E-Signing of Loan Based on Financial History.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>E-Signing of Loan Based on Financial History</h1></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"logo.jpg\" width=\"900\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>ESignature:</h4>\n",
    "<br>\n",
    "<p style=\"text-indent:5em\">Electronic signatures aren’t exactly a novelty. They have been around since the American Civil War, during which contracts were signed through Morse. In a modern setting, an e-Sign refers to a unique, digitised, encrypted personal identifier. This is, in essence, different from the ‘wet’ signatures created by hand. The e-Sign is meant to complete transactions, loops, and agreements electronically.</p>\n",
    "\n",
    "<p style=\"text-indent:5em\">In India, the e-Sign has been granted legal status by amendments to various laws, namely the Information Technology Act, Indian Evidence Act and the Negotiable Instruments Act. Early adopters in the financial sector have started using e-Sign to get customers to sign loan and card applications, and loan agreements.</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing library for reading, writing and perform basic operations\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Importing library for Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "#Importing the library for evaluating the model\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_id</th>\n",
       "      <th>age</th>\n",
       "      <th>pay_schedule</th>\n",
       "      <th>home_owner</th>\n",
       "      <th>income</th>\n",
       "      <th>months_employed</th>\n",
       "      <th>years_employed</th>\n",
       "      <th>current_address_year</th>\n",
       "      <th>personal_account_m</th>\n",
       "      <th>personal_account_y</th>\n",
       "      <th>...</th>\n",
       "      <th>amount_requested</th>\n",
       "      <th>risk_score</th>\n",
       "      <th>risk_score_2</th>\n",
       "      <th>risk_score_3</th>\n",
       "      <th>risk_score_4</th>\n",
       "      <th>risk_score_5</th>\n",
       "      <th>ext_quality_score</th>\n",
       "      <th>ext_quality_score_2</th>\n",
       "      <th>inquiries_last_month</th>\n",
       "      <th>e_signed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7629673</td>\n",
       "      <td>40</td>\n",
       "      <td>bi-weekly</td>\n",
       "      <td>1</td>\n",
       "      <td>3135</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>550</td>\n",
       "      <td>36200</td>\n",
       "      <td>0.737398</td>\n",
       "      <td>0.903517</td>\n",
       "      <td>0.487712</td>\n",
       "      <td>0.515977</td>\n",
       "      <td>0.580918</td>\n",
       "      <td>0.380918</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3560428</td>\n",
       "      <td>61</td>\n",
       "      <td>weekly</td>\n",
       "      <td>0</td>\n",
       "      <td>3180</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>600</td>\n",
       "      <td>30150</td>\n",
       "      <td>0.738510</td>\n",
       "      <td>0.881027</td>\n",
       "      <td>0.713423</td>\n",
       "      <td>0.826402</td>\n",
       "      <td>0.730720</td>\n",
       "      <td>0.630720</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6934997</td>\n",
       "      <td>23</td>\n",
       "      <td>weekly</td>\n",
       "      <td>0</td>\n",
       "      <td>1540</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>450</td>\n",
       "      <td>34550</td>\n",
       "      <td>0.642993</td>\n",
       "      <td>0.766554</td>\n",
       "      <td>0.595018</td>\n",
       "      <td>0.762284</td>\n",
       "      <td>0.531712</td>\n",
       "      <td>0.531712</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5682812</td>\n",
       "      <td>40</td>\n",
       "      <td>bi-weekly</td>\n",
       "      <td>0</td>\n",
       "      <td>5230</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>700</td>\n",
       "      <td>42150</td>\n",
       "      <td>0.665224</td>\n",
       "      <td>0.960832</td>\n",
       "      <td>0.767828</td>\n",
       "      <td>0.778831</td>\n",
       "      <td>0.792552</td>\n",
       "      <td>0.592552</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5335819</td>\n",
       "      <td>33</td>\n",
       "      <td>semi-monthly</td>\n",
       "      <td>0</td>\n",
       "      <td>3590</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1100</td>\n",
       "      <td>53850</td>\n",
       "      <td>0.617361</td>\n",
       "      <td>0.857560</td>\n",
       "      <td>0.613487</td>\n",
       "      <td>0.665523</td>\n",
       "      <td>0.744634</td>\n",
       "      <td>0.744634</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   entry_id  age  pay_schedule  home_owner  income  months_employed  \\\n",
       "0   7629673   40     bi-weekly           1    3135                0   \n",
       "1   3560428   61        weekly           0    3180                0   \n",
       "2   6934997   23        weekly           0    1540                6   \n",
       "3   5682812   40     bi-weekly           0    5230                0   \n",
       "4   5335819   33  semi-monthly           0    3590                0   \n",
       "\n",
       "   years_employed  current_address_year  personal_account_m  \\\n",
       "0               3                     3                   6   \n",
       "1               6                     3                   2   \n",
       "2               0                     0                   7   \n",
       "3               6                     1                   2   \n",
       "4               5                     2                   2   \n",
       "\n",
       "   personal_account_y  ...  amount_requested  risk_score  risk_score_2  \\\n",
       "0                   2  ...               550       36200      0.737398   \n",
       "1                   7  ...               600       30150      0.738510   \n",
       "2                   1  ...               450       34550      0.642993   \n",
       "3                   7  ...               700       42150      0.665224   \n",
       "4                   8  ...              1100       53850      0.617361   \n",
       "\n",
       "   risk_score_3  risk_score_4  risk_score_5  ext_quality_score  \\\n",
       "0      0.903517      0.487712      0.515977           0.580918   \n",
       "1      0.881027      0.713423      0.826402           0.730720   \n",
       "2      0.766554      0.595018      0.762284           0.531712   \n",
       "3      0.960832      0.767828      0.778831           0.792552   \n",
       "4      0.857560      0.613487      0.665523           0.744634   \n",
       "\n",
       "   ext_quality_score_2  inquiries_last_month  e_signed  \n",
       "0             0.380918                    10         1  \n",
       "1             0.630720                     9         0  \n",
       "2             0.531712                     7         0  \n",
       "3             0.592552                     8         1  \n",
       "4             0.744634                    12         0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"financial_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9639\n",
      "8269\n"
     ]
    }
   ],
   "source": [
    "#Finding the count of each class of dependent variable\n",
    "print(sum(df[\"e_signed\"]==1))\n",
    "print(sum(df[\"e_signed\"]==0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>entry_id</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pay_schedule</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_owner</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>months_employed</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years_employed</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_address_year</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>personal_account_m</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>personal_account_y</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_debt</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount_requested</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_score</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_score_2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_score_3</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_score_4</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_score_5</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ext_quality_score</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ext_quality_score_2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inquiries_last_month</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e_signed</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percent_missing\n",
       "entry_id                          0.0\n",
       "age                               0.0\n",
       "pay_schedule                      0.0\n",
       "home_owner                        0.0\n",
       "income                            0.0\n",
       "months_employed                   0.0\n",
       "years_employed                    0.0\n",
       "current_address_year              0.0\n",
       "personal_account_m                0.0\n",
       "personal_account_y                0.0\n",
       "has_debt                          0.0\n",
       "amount_requested                  0.0\n",
       "risk_score                        0.0\n",
       "risk_score_2                      0.0\n",
       "risk_score_3                      0.0\n",
       "risk_score_4                      0.0\n",
       "risk_score_5                      0.0\n",
       "ext_quality_score                 0.0\n",
       "ext_quality_score_2               0.0\n",
       "inquiries_last_month              0.0\n",
       "e_signed                          0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for percentage of missing data in each column\n",
    "percent_missing = df.isnull().sum() * 100 / len(df)                \n",
    "missing_value_df = pd.DataFrame({'percent_missing': percent_missing})          \n",
    "\n",
    "missing_value_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-indent:5em\">We see that the data is not highly imbalanced since the ratio between the count of two classes of dependent variable is not so high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x864 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dropping the unnecessary columns\n",
    "dataset2 = df.drop(columns = ['entry_id', 'pay_schedule', 'e_signed'])\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "\n",
    "plt.suptitle('Histograms of Numerical Columns', fontsize=20)\n",
    "for i in range(dataset2.shape[1]):\n",
    "    plt.subplot(6, 3, i + 1)\n",
    "    f = plt.gca()\n",
    "    f.set_title(dataset2.columns.values[i])\n",
    "\n",
    "    vals = np.size(dataset2.iloc[:, i].unique())\n",
    "    if vals >= 100:\n",
    "        vals = 100\n",
    "\n",
    "    plt.hist(dataset2.iloc[:, i], bins=vals, color='#3F5D7D')\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8ce42d7b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qekyS/5rlLUBxfpKfTfKEJD+Z5B5J/iXJB5Oc21r75HLGCgAAAADjmOaVduluST1pmecOX2E3/9hNSV7WvZbb95W5+1V7C537zoz/zDsAAAAAmMi0FqIAAAAAAJZJaAcAAAAAPSO0AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAHaNmvC1fYK2AAA/ZIR2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICemWpoV1Ubq+pdVXV9Vd1eVddW1Vur6v4rrPOArt21XZ3ru7obFzj/2qpqC7z+eZF+jqyqj1XVTVV1a1X9Q1W9vKr2XOncAQAAAGC59ppWR1X16CSXJ9kvyUeS/FOSJyZ5WZLjq+qo1tqNy6jzwK7OwUkuTrItyaFJTkrytKo6orV2zYim30ny1hH75xbo54QkH0pyW5ILktyU5OlJzk5yVJJnLzVWAAAAABjH1EK7JG/PILB7aWvt93furKq3JHlFkrOSvHgZdd6QQWB3dmvtlfPqvDTJ73X9HD+i3b+21s5YzkCr6r5J/jjJnUlmWmtXdPtfl0FQ+Kyqek5rbdty6gEAAADASkzl9tiqOjDJcUmuTfK2ocOnJ7klyYlVtc8SdfZJcmJ3/ulDh8/t6j+1628Sz0ry4CTbdgZ2SdJauy3Ja7u3vzJhHwAAAAAw0rSeaXdMt72otXbX/AOttZuTXJZk7yRPWqLOEUnuk+Syrt38Oncluah7u3lE23tV1fOraktVvayqNi/ybLqd4/34iGOXJrk1yZFVda8lxgsAAAAAKzat0O6QbnvVAse/1G0P3o11/lOS8zO4DfetGdzm+qWqOnol/bTW7kjylQxuLZ70ij4AAAAAuJtqre3+TqrOS3JykpNba+8YcfysJFuSbGmtvXGROlsyCN3Oaq29dsTxk5Ocl+S81tqL5u0/Pcknk/yfJDdnELa9JMkpGSw0cURr7fPzzr8qyUFJDmqtXT2in8uSHJnkyNbapxcY6yld/ey///6Hbdu2Oo+/m5uby4YNG1al79Vk3uuLea8v5r2+mPcas32y5nMb57Jhx5jzPmyyvicyd+tkze+8Ixv2HPNR0xv2nqjv1bRmf59PyLzXF/NeX8x7+jZv3ry9tbZpVTqfgmkuRLGY6raTJogj67TWzhw67x+TvLiq5pK8KskZSX520n6G+jwvgwAxmzZtajMzMysov+vMzs5mtfpeTea9vpj3+mLe64t5rzGjHlCyArNbZzNz6sx4jXf/v0Mv7JIrlj5nEbM335CZffcbr/HRa/fvKWv29/mEzHt9Me/1xbzZ1aZ1e+x3uu39Fjh+36Hzdnednf6w2z55N/cDAAAAAMs2rdDui912oWfWHdRtF3pW3a6us9MN3XZ41doF+6mqvZI8KskdSa5ZZj8AAAAAsGzTCu0+0W2Pq6of6LOq9k1yVJLvJvnMEnU+0513VNdufp09khw31N9Sjui2w+Hbxd32+BFtnpzBSreXt9ZuX2Y/AAAAALBsUwntWmtfTnJRkgOS/NrQ4TMzuNLtva21W3burKpDq+rQoTpzGawAu08Gz6Gb7yVd/b9urX0/hKuqx1bVA4bHVFWPTHJu9/Z9Q4cvTPKtJM+pqk3z2tw7yW93b/9g9GwBAAAAYDLTXIjiV5NcnuScqjo2yZVJDs/gkcVXJXnN0PlXdtsa2r8lyUySV1bV45N8NsljkpyQwe2uw6Hgs5P8ZlV9IslXMlg99tFJnpbk3kk+lmTr/AattX/rVqK9MMlsVW1LclOSZyQ5pNt/wcqmDwAAAADLM7XQrrX25e6qtddncNvpTyX5RpJzkpzZWrtpmXVurKojkpye5GeS/ESSG5O8O8lprbUdQ00+kUHQ9uMZ3A67T5J/TfKpDK7aO7+1drf1xlprH66qozMIE5+ZQcB3dZJXJjlnVBsAAAAA2BWmeaVdWmvXJTlpmecOX2E3/9hNSV7WvZaqc0mSS5Y7xqG2l2UQLgIAAADA1ExrIQoAAAAAYJmEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgBgF6sJX9snaAsAwA8HoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaQY9UTfbavn38tgAAAEB/CO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGeEdgAAAADQM1MN7apqY1W9q6qur6rbq+raqnprVd1/hXUe0LW7tqtzfVd344hzH1hVL6yqP6+qq6vqu1X1nar6VFX9clXd7WdQVQdUVVvktW2SnwMAAAAALGavaXVUVY9OcnmS/ZJ8JMk/JXlikpclOb6qjmqt3biMOg/s6hyc5OIk25IcmuSkJE+rqiNaa9fMa/LsJH+Q5BtJPpHka0n2T/JzSd6R5Cer6tmttTaiu88n+fCI/f+49IwBAAAAYDxTC+2SvD2DwO6lrbXf37mzqt6S5BVJzkry4mXUeUMGgd3ZrbVXzqvz0iS/1/Vz/Lzzr0ryjCR/1Vq7a975W5J8NskzMwjwPjSir8+11s5YzuQAAAAAYFeZyu2xVXVgkuOSXJvkbUOHT09yS5ITq2qfJersk+TE7vzThw6f29V/atdfkqS1dnFr7S/mB3bd/n9O8ofd25kVTAcAAAAAdqtpPdPumG570Yjw7OYklyXZO8mTlqhzRJL7JLmsaze/zl1JLurebl7muP69296xwPGHVtWLqmpLt33cMusCAAAAwNimdXvsId32qgWOfymDK/EOTvJ3E9ZJV2dRVbVXkl/s3n58gdOe0r3mt5tN8oLW2teW6gMAAAAAxlGj11/YxZ1UnZfk5CQnt9beMeL4WUm2JNnSWnvjInW2ZPDsu7Naa68dcfzkJOclOa+19qIlxrQ1yauSfKy19rShY/sleUkGi1DsXNTicUnOyOAqvquTPL61dssi9U9JckqS7L///odt27Y6C87Ozc1lw4YNq9L3alqr896+fbL2GzfOZceO8eZ92GGT9b2a1urnPSnzXl/Me22Z8Os8G+fmsmPMea/q1/mEE5/bOJcNY/5/bFUnPnfrZM3vvCMb9hzz39I37D1R36tprf73PSnzXl/Me30x7+nbvHnz9tbaplXpfAqmuRDFYqrbTpogLqtOt2jFqzJYwfbE4eOttRuSnDa0+9KqOi7Jp5IcnuSFGSx8MVJr7bwMAsRs2rSpzczMLG8Gu9js7GxWq+/VtFbnvXm5N3YvYOvW2Zx66sxYbaeQ3+82a/XznpR5ry/mvbZM+HWerbOzOXXMea/q1/mEE5/dOpuZMf8/tqoTv+SKiZrP3nxDZvbdb7zGR6/dv6es1f++J2Xe64t5ry/mza42rWfafafb3m+B4/cdOm+31amqX8sgbPtCks2ttZuW6PP7Wmt3JNl5peCTl9sOAAAAAFZiWqHdF7vtQs+aO6jbLvSsul1Sp6pensEqs/+YQWD3z0v0N8o3u+2iK90CAAAAwLimFdp9otseV1U/0GdV7ZvkqCTfTfKZJep8pjvvqK7d/Dp7ZLCYxfz+5h//jSRnJ/lcBoHdDSudRGfnCrfXLHoWAAAAAIxpKqFda+3LSS5KckCSXxs6fGYGV629d/7CDlV1aFUdOlRnLsn53flnDNV5SVf/r1trPxCoVdXrkrwpg8cjH9ta+9Zi462qw6vqniP2H5PkFd3b9y1WAwAAAADGNc2FKH41yeVJzqmqY5NcmcGCDpszuJ31NUPnX9lta2j/liQzSV5ZVY9P8tkkj0lyQpIbMhQKVtULkrw+yZ1JPpnkpVXDJXNta+09896/Ocljq2o2yY5u3+OSHNP9+nWttcuXmjAAAAAAjGNqoV1r7ctVtSmDAO34JD+V5BtJzkly5nIXhGit3VhVRyQ5PcnPJPmJJDcmeXeS01prO4aaPKrb7pnk5QuUvSTJe+a9Pz/JzyZ5QpKfTHKPJP+S5INJzm2tfXI5YwUAAACAcUzzSru01q5LctIyz73b5XDzjt2U5GXda6k6Z+Tut9Iu1eadSd65kjYAAAAAsKtMayEKAAAAAGCZhHYAAAAA0DNCOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAzwjtAAAAAKBnhHYAAAAA0DNCOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAzwjtAAAAAKBnhHYAAAAA0DNCOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAzwjtAAAAAKBnhHYAAAAA0DNCOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ7Za7UHAAAArD11Zk3UfuvBW7P5zM1jtW2nt4n6BoC1wJV2wKqrCV/bJ2gLAAAAfSS0AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICeEdoBAACwqJrwtX2CtgDrldAOAAAAAHpGaAcAAAAAPSO0AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjuA1VITvrZP0BYAAIBeE9oBAAAAQM8I7QAAAACgZ4R2AAAAANAzUw3tqmpjVb2rqq6vqtur6tqqemtV3X+FdR7Qtbu2q3N9V3fjruy7qn6sqj5YVTdU1W1V9cWqOrOq7rOS8QIAAADASuw1rY6q6tFJLk+yX5KPJPmnJE9M8rIkx1fVUa21G5dR54FdnYOTXJxkW5JDk5yU5GlVdURr7ZpJ+66qw7v690hyYZLrkhyT5LQkx1bVsa2128f5WQAAAADAYqZ5pd3bMwjNXtpa+5nW2m+21o5JcnaSQ5Kctcw6b8ggsDu7tXZsV+dnMgjg9uv6majvqtozybuT7J3kWa2157XWfiPJ4Uk+lOSoJK9YyeQBAAAAYLmmEtpV1YFJjktybZK3DR0+PcktSU6sqn2WqLNPkhO7808fOnxuV/+pXX+T9H10ksckubS19tGdO1trdyV5dff2xVVVi40XAAAAAMYxrSvtjum2F3XB1/e11m5OclkGV7U9aYk6RyS5T5LLunbz69yV5KLu7eYJ+97Z5uPDA+huvb0qySOTHDh8HAAAAAAmNa3Q7pBue9UCx7/UbQ/eDXWm1QYAAAAAdolqre3+TqrOS3JykpNba+8YcfysJFuSbGmtvXGROlsyeP7cWa211444fnKS85Kc11p70bh9V9VFSZ6S5Cmttb8d0eb9SZ6X5HmttQ8sMNZTkmvc64MAACAASURBVJySJPvvv/9h27ZtW2hai5u7dbx2O5vfeUc27DnmeiMb9p6o70ls/8b2idpvvNfG7Lh9x1htD3vIYRP1vZrm5uayYcOG1R7G1Jn3GuN7bSxr9Xtt+2TTzsaNc9mxY7zf54et3a/ztfvf94TMe31Zq/P2vTaetfp5Z8LPe27jXDaM+XlnNT9vf14biz+vrdyk32ubN2/e3lrbNFmV/pra6rFL2PlsuEkTxHHq7JY2rbXzMggQs2nTpjYzM7OC8vNccsV47TqzN9+QmX33G6/x0av3+37zmZuXPmkRWw/emlOvOnWstu25uz/I3l1mZ2cz9u+1Ncy81xjfa2NZq99rmyebdrZunc2pp86M1XYK/y6526zZ/74nZN7ry1qdt++18azVzzsTft6zW2czM+bnPfHfjifhz2tj8ee1lVvL32vTMK3bY7/Tbe+3wPH7Dp23K+tMqw0AAAAA7BLTCu2+2G0XegbcQd12oWfITVJnWm0AAAAAYJeYVmj3iW57XFX9QJ9VtW+So5J8N8lnlqjzme68o7p28+vskeS4of7G7fvibnv88ACq6sAMwryvJrlmifECAAAAwIpNJbRrrX05yUVJDkjya0OHz0yyT5L3ttZu2bmzqg6tqkOH6swlOb87/4yhOi/p6v91a+2aeW1W3HeSS5JcmeTJVfWMeWPaI8mbu7d/2KaxigcAAAAA6840F6L41SSXJzmnqo7NIBQ7PINHe16V5DVD51/ZbWto/5YkM0leWVWPT/LZJI9JckKSG3L3YG7FfbfW7qyqkzK44u7CqrowydeSHJtkU5LLkpy9grkDAAAAwLJN6/bYnVe8bUryngwCs1cleXSSc5Ic0Vq7cZl1bkxyRNfuR7s6hyd5d5LDun4m7ru19vdJnpDkIxncdvuKDBameH2Sp7TWbl/ezAEAAABgZaZ5pV1aa9clOWmZ5w5fYTf/2E1JXta9dnnf89p8IcmzV9IGAAAAACY1tSvtAAAAAIDlEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICeEdoBAAAAQM8I7QAAAACgZ/Za7QEAAABAL7UJ28/ughrAuuVKOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAzwjtAAAAAKBnhHYAAAAA0DNCOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAzwjtAAAAAKBnhHYAAAAA0DNCOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAzwjtAAAAAKBnhHYAAAAA0DNCOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAz+y12gMAYJ05etNk7WdnJ68BAADQc660AwAAAICeEdoBAAAAQM8I7QAAAACgZ4R2AAAAANAzQjsAAAAA6BmhHQAAAAD0jNAOAAAAAHpGaAcAAAAAPSO0AwAAAICemVpoV1VHVtXHquqmqrq1qv6hql5eVXuOUevHquqDVXVDVd1WVV+sqjOr6j4jzj2oqn6jqi6uquuq6ntV9S9V9ZGq2rxA/V+qqrbI68Xj/AwAAAAAYDn2mkYnVXVCkg8luS3JBUluSvL0JGcnOSrJs1dQ6/AkFye5R5ILk1yX5JgkpyU5tqqOba3dPq/J/5vk55N8IcnHur4PSfKMJM+oqpe11s5ZoLuPJPnciP1XLHe8AAAAALBSuz20q6r7JvnjJHcmmWmtXdHtf10G4duzquo5rbVty6i1Z5J3J9k7yQmttY92+/dI8sEkz0zyiiRvmtfs40ne3Fr730O1jk7yN0l+t6r+rLX2jRFdfri19p6VzBcAAAAAJjWN22OfleTBSbbtDOySpLV2W5LXdm9/ZZm1jk7ymCSX7gzsulp3JXl19/bFVVXzjr1nOLDr9l+SZDbJPZMcuezZAAAAAMBuNo3bY4/pth8fcezSJLcmObKq7jV0W+uKarXWrqmqq5IcnOTAJF9extj+vdvescDxx1fVy5PcO8nXk3yitbZjGXUBgCStTdZ+dnbyGgAAy9FOn+wPHbOzs2nP9QcXdp1phHaHdNurhg+01u6oqq8keWwGQduV49bqfCmD0O7gLBHaVdUjkxybQWh46QKnvWzo/Z1V9Y4kL++uFAQAAACAXa7abv7n6+7qt4OSHNRau3rE8csyuD31yNbap5eodVGSpyR5Smvtb0ccf3+S5yV5XmvtA4vUuVeSv8tgEYxXt9Z+d+j40Un+ryQXJdmR5H5J/muSNyZ5dJIPtNaet8RYT0lySpLsv//+h23btuQj+0abu3W8djub33lHNuw5Zja7Ye+J+p7E9m9sn6j9xnttzI7bx7so8rCHHDZR36tpbm4uGzZsWO1hTJ15ry9rdd6+18azVj/vSZn3+mLea8v2yb7Os3HjXHbsGG/eh63dr/M1+3lPas3Oe53+PXRSa/XzXsvfa5s3b97eWts0WZX+WtZ/RVV1bZJHrqDu+1trz1/muTufP7cr0sMla3WLWZyfQWB3QZKtw+d0z7u7ZN6uW5P8WVV9Jsnnkzy3qt7cWvv8Qv201s5Lcl6SbNq0qc3MzKxsJjtdMtlCtbM335CZffcbr/HRq/f7fvOZmydqv/XgrTn1qlPHaruWL2eenZ3N2L/X1jDzXl/W6rx9r41nrX7ekzLv9cW815bNk32dZ+vW2Zx66sxYbdfy4wLW6uc9qTU773X699BJrdXPe9Ihz87O5ud/fsIijLTc6PvLSVZyO+j18379nW57vwXOve/QeYuZqFYX2L0vybMzWG32+W0Flxq21q6rqo8l+YUkT84gwAMAAACAXWpZoV1r7dgJ+vhikk0ZPGfuBy66rKq9kjwqg4UgrllmrXS1Rjmo297tmXddX3+aQWD3p0l+sbV25zL6HPbNbrvPGG0BAAAAYEl7TKGPi7vt8SOOPTnJ3kkuX8bKsYvWqqoDMwjzvpqhALCq7pnkwgwCu/cmOXHMwC5JDu+2ywkZAQAAAGDFphHaXZjkW0meU1Xfvzm9qu6d5Le7t38wv0FV7V1Vh1bVI4ZqXZLBCrNPrqpnzDt/jyRv7t7+4fxbXrtFJ/48yQlJ3pnkpNbaXYsNuKp+YsS+qqrfSnJEN5+PL1YDAAAAAMY15nIuy9da+7eqOjmD8G62qrYluSnJM5Ic0u2/YKjZE5N8IoOQbmZerTur6qQMrri7sKouTPK1JMdmcAvuZUnOHqr1h0l+KoOg7etJTquqoVMy21qbnff+0m7V2//VtblfBgtX/OcMFqX4hdbav63oBwEAAAAAy7TbQ7skaa19uKqOTvKaJM9Mcu8kVyd5ZZJzVrgYxN9X1ROSnJnkuCT7ZnBL7OuTvGnEbbaP6rYPSnLaIqVn5/16awbB4TFJHpDkrgzCwbcleUtrza2xAAAAAOw2UwntkqS1dlkGV7wt59zZJHe7HG7e8S9k8Hy65dSaWc55Q21+faVtAAAAAGBXmcYz7QAAAACAFRDaAQAAAEDPCO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGf2Wu0BAAAAAD1y9KbJ2s/OTl4DcKUdAAAAAPSN0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAzwjtAAAAAKBnhHYAAAAA0DNCOwAAAADoGaEdAAAAAPSM0A4AAAAAekZoBwAAAAA9I7QDAAAAgJ4R2gEAAABAz+y12gMAAABYK1qbrP3s7OQ1AFgfXGkHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAnhHaAQAAAEDPCO0AAAAAoGeEdgAAAADQM0I7AAAAAOgZoR0AAAAA9IzQDgAAAAB6RmgHAAAAAD0jtAMAAACAntlrtQcAo7TT20TtZ2dn0547WQ0AAACA1TK1K+2q6siq+lhV3VRVt1bVP1TVy6tqzzFq/VhVfbCqbqiq26rqi1V1ZlXdZ8S5B1RVW+S1bZF+XlBVn62quar6TlXNVtVPr3S8AAAAALASU7nSrqpOSPKhJLcluSDJTUmenuTsJEclefYKah2e5OIk90hyYZLrkhyT5LQkx1bVsa2120c0/XySD4/Y/48L9LM1yauS7Ejyx0numeQ5Sf6iqv57a+3c5Y4ZAAAAAFZit4d2VXXfDEKvO5PMtNau6Pa/LoPw7VlV9ZzW2oJXvM2rtWeSdyfZO8kJrbWPdvv3SPLBJM9M8ookbxrR/HOttTOWOeYjMwjsvpzkCa21b3f7fzfJ9iRbq+ovW2vXLqceAAAAAKzENG6PfVaSByfZtjOwS5LW2m1JXtu9/ZVl1jo6yWOSXLozsOtq3ZXk1d3bF1dVTTjmF3fbs3YGdl0/1yZ5W5J7JTlpwj4AAAAAYKRphHbHdNuPjzh2aZJbkxxZVfeapFZr7ZokVyV5ZJIDR7R9aFW9qKq2dNvHjTnm/zl0DgAAAADsUtN4pt0h3faq4QOttTuq6itJHptB0HbluLU6X0pycPf68tCxp3Sv76uq2SQvaK19bd6+fZI8LMlca+0bC/SRrg8AAAAA2OWqtbZ7O6i6KslBSQ5qrV094vhlSY5McmRr7dNL1LooXfjWWvvbEcffn+R5SZ7XWvtAt2+/JC/JYBGKa7pTH5fkjCSbk1yd5PGttVu68x+a5OtJvt5a2ziij3sk+V6S77XWFrw6sKpOSXJKkuy///6Hbdu25CP7Rpu7dbx2O5vfeUc27DlmNrth74n6Xk1zc3PZsGHDag9j6sx7fTHvtWX7N7ZP1H7jvTZmx+07xmp72EMOm6jv1bRWP+9Jmff6Yt7ri3mvL+a9vpj39G3evHl7a23TqnQ+BctKc6rq2gxuO12u97fWnr/Mc3c+f25XpId3q9VauyGDlWXnu7SqjkvyqSSHJ3lhkt9bYV+Ljre1dl6S85Jk06ZNbWZmZoXlO5dcsfQ5i5i9+YbM7LvfeI2PXru/72dnZzP2z3wNM+/1xbzXls1nbp6o/daDt+bUq04dq2177u79B7r/n737DpekqvY+/l0TYJgZhiBZspIkiDKSJUsSRAQERaKSBJUgl2uWKAjIFQPRC6hIEBEVfNGrMKiIKKAiCg4wIBIUEARGGIaZs98/1qrpfWr6nDmpT1V3/z7PU885XVXdXXtX9a6qVTu0Urvu7+FSuruL0t1dlO7uonR3F6VbRtpAq2A9DMwaxOc+mf3/QvxdrI91p5TW68+IfVY0zb0UD9ptRSNot6DvWKy0noiIiIiIiIiIyIgaUNAupbT9ML7jr8BUvA+4Xm2DzGwcsBowh0bT1QV9FvTdn9wa8bevPu/Knom/k4oZKaX/mNkTwOvNbPkm/doN9jtEREREREREREQGZTRGj70l/u7cZNlWwETg1ymlV4fzWWa2Oh7M+xsDCwACbBp/y+v3t827lNYREREREREREREZUaMRtLsOeBbYz8zmdZJmZhOA0+LlBfkbzGyima1tZiuXPus2fITZrczsXdn6Y4Cz4uWFKRtdw8w2MbOFyhtlZtsBx8XLb5cWXxh/P2VmS2TvWRU4GngVuKyvBIuIiIiIiIiIiAzHEIcVHbiU0otmdhgevJtmZlcDzwHvAtaK+deU3rYxcCsepNsm+6y5ZnYIXsvtOjO7DngM2B5vgns7cF7ps84C1jWzaUAx7N4GwHbx/2dSSr8ubfOvzexLwPHAvfE9CwH7AksCH0kpPTrozBARERERERERERmAlgftAFJKN5jZ1sCngL2ACcBDeFDs/Lxm3AA+604zextwMrAjsCjeJPYU4MwmzWy/BewJvA1v2joe+CdwLfDVlNIv+/ieE8zsXuAY4HCgB7gHODuldONAt1dERERERERERGSwRiVoB5BSuh3YdYDrTgOsn+V/AfYZ4Gd9A/jGQNZt8t4rgCuG8l4REREREREREZGhGo0+7URERERERERERGQQFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZpR0E5ERERERERERKRmFLQTERERERERERGpGQXtREREREREREREakZBOxERERERERERkZoZtaCdmW1uZj82s+fM7GUzu9fMjjWzsUP4rDeZ2bVm9rSZzTKzv5rZyWa2SJN1LzeztIDp56X3HLyA9Y8cTl6IiIiIiIiIiIj0Z9xofImZ7QF8D5gFXAM8B+wOnAdsAewziM/aBLgFGA9cB/wd2A74LLC9mW2fUno1e8sNwKN9fNwBwOrA/+tj+Q+APzSZf9dAt1dERERERERERGSwWh60M7MpwCXAXGCblNJdMf8zePBtbzPbL6V09QA+ayxwGTAR2COl9MOYPwa4FtgLOA44s3hPSukGPHBX/qzFgf8CZgOX9/GVN6SU+lomIiIiIiIiIiLSEqPRPHZvYGng6iJgB5BSmgV8Ol4eNcDP2hpYB/hFEbCLz+rBA3AAR5qZDeCzDgAWAa5PKT07wO8XERERERERERFpudFoHrtd/L25ybJfAC8Dm5vZwqVmrYP6rJTSDDObDqyJN3l9eAGfdVj8vbifdTY0s2OBCcATwK0ppccX8LkiIiIiIiIiIiLDMhpBu7Xi7/TygpTSHDN7BFgXD7TdP9TPCg/iQbs16SdoZ2abAesD01NKt/bzfR8rvZ5rZpcCx0ZNQRERERERERERkRFnKaXWfoHXflsDWCOl9FCT5bcDmwObp5TuWMBn/RR4B/COlNLPmiy/Eng/8P6U0lX9fM5lwMHAiSmlc5os3xoP6v0UeBxYDNgS+ALwBuCqlNL7F7CthwOHAyy77LIbXX31Arvsa27my0N7X/H2uXOYPHaIsdnJE4f13VWaOXMmkydPrnozRp3S3V2U7vZy91N3D+v9Ky68Io+/OrTK3hstv9GwvrtK7bq/h0vp7i5Kd3dRuruL0t1dlO7Rt+22296dUppayZePggFFc8zsUWCVQXzulSmlDwxw3aL/uZGIHi7ws8xsMeC99DMARUrpNuC2bNbLwHfN7DfAH4H3mdlZKaU/9vU9KaWLiaa3U6dOTdtss83AU5G7bXgD1U576Wm2WXSZob156/Y97qdNm8aQ87yNKd3dReluL9uevO2w3n/Omufw8ekfH9J70/ta+4Culdp1fw+X0t1dlO7uonR3F6W7uyjdMtIGWgXrYWAwzUGfzP5/If4u1se6U0rr9WckPusD+OizVw92AIqU0t/N7MfA/sBWeABPRERERERERERkRA0oaJdS2n4Y3/FXYCrez1yvtkFmNg5YDZgDzBjgZxGf1cwa8bevPu+gMQDFRQP4vmaeib+Thvh+ERERERERERGRfo0Zhe+4Jf7u3GTZVnitt18PYOTYfj/LzFbHg3l/o48AoJltArwZH4Bi2gC+r5lN4u9AgowiIiIiIiIiIiKDNhpBu+uAZ4H9zGxeJ2lmNgE4LV5ekL/BzCaa2dpmtnLps27DR5jdyszela0/BjgrXl6Y+h5d4/D4e3F/G2xmb28yz8zsE8BmkZ6b+/sMERERERERERGRoRrisKIDl1J60cwOw4N308zsauA54F3AWjH/mtLbNgZuxYN022SfNdfMDsFr3F1nZtcBjwHb401wbwfOa7YdZjYF2BcfgOKKBWz2L2LU298BT+B96G0BrIcPSrF/SunFgaRfRERERERERERksFoetANIKd1gZlsDnwL2AiYADwHHA+f3UzOu2WfdaWZvA04GdgQWxZvEngKc2U8z2/3xfugGMgDFOXjgcDtgSaAHDw5+DfhSSklNY0VEREREREREpGVGJWgHkFK6Hdh1gOtOA6yf5X8B9hnk919AqRluP+ueOJjPFhERERERERERGUmj0aediIiIiIiIiIiIDIKCdiIiIiIiIiIiIjWjoJ2IiIiIiIiIiEjNKGgnIiIiIiIiIiJSMwraiYiIiIiIiIiI1IyCdiIiIiIiIiIiIjWjoJ2IiIiIiIiIiEjNKGgnIiIiIiIiIiJSM+Oq3gAREZFukD6XhvX+adOmkd43vM8QEREREZH2oZp2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM0oaCciIiIiIiIiIlIzCtqJiIiIiIiIiIjUjIJ2IiIiIiIiIiIiNaOgnYiIiIiIiIiISM2Mq3oDZAG2njq890+bNvzPEBERERERERGRUaWadiIiIiIiIiIiIjUzakE7M9vczH5sZs+Z2ctmdq+ZHWtmYwf5OR80s4vM7M74nGRmpw3gfbuZ2TQze8HMZsb7D1rAew4ys9/G+i/E+3cbzPaKiIiIiIiIiIgM1qgE7cxsD+AXwFbA94GvAQsB5wFXD/LjzgUOB9YAnhzg9x8D/AhYD/g2cAmwAnC5mZ3Tx3vOAS4Hlo/1vw2sD/woPk9ERERERERERKQlWh60M7MpeNBrLrBNSumDKaUTgQ2BO4C9zWy/QXzkfsCqKaUlgYHUsFsVOAd4DpiaUjo6pXQcsAHwMHCCmW1Wes/mwAmxfIOU0nEppaOBjeJzzonPFRERERERERERGXGjUdNub2Bp4OqU0l3FzJTSLODT8fKogX5YSunmlNLfBvH9hwILA19NKT2afc7zwBnx8sjSe4rXp8d6xXsexWsJLgwcMohtEBERERERERERGbDRCNptF39vbrLsF8DLwOZmtnAF3///SusM5z0iIiIiIiIiIiIjYjSCdmvF3+nlBSmlOcAjwDhg9Qq+/yngP8CKZjYRwMwmAa8HZsbysgfj75ot2FYREREREREREREspdTaLzCbjg8asUZK6aEmy28HNgc2TyndMcjPPhi4DG/G+uk+1pkNjAfGR5CwvPwJfFCKFVJKT5nZCsATwBMppRWbrD8emA3MTin1WTvQzA7HB8xg2WWX3ejqqwc73sbImDlzJpMnT67ku6ukdHcXpbu7KN3dRenuLkp3d1G6u4vS3V2U7u5SZbq33Xbbu1NKUyv58lEwbiArmdmjwCqD+NwrU0ofGOC6Fn9bGz0c+e/vd/2U0sXAxQBTp05N22yzzeC3bARMmzaNqr67Skp3d1G6u4vS3V2U7u6idHcXpbu7KN3dRenuLt2a7tEwoKAdPorqrEF87pPZ/y/E38X6WHdKab2R9gKwVHz/v/r5/hdL29HX9i5WWk9ERERERERERGREDShol1Lafhjf8VdgKt4H3N35AjMbB6wGzAFmDOM7FvT9S8X392p+a2bLA5OAx1NKLwOklP4TTWZfb2bLN+nXbo34O18feSIiIiIiIiIiIiNhNAaiuCX+7txk2VbARODXKaVXK/j+XUrrDOc9IiIiIiIiIiIiI2I0gnbXAc8C+5nZvM4BzWwCcFq8vCB/RjVriQAAIABJREFUg5lNNLO1zWzlEfj+y4BXgWPMbNXsO5YAPhkvLyy9p3j9qViveM+qwNHxeZeNwLaJiIiIiIiIiIjMZ6B92g1ZSulFMzsMD95NM7OrgeeAdwFrxfxrSm/bGLgVuA3YJl9gZh8CtoyXb4y/u5tZMdLrAymlM7Pvf8TMTgTOB+4ys2vw0V/3BlYEzi2PWptS+rWZfQk4HrjXzK4DFgL2BZYEPpJSenQI2SEiIiIiIiIiIrJALQ/aAaSUbjCzrYFPAXsBE4CH8KDY+SmlwYzcuiVwUGneBjGBB/rOzBemlL4SI+B+HDgQr2H4F+DTKaUr+tjmE8zsXuAY4HCgB7gHODuldOMgtldERERERERERGRQRiVoB5BSuh3YdYDrTgOsj2UHAwcP4ft/BPxokO+5Amga1BMREREREREREWmV0ejTTkRERERERERERAZBQTsREREREREREZGaUdBORERERERERESkZhS0ExERERERERERqRkF7URERERERERERGpGQTsREREREREREZGaUdBORERERERERESkZiylVPU2dDwzewb4W0VfvxTwbEXfXSWlu7so3d1F6e4uSnd3Ubq7i9LdXZTu7qJ0d5cq071KSmnpir675RS063BmdldKaWrV2zHalO7uonR3F6W7uyjd3UXp7i5Kd3dRuruL0t1dujXdo0HNY0VERERERERERGpGQTsREREREREREZGaUdCu811c9QZUROnuLkp3d1G6u4vS3V2U7u6idHcXpbu7KN3dpVvT3XLq005ERERERERERKRmVNNORERERERERESkZhS0ExERERERERERqRkF7URERERERERERGpGQTsRERERkVFmZlb1NoiIiEi9KWgnIlJzZjalE2/uzGxdM5tU9XaIiIwmM1sRIKWUOrFsFxERkZGjoJ3IKDKzsVVvg7QXM9sK+CHw9qq3ZSSZ2drAn4BrFbgbvnLZorJGpJ7MbAvgMTP7b1DgTuqjG45DMxvT3+tO1S3pFOlU+gGLjBIzG5NSmhv/f8HM3l31Nkm9mdk4YGNgK+BzZrZ5B11UzwauB3YBLlPgbuhKZcsnzGy54rW0ryaB2GH99svvj/JFRt8iwKvAyWZ2HChwV5X+AhmdvD+aBK5eB34cVrNFoyPOlT3x/1YAKaWeTt7XMF+6zzCzTaveJml/Cyg/FWMaYcpQ6egLkzrJTpifBk4C9jazRavdqt5UyNZLSmkOcAVwArAp8AVgs074zaaUZgAnAt8G9kaBuyHLypZTgNOB0zutpl0nHPODlQViDzWzCcMJ7JiZFTfkZrZYfP6ceH2Wme09Utst/Usp/QzYDXgeOHe0A3f5d/T1fzcoBTL2imDGBWb22eH+3uqslO4jzeybwDQzu8HM3hzzOy7d0Otc+XM8zfvE/I7c1zDf/v4Nfj25WKemt9VUfrrScbWvmZ1tZleY2ZlmtngEw3VPOYL0lFU6/sla1cxsbHbztQSwB3ApcHZK6aVKNy5TKoDXAJYDJgD/Tin9rtKN62IppWfionoc8FngTOCTZnZ7O/9243fxiJmdDPQAB/psOySlNLPizWsLpbJlTWA/4Hzgok6qaVdK56LAuJTS89lya+ffQn/M7AjgAmAh4MKhpjML2P0MWMbM3p5SesHMvgx8BDjVzG5MKc0aqW2X+cWN3ZiU0s/N7CDgm8A5ZjYupXR2ETxo1fGc/5bCROA/0AhcdOpvKVe63vkusCteA7KwrZm9O34jHZMnpXR/D9gJeA14GVgXWN3MtkspPVvhZraUmR0LbBsvLzOzuSml6zvx+C/t718BawLHA7/K09tp6W6VJuXnZOAlmFd+zsvvTtakHNkFv1/swSuEbW9m70wpPd0teQK986UVFAHtMma2hpkdb2bfNbNL4v8JVW9XJ8tuNt8DvBtYB/hWSunBSjcsUyqATwD+D7gN+Alwp5n9r5mt3W1Pkqpm0XwtpfQv4H/xmnab4DU1t2zX/VG68Hka+H/ArcBewFfMbHJlG9dGsrLlrcDmeKD9ipTS/ZVu2Aiy3k1/PwncAjwY5689obNrSQD34DfVw+7TMh4a/RNYD7jKzC7BA3bnAJcoYDcq5h3PKaWf4DfQTwOnRDChZcdzKfh9iJl9B7jfzH5qZqd3y417pLO43vkJsDNwFR64excwHdga+Cp0zoPtJuneDjgXWAMP2F2Klw375e+pYFNb7U7gUeC7eND6qrg+76hzSZOA3frAp4HLU0rzAvX5X+lbqfz8YAT7p5vZL8zsPDNbuBuCU6Vy5GZgR/zh0w74Q4B7gI2Ai6BRu7UTlWpaji3V3D7JzE4179Zo4oh8YUpJU5dMeN9YjwOzgCeBf+NR8Zvxk/f4qrexUyc8GNGDByb+CEyJ+WOr3rbSdn4qtvMm4ANEf2N4/2O/Bjatehu7ZcqPDeAo/AT4QPx+e4AfA1sAVvW2DjJdY7L/TwXuB/4BPBTp6gGuBCZVva3tMAHHRJ7dAPw05lm7HRcDSOcZkc4Z+EXhXOA54PhsnU5L8xi8ht01wBxgixH4zCWAsyL/evDAxDJVp7UbplLZ99/4g7F78JpuRdn30WydETue88/CAzWzgMfiPPLX+O6f4DdbHfU76idPzgWeAY4DJmfzV45r5enA4lVvZwvSfSEeKD6xlO43xXFwcNXb2OL0bwDMBDYDDok0zwLeE8trdV0+Aum9DXgROAxYtEgjHrA8AvgY/iB4ZWDhqre3jlOT8vPVuBa5Hr+n6wF+FdfkY6razlHOk7Oj/Dy+OK5i/lJxPf8o8Lqqt7OF6d8U2BOYUJr/xex83oM/KD1vJM4llSda0+hM+FO0p4G7gANi3opxIPUAvwNWqHo7O3UCXg9ckv2I3131NjXZxp3xPna+BaydzT8QbzrxL2Dpqrez26Y4Mb6MB2VOjBuMu/Cb7lvxGlZtd5MFnBJp+HrcKC4PvCPS1oMHKiZXvZ11n/CnnL+OPHsZ2KDqbWpBGjeKC+SvAG+MebsAz0a6P5Gt23a/hQGk/9BI5wV4M/kh3RQU7wOujc+bGzcakzo17+o44V0czMJrd+2MP9T7dHZ9cGy27ojukziHzMGDtevFvFWA6+K7r6DDghZ95MNGcVP5w+JmCg+Sj8MfehT50VHlaRxvzwDfKN9ExrHRAxwb598ro+x5fdXbPcJ5MBb4C3BGvD6JRuBun5i3NrBL1ds6Amk9M9J2NbBizFsiruv/QO/gwnQ8ALNU1dtd1wnvD7AoP98U85YHvpNdt3ZD+blhXJPdBCwR8wzvugT8PrIHeFvV29qi9C8N/A2Pq+xBBLvj9zMrys698Movv4+8uKxc5g76e6tOuKbWT8B4vGndP4G9svlvBL4XB9Phpffo4n1k8j5/OrMCjSDpNODNVW9faVs/jz892qbYdrw57/14zcxViuNJx8mo7ZP943i5BFg5m78mHvR6GW8u2FY17vAn+s/izbBXiXlFQGFtGkGFb6LA3YLycgzeP8/NkWfn0uY1p8rHMt5s7e/Ew4TsWNkq5rd14K6vi3x617a9DXgkv0AexvedFb+tyyLvfl4+ZtotD9tlwh9MzMZvolcpLdsVeIEW1biLa5A/44HatWLeQniT0KJm2dKl93TkcYAHpnpoBC7HlJafTgfedAK7491RrFyav2McG3PjmuKveHBvNnBx+bho9wn4KXBL9voEGoG7j0S6nyjnUztNcW2wa5w7/g18EK+scQRe8+4evAb7AcAP8AfzTwNHxns78rc/jPxcFq9Vd0dWfi4c5eff+yg/OzKAh7f+6SHuY5uUn0WrrS2r3tYWpX+R+N08jNcqfE/MuxK/f1mttO7tkR+XM4zAXeUJ19T6Ca8C/QhwYzbvzfhT3h7gsGx+R52YK8jrfmtA4DXuLqARdV+n6m2O7RqHB1BmZPP2xJtj/hNYNZs/FTi66m3uhgmvZfdqceKjd/Oq5YAvxbH0I7zPq7a4yMKDTPNuTPEn33na1osyq6j50fWBO3o/ABhT+js28vRXeKfIxwznwqDidOaBqvFx/toJuD3mjctvKOK4b/vAXWzzh4E9SvPGR5pPiTR+frjHTZaPi+I1BorA3dJN9sGyVedLJ000bnZ2KvZJab/sQaPmy3HN9uMwvnuT+NwjsmNrP7zGwCNEDZsoT9avOq9avB+WAE6Lv3n+F2Xq5yOvNinypOptHqF0L1T+ncdx8We8mfauwJQ4Lt8O/BIP+Gwb67ZduVpKf5HmLwL30btZ34do1ED+J7Bv1ds7AukdA2wD/BZvSfNlPDj3E2Cx7Dy6BF7jcBZwL+qepFlevjWOj4/E6/HAvk3KzzHAW6re3hbnxQR8YLylSuVncTwVtVffHq/HVb3NLciDRWL/P4YHbA+JcuMD2Trj4+9YRiBwp4EousPi+A3+4wBmtj7en8q+wFEppUuydc8zsy+M/ia2v1InlOub2c5mdqCZrRMjHpJSegK/UPwGcBBwkpmtk31GlR3gzsGHgV/HzHbHBz1YAr9ofTRb7xPA58xs2Qq2sdu8Ce+E/vF4Pe/4SCn9Ay/8nwPeiZ8ktx7l7Ruq4tyzGviACinrrDaldB/erAP8adb3R6wj1zYUHUqnbNYi0OjgN3nnyLfhTzf/jNcSOSAGHmgbpUEnPg78DG/C9jk8wERKaU78LUa8+yXwfrxWxKlmdlqxfPRTMHRmthceQPu+md1oZv9lZgvDvDRfhPf9uEMxf0Hnizgn5fkwqXhPSmlO8tHLz8CbqG8LXG1my2b7YDfghvgrw2BmRZm3Yvx9HfhvOKXUk+2XH+APVAE+b2afjfmDOp7NbGzpteHnc4DZsXwvvNZlD7BxaowYOga41syOHMx3tov4XTwPfC6l9Hzp3FP8X4xgnpexmNnWZja1nL/tIMrL2SmlZ8DTFMflksArwA4ppR8D/4l8uBN/IDiFGG213crVstQYAOs3wFrAatm+vAHPB8PzZDb4b7ddB6eI/fgL4L/wJsEfwR/G75xSegF6/R7Ox7tKWg/vr6tr9fH7Xjz+9mTlZ9F/Wbn8vMrMjmv9lo6+OF5mAaemlJ4tlZ9F+fCf+FuUn3PivduZ2RbFIHvtKq5VX8G7VzgRf7h8eix+JFvnNfOR4efiLUPuwJumn2tmSw72exW06w6v4ieiNc1sK/zGrgjYXVSsZGbvwKv5Wrv/oEZb6Wbz83jnzj/Ggyp/AC4ws81hXuDuM3jg7kDgv8xs7Vg2ahdE+UVIFKg34Selk/AaXvMF7MzsUPxkfgX+1E5a6y/4yWAPmHeRbTDvmLsXb+L+J/wJ+UfNbKGqNnYQZuAXxO8ws6n5guxi6Sm8Ce3P8Y6juzJoV3oYcFCM+PhnM7vazD6U3ez34LUiTsKPm9OAD5jZ4n19dt1k6fwCfjG8Kt5cZ0NgPTP7XLFeEcjMAnfvwx88HGpmUypJwPD8ANgHb4q2CR60vgc43czeFueNb+J9WO4D858v8sB2dqGImR1tZjfgN2s/M7MzzGyJWOdJ/GLza/iN+XfMbJMo68/Am+L/qZUJ7wbZjc2d8XezPBgQx3JR9j2Ml38TgcOLh34DVboeOdHM1otj5bFYZVP8nJIH7J7JPuI0/EHvI4NKZJtIjdF75/azWrFs3n1SPMy8Bi+b2u4audn1ZRbU2TGldEecb+ZG2TAbr6EFjSBmp3gcr/2yfKR3fbxJ8PN487axwDfNbP8IrLdtsDLbx5/Fm++dH+XNmOTmmo98+gqNsr7tju+RUio/P2lmRQDzUfy6dTO866C+ys9T8Ka000dto0dRVn7295t4Lf7m5edu+PF3Nm10fGUP3IrXC2UPzF/Bg/0nEP2+A3uZ2aRsnTmlwN0v8Vp5nxn0w4DBVMvT1B4TfrG1aGne2fhFSDHKzZExv6jKuh7+RO1BYPOq09CuE41OX38I7I0HUi6MeY+Sjf4X++kS/CRwPbBGi7etadNdGs0FpuIBxrl4fxerl9bbEz+h30epLx5NLdtnO9DoIHjbbP5C2f9340HUQ4GVqt7mbLvmO96K8ib+PzvS9nWig+TSumfgNa1WA5bs6zM7eaJ3s4OiqfRTeJ+YT8Rv9Rul9cbgNS7vwIOeJwGLVZ2WQaRzKl6j7KvAG/CHB+/E+298DfhY+X3ZeWxjotP0/Fhrpwmv5bEyHkj7TfxGXsVrQXwTb750DTCplG/bAN8FNix93jk0RjC7D28+XdzE7UKMfEajuf3MWD4bD66vFss7onngaO3D8jGYHatvpNEx9V7l98T/3wH+B2+OtWJ5+SC247T4novw2rlT8IeJPfgNxkPM3wfTB/Cg4Q3EKPfdNGX7rhgYpOhsfg/8YchTxGA4nTqVjsVLo8zZsrysnSe8aeOzeP9t68Tv4UngwFhejMo+I347bZ9uPBC5HvPfH+b7+1a8uedyVW9v1RNwahwD/xvn28k0+oF/LsrPpUrvKcrPH3Zj+Znlw/GRTxvG6z3w/tHbqvws/TY2ofc115nAQfH/RLyrielRjryX0kjMNAbpGBfn11UHvT1VZ4imkZ3ignBmXKwtls3fDg+4zAWuLb3nbXhzjNcoDUihaVB5vyd+Q3QZEYCLk+Ruke/3Ubp5xkcdugZ/6rdkC7dtTGk7TwI+SWkwjLiA+RteY+XDeKfZK8fxNAPvmPhNVed1p0yUboSj4C936PqFOH5+Qmk0Mzww/AC9b/4qv7mmd59Yq+PBlM3ykzVeY+gXNPp42KGUrvvxm9de/bd144Q3Dy06BC86/l0DD2T14E8vy4G7rfCaAw/QJn0C4jUq3xPbXXT0XNxEb4rXGJ9F79E15+swuw6/gSGkvUjnvH4K4++H8Jofs2gE3J4t8ifWWYjGU/+riP7I8NoA/8YDfuvgN6obxjnnZfwhTf4wYMnI//PxG5bl2zU/K9yPedm3OD74w/JkffrQCAj04LXtl8iW7YHfDJ7e7DMH8d3rxu/oy6Vy9900Rl0+qfT+I+K7p9MIFnZluUtjYK4N8Ievf8UD30UQu+P6aCrvb7xriqeAGykFejphwpuC3kEjYHcQvW/Sjyj2d6dO2fnGgMNojHy5SNXbVkFe5OXn2niQ/vxS+blj/CZ68Oah+bn7KDxg96DKT07C71s2wh8OtnX5iVdq+jteIxl8wLce4GRgYsybSKOPw4fwe+2mgbu+Xi9wO6rOCE0jelAZ3rzlt/jNzfGli8GDo0DpwYezPxWvvfFQXJwcn39W1elptwmvpfA88NZ4PQ5vsvUovTspXZjsJhpYhlGqSUTj6XExvYL3cbFCts6heO2tfL1X8Sq9tRg4oxOm0gXCIcC342Q/DW/CvngsWwWvTdWDjyz4xTgxnBUnhwepUWfx9L7o/2ScrIvj6AngvGz5O/BR3F7DHzb8BA/kvRQnyNrUHKwwP3fFm7VdDKwZ85bEa00/E/u/GGm3HLjbtDg26l6m4zfJs/CRC2+OeePjvFbcWGxCI3A3X427TprK+yv2ZdHnUE8cD/lI3ivjAf45+Pl97cjTO4knujQCga/Ha7LOwUeSbFYrdmz+V9OA9llepn8Ur7XyNH6TdyWwT7b8k5H/c/AaxWcB38JvbB4laowOcTvehD/pnwlsFPPysuFQvKZID9459pXAXVHu/rV8vHTjFPunB39g8kd633B2fL7gAawH49zTUQGI7HxySpxPZgAHZ8sXKq3fVgGGIebJ/niwfgZxP1D3a4YW5sWGeH91LxOjR9M7mPtePMjbg3dhcX2UETPxh82rxnodX070k4fHRf6c1u7lJ34deix+/3UHXouyB28NsUpp3XxwiqaBu2FtS9WZoWmEdiS8Bb+I/wONm+S5eLQ7D9ztijeley3WKS7a35ut0xEn5lHM+zF4TYd7gXtj3gR6j8q2dLb+2vjQ65PLn9Pi7dwbb/Z6Lf606Gg8EDcbD97mQ1S/MU5an8UDfVtRqgauaVj7Ir8AOAcPQjyBB7Cm02hivU2ssxLwcTx4WgTA5sTJcNXROH6GkMai+evNeGD4IPyBQk+kc5FYb33gY/FbeSF+L9cTAbt2O8GPcB4ujN/M/xPvNwW8icaf8UDAHni/b4/QpMZd9jm1Ojb6SOs7o3zqAX5X3nbmD9y9RDZabCdOlJpZ4g+CpuDNK//M/DW3V8SD+nPw2nT/h/dfVLw3L3dWwR8QzBtNNP8uTUPbV/H/Ofj1171406pv0qgheVq23gFRzhfXYy/g5+RVYvmgy74oS3vw2lE359tH78DdTng/hk/igcXf4YHc5Yb63Z004cG6OVHOPE10F9LJ+RJlxFp4c+on8GuRVTs13Xht1NPoHbCr/blyBNM/Br/WPw9v7fNwJ+/vAebJh2lct95Syqu8jN8qyvkZkXe/xmtdFQ9JuzL/svw5McrPmZ1Qfsb+3we/X56LN5Mugtvl1lHlwN0eRDckw96OqjNC0wjsRO8D6Pm46Dodr4p6Dl4LYy5ea2eJ0ntWwZvLrERU7Yz5XXPCasF++CEeeFgebxI7X8Au1rsNr8k2pCGfh7BdxQ3fF/Gn6mtny96C94M0Bw+yrFp1PnbTlJ3YvkKjSduawNVx4XApvW+03owPFvNRPPD6uphfqxMhXsP0Fby/utWz+cUF0dPMH3AompK9jngyVbd0VZCPY/AObveP1xPiYvIZPPBfVMv/fBxHL+N9VrVl4AWvKf5PSk33mD9wtzGN/obaounvCOVPUfvtc5H+Q5usUwTuZsc612fLyrX33hnrnFF12jplwruYeI35m1WdHHl9H71r2k+Ocn2nOB9Pyff1EL5/Lxp9Ib7E/H0clm8wloxyd2x2rdDV5W7kwRmRh4/QJYEMvEbJjVF2XEXjprRj0012HV7+bXT6FPv753Gc30CjP9iO3d8DyJN30niYNQsfjC9fXj6HLoq3ljKVn73y5TNZ+blKJ+QLXjO36O/3XmD7fo6LInA3A++iZOeR2Ia2Gb1DmjOzpfAadi8An0op/TQW/T8zm4bXzjkFmGtmF6eUngNIKf0t+4xiNEpL2dDNMmjT8WDdhXgH0q8Bm6VsVCEz+xj+ZOsyWjgaV4x+VIxck2L20sBNKaUHYiSbOSml35vZyUDCqzObmZ2fUnosPsey98sIMrPl8SbrtwNfSSlNj1Gb1wW2wE92/518pExL7o947br8c+aNdFUj2+EXPBellGaY2Xi8pueJ+NPcTVNKL5jZhORDxwO8kFL6d/EBkea6pWvUFOWxmf0PfnENnodvx8uYq1JKL8f8v8fUg/cVuCTeT09bSSndamb74c07jzez51JKl6TGaLHF39+a2UbAsymlmXUup/KyeAQUn3N3/J1QXiGl9LiZfZXo8gLYxsx2SyndmJKPtovfnM7Fa2sCLDVC29e1Il+n4L/RGcAFKaWHsrJv/5i/dRyzE1JKs1JKM/Ey/Y/ZZw25TE8pfc/MZuPNebYG9jSzGSmlF2P5vGMxfjfPNfmMri13M1/C+2E9NKX0aDGqatUb1UoppdfMbH/8ochvUkovdXq6i2uObrz/if1dDJh3U0rp352+vxckpXSTmb2GPwDdCdjbzKanlJ6P5fOuM+KYeQl/OJJ/RtfmX+ZcvEuPY1JKf2v34ypGkH0ZD0bOxh+cftHMPle6tiLu1V4xsx/g12jH4EG+YVPQrv0tiY8G9K0iYBfDEc9OKd1oZnPw0RdPB14zs2/kN8bQKITqetNTFwO4+ToDr/20O37DvG1K6Z/Z+9+LN0l9HG+yNKfV22lmO+Ed1o/F+2l4DOYNQT0m+VD295nZKfH2Y/EA7wUppUd1TLTUSnht1yMjYDceryVxFl5rapOU0rMRyFudPoaPr9uFpplNwU/WD6SU/hjb/x4aIytvnlIqAkrrmtn6KaXLy8daNx17zcqWLP0pC2yuj5+3v5IF7MDLnV8DhwOTUkr/GuFg0aiJwN2+eA3gU8yMPgJ3vweo08VgOc+LhyPx/9rAQ8Mp9+PCcHG8ac5c/CIy//4iuP+YmX0Dr6l5InCUmT2VUro7jqsiv7aJv38a6jaJi32zJB7wuCqldL+ZLYT3aVOUfRtnZd8b41rtniaftcDfbbNAdbb/fxT3D5Px/f+YmV2ZlSPztnkISe148bt91sy2i/06bji/23YRZekLeLP6rnpw1o2/hdjfz+PdatT1AXBLLKD8/GmUn5Pw7gb+bmaXlq65uvKYGYgoL1/Gay3SjuVn+fiI684v4t36/MfMnscHeTo5Vr2pFNBdMfkD1KvwwT9fGYlr1THDebPUwuL4TdzjMK8Qnp1FfG/Gm9gZ3mTmqLiQlEGIfC0CYXuZ2YlmdoqZbWZmE2O1l/AmMH/Fa8YcbmZbmNlbzOzL+A98It5/4NMRuR9x2XZ+Gu+v8Hy8z4o3Axua2UrFesU2pJTuw2tkXotf5B8awRYZAX3s60nxt7iReg+NESA3Tik9G/PHAb8ys4NbupFDYGZjm8xOeBrGx/K98bKnSNcz2bpfAvYzs0VbvrE1VSpbNjWzfc3so2b2rrip7ynKc7y27Hi8X7fi/fvgI4A/nVL6T1G2tGPArpBS+hnef8gEPHB3WMzvKaetTjcZ2X78vJlNzQJ2FwOX4MH3IYvjYE18IIGbUkqXx/xx8Ruakm3Lo/Gd5+FB3bPN7IBYf4KZvR8fHfFhvNyXfmS/weJ1s/PjGPxa6z/xei+alH1mtjA+6MSefZShC9qWscUNgplNNrPVzGw5vG9dAFJKP8L767ofv/bY38zmq5nZSfJ91Nf/A1G+waz7DecIpntu6XWtgxIjle5208L93bbXDINRKj8XM7M14r5ocrFO8kowp+P9MJ8NfCi71+tII11+ZnGIWpefZaXjYykzW8LMlkheyaU4t1+Jd1O0Fn6Nulv2/j2A75rZe5NXoHoFRuhaNdWgnbCmoU34xeGb8AvCB4A1SsvHxd834YGk2/EaPO8v3l91GtphyvOJxgV4Mf0DDzwUI30uhjeR/T2Ntu/x4UZzAAAgAElEQVRz8TbtP2WUOtcH3o/3KfZd/GnHx/E+D+fiwbkp2bp5n2kb4h1na5TY1hw/b8j+3yCOkUvwDsmbDVpieC2NfwO7VJ2WftJ4ENmIa3gffT14FfmHI13Llt7zMbxvto/TZX3JZHmQ//ZOpdGfWzHdE7/JhWKdzfFavH/CRwq9AO8w/FGyEaA7ZQJ2wPtrfQz4aNXbM8BtLgYCuBbvK6w4Z5zPEAfzKZUhKwD7Za8PxwedeABvgvEJYsTQWL5KlCHFIDZ3RlkzPdZfNdZr6/5mWrxP89/plNKyA4FF4//l8Gutf+JNk4syfZnSez4RZfoBQ9iWfITaj+HXdT14rcs/4wNgrZitszvenHom3g/miHSIXbepfPziNY6b7sMBft5818fN5lU9Kd1KdzekuxX5h3cJVAyO9hrefcGhZH174w+8fhXnz4+S9QHfSVOT42rR0uuROK5qf51fOtd/HB8t9iG878fdS+tOiHPqzLiWOgI4DL8+n0VpZNkR2b6qM0jTCOxEHw22GFp56ZiXH3jvA56LwujRuFhcu4ptbecpLsJn4RH2vfGL9T9G3v8vvUfpXTh+vCfho69uzTA7mF7AtpU7l/56nGjyQSe2wTuxn4u3y+8rcLfQSG+fpgT+1O627PU4fJTUOfH7nMH8Abv3x/zvU7pZrMsEHBW/gQ9l895NI/D0RJP37BsnwjsoDdTSjRONTs+vxIPsb8CDPa/gI05uHutNysrxHny01duAlWN5xwVe8P4RiwDmpKq3ZwDbuwjw1djmh+LvKUQn30P4vDxgtx2wRfb6XPxm429Rtt8dr28DDsrWWwkPCs/EA6BFfzNLdOpx06J9+6PYl0WQ7vzYv+/L1jk15hV5vXC+L4H34g8yfkZpgLBBHgtfinPHH/A+cm/JvvfLwFrZurvFsfFvvH+dhQeb9jpP9L4RPwT4TuR9UVtmUEGIUj5vAuxUdRqVbqW7W9M9gvmXp/fcKD/vxvsILgZgmYX3E79+tu4O+P3UTOC/8SaSlaenRcfVB/HKHk8Bv8Br6g/qfFHK502Bd1WdxiHkSfGw9UngLhr3M8eU1puAV7r4dyx/lRaOul15xmga1EG0FLAR/uT0zdn8TfDI7vN4MCYfrXFt/Cn8j+P15+PA2qPq9NR9oncgazx+Q30tsFo2fxIeeOjBL5z7HRGWFj9piP3/WTzI87GYNy5bvjl9B+469ula1RPejH1aHCc7Z/P3xm/s5wIn5PsCH4XwoZhWHI3jZ4hp2xwPFFxRmv+p7CT2Trwa+TL4w4VH8YETimBT7dI1ivm3U5zwLyuV3e/Hm9z/g9IId3iNnr3xjtJrG3gZqf0KbElWe6gdJrzm22txAdfnKGML+Iz84neHKAuexpvBHhflxleAN8U6ywAXxe/uu/QOGK1KI6B0PqUReTUtcF+sizc1fQGv3Xhx5OUXi3Is1lsEv07owbunWCHK83H4Q7yH8SDrkMv0ODfMBv6Hxs2B4TWefxnLvkhWsxPYBa/19yBtEPweRF6Ub8Rn4YGMH+O1HnuAn+DXzgv87ZU+b0e81cRtlEY7r3pSupXubkh3i/LyMPy6dF75GfP3xQOgc/Bz5LLZsu2Av0QZ2jEj1jc5rl7FKwpcT6NSyq/wwfEWeK5qclz9Ae9veVAPqCrIh/x+f0e8FdD5RMWXODaK/Di2yfs3wFs0fBRYLuaNfAWdqjNK04APqLfizSBephHx/QqwQSzfF7+gnIM3fzkSH1SgGM67CODsFK9PqCot7TYBJ0R+Pg18IJs/vvhLk8BdsTz+b3lADG8GNZ1GoOST2bK8QMoDd5/qhpN0HSYatc++UJr/4bj4mh3H0eXxG34Bv/FfNdarY1DG8H6Uvhfbv3Fp+Yl4/07FMVk0Gf8lHTIM/Ajk4SciXzYr8gOvHf1IXDwtFfMn9lWODORiahTSUa7tmz8sWDt/PYzvaItjJX7rL+DNFXvw0XDXH+Rn5Be/78BrGj4X1wLLxevfEN1i4MHcfeOYeQR4XZP9sBpe83vl4aSvGyc86PY2vJP+oiz7Svb7tGKf4SPEfzcr7+7C+x2ejd8crxrrDfp4jv18U+zjYt+Py5Ztg9ceeR4fqTZ/7/bEDUWnTXGumYPXcl0v5q0Sv70evEVKv/nd5Dd3F37N/eZWbbfSrXQr3aOehzfg19xrxetyxYbb8Vp1O5fe93YikEeHVXLA73OL46p4CLg8XpuzB6/8M5Tj6j/tdFzhDz53x++91iot25FGc+rjsvnzXX8vKK+GvH1VZ5CmAewkv1B8AX9Cey5+Yf4/8QP7HnGjjF+sFQV3MT1HFhWO972Kj2xaedrqPuHR81fxqsJP0WimVtRSKC6Wx+FPE3rwgT+WrGh7d8Kb3cwBvk32RIj5A3c3xvae2GknoDpOwBJ4MO55sr7tYtku+A3gc7H8LrzPspY9sRnhtB0Wx9JX47eQ92+3OR70/hrehGNnGoHtWqdrFPJtDHAV8O94bXifVM36N1wP78Ou1nmG1+aemr2+GA/Srln1to1iHrwBr126Mo3+HW+gd5ObPvdjHAerA6+nEbB7kbj4xQcV6gFOitcLlY6bvIbV6mQPZrJzV62Po7pONGrRvYo3lV0kW2aldT+GB+/+EH+PoRHkG1L+44PRvAT8NP8cGgHDMXhXHj1xjrfyd1GDIP8I75MV8AD5r2jciC8EvAsPlk6n1A1DOQ9oHiR/kXgwXsdJ6Va6uyHdI5yHS+L9At8Wr8eSPXCJeUdG+XkLfj3b6eXnsngtsjuy42rhOK7+3sdxVc6Ttj+u8G4lnsGDttcU+5rezYffQSNwN1+Nu5ZvY9WZpGkBOwjWwGvQ/RbYLZt/No3A3E+BTbJl2+Ojlu0MvDWbvw/eafmdlDpG1jQvj8oF0RS81ssfaHSsP7G0Th64+0Ws9yVGMRBWKlR2otE59Uml9fLA3dZ4kFeDTozwfuhr39Nonn5SXCgsVFq+FI1+KS3/zDqkq7/5+IXkQzT6buy3ZlWnXfgMIw8vx2vgrIs3eZ0vYBfrXY8/uFmp6rT0k8aWDsJQx6mv45jo8B+viX1h5MP36R24M7zPly2y84jhD4tmxz7/E1nALtZ5W3zekfG6r0Dv5PhNHlx1PrXrlJfDeI2Wu/AHYr/Ba2N8nN5B0mZBsgml1wMq+5od+8Cisa8fplRrLtvWxfEal7+tw/ljFPbRJvF7OCJej6dJEDv24Xw1Xun7hrPWNUSUbqW7G9I9jPxqVn5OxLsIeIKsaWy+Pt7Fwf344AId38c3Xnu/B/hIdlzt2+S4GgO8pVOPK7xrqR68duB1efqapLEI3H1qVLex6kzS1M/O8Uj3F6OAOSibX3RafiE+4MBc/Inqdv181iH4xf+zKEgzkLw/kAhs4jc++8V+eBIPfi5cWj8P3P2gfDIY4W0bSL8C78CDs/M1haZ34K6jOlSt+JjJC/VVSsuKptSvw2+ib1/Q/mx2wVH1hD+BfGc5bXHcnx7H22f6S08d0zXKefhJYNPs9V40Hr48gt9sl2/Gj4iLzLPrfBFJawdh2J5Sc7+qJ3oHrbfEa8y+i/mDNuPxWpJF4G6dmL873r/jD8gCO/jDunvj3P4q8I48T/CadnPwPoyOou9RSk/FL57fXXVeteNUOlcWQdgV8ZoJG+EP6WbiTYuK5sjzyjlKtRoHcu7u47vL1xvXx7H0WaI2fen7psQx8fOq87AF+2S+Gh74A+oe/Dp3LH0HscfjN+NH5u/P/q/tDafSrXR3Q7pHMP/6vM8Bvhn5eAbNa6FPwq9ffll1Olp9XMW8YrCvoxdwXI3Dm43mTUM76rii8eC5h2y0WOYP3G0fx8hTlEbaben2VZ1BmvrZOV5w3Adcn80rOnf/Ov7Ud0Ua/al9F9im9BkT8f5P5kQhvm7V6ar7BHwo8vMYGsG4iVGQPYYH795NH4G7vl6P0LblJ6Lt8U4vr8AHlXh7ad3+AnddHThp8fHzNRpB9f1KyyYD36A02mo7THiwujiZ/QC/UR2f/UZWxgdNuI0ILOk4my8P3xH5d0U2b43sd/o8pU6O8Zq+D+J9YRXNpWudr7RmEIYHiUEYqk5fbFNeFp+OB2+K38edeFCnPJhREbj7I17D8mHgn8zfXH5XPGg3J/LxTEqBT7z/1B58EJNHKY0GHuerR/ART9Vv6eD3bx6Q3T/Os2/Lj088UHsbjcDdMtnyPYGzGMKgD6Xvfl8cK1vSuKncLPb5DHzAmiml9x8U23Ra1fk4wvsk/z2dSKNPrzfFb+Ei4D30XVv5rChj5xslE++v6B68K5pa3XAq3Up3N6R7BPOvXHZfjbdAKsrPt+KDdzwOHEqpOyN8NNCX8Eoztb7WGsZxNe/hMd6Fxiy8Bvle/RxXZ8Rx9c4mn902xxVNKhDQu0ufj8Tv7GFK16+l97ydUb4mrzzzNC1gB/mPqej48t14W/zryPoHigL8lTjIfgMsX/qMPfHaDqtUnZ52mPDONx+lNOINXoukCNw9RJPAXYu3q1zgPoff0M2lcbN4cunY2IFGQGDU29934xS/tf/L9sn/4YNNLB/L18WrX3+76m0dZLoWAt4LXBLlUA8eWDiTaIZPo9n+flVua10nYDE8oPUQvUec3DbKlaJz+/fhTWEuxGvY/Z3GwB21blZMiwdhqDp9Tbb1M5HOn8XF3g3x+54R5W+5xsQXaPQ3+5tsvxbB7+WAW2Od3fGaeUXgLj9mtsD7CuyJZZOyZR+NY+wRajzydF2n0rn2VDyw+iKwZ/k4jf1QBO5Owvsz3D07/pcexnd/AW8d8Ry9W1tMxvutezbKh6/gN/TL4bWhp8f+79RBJ06jEbxYBK9Z+OOY969Ie/mG8wP4TdgNzB/kXCd+SzOp8Q2n0q10d0O6h5ln5fLzX1F+H5LNXwS/Jn8KfxB4Kf6QbUW8ttn0yMPlR3v7RymPilHk/xevHDQZ7x+/uC55iFJ3Jtlx9cMmx9WaNM6BtT6u6B3QHYdfk4+jdH1Eo1/YB4Adsvm9AnflY67l2191BmoaxM7yG+IXiSZCNKLFl+BP008h2qQ3eW/H92syxDwt/1DHx/Tf8YM9t7S8HLjbg1J/NaOwzSfEtl2Nd3b+5tjeR/AA3iXA6tn629Po4+7oqvO8Uyd6jxa8ELAVXg3/75H3T+F92u2I19rooWbN/bLt77MpK159fnU8UPC7SMcrwHmR3ln44AqTRvNkVveJRhO5oymNPhXzt8ADP7NoBHxn4l0frJR/Rp0nRmAQhuz/2jW3KF30TcG7nbi8KHPxC+Cj8Kf4j0YayoG7jfDg/XwDssQ5Zt9YvgxeQ+AB/MbjDCJwh19o7kvjocx0fIS33+NBwz9T45Gn22GKMm5OnFPX6+t4jd/uz+P8+wR+4/M3YLVYZ9DlIH5jNRcP3M/XpQneofqH8RYURVnxYpTF93fSvi/9PtbFa8l8GXhjNv/deBCzWV++R+DXa9NpEsTGu6L5n772sdKtdCvd7TXhFRjm4rXb126yfApey+73NEb5fgW//uqoc2fpuFob+Avez3B+XO2I36P0AKfGvOI+4Cg8YPdgH8fVGDxAWuvjqpQPB+PXS9Px+5jzgC1L6x8X+fFXSoG7ytJQdSZqGuCO8iDAPfhNwMLZ/A2jgPl0af2OqdLbwjzNC531SsuKAUCaDftdBO5m4FWFd271tmbfvS5+M3hTXuDGsh3wPo56gBNLy3bBAwJvqjrfO2ViADdicawsizefK5qxv4Q3aXsVH9hkvtGpKk5XfmKbHNu/YrP14mR9BN7H0quRth48wPDG0drmOk/lshgP2LwSF4srlMqhZfEg/KF4E411aPRZVZtjJNveER+EIVteu4BdKY0HAhvjF8Cb5fsImAB8kH4Cd83ykEbzHcMD/LfjtRaLwP+/yWrcxW9wPfyB3sN4rYJfxnuXzbdJ06D37774+f1CsodgsWwZ/An9EsV+i9/q+fgNwBU0bm4Gnf/4ufwZfECXVftZb2FgVXzgq2vxm5BjafTH21H7Hq9N+F78umyjIu+z5YfiAdOe+O1ciQ8c8hJ+47VqOV/6KsPqNCndSnc3pHsE829bvAbddc3KTxrBqPH4Ndip+APnb+EPVTu1/NwQb/76MtHVA72vud6L99veg197XY935TGTPh4EtctxVUrnufjDuGfx+Mk/I80vA/uU3lcE7u6jSbPgUU9H1RugaRA7yy/MeoDD4vUmUci8SJO+CzQNOF+/gDc/OgV/el3cQO4Z+X0ZHnyZVy02Xh+MjyCzwihu6y5k/aHFNo3Llu+K32i8SulGl9Kot5qGtR/yk9YuwH/FCe7j9B7luVkNmmtpNC/9KzXqb6q0vUfhTTEex/uquxw4IFteHvl2S7w/v3sibReQ1T7slqmvCz16X3SfHRcN2/f3nmz92j2EKR0rIzYIQyyre8DugOz3OwPvUqFX7VTmD9zN11S2n88/LY6Pr+FPwFcGPhF58iqlprLxnslFWZJtQ1tcUNdxwgdUeYbGzc0YPEh2PB6Yux/vA2jj0vsWpTHw0JBu+uI7ZpMNQDKEz+iofU+jg/AbgZuz+UbvsnWn+N08id+4/w6voVr0PdRWN+JKt9LdDeke4Tw8Br+n23UYn9Fp5eeH47i6GbglTye9A1pbAefg1zWP491EnUyHPATEg3BzIo1rxLyVI41FC5dyNxjFb/IXjGKXWE23v+oM1DSInQVvjB9S0RTmX1EwHVf1trXrhNdqeT77sf4mCrcpsfybeBXr3eN1HribQIxKNFoFGY2bxQOL7cn/xv9Fv2LvLi/TNCL7oNxnxkv4jXRPNp1Do9ZNeYCSyXhNzmti3c/kx1WF6cqPoS9F2fIYXntzBn4T+RpZ5+aUakjhtQYXx/u6u5eaDBpQUX6ehQdX1qJR86w4JnajMWLs5Kq2cRhpa/UgDL+jpgG72MaJ+E3VnNjODUr7txy4eyTyaKsBfPaWeO26m+jdh93YyNf/w2tqlvu4G9vsfKBpSPu3aNnwBLASHqzbE7/h6cGbvhZ91t2AN7UasZs8PLg9k0aT63IgvKiRObFb9jdeQ+Q3NGqrb9gsT7LXS+K1aPLfRdvdcCrdSnc3pHuE8/A7eDPX4gFhX+XnpPjbDeXnO4FpcVzNAjYpLS+3CFkUr1FunXJcAa/Ha6TeR6n2fCw/JvLnSeav9HIY/dR6H7U0VL0Bmga5wzxw9xX8Ke+1wPuyZR31ZGAU8/T8+KGejV+oz8SrnK8TJ9Bn8Lb8o9bcr6+TCI2b/R8x/4AjxYid7411Plp13nbyhD+ZmYMHILbAm/odhjdh6wEuzdbNAxjFCXDZODncVHVaSuk6OtL1RWJQE3yk6v3x6uM9wOf6eG8RtDgl1juo6vRUlIdvpRHEegqvhTmVeFoZ69yMX5QXAZ+2K79p7SAMG1SdvtimvmpOToj92oMHGRfL189+5wvHxeBdDKBWdpxzeoAPZ/PyZrNb4zX8nsVrVqxUdR510pTtt+PxhzG3xPXATDyIdyDe8fYkvB+75wayX/v7rib7uQhwH9DkPfMCdngAd2rVeTaK+2b3yPM5+Pm36UOhcr62+6R0K93dkO4h5FO5/CzOvedF+XlkX+/BW75MA95edTpGMb92xFvPzMXvd5foY72OPK7we/qXgItK8/P7s6JF4+Hxenxp3XGt3MYFpqHqTNQ0xB3nF2xt16684jzr82kL3pn4TXhNoePxm87n4oT567hg/ySlJoEt2s5efe3Rux+o8XjtnBfxfi0mNnnPGfiN8xZV53mnTsDb8GDu9ZSevgCb4bWJeoCT+3h/0XzqhljvDa3c3n7SkdeuM7yW3K1x/K/aZP0t8Vo+/yIbCr38eXgzyR7giKr3VYXHyDp4c+hihM+X8UDV4Xh/WHtEufL1qrd1EGlq9SAMC0eerVt1Wpuk/XBKA8fggbti1LWb6T9wNylf1s/3fCg+75h4Xb5oHB95XvQd+VU6dJTQUdinfV43xTF6Jn6Rfz/eT90y2fKx8du+nyF0PVE6Z5dH49s79u/P898C2fUH/nDlVWD/qvNxhPdJs8GP8vPU7nht3pfxWqyjOhCY0q10K93VT6Xyc/HSsqIboduBt2Tz8/7gj8KvZQ9p5XZWkC8LOq52xEd6nY2PMt813Sbh921z41ptAr27liriATvEsXNVX/lZaRqq3gBNmkZ7wmsMrUKjZtq4OCnOAU6IeW/EO6B+gcaIOjMo9SPUgm3LT0TH4KMG9hCjH+GBlQNjm/4RF+6rZO95J950+rdkNxiahrQv+sw/fCCSeX0f4P1C5Ptuqzgp/iu/aCh9xiS8xuQTjG6/iFsAO2av8xP6KnhNwZvKy7OT2kci7Z/u4/MXx5/ivUaX1rQr5ccYPIj5ZRrNqP8I/DB+x/fRZHTIOk+0YBCG8vFWpyl+zz1xsbtpadkEGsH3n9BH4G4Q37VNfNZvgUXzfKIR7N8VH8jkXrwJ++JDSVc3T/QOFm8Y5873A9uW1lsOr5WRX+BbrPsk3q/UoJ6+l757f7xvvA9k86bgTbxeA75OdEafLd8br215O7Bk1XnZon0yGVgt8n/h0nq7A3fjDz3aPqChdCvd3ZDuFuXfgXirsyOyeZOAS/F7usuAzUvvL8rPO4DXVZ2eFuXLYng3PCsV1xHZsh2BX+HXo10TuANehwfCn6XUdJrGNdYykS8XVL29TdNQ9QZo0jSaE707EP8MjVGClsc7mXyAbCTZKNyvotHUrWWBMHoHTz6HB31+AOxbWm88cAI+ouDL+M3dp/GaAI9EgaRRYoe3LzaJ/D+oNL8o2P87jocP9vMZReBqvkFi8D6Tjo3PGLWTA7Ai/qRpFk06OY8Lx3/gtUub9o+FN/Ms+mObwPy19TbBa6l+v+r9WPXE/LXM3gYciQfWX8jKlfmawdV1osWDMNRxigu5k/AazrcSgcps+QS8H7KmNe4Ge8zgwcEevKlGMXpwXkvgG/EbXY246Sj/TjX1m8f5A5aT8QcneZ+kP4m8zW+C8nLuQ3H8T6fUTcUgv/s0/MHO/WRdncSyrfGadnNj+WfwAN+lUUb/ncYowm3f0qKU1x/DA5JFDeU/4w/KVszW6YiAhtKtdHdDukcw/8r96T4X+VYuPzfD+52di98XnRn5dzleQ72Ty8/j8PvCHvweZAbeMmvVbJ2ODNz1tS9pXJueQWM02BViXtE9yzgag058uI7HRuUboEnTaE74hfgRwB/ih/kAsHMsezNeXfqc0ntWwJ/Cvz5et/RHDHwADxhdRNSwy5bNq3GB9330XRo3Gi/gNxtrt3L7umEC9sFrQf2nfDEQy4vq9xcx/0ATxZObo+mniSjwPuD67PWo3HTjI93OiguXvMbdOGAJGp3V7ptvW5ausXGsfauPz18J2C97XauTXkXHUznw+Tr84vySyOt7GeTNf4VpadkgDHWegKWAE6NMWFDg7k5KT7cH+B1FHq6LB4RmAxeTjTCNP0j6E3Budj7o+t/YEPfpmbG/rgXegwfVL6URlN4gW3c8PsrcNXgw+n7iJoihBWeLm4eLS9+T35RujtfkywOKz+Hn+ZWH+t11m+gdEP1SlC1/wGvJ3BLpnonXVl4rW3c3PKDxb7xlQqUj+yndSrfSPWp5eTqNa/C+ys+pNB6eF+Xns3i/bivFOp1Wfp4bx9XdeGuxG/HriFlxrsm7W9oBD9zNxCsiLFJ1WoaZD3ngcnNg5zgGFsnmW+z/HnwwtC1oVN45AA8A3wcsXXV6mqax6g3QpKlVE1ntD0o3NXjzkwvxkWNfAa6MAuxE/MnDDn18ZssK+NjOhaKQ/VupcP3/7J13vFxF2ce/c9MIIRAInZCEXgJIlyJNOgICgoCAoQmCNENHQJAqHaUJSAcLCKj4Ir7yAqKiVEG69N4hUhISsvP+8XtOzuzc3dvvPWd3Zz6f57O7c+acnWdmzswzv3lKOCnHvHwJBUEYRw82ionq9sd2aOM2HfhWdG08cixfoRqgCvvpJza+1urCf/X7pjsSZg5GJ5BVwJ1dm2h8vYVpCZKDCYMR6F0BDqOTqLcDwVejUY339zITqr4Sj6Giqd58Rz8FYSiaYn5r/J4bgd4dAXd3IWClx0KfvVcbImCoAjyHTChvQ0Dpy6QAFL3t662sLS8jiCQH7G4ywVtUg6ULoQ3ie8C15Kf0PQHstkcHHxcRuLewa6MING0sb210SLcPMuWdvaf/XWZCWsjTgPPIAVFna9K9du0MYO7gns3RwcB/ML+RjUaJ78R3K/Ddh+23rc2fF9eYP0fT3s/0ykjxYiI6EMvcTjTb/PkdtH89j2qtuh2RZcwXaF8SBkT7KnJz8iKm1d+IRPXe6zRyVzTTkSJCyLMjl1+nIq3L7JD0xeCdLN3+pfAKJErUHxRPxuROz2On3lsA19jLOwWZo7yHzFLHDERdo/osaJPNDbX4qHNPaTb5zUBUg1uZ74tawN0+5Kd3exL4xgC2QRvruwg2fkUTXQfuTja+PjbexiANu73QKdQz4SKYqEd9kankf9Xa+tKi69RBXQckCENZCPha8L0ecDcVRe9cOxIYh2Gbrd4KfbYe3IROhCvIjPNPNJGWVYF9fLIJ6avb78FI+/lFa++sD2cJ7lkeaeP1ajwjLZtPgRXtd3ZgdyS5Kdy1wGodPKN0G4pe9kcbCgb2IrBE1ifBtfWtbT6sMRdtSIMGY0l8J75bge8+bsPTbf1dyX475Hf0aGS18DkCZdauN0/ShPsm5Fv3FUxLk2o/rGuRR0HfLLpvHUyeb/R2QYo305A23YHooLOC3LSsEpRz6ED5BrRX+zPSylzArpdStiq8AokS9TVRrSK7JzIhfdsmrOuJTmas3K7I5OgTcvT96wXUfQFbcG6rcz0zh5obWL/otm5WouvA3aG2QMxAfqjOQ6ZWbyETqnHx84omug7cHUcOSr6LnK5PRydS461MKRe2RiJ08jsZuKToutSp3+/snLgAACAASURBVIAFYSgDkZu1nxPkxcDdQuSmzTdTQ5u2t+88Oeg5CGkPrArMR+7jLr17PW/bIcDtwKtZWyNz9ZfRZnqeoOzKwKn1+qcH/z3IxswnwXuyHQJjK0jr4XH7fn3RbTWAfTIPOiT6U9ZOYTsjQGOStcttBC4bgmeUZp1NfCe+E9/90n5taB83jVzjeAcEumRBvh5Ccm3L+FUG5kL+Ue/JxhWRJQzS8KwgU+zBzTSujNfZbBz8gnyPMhSBuZMRoLlKjXsz89gMPC+tbFV4BRIl6kuKJqizEcjwuk3kL5NrK2xH+0hNKyCTv8xB/ID7mEJmu68ibb+Nomshbz+zhb/0JmeNSnQduNsV+bLK1LHfQEBGKX1m2OIWnsCFwN2mUdltkE+lR1Ck2xPJT+RKxddAjoewLXv5zDmA45HZwpFF81injgMWhKEMhAKpZE6czwryYwE382s5HfklaicM9kFdWkZLoB/7s11boQ3N75A2y6LAN6kB2FnZ25CZcp9puAAnkPvNux8dEr6BIiGOQRqW/7QyE5qtv+v0yUjrg+fjtiYHNEYhp+r3N+L8kvhOfLcC3wPQrkfY3PiKrb2fo33dLjZ3zkXum3mlFpk/Z0Wm06/T3jw4G1fDbS17DBhaNB990A6xq5m5rA02tt9Dsk+kYFEF3AXt0hb+LjO1kVJKTZR89uY5dyiKiHMBcoD+JeQL5mK0Ub4EqZrjnBti9z7mvT8LCckLee/fdM4N6us6OudqvnfOuUHe+/8iJ7SjgD2cc4vV4G1b4CtIU+Djvq5fq6a4X7z3lSzPe38TitD7AnC1c+5bQbnrkLCwErAB6ptvee9ftT6dMVA81Eo1+PLe+y+C3+ejk915gGucc5sG12713n8PmZBtBZzgvX+7DHwNZDJ+K/Z9aefcEpC/k71Is6MDhDu99z/u5bP6JXnv30HRSk8CVgdOdc6tGVyfiswKf4sikv3JOTeyUceH9/6f6ET6fmCSc+4sy5/hnBvknBtsRZ9CvuYuQwLzq/GznHMu+t0tmSsbczXyezvuWiI559ritnLOORubv0OywI/JA1Ks4b1/Nyi7H7AK0ox7vw/qk42HU5AMAtpQXIlMva7x3r/mvX8Duet4FHihmfo77BPn3LAs33v/MdKQWQTYxzk3m5Vx6KAJ1EeDgI8bbX5JfCe+ofn5HojkvT8DKWV8RB6saSXv/fXe+ze89x+gw7THgf808fw5PMv33n8G3IestfZxzs0R3mafbWi9mey9nzZAVe6XFMnkyzvn1gUWBzy2L/beT7f2mo721T9Ca/4tzrmVs3bMntMQ46Ro1DBRor4mBD48iLSDFrO80JH+kWhRfBk7VSdC3MN7+rhu4fMnoCASK0ZlViCPbvMbpOU1DKn57oFOll7F/GEk6pN+CU2q5waWRBpGc0TlvkkNjbts/ERlCz+1ifjaHIERZwNfj8cPcAg1TGXtnWnZKJVRG05CEad/Q3Sa2Y3nhRqzI6gT+axIPuv8HpAgDAPIby3NyXB+XgX4B5HGXXD9hwhUWZjcZLWtzrPG1er/RAPW12cCp0R5K5MH+niP9pr3O6FT+wfpQ/+d5LJIGzKzHkW15nMbOgR6Dx0yNrxGRMy7fd8ZuAodcmXry5rI99ALwLcw87fgnonIrPjkonlJfCe+E9+Ft+18SHMxnD+dtem76ECtaSLtRrzvAvwS2DQYVyuj/clryDXUXNH9uyFA6wwaWA6hWoY+BVmBZD64vwB2DtsraJ/BSOPuPeAzgmCPjUKFVyBRor4mYBkCn0REgIO9uNdbmWMsr98nsGiiORSZw3xGHtFn9eD6OlSbXD5mk/EUBDYuV3Q7NwtRvbk+GvkUqiDw4d/AlsCcQZmawF3ZKBpvpyETrC/I/dS9DmwV3ROaym5cNA9FUzQ2zjBB+i6CQAW96JPNkDbwvPF/FcxzKYIw9DOPofA7HpnErgF8KSq3KjlwdwEyXRyOTOKfAn5F7gfF1Xn+/sDDwC97Ud9ahwKlbd8yEdJoyeb0o6Jr30QadBXgByjC4ARbj19Dh2NZ0I8+a+9a/Rlc2wMdDDxNHqG2YTdYAV/hXHoa2jh9AEwM8mdDByPv2fr0U2BZYH504PQs0m5tGGf8ie/EdyvwPcBt3NH8uZfNnc80+fz5PpLT9wjyh5u88SbwDnA5Onwcg3z1PovMsgfc9VM/tcnRaB92B7KEyoJOvA0sY2VqAXfHI/ltoaJ56DbPRVcgUaK+JnTakDlGHxldy17cVZDGyM8LqF/mj+FRpNZ9PwJT7iIASpC2127AA7YI/Q2dKowvuo2bkZCZVNYv15E7tp2KIg2OD8ruYELBZ8CeRde9E76OQiYEv0GmmLsgc8cMvJsYlT/YeK4QRS9rVUIb+mko0MhiHZSrq51LNaizEfCEPbM0AhQlCcLQzzyGwu9RNrdm78IXwLnAUsFasarNzVkEsqes317BAJ0Onv9jZMLzF2C3HtY3HDfrAXsV3YaNRtZu9xAc1AXXvo606SoBTTH5YUD8kiIt+pVt3XnT1pZxA/HfBfTFSehg6BJsYxVdnwttPDMtyE+QJsUUyxvfiO2S+E58twLfBbX1UOS64xcoCNzTTTx/nmjj6mJg6RrXZ0dado/YuJpmY2oqkjkbdlxRfRjahg5UryOQw4BTje+3aG9pFwJ3IxuxHQqvQKJEfU3ITOsxdEq+bnQte2mXsInsqn6sR/Zf4UQzFpm3XgEsGeQdhrTq7iXScEI+CJrGRKYsRPXmekW0CT8nWgD2Rf5HpqGTnKHBtW+gk9KXgFmL5ieoVzjeZkWb1SuIoiYjLa/PbIHbMLp2tL0/DXcS1Q/t+SV0OvkHYPHo2gYICN0tyGsnBFANvGyMNK8+JDKNL5ooURCGAeD1TOPhbnQYcgYyqamgSJ7rBHP40vb+P4W0tm4CxtTrb8v/IRKuL6CGcN3FOobjZhMkiL+AaWcm6lb7rQf8ldrA3Xi7fgjaSK9EHpmw34V6tAG5EW2srsCA/IH47wHuj43sHfs1HRw+Io3d8bYe/xpptB5CrpXcUO2S+E58twLfBbf5VUievbyJ588NkAbdTbXGFbmbpyEoIMdJwDXAtehAtinGFfB9ZAH1MKZYgAWdsO8n0AlwF7ZXI1HhFUiUqC8JC3ENHEO++VqYauHdkWsTHZDl9WEd5gq+hxPEqsByKIJNDJKMsDplwN1GwbUQhGm4SabshMyhvkO1SnXoI2MrdHI3lUirCJlTlUZTKqrbocjfxZOYySPtI8dmWp/3I/8g4VjL/HQ19ALfB+24OQJfvpeNDQT6n0+unVUBbqlzfy3A7r9EpphlIaTx086XG3KMnZmCjkemFhfZZ0OBSMjnzRRkBrlwkL8KCgowHZlazBvdNwwdCg3N2qTO87+ChOvraB/JbWlkVrwwMEsHdYzHzUPIZ0spx03RVKsvbL6Lgbt7bWwf3YVndkljtKvlOnnGEGAtYERHY6uRCZkETqMXrhf6oq0T34nvxHd5qK/mOmA17AC9SefPA0w22aIXz2jocQVsQR45+HVgefJ9f7jfPoE6wF0jU4oem1JTJW8JbZZ+j068rgW2cc7NacV2AfZDPiN+ld3XF//vnPsScJ9z7iB7bhbdZhcEjFwIPOW9v9PyB1m5T5HJ2RFIzfsE59xX7drM6FF9Vc+UlCzK8CMI3HrQe/+URXickUX6897/Hm3khwKHhhEkvfd/8P0UZbg3yTm3HtIkugmBA1MgjxwbRMU9A5kBj0PCzozg2idBpMWWSHG0T0ujkUCwhnNuVeBwZBa6F5o/DkKg7tedcxPj52XvrHNuY2QuuTiwjvf+0X5jpJNUK4pp0O8Po1PZdtFTfR51eCIaU6cBK3vv3+luZNSC01cQEHuVV5TnNuurh5B5xW+RcPjd7Aa7/rn3fqr3flon78ZSyP/fdd77l+z58zvnTgf+joCjvwEHOudGxjfXGTdLAGsXOW7KnLK+cM4d6ZzbzvKyNszm8nuA45ALhFOcc5M6eWbN6L1hsuh0M6PY9aTuFglvuvf+7977T5t43l0HgRnPQy7/ZCmbQ5xzs9pnu/m4K31SwpT4JvGdpSbmu0cpmLtX6cn9wT7qAe/9Z008f66F5JYXocNxNcI+m25cee//B/mAnB9Fyh1n67zz3leCsXACihY7N/Csc258M4yJRhKyU0qpS8km7FeQptGN6PTlJuAR59yzyI/cUGBL7/27fbzZXABtrlZ1zs0S5L+BzLBWAZZ3zq0O7QC5z8iBuxWB8w18SakfkvX7i0hTaDtgHefckt77Sgb+BmPjEis7Du0FvwifVcLF4B/InO8NpMW5QTgebXEbaj//gyIuL59dC8q1DEhsm+9skz97cOlGFBRmF2RedxI6wdsU2N97fxE5uBO+8yFoUCbAblAAMox3zn3ZObcG1v8ABl4dQA7cXeCcG+OcG+6c2xVF1nwaeDMAdxtCGHTODUPahO8ZQAnkfeW9/w+KsDwN2Nk5N3sIosXl66QR9jmbc2440uT9LVqT/oFMIKchcHTuqH4dAb2P9Yzr5k9O6csISD7HObcF1ATu7kbCPMBZzrkjuvMfcV7wLl2CfF52OwWb1qyODT3vxu0UrKNvIZcNa0P1upmBnwZk/NY5t2qjtUPie+bvxHcT890fyTl3KvCAc27Z7t7bAvNnBs69jfau60G7cZWBVsOBPzjn1mn0dohTAMgdjCxdAH7unFveeG8zxYMQuDsXtdv0Iurc1ymBdik1XTKwxXnvn0P+IfYG/og0Q95EPobWNg2ImRvYPvrvP6IohEd476c651aw/LuQA9F7UYSf3Zxpa0X3Z8DdycC8KFJsSv2QrN9vA45E2i+zAXu4XCMTpGGVfQ4CppUdnHDODfbefw6chfx8vIvegdWCMm3e+2n2cxSKbvb8AFe1NCkCsvYAbnXObQNgbbk3Ure/GAEta3nv/+a9/9AErC2Q+fRTNZ69JQISFqN4wK4tEHCPQvPifWj8P+ScO9c5t5SVexABd/cgP19/Raa9VyBQ6vAMvC6rcNiB8DsFGOuc2wqqgWobC/eh4BNjgeE94O9faOP2a/u8GIFzWwG7eO/3Rk6zxyI/gjPrW0agt6wp7F87Z/knej/HAhc4576WXQvLe+9vQeP+OeB059yBXfzL2eP/td/zANsiR981NVm7wktQzyHdvb8sqdbhR/B+3WmfuzvnJgT3DA3K7AGsi7RVGyYlvhPfrcD3AKS1ob0WWWcpmj+H9UfFBiJF42oUVIFzf7LP3ZxzKwX3DAtklN2RTLH4wNR44FIEyB2O9jjzAL90zi0XAXdtQbmlvfevd3dMlTL5EtjoJkrUH0Tk/w1FaxpCjQAR/fT/hyPHqHsFeeuQRyU9qYN7ZwXmLLoNm4XowI8DpnWJTKbeA/YB5o7KTEQnNRd39KwC+OpwDBtvRyI/ii8h/2xzBte3QaHj/wLMUTQ/RY8NBJa/i/xldBoVGIG530BaZ3+O2xBp3l1r7/sKRfMa1KtfgzCUgaJ+HR1d28d4vQpYMMgP/T3+A3i8Ho+dzQMoKunPkAn14cCo6PplyCfLEjXu3RSZ7U8m+bDrSv/OGl37rvXvC5g/T8sfYp9Dkeb0xUiLdlwX/m8D66+Nalwbi/xUHt9DXkK/e1siX5EN5SfS6h76RN0FuSnZNcibHbgBraUXEQWwAbZHkXP/RuAbuOyU+E58twLf/dSWWfCE8UhGva+nz7DvWyJ3Ngv3Rf0GuC3CcfVtdOi3b5A3AgXa+MJ4jH1sZ+PqPiKZp5koaqezba1/EljO8trCz3iMNDIVXoFEifqbinpZkcnl52jjsEeQ/xV0ElcBTi66fZqdogl+HDJfni0qMxQFlXgC+MgWxrWAMcCBlv8yJYqmGvG1DfKt9h1gyUiIGYpMrj9CwMzvkObUTQiUeAmLmEuJAMkC2jMLFX8ZFpCkTrlMyGxDYMwLKNLuwrXa0ITRdsBMgXz2axCGMlD0bnwHbY5OC/KWtjn4C6RBuWh0/04ouu/PCaKS1Xn+YsD6SGtifFQuA4niA6Rv2nxya425aCwCkj4jAXZd6euTkRZ7PF73JQfutoqu7YfcIswMvkMA2Nb5nyxoz0vA+mG/2hj4GGmftuvvTp5bK+BIJR6TZSfaH368j4D+naNy69m7N8OuH4eAj8uRVuqrNNB6lPhOfLcC3wPQrsPRAVcFA0C7Mo/WmD8ftGcsXjRPvRhXpyDrlydqjKs1kXyW+bY7HflXvgqZgbbEuKI+cDehmXkvvAKJEjUzIbO5T5DmTj3grq7GXaJet3+4EJ5oC1oFeAA4Lio7BAF3D1uZGVb+Geur8VauVGAFudZURk+iSMQh75nGXcb/E8D1yLfTQmXka4DbMNM4vBRYJLq2IArmMXuQt5K180fItDQTkkrfhujk/xMUQAIEPmbgwxIIzK0QaA3FwnNXhOkC+QvH/RlIW+1B4OAaff4YAu5+b4LvEijy+DPUAemj5//AymXv3odIK3fWDur3PWSW+QK5xmIc3fx7yKSj8PYsG0XtP7/N158iVxjzRWW/G/TL96x/D7T+/Sfd1C4GDrPnvY4Bd5b/ZXRAt5f97tI8QPsN58M2p5RGK7cH/ZMdflwa8hH121ooKFe4bn0A3NFIc2niO/Hdanz3ss0G1fpNrhm1Ijr8uKyLz6s1f05u8PnzFBsnP+tgXK2K5P7pwbh6D/gf8sPjph9XVAN3Z1k7vA0sVXTd+o3noiuQKFE9ooONYUfXykYICOoMuDun6Ho2M5GH/34ImTK8ab9viMplprL3ItDuFORzpEvaGAXw9V0bW9ciDar9UPCJCvKHFS5qQxEg8bKVWTK41vQLfCfteDLa+K9jv9uQj8Nj0Cn5xyiQwDZ2fWXgGuCHwDyN0oZIW+4fwEtBXqwZuCa5f77ZG2mujfg41t6Dn0ZjPRT0t0CRgMNN1QybJ8bH/RrdmwmJ/wvsavSg3X8YASBE7jz6TgQg/Qszyaz3/EQ1+zRsq83QQcRt1g9voUAf80T37IG0FsM+fjFo/05P5KP/zTTuXgfWs7wNLG+7bvBSa8P5XxpYuxKZZ01GBwPjomujMJA6yFsbuRfYB23YZ4/buxEo8Z34bgW++7D9NiU4BM3aAhgN/J/Npet18oxmnD+3tXF1cY1xNZr2mvwro/3lRGACMLLVxlW0Nl8CvAMsUHS9+o3foiuQKFEtil7EcSiy4Vq2ILq4TBeeF07wwwrgpx5wtzbaIH5KtNlI1Cft7oBFkEbNJeQb8SWR764KcHN0TwbcPQG8hjbjI4rmpQ5/P0daQosEeePQprQecHekjbcXMG2rVifkO+QzZMo6AtiRHFD/N/L5NxX5rZvVxtUIDMSlhKr4tNeOG4RAu7uMr61q3JOB07fbGJmvv+vZT7yvZHPt7bHwG/cX8nW6GQJgT0QaeKPD9qhx/0FIuP4Jdqpr79Zz5MDfMVQDd/vYPPTjrF27s4a1OkVr+JlIM/bfyKT770jTbTIyWY99kq4LHG1lj8GE+u60P/WBu1WRfDIV2BltruZAprdjkabu/MiEet4avDTFhtN4OcfmjRUzPoM15yHyA6bVOnhG6ebSxHfiO/Hdq/YK19ujbe58Fh2yrBaV3cuu/8h+t5ujm3j+PN3WkZWCcTXc2uwxW+NuRvvGmuOHBjj4i/qvrTvrcJ3nhWvzqHrjphmo8AokShRTNMEfiQIETEFmTI8g3xCZr6BOX8xogtgcaWCM6cs6d5GvesDdGnTBEXai7o8f+/0lpIWRmQNmQMs4pD1VC7jLTGWftHt3I/I9VQK+2tCmdVKQl/G2AB0Dd0eYkPNcows6vWzT7ABgD3Ltssfs+yvIt9lYpHWX+VtZruh6d2es0A9BGMpOSBNipm+cztqoO9eRP7yHkd+5zH/KHDZ23gROolrjbq7g3rHYoVFn/5+obr8cbH17HnZYgQDpiTZuP0XAXc1DMLp56EedTZD9RwVpLV9NbqL0uc0fmenSDHKAb87oGc204RyENpSfkIP/25Efjj1pc0oFuL7o+ia+E9+J735pn1hOHRZ8XwJYAR2gZ2003ebyb1iZWVGk+leJDl9q/NdGTTR/tiGXNdPINTF3IA9c+CgChGcAtxRd374aH9G1vYmCtvTkufXW7GagwiuQKFFItD9NryAtl2OBSUg7aAbyCdHOQXgnz9sYbcgrFADaWR0y4O55YL+i27vZiGpwah4USGI1pDU3whZGR+5DYyydA3f/Rhuz71KAlmYNvra0xW0vBBzskNXXPmsBd6fRHrg73ISdzzD/Ks1MnQgL8yP/ZI8hwOUC2oNdtyOQc2TRvHRjrPR5EIZGIHTYUwE2jNskHAtUR1LukqBn88mnwLb2ewQSpt8GdrG8ne3/v0AAeTyWmlao7Od+HYQ2Ma9gjsaD+W4QMlP9l43fSQSaoj1pc6o3AovQ3mfeEWiTNdnmhh8iX0SXAOciv1c/Rm4Wxkf3bm7zzYc0+IYz4OkEG/fPAPcj8PINFAlxDNI6/KeVmdAs70HiO/HdCnx3s42OIjBTRG4qbifXNh6N3LncbXNoBZnG7or86n6B1vE2ashuSDv+8SabPzMN7ldsHfscHfbsYmNqLmuvCrImaNhxZXz8PPh9sfH7bUrmhqhMVHgFEiWqRfbiTkU+I5YI8vewCes/dLJ5prYK9YeYSnuBvG1BbnbXLUfYiTps13CDdRxSv89OPt8hd/peZdJINXB3Z/TMIdZf7wEXlIDH06l2PltB0WCrtHeoBu6ydviB5WWnw0ORVlCXnP42MlENZH3FBMNjEQC6YNAesyFzhLB8GwKyXgeuoCDgtot89msQhrIR7U/1s7F9kI35n2DRboMyYfTfJ4EjuvmfsxBo7SJH5B/Zfw63/JFoQ5G9ewd35z8S1W370dbW92XtX6NPJ9GBj7tu/Fc4B3wPaX/chDZO4Xt2FNpsvAF8pZNnZvUdhg4JKhQsj/RRv2TtPwTJbM8gq4iLaQ903m3Xhhdd78R34jvx3S/tlEXzvNJ+/9h+nxnPx0hLfXXgBptDpyDXB+0O0oN7hto8P70Z5s+ItzORbHY/AjrjqOj/a9cLtfzpJY/jUUCWCgqGd459P5fA+qObzwz3+6NoYECzQz6LrkCiRCFhWlDAL2wCX9byB6HThueRtl3mc2iWes8JvpfOBAXYhBQdsL/a9iRbAP6FwLjX7PfvgjJx1KqFgXuQBk3sD2loGcYNMgubgsw090Cg0zPG2w/JAYMYuBsD3Edggh2UGRTnNRtRvcE+GWlEhUEHnqVj3zPfsXZ+nhzgK7VAQD8EYSgbRWN3CYJDHBvzz6PDnfWC931o1g5IW/VN7DS/o+fXuDaLfS5g69GfCCLGIqHxQySEXkcLaLIOUJ+3oc3MG0SRd4PPOazf37f5/Ih4Tu/K/wTfz0Sywz+BLeuUyTQ73wC+GtS1I+3eMTQAMN6NNpu5pgIL2TsQmty3IRnuPQRyDC2inonvxHfiu9/baAi5WedT9nliuA7WmLeH23p6Krnf3QqwU53/mJ8mCjhAtTwzHzr4C8eVQ8Hm3gUuo8SHx53wmfX3EsjkN+vnU4jcR3T3mfZ9c2Qp0+EBWqNS4RVIlCgmdJL9GvBH+z0Lcgz/MjL3mycouyyRQ3VKDtgl6vPxEi52Y9FmfWb0JbQ5ut8WhmuDRSMG7hYijwRaz5/VgAFbVG8K25DZ1c1U+3H6EgKUPkOaG/WAu6rf9t3V+t6sZEJBBZ3obol8Gp6OtGQqwFpB2WEoWMmtCNh5ghrRPstI9HMQhjJQ9M5PAp5G4FkWJGRW42c60pDaimrfOt9Em4kHaX+S3RY9f2sEykwCNo3Krmxj58SwbsD3rT5jg3mmtO3ZSARcbm1+CSbkU+3yYCQCz05CbjQ+o0Zwii7+13E2P5xPJ4ds5KZNLwObFN1OBfVN3XUEHTQ9bdQQhx+J78R34rtL7TEBWMy+hzLm28j09Qlgecvrih/yOZGfwApwcSu0YWc8ImuIp5G839DjinwPdgs5aPez4HqXzWNpv9//lz1vkb6oa9mo8AokShQTOqV5GrjDfm9PbcCuDWmFXEcNjTsSYNdShE5YNkS+hVbIxpJ9Log27/WAu6poRkXzEvF1LAIg3gK+E+RnPCxLB8BdzF8rko2Lj5DD+MWC/G2RCembVAcMGAlcaPPOZfQg2mSBvPZbEIYyUPSuno20T38H7BaVG4tMV6cg8/jfI/DtFqSF9So5ELsWMLHGf2V+VUNNxHOC68vas14C1rC8iQgQ/Csl939YRurC2FwIbQI/sP6cK7o+EWnPrgh8GZmpfYyAu3k7eO7g6PeaaNN5I+39Pq6ENDhXjvIPtXHyEAL+W33eHYqA7etsjn2GBjn8SHwnvhPfXeJ9EQTMPUweFd2hw65M+zgLfDW3Xe/wUNzuH4Z8707G/Je2Gtm4Wh1Znr2F9sUNO66olt3mQ3LbL5FmfAU4O7je3SCT2X7/I5p4v194BRIlCgkBcSOQlkhm4/4KAuxinxGHos3YwfEigDRpHmr2FzjRzP7+vo2Xv6OTliEEDsrtcwE6AO7KSOS+H9434WdTy483mCFwdzQt6Eelk3bMtGAyYGUwMjV40SjTLpsVGGHf50OajBkIWtpxEvHab0EYykTAIQhEO7eeUI8AnoPIT1+zTcSN5OaVcyNQZzIB8Gfry1TgGqSBuH0gXF4RlMvM8afZWlWxMTWuUdu2wD4NNRw3sz64EB1GLBpc2wEBpZ8i7eMvA0sh33NPI+E9e4/XRA7OK8DXov/bkMAZdnTtW3bP5vZ7CNLaPgMdBGaBRr4f1Xs/kjl01hZt9q5NRf5AG+bwI/Gd+E58d4n3McCVwK+zOdfyl0IH6QsH8+915LJWW621kfZ+eWcAaxfNZ4HtexWS6y9v5HEV9etI+8w05RdF/oVj4G5o8H326HktaVFXeAUStSbR+Wn6DrPmYwAAIABJREFU18g3Wa9Hi0Eb2kA9B9xLex9kmZPYCqZxlai5CfkVeZp8U151okdt4O53RdS1m3y1AesCD5Cbds5m11xUdlmkgVIBDiq67kW2WfR7CHA9AmYGG9Uzt18MaWfFzpJLB7zU4LPfgzCUhZA/m0fQwUynZhDW56sC6wDzEgGxCJT7AIFue1je9egkeJHgOcuQ++q5Osg/CGny/dnafcHw+Ym61KehUH+6CeAz0IalgnxE7YWCUQxBB3MP2rUp5GbuzxOZhSOtuGOivOFBX+4f5GdjIgtocayVPcj+b6qNiwvQoeEX2GFA9PyG7/t4junhM4YgTdYMRC19uyS+E9+twHcftt9ocj+ve2DBA6n2IXsPEXAX3L8qsGaUNxuKYP8xsE7RPPawXfpkDKCI9bP25TOLagfk7/FyIssGYHlqA3eD0AHe+eQWVC0J2HmfQLtEBVD0Am+AIjmuXUPQPoY8rPrWyP/S3CjazEvI791YKxtvYOclMmlJ1JxEbgI7Em3iK0jlOhOeYuBuPnLzqtEDXd8u8hQuSoMQ2PAo0iw5KOAtBu5WAP4CLFw0DwW1Wzi3rBR8vwD5N1vU5pJ2gJ2VuxWZNpa6/SI++zwIQ9kJWMXe8x/G7RGU6RBorfHufA34BJnNTkKHRbsEbZa149LkYM81wf2jMFC4Xp0SdalvT7S2vQxtgp3JCJmW495B2dmQZvElyIToaGD+rP1rjQGqwcHlkT/HuWqUW4I8imGmWfcs8NWsPLlW6+5Ft1s/9ENVO/XwGbGWb+kOPxLfie9W5LuXbbYmgS9x8sPALWw+vJ3cx13YvneTW7pkMuxWSOaa6ebI5u7MvPbq/uZnANprlVYdV1H/n4L2Xc8C28VlgOXIgbvzLG8HpJDxMu39D7cUYOd9Au0SDTBRDUZkYcAzuh/YNrg+N3BUcP1NezmnIjPIcVYubY5ahKgDMAST/khyU7gLaB+YIdt4z0Puf6Nw0KKzOqBT3HVs8Xob2J/6wF3LgwbAWTZfbGG/v2lj4j4E+D9PFHkM2BcdBJxDiSO+UQ3Y9WkQhkYhdMhTAX5U53r2vi9IDrB0KvCSA3cv2DhZx/KHhM+gGriraV6ZqEf9+mXku+dXVJvD7oi02l4L+rOjyKxd9ocTfP4EuCUqMwFtMC8FDgPmiK5fZPVdsei262W7d+QA/RLgzv56fuI78Z34bgwy+WIBW/c+JIimbdfHIdlrCnAbHQN3fwPOs7X2A9r7DV0XOD74Xbic3sM2O9X4XbaVx5W1wwx7z1aMrrlgHV6OfA/3BtK0fDkbH+Sy3VZISeNDWgSw8z6BdokKIqQtNBX5QdgfONle0o+BfaOy6wCnIZ8RF5oAnwnuLQtMtBpRDVYsiU77NkeOcEMweHbg3zaeLqQ9cFcVlbVkfG0JHI/8aJ2E1OKz+g8lB+7eoQPgrtUo6v9dELj/E3IzjfnITYw/IAotD+yENHkeIdfUKV2bRnz2VRCGwt+BHrRDFrX1/qyP67TRXeiAp8sgrL2Dk+355wb5beHzkc+eP1q5s4puk2Yg4NvWnpvY78HAzghAfZHc7cEsGBBNNyLNRf8VRjlc3N79CnB5VG6mhmqUvz3acP6BBg84goGRNXicBx0S/cR+d3uuiN7HIUXzmvhOfLca3/3QjvvbXPkSgcadXRuDAjhNpz5w92vkA/a/toaPs/xQFi5tcLhutlUG2n0n5rEH42pYX9VrgNtge3QYehHtLermwGTuIG8JBO7dhjQwF6oxPjKN/JYB7LxPoF2iggg5j745fIFRiO8PbDLftwvPaNiJPFH3KFrwj0Xq1TNs0n7ZJvghQZkQuGuncVcWivj6MfLfNJXcLOs9E4Ayh61tVAN338V83LUqxX1q/f1AICxm2pVLIO27CtLk2R6Z51+OTCFDIKvUhwH0XRCGUvNZg6dQgL3O1oojgvcj9Nn3baRR2c63Xxf+Z1MUxOhTYK94rAX/MwGZ4o8vum0ajeKxh07bT7Bxmjnc3onavieXQFqkc/fwv8NxdBHyobcRcAftA420Gzso+MRzVq8x8TMbiWwOfB3YqMa1sWhjfXwPnx2285bW1qXQ7k18J75bge8+bsNQXt2b3gF36yKN+aZUwAhkhPHWRvf1wbi6kpK7bqnDx7kItFs+4wspIRyOLD4+RHLU4sE9w6LPWi5QGq4tet2WRVcgUfMT7TfVw9HmeUf7PdP3DPB1e4GnAfsE9wwJypQKeEk0oGPpDBMU7gAORJu6+y3vSaSZkW2sQ+DuGsxfRhkJaddlfpy+ZAvaKuR+nE6nGpBYB3jcrk0suv4D3FY1QUpk1noF0jw7NGsr+8yAu8WAOxEwmgFZk4HfZgJA2YVH+jgIQxmpxpoR+orL3oOtEXj/KdLUXim7F9gdmQA/QWQG3Y06bIE0v18J3zHaA3fZ2OqRxlcrEtWbt3WC75k7jJ2QGXc935OXo012j3xQBc85mNwH6lAEBmZmzyFwNwhtNDZCPnTeR5uNceEYaEQij6z9ErC+5WVjezF7Bw4P87v43NhZeOZvthS+hhPfie9W4Lsf2rG3wF2HfkabjdB+91fWTrvWa4MujKss6FLNQ9qyEpLdfofk7Cxq7HbkB2RPkQcRvLEW/zWe2bDrba/bs+gKJGpuolqddW0UpW814E/AhpY/OJqgQuBu74Gsb6LyEjKL/gxp1S0W5GcmVR+Qmz9kG+nZkXZRO5PIshCwOtLyuo3cnHMw8tnwKgLu5oruGQRsiCJyLVQ0DwPYVhsic7TVgrw25LvsH+RA3Pnxoh+MiVHASjZudkERQRsm4hv9EIShTBStGTshE/c/oUADqxKA7yhS3WPWHm8j8PVBBOS9gGnA9bRfyX3c1QTuEvW6r8+2vtvefi9p/fiMzX0v0B6w28P64zIMgO7J2LLfv0Am4+EJf+iv8Iqo/Fpok3EGpknTCHNGF9rlMOP3dQzQsPwvo6i8e3WHV9pvOB9GmqsrFM1r4jvx3Wp890M79gq4azaqsa5ksmZ2wLciAoUv6+Lzao2ryY06rsjdXz2FrGCmIsWd3ZDsPic6iJ5KLw/imp0Kr0Ci5qVo4jmVau2WCnBKcL2N9sDdO1Zul6J5SVQckZ+G/gyBuVnY70EIdHkBmcGNtvzh0f2zkZtblQ6sQD6bvsCiKSHAbke0MZ2pZWL8jiHQOCL3t9TwG8cuttW5Nif8Flg5urY62oBPRZvudqrzHfV/GcdGnXr2SxCGMlC0GTjD+vJTpFE3GW2IDgAWDMqtZ2XfRwDbv5BJbPbO9+rdoBq42603z2p1itb4LUxwv4TcbGYO4GJyNwErRfd/Gx1iPEEv5nQUZfZ4kzF2y54TvDtLUR+4m5PcZKehwVuqAfJME+l1YD3L28DytuvGM2ttOEsV3S/xnfhuBb77uU27C9x9hlwajC+67v3YJpsCs0d5g4DRwP9ZG63XyTMaclzVWoej9+4iGx8PoTU/9mN3DzpwnbVoXspMhVcgUfOTLZKfow31UVRHjd0zKBcDd99EwnnL2a0nqiZgBDJ1fch+D0PAVi1/RxOA7xBswiy/lMAWCrIyU+0d+FYdvsYgM67liq5zgW3VhqKTVZDGXbypX93yM1+GDQFWdbMN+i0IQ8F8hXU/AfnsuxgDZ5HmaQWBc0fRPvrvaORQvI38pLtP3nkE3H2IAnvsUHRbNSJF/Tsa+IHN6YuG14FFUeCUCnAvcIrNib9CINtL9EKDEmnSVZAm3yuYjys6jhDcTkOiWeYW6gMaqwLLI/B0Z+uzOVBQn7HoUGB+YH1yrcOG2XAmvhPfrcB3H7VdzbmOaj/SnQF3F9u1efqrngW0S7i/ONr4fxY4lMAaxK7vRXDYSicWEo00rqJ3a1Z7b4YTuSRC/v1GRuPGIeWL922MNIS8WlhbF12BRM1H8WQE/AZFCxob5H2dfAM2MciPgbtZaj0zUWuRjYv7gEfs9/bUBrYcOs37Hxokol+wmE9CwEQ9P06X2ALekCryfTwWzicH7laNrq9i/V9B5nfNsrkekCAMRROwrb0DV5AD2bMg04q3yP3YVQF39LPWE/LD8gyBll+iHrXjqcDdyOT5p5aX+YzLxvA4dLj3HPkB31vUiSTXgzpsSR7s5+J4DAX1CCMEn1102/VxP9TbiB9OHrTmavKASJ8jYGO65WWBoF6nfTTu0m44E9+J71bguw/bLwRk5kc+l5fFNKKi6x0BdwsAo+x7w2kox3UmiOSKfKGugGT0zNf0dOA84BtWZla0N3mVTgIokftOLf24ivr/QOBvxv+LaL0eb9fqvYd7Ip92T2GyVb2yifxMwSSllPo8OeeOQIL4IcAB3vvfWH6b977inNsSOaj8CPi+9/7q7DrgvffeOed8GqQtnWw8OOCnKFrq5UgNvQKs6b1/Kyh7MHAMMqM803s/Y+Br3L3knFseqYVPRmaynyMNso+CMnugEOd3Aft57z8roq5FJufcoLA/nXPnIyHhd8jU/oHg2irIj8amaCwc1ijzSDY/Br8HA3jvv8jmQ+fc1kjjcCHE32+894/Yu/Jt4Ej0fmzkvX9z4LnoWXLODUdg3RrAtt77fznnZgP+CcyF+PoIgbazowAt13jv3x6g+s3ivZ8aj8WUupaccyOBk4D9kRuA33rvt7VrLlzznXPDrMzKCIh+HPi0r9rfObcp0t4bCezvvf+Z5WfySVaPCcBxwFHe+5d6859lSeEc45xbBPgsfIdMdjsZaZa+C1yLNI685U1BQOsXSAvxpeDezRHgujDyG/boQPDUlZT4Tny3At99lcJ51tpqd6SB/AU6wNoH+Gckr+wNXIq0mA/w3t8WPbOh93TOuaOAqzO5yjn3U2BxpHzyjnNuNLIS2xH5QB2MDqmuQD7dt0Fa5mcChG1nz9uMXLYr9biK3q+zUFT1J5GG+hjgG2gcbOy9/082nkymXQk4CAGU/wU29d6/lGSrTlLRqGGi5iR06lBBJ+XPY9owWNAJ8pPsLcmDCCR/QYnqEvJdlWldvEH1SZcDdrDxdh8NooIfvAffR2BdBYuqHJTZAwlIT1Ji33z93E7had7uSI3+Vmuvz5A5XezjbhXgditzRiO0WcRnoUEYCmyDg4BJ9n0W4H+RBsReQZnryDW1T8H8WSYqP6HNyLHokOJN4GvBNVfre3R/n73HwOa0WITgaI75HtL+uAmB4qG511G2Jr0BfKWTZ2Z+VoehDWkFWLFoXhPfie9W47sP2y9so7ORxuF9SFY9EWkevomsXoZF92Yad58gwKZwfvqoTbLASVfa78zV05m0t4yZA7lrucHG1hRy7e6b6zx/KLK4md5I4wqZBk9BLmmWtbzhNj4qyK1FFmRvkNEVdu0q+sj/cCtQ4RVI1LyENp2f2It5UpCfaU6FwF2mgp78BSWqS+SmpJ8gtepF0Enn6Ugl/zXMDJsGUsFHPiB+bIv1y8hp68EIjJpMA4MwfdxOZ6FTub+iSHBXkkeN/QO1gbvMP9bxRde/E95KF4ShaEIA7afIpHJokL8fcmj8sL0bsxdVx0Q96tcxyG/hVOCOcIPCAIPrtFCE4GiOOdPm0n8CW9Ypk2nsvgF8NbveUdtY35YqonniO/HdCnz3U1tOsvnxfGAZy5sDydsVpKG4I+2BuwORH9imcScBDCH3c/qUfZ5ItesnF30OR6bBpyJLmUzxYKc6/zE/kb/eMhBBALwofy0ko14DLG15cyJ/9G8j91iZe4swSvtgYE1ghP1uaFl1wPqh6Aokai6KFzmkCjzFJu9dw3JUA3fb2oI5pmgeEpWPqNbAmGhjKtOyqqCT0nvIAbuGWwCQ4+O9yE/lKihS4s/JfT00HF897eca13a3NrmQHMBsA5ZEkYUz4G6V6L41kNnLuKL56wrflCwIQ8Ht8jME7IyJ8n8P3AmsSB5dufSalImq+nBBZCo7DZm4r1RgXVoqQjAy9/0cbcSX7qRs5rT/ZWCTouue+E58J74HrP1WRMGCfk2uQTUKWX28iVzWvIbAmR1oH3igoQEZFNRuMfs+OMh/29atJ8gjn3fKIwKztiPwpdoIcguKrnxOLGuhPfx3bO1cP+tzZAHylo2JQUibLrMIWbLG80vfBmWhwiuQqLGJLpxGI/Xpz5Hp4k7hvVQDdynoRKIuEfKHsJ8JDScDm5E7uW3o8YNOcJe2hXKuVnovyE/zakXWugyZsk2IyyAnv5kz6d/RPnLXkPD5ZSVKGoShwPa4BGmffinI2wmd8h/Z7Pw3O1E+4K7pIgTHcx7SbngbuBGL3BtcWwlp78Yay4fa3PoQMg0s/SYr8Z34bgW++7lNM/dFm9rvEcAj1q47Iy2y48kDdexA5LamaB56wfsiti49DMyX8QNsTa6VmZl3zm3Xa8ohWb7dPwwdOE4m0DwrKyHtwnvJzYAzXrN9+zrI/zDIvPcmdLi8L3mwko3Q2vqpPWfRgeajWajwCiRqXKJ60/xl4FsoCMBW2OlEcH0H6gB3wfeGneATDQx1NkbqLZqNQvX4a4V3A9gQmQ/Ma79naoyZ4PCICTpjarUH0qh7A5363QysUTRP3eR/OPJd9yJmLgjMhk5z30QBJra26++j6HfzFV3vfm6TfU3Ie942CWcjc9jnaCKzm1YmqoG7m4kA9wGuS1NECLa59Od1rn3L3qnN7fcQm1PPQGaEFeRo/vuRjLcfgRlYGSnxnfhuBb77qS3b7cWQVt0GQftdi8CX/YHhlr8Ykrvesjb9etG89FF7jEHuV36NaQxa/lLIF+rCwP8Zz9dhfnUxZZRO2vcMZE2xdtF8drEtFiQ37T2HKPotuWLBusg//VUYYGf5qyIz6huRLJss6nraF0VXIFFjUjQB/Qg5Cq8E9BjwveieELj7ZtE8JGpsolpLsxSgVlyPstSrEQj4pc0dfyZXww+F6QuR1tV69rsdQAv8BZ0CV0zAHFI0X91sg6YLwkAvNUTJ/Rhma8tDmKlzb5+dqBxkm4ITrX+vLPK9pcE1mxH4n/ld2j/Izw5BJtm1Y63sQSiAzVSk7XgBchz+BTUOPsraLonvxHcr8N1PbRm655jNPjPtsMxKYQkEuPwGA+wsfxFbn89DGmRNA8ggtyPZerAHeTCFoVm7Ibc8VcBdcP+qwJpR3mzI5c3HwDpF89iNtliAXONuJnAXjZ3d7fomQV4bcC7yKdkWjK+Web/6tB+KrkCixibkXPMLdBqxFbAbMlnMAkscE5XfAWnCfARsXXT9ExUyZhpaG64rfJEc4/ek/QaTA1J3kwN3mdD4XfIDgYUsb1DU7vcjE5fDML93jUo0eBAGYDnMVKYnAhrVgO2qyHR4fWDOnj4zUb/1da8PJ5Bmw+G0oJZLP/TH8sAPgblqXFuCPIphBoY/C3w1K0/umH/3onlJfCe+E98D1p7nAI+Tm4OGstXG1lYTgzyHoug+hXwy93i9LwMhk+qtQv7scwvj/XZyH3dh29xNflCc+fHbytrlOoKDIHLz2quL5rcH7TM/OhivICAuk9EzgHcPu3YBubuib9p7dxMB2Fk0L41KhVcgUeMSsKkthlcTOHlHaPo25JoRe0T37YY2mw1tgpKoR2Mm3IhvAGzUy+eVYvKP+NrFFupde/G8Wur1peC1n9ov82U3GPgVOXA3b1TuNvKAEwtH17ZHvs6+ET+3EYkGDsIAfMn66Y7eCPJ04iMmUSF9GwebCue+XmnIBcJ/Q276ykBE2ucomvQtUZkJtsG8FB1wzBFdvwiZu63Y3/VNfCe+E9/lIJOrKsgUMgPuMtlsDbv2l+DazsidwJ8ItO8ajRD4uIDx9yFBlGG7Pg5p/E9BMmhHwN3fkNbhC8hUNPanuC5wfPC7oWQZ6gB3dm1I0A73An9ELm1eIZLXE/Ww/YuuQKLGJaTRMh1Y137PdLZpn7sgLbzbgJHRvZk/hCSctwhRvbk7GkWdqmBh5HvwvFAtu91J6wDyFS7cJ6PT3aeAnfuAr5VaQaCMBRdkgjENmR6EQsFocnOEF5DW2RYo4upz1u7zDGTd+7FNGjYIA4qS9nfrp5tp8BP4RDX7eBsCn4ooeMqVNDBQ3gxEdZTDxZEv0ApweVSuptYDOvx4AW3gR/ZnXRPfie/Ed/FEvmdrQyBnFTgXlLvRrr1i7T3F5JGxtdq40Qj56qsYT1tF18agQAzTqQ/c/RrJrf9FVh/jLD/c+4Tyfelkty62U0fA3RJIq+5Te79uxQA7kvzX+7YvugKJGo/IwblsAl8nzA/KzYk0Qr4AVii63okKHTPhQnU20iC6AdiwD563KfL99dWCeTzV3odLezrea/D1TwRgzdoXdSwjRQLN3giAu8cEwgpy9jt3UGYE8n+XmeBX7PsTmElsowpDUbs0ZBAGcp9Co9AJfALumoyA861f97PfZ9vvizCzmB48M/kD7X2/hOvHRcDpKHLfHdY/VwTXh9a4//s2v7yIafg2Qj8kvhPfrcB3P7dptm47cjcl9wLzR+XOQb4B/wVcE7Rfw67rVANve9M74G5dYG1yE+yGbZdO2qwucGfXl0O+ajNz4aZshwFv96IrkKhxyRa+CkHACdqrrJ9pZTYour6JiifgYATGnEcn4c6pbxoXCmwbI/9eU4AJBfK1PVIDv4jAVNyujaILIEsNvjKnyU0LeNMezP0InVAejzR3/k0O3MVCwSboZPRkYFdyx7ilEQ56WxcaNAhDsAGYkz4A7qixoSJpdBXVtw5Fz7sXmRJlmq+nYL4me/LM4PuawCJF89nIZOtsBR1uDEXaD5nT/hDQGGT9uRHykfm+rTvjsutF85L4Tnwnvvu0zUKAKVuPs/1aCNx1pHE3Gpi9p+t5GYneA3cdRoxtRqIT4C4o19KAeJ+2edEVSFRu6mgyBra0l/VTgig4VKut/xL5jOgQoEnU/GQL/QPAfdlCF1zbFjgJgTV1AV7aA1sPI1BjQIGtQMhxRufYe7BikD8UOTp+CAVfuRpYrcx8FTg2Jtpcch62YUemGiPJ/axUadzVeU4phCRSEIZwA9Ar4C56N7YCrqLBogI3IwEr2No+A22UJwTXuiyk15j7/o2A+2FJ2O9yGw6Kfv8CuCWUu4ClqQFo2LW1kGuBMzA/oo00xyS+E9/NzHc/teUJKLLuHPa7FnB3i7XjPZjGHe0tqppmjqaXwF0rEtXA3VlEfqgT9XF7F12BROUktGEON45bIwBiErBZkH+GvaxPEpknIp83b6Dw6iOK5ilRsQQsY2PlPPvtECBxNbkmUQU5tt2qxv31gK0vDUT9g/9uJ+QBv0XAXCbwbEcOVDyJInJVgOvLyldBYyITFK8APgeWC66FwSn+j0jjjpICN6QgDGFdewXc1Xg3HrBnpEOg4vo0e2ezyItvAB8jc+5u+RatM/dNpgX8ePZT3xyNDr7eAXbL2pjcpclS1Ac05gzey4aZYxLfie9W4bsP228M8ik9GTiE+sDdcCSPZ8BdjzSpG4noPnD3GfBXzDVLKxIC7jIZ/QSaCMgtGxVegUTlIWSWMrFGfmbiGvqPOj+4fmlw7VzgIOBC5Kz0NXInlOlFbmEC5kF+RB4FvoGifb2ETKxOQtpEhyIfiGdE95YC2IrqcQ5wpX0/nhxwvB+Ztb4BfNsW9wWRf7oKimjmajyvpQC7gO/ByPflZPIw8Vn7ZMDdOJtPsshm8xdR1y7y05JBGOrN7+SOwLsF3NV5NybTItqnZScU/fsQm8v/avPWQZg2qJWpuylOc1+f98fS9m69anPlRpY/JGxvqjWRLuuoXxqBEt+J71bgu5dtFmvHtQFfRlYgHyJljBi4y9bnO4CXg3W7aYDODmSWIcH3zoC7i+1aUwRA60VbLmjjY2zRdWlmKrwCicpBwFzotHwydnJl+YciAOIapDm3PfAfm8SuCsqdALyNIudUkM+IO8mjCjX1hjVR1ViqBUi1IXPRH9o4qSA/dH8DliXf2C9nY+iaOs/OfNgVurlDWiZTUZSkOYy3ixBo94gt5LEfkLvt2vAaz9uEFt60kjs+/lo2XoJrg9GJ74PA61bupjIK3eQn1C0VhIFqrexZEUA/b5CXzQldAu5IgE6piPpanxmovh5ye1ALuHPA8llfp/7t137a0mSvCnBx3H/Be7gU8Ecrd3bR9U58J74T3/3WVuFcOzTMB1ZHASVi4C50cXQ3cBgyfRxfND992C6hzDI/spBYFgv6Fl3vCLhbgPywuWkAzR62afb+NaWcWwYqvAKJykMIlPsAnV7tYXnXI/PWRYJyy5CfYF0d5K8KbAh8B53iZAtAeoFbhKKFbg5gdHR9BLAKsBvS7Jw1un6ICRC713j2+sjU9CMKNIk1vv6EgOyxQX4bsBACbAZH+bsA7yEwb2j07JWQFt6nA81X0RQs8jsg/yC3U33KGbb7vcAxSJt3fNF172ys0CJBGKI++q714RvocOd4YIGofIftQgJ0SkVR/45DBysLxWVQ1LwQuBtp175m42GmW4TUv/3aX5vaGjkD2DfIjwGNCcjn8Pii65z4Tnwnvvu9vc5CANzsQV4M3B1OHtCrDdgd+S1dK7inNLJHL9oiXNOOQPuKClIY+Dfam8TaiSFwt2WNZ5buELkL7VB1OF50fRJ1oc+KrkCicpEJ2J8g1fNJSLNlF7vmyDdWoer51R08r6VPHpqdCLTGogXgUGQm+jpSr9+MTvwdATsDT9t9c0fXhgEHAC8AKxXI797Iv+N7wNZBfkcBW/Ywvp6mRhRZpGJ/M01s9tfZPIDAzjvJI8GNCuaaQcCOSENzg+Ce0goZ9BFwRzXAUbogDNE7fzYCXl8CfoU2CBWkRblqdF/YLncSAdlWJgE65erfH5KbqD8PXEL15qcNAXd/tz47D7k9eAJ4l8gPoa0JD6b+7Zd+2xxZTrxC4PKE9oBGNk+Vdi5NfCe+E989aptwbh6G/C5XgBuoDdw9gg6Of4k0p48nd2fTYQCwRqIaMssMdNj0feBEtGd5E1n4Pd+AAAAgAElEQVSVDYvuzYC7T4CNi+alD8fHdshP5IRePK/hQMtGpMIrkKh8RA7cvYA2YOtYfrd8RiRqbrKF/Ulg2Sg/84H4KgrAMA05DD6y1uJvAsWPbKy9CIyz/PikayEK9GWGfDhlfL0JLF2rnpY3FFgZARZvIrPZjK9wsczepdIAMf3QbiG/KyMfWLsA60bllgUeI3d6fBiwmgmPz6DNf8P4DaGXwB3tNZJKG4TBBL5pwPkYAIM0HD6wOv+W9sDdKOPpg3heQFoU/0an/wnQKb5/T7Z+fAgFjXnWft9Ne+BuLeB/qNZcGG/XM3PaZRC4N50mPqwouM8yOa4moNGslPhOfLcC3520STgn7wn8AB0av0N+kBYDdyuQBxMIg8KNb8b2REopn5jMsozlzYH2IRV00LQj7YG7A00uaXcA3yhENXB5ovE6Be1xut3Pkay6OAnA67++K7oCicpJyGfEZJu8zg3yO/IZcVbR9U40YOPDAcdZvz9GDmBtiECq85EpVRuwBfAPWyB/QLX6/Vp2bRoCgLOgJaUzqQbmBY5FWiYVZErQTkMo4O1G5PfuCsxEsIx89XObhcLBCeT+DCsoWmysrbM08BtysCejp8osPNYTUmiBIAwm6D2NTvCXtLzZrM+y6OFfALcCq0X3zoH5vgvWltlsrEwhRREtqk/Dd3IJ5Ij84uAdnBtpZFSQX9LYJ+FgpKmwZTDfh89cHAUiWr5oXpuZqAY0diu6PonvxHfie0Db5SykyXwPcityJrISyfwCj6xxz3cQoLUfdkjabHIrsCI6TPo1pnSADhGfRPuXn6Igim8j1y2zRPePaNR2iWTLM9De61KCQ9WoTIcAXFR2U+QW5YdF89msVHgFEpWXyH1GfArsFeQnnxGJQP7pfmCb66dQ9KDdbdGbEJQbhJy8/p32wN0o+70H5ry8jAthMNbnsfq+i7QI1+jgniEIlGzYBb4v2sy+/9gExVtQlOAVgNvIT31HBmVHI428Y4wmUmPjXxaiGoxoqSAMCLw/EYFyq1veCCQQv4VM3pchN835BZHGnd0Ta9UuhwH4iQrt37WRWfZLwHKWl2nLzYXMtSs2t2f57bSG4/61vGH9UedE7dr5a0gzZAqwQ9H1SXwnvhPfA9Iee9jcfB5m5WH5E9DBaAWBVrNbfk3T4Vpzd6MTOkyqAJva7xHIPPhtk1mGIwuPCjKX3YE6gZQaldBe7TPgQmDR6NpsaP/SYWCJGrLqA0hRYeWi+WtWKrwCicpNSEsq+YxIFI+LrP9nRRp3n6NTqmuAi7KxQbWm1QpUA3eZhs3g4HmFCwjBmK6Kfht8z4C7j42fdtpP8SLXDIt8L9pzP6Q5dwGBaSfSzvqE3I9du1PfWmOuTEQ1YNeSQRiQduTe9j3znfM+sA85kLOP8Tzd3pmkYVVyQprEFeST9K+Wl7nIyObrUcCVtAfukhxQIkI+i56hgU26Et+J78R3l9vBAT8nAFCo9km+WCCHXEsO3HVZw6pRKJLdM9l+FOYfGYFT1yLAd3/MT7e10Rvo8LECfL1oXvq4XX6B3BFVufkxOfZ/kOXTWYGM6qL768mqpbMGaSbKBnBKKdVNzrmvIcfiHwDHee+vtvw2732l0MqlVEhyzg3y3s+w77Mh/2MHIO2L+4AtvPeTa9y3AjKJXAaZW53tvX9/wCreSYr4WgAYifmx895/HpSbG9gX+fJ6DPiu9/6xAqpc6uScG4vmjo+Aw7z3TzjnZkfBRuZAQsHGwCZIcDrAe/+xc64N8N5775xzvoQLVTj/OefORhEzX0eRgOdDTvlvQFEzHwzumxO1yUbAXcDm3vtp0bM3RtqJiyOfoo/2P0fdT1kbBJ/rI3PYG4CDMr4s/+eI368CX/bev1tQtVPqQnLOrYiA5fHo0G757N2M+nwUMr2aiDbMy2VzaErlSc65Wbz3U8M1rhVS4jvx3WrJOTcEHbZMAMZ476cH87UzuWolJIcNQhYQu3vvPy6w2n2eQtnROTeb9/6ToB2GWLssAfwFHTrt6r2fYuUXQUE4rgCWR0orrxXESp8l55xDsvczCJRc23v/mXNuM3TAvhUC3wYhDcRLkFxeCZ8RtGtDyKrNktqKrkBK5U/e+z8gh5xzASc553az/ATYtUAyAKUqBcDW0t77TxD4ciFaBBZBWnXtkgFb+6LTq+2R5k0pUgTYfR+Zbz6K1OavNfAaAO/9e8DPgNMQr5c455Yf+FqXPg1DpgZXG2A3KzrBmws4ynt/NnAwMq/cDfiZc26k976SCQVlBOwgn/+cc0cj58QXodPYHYHvIaDyW8BxzrlVg/s+BL6JnPqvBMwePtc5tylwDnqPSiEE1ZoDQG1gAly2FqyMTCsuj4DI7ZAg+COkNfhuvWemNPDJOTco+u289/9CZkSPA2PR2j9rBNi1ee8/QpH3bkUuEuYe6Pqn1Hny3k+1z5YCMhLfie8WTR8jq5AdIJdXDLAb6r1/BEVufxy5LLnGOTeyqMr2RwqApXOAfzjn5gvWrWzvMR4dsv4uAOwckt1eRzL+Ft771+J1shGTV/oIWURMQDL3bUjzbg2kbbgm8mX7huXNlFETYFdsSkJzSl1KAXA3ErjUObdDwVVKaYBSAE6cEgJTzrnLgCedc0sacPdj4DJ0inOZc26pOs/7N9rEr+O9/68tkIUmW8QzwO4sFAq+DQGRP0UA4znOuT2zeyLgbhng1865ZQa67iVPr6DTy1+awHMSaqvTkEkswHPIF8YjwE4IAG6I5JzbAGkY3QRc6L1/1DRPb0KmKbchXzvHOudWy+4zoWkjZJrwXgZg2b1rIiFogzIIQQZmZ3PAls65Q51zB5v2XLYJyN7hTHtuo+D+HVAbPAy8btpaIdCXUsEpmPu2tLkw03B9Evn4eRYdthxsmiy1gLvdgUW8928nQDallFJKqZhkgNQV6FB8B+fcotk10zDLDtQWQpYxVwJfB65uNuDO0lLAssAvA+BusF3LtAv3cs7NZ993Ar4NvAr8N7OyaTQguJN1+GcIuNsFaRLehUypL/HeP4Vkuf8iH5GfZjdFgN1pJMBuYJMvgY1uosYhks+IliRgb2Qm+jukJfUTpB31U2ChoNysyJdXFpxi6U6eWyo/ZcARyDnrT8K6o0hSFaQhuHt0z2jgVLSxna9oHspGVAdieBiZjw6LyjxvbX4ugdPkMhMtEIQh6zv7fgbVEX0rwNFR+VWCd+XnKILyWyiQQSl4SlS3rzPH5ecG72z2uRzy0/gpCg4zi+W380Vatjk9UaJEiVqNkHb0TTanX0h1cLhMk+wl4MvAGHLfpL/BfNw1OgXrVxtyv1JBprDzReVutGuvoMPjKdY2Y8PnNBJR7W95PDoMXp1oT2Yy29w1ZPLdUaThU2vIqKuZzD6Z5MNuYPu16AokajwKBPbSRXJM1G99vihwMtIeetEWuDOA+YMyYXCKLgN3ZSFbvB5H/saWsrzZUcCEV1AUrgo6gdozundOcme+adNafwx9CNwU5A1BpqRvRUJlQziyp0WCMACTULCZG1Bwoj3t3a4Ap0VlN0On91+YUHdnIPymNaOkhDZvf7Q+PYeuA3cNt6FJlChRomYnYH0EUlWAe1BwoeWBY5HyxRMYgIWAnSus7MFF170P22BmkC/gOuPv3nDvYtfPAR4E/oUC6o0J728kovoQ7VjgBfKD1inITcm4Du7f3cbH43E72fUJwP+SosQOOKVAFCmllFKXknNuGPA35IfrJaRxdq9dy5zbZiZTs6LgFEejjd7O3vsnCqp6p8nM+yYiE9+dvPd3OedGIJBlfuBw7/01zrmDEHj3MvAj7/2V8XN8mlRrJufcaATmLAQcAlyFtHsOQUDApt77DwqrYDdTMwdhiJ14O+duRgDrQd77Fy1vTaRpuBFwJtK6y8xo50UaudOBt7z3n7ayY/BGSeac/BQEvJ4HHGrzeja/L4ecls+DtDd+5IMAPSmllFJKKRWbIr9jayM569vA4KDYs8Bm3vuXnHODvfdfOOcWA77qvb9s4Gvdu+SqA4MN895/Hqxbg7z3M0zOvwaZhP4V2MF7/3bwjNFIZvnc7m9omcU5dwbah92O3NEMQq5+NgN+j2S2p4PyY4Dj0MHsVGBjGx+xPNiGNPOmDBgzKQEk0C6llFLqWnLObYU0ie5HWml/RJu6Z6NyIXA3CZ3q/AXYyHv/xQBXu8vJObc4MnO8wTk3FJ06bgkcBVzhvZ9mQQJuRwv7EGBH7/2NhVW6QVIgPG2EhIVhwCcoaMGLSFB82ZUwInVHdYqE40nIH9/qvjpa7E+AdZDPmA99EIFzAKrfq+ScOw6Zi+8FHOu9vzkSjldHGrjtgLvoOQnMLkmqI4D7YByvjPo0A+4O89VRBycgTYX3gVW89/8deC5SSimllFKqlyLZZATy67YBkr1eAO70Cgg1E9AK1+hGkVHi5Jw7AXPd472fXAe4uxnJY/ciGf6tmN9Gl1mcczsjH+PXAmd575+3/AOQK5oXUFCwTy1/MeRzegd0+HyQ9/71Rgcumy0N7rxISimllBJ473/vFEH1XbTgHQkMds4d5L3/D+QbQlsAP3POnY82/TeWEbCLtKWec869YJcmINOCPwKXBYvWo8gv2x8RkHHfQNe5EVOgrfNn09CaRO5D5CITmkonHLjqiMJbIsH3C+BR7/3doRYS1UEYHrR7siAMd6MgDJlwXGph2ATb5ZEm3euAR6bNAG3Gsvfe3++cO9byDwdmINPJqtTIwm+zpWA8r+i9/1c2/6E+xnv/sPWpQ1qw051zxwRj9wl7hz/2Fkgo9W9KKaWUUv+l7s6zUdnPvPcPI9k1fObMAGzxs8suo9RKpim2NwqY+Ilz7soAuJsR7E++hcxg1wF+5Zz7lvf+9fBZTbCmbQBMAy733j9vigjbIM2754E1zQJiiFfgkheR9vzlwAN2uFw6mbzVU9K0SymllNqlzk7ZbHE8EPg+8GfgYOD5TCMD2Bz4yHv/9+CewheAGqdpsdZJtqjvgk6oDvDeX5RdQydRE733CwUmBYXzNZAp0rTq1mlsAJIONc3F7Hfp2jA6qc7MDML0A+/9aUH5VZAm6oLIqfPsSCiciqJrvTogFe/DZMLtdfbzOO/9KZYfa2ethgC+zZBG3qlF1DelriXn3JnAocC3vffXWV48N66JgqeMBU5H/VqJ3ovSvbcppZRSSo2caszFGbBCJncWV7vypBrt1IaCJFyEfCifBPw80rjLTGfvQD6JFwZuBbZvJKDS1uf5gduysRFcGwE8BLzjvV/X9i47IPc/FWQN8q6V3QAY5b2/JXpGQ2paNnvqKBxwSiml1ILJNmIZKLO2c+6bzrkNncxHAfDev4ZUrM9BmkU/ARZ3CqO+CXAJcKtzbhZbSCl6cxfxtaNz7jzg7865C5xze0Z1fAlpVK3hnFvQ8nZAJ1UPOpn+zojuabrkopDxrr3fkAyk7VIKhIBMyMi0e0rXhgEwMQmB0r9E5tJ7Iye9pzjnTgvKP2TX/on8I26CHD2v671/1QSn0ifnXFvwzt6AfKAA/MhAvKwfXdb33vsHkEnlL4HrB77WKXUzZRoXZ9oBBYHGHfb7PhRJFuQi4NJY26OM721KKaWUUqOmUBPfObcCQADYnQAc3SiyRH+mqJ2Gwky55H5gP+R3+jhgL+fcHAbYDfa5D9ZhwE/RHmZSIwFUzrl5gKtRpN+V6xSbAczlnFsY2I4agJ2lU4CTnHOzhzc3Unu0UkqadimllNLMFGlRnIbMGIfY5cdRpMhfBOUXAg5AZlRPAK+iQBWDgPW9+VEoOkVg05nA/iiK0pso3P0cyIfdQV5mvXOjxXwXdGI1HZnMfoRAmFdaySzMObc78D/e+3fs99VIWFjFW8CFZkk1tC+bOghDV05Uncx8fwW8DRzivf9Vdi/VGndDvPfTkzZAeVOgcbAN8u3zPurT6+16G4qYN905tx3y0/gOsBiwhPf+o6LqnlJKKaXUCsk592cUuOor3vu/O+d+CnwP+AHy1/ZJD57ZdDKrc+4sYFVga2/+Ve0wcTXgUmAccCpwpff+PVvfvo20x7fLrIEaSWYx0HZPYAWkAT+5htbhecC+wM+QsoEnAuyccwcjdybnAWeUVUZNKU9J0y6llFKamYLN93HAEcAd9nkmsBxwhXNun6D860jL7hhkFrgB8lP2FS8/CqXwmxkAKj9AQOS1KDDG8kgwuh8tgj+y8u+hzeqPgcVRtMQ/I75eMRCmqYSfesk5dxICNPez3+cDu6JIwqN6+Mx22nnd0djrz+Rzn1/HOecORSYUV3rvXww00O4DjkVj4nDgtODaO977p733zxtg58oqDEXap2s553Zzzp3qnNvaKVIoAF7BVnYG5gPOd87taPkV5PssKzfdPhtC+G32lL1T0bs1CMB7fyvwDWA0cF6ocReY22yI/HjuDizrvf+oLO9pSimllFITp/sR0PIL59xvEGB3NnBdbwE759yizrn5+rS2A5RCLUPn3DBgCWBd4JJMW8z4fADYB1nNnABc4JxbD8ltx6IDyJlB9BpFZgnkycuRhuBk26/t5JwbEhS9HR22HQjMAqwaAXY7IYWLF5Hfu1LKqClVp6Rpl1JKKcUO94chsO5l5MfqFcvfDi0UsyFfb5cG9zsEbM0HvORL6MTUObc0CiDxDHCg9/5ZU6vfEIFSHwNrR6rjmebUx0gWmFo2vvo7OefWR+ahWyOtw1URiHue9/7NHjwvFB7XAmbx3v9f39W4d8nG8vLIUXEWhGGi9/4uA6FnBPUPo6ee7r1vF4ShrCnSPv0RAmVHB0VeBk7y3l8R3LMj8nP2NtI8TJGTS5qiOX1OpE08DZjqvf8gKLcdcBMKNHK09/5SA6C/AZyGfOYcYmWTn5uUUkoppX5KkXz0XeACpGDzK+SDdHp3NeaiZ26OzEZvQ9pVDQFWQbs1bU9gAWA3ZNkwN3ADsH+kcbc80iRbP3jUf4BNvfcvNeKaFrXDcsDfkWXH3sAfMusX59yJCOwFAZivI6WKA1G7eXJFhIZrh1ZMCbRLKaWUZibn3G4oqvRPkOr4/4YmcM65rwNXASMIgLtYtbyMavjOuS2QoLKj9/5GA2C2J/f1sJqpzw8BFvXeP2P3hQJP6fgaiOTkz/AOYDzy2ba39/5Ju9blNonachMECgBskAlaZUmuRYIwOOdORdq0twLXAHMiE/eDrMgk7/15QfkMuKsA23rvfz+wNU6psxQJ9Qcgf5wroaAozwJXe+8vC8pvi4A7h07ohyDzov+ig4zXBpaDlFJKKaXWTC4P0HU80hLLotNv4/3/t3feYXaVVfu+nxSKNBFRQUCwgfIJKtLhhzQRsCFFEGlSBeFDEOkdhYRQlV4kGDqotACKtA8RKYINQYp0QUIvoSR5fn+sdyd7NpOYQDJnn5l1X9dcM7Pb9e6zzzl77eddaz2+dVoElkbMtSZRKvp54LO2/zpjzmDGoiiJ3Q64i+jPOg7YihDvfglsZfvlxj7bEq6yY4GLbT/TjRPwjXv7wkV43IAw3ZgH2IFoZfN6ES33IsS8RYj3kYA3iEzOzWqVQ131OgxUUrRLkgQASWsRD2y3EM6XqxURazAwoXbjrwt3O9o+vUNDniy93YQk7UjMWn6ROMcNib4WTTelOYArgWG2r+zLcbeNKuCTtDNwHGEV/zFCpDre9vPTeqzy95rEa78osILtv0z/0U87tRLXKgOtykIyEeCcW9uuLtytQJQa7G37kU6M/Z1QrsMvy88B9bFL2oRJphLfqc69rNuCKB//rKNEPmkJjc/ZUUS/0QeBewhBduWy6Qhgn2qyRdLqRJuDZYiHoLuJDNMM6pMkSWYwDUFGwLcI9+73EuLLU8Cmtq+vbTOxrU21rLdJ5nKvr9q9rNSWmGtakbQVcAaRWHBMFbNIWpxob1NNQG1j+6VmQkHtOF2dWSbpGEJ83Z0QLjcizn9uopfdVbbHlm3/h3ju+Qgh2N0I3O5od5H39i6iFf2mkiRpBbcTmU97EbMx6wBn2x6vgoNLFcYEpxGOgmNdmpi3hVrgcwhx8/oD8FBZvTTR4+Ftgl3hcODTwJi+G3E7qQWDFxI3+38Tvf8OBIZKGuHSmL55828GoJMJHjsq2DUDt2YQZ/uXJbPsAuAoSeNtX1BmwQdJonwmbpF0u7vPhOEzhPh+uu1H6qKl7fMUpfJnAttJ+h0wxvZ42yMlXWh7bAZ97aL2OdueyJY8BviZ7YfL8vUIAf6HRLnsfmW/30m6k3hAHAQ87ZabqCRJkvQXavHSMGA0EXepxOAvEDHrOZIq4W5QbZ+PAY95Umnk5GKulbtYsBOwEnHfOrvELCJeh79L+hGRTbcB8IaknYpw9zYhs9sEu8Y5bEuY6Z1BxGQTJFWtSg4BTgZ2kDTa9uu2/0YYCTaPOSjv7d1FGlEkyQCnNlv3HOGYemBZ9b2SQVR/EKy2vZTILPoHcFNfj3lqKOWX+wHbKkpeHyCEuyOJTMG3gGXdsznrlsC6hMHA225yA4FKuKlj+2nbpzpKIY8gMhH3AX4o6b1lm0rcXVnSbFMh2HU0eNQANmHQpGbOny2/q2vuEgBWn/OziD6QnwPmrq5x2fb1sk0GfS2ifAZnA75KNKI+rSbYyfavCGOJF4B9FKU1ANh+wfbDth9yy01UkiRJ+huSvkqYW81WjzFsDwf2BD5ECHer12KudQmh5vvlGIOnEHP9uc9PavoxhCjzfBGoSnsrUVO2HyReo/HApsDPJc1Rz0Ss/90tFHGtPu6liPLWYzypL9944CLgAOLefjKwjnqaU/Sg24TLJEW7JBlwNEWZxg3tWeLL/sfAssCeimb7vQl3FwJL2X6sJgK0ieuIEq8VgDltPwCcWNZ9iOg/9p9q4yLY7UuIEbtXD619O+TO0hCyFpW0oqSFJM1VbWP7j0T/jEq426O2/kvAJUTvwNYGj/UZxpKNeSkwksgy/TVwuaLRMQC2LyCEuw8QTpsbluVdEfQ038c1IeZP5fdnqk3Letc+0/cTs9cfrdbVfyftolyX2YAlgPtc681Z2+Y6JvUsXKO5vnGsJEmSpG+oTIKWKL/H1zLgj2SScHdhmXDZnmh18AVCtKln7K1BS2Ku6cjLhOldjxisxCwz2b4L+B0x6b4ecLai5U3XUCocJlKLyY+SdDIRr51j+/5qfW2CrSncfWVKwl3SXWR5bJIMINSzZPGTwIeJcqh/Eq6vrzr62B1LiPp7x6Y6zPZt5caoWvbUG9C+bJtynuMkXUCUu+5IOGEeXTLD9gNGKcwpniAEys8RAdPatp/QACsLawhZhxJuou8jXpMbJR1g++8Atm8r20wghK6FJL0IrElkMG5XtquOtxZxHT5GC4LHWhA0JROG0yXN6WLCYPuComucB5wn6XV3gQmDerrEvh94xfbrZfVfgNeAIyTdZPsvtQeE6r3/AaIs+v4+HnryzhkCDAY+IemjJXOuPukyiJjUeAZYQeGiPY7o3ZgkSZJ0hseIuHoBmDhx4uo+bvtISW8RZZAXElllDwKfrybQS+bZqkQLmzlpQcw1PXC0HzmTMP3aUNKtth8CkDTUpTSYeK65hWj5sxUwUtIWbphTtBFJKwPflHRCSTSoli9AxNVDiBh7trJ8iO1x1bNZufZVqez+xCT6ukS/8qTLSdEuSQYIDVFmf8JRaMGy2sBFkkbavsr2s5KOLuv2Lvscavv2tqWa1wTEer+KSnC4nJh1WlvSSbbH2D5A0mNE5tQGxAPsw0TT/Z/YfnKgCXbQQ8g6hMg4/D/gz8BixIzlpyR9x/afyva3lW2fIfrcjSNmN9cqKftDinC6KCGMLU70D2xF8Fgy/3YmrnvdhGGkpD+W5UdL+o+LCUMR7mYhZrb/1Ntx20RDpN+OaGx9qaQzbb9i+zpJpxJmBZdL+qbtO2v7f4NoYHw78GTfn0EytdS/B8t32GiiDHYV4KHa+kElsH+KCP4n9kFKkiRJZjxTiDHfJFy7lykTaINtv1WyqSrh7lhJ9xNGXjMDZ9p+unHMh4mJmy+1JeaaTtxFxPXfBJ6UdKLtvxdBT0RcPwfRi/cJIr7fAjhL0la2X+rUwP8bkmYl3F83Af4i6cHaPf1xRcufs4nJ76oCalx13XsR7mYBtmZSKXHS5aR7bJIMMCQNB3Yj+rZdQrhTrUg8nD8M7Ono44Wk9xHuRHsAtwK72L6770fdO5MLfMrNWyXQGU40XV/H9tW1beYg0uznIHrdja3fAPvoFDpOQ9j5IHA9IdgdZvuxsvxI4n3wIPCtSrgr62Yl0vXnAP5k+/nGMd9HZOONrDL12oCk3QjxbRXb/6e3O8duSQR+NxFi15jaOc3qlpswNDLsDieCwTHAXrYvaawfCWwGjCXKhP9OiKzrEpN7y5VZ/InCUNJZNBn3u2q5pI2BnxMPLevY/l1tmyHA5sApRPnUAdTckJMkSZIZj6QdiVLGMUQ7l0HAqUS/2U9WWfHVvVdRAvq2SZZGzFXdA3rdttuR9EUi03AlIla9gui9+3UijhkHrFaEzIWJ+9uWwA9sH9f3I556JH2WmGgb6XB3fa+L2VtZvwxwPrAwMNz2XmX524zfSkw7c9tj1WTqSdEuSQYQih4YvyAezA/3JLv0hYib2kHAfcD3bN9Q1r23LN8A+Jx7Oq22AkkHE2UARwIvOvrRVYHLeoQ4OZpoTvtybw+7A52SefZ+4AQiW+521ZxQFaWke1ET7oo42iPjsiEGVcFDawKGWvnI2cB3gFVt31gbaz1zczQhaC9r+97etmk7kg4j+uCcBhzn0uOsl+1+THzGP1EWvUhkE25l+9E2XcOBTiNA/zowP9GL87paxiiSDiIeWN4iMqZ/T1zTbYim5TMDK9l+qk9PIEmSZIAjaSfgp7VFrwGvAEMJ8e4Y4GbgeeDxaY29uylOmRoasdmKROnr5vSsGvwn8OVGtcfHCBHvtL4f9bRTew0PLh0AACAASURBVHY5nDBAO7hxX/8CURq9MHCI7YPK8rcJd30++GSGkqJdkgwgJI0g+iKsYvuuhsAymDCg2AMYYXvP2n5zEt8XL04uw6NTlJmpKvPrCeCPRLDz92qGStKlwP8jxJd/tu0cOo3CdOF0ouxgHtsrVYIcpZyubFcX7jZqvofayOSCF0m7Em7Ju9j+WfOzUIS944gS2q/YHt23I3/3lHKKUcQs9MEOd7Vq3aeInoSzuJTPlGUfIQLFe4F/2H4pBbv20HhwqbKIK54jHmR+W8vQ2Af4XyKrGOBVoh9O/eEmr2+SJEkfUeKr/yF6180KfJ5wRp0HWJ0oba3zIvGdXWXinT4QRZnG/W82okR4VWIC6iHgd7afqcVwPeK/tserFZI+QGTKrw0cS0y41oW7esZdr8Jd0v9I0S5JBgAlTXoQUeL6cSJYeJJGSZSkJYHLiCDiM7afbhynlbM3JVNwLSJTqDJDuJkQoU4FNiacY8+xvXWnxtlWilhzNPEaTiB6z9VLYOszeD8msnZeJBoc/60DQ54qNAUTBkmrEe91gBXc04Sh2uc8Quz9ootTV5vpJUDdg3D6/Zrt35SyyLmJLKvtiED3eSLr9vTJHLMrgtyBhqTtic/sZUSfm+WJzNEPEULeKJf+PaWcaFmiBcJ/iF6Vo2z/J4P8JEmSGcu03EdrGXh7A48SrSqWBuYjvt+Xtf2vGTXWvuTdPFNMYUK262KW3sYs6dPEe+DbxPvhmCkIdwfZPqTvRpx0gkGdHkCSJDMeR/PaccCdwOzAor3d1ErGzZ1Ef7I5e1nfOsEOwPajtk+zvRawEVHiuTzxUHs9k4S8JSQtCBNnOgc0tfLWfxDZOBcR94VdihBKWT++ZGJie1/gZ0TD5Of6fNBTSREjKvFtO+ACYDtJswPYvo4QdN9DmDAsVT4n1T6VCcOddIEJQznfagb6w2XxJ4GZgDkUvQU3A34F7EO41F1DzO4fKmmx3o7bbcFvf6f2vbUy0W9xX4d50AFEVuifie+9zRR9O7F9g+1hwDdsb2H76BTskiRJZjyNWGRxSWtI+qqk5WvbDKp9t79Wfj9r+zzb+9ley/YSwEdt/6uKx7qJalK09v/QWswyzcaYk3se6baYpYiP1fvjvdVy2/cAhxPC3M7ADyR9pLb+NiIh4QHgIEm79+nAkz4nRbsk6Wc0b4xlWRUM/IP43A+T9Ek7+nM1AoA5gceBZ2f8aKcftSypi23vRoh2exBZg18jSsKWIuziWytAzkia743Ga3A/0f/qSqL334418acp3O1CNEl+so3Bo3o6JR9ONNtfCHjC9iu198puRI/HBYGbJJ0oaSdJJwLHEVmHOzl6JLZW5C1BX3W+xwPHS1qEKK94CjgJuIMQKecnnNfWs70JMJwoh527E2NP/jv1z235zh5EZF2Msv2QpKFl3ZXAgUS7gKOAzUsJUcVb5XiVWJ+CXZIkyQyiEYscCFwF/Aa4lIg5zpU0X5kwrOKxu4nYY/Gy35DaPeDV+v2+W2gIU0sA2K7uRwcBe7cxluwLasLlUcCfFOYZ1bp7iLZFUxLutiLaAl3cd6NOOkGKdknSj2jM6K0p6buS1iBKYrF9LFFO9Xkiu+ZTDqqgYn3CteqPREPcrqE5u2b7z7aPIs51VyL7DiLb6oN9Pb5O03hvfFzSCpK+IukDkmYpgcM/CZfY3xIOw7v0ItxVgteLbQ0ea+d5GFEqeB7Rl+6S+vry9xbEbObjhMPqT4FNiNnL5R2uqYNrAXXrqAV9OwLfI9zoXieyBHcgehA+SPR6/Jzty20/UXb/CFEy+Xhfjzv57zQ+t6tL2oJoA7AAkSWK7bdqn8vfEMZBdeGu2m5C+d3a93KSJEl/ofbdPYyYULmDKHdckzBI2xi4TNKitd3GENl285ZjjKt/d3fj93ctRrkWuFvSCuX/nxKTxeOICfZpps0TqtPIAkSp6/nTKNz9Hvh/th95JxmLSfeQFzdJ+gmNbJtmg/K/SjrK9tnAD4C5gA2BpUsm0pPAckQwMRbYy/ab5ZhdFyBUlAfecYRzJuXevh1REvj0FHbtVzRme/cDtgA+VlY/DZwr6RSHScf9RKnsccR7xZJ+Wok8DcGrte8NhQnDdoRgd5SnYMJge19Jo+gyE4ZexrY0cB3Ro+7fZdllkq5wuJH1cPYlxJ/lgBuJB4WkRTS+04cR3+n1B5RPSZrJ9pv16+voXwiwHzFZMRY4q4+HnyRJMuCR9E1gJyIOHWb7obJ8EWB9Qqz5d1km4l78b6Kdy9y2n+/IwGcMtxHGEedJugNYj5hcGmV7mhMF6s8okj4KvOpGL+62U52D7W9Jep6IWy+StKHthyGEO0U/aQjhbrykE6r3EiF6Up53kn5KinZJ0k+o3bi+T3ypX0A8wM8LHAacVgKA4yR9m7hRbgycUg7xGvA34Fu2H2+zWDG11B54BwEmMsh2ArYEbu12UXJqqQk1lZh7LVEyOo4Qb3cFVpG0qe37gPslVcLdzkRPtENtP9WRE5gKermWSxKl3qNsP6jJmDBIOtz26Y6+fv9oHHNQmz8D7unqOxhYETjC9gPVNvWylEY26o7EdRewm+2xA+Xz0C3UvtN3IYT0i4ELgS8QJTG7EN/ZZ5btm8LdTITRyO86Mf4kSZKEFYj782mOdgZDiLhrX+ARYLkyQTiz7TeAsZKqvnavd2bI05eaMLWPpEeJvsgLEs8p+5Rs8WmKPxqC3drA/sAVkoa3WbxqnqdtV89btncozyvbMHnhbjwxof6ypEPcs7Q66cekaJck/Y+ViQbze7k4DUm6ExgJHFNuGMcC35Z0MSHqzQ/cDtxi+7n+INg1cLkx3kO4no6vFnZ2WH2HpI0Jwep0YETJqhPwMmErPxNlthfAdiXcnQ2sSwSYrUQ93W0/XLICmyYMXwe2Jlw07yLKRb9FlInfbPve5nHd8obG5fotAOwFvERkVFXuuENtv1V/jyt6n32ccFL+NHG9v277iX74me9aerkWqxETMHs5XAMvkXQ3UW51mqRxJYu6KdxdIem3tt/I65skSdK3SJoZWAZ41PYdRZDZADiCiEOXs11luX9W0uq2fwJcDpzRXybTSvxdZfp/gGjPZSLrbingVmICcarOsyHYrQkcQrTC2b7lgl292mFW22NhUs/oItxtVzLltwEuLMLdI2W7eySNIOK9M9seoybTF3X590CSDGjUsAkvmRUPEjNXv9CkPkcTFL3tRhE3zD1tHzk1x+wvSJqL6NO2P1M4//6KpNMJQ47Vbf+1zPZuRGRhClja9pgSZMp2Jf4sArxi+5k2Bo+N4O144MNENuF8RGbSEKI/44KEY+r/An8qQtURwI+AFW3/oRPjnx4o+sNcQ5itjLS9VVne/H6YnSiN/h5wA3CY7adS0GknisbUDwDrENf1YklDqocSSesRvW4WBbaqhLuyrnWf1SRJkoFEiaeuISbLlgNWIsyfxgHL1AQ7JF1DTKB/oWTc9TaB03U0JlVFTJYuBLyXmHB8CtjU9vW1bXpMqjfivKZgN4x4fVey/Zc+O7F3gaRDCOHyGNvP1pbXX6tziP7KdwAbVRl3Zd0Q2+P6w/sjmXoy0y5JupTGl/sqRC+ufxPlUtVNYDCTeh1cK+k7hHA3TNJbJeOuB/1RsCvMC3wJGN3fBbtmgAPMDqwB3FsEu1mIzLPDid5u9eDxo8BikkbbfqNk9rRWzK2dZ2XCcDY9TRj2JkS7i4Ef236xtnvXmjAonNYmAINt3yJpVaI33RaS7rN9hBt97BzOuaOAK4Cnbb+eQV87UTjs/aD8+wrRhBqil02VSfer8nzzY+DnkibYHgUDK4s4SZKkjZQs58uBI4kJ0lWImLyeYYeknYiWHidRKkHK/l1/b3bPvqyjiRYPKtllLxBZh+co2rNcD9R7MH8MeMz2m+X/yQl2K3eRYDcfIcZ9mChxPb0S7uoZd8DmwGeIdhgXlNfngbJd9VzX9e+PZOpJ99gk6ULKjau6qR1OWMifTzy0r1V+Kkt11WaurgW+QxhPHC1p2w4Mv1M8Duxq+yswsc9dv0M1p1NJ8zp4mRB0Zy8zv18iZnsrwe6Z2iHOJvqdDa4ft22CXRGt6vQwYSiC42VEFt2aRGnhi2VfSdqQLjJhaL5fSxmFa8HbHcAXCcHyoFLaPLFcsrbfi7YfKYJdK91/EygPIFsA9xOi+2JluQlzmCqL+ldE6frfgbMlbdCZESdJkiS9cBHwKCHCvAf4hO3/VCslbUL0l30UONEtLu98p0j6KrAHMFuJJatnkuHAnsCHCOFu9SJcSdK6wMlEW5dmbNsU7P7c5yc1lfTyrPEUUeVyF2EWtZ2keaqV5fxnLrHZI0SFyNLA8b3EvckAIjPtkqQLqd24fkiUfF4O3EK4wu4D7Czpfts/qx7aJVV28ddK2p7IzrimU+fQ1zjKPf8I7c0ae7c0xNxjgLkU5hP3AQ8TZQmnEj2y3gKWrwt2knYjeqT9Cnijb0c/bXgAmTA0smq/RvRuWYr4zN9l+2oA27eXjLsbgOHlM39c+Q54W0ZdW893oFG992q/q0y6X5T5liOAfSXdY/u8sg3qmXE3C7A94c6XJEmStADbj0pan5ggnJcQae4jBJmdCFMKA+s72pD0x/j0ufJ7CSLbrp4xfmS5zw0jerhtD7yfaGXyIeC70CPmW4PuEewmxqAqPezK/ftu4tqfTDyzIelU28+W0tcq/p4NOJ54tjszJ1kHNtnTLkm6iOaDt6TzgKHA7p5kOrE6IcaNBfawfXJZPohiyFD+n9nZoLxfonCbPBY4AzjY4Qa8KCHmfJAoCf287Sdr+2xICLljCHOCZ9524BZRskcXIALfyoRhd9vnqpgwNLafnAnDI23+DKhn4+JhRKBnwkBkXkJ8Pdb2PrV9liWutYjvgJ/29biTqaMhyM5BuBoPtv10bZtNCbfvDxDu3heV5T16/1QPBW1+PydJkgxEJC1JVMQsWlv8JnAz0ZP0sf763S1pIWLy+Azb368tr8c3uxKGErMTJcIPAmvb/lf1upRJyTOBOYHV2izY1ZF0JFEavbZLKWy5fy9JCHeLE0Lk6Y4+w4OAzcqyTTyp39/EnrbJwCNFuyTpQiTtQ5S37wT8wPb5ZXl1Y/sicC1RKreH7ZPq68vfrc0sSqaNXsTci4BZiPfGAyUAGETMWB5CBIpHAVcT/VW2AzYtu69QZoa7YrZXA8SEQdIBwEFEwHoScDfR1PpMYBFCnD249h2wLPBbIgDe1vYZnRl5Mjka38c7AN8APkU8sJwCXG77nrJ+U+BoQqidrHCXJEmStBNJ7weWJzLOxhLZ8vfYfqlbYpEpMblzkPQh4M/EJOtyxMTUW2VdXbhblxA1ZyYyy55u3CcXITIW17N9Z5+c1DugMeahwDmEa/ClwDa9CHcnERUUVxEi3vJEK6PXgFVd63+YDFxStEuSLkPSYsA9RJ+D8cB3HI3oh9p+q7oBNoS73Wyf2rlRJ32BpIOJzKstgP0rMbe2fh7CQfYnRMbdy0Sm5gTCoWrzIti1OnhUTxOGcZK+QARysxLOyUeU7ZrC3VyEY1lXmTBIWgq4jOiBspvtfyp6E64C/AJ4gXBOe6ax30rAWUTQ91jfjjqZEo0HlaOIUqAngNuBuYn+hBcBp9Rm2evC3Ya2L+nA0JMkSZLpSLdMkk4tCmOwF4jKjbuJSeNTgc8Cn3S0q6m3hpjJxWyicZy6+FU92/S6bVtojHlbwnBiUyI7cF7g18REat01djFgBOEUX/FP4Mu2H+5v74/knZGiXZJ0IYqmrmcRD3fH2t6tLK9uatXvVYhsqpmBLWz/omODTqY79etNZOj8FXiGELS2t31Zb+n0kuYHtmJSeeWNwM22X2ijkDU1AYukpYnzGATsafu4Ke3bTZmmkr5NuD5/1faVRbTckCidmAAsbXuMpJmA+W0/XNu3EvNbd12TiVnTBxEz7afZ/pvCMe92QmC+Ghhu+4ay/abEdZ8f+JrtKzox7iRJkiRponDCrbfkeI1wQB9KxGfHECXBzwOPT2srlm6J3SSNIPrM3l5+3gS2JES8a4iEi2cb+2xG9K97HbjU0eMwY7cESNEuSboWSWsBFxCGMv9blb/1ItytCZxGZOI83sEhJ9ORRqZO1cvqm8DFZZOjbf+wl20nK4C1cTZPU2nCUNZXvdwGAT+qCXddGfTUPsNHAD8ihNmHiDKLI2i4/5ZMylOAEbZvLcu6IsAdiJRs6JOJDMoDbN9fyrjvAOYgTCW+RgT4I2xfV/bbGtiZ0pOxE2NPkiRJkjql3PN/iH7DsxLx2iLAPMDqhGlYnReJjLIqE+/0/hCvSNqcSKz4GXCUJ/Uc/xjRomYTohR2c08yn3hbr7o2xuRJ50jRLkm6GEnrEMLd80Q55MiyvCnczWT7zW4VL5LJI+lU4MvA4rZfLmLuVWX1DlVZdC+lohPFnLYKO0oTBgAkfQc4m2hM/DRhMNJDsCvbnQF8FVjd9l87MdZk6ijZsYcBPwBWsX1bEexuJZzzdgf+QAT43wZ+CZxQK5V9j+3XJhfsJ0mSJMmMZFpEpVoG3t7Ao4T5wtLAfIRL7LK2/zWjxtqXSDoJ2AZY3vYdZdmQ0s5lQeAE4CvUSmW7ISZPOsugTg8gSZJ3ju3RwMbA+4BDJW1Rlk9o3EzfKstTsOtHSPoeUSZ5F1Euh+1rgHXLJoc33xPVvvWAoK3BQU2wOwDYg3Be+yKwILAG4QC7l6QDy3aDbf+xbPMmcFzJSmo99WvTCw8S/StPBEYS5iHLNgS7bYiZ7NFl+6TFlPf2OUQp922SZiFKoOcD9gfOt/0QIdxBiLHDJC1X9n+t/E7BLkmSJOlTSrxVxWiLS1pD0lclLV/bZlDJvoMokwV41vZ5tvezvZbtJYCPurjE9vFpTHfKOXyEKAmujKQGFcFOjv7CPyJej28AoyTN0w0xedJZUrRLki7H9pXAt5gk3G1Wlk+obZM3gH5AXdgpD/lfIHqD7Gz7vipAsn0VUVY3N3DM5IS7bkBhwrA9IUYNdziGDSH6NM4O3E+IWRNF6SLcrUOUkv6mA8OeJhrB79qSfiDpEEk7l2DuD8ABRMnkvEQ5/H9q+29GBIGvEEYcr9UC5aSl2P677ePLvysSxiIXAT+vTbDcTgT+ZxPmMSnIJkmSJB2jiFBV25IDieqO3xDuqDdJOlfSfLYn1J4/7iYqBBYv+w2pxaOvlti1vyQWvET0ptsYJj2P2RNNN+4Frgf+BqwJnCvpfZ0abNIddNXDW5IkvVMT7uYATpW0YYeHlMwAasLO/sBuRGbV1Q7HV9XWy9Gg/mtEM/tjehNzu4RFieyjkxyuqYOB9Yg+ja8BK5ZmvTNJWrjayfbNwKdsP9bm2dt6oCppOHAFcBSwH3AcEQD/LyFMDiMEy7MkDZO0u6TLy3ZDga/YfrKIgCnUdxdLEUH+BY3suc2JWG1f4NPlvZ6xW5IkSdIRGm1LDiT6sH6bEKAuIcSqyyQtWtttDBGzzVuOMa4uZnVbzFKfGK3uyTUx80zCTGLD+mugMAarnG8XINphnEK8buelcJdMiQz8kqSfUIS7rYleEb/v8HCSGYSkjwPbEiV08wDP1dc3emJUwt1cwMhuEnNrwsQS5fcDkoYCGzHJNXUZ22PK+jmAEVX5YGEctLssvHa9fkSYC/wCWJvIujqZEF2PAPYkXEZ3J3r67QocScxa/5owmnk4+1Z2LU+W30tWCyRtBKxFZNM+Z7vKRug24T1JkiTpRyiMz3YiJlB/aPt8278DriXaeSxAtDCpBK4x5f8lJM3dmVFPH3qZGBX0mBT/B/Ar4v69p6TPlvVvKajaGo0CDmaScHeupHn76DSSLiONKJKknyFpFtuv58N7/6DZkFbSEGA1osfb6sBlwI62n2zuBxPT8dcjGt8u4y5zEFY/NWFofj4lXUSYbPzQ9qNl2ZzAysAI4MNEGfRIhQPZ7IRhwd+AF2y/kZ/57kXSh4HrgE8Q7/F5gBWAscDK3fa5TZIkSfovkkYQot3Ktu8osemGwE8I0W4522MkzWz7jbLPXWX3FWyP7cjA3yX1OEvStsDywMKEKeDo0rMOSSsSE62rE07wVwFXEj2nNyUmlVez/ZSk+Ynqih2AvWwP78tzSrqDFO2SJElaiibjzFUT7g4gyur2Bs60/VJju7pw11oxd3LnWdYtD9xElFW8QpQcLN/o6bYNEfDcQAiYr/VyqFYiaW+itHUnYDfb5zSu21BgfUJ0vdf2ipM5TrqNdTmSFiPc9VYneuLcCWxZlXi37XObJEmSDDwkzQz8Fvig7UVLZURVBTGeWhWEpGWJydSfSDoEOMP2I90esxTRcjciNn1PWXwlcIzt68o2yxITzlsBs9Z2vxdYp1RHDC0ZeAsSr9NZfXUOSXeRol2SJEnLkXQ0sJDtDWrLBgOrEqWTixDC3QW2X5zMMVoZIDVmLdcGFiMMNJ4BzrX9bBG2fkzMTH6zlP1W+29GlAq/CXyp9HRr5bk2KSLNPcBjRHnFDrZHV0Fcbbv3A+cSjrmr2r6xIwNOZjgKg5mFiQeBZ0tJbAp2SZIkSSsoot01wMeB5YCVgOFEjFZvW4Kka4D5gS/UMu667p5WjytL3HkicB4x0TY74QS7K5FVd6jt35RtZwcWJCbaZwH+BdxUshAH2x7fS0XNZCeyk4HLkE4PIEmSJJk8kuYi+tJ9VNJptreF6NMm6XpgL2J284jYXOf3Jty1UcTqxYRhd0pvkMIOkk4lgqM5ib5uZ5VS2P8AXyRcN19kkmDXNcGg7XslfY0o/30vMVM9usy6DnK4/Q4pwd2vCdFulk6OOZmx2H6dmIUHen5GkiRJkqTTlHYclxN9dQ8jevCOo5TEVttJ2ono03oSkYFX7d8197Sqv3JDRFuWmHAdbvuBst09RM++YcABRYf7re1XiB53/2get3odmvF5CnZJb2SmXZIkSUupZt8kLQScT8xo/tz21rVtqoy7YUSGzl7ARbZf6MCQ3xHFhOFgoifIeUSW0SbA14lmvceU9TsCuxCztkOBh4mS2P1tP9FNgl0dSWsR5z4n8H3bJ5bl9V4wpwNbAsvavrNTY02SJEmSZGBT4tKbgIWIyoj5G316NwEOAZ4H1q33IG47pax1MdsjG8uPImLPBYAbbB/fqBZ5D2EUNxy4HTiwmHNk9lzyrknRLkmSpMXU0ucXBC4GlgbOsv3d+jaEcPfjsn5z26M6MuCpIE0Y3k4pDb4QeBUYYXtEbd36wHHAE8Cazd6FSZIkSZIkfYmkpYAbiZ5uOwH3AY+Uvzck4roVS1/WrhCtJL2POIfZCKOIG8ryjwC/ZpLD++G29+1l/7pw9wdgmO2r+mDoST8nRbskSZIW0JvoVMu0qwt3FwHLEMYT29T3J+zldwG2cRe4TaYJQ08krUtkGs4OXEsYEXwS+BwR/K5q+9FuCX6TJEmSJOm/SFqSqARZtLb4TeBmYKtuNFKStBHRlmbH+iSppFWAPYAvAZcDW/dW1VKEu+8CxwOXAhtXVRNJ8k5J0S5JkqRFSPoh0a9tVOlp1ptwdzXwKcKFa9vavoOBId2QeZYmDL0j6cuEcDcX8E/gLuBW4BLbj7f9uiZJkiRJMnAocdrywBLAWOAW4B7bL3VrzFL6CY8rk8sv2T6hLF8F2I9weR9me+/J7D8bsDHwG9uP9dW4k/5LGlEkSZK0BEmfIVLqHwTGSrqkJtyNL8HPY6Vc8jZg65J1tTVMbO47vvZ3a0kTht6xfXXpBXMRMBi4sip1ljST7Tc7OsAkSZIkSZJCMZ+4vPxMpG620G0UwW4xou3Mk5JetX2W7RsljQcGAXtKGmd7/172fxU4A7rTLTdpH4M6PYAkSZKBSuVKVf1t+6+EAcOchCPXBmW5a8LdTMCjwANEj7OtSnPcrsP2FcT5vgRsLmnHsnxCMWEYVzb9PDABGNP7kfoXtq8mXpf5gJ9I2qosf7P+nkmSJEmSJGkj3d7Gw/a9RJns7MAhtVjsZuBA4HpgX0mH/pfjpGCXvGsy+E+SJOkAZeZtQvn7K8A+kpa0fQHR420e4FB6CndDbb9p+zXgDeAUYmbzpx06jXeN7WsIgepV4IBSHkzNNXV94MtEf7f7OzXOvqYImt8i3HMPlLRFWd7VQXCSJEmSJEk3UGKxTYlY7OApCHcHd26UyUAge9olSZL0MXUjAUmHADsQfUD2sn1eMWD4OnAykV12MHBxKR0dQjhTHQAsW3NbHVLLTOs60oShdyStA1wAzEw4md3c4SElSZIkSZIMGEqMegHwHHCg7Z+X5SsR8fgawJ62j+zcKJP+TIp2SZIkHULSEYQT1UjgBNt31tZVwt1JhKA3CjgNWA/YHngB+LLt5/t63DOKNGHonSrbsG46kiRJkiRJkvQNUxDuViPi8j2qifQkmd6kaJckSdIBJH2DMGG4ADi0ljEnly/mklW3FiHcLVDb/WEi6+rh/pZ5VoS7i4CngIPThKEn/e16J0mSJEmSdAMN4W5/2yPL8plK3+GurnpJ2kuKdkmSJB1A0nBgV2Al27f9l20/ULYdSmTYnW776f6aeVZ6/J3P22czU7BKkiRJkiRJOkIR7kYBswBbll7USTJDSdEuSZKkj5E0C/BbYGHbC5ZlEzPsyv+Di1vsHLZfrv2vYkrRLwW7itps5hhCuBvZ4SElSZIkSZIkAxxJ3wQOJ/otP9np8ST9n3SPTZIk6QwG5pO0DEBDsBtUBLr3ACdI+nRToOvPgh2A7SuBjQgX3dNKs98kSZIkSZIk6Ri2fwksaftJSYM7PZ6k/5OiXZIkSR9j+3XgCuI7eJ36utIPoyoB3RNYFfhgbd8Bkx5tezSwJTAyXVOTJEmSJEmSNlBi+X4/iZ60gyyPTZIk6QCSFgZuAT4EbGv7jMb69YnU+38BG9l+sa/H2Dayp12SJEmSJEmSJAOJFO2SJEk63kDHcgAAAUdJREFUhKTPAXcAAk4Efg/cSWSXbQIMBlaw/XgKVkmSJEmSJEmSJAOLFO2SJEk6iKQlCReqxWuLDfwR2Nj2o/3ddCJJkiRJkiRJkiR5OynaJUmSdBhJ7wdWAJYGXiOy7263/UIKdkmSJEmSJEmSJAOTFO2SJElaSpbEJkmSJEmSJEmSDFxStEuSJEmSJEmSJEmSJEmSljGo0wNIkiRJkiRJkiRJkiRJkqQnKdolSZIkSZIkSZIkSZIkSctI0S5JkiRJkiRJkiRJkiRJWkaKdkmSJEmSJEmSJEmSJEnSMlK0S5IkSZIkSZIkSZIkSZKWkaJdkiRJkiRJkiRJkiRJkrSMFO2SJEmSJEmSJEmSJEmSpGWkaJckSZIkSZIkSZIkSZIkLSNFuyRJkiRJkiRJkiRJkiRpGSnaJUmSJEmSJEmSJEmSJEnL+P/a5H5X/es9GgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation of independent variable with dependent variable\n",
    "\n",
    "dataset2.corrwith(df.e_signed).plot.bar(figsize=(20,10),title=\"correlation wigh E signed\",\n",
    "                                             fontsize=20,rot=45,grid= True,color=['pink','green',\n",
    "                                                                                  'blue','cyan','magenta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initiatizig into new variable and creating the dummy variable for categorical data\n",
    "data=df\n",
    "dummy=pd.get_dummies(data[\"pay_schedule\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>bi-weekly</th>\n",
       "      <th>monthly</th>\n",
       "      <th>semi-monthly</th>\n",
       "      <th>weekly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17878</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17879</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17880</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17881</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17882</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17883</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17884</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17885</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17886</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17887</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17888</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17889</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17890</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17891</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17892</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17893</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17894</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17895</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17896</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17897</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17898</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17899</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17900</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17901</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17902</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17903</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17904</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17905</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17906</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17907</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17908 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bi-weekly  monthly  semi-monthly  weekly\n",
       "0              1        0             0       0\n",
       "1              0        0             0       1\n",
       "2              0        0             0       1\n",
       "3              1        0             0       0\n",
       "4              0        0             1       0\n",
       "5              0        0             0       1\n",
       "6              1        0             0       0\n",
       "7              1        0             0       0\n",
       "8              0        0             1       0\n",
       "9              1        0             0       0\n",
       "10             1        0             0       0\n",
       "11             0        0             0       1\n",
       "12             1        0             0       0\n",
       "13             1        0             0       0\n",
       "14             1        0             0       0\n",
       "15             1        0             0       0\n",
       "16             1        0             0       0\n",
       "17             0        0             0       1\n",
       "18             1        0             0       0\n",
       "19             0        0             1       0\n",
       "20             1        0             0       0\n",
       "21             1        0             0       0\n",
       "22             0        0             0       1\n",
       "23             0        1             0       0\n",
       "24             1        0             0       0\n",
       "25             1        0             0       0\n",
       "26             1        0             0       0\n",
       "27             0        1             0       0\n",
       "28             1        0             0       0\n",
       "29             0        0             0       1\n",
       "...          ...      ...           ...     ...\n",
       "17878          0        0             1       0\n",
       "17879          1        0             0       0\n",
       "17880          0        0             1       0\n",
       "17881          0        0             0       1\n",
       "17882          1        0             0       0\n",
       "17883          0        0             0       1\n",
       "17884          1        0             0       0\n",
       "17885          1        0             0       0\n",
       "17886          1        0             0       0\n",
       "17887          0        0             0       1\n",
       "17888          0        1             0       0\n",
       "17889          0        0             1       0\n",
       "17890          0        0             0       1\n",
       "17891          1        0             0       0\n",
       "17892          0        0             0       1\n",
       "17893          1        0             0       0\n",
       "17894          1        0             0       0\n",
       "17895          1        0             0       0\n",
       "17896          1        0             0       0\n",
       "17897          1        0             0       0\n",
       "17898          1        0             0       0\n",
       "17899          1        0             0       0\n",
       "17900          0        0             0       1\n",
       "17901          1        0             0       0\n",
       "17902          1        0             0       0\n",
       "17903          0        1             0       0\n",
       "17904          1        0             0       0\n",
       "17905          0        0             0       1\n",
       "17906          1        0             0       0\n",
       "17907          0        0             0       1\n",
       "\n",
       "[17908 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy=dummy.drop(labels=[\"bi-weekly\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data=data.drop([\"pay_schedule\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data=pd.concat([data,dummy],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17908, 23)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating the data into dependent and independent varioable. Response contains the esigned column which needs to be computed\n",
    "#from the dataset\n",
    "response = data[\"e_signed\"]\n",
    "dataset = data.drop(columns = [\"e_signed\", \"entry_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transforming the data using StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X= StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into Train and Test Set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataset,\n",
    "                                                    response,\n",
    "                                                    test_size = 0.2,\n",
    "                                                    random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fitting and transforming our data\n",
    "X_train=sc_X.fit_transform(X_train)\n",
    "X_test=sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14326, 21)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building ANN Using Keras\n",
    "\n",
    "<img src=\"keras.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div>\n",
    "<img src=\"ann.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Importing the Keras libraries and packages\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "\n",
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\md sohaib uddin\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From C:\\Users\\md sohaib uddin\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "#Adding the layers in ANN\n",
    "classifier.add(Dense(units = 10, kernel_initializer = 'uniform', activation = 'relu', input_dim = 21))\n",
    "\n",
    "\n",
    "classifier.add(Dense(units = 10, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "\n",
    "\n",
    "classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
    "\n",
    "\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\md sohaib uddin\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Epoch 1/100\n",
      "14326/14326 [==============================] - 3s 205us/step - loss: 0.6739 - accuracy: 0.5754\n",
      "Epoch 2/100\n",
      "14326/14326 [==============================] - 2s 154us/step - loss: 0.6604 - accuracy: 0.6022\n",
      "Epoch 3/100\n",
      "14326/14326 [==============================] - 2s 160us/step - loss: 0.6535 - accuracy: 0.6086\n",
      "Epoch 4/100\n",
      "14326/14326 [==============================] - 2s 170us/step - loss: 0.6489 - accuracy: 0.6145\n",
      "Epoch 5/100\n",
      "14326/14326 [==============================] - 2s 166us/step - loss: 0.6457 - accuracy: 0.6173\n",
      "Epoch 6/100\n",
      "14326/14326 [==============================] - 2s 165us/step - loss: 0.6436 - accuracy: 0.6199\n",
      "Epoch 7/100\n",
      "14326/14326 [==============================] - 2s 154us/step - loss: 0.6422 - accuracy: 0.6242\n",
      "Epoch 8/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6405 - accuracy: 0.6272\n",
      "Epoch 9/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6395 - accuracy: 0.6259\n",
      "Epoch 10/100\n",
      "14326/14326 [==============================] - 2s 149us/step - loss: 0.6392 - accuracy: 0.6232\n",
      "Epoch 11/100\n",
      "14326/14326 [==============================] - 2s 149us/step - loss: 0.6380 - accuracy: 0.6259\n",
      "Epoch 12/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6372 - accuracy: 0.6272\n",
      "Epoch 13/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6369 - accuracy: 0.6299\n",
      "Epoch 14/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6364 - accuracy: 0.6312\n",
      "Epoch 15/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6355 - accuracy: 0.6288\n",
      "Epoch 16/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6353 - accuracy: 0.6310\n",
      "Epoch 17/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6355 - accuracy: 0.6333\n",
      "Epoch 18/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6347 - accuracy: 0.6324\n",
      "Epoch 19/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6346 - accuracy: 0.6323\n",
      "Epoch 20/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6338 - accuracy: 0.6308\n",
      "Epoch 21/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6338 - accuracy: 0.6332\n",
      "Epoch 22/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6337 - accuracy: 0.6302\n",
      "Epoch 23/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6335 - accuracy: 0.6326\n",
      "Epoch 24/100\n",
      "14326/14326 [==============================] - 2s 154us/step - loss: 0.6332 - accuracy: 0.6352\n",
      "Epoch 25/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6328 - accuracy: 0.6359\n",
      "Epoch 26/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6320 - accuracy: 0.6359\n",
      "Epoch 27/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6325 - accuracy: 0.6340\n",
      "Epoch 28/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6326 - accuracy: 0.6323\n",
      "Epoch 29/100\n",
      "14326/14326 [==============================] - 2s 156us/step - loss: 0.6322 - accuracy: 0.6342\n",
      "Epoch 30/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6313 - accuracy: 0.6365\n",
      "Epoch 31/100\n",
      "14326/14326 [==============================] - 2s 166us/step - loss: 0.6311 - accuracy: 0.6363\n",
      "Epoch 32/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6316 - accuracy: 0.6333\n",
      "Epoch 33/100\n",
      "14326/14326 [==============================] - 2s 169us/step - loss: 0.6312 - accuracy: 0.6341\n",
      "Epoch 34/100\n",
      "14326/14326 [==============================] - 2s 168us/step - loss: 0.6304 - accuracy: 0.6355\n",
      "Epoch 35/100\n",
      "14326/14326 [==============================] - 2s 159us/step - loss: 0.6305 - accuracy: 0.6367\n",
      "Epoch 36/100\n",
      "14326/14326 [==============================] - 2s 154us/step - loss: 0.6299 - accuracy: 0.6369\n",
      "Epoch 37/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6295 - accuracy: 0.6374\n",
      "Epoch 38/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6295 - accuracy: 0.6372\n",
      "Epoch 39/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6296 - accuracy: 0.6388\n",
      "Epoch 40/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6286 - accuracy: 0.6416\n",
      "Epoch 41/100\n",
      "14326/14326 [==============================] - 2s 149us/step - loss: 0.6291 - accuracy: 0.6368\n",
      "Epoch 42/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6286 - accuracy: 0.6381\n",
      "Epoch 43/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6286 - accuracy: 0.6371\n",
      "Epoch 44/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6284 - accuracy: 0.6416\n",
      "Epoch 45/100\n",
      "14326/14326 [==============================] - 2s 149us/step - loss: 0.6279 - accuracy: 0.6393\n",
      "Epoch 46/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6281 - accuracy: 0.6407\n",
      "Epoch 47/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6280 - accuracy: 0.6396\n",
      "Epoch 48/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6275 - accuracy: 0.6387\n",
      "Epoch 49/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6269 - accuracy: 0.6404\n",
      "Epoch 50/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6269 - accuracy: 0.6377\n",
      "Epoch 51/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6268 - accuracy: 0.6395\n",
      "Epoch 52/100\n",
      "14326/14326 [==============================] - 2s 172us/step - loss: 0.6266 - accuracy: 0.6401\n",
      "Epoch 53/100\n",
      "14326/14326 [==============================] - 2s 156us/step - loss: 0.6265 - accuracy: 0.6407\n",
      "Epoch 54/100\n",
      "14326/14326 [==============================] - 2s 157us/step - loss: 0.6268 - accuracy: 0.6409\n",
      "Epoch 55/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6262 - accuracy: 0.6398\n",
      "Epoch 56/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6260 - accuracy: 0.6415\n",
      "Epoch 57/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6257 - accuracy: 0.6414\n",
      "Epoch 58/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6257 - accuracy: 0.6405\n",
      "Epoch 59/100\n",
      "14326/14326 [==============================] - 2s 156us/step - loss: 0.6255 - accuracy: 0.6396\n",
      "Epoch 60/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6256 - accuracy: 0.6415\n",
      "Epoch 61/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6251 - accuracy: 0.6395\n",
      "Epoch 62/100\n",
      "14326/14326 [==============================] - 2s 159us/step - loss: 0.6252 - accuracy: 0.6407\n",
      "Epoch 63/100\n",
      "14326/14326 [==============================] - 3s 175us/step - loss: 0.6249 - accuracy: 0.6403\n",
      "Epoch 64/100\n",
      "14326/14326 [==============================] - 2s 168us/step - loss: 0.6248 - accuracy: 0.6414\n",
      "Epoch 65/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6246 - accuracy: 0.6403\n",
      "Epoch 66/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6245 - accuracy: 0.6404\n",
      "Epoch 67/100\n",
      "14326/14326 [==============================] - 2s 153us/step - loss: 0.6235 - accuracy: 0.6409\n",
      "Epoch 68/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6235 - accuracy: 0.6404\n",
      "Epoch 69/100\n",
      "14326/14326 [==============================] - 2s 159us/step - loss: 0.6239 - accuracy: 0.6398\n",
      "Epoch 70/100\n",
      "14326/14326 [==============================] - 2s 157us/step - loss: 0.6236 - accuracy: 0.6415\n",
      "Epoch 71/100\n",
      "14326/14326 [==============================] - 2s 156us/step - loss: 0.6230 - accuracy: 0.6411\n",
      "Epoch 72/100\n",
      "14326/14326 [==============================] - 2s 155us/step - loss: 0.6227 - accuracy: 0.6402\n",
      "Epoch 73/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6233 - accuracy: 0.6407\n",
      "Epoch 74/100\n",
      "14326/14326 [==============================] - 2s 146us/step - loss: 0.6229 - accuracy: 0.6434\n",
      "Epoch 75/100\n",
      "14326/14326 [==============================] - 2s 145us/step - loss: 0.6227 - accuracy: 0.6411\n",
      "Epoch 76/100\n",
      "14326/14326 [==============================] - 2s 146us/step - loss: 0.6221 - accuracy: 0.6415\n",
      "Epoch 77/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6223 - accuracy: 0.6402\n",
      "Epoch 78/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6224 - accuracy: 0.6421\n",
      "Epoch 79/100\n",
      "14326/14326 [==============================] - 2s 145us/step - loss: 0.6221 - accuracy: 0.6413\n",
      "Epoch 80/100\n",
      "14326/14326 [==============================] - 2s 145us/step - loss: 0.6219 - accuracy: 0.6442\n",
      "Epoch 81/100\n",
      "14326/14326 [==============================] - 2s 147us/step - loss: 0.6214 - accuracy: 0.6384\n",
      "Epoch 82/100\n",
      "14326/14326 [==============================] - 2s 148us/step - loss: 0.6210 - accuracy: 0.6447\n",
      "Epoch 83/100\n",
      "14326/14326 [==============================] - 2s 147us/step - loss: 0.6206 - accuracy: 0.6411\n",
      "Epoch 84/100\n",
      "14326/14326 [==============================] - 2s 148us/step - loss: 0.6207 - accuracy: 0.6397\n",
      "Epoch 85/100\n",
      "14326/14326 [==============================] - 2s 148us/step - loss: 0.6207 - accuracy: 0.6411\n",
      "Epoch 86/100\n",
      "14326/14326 [==============================] - 2s 147us/step - loss: 0.6209 - accuracy: 0.6451\n",
      "Epoch 87/100\n",
      "14326/14326 [==============================] - 2s 148us/step - loss: 0.6209 - accuracy: 0.6441\n",
      "Epoch 88/100\n",
      "14326/14326 [==============================] - 2s 160us/step - loss: 0.6199 - accuracy: 0.6434\n",
      "Epoch 89/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6204 - accuracy: 0.6439\n",
      "Epoch 90/100\n",
      "14326/14326 [==============================] - 2s 148us/step - loss: 0.6197 - accuracy: 0.6423\n",
      "Epoch 91/100\n",
      "14326/14326 [==============================] - 2s 150us/step - loss: 0.6199 - accuracy: 0.6431\n",
      "Epoch 92/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6199 - accuracy: 0.6434\n",
      "Epoch 93/100\n",
      "14326/14326 [==============================] - 2s 166us/step - loss: 0.6194 - accuracy: 0.6423\n",
      "Epoch 94/100\n",
      "14326/14326 [==============================] - 2s 162us/step - loss: 0.6192 - accuracy: 0.6434\n",
      "Epoch 95/100\n",
      "14326/14326 [==============================] - 2s 151us/step - loss: 0.6191 - accuracy: 0.6451\n",
      "Epoch 96/100\n",
      "14326/14326 [==============================] - 2s 149us/step - loss: 0.6193 - accuracy: 0.6446\n",
      "Epoch 97/100\n",
      "14326/14326 [==============================] - 2s 149us/step - loss: 0.6187 - accuracy: 0.6431\n",
      "Epoch 98/100\n",
      "14326/14326 [==============================] - 2s 149us/step - loss: 0.6189 - accuracy: 0.6425\n",
      "Epoch 99/100\n",
      "14326/14326 [==============================] - 2s 152us/step - loss: 0.6185 - accuracy: 0.6431\n",
      "Epoch 100/100\n",
      "14326/14326 [==============================] - 2s 145us/step - loss: 0.6184 - accuracy: 0.6457\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x2a8d58846a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the ANN to the Training set\n",
    "classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict functioon the test data using the model\n",
    "pred=classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pred = (pred > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6155778894472361"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Machine Learning Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "    \n",
    "<div>\n",
    "    <br>\n",
    "<img src=\"ml.jpg\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### AGE\n",
    "\n",
    "We will group our age data as follows:\n",
    "<div>\n",
    "    <br>\n",
    "<img src=\"age_group.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initializing the Empty List\n",
    "AGE=[]\n",
    "\n",
    "for i in range(len(df)):\n",
    "    if(df[\"age\"][i]<=45):\n",
    "        AGE.append(\"ADULT\")\n",
    "        \n",
    "    else:\n",
    "        AGE.append(\"SENIOR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Months Employed and Personal Account\n",
    "\n",
    "We will calculate months employed as follows:\n",
    "<br>\n",
    "<p style=\"text-indent:5em\">month employed=month employed+ years employed*12</p>\n",
    "    <p style=\"text-indent:5em\">account_m=account_m+account_y*12</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "EMPLOYED=[]\n",
    "for i in range(len(df)):\n",
    "    x=df[\"months_employed\"][i]+df[\"years_employed\"][i]*12\n",
    "    EMPLOYED.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "PA=[]\n",
    "for i in range(len(df)):\n",
    "    x=df[\"personal_account_m\"][i]+df[\"personal_account_y\"][i]*12\n",
    "    PA.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_risk=[]\n",
    "for i in range(len(df)):\n",
    "    x=(df[\"risk_score_2\"][i]+df[\"risk_score_3\"][i]+df[\"risk_score_4\"][i]+df[\"risk_score_5\"][i])/4\n",
    "    avg_risk.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ext_quality=[]\n",
    "for i in range(len(df)):\n",
    "    x=(df[\"ext_quality_score_2\"][i]+df[\"ext_quality_score\"][i])/2\n",
    "    ext_quality.append(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging all the features into Single Dataframe\n",
    "\n",
    "<p style=\"text-indent:5em\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting all lists into DataFrame\n",
    "AGE=pd.DataFrame(AGE)\n",
    "EMPLOYED=pd.DataFrame(EMPLOYED)\n",
    "PA=pd.DataFrame(PA)\n",
    "avg_risk=pd.DataFrame(avg_risk)\n",
    "ext_quality=pd.DataFrame(ext_quality)\n",
    "\n",
    "#Concatng all the features\n",
    "featured=pd.concat([AGE,EMPLOYED,PA,avg_risk,ext_quality],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "featured.columns=[\"AGE\",\"EMPLOYED\",\"PA\",\"RISK\",\"QUALITY\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>AGE</th>\n",
       "      <th>EMPLOYED</th>\n",
       "      <th>PA</th>\n",
       "      <th>RISK</th>\n",
       "      <th>QUALITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADULT</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>0.661151</td>\n",
       "      <td>0.480918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SENIOR</td>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "      <td>0.789841</td>\n",
       "      <td>0.680720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADULT</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.691712</td>\n",
       "      <td>0.531712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADULT</td>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "      <td>0.793179</td>\n",
       "      <td>0.692552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADULT</td>\n",
       "      <td>60</td>\n",
       "      <td>98</td>\n",
       "      <td>0.688483</td>\n",
       "      <td>0.744634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      AGE  EMPLOYED  PA      RISK   QUALITY\n",
       "0   ADULT        36  30  0.661151  0.480918\n",
       "1  SENIOR        72  86  0.789841  0.680720\n",
       "2   ADULT         6  19  0.691712  0.531712\n",
       "3   ADULT        72  86  0.793179  0.692552\n",
       "4   ADULT        60  98  0.688483  0.744634"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featured.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>ADULT</th>\n",
       "      <th>SENIOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ADULT  SENIOR\n",
       "0      1       0\n",
       "1      0       1\n",
       "2      1       0\n",
       "3      1       0\n",
       "4      1       0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating Dummy Variable\n",
    "dummy1=pd.get_dummies(featured[\"AGE\"])\n",
    "dummy1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYED</th>\n",
       "      <th>PA</th>\n",
       "      <th>RISK</th>\n",
       "      <th>QUALITY</th>\n",
       "      <th>ADULT</th>\n",
       "      <th>SENIOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>0.661151</td>\n",
       "      <td>0.480918</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "      <td>0.789841</td>\n",
       "      <td>0.680720</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.691712</td>\n",
       "      <td>0.531712</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "      <td>0.793179</td>\n",
       "      <td>0.692552</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>98</td>\n",
       "      <td>0.688483</td>\n",
       "      <td>0.744634</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EMPLOYED  PA      RISK   QUALITY  ADULT  SENIOR\n",
       "0        36  30  0.661151  0.480918      1       0\n",
       "1        72  86  0.789841  0.680720      0       1\n",
       "2         6  19  0.691712  0.531712      1       0\n",
       "3        72  86  0.793179  0.692552      1       0\n",
       "4        60  98  0.688483  0.744634      1       0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping the AGE Variable since dummy variable is created\n",
    "featured=featured.drop([\"AGE\"],axis=1)\n",
    "\n",
    "#Concating the data and dummy variable\n",
    "featured=pd.concat([featured,dummy1],axis=1)\n",
    "featured.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classifying the Data into dependent and independent Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dependent Variable\n",
    "dep=\"e_signed\"\n",
    "\n",
    "#Selelcting all the column as independent variable\n",
    "ind=df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['entry_id',\n",
       " 'age',\n",
       " 'pay_schedule',\n",
       " 'home_owner',\n",
       " 'income',\n",
       " 'months_employed',\n",
       " 'years_employed',\n",
       " 'current_address_year',\n",
       " 'personal_account_m',\n",
       " 'personal_account_y',\n",
       " 'has_debt',\n",
       " 'amount_requested',\n",
       " 'risk_score',\n",
       " 'risk_score_2',\n",
       " 'risk_score_3',\n",
       " 'risk_score_4',\n",
       " 'risk_score_5',\n",
       " 'ext_quality_score',\n",
       " 'ext_quality_score_2',\n",
       " 'inquiries_last_month',\n",
       " 'e_signed']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing the dependent variable from independent\n",
    "ind.remove(dep)\n",
    "ind.remove(\"entry_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting the Data\n",
    "X=df[ind]\n",
    "Y=df[dep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>pay_schedule</th>\n",
       "      <th>home_owner</th>\n",
       "      <th>income</th>\n",
       "      <th>months_employed</th>\n",
       "      <th>years_employed</th>\n",
       "      <th>current_address_year</th>\n",
       "      <th>personal_account_m</th>\n",
       "      <th>personal_account_y</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>...</th>\n",
       "      <th>risk_score_5</th>\n",
       "      <th>ext_quality_score</th>\n",
       "      <th>ext_quality_score_2</th>\n",
       "      <th>inquiries_last_month</th>\n",
       "      <th>EMPLOYED</th>\n",
       "      <th>PA</th>\n",
       "      <th>RISK</th>\n",
       "      <th>QUALITY</th>\n",
       "      <th>ADULT</th>\n",
       "      <th>SENIOR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>bi-weekly</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515977</td>\n",
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       "      <td>10</td>\n",
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       "      <td>30</td>\n",
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       "      <td>0.480918</td>\n",
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       "      <th>1</th>\n",
       "      <td>61</td>\n",
       "      <td>weekly</td>\n",
       "      <td>0</td>\n",
       "      <td>3180</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.826402</td>\n",
       "      <td>0.730720</td>\n",
       "      <td>0.630720</td>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
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       "      <td>0.680720</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>weekly</td>\n",
       "      <td>0</td>\n",
       "      <td>1540</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.762284</td>\n",
       "      <td>0.531712</td>\n",
       "      <td>0.531712</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.691712</td>\n",
       "      <td>0.531712</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>bi-weekly</td>\n",
       "      <td>0</td>\n",
       "      <td>5230</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778831</td>\n",
       "      <td>0.792552</td>\n",
       "      <td>0.592552</td>\n",
       "      <td>8</td>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "      <td>0.793179</td>\n",
       "      <td>0.692552</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>semi-monthly</td>\n",
       "      <td>0</td>\n",
       "      <td>3590</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.665523</td>\n",
       "      <td>0.744634</td>\n",
       "      <td>0.744634</td>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
       "      <td>98</td>\n",
       "      <td>0.688483</td>\n",
       "      <td>0.744634</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  pay_schedule  home_owner  income  months_employed  years_employed  \\\n",
       "0   40     bi-weekly           1    3135                0               3   \n",
       "1   61        weekly           0    3180                0               6   \n",
       "2   23        weekly           0    1540                6               0   \n",
       "3   40     bi-weekly           0    5230                0               6   \n",
       "4   33  semi-monthly           0    3590                0               5   \n",
       "\n",
       "   current_address_year  personal_account_m  personal_account_y  has_debt  \\\n",
       "0                     3                   6                   2         1   \n",
       "1                     3                   2                   7         1   \n",
       "2                     0                   7                   1         1   \n",
       "3                     1                   2                   7         1   \n",
       "4                     2                   2                   8         1   \n",
       "\n",
       "   ...  risk_score_5  ext_quality_score  ext_quality_score_2  \\\n",
       "0  ...      0.515977           0.580918             0.380918   \n",
       "1  ...      0.826402           0.730720             0.630720   \n",
       "2  ...      0.762284           0.531712             0.531712   \n",
       "3  ...      0.778831           0.792552             0.592552   \n",
       "4  ...      0.665523           0.744634             0.744634   \n",
       "\n",
       "   inquiries_last_month  EMPLOYED  PA      RISK   QUALITY  ADULT  SENIOR  \n",
       "0                    10        36  30  0.661151  0.480918      1       0  \n",
       "1                     9        72  86  0.789841  0.680720      0       1  \n",
       "2                     7         6  19  0.691712  0.531712      1       0  \n",
       "3                     8        72  86  0.793179  0.692552      1       0  \n",
       "4                    12        60  98  0.688483  0.744634      1       0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Concating the featured data and unfeatured data\n",
    "X=pd.concat([X,featured],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the unnecessay data from our dataset\n",
    "X=X.drop(labels=[\"age\",\"months_employed\",\"years_employed\",\"personal_account_m\",\"personal_account_y\",\"risk_score_2\",\"risk_score_3\"\n",
    "                ,\"risk_score_4\",\"risk_score_5\",\"ext_quality_score_2\",\"ext_quality_score\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>weekly</td>\n",
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       "      <td>0</td>\n",
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       "      <td>bi-weekly</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>semi-monthly</td>\n",
       "      <td>0</td>\n",
       "      <td>3590</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "      <td>53850</td>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
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       "      <td>0.688483</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pay_schedule  home_owner  income  current_address_year  has_debt  \\\n",
       "0     bi-weekly           1    3135                     3         1   \n",
       "1        weekly           0    3180                     3         1   \n",
       "2        weekly           0    1540                     0         1   \n",
       "3     bi-weekly           0    5230                     1         1   \n",
       "4  semi-monthly           0    3590                     2         1   \n",
       "\n",
       "   amount_requested  risk_score  inquiries_last_month  EMPLOYED  PA      RISK  \\\n",
       "0               550       36200                    10        36  30  0.661151   \n",
       "1               600       30150                     9        72  86  0.789841   \n",
       "2               450       34550                     7         6  19  0.691712   \n",
       "3               700       42150                     8        72  86  0.793179   \n",
       "4              1100       53850                    12        60  98  0.688483   \n",
       "\n",
       "    QUALITY  ADULT  SENIOR  \n",
       "0  0.480918      1       0  \n",
       "1  0.680720      0       1  \n",
       "2  0.531712      1       0  \n",
       "3  0.692552      1       0  \n",
       "4  0.744634      1       0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   monthly  semi-monthly  weekly\n",
       "0        0             0       0\n",
       "1        0             0       1\n",
       "2        0             0       1\n",
       "3        0             0       0\n",
       "4        0             1       0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating Dummy Variable\n",
    "dummy2=pd.get_dummies(X[\"pay_schedule\"])\n",
    "\n",
    "#Removing the trap\n",
    "dummy2=dummy2.drop(labels=[\"bi-weekly\"],axis=1)\n",
    "dummy2.head()"
   ]
  },
  {
   "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>home_owner</th>\n",
       "      <th>income</th>\n",
       "      <th>current_address_year</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>amount_requested</th>\n",
       "      <th>risk_score</th>\n",
       "      <th>inquiries_last_month</th>\n",
       "      <th>EMPLOYED</th>\n",
       "      <th>PA</th>\n",
       "      <th>RISK</th>\n",
       "      <th>QUALITY</th>\n",
       "      <th>ADULT</th>\n",
       "      <th>SENIOR</th>\n",
       "      <th>monthly</th>\n",
       "      <th>semi-monthly</th>\n",
       "      <th>weekly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3135</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>550</td>\n",
       "      <td>36200</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>0.661151</td>\n",
       "      <td>0.480918</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3180</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>30150</td>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "      <td>0.789841</td>\n",
       "      <td>0.680720</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1540</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>450</td>\n",
       "      <td>34550</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.691712</td>\n",
       "      <td>0.531712</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>42150</td>\n",
       "      <td>8</td>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "      <td>0.793179</td>\n",
       "      <td>0.692552</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3590</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1100</td>\n",
       "      <td>53850</td>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
       "      <td>98</td>\n",
       "      <td>0.688483</td>\n",
       "      <td>0.744634</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   home_owner  income  current_address_year  has_debt  amount_requested  \\\n",
       "0           1    3135                     3         1               550   \n",
       "1           0    3180                     3         1               600   \n",
       "2           0    1540                     0         1               450   \n",
       "3           0    5230                     1         1               700   \n",
       "4           0    3590                     2         1              1100   \n",
       "\n",
       "   risk_score  inquiries_last_month  EMPLOYED  PA      RISK   QUALITY  ADULT  \\\n",
       "0       36200                    10        36  30  0.661151  0.480918      1   \n",
       "1       30150                     9        72  86  0.789841  0.680720      0   \n",
       "2       34550                     7         6  19  0.691712  0.531712      1   \n",
       "3       42150                     8        72  86  0.793179  0.692552      1   \n",
       "4       53850                    12        60  98  0.688483  0.744634      1   \n",
       "\n",
       "   SENIOR  monthly  semi-monthly  weekly  \n",
       "0       0        0             0       0  \n",
       "1       1        0             0       1  \n",
       "2       0        0             0       1  \n",
       "3       0        0             0       0  \n",
       "4       0        0             1       0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping the pay_schedule since dummy variable is created\n",
    "X=X.drop(labels=[\"pay_schedule\"],axis=1)\n",
    "\n",
    "#Concating the data and dummy variable\n",
    "X=pd.concat([X,dummy2],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Transformation and Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=StandardScaler()\n",
    "#Transforming the data\n",
    "X=scale.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(X,Y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initializing the object\n",
    "model=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "    <br>\n",
    "<img src=\"random.png\" width=\"600\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the model\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predictions on test data\n",
    "pred=model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5874469510833147"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest,pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div>\n",
    "    <br>\n",
    "<img src=\"gradient.png\" width=\"600\"/>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "<a href=\"https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d\">Read More</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1]\n",
    "\n",
    "for learning_rate in lr_list:\n",
    "    gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=learning_rate, max_features=2, max_depth=2, random_state=0)\n",
    "    gb_clf.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate:  1\n",
      "Accuracy score (training): 0.622\n",
      "Accuracy score (validation): 0.583\n"
     ]
    }
   ],
   "source": [
    "print(\"Learning rate: \", learning_rate)\n",
    "print(\"Accuracy score (training): {0:.3f}\".format(gb_clf.score(xtrain, ytrain)))\n",
    "print(\"Accuracy score (validation): {0:.3f}\".format(gb_clf.score(xtest, ytest)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XgBoost\n",
    "\n",
    "\n",
    "<div>\n",
    "    <br>\n",
    "<img src=\"xg_boost.jpeg\" width=\"600\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implementing xgboost\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,\n",
       "       n_estimators=100, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\n",
       "       subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Initializing the model\n",
    "xgb_clf = XGBClassifier()\n",
    "xgb_clf.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6278758096939915\n"
     ]
    }
   ],
   "source": [
    "score = xgb_clf.score(xtest, ytest)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Machine\n",
    "\n",
    "\n",
    "<br>\n",
    "<img src=\"sv.jpg\" height=100px >\n",
    "  \n",
    "  \n",
    "<br>\n",
    "<p>For more reading click <a href=\"https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/\">here</a>.<br><br>\n",
    "    \n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=0,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(random_state = 0, kernel = 'rbf')\n",
    "classifier.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting Test Set\n",
    "y_pred = classifier.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5890104981014072"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the Accuracy Score\n",
    "acc = accuracy_score(ytest, y_pred)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<br>\n",
    "<h4>Models implemented:</h4>\n",
    "\n",
    "<ul>\n",
    "    <li>Artificial Neural Network</li>\n",
    "    <li>Random Forest Classifier</li>\n",
    "    <li>Gradient Boosting</li>\n",
    "    <li>Support Vector Machine</li>\n",
    "    <li>Xg Boost</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h2><u>Conclusion</u><h2></center\n",
    "    \n",
    " <ul>\n",
    "    <li>XgBoost Algorithm performs the best and give the accuracy of 62 %</li>\n",
    "    <li>We see that the ANN with no feature engineering performs far better than SVM, Random Forest with feature engineering</li>\n",
    "    <li>Though we didnt get very high accuracy but this can help the banks in knowing whether the customer is risky or not.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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