1kDWvGjkmLnaFxhU1SQipnTpBDKAa7W4bRTADJs2cGeft1S6uE89GdcNIwVVUlV4ipZcc1GTIMm91nhtBNm+UVY5lIy8v9PHpUx+1SWF87NpvWCg9cSzqdy8CRW7htfQ098RxvHed2mitel8odV9dQHvQUDNrYuuPEuBhCYpaMkbV1C5eBE89OnfGvqyvldH+KXacHqSrx8u6tTXzk5hVopk192E9nNIvXpUxp7blYWfBcSCHE9UBQSvkqIcTXhRA3SCn3TrdtPGvQOpgmmtFAOOX53fEcX328hYDHVdA7cRra9Fs2ppTUlwWoLfXR2p/CBg53HwRgKKWRN53+BTeuLCerm0gpSWsm1zWFqS71sbo6iCoEf3bHWrrjOZBO1fHhrgSaYVNb5lQBGrbEtCWaYeFRFdJ5k1jWQFXgquoSMprT6vBKJ5k3eOHsMNVlXj51+9qiHbd33LU51j1vdY2vGFIFGWdN00ZjaeNTnLviWQaSTj/fE31jD9vDXQkQTl3Ngc4Ym5dNdc+IwoNaEc6KULfsomUYlfk9KEJMqTU40jl11j8Tp/unriAuB7aUJLWpIe+eaI4zA47W0kBSI54d62/9zOlBvrW7Dbcq+Is3rV/QdNILZcENAo5I3uOFnx8HdgDTGgRVEbhUhZDPTWXIQyxrsro6ROtgmk0NYXrieWx7LDDWFA5w7bIIQsChzji6aZPMG6iKgm7ZGKaNEILhtM72leXsORulqsRHTzxPJOBlc2MEVXEyMkI+FxUlXtKa09s1ljXY1BjmLdfUEXC7uO+lDvoTeaeVoQCQKEJxCmLqyxZlo5I5Iabvs3xJKGI0S8D8DSvgm0smkigows4kXdGXzBcyXwTx7Jj/PRzw4FEVFEUQDowFpD2qQLcc3aOReftgWqNj2DHUR7oSrLrEh1mZ3832leWMNLgaz/nkXyYjpmmgcznQZ/hi9sRzbGwoI5U3qQ/7qBhX7HesJ0HesMgbcLI3dUUYBDFbJ6B5H4AQnwP2SykfFkLcBtwkpfzCuPdHW2hWVFRsaW5unvFYiZxBImegmzaqIgh6nYyjkUCZYdnEMgYSSTjgwVtkUSndtIkVlpVel0Iyb2LZkpDXRWWJl5ePt+Aqq8alCNbPc9XkfNDe3s75rv9i5HhvclTLvsSIXXHjH8+VeP1HuJSxn+pLoRc6pTVF/BOM2eVipvEf60mOaqetry3FtcB1BDOxf/9+KaWc9YG3GFYIcZxuaTBN17TJLTT37ds344G++3w7z7QM0j6UoTzoYcvyCB+9ecWo/+5IV4LHTziafDeuqmDHyoqi/iH7O2I8c9qRclQVwcFzMbK6xaaGMj51xxoiy9dT96Gv4nUJ9n3xTUU99+Vg69atnO/6L0bWfO6h0Qb0lY9//oob/3iuxOs/wqWMfePnHyHtBJl4z7ZlfOmuTcUc2gUx0/hX/8VDmAWvxP/5+DZuWLE4OyMKIQ5cyHaLwSC8AHwC+AlO17TvzPVAr1tfjRCwvq4Er6qyfWXFhGDOuroSBtN5DEtO0CcvFpsaypw+zFDoqepmMKVx+4ZaAh4XXpdC0KPysVsWR4rZbwIfubmZ779wjqaKAK+EnnWXW0BvMfCXb1rH3z54gkjAvSDG4Hy8Z2sTvzjYzaqa0KI1BhfDghuEQjvOvBDiWeCQJ1eJVgAAIABJREFUlPKluR6rMRLggzc2z/i+W1V47br5S/nyuBRu3zB2/A9MGsuamhL2feEN83b+Jaby2Tdt4LNv2gDA1u8v8GCWmBPv3b6c925fvtDDmJYv3bVp0RmpS2HBDQLA+VJNl1hiiSWWuDwU3SAIIWqBbTgFlHullH3FPscSSyyxxBLFp6hpNkKI38XpjXAX8FvAi0KIjxbzHEssscQSS8wPxV4h/DlwnZRyGEAIUQE8D3y7yOdZYokllliiyBS7yqMLGF9KmAI6i3yOJZZYYokl5oFirxC6gT1CiF/ixBDeDrwkhPgUgJTyX4p8viWWWGKJJYpEsVcIrcAvGOsX80ugFygp/JuCEKJZCNEvhHhaCHHBaqm2LemN50hMIzh1oWimRSI3setUVjfJaLP3TLNsSSyjzygfMB2GafPo0d6LHucSl8ajR3vpS2SREhLZiZ93Mm8QzWhkNJNE1iCa1sgb89uMfYmL56lT/XQMZRZ6GKMYhsGDh3sYSl35GmXjKeoKQUr5NyM/CyEiQFxe2BPzMSnl3Rdzrh/s6eDXR/soC7j509vXXrRWUFY3+cGL50hrJjvXVXNtU5i+RJ6f7utEAnde1zAqGzwZKSU/3ddJbyLPhvpSXj+pCcZMnB5I84c/PMimhrPc/wc3X9R4l5gbb/vabk72JfG6VVIZjW8/18ZNqyrYvrKCk31Jfrqvi9N9KSpDHhCOhv2W5ggf3NG8IBIJS0zld7+7l2dOD+JSFb730W1saV54efQ3/ttznItmCXhcPP/nOwkEiqsAvFAUZYUghPhrIcS6ws9eIcSTOKuF/oI+0WzsFEI8O107TiHEx4UQ+4QQ+wYHB0d/f7o/jWVLElnDUSK9SKIZfbQcvjPqWPneRA7Tlli2PO8xTVvSW5AgHtn3QhixjSOiYUvMPyMqs5phjWoadRY07buiOVJ5A92y6U9pDKQ08oZNMmsymFq4JvRLTGREHtu0bJ49M7DAo3HoL3w/srpJZ+KVcz8Xy2X0HuBU4ecPFY5bBbwa+PIs+/YCa4CdwG1CiM3j35RS3iul3Cql3FpVNVYaftd1DayqDrFjZfmcZCjqy/xsbiyjIexn+wpnxrG+rpRV1SFWVAbZNE1zixHcqsKta6qoLfPx6jUXXq7udauEfC4+uiRdcdn4wI5lBL0urq4vJehxUR/2cdMqR8Nqy/II1zVFuLq+lNetr+bWq6rY3FjG1uYIKyqDsxx5icvFPTtXUeJzs7wiwMdvWrXQwwHgndc3EPS62LI8wtq6mZ8VVxrFchnp41xDrwd+KKW0gBNCiPOeQ0qpgdOdTgjxK2AjcHim7bvjOfqTeRQBN66oYE1tCCEcYbmmiJ997VHahjK8YVMdJT4X7UNZWgdTJLIGb9lcT2PBDaQoUzsX+dzqhB60z7YM0pfI85bNdfg9E/+MNTWOiyoyTu52pOH3UEojltWpKfVRU+ZjXaFPql1Y0ZT5FkbCdy6kczr3Pts2o/tssZPXnd4UXjWH26Ww+/QAvz7cQ3WpD8O2UYCrG8JsWV4+2uhINyVZw6J0Hnv0LjGR933zBdqGM3znQ9ezrmGiS+j6xlLcqqC6xLegrpmzg2nSmsnvf28vfUmdV6+J8O2P3Lhg45kPimUQNCHERqAfZ6b/Z+PeO++TRAhRIqUcSVW9GfjaTNtaUnL//i76k3nahtJYNqysDFJV6kMA56IZjvUkyWgmBzrjrKst4VRvikPdcTyqwsm+FF+6cxNB7+x/9tHuBP9e6I06kNK4Z+fqCe//96Fe+pN5fG6VT9y6EkURPHVykOdbh3iuZQi36vRueM3aKhQhWFNTgm7ZWBI+98sTvP/GlbOOYTHwxQdP8uyZwdk3XKTcu7sDgL6UTjya5ddH+zHlmO6/EPDEyUE21Jfic6mcGUxTVeKlI5rhk6+9auEG/hvE5352mBfanCY5d35jDyf+9o0T3n/H1/eQN236khqf/+VR/ubtGy/7GA3L5pcv9/DDPR0MpJ1ElidPxfjM/Yf4+3dec9nHM18Uawr0R8B/ASeBr0gp2wCEEG8CDs6y76uEEPuFEM8DPVLKPefbWErn30hTDYkz8wawbUbzm6SUznaMxbRtCReaE2SPi4U7bQWnf18iR485cs7x55CTjnWlYV7BY5/MTH+J8xmNfbeQIC+ma8sSl4Rhjd1f033d5LhPzrQWpoPSyLgmfy1M65X1PSnKCqHwEF83ze8fAh6aZd9ZtxlBFYI7r2ugL5lDCDg3nGVdbSkNET+n+lK8Z1sjL7Y6LqM3b64j7PfQOpjiVG+aZN7gzusbCF3A6gBgc2OYT9y6kt5Enndc1zDl/bdurudkX5IVlUFUxZlv7lxXTWWJl+0ry4lldepKfNSG/aytcTJu3YpAAf709itn5vlXb1nP159qpT7s49/+a6FHc/G8d0s9P9rfQ7nfRSji58a1VaTyJpUlXuyCAd/UGGbbinIM06Y9msWtCG7fcGGZY0tcOv/4rms53Z+mM5rl2x/eOuX97390O7/3/f0sLw/wpbs2T3OE+cfjUnjz5jpuXhnhnvsOMJQx2d5cxt/defWCjGe+KGraaUGq4vPALTgTr93AF0akLC4Vy5b89+FuXmqLsbY2RGXQy1efaGFNdYg/fO1q/B4Xa2uN0WySE71JYlmdd1zfQHnB15/WTJ440U8iZ+BxKQhAEYIbmstprgzyfOsQPfE8t6yu5LWTYgxpzeSbu1pJ5U0+cnMz21dW0DGc4af7OllZFWLL8gg3NJezvyNGTre4qraE5RVjwUnTltjAMy2D3PO6NcW4JPPOse4ET5wcIBJwcy6a5e7/3MNfvWU9a2uL1/EtlTd48uQAfrfKa9dV4yqi7/54XxqAtG7hEwKhKKR1i9c2hWkI+3ng5R7ahzPcvqGGq2pKuBHHV/zo8T7qw34OdMQYzui8b9sy6sI+njo5QCpv4lYdN+B10/QlXmIqac3kf/7XIbYsj/CeG5ZNeE/TNDrjWTKGyd72GNcsmxhDONARJWOYnIvn0DQNr9d7OYc+SnWJl/fd+zzDGROfS/Dvd2/F7b70mMbL52L84yOnqCrx8r/v2ozHU5we1nOh2FGzHwGDwDtxxO0GgR8X6+AZzeTRY/2cGUjx0JE+HjjUw/GeBC+eHWbX6SGklDxxYoCuWI6fHejm5c44HcNZXmobs0cHz8U4O5jhiRMDHOyI87MD3bQPZ3j61ABDaY09Z6N0RrPsPjO1ncrulkFeaotyojfJzw50A7Dr9CBdsRzPnB4kp1vkdItnCr/bdXqi731kcbmn/cppJP/Pj7XQl8hxojdJVrdoG0rzH0+3FvUc+zucz+RYT5Izg+miHvtwt5OyqFuSaFbnwLkYHcMZ7t/fxQ9e7OBoT5J97THuP9A1us/Tp5zP78d7z/FsyxAnepPcf6BrdJyPHe/jSFeCXacH0cylIrYLIZrR6RjO8rMD3VOKST/7i+MMpw3yhuSrT5yZsu/Xnj5L3pAMpXU+/98nL9eQp/Cd584ynHFS1fOm5LP3HynKcf/j6VY6hjPsa4/y4AIXrhbbIJRLKf9WStlW+PdFoGitydyqQiTg9EiO+N3UlflwKQo+t8qqqiBCCGrLnA5pKyuD+NyOpa0r848eo67MhxAQDrgp8alUl3pRhaAu7KfE56LE5yya6st8U86/vDyAz62gCEYL4UaOXRny4HEpeF2KU+Q06bzjKXIr53llfa3j7lJVZzUlhDhvSu5cqA/7EcJZlleFijv7G9/i1udS8LlUFCGoK/OxrCKARxX43cqEBuh1hc9+eUWQUp8LVRGsrAyO++54CHhcVIa8uJUr6MNcQDyFVV91qZfgpIy9166tpuB1pSE89b4buRcVAa9Zt3Bdya5rmrhyec3a6qIcd13hHvO4VDbUL2yvdXEx0guzHkyIfwL24bTDBGeVcLWU8vPFOP7WrVvlY7uepzuWJRJyA4JUzqDU5yESdONSBLZ0ZiOVIS+mLUlpBuUBDy5VQTctTMsmljUo9bvJGzYuFUwLfC6BIgSWlOiWpDLkRTMtPKpCWjMJuFUMWxLN6CSzOnXhAGUBN1I6Mxe/WyHkc5aPummTyBlUhjzolo3X5RimleuuZuMffI0H/sdri3E5LhvffOo065qC3PPOt/C1Hz/EznV1RT/HgfZBwgE/Ky+y4vxCeNu/PsmHt9XzpT+8m/+8/xFMCQ0RP+VBL60DKYJeF8vKgyiKwLBsDMNmOKfhVVUM06A7obGpIYyqKGQ0A0UIsoZFZcF4KUIgcLLgRl6rikBKiWHJ0Z8v1RW2detWhm77m1m3W4xtNbdu3coHvvxd3n1tDXXVlVPef/hYFwfbk3z2zRum3f9fHzvJlhVhblk9NbYjpZxwn80HW7du5fnn9/DosS6+91wH797RzJs2N+BWBXndRFVVFIXRMYx89p4LnP09d2aQZZEATRXzU/8ihNgvpZwaoJlEUWIIQogUjkdEAJ8Cvld4SwXSOHGFohAOuPnZwSgPHe7DsG021peSyBkMZzTCfg8NYT8uVaG61Mdt66t58IizBLtheYT/fPYsh7uTlPhcvHVzPevrSjnSnSCrGzx1coiUZrB1eYS7rm8E4FBnnFP9KYbTOiDxu13kLZOzA1mEgLu3L+ePb1/DC2eHaR1Ic+2yMDvXVjsz3RIvvz7Sy8m+FBsbyrh9Qw3dCR27J8eqzz5I698tvpt2Om7420cZzDj6P/2DGX7v+y/z4Ztio20pi8HHvruXx04MIIC/edt6PnhT8VJyV37mQWzgU79sJTaQ5p4fvkx1iZeA18UNzRFURSHgUXnvtmUMpjS+vbuN51qH0AyLvGGRyBpIIBJ0s2V5Odcui2BZNkIItq8o54XWYccgKDCY0pC2pCES4J1bGnn0WB9nB9NkDYu6Mj93XtdAfXj6VeMrnSPdCb7yZDtfebKdlz9zM+HwmONgb9sg93z/EDZwpDvJfR/fMWHfLz94nB/t7eS+vV38+OMhmivHJg1SSu4/0E1nNMv2FeXctHqqsSkGumlz3RcfJaM7WU57/usIX3jwBDeuLOdoTwrNsHj1mirefE09t15VNSptc+uaSrYsP7/UxgOHurlvzzkqgh7+/p2bKfEtXK1FUda7UsoSKWVp4X9FSuku/FOklEVdA+mWzZGuBDnDJJ7VaR/O0h3PkciZ9CXydCdyxLI6yZzBoc44mmGjGTYvnI0ylNHJaCbpvMnLXXFODzjlD3vPxsjqBlndojOW43R/mtP9aUxbcro/hW5ZdMfypDWDzuEcWd1EN232tA1j2ZLWAcfv3dKfmjDW0/3pwv8Tf38lZaqNGAMYSauVPHWquHUJLxZiPBL4yd6u8298kYxPUrQKM8lz0SyWLdnXHkM3bVJ5k954jtaBNN3xLJphEcsaZPImZiHFOZ41iWcNWvtTxLMGummztz2KaUv6U3m6YzmiGZ3BtCOJcmYgRW8iTyJv0hPPo5s27YtInG0h+dHB/gmvf7CnC6uQ8nu4OzFl+2fPOPHBrGby1MmJ0hU5wxqVj5l8nxWTtGaS1SemvGY1k2M9SXK6M3loH87Q0p8imTNGpW1a+mePie1tiyElDKV1zgwUN4Z2sRRrhbBOSnlSCHH9dO9LKQ8U4zzgLMlu31BDWjOxJVzTWEYsqzOY0qkIeRx/NFBT6uOWqyrJ6BZSwo6V5Qyk8uR0ixKfizdurGNFZZCD52LctbWBB17uJZk32LI8wvaVjkU/0BHjdeuqaR/OsqzcSS9tivg52pNEAndd34CqCLatKOdEb5IbJolu7VhZzpHuBNctmxhGifgXRSvrC+K6xlIOdjmBWVUReN0qH76puA3P796+nG8+cxYF+Ou3Fm/lAVDiUUnpTuDX61KoCHpYWeVUt+9cU03OtAh6XCyrcFyAJ3ojJLImVSEvsazBYDqPlNAU8bO62skk0wwbG8mOFRU83zpEfdiPIqA/6TwEakp9bGooI5Ez8bkVGsI+yoPeBfcPLyTjQjn83s61E977s9ev5tFj/eiWzdvHKQWM8P5ty/i3J89QHvRw53UT3w94XFy7LMzZwQzbVlTMx9ABKPO78Ye99MTHNK5qy3zcsaGWF9uiaIbNDc0VbF9RQTjg5ur6UjpjObY2z56F9pbNdfzf5/LUh/1cXb+wMhjFejJ9Cvg48M/jfjd+HlwUp7lu2fz+9/bSG8vSl8pTVeInkfHSE8/RE8/Rn3ATdAn6MzrHuuM8cqSHoYyGIgSnexOkshqmYaKrgm8+3YLfrVIR9NCXzBHLaFSV+OmNZfnCA0dZVR1CSEehtCLkonUgQyJrUhH0sHNtFe2DGX6+v5sHDnahGRZ9CY1f7O+kLuzHpQo8qoJl2/QkdXoSWapLxoJlgfzs8tqLhVebydHKQtOWpPImFfFTQHPRzvHpN67n67vOYgPbVhZ3yV9qW6Mdm8r8LkDSNpDkVWtq2NIcZveZYVwqPHGinyeP99EZzdIVy6DrNvXlAWpLSjjRl6JtKENfIsf9L53D5XZqSjRDIhRBwC1QVRdraoKYtkAV8IuDXXRGc6yt8lNTHqA3nuWqqhDbV1Xwq8PdpPMWq2tCGKZka3MEVQjODKaJBDxUlSxMWuV8cr5FcWOkhGxBcryhcuoMeduKEmIZDa8LwsGpQeeakJfWgTQ1pdNft58f6MKlCN567dR6or5Ejl8d7uU1a6pYXTNVob8v4XgGVEXQG58oeNgZy/Ot59oJuSFtwPCeDN3DKU4OZNjcUMrNa6r52f4uqkt93LqmakLigmHZtA6mqSnxcUNzORUhD6U+9wXHHOaLYhmE/xRC1EopdwIIIT6Ek3raDvyvIp2Ds4NpksfGlox9qRRHeiYuE/edmz6lc2Kq5/RKpn3pNEd6nS/ky4VZ8WR6khpHemdYmiY0jvZN/UIfOBfncOfYUrj7CnIZfbVv6u8+8ZRJ++uLd47mzzw44ediBkW7x9ne7ngeu99x25wcaOPnL3ehCEFGt7Atm5w58YOJ9U78LA2t4DLQYfQRZ0kyhgR0+lJTe3Oci+WAKAJ45PgAf/y6q/jmM2fJ6RZBr4vGiJ87r2ukpszHgY4YLkXwwRubKXuFyClPx4rPPEjbuM94/Of/jw9F+YNbJ25/x1dfBKAzpvG2f93FA3/06tH3EjmdT/7oIHnD4qmTA/zgYxPjD/fuauW7L7QDkNZN3rdt4ur2D+87SG8ix4/3dvLwH92Cqo4FpgdSeX68txNbSk72JplpqpIueFVjeckDR53n0+mBDD9/uRcBqKpgb3uUj926alSI89Fj/ZzuT+F1K2yoLeVgZxxFCO7esYyKImfaXQzFMkffoHCbCCFuBf4O+C6QAO4t0jmYRkHiiiGrL+WrLzZ0w8aWjvSJdRkkOkxL0lOQ3pZIDMs5f0ozyOmO5TJt+YqvbbiUK903SZZcMyx003kwZLSp1y2aGTPSQ9MY7EzhuuumxeRbVDPsUdkZaw5SJiMyNlI6NVRZfWx2MnJew5SjMvy2lOTNhX3IFWuFoEopo4Wf3wPcK6W8H7hfCPHybDsLIb4CbAUOSCn/aKbtGiN+Kso8DCd1dAk+FRrL/SSyOsm8hUtRWF7hJ6NZaKaNRJLWLFTh+PviOYNY2kAIx7ioCvjcCmnNxgK8CgR9LvK6RXnIi2XZxHM6bkWQ1SUW4FFgWWWQaEZD0y1ciiBv2mgWeFQIeZ28dZciMEybrClZXh7gM29YxxN/X4QrvQgoL/KqdlOtypE+52788Z3zkyUCUOpz4VGcm/Sq2hI+fvMKnm0dwqUq+N0qT58aYCCRI2c623hVwCpI8c6CAEJuEIqKqgh00yJvSkp9KhUlXlQEb7+ukd+9eQWGlKTzJmtrS1AVwduvbcCtKvg9LiqCHqpLp7pFXklMXgG2//2bR1cJJdN8t377hnru29uDAF76yzsmvFdd6ud/vO4qdp8Z4gPbl03Z9w93riKjm7hVhU+8amr22ufevJ6f7uvitnXV+CdVCDeVB3jtumqSeYMN9aVciNxCxKeQNSRVJV5uWB6hM5qlvMTH3TuWsXVcttEdG2o42BmnKeKnIRyg1O8mHHDTsMBZaEUzCEIIl5TSBF6HE0+4oHMUAtFBKeWrhBBfF0LcIKXcO922lpT0JsasfN6CM4Nj7h/NsjneN30mR3JgXBOLgrE3bdC0MYus2aBlHWudjedHf58bN6fRbGgZGH+Oce9ZY/uP53hfio9/b9+047oSiRZ5EjNiDAD+5dEhfry9uMcfQUooC3jwKBDPGHxj91nKAx5CXpU4CvGsxviPb5oJ58zHBlIGwMSdYjmLRC6LS4F/euQU//uRU3hUp1ZBEYJSv4v/PtTLZ964lh/u6WAorfGWa+poigS5eXUlfYk8x3uTbKj7zQhIK9N4yp5vGctCm46NDWWO+618ag6/x+3itetqEAKU8VWKBXwulapCGvJ0XFNw8RztSTJb9Y0KXFVbimHDinI/BzvjZDSLWM6kL5kfXW188r4D7OuI4XcrXF1fRk2ZjzdcXcvmxqLV8AIQS+v8r18dQxFT/+6ZKJZB+CGwSwgxhOOgfxZACLEax210Pm4EHi/8/DiwA5jWIHTHclypkmN58woKHCwge+YxMzOtmQymxyYUvQmn+tXrUpyCtHlardvA+IxFx9A4sqoZQ2c4Y/CnPzlENGM4iQjxHO/augy/R+XlQup0+/CFX5jxPnlYfIVqk+NE48ebmGY51j4umLvqMw/SOm7fvGHxyLE+pIThjM4HdkyMERzuivNypxM/jAQ8ow/4Ee595iyJnMHxniSvuqrqkoK6FvBSe5ygR+VQZ3zUZTSU0bl311nqywIMpnI8eXKAjG6hAJ3RHOvqSukYyrC1yK1Bv/5MK/vao7NvOI5iqZ1+SQjxBFAHPDquWY4CfHKW3cM47TbBMR4T5AOFEB+nsOLwlBWnVHyJ30yEcFw7IxWUinCkLVwuBQkYCxSkUoSgIewnkTOQiNHCpPKgh0jAQ18iT+QS+jtPNhAwvZGYbrsL2W+248y2T9AFmQtMvKstnbiEcCmCkNdFKm8SmSYQHy78TgimvYaVIQ+JnEGZ310USRmlcC6XIrAsx80shCAccFxCIa/jUhQAwknldqmMVr0XkxWVTisacRErhKJKV8wFIcQ9wKCU8idCiLuARinlv023bWVlpWxubr6s45sOzbSJFwS6/G6VUv+FZYScaGmlvKYBj0u5pBt8oWhvb2cxXP+LIZrRR/X2c9G+eRu/ZUuG0s5M1qMqEzrpFYvprv9ASkNKiSLEok5XvRK/O+OZPP7htIZZCDRXlzgaV4uZ/fv3SynlrCZvMVRIvQB8Akf/6DbgOzNt2NzczL59C++Lt23JU6cGSOYNdq6tJnyBD/d1m67li9/5FTevrphR+G4xs3Xr1kVx/S+GrliWF1qHWVYe4J53v35ex//i2WE6o1luWl05L8HB6a7/0e4Ex3uTXNsUZs00efSLhZGxL3Z31kxMvvZtQ4466arqENdfARLoQogLKg5ecIMgpTwghMgLIZ4FDkkpX1roMc3GdP2YL4SQ18VvbWmchxEtMRONkQDv2np5+kHvWFnBjpXzVy07HRsbythYZPXZJWZnRWWQFZXzI0S3kCy4dq8Qohl4L05MZtauMfGMzqHO+Gju7uVCSklXLEtPPDsqUXCh9MRzpDWTvGHx/RfaMc0rp1IZnGrNRM6YfcNFSDZr8LUnTnOoMzYvx9dMi4cO9/DI0V56E9MXPBaLrG7RG594juG0xq8Od5PITc2xX2J+2Nc2zHd2n51Q4/BKoegrBCFEA7B8/LGllM/MsttjUsq7Zzu2aUu++OAJOmNO68xPv3EtAc/lWeQ8dWqA588Mc7o/xebGMt6wse6CZmbPtw6x52wUn1vl7FCGf3j4JA8d7eO+SRWVi5WD52I8fWoQ9zQpe1cCb/v6c5yLZvnGrrPMh5Pur39xlIeO9GHaNjeuquSenavZsnx+XAh9iTyf/tlh/uGuzdQVXFK/8519DKbz/L/IOX7yiRvn5bxLjPHs6QH+4L6DaKbNj/Z28n8+dANN5ZdnBXo5KHYLzX/AKUw7zlhCtgRmMwg7Cy6jn0kpvzLpmKNZRvWNTaQ0Z6YazzmqkpfLIAyldfKGU/BmWmMBxAvZD5z0uJH4fX/i4lYYC8lwYfzGlSTROo5odmT8dtENgpSSjuEstrSxbEk0rTF8gd+LuaIZNgOpPHVhP5ZlEc0655vv8y7hcKI3hWnZIKVT6JrVlwzCeXgHsFZKeTHfzl4cV5EG/FII8YSU8vDIm1LKeynIX2zdulW+/dp6DpyLc+tVVRME4+ab16ytwutSWFUVoi7sm6JsOhOvWl2JWsgAKfW5aCoP8MmdV83zaIvH9pXlGJZNqd/NDxZ6MHPgU7ev4etPt7KutpRDjxT32EII/ucb1vDFX53EsiXvuaGJHavmL4bg96i8/uparmlyViCqqvKxV638/9s77/C4rjLh/97po95lyZYsO+49ruk9QBJIAUIgBAiQDexH51sgSw9Z2u5H2A0BEiBLKIGEBKeQ6hAncZod9y53SVbvUzXtzvn+uFfSyOrWjIpzf8+jRzO3nPveO+ee9jZePNjE+5b1jxJqknw+ecEs3jzWxonWALesK2fhGeYwmOwO4ThgZ2Te/gAYnUcYQESeBpYAewY7/uqlpVy9dPwrf1Gmi+sGiJY4HLnpDq5Zpvs4luWl8eyXLhrmjMlFpsvOVUuTnyFtvLjlnApuOacCgNX3Jr/8lTPzWf+585Nf8ACUZLv41AWz+mz72LkVfOzcinG5vomexvfBT62daDFSRlKUyiLyCxG5BwgCu0TkfhG5p/tvmHMTbeXOp9dJ7bSo7+yirnN45V5NW5DmBOVwdVuAZl//pZyuiMbRZh+h6NgDjgXCMe556TD+KRT+OpFYXFE9Co/ZyYC/K8JDm6s50DCcw/xpjwJPAAAgAElEQVTweIJRjjb7+wU6i8TiHG324w/HRlVfvCG9vJg2Moe4cCzeT5FZ19HFn96qotmbWoW2iY4nGGX9jlp2nxw4qvLpoJTiRGtgxMvQqSRZM4RuA93twFOjPPdCEbkLfZbwulJqy+kKcazFz1O76gF43/IS5hQNbJe962QnL1c2IwIfXlNOg6eLVw61YBHhw2vLKE4ILvbY9pO0+iMUZ7m4eYDgWaOhuj3IbzcdZ9PhVh771/PGVNZE0O6PsH5HHZfML+TsKWB7DfCVR3dzoN475ny7oajGX96uIRTVWFyaxbsW9wZReW5fA8dbAmS69MCGncEopTkublozeH0JxzT+uqWGYERjwbTMEc3COoMR/rKlmo+dW0G24Qx5+5+20hmMsn5HHY+P00zlnUooqvGtx/ey82QH6U4bP37/sqQYEGw50c5bx9qwWoRbzplJXgqcGkdKskJX/AFARNKBkFJKM75bgSHdJ5VSzwLPJkOOxJG3d4hRuC8UNa4N/nAUX6g3/KwvFKM4YVmwu5zuc8ZCt1J5qppwKiO8mG8KzXA6gvqzjoxwFD4Y4Vi8Jyz1qfffXUcC4VhParDhnlFMU3QZswjvKOpWVFOEohrZbjuapuEPDyyTSfIJx+J4jN8qpilafckZ0Xf/dlpcEQjHpn6HkMBL6N7G3ZlF3MAGYFyGw4tLs/CHYygFy4YwCV1TkYcWV6Q5bJxVmMGM3DQ0pUh32DirsK+zyfuWlVLZ6E1K+sPcNDvzp2Xx1SunjlI5kQynnq5w7azkBuFKJd+8aiG/e/0EK8tzuO/R0y8n223nXYumUdfZxepTRoXvXlTM7loPswrSsVuFQ40+Fg9jkpzu1NO41rQHWVk+siiXaQ4rly4o6pnBWq1WvnrFPJ7f33ha+i2T0ZHttvOly+fy583VzCnK4PIFyYmtdt5Z+VgtkO12TLjFUrI7BJdSqifNlFLKLyLjdoc2q4Xz5wwfT99lt3LJ/KI+3y+dP/CPW56fRnl+cm6hNMc9JZeKukl32gZ9TpOVlTNz+ZXRgN83xrIWlWYNODAoynJx5aLeZcaZ+SPzYJ0/LZP500YebiLTZe/JuNXNdWdP57qzzc5gvFhdkZf0qKTpTj1E92Qg2Z7KASO/AQAisorB8lUmAaUUhxp91HYEhz94COJxRWWjl/oRKKMHorYjyKFGH9VtAY40+egOGNgRiLCvztOjYAxFNZ7aXdcnc9JkJxLR+N1rx3l+3wC5NJNEPB5nw/5GthwfSQqS0dHmD/PLl4+w5cTwZQ9Un052BHlyVx2t/tF7px9s8BKP60s8++o8dIzRszUU1Wg8xYflUKOXX758hKrWvuk+6zq7qGzUr2+SPF451MzdGyr5y5aqUUcsmAoke4bwJeBREak3vpegO6qlhK1VHbxxtBURuGlN2WkHjNt8vI0tJ9oRgZvXlY/Kv6HB08Vj22vpCEQIRDSm57i5dEERi0uzeHjrSUJRjSPNPm44ewYNnhAPba5hf52Hf7960WnJOt7c9ewBNlY2jyqE7mj569aTPcYAX3v3/KSOwP7vo7s50uTj0W21wx57an0qznRy51P78YVivHKohZ/ftGJE12zxhXl0Wy1xpWjzR2j2hahuC+KyW7ntwlnYrac3DvN0RfnbtpN8/NyZPQEVv/jXnfjDMZ7f18g/vnAhoOcCfnTbSZSC9tkRzjsrdVno3knsrO7ge0/up8kbIs1h5Y2jbfzyo6smWqykkrQOQUQsgANYAMxHV69VKqVSpkHtVvIpRU9e1dMrJ95Tzmg9ciOxOErpCiHNiKcfjmpocdVjThiO9pYPEIxMneTQ3UrLVIZJDybEpUp2jKqQkSg3NoKR8qn1KR7vrRujMTvWcyWrnjK7y4glbD9dtLjqUZBrmkbEqK/hhOw+3XVSv/7UqWuTnUA0RlwpFApNQShVGZUmkKR1CEqpuIj8TCl1LrAvWeUOxbpZ+VgtQqbTPuJ124E496x8nDYLWe7R5zSdmZ/OFQuL8YYiOKxW4kqxamYuNquF61ZMp6otwLIZuoKxMNPJxfML+cDKqRPx9NvXLOTuFw8xLcvNHx5LzTU+es5MrBYLWS5bH91OMrjzusU88PoJVpXn8l/DKJUHqk9fvmIem4+3jUqBWJrj5l2Li/EEo6ycmUsoqrGn1kNFfvqYzF/TnTauWjqtZwZrtVr59jULeG5fE9ef3eusOSM3jSsXFePt0q9vkhwumFPIp86vYMuJdqbnuqfUezxSkpogR0TuRPcyXq9SMKRcvXq1mmrx+BOZivkEEjHln1imsvxnWj6EqYaIbFdKrR7uuGTrEL4KpAMxEQlhZCxUSiUt4MeO6g6qWgPUdQZ47UgbDpuFi+cVUpDh4KlddfjCMYoyXcwpzmTdrDye3dtAiy/MjatnUN0WZHdNJwtLs7lgTj7P7G2gqtXP3KJMqtuD5Kc7Kc93E4zEyUuz8+zeBgKROHe8ex77G710BqM4bVYcNit5aXYCEY1L5heS5rTx3N56PMEYjd4u/KEYWW47WS47mS4b80uyKM1xs7fOQ8Udz7B6ejqPfeGSZD2SlPLTp/bw6zdPAtBgyP/nj6/lgkWFSbvGrqpOPnD/GzhtFg7cdVXSygW46CfPU9OpL/eEO7tYcefzOG1CbpqbZTMyef1wCxrCNUtLqShw0+oL89bxduwC03Lc1LYH6AhpTMt0srwsh6UzcohocZq8YVr9IQ7U+/j8ZbNpD8SIaXHes6QEm9XCidYAncEIS6Zn9+gM4nHF/novLruFuaeRzKa7/iQ2otf8/BX2NwU4pyKbhz97Qc/2e186QnVHkG9dNZ+cdH1G0eIN8/Hfb2F6jpvffWJNz7H3v3qE326q4pplJdx53ZKe7XdvqGR3rZcfXLuYmacZ+//OJ/ex6UjraZ072fjFS4f52YtHer6n22BRaTaVTX7mFWVw5/VLsFos/PS5g2S5bPzHtUt4cHMNEU3j3YtLWFyahcUinGwP0uIPs7g0C6fNSn1nkG89vo/5xRncMcG6xaR2CEqplKZsCoRj/GlzNbtPdlDTFqQ7b/2O6nYsIviNtXnBg/tQCw9vqaYjGCWu4M1jbYgIXZEYLx9u4YmdtVS3B4lpihcPtOj5dgVcNgsWi4VYXCMY0S/wyT9uI8tlx9cVBRHSHFa0uKIw08mbx1qZkZfGi/ubaPaFiMbixBVYrGAVIcNhoyTHzSXzexvQbXVTJ/xDd2eQyC1/fDupI7sb7nsDBQSjcVb+4AV2fPfdSSu7uzMAPZ2ms0v/3uTzUdnk69n3+zeryM+w0xmMJegbesMTHGkOsLW6g6Ist64LIE6zN4JVYOfJThaWZCFAKKq4aF4BT+6qQyndae3iefpvv72mg9eNxvGGsy1UnGYju+x7z7DnTv3572/S69Lmqt7QHOu3n+T+TXoEmCZPiD9+eh0A1/3ydeo9IQ42+PjW+r388P1LAfjJc4dRwB/equaOd52F2+1mw74G7t90AqUUn3xwKxv/7ZJRy/nqoSYe3FyNUjDV1dqBcKxPZwB6HuitNfpz337Swzce24M/onGyPYhFYHedh1AkTjAS41CTj9svOos5hRms31HXY3Bw5aJiPvn7rZxoDfDmsTbOKsrgxtVji4gwFlKRDyEXmAv0mOqMIB/CiFAMrNwcbG0q8VCl6M17qk7ZOYJzB7vWoEUZmdy795vGfyNjQlN8D3NtXTbV47HdZ4dRweLE+9SJRCXyUPVoNAynJ9YSLpR4aOL1uw0gBiOaoIQfqyL8nYJC9a2/ib93vLcd6K4/3c+1+xzdOGV8ZB2MZOdDuA3d9HQGsAs4Bz1n8mXJKD/daeOWc2ZyvDWf+vYgrx5pxWG1cMn8IvIz7Dy5q45AWKMgw8W84gzWzc7nmT31tPrC3Li6jOr2IDtrOlhYksVF8wp5ek8DJ1r9zC3KoLo9SK7bwazCdIIRjbx0B8/ubSAYifH1d83nYKOf9mAYt92K3WYhL91BIKxx6YIiMpw2ZuS68QQiNHi78HXFyE6zk+myk+m2sXBaFjPy0vi2cR+Li8cvbPdY+cjyYv66u6nPtj9/PLnRHh//7Pm8/743cNiEnd9L3uwAoDgDmgwT/dw0B1lOC3abhVy3kyXTM3jraBuaWLhqcTEV+em0+ENsPt6BzSKU5jip7QjSEdQoznKybEY2y2bkEtPiNHpDtBhLRl+8bA6tgQgxDd67vAS71cK1K0rpDEZ7DAoAVs3MxWGz4LJbxpR+8cBdvbOz2QVujrd2sawko2fbjavLqesIUdUW4NvvXdiz/dF/Xcunfr+DadkufvLB5T3b/+1d8/jd68e5ekkJbrduVHHNslL213nYXevhzutObxnj4vnF3LymnDeOteAf/vBJTbrTxu2XzuZ/Xj7es81lg7lFmRxvDXJWYRr/ccMyBPjpc5Vkumz84Lql/GFzFZGoxtXLSlk6PRurRbh+xXRa/WGWGnXj959cwzf+vof50zL58NqJmx1A8pXKe4E1wGal1AoRWQDcqZRKii+CqVSeWEz5J5apLL+pVJ5YJkqpHFJKhUQEEXEqpSpFZH6yCo8rxU+eO0hdR5A5RRm0B2PMLUxn8/E2glGNdLuFnSc78XRFmZmfzjVLS/CHYzy5q45ITLFqZg6Hm3z4wxr5GU7WVOSyuCSHJ3adJKYpSnLcOGwWLCIsLM7g6f2NuG1WCjMc1HtCiIDDasVqEcIxjROtfrLdDs6fU8Duk51kpznISbNxsN6DNxRD0/R1o/klWXzt3fN6lIJu4OAUeREu/dEznPDqn7uVyl+8qIKvXr04ade4/6XD/NhYn012AzHrjmd6Zu7tTb6eBinLaSXXbcVqt/HRdTM5uyyXP22uYvOxFoLhODnpTuYXpVHb2YWmoDDDicNmwReK0RGM4nZYiGkKm0UIRjTCWpzSLBfeUAyH3cJVS0rwhaLsrfWQl+5gVXkeGW4rSkFNexeFGQ7ev3IG+xu8OG0W5hVnsqOmg8IM56AK54GUyokNbOL2D/36TRq8Ie776CoWGyPRI41ePv3HbRRkOFj/f3oV0D/8xz4e3lbLxXMLufeWM8vRKpks+vYzBE9xk8l0QJrThqYsrKnI5aK5hfjDMdbOysMf1jje4qOuM8SFcwpYNzsfm9XCPw80cqItwI2ryqhuC7D5eDuXLyhizmkYGgxFPK7YebKT0fiUJrtDqBWRHOAJ4EUR6QDqhzlnxLT6Izyy9SShSIwXDzSTk2bj0a6Y4azTd2V3X71Pj12vKaLGjuf2N/fsb/JFONrkw2qtJRTV132tNZ2ICC67hcd3xtE0hUZPAMtutUCf69R7IlQ2+hH09VoLfddtARq9LbQkREacSpHruzuDRO7ZVJXUDuHHCcq6Uxu8sZL4WyU6aXnDGt6wBkS4e8Nh5hSms7feS/fSuSccpLq9N4TFoaZAv9/+VGo7ekMZHGr0YxH9mlaB14+2UZzlJBKL4w/HKMpyUdUWJN2pv4I7azp7ch3cco6DwszBgwR3P6O33nprwP3fWr+Xt6s7APjEg2+z7dtX6uU+8DZNvjA17V189ZGd3H3T2QD87o1qFPD0vkZ+4O0iLysV2aenNrUdQVwD+Ez6IuAzQtE8t6+JbdUdTM9x88zeBsrz0thu/A6VDT6sVgs5bju/fe0EAK3eCAcavURicXbXdvKrJHs9763zsOlwy6jOSbaV0Q3Gx++LyMtANvB8ssq3CIjRPFsEQLBYBE2pAV9Wi0X0WC6DvMUiYLVIT4Mv3X8CFhE00c9N7BCgf6M/bAcs4LQlO2yUSbKwWASH3aqPpIZo8YfrEBKxGnUI9FSbVotgtViwWlRPPXM7ep3UXHa9flhEsFmGrlHdwZHPPfdcePKZfvtz0myI6EpKu6W33tmt3fJAtss+YNnugTenjFOXkGByLiNZh/lNQP9NrQgWwGq1YLMKFhHiSmG1CHarBaexAhFXCpfDgt0qRGL6ykOyOZ0QKUnpEETEBXwWmAPsBR5QSr2ajLITKchw8qUr5lDnCTG/OINmb5iFpVm8cayFcCROpsvC5uPttHojzCvN4n1LS/FFojy+o45gKMplC4vYXuPBH45SnOlm7aw8FpVm8/iOWuJxRWmuvmSkxWHZjCye2lWP22GlNMdNVWsQl92CxaL/4FpccaDBQ266k8sXFPHW8TaKs1xku+zsb+ikLRAFpYhqiqXTc/jXS8/i6bv0+zi7dJzfujHwxcvSuGdj3+CBf7xtySBHnx6vfHYxl9y3H0h+Y1CaAfWGRjPTacNmBYlDWX4apVlO4gK3njebOUUZ/GNXLa8eacXXFaUgw8WKmTkcbwoQ1eKU5LqwWYRAOEq9J0ye20ZYUzisVgKRKL6uGPOnZdLkDeOwW7lqcTG+sB7ULj/DwfKybNw2K0qEhs4ust0OrlhYyJGWAE6blbJcN5WNPvLSHeQOEw//8DDP6GvvWUiTL8TxlgD339Lrb/Ds5y/gU3/axozcNL6X4G/w4/cv5hcvHeVDq8t6lMomfSnJdpOb7+RIW+9MP8MBBRluitJtdEYUl88v4tyz8mkLRFkzK5f2QISrl5RS5wmyZmaebposwtffM5/qtiDvWVxMky/Mtqp2LpibPL+ebhaVZmG3CiLCV0d4TlKUyiLyCBAFXgOuAqqVUl8axfk/B1YDO4Y6z1QqTyym/BPLVJZ/MKXyQJw6KJgMs4ip/Oxh/JXKi5RSS40LPwC8PdITjXDZ6UqpC0Xk1yKyRim1daBjG72hEVWoyUhxpqNHKQiTc1o8EInPuyFF8ide42YL/OhHqSm72zmq2Rtib52HOUV6cqQX9jXw1O56Nh1q6qc0TCZ2AYfNgt1qIaJpiAjTs90sK8vmtgtn8bvXq2jxhnjfiulMy3KzbnYezb4wlQ1eFpRkjUqpPPuOZ4gDX77sLL78rgWAnoLzz5uryUt3jjkd7DuNxHd3KNLtQrrLzux8N4ebAmS67Vy+sJhvvGcBTru+LLS31sNftlSzraqdyxcVccdVqfFODkV0R17LKLTKyeoQeiKaKqVihpVRBbAFOAhElFLvEpGvAdcB1cCtRiTU7wALReQh4El034UBO4QWX5jhM89OTpp8Y4uF/07hL3H4UYqv8ezeBjqCUQ7Uezl/TgF/eLOarVXt/YwBkk1UQTQah54omYrDLQHqvSH213up7QihxeMcbQnwwVVl2K3CzpOddEU0jjT3WvJ3dwo//OHADdSauzb03Mt/bzzW0yH88a1q3jiqe0qX57lTskzxTicQVQSiEZqN9729K8b6HbUsLMnixtVlNHtD/H1HLet31qLFFX94q4b3LitlyfSRZc0bDY/vquefB5uHPzCBZHUIy0Wk2x5F0FNn7kX3Vl6plMoSkULgUqXUBSLyDeB6EXkFWITuzLYcWAL0GaOJyO3A7QDWLLMCm4yddKfNMB21kumy4rQLVgsM47ybMmwWITdNN22OK8FljCTTnTbSnTa6IlqPNVIi3/rWNfx2gFFraa6bloA+RkscG+al6bori8iE5u1NBaezFDXYecmevduslp7n7bRZcdotWC2CFlfYRMhPHzLt/GmTfxq/cVId0/oUrM8Q3gCOA+uBw8BipdR/GpnUbgY2ArcBfwWOAd8FXlJK3TNQmQUFBaqiomJUcrQHIkQNf/CCDOeIrAWGIxKL0xHURwDdPyzoqTizhzDTqKqqYrTyTzQ17UGCRk6BcEcjztxppDusE5779XRI5vOv7Qj25IrITbP35DlOJbsPHsGZO42SbNeQ9Wwysu/QUdLy9Pn9zPy0Pu9h4vs03Ds0UUzFdzeR7du3K6XUsGZHSY9llEADMA8Ioy8FZQHdMRA8QC6QAxwALgd2GMf/MLGQxBlCeXn5qBU7J1oDvHG0lfK8NC6al5wZRjyuePFgE23+CBfPK2RvXScdwSiXLywaMtvaVFRMbTnRxk+erSQv3cGT3/84F339d3z3vYtYUT714uwn8/lvr+7gv16oxGWz8L33LWJWYUrjOgKQU76AD//wIe65aQUOR/LNFFPJvCXLec+3HmT5jGw+feHsPvu0uOLFA436O7SgiKJx6FxHy+rVq2m94s5+26eKLlBEdozkuJR1CEqpMHpngIg8DXiB7mzgWeihJDvRO4cM4G/ons5vn1LOb4DfgG5lNFo5ZhWkjyluzEBYLMK7F0/r+T4998w11Vs3K5/HP3c+ALvvTWf9/zl/giWaHKyamcvDt587rtecU5TBfR+bmp7EWS4793zk7AH3WS3Ce5ZMVe3gmUXKvKVEJHHIdD5wFLjY+H4FsBldeXyxYWr6MPDjVMljYmJiYjI0qXSfvVBEtovIm0C9UmoLsElEXgdWAE8opZpP3ZZCeUxMTExMhiCVS0bPAs+esu2nwE+H22ZiYmJiMv6YAXZMTExMTIARdgiic4uIfNf4Xi4iyc2SYmJiYmIyoYx0hvAr4FzgI8Z3H/DLlEhkYmJiYjIhjFSHsE4ptVJEdgIopTpE5MxydTQxMTF5hzPSGUJURKwY4eCNMBQTnA7axMTExCSZjLRDuAd4HCgSkR8Cr5P6GGQmJiYmJuPIiJaMlFIPich29BATAlyvlDqYUslMTExMTMaVYTsEEbEAe5RSS4DK1Is0NJ6uKJsOt5DpsrG2Io9NR1qwiHDJ/CIcw6Sp3FfnobLBSygWpyTbxcXzCrFZLfhCUV493EK6w8ZF8wpHHQDvUKOPw00+ls/IoTx/8KBvbf4I3358LzevK2dRafaorjFRbK9u57tP7icvzcGhRh+r7trAz25cxiULpg1/8hQkEovz9x0n2VvnIT/doUdFtVu5aU0Zc4qSG69oR00HNW1B1s7KozTn9MKfVDZ6OdjgZen0HOYUZQAQj8d54PUTNPvCfOr8WZQYZR9q9PKjZyvJTbfznzcsmzLxkF7YV88Pnj5IUaaLxz57DtZRpJsMRTVeOdSMiHDpCNqIdzrDdghKqbiI7BaRcqVUzXgINRRbT7Rz1IgN3xGMUNUapNEbYntNB1ctKWFF2cBxxU+0+rn7xcMEwjHcdivLy3IoyXazqDSLbdUdHGnSy5yR62Zu8chffC2ueGF/I1pc0eQNcdspgbsSaQuEef1oKx1dUX5588pR3PXE8YOnDnC02YcgRLQ4bYEod6zfx+Zvnpkdwv56D8/tbaSus4tILE5XRMPtsNIeiPCzD60YVVmvHm6hpj3IBXMK+sXT8oWivHpIT4DeFdX4yNrhE9acbA/y0+cq+b9XzsNms6CU4sX9TcTiiiZvuKdDePNYW08c/D9vqeFr754PwL0bj3K02QfAoztq+eg5M0d1P6kiEovzsw2VNHkj3HbhLJZM7ztY+o9nKmnxhWnxhfn1K8f4/OXzRlz2nloPBxv0ey7Ocg3aPpjojLS7LAH2i8hLIvJU918qBRuMoiw9drjDZmF2QToi+osSjcV57XDLoOftqukkpsUJRmJYLIJSiv31HjYdbqHQiEdut0pPPlt/OMa+Og/P7Wvg5UPNxLT+OvTt1e389z8Pc6LVT1SLU5AxdFxzLa5oD0ToCqcwLVeSsVgUkZgiknD/Sp259gRZLhvBiEYoqpHpsqEphRaPEzBCXZ/K0WYfT++pZ29tJ8/ubWB7dTsAnmCUHdUdtPrCbD7e1u+8xDDPRZkji4fvDcV4bPtJdtV2AiAiKBSHm3wk5lOfnuPGbtVnuTMSAi/OMwY6NquFhSVZI7pmsmgPRLj8Z6/wnSf29tu3o7qDXSc9NHi6eHxnXb/93fdgsQhnl40uym5hphMRPQdEQYZpGDkcIzU77R/3dYJYNkMf2bsdVjKcNsrz0inNdlPX2UXFEFFNZxdmsHR6NhaL8KHVZWw50caG/U2kO63cftFsPnbuTJw2C5kuO0opHtl6koMNXjqDERaXZpOX5mB5wujicKOPuzccptkXxmET8jOcFGc6Wb+jlnNm5w+6BKCUIjUZKFKEOuU/wChS8k0lNh9v461jrcwrzmBuUTrnzy2kpj1AfUcX2W4HP3vhEMXZTq5YOI1p2S6UUjy3t5FYXPHigSbsVgverihfuNzCwmlZFGY6afGFqcjvXy/tVgs3ryunMxilOGtkHUJcKVr8EQ7UdrC6Ik+vSwpKc9zEtN4faFZhBj/+wDLa/WGWJzSgX7h8Lmtn55Gf5mDOKGbByaDBE0JaA9S0B/nMRWcxIyGfxuxCN62+EP6wxpULi/qde+/NK/mPZw6wsDiL8+YW9Ns/FLMK0vnEuRVYRMhOm3x5FiYbI1Uqv5pqQUZDYcKIKjfdwY2rZ+APx8gYIKtUN8vL9DVWp82CzWrhH7vr8XRF8XZFiWqqz+g+riBoLC21aHFE6JdhakdNBxEtTrs/Qk66nY5AhBcONJLtdhCJxfnwAEsAFhEyXPaehDpTAU9I69f+hyJn3gyhPRDhrWNtdAajtPgjLC7NYlFJFlcvKeFAg4endtez+6SHwkwnDpuVD60uQ0TISXfQ6guT47ZT2ejDahF2VneydHoOH1lbTldUG7ReuuxWpmWPfh2/1hsC9BlCfoaTuIL8U2anZblplOX212etm5U/6usli+5abzllXeJEaxe56U6y0xRNvnC/8zYfb6c8L51AVKOus4sZA9zXUOSeYdnhUsmIOgQROQf4BbAQcABWIKCUGt955yCICJmu4Xv/dKeNSCyOUorVFXm0+SNkuq09a6/dWC3CtStKOdrsZ1p2GcVZrn7LQeV5aayemUdJlovcdAcFmU6sIoRjcYqz9QQfSimimupRZGW6bCwuzeLGVTOSdOep5/oV0/n9G8dx2izUAzaBG1ZOH/a8qUa600qmS38d1s7KZ82s3J5kR2W56WS4bDhsFjKdtj6pCW9cNYNmb5i8dDv3bzpOTItTkqOfZ7XIkIOU0SJAusPK9St668+Nq2dwsq1rSGOGycC0LCcLSzJZWZ7LtKy+s+fyvDTSHB/w97kAAB1xSURBVFa6ohqzCzP6n5vtorLRS5rDSk6a2binkpHW1nuBDwOPAquBjwNzUyVUMlBKIcbQtq6zi45AhH31HnbVdJKb7uD2C2cz+9KzyHDaenLYdtPg6cLtsHL5wuKebaGoxtFmPyXZLvIznJw3p4DZRel4u2I4bRYKM/X0nJ6uKIUZTqJanEe2nqTVH+ayBUUsm5FDTpqdNRW5XDhn6uSGXlyaic1qIcttx2YRXA4rl82fOvInosUVW060Macwo9+I2mmzcss5M/GGoj0dgS8UpT0QwWmzcO2yUm5cVcbLlc3sqfVgEeHSBUWEY3GafSGavCE+uq6cjYea2Ven70+sP6DXySPNflw262k14A6bhfPPyuvTaK7fXsvGymauXlrCDSsn70AjJ83Bx8+ZyTmzC7CcYsWXm+5gRq6b2o4ulk7vb33nsgkH6j3MzE/HloQUuCaDM+Lhi1LqqIhYlVIa8Hsjz8G44wlGeXT7SWJxxfvPnj5gur0X9jdS2eBjTUUuc4szefCNE+w+2Ul1WwBvV5SsNDtaXOP6FWU48yx9OoRDjT6e3duACHxg5Yye3MHP7WugqjWI027hw2v0pYDKBi97aj24HVZuPa8Cp81KUaZeVkdAt4oAONzkZ9mMHE62d/HA6yfYfdLDr26ZGpmvvvX4Xlr8EVr8EaJxhT+s8bm/7GDvnVdNtGijptEb4u4NhynLTeOOqxf0m/W57NaeuhCKavx203Gq2gIopeug3ru0hCav/psebPSS5rTyj1311Hu68ASjLJ+RTYbLjohwuMnPuWfl0x6IUJrtxmIRNhxo4o9vVmG1CP9+9QIWlozO9Dgci/PPyhae3lPPB1eVoZTix89VEozE2F7d0adD8HRF6YpoTMueHOkoT7QG+OYT+8h123nt65fidvbO6N8+3sbLh1qIxxX3v3qMX5xigfeT5w6x62QH26o7uGBuAZctKD61eJMkMdIOIWjELtolIv+Jni85uXkpR0hVWwBfSLfSOdrs79chaHHFgXovSil2nuwgL8PBvjovtZ1dBMMxumKKsC/Co1vriMYUkVicL14xh9qOMEWZTjqNZN9K6S9VmVFuJKavmwfDGn96q4qoFiemKZx2K10RjSZviBZfmLx0B+2BCBUF6SyYlkmDJ8SqmbpiLxZXBMMxjrX4x+VZJYNGX6TfNl94auoQ4obuJhCJ4QvFKMhwopTiYIOPcExDiyuKs1yU5aXRHgizrbqDjmAEu8XC7MIMvOEo62blsafOQzwe565/7Keuswu7RQhritZAhI+uLcdiEZZOz+ahzTX4wzGWTs/mikXFHG/xE9HiiAbVbcFRdwig67d213TwwVVlaJqG13gXOoPRnmM6AhEe2lJNVFNcPL+QlZMg/3VXVEOLQ2sgypFWH8um5/XuFCEc1dCUIhzrb83VFY0SjGjYrL3voUlqGGmH8DF0E9XPA18ByoAPpEqooZhVmE5+rYOoppg3rb+lhNUirCjLYf3OWpw2K/vqvMwqSCfDaeHtEx1YJG5MOxWvH22jK6pxvDXA2ln5WES4eV05W4630+wP8cBrx6nzdFGRl870PDfleWmUZLm4b9Mx3SzRbSfbbeeqxdPYWNlMZzDKwQYvC0uy2HWyc0CfhK5onAJTyTUh5KY7WDkzl1XluVQYSzYHGrxs2N/EkWYf2W47NW1B2gMRFpZkkem00eILkWa3Mm9aBmcVZnDPxiO0+yM4rEJNexexeJyQgjSHjUynlRl5aVy9tITdtZ08vacepSDNbuWKRcWsnpnL8/sacTusAy6NjJR5hoOcJUE7m9hMegxDCdCdIScbTkvfJdqSHBdpTiuBsNZPnwfQ4ougKVAahKMDm/+aJIchO4RuZzSlVLWxKcQEm6Bmuexct3w6rf4QJ1oCCL0WQIGIRprdyiXzC9lW3c6+Og//+/px5hRmEIsr0uwWBBtpThu3XTiLR7adJM1uoaY9SE6ag65IjOo2PztrOgjHFB3BCGkOK9WtAS5fUMysvDSKs50UZbpo8YUQdGuOiBanwdNFszdMKKpxvMVPo6eL2o4g58zOZ2Z+OrMK0lFKEVdQ1R6cyEf4jiXDaeMb71lAiy9Mqz/Sx1oNIBqLs6/OAyiafCHOmZXPWYUZtPkjVNZ7ONbk4x+76lFGWZpSWBAcdgsFGQ5WlOXSHgzzwGvHCIQ12gIRguEYGw40Mn9aBvWeEGtn5eG0Wajr6MJtt5Kb7ujRdY0UZayjR6PRAfeX5bpx2iy0+MMsGmd/g5EQOMUPp6kzRFdYIxLVqOvo/26EonG6n1B1m/nupJLhZghPACsBROTvSqkJmRV0E4nFeWybHlbgnwca8YY1ct1WZhVkcKItiMtmYVZhBhfNLWRHVTu767xEYhqVDT7igNUCdouwvCyHHTUddPgjhGJxlpdls6fWQ7rDyvbqDkKxOJqmsFnB2xXHYhGe2lPPm8fbKM50cLjZj91qoSTbTZs/zPZQlKf3NBCNxkh32YnHFcGoxr56L8/va2TtrDzOn1OAQje9q+sMTeRjnHS8fKiJrzyyC5fNyhOfO49p2cmzmHl0WzX/74UjLJ+hj8hPtAZ4cpfu/HT9iuksKslCKbhoXgG7ajp4u6qN2o4QgsaLBxqJxfXfbNfJDuxWC9FYnJjSzVQF0BRIRKMrqnHJgiKe2d1ATXuwZw0/rsB7spPPPrSDWQXp5KXZafVHeWJnPbnpdtbOyufLV8xFAVuOt5Of4Rh2iWfj/jo+fm4FSg1svnyoyc/6HbV0RTWKMp386yVzkvY8R8vtf9jK7lpPn21bj5xkZUWv+WtlYydtxpLX60f7O/HNLUyjyRdGKbh2xZln4TaZGK5DSBy6DB6TYZxoD0T4y9vVNHpCNHvDxBT4QjHqPWHicX3afKQ5wMuVzaQ79GjdWrzX/lkUaHHYVtVGKKZvtVmFcDSOUnH8YY1ITCOm6edocchPt9EejBGPa1S3BantCNK9jNkZiNARCBOKabQHYmhxRSAax2YVQpE4cSASC7OjpoOF0ybfSG2y8M3H99EZjCHE+Obje/nfW9clreyvP7YPBWw42Eye0r169YZUaA9GDAdHK9OynBxq8hGJxbEAMaU39t1E4xCNJ3pr9+5TQFSL89bRVk60BuiKaMQS9scUxKJx6ju6KMxwYrXoegxNxdl0uAW7VZhdkMHRZh++cIyCDAfleYOr6N44qnsqB4MDj5a3nGilwRMirhSvVDafdofw9olWdlZ38pnTPP+fBxp5sbKZU/utTUc6+MyVvd83GmE2ANoC/Wc9bxzv6Pn83Sf28Mfbzj0teU4HT1eU0DtomWq4DkEN8nlCCEZiHG3y4g33FSVRz6SAiKbQQjHyMxy4rVG8Id2jM9Ntwx+KkVjnVEzhD0exoWjwRoir3hvVFDT7+05vE68ViUNNR9/RvqYpIgktiaag3R/CP4XCVYw34ZBuuaOANGtyzQoTa8qxZj+fe2g7FqAw08Gzu+to9gZJdzqYkZ/G8ZYArQM0SCMhFod9dd6eWeBAhKIa5bkumrxdxOMKt9XCtGwnVoSuSIxt1R2EonFeOtDMJy+YBUA4ptHkCVOc3bu81S1hKDTwTLMiP51wLE5cqX5+ENtPtFCU7aYsr3et/sZfvcbWGi8VeS5e+frl+nFV7Xzif7eixRXP72/k8c9dMOpnku22IgM8j3i8bwPrDw49Y048vyva/z3aVtXOzpoO3rO0ZEBnvGZvCIsFCjL6W1xtOtTEg29Vc/WSEj64uqzPvkTl/GBU3PFMv21VP7lmiLuZ3AzXISwXES/6TMFtfMb4rpLlmCYiP0f3b9ihlPrSUMee2hkMhqag+RQLmY5g/8oUBw40JM/qZ6CxRDAKv339eNKucabRHur9TZ/e38K9KbpOKBbv6dDrPBFArx+eSIQGX2TMI57h7F/CmuLPb9fSbUrvD8fwhmNEYnG+995F/H1HLU6bcLDR23PO4zvqaPCEKBnAfDQra+DXb+PBBmKGRdXbJ3qXYK76+ascNII43nXtIj52nt7pbK3Rr1fVHqKp3UdxXiYHG709HvWN3v7ewyNhzaxCvnrlPDZWNpHYbB6r7VvenqaRL6Huqfb2+e4LRfnKI7vwdEXZcLCJRz9zXp/9rx1u5q5nDmIR+OENS1k1M6/P/i//bTf+UIzNx9p437JpOB295rCdCcr5dwpDBrdTSlmVUllKqUyllM343P09WZ3BSiBdKXUh4BCRNYMdG4xM3albJPbOqlhTjfH8deJK/9MUtAeiHG7yc9+mE7QFItR2dtHiD3P/q8do8oZoC+idVvf/RLZs2TJg+c/tber57EsIM1LZ1DvwuWfjkQHPjSq9t7rlnApWludSmOnkm1ctGP1NGnz+srms/z99ZxfNgxw7Ek7tmlp9IZp9ujHH0ab+A7uXKpsJhHUz442V/a/cHbNRX1nou68iP401FXnMH8Ca8UwleX71p8+5wD+Nz/8EzgG2DnSg2aSanGlYBJw2C9VtAYqzXHiCUWwiBCMaR5r8XL20hP31HhaVZPHVU84tLy8HDvcrszjHRWtQbxwTF+Cm5zip7dSb1FvW9Ya+Lkyz0BKMk26Dktxe3cXDnxm/tfqRUpHdd0lxWpaLGbm6ccfyGf1DW1+7vJQd1Z1YLPC+ZaX99n/zqvk89HYNVywsJtPdN/yNiHCBEUzvu6OR8ZRlpKm0hDQZOoQc4Jjx2QMsTtwpIrcDtwOk50/dGPz2M8Tl3iqwJIXJfVIZkcdls2C3gMMKeRkurllaohsPaIotVW3srfMl9Xrd6+d2K6ypyMMT1JcgHFYxrN6EDJeN/HQnVywsYmNlCxX5ule0y/B9KMp09cul0M3cuXMZqEP4/a1rufxnr9IV0bjzuoU921/52mX84c0TzMhz8+7FvY3jvbesZVt1B4tKskadHOp0+NLivt83fOE83vULPfBB0QBBgr9yxVn8/J/HcFngH1++ss8+t9POQ7etZU+tlwsHiIS6cmYef7ptHRZhwHhnN62dyU1rxz8vxEC6h1OZiI5EBjNdGzcBRD4HtCil/iYi7wdmKKXuGeTYFiAAtI6DaAUpuM5KoCYF5Y4HBUA5qZU/Fc88sexUyz/YdZN1vYmoP8mSfyWwI4nlJZvh5Jqq7273fc1USg0bhGwyzBDeAj4D/A24AnhwsAOVUoUisk0ptTrVQqXqOuMlf7Ix5K5IpfzjUHZK5R/iukm7nil/ahiJXJNV9qEYrcwTnmBUKbUDCInIa0BcKfX2RMtkYmJi8k5kMswQGM7U1MTExMQk9Uz4DOE0+M0Uv854yZ9sfnPK/1ReI5Vlj/fzT/b1TPlTw0jkmqyyD8WoZJ5wpbKJiYmJyeRgKs4QTExMTExSgNkhmJiYmJgAZodgYmJiYmIwKayMhkJEVqGHs8gFOoHNSqltEyvVyJnq8iciImuUUlu7/yehvMWAppSqTNi2Tik1cJCe0ZW9CjgJzAQ+DbiAbUyx5z9R9UdElgBLgGNj+a0ne/0f6j4nu+ypYFIrlY0oqE70GEceIAvdeU1TSn0xidexAtdzyo8PPKGUOu241eMlf7IRke6ZY2IcAwGeA94DPK+UurLfiaO7xs+AYiAG5AOfUkq1iMhGpdRlYyz7AUPeVUAGUAl0AEXAEVL0/JNdj8a7/ojI80qp94jIl4HLgWeA84E6pdQdp1HepKz/I7nPySr7UCSl/imlJu0fsGk028dwnT8BX0N3Tz8LONv4/uepIH8KnnsQ2IgezbsdvTHtQA/F/zLQloRrvJrweRnwCrAG2JissoFNwN6E7S+n8vknux6Nd/3pfvbAq4AlYfvrU0H+ZN7nZJV9mPsac/2b7EtG20TkPvRe2oveS1+OHhMlmVQopT52yradhvf0WBgv+ZPNQeAG9E7hMqWUB0BEXlRKXSkiLybhGjYRcSilIkqpPSJyA/BnTglueLplG/+3AQtF5IPoz79IRH5N6p5/suvReNefRSLyR/TGxAl0Gdv7J2MYGZO1/o/kPier7EMx5vo3qZeMAETkbPQQ2TnoU6C3lFI7k3yNfwMuQR+leoFs4CLgNaXUf46x7JTLn2xEpARoQ1/KaVNKRYztNqVUrPv/GK+xFqhSSjUnbLMCNyqlHh5j2YuBSqWUlvD884FS4Depev4i8jXgYnrrUZbx/bTr0XjWHxFJDPtZr5SKikgGcKFS6rnTLHPS1f+R3udklH0oktGOTfoOYbwQkQJgLfpD7AS2KaVaJlYqk6lGQj1aBRwFjqokKOBNTEbCWNuxyb5kNC4YI9OL0UcDuejr5ekiMialssk7iwRl5TxgHdACfFFETkspa2IyGpLRjpkzBEBE/gTspb9FwXKl1C0TKZvJ1KHbQkpEXgUuVUrFje2vK6VGn6XexGQUJKMdM2cIOqlSKg+JiHwLuBndmicOfEaN0QZfRK4FFimlfpIE+fxKqYxRnqOhV0obunL6E0qp4CDHfh/wK6X+31hlnSQkWyl72gxUt4B/Ae5WSh0Y7LcVkXOA/0GX3wk8opT6/rgJzujq0CjKvBVYrZT6/NglnLSMuR0zOwSdp0TkaforA/+RqguKyLnAe4GVSqmwsfbnGOG5gyp1lVJPAU8lT9JR06WUWgEgIg8BnwXunkB5xpN1xv/voPtXYCgrvzOeQgxWt5RSt43g9D8AH1JK7TaWIOanUtZBOO06JCJWpZSWSuEmMWNux8zQFYBS6r+AW4ED6M5SdcAPxmphNAwlQKtSKmzI0KqUqheRKuMFRkRWi8grxufvi8hvRGQD8EcR2WJY02Dsf0VEVonIrSJyr4hkG2VZjP1pInJSROwicpaIPC8i20XkNRFZYBwzS0TeEpGtInJXEu7xNWCOUfbHRWSPiOw2prZ9EJF/Ma67W0T+LiJpxvYbRWSfsX2TsW2xiLwtIruMMucmQdYxo5SqTviLGtv8p2uhMwYGq1uviEhP9iwR+ZmI7BCRl0SkO71iEdBgnKcppQ4Yx35fRP4kIhtF5IiI/Ms43UtiHXrCqLP7Rc+13n0ffhH5gYhsAc4VkTUi8qZRZ94WkUzj0FKj3h8RkVS+2xNCMtoxs0OgRxnYCnQrA7PRlYFjXnYZgg1AmYgcFpFficjFIzhnFXCdUupm4GHgQ9BjJlqqlNrefaDhO7AbfYQA8D7gBaOh+g3wBaXUKuDfgF8Zx/wP8Gul1BqgcSw3JyI24Cpgr9FxfQvdp2E5MFBCpPVKqTXG/oPo4SYAvgu829h+rbHts8D/GKPI1UDtWGQ9AxlJ3UoHdiilVqI7aH3P2P5z4JCIPC4inxGRxOWuZcA16ErL74pIaQrvoU8dMjZ9yqizq9Hfz/yEe9mnlFoHvA08AnzJqDNX0Lt0twK4CVgK3CQiZamUf7xJRjtmdgg63Us1NwDXKqXuM9biUqYIVEr50Rv429GtUR4x1jmH4imlVHfl/htwo/H5Q8CjAxz/CPoLAPBh4xoZwHnAoyKyC7gffUQJuvv+X43P/UbxI8RtlLsNPSn5A8BlwGNGZUUp1T7AeUuM2cpe4KP0Oqi9ATxojEitxra3gG+KyDfQk4d39S/uncsI61YcvX6A7hB4gXHuD9Ab3A3oOojnE855UinVZfyOL6ObN6aCgeoQ6I3bbvRwDGVA98xQA/5ufJ4PNHSb+iqlvAnLqy8ppTxKqRD6KDrRH+FMYMztmKlD0JkQZaCx1vkK8IrREH4Cfe25u6M+9fqBhHPrRKRNRJahN/qfGeASTwE/FpE89AZiI/poqrN7jXYgsU7zdrrpOrVsEZERlPsgcL2xdn0ruoMNSqnPisg69JHpLhFZoZT6i7E8cA3wgojcppTaOEa5h0V0b+r1wEKVEJBvMjJI3RrylIRzjwG/FpHfAi0JI/FTf8NUmSgOVIcuQR/tn6uUChpLqd3vRyhBbzBUXQsnfNY489q/Mbdj5gxBZx264u98xkkZKCLzT1n7XgFUA1XojTfAB4Yp5mHg60C2UmrvqTuNkeLb6EtBTxtrwl7ghIjcaMghIrLcOOUN9JkE6KP0ZPES8KHuhsXooE4lE2gQEXvitUXkLKXUFqXUd4FW9KWQ2cBxpdQ96J3esiTKOhQfAV6n9xlNSoaoW4lYgA8an29Gvy9E5BqjAwd9BK6hOzgBXCciLuN3vAQYT4e7bKDD6AwWoAdwG4hKdF3BGgARyTSWnt4JjLkde6c8qCFRSp36snQ3pqlUBmYAvxCRHPQf7yj6FH8h8ICIfBMYzgT1MfTGfigF8CPoy0mXJGz7KPoI8NuAHb1j2Y2+tv8XEfkSvVPwMaOU2i8iPwReFd2kcCe68iuR76DfbzX6mnG3IvC/jMZN0DuW3cAdwC0iEkXXdfwgWbIOhvFinQ9cit4JfV90hf296HqaE+iN7P8qpR4TPXTy3ei/cytwq1KqIdVyGgxWtx5LOCYALBaR7eg2691Lix8Dfi4iQePcjyo9BAjog4tngHLgLqVU/XjcjMHzwGdFZA9wCH3ZqB9KqYiI3IR+/270UfIV4yfmxJGMdsx0TDMxGQEicgu6s9mnReRN4PPAbOBT6CaeRejK8H8BnkRX1F6n9JDeN6Erxj81MdKPHTnzfEZMBsCcIZiYjIyPAP9tfH7Y+G4HHjU8khtF5GVj/3z0pCsvGiNrK4Ypp4nJZGbSdgiSAm/FicJQiP2bUuq9A+yrQvegbB1vuUxGhrFmfhm6JZRCb+AV8PhgpwD7lVLnjpOIKWe8vZXPFBLasW6uV0pVTZA4wzKZlcpdSqkVSqklQATd9nzK8Q5SaJ3JfBD4o1JqplKqQilVhq4zaAU+ICIWESmmV09zCCgU3WMY0Z0Bk5HnwWTq0d2Odf9VjeQk0b3Ex53J3CEkMqS3oohYReRB0T1a94rIV4ztXxSRA6J7sz5sbEsXkf8V3St2p4hcZ2y/VUTWywCejCLyadGdfF4Rkd+KyL3G9kLRvWq3Gn/nG9v7eBUn3oiI5IvIBuPa99M3TaXJ5OQj9J8N/B09v0ItsA/dn2ML4FF6/ogPAj817OZ3oft+mJggIhWi+9zsMP7OM7ZfIiIvi8hfMGYVInKL9Hrl35/yjkJNgtRvA/2hK7BAXzJ6EvhX43ue8d+N/iLmo5tpvphwbo7xvx5wnrLtR8At3duAw+i2+bcCx9HN21zo1i5l6C99FZCHvmb8GnCvcf5fgAuMz+XAQePz94HtgNv4fgm62SfAPcB3jc/XoC89FEz08zb/TrueZhj/84FjwLSJlsn8mzx/6Ga7u4y/x41taYDL+DwXPWdBdzsRAGYZ3xeixyGyG99/BXw8lfJO5uWMbm9F0BvhRG/FG4zP3d6Kh4DZIvILdLO4Dcb+PcBDIvIE8ISx7V3AtaJnFwK98S83Pr+ketNFdnsyFqDn6G03tj+K7hoOujnbol6zbbKkN25KoldxIhcB7wdQSj0jIh0jfSAmk5KnDfNOB7op5phCfpiccfRzskMfWN4rIivQO4x5CfveVkqdMD5fjj7Y3Wq0MW6gmRQymTuEEXsrKqU6DOeqdwOfQw/l8Cn0EfhF6DFwvmOs4wrwAaXUoVPKXsfAnoxDLelYDFn6NPzGjxcY8Awd09b3DEEpdclEy2Ay5fgK0AQsR29DQgn7EtsNAf6glPr38RJsqugQuhnQW1H06KAWpdTf0R2cVhpOQ2VKqZfRvXlz0B12XgC+0O2NKXre1KF4G7hYRHINBXGi9/AGdHt0jLIGCweRyCYMT1wRuQo9s5GJick7h2z0eEtxdEfAwfQCLwEfFJEi0D38pW8+6KQzmWcIAzGYt+J04PdGJwDw7+gP+c8iko3e0/5cKdUpeljn/wb2GJ1CFbpj0YAoPWbQj9AVhvXoQbE8xu4vAr805LGhN/bDWUPdCfxVRHagOy/VjPTmTUxMzgh+Bfxd9PAxLzPIaoLSExl9G9hgtG1R9BWQfh7JycL0VB4BIpKhlPIbM4TH0cMTDGaDbmJiYjIlmWpLRhPF9w0F9z50+/MnhjnexMTEZMphzhBMTExMTABzhmBiYmJiYmB2CCYmJiYmgNkhmJiYmJgYmB2CiYmJiQlgdggmJiYmJgb/H8YofUCk7xWWAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 49 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pclass        -0.338481\n",
+       "Age           -0.077221\n",
+       "SibSp         -0.035322\n",
+       "PassengerId   -0.005007\n",
+       "Parch          0.081629\n",
+       "Fare           0.257307\n",
+       "Survived       1.000000\n",
+       "Name: Survived, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()['Survived'].sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "2    1\n",
+       "3    1\n",
+       "4    0\n",
+       "Name: Survived, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df.Survived\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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>const</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Sex_female</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   const  Pclass   Age  SibSp  Parch     Fare  Sex_female\n",
+       "0    1.0       3  22.0      1      0   7.2500           0\n",
+       "1    1.0       1  38.0      1      0  71.2833           1\n",
+       "2    1.0       3  26.0      0      0   7.9250           1\n",
+       "3    1.0       1  35.0      1      0  53.1000           1\n",
+       "4    1.0       3  35.0      0      0   8.0500           0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = df.drop(columns=['Survived', 'PassengerId', 'Ticket', 'Embarked', 'Name', 'Cabin'])\n",
+    "X = pd.get_dummies(X)\n",
+    "X = X.drop(columns=['Sex_male'])\n",
+    "X = sm.add_constant(X)\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.Age.isna()]\n",
+    "df[df.SibSp.isna()]\n",
+    "df[df.Pclass.isna()]\n",
+    "df[df.Fare.isna()]\n",
+    "df[df.Parch.isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.Sex.isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Age_mean = df.Age.mean()\n",
+    "df['Age'] = df.Age.fillna(Age_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>        <td>Survived</td>     <th>  R-squared:         </th> <td>   0.361</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.355</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   55.54</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Mon, 23 Jul 2018</td> <th>  Prob (F-statistic):</th> <td>2.59e-54</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:18:00</td>     <th>  Log-Likelihood:    </th> <td> -278.91</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   596</td>      <th>  AIC:               </th> <td>   571.8</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   589</td>      <th>  BIC:               </th> <td>   602.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>      <td>    0.6856</td> <td>    0.087</td> <td>    7.841</td> <td> 0.000</td> <td>    0.514</td> <td>    0.857</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Pclass</th>     <td>   -0.1444</td> <td>    0.024</td> <td>   -5.909</td> <td> 0.000</td> <td>   -0.192</td> <td>   -0.096</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Age</th>        <td>   -0.0046</td> <td>    0.001</td> <td>   -3.442</td> <td> 0.001</td> <td>   -0.007</td> <td>   -0.002</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>SibSp</th>      <td>   -0.0375</td> <td>    0.015</td> <td>   -2.518</td> <td> 0.012</td> <td>   -0.067</td> <td>   -0.008</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Parch</th>      <td>   -0.0259</td> <td>    0.023</td> <td>   -1.120</td> <td> 0.263</td> <td>   -0.071</td> <td>    0.020</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Fare</th>       <td>    0.0006</td> <td>    0.000</td> <td>    1.531</td> <td> 0.126</td> <td>   -0.000</td> <td>    0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Sex_female</th> <td>    0.5029</td> <td>    0.035</td> <td>   14.249</td> <td> 0.000</td> <td>    0.434</td> <td>    0.572</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>28.347</td> <th>  Durbin-Watson:     </th> <td>   2.020</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  31.545</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.564</td> <th>  Prob(JB):          </th> <td>1.41e-07</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.007</td> <th>  Cond. No.          </th> <td>    359.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:               Survived   R-squared:                       0.361\n",
+       "Model:                            OLS   Adj. R-squared:                  0.355\n",
+       "Method:                 Least Squares   F-statistic:                     55.54\n",
+       "Date:                Mon, 23 Jul 2018   Prob (F-statistic):           2.59e-54\n",
+       "Time:                        11:18:00   Log-Likelihood:                -278.91\n",
+       "No. Observations:                 596   AIC:                             571.8\n",
+       "Df Residuals:                     589   BIC:                             602.6\n",
+       "Df Model:                           6                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const          0.6856      0.087      7.841      0.000       0.514       0.857\n",
+       "Pclass        -0.1444      0.024     -5.909      0.000      -0.192      -0.096\n",
+       "Age           -0.0046      0.001     -3.442      0.001      -0.007      -0.002\n",
+       "SibSp         -0.0375      0.015     -2.518      0.012      -0.067      -0.008\n",
+       "Parch         -0.0259      0.023     -1.120      0.263      -0.071       0.020\n",
+       "Fare           0.0006      0.000      1.531      0.126      -0.000       0.001\n",
+       "Sex_female     0.5029      0.035     14.249      0.000       0.434       0.572\n",
+       "==============================================================================\n",
+       "Omnibus:                       28.347   Durbin-Watson:                   2.020\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               31.545\n",
+       "Skew:                           0.564   Prob(JB):                     1.41e-07\n",
+       "Kurtosis:                       3.007   Cond. No.                         359.\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = sm.OLS(y_train, X_train)\n",
+    "results = model.fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 1, 1, 1, 1, 0, 1, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(X_test)[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8169491525423729"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}