handling-categorical-variables-encoding-modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About This Kernel\n",
    "\n",
    "Hello, kagglers! In this kernel, we are going to fight with categorical variables with some exciting feature encoding techniques and modeling.\n",
    "\n",
    "I hope you enjoy my work and bring me your feedback. If this kernel is useful for you, please don't forget to upvote the kernel and You may ask any question if you have, Let's get started:\n",
    "\n",
    "Vikas Singh,\n",
    "\n",
    "Happy Learning!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objective:\n",
    "**Objective of the kernel  is to show complete exploration to understand the categorical data, technique of handing categorical variables and after it I will build a Machine Learning Model.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Competition description:\n",
    "\n",
    "Is there a cat in your dat?\n",
    "\n",
    "A common task in machine learning pipelines is encoding categorical variables for a given algorithm in a format that allows as much useful signal as possible to be captured.\n",
    "\n",
    "Because this is such a common task and important skill to master, we've put together a dataset that contains only categorical features, and includes:\n",
    "\n",
    "- binary features\n",
    "- low- and high-cardinality nominal features\n",
    "- low- and high-cardinality ordinal features\n",
    "- (potentially) cyclical features\n",
    "\n",
    "This Playground competition will give you the opportunity to try different encoding schemes for different algorithms to compare how they perform. We encourage you to share what you find with the community.\n",
    "\n",
    "If you're not sure how to get started, you can check out the Categorical Variables section of Kaggle's Intermediate Machine Learning course.\n",
    "\n",
    "\n",
    "Have Fun!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.  Importing Packages and Collecting Data\n",
    "After importing required modules, read train and test data from csv files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
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   "source": [
    "'''Import basic modules'''\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import string\n",
    "\n",
    "'''import visualization'''\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline\n",
    "\n",
    "'''Plotly visualization .'''\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "py.init_notebook_mode(connected=True)\n",
    "\n",
    "'''Display markdown formatted output like bold, italic bold etc.'''\n",
    "from IPython.display import Markdown\n",
    "def bold(string):\n",
    "    display(Markdown(string))\n",
    "\n",
    "'''Ignore deprecation and future, and user warnings.'''\n",
    "import warnings as wrn\n",
    "wrn.filterwarnings('ignore', category = DeprecationWarning) \n",
    "wrn.filterwarnings('ignore', category = FutureWarning) \n",
    "wrn.filterwarnings('ignore', category = UserWarning) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Read in train and test data from csv files'''\n",
    "train = pd.read_csv('../input/cat-in-the-dat/train.csv')\n",
    "test = pd.read_csv('../input/cat-in-the-dat/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Variable Description, Identification, and Correction\n",
    "Describe what each of the variable indicates and identify our response and predictor variables. Then seperate the categorical variables from numerical variables (i.e., pandas object, float64 or int64 data types)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Preview of Train Data:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <td>Blue</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Lion</td>\n",
       "      <td>Russia</td>\n",
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       "      <td>ae6800dd0</td>\n",
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       "      <td>Lava Hot</td>\n",
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       "<p>3 rows × 25 columns</p>\n",
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       "   id  bin_0  bin_1  bin_2 bin_3 bin_4  nom_0      nom_1    nom_2    nom_3  \\\n",
       "0   0      0      0      0     T     Y  Green   Triangle    Snake  Finland   \n",
       "1   1      0      1      0     T     Y  Green  Trapezoid  Hamster   Russia   \n",
       "2   2      0      0      0     F     Y   Blue  Trapezoid     Lion   Russia   \n",
       "\n",
       "   ...      nom_9 ord_0        ord_1     ord_2 ord_3 ord_4  ord_5 day month  \\\n",
       "0  ...  2f4cb3d51     2  Grandmaster      Cold     h     D     kr   2     2   \n",
       "1  ...  f83c56c21     1  Grandmaster       Hot     a     A     bF   7     8   \n",
       "2  ...  ae6800dd0     1       Expert  Lava Hot     h     R     Jc   7     2   \n",
       "\n",
       "  target  \n",
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       "\n",
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      "text/markdown": [
       "**Preview of Test Data:**"
      ],
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       "      <td>Square</td>\n",
       "      <td>Lion</td>\n",
       "      <td>Canada</td>\n",
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       "      <td>46ae3059c</td>\n",
       "      <td>285771075</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>Lava Hot</td>\n",
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       "      <td>A</td>\n",
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       "      <td>5</td>\n",
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       "      <td>Blue</td>\n",
       "      <td>Square</td>\n",
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       "      <td>b759e21f0</td>\n",
       "      <td>6f323c53f</td>\n",
       "      <td>2</td>\n",
       "      <td>Expert</td>\n",
       "      <td>Freezing</td>\n",
       "      <td>a</td>\n",
       "      <td>G</td>\n",
       "      <td>tP</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
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       "       id  bin_0  bin_1  bin_2 bin_3 bin_4 nom_0     nom_1    nom_2    nom_3  \\\n",
       "0  300000      0      0      1     T     Y  Blue  Triangle  Axolotl  Finland   \n",
       "1  300001      0      0      0     T     N   Red    Square     Lion   Canada   \n",
       "2  300002      1      0      1     F     Y  Blue    Square      Dog    China   \n",
       "\n",
       "   ...      nom_8      nom_9 ord_0   ord_1     ord_2 ord_3  ord_4 ord_5 day  \\\n",
       "0  ...  9d117320c  3c49b42b8     2  Novice      Warm     j      P    be   5   \n",
       "1  ...  46ae3059c  285771075     1  Master  Lava Hot     l      A    RP   7   \n",
       "2  ...  b759e21f0  6f323c53f     2  Expert  Freezing     a      G    tP   1   \n",
       "\n",
       "  month  \n",
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       "2    12  \n",
       "\n",
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   "source": [
    "'''Train and test data at a glance.'''\n",
    "bold('**Preview of Train Data:**')\n",
    "display(train.head(3))\n",
    "bold('**Preview of Test Data:**')\n",
    "display(test.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Shape of our train and test data**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension of train: (300000, 25)\n",
      "Dimension of test: (200000, 24)\n"
     ]
    }
   ],
   "source": [
    "'''Dimension of train and test data'''\n",
    "bold('**Shape of our train and test data**')\n",
    "print('Dimension of train:',train.shape) \n",
    "print('Dimension of test:',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Variable Description'''\n",
    "def description(df):\n",
    "    summary = pd.DataFrame(df.dtypes,columns=['dtypes'])\n",
    "    summary = summary.reset_index()\n",
    "    summary['Name'] = summary['index']\n",
    "    summary = summary[['Name','dtypes']]\n",
    "    summary['Missing'] = df.isnull().sum().values    \n",
    "    summary['Uniques'] = df.nunique().values\n",
    "    summary['First Value'] = df.iloc[0].values\n",
    "    summary['Second Value'] = df.iloc[1].values\n",
    "    summary['Third Value'] = df.iloc[2].values\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Variable Description of  train Data:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <td>bin_3</td>\n",
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       "      <th>5</th>\n",
       "      <td>bin_4</td>\n",
       "      <td>object</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Y</td>\n",
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       "      <th>6</th>\n",
       "      <td>nom_0</td>\n",
       "      <td>object</td>\n",
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       "      <td>3</td>\n",
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       "      <td>Green</td>\n",
       "      <td>Blue</td>\n",
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       "      <th>7</th>\n",
       "      <td>nom_1</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Triangle</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Trapezoid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nom_2</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Snake</td>\n",
       "      <td>Hamster</td>\n",
       "      <td>Lion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nom_3</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>nom_4</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Bassoon</td>\n",
       "      <td>Piano</td>\n",
       "      <td>Theremin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nom_5</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>50f116bcf</td>\n",
       "      <td>b3b4d25d0</td>\n",
       "      <td>3263bdce5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>nom_6</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>522</td>\n",
       "      <td>3ac1b8814</td>\n",
       "      <td>fbcb50fc1</td>\n",
       "      <td>0922e3cb8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nom_7</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>1220</td>\n",
       "      <td>68f6ad3e9</td>\n",
       "      <td>3b6dd5612</td>\n",
       "      <td>a6a36f527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>nom_8</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>2215</td>\n",
       "      <td>c389000ab</td>\n",
       "      <td>4cd920251</td>\n",
       "      <td>de9c9f684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nom_9</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>11981</td>\n",
       "      <td>2f4cb3d51</td>\n",
       "      <td>f83c56c21</td>\n",
       "      <td>ae6800dd0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ord_0</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ord_1</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Expert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ord_2</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Hot</td>\n",
       "      <td>Lava Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ord_3</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>h</td>\n",
       "      <td>a</td>\n",
       "      <td>h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ord_4</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ord_5</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>kr</td>\n",
       "      <td>bF</td>\n",
       "      <td>Jc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>day</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>month</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>target</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  dtypes  Missing  Uniques  First Value Second Value Third Value\n",
       "0       id   int64        0   300000            0            1           2\n",
       "1    bin_0   int64        0        2            0            0           0\n",
       "2    bin_1   int64        0        2            0            1           0\n",
       "3    bin_2   int64        0        2            0            0           0\n",
       "4    bin_3  object        0        2            T            T           F\n",
       "5    bin_4  object        0        2            Y            Y           Y\n",
       "6    nom_0  object        0        3        Green        Green        Blue\n",
       "7    nom_1  object        0        6     Triangle    Trapezoid   Trapezoid\n",
       "8    nom_2  object        0        6        Snake      Hamster        Lion\n",
       "9    nom_3  object        0        6      Finland       Russia      Russia\n",
       "10   nom_4  object        0        4      Bassoon        Piano    Theremin\n",
       "11   nom_5  object        0      222    50f116bcf    b3b4d25d0   3263bdce5\n",
       "12   nom_6  object        0      522    3ac1b8814    fbcb50fc1   0922e3cb8\n",
       "13   nom_7  object        0     1220    68f6ad3e9    3b6dd5612   a6a36f527\n",
       "14   nom_8  object        0     2215    c389000ab    4cd920251   de9c9f684\n",
       "15   nom_9  object        0    11981    2f4cb3d51    f83c56c21   ae6800dd0\n",
       "16   ord_0   int64        0        3            2            1           1\n",
       "17   ord_1  object        0        5  Grandmaster  Grandmaster      Expert\n",
       "18   ord_2  object        0        6         Cold          Hot    Lava Hot\n",
       "19   ord_3  object        0       15            h            a           h\n",
       "20   ord_4  object        0       26            D            A           R\n",
       "21   ord_5  object        0      192           kr           bF          Jc\n",
       "22     day   int64        0        7            2            7           7\n",
       "23   month   int64        0       12            2            8           2\n",
       "24  target   int64        0        2            0            0           0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bold('**Variable Description of  train Data:**')\n",
    "description(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Great!! As we can see that data don't have any missing values but in target has missing values (due to concatenation of train and test set) that we would predict learning from the train dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Variable Description of  train Data:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<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>Name</th>\n",
       "      <th>dtypes</th>\n",
       "      <th>Missing</th>\n",
       "      <th>Uniques</th>\n",
       "      <th>First Value</th>\n",
       "      <th>Second Value</th>\n",
       "      <th>Third Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>200000</td>\n",
       "      <td>300000</td>\n",
       "      <td>300001</td>\n",
       "      <td>300002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bin_0</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bin_1</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bin_2</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bin_3</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bin_4</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nom_0</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Red</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nom_1</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Triangle</td>\n",
       "      <td>Square</td>\n",
       "      <td>Square</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nom_2</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Axolotl</td>\n",
       "      <td>Lion</td>\n",
       "      <td>Dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nom_3</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Canada</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>nom_4</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Piano</td>\n",
       "      <td>Piano</td>\n",
       "      <td>Piano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nom_5</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>0870b0a5d</td>\n",
       "      <td>a5c276589</td>\n",
       "      <td>568550f04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>nom_6</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>522</td>\n",
       "      <td>9ceb19dd6</td>\n",
       "      <td>1ad744242</td>\n",
       "      <td>1fe17a1fd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nom_7</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>1219</td>\n",
       "      <td>530f8ecc3</td>\n",
       "      <td>12e6161c9</td>\n",
       "      <td>27d6df03f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>nom_8</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>2214</td>\n",
       "      <td>9d117320c</td>\n",
       "      <td>46ae3059c</td>\n",
       "      <td>b759e21f0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nom_9</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>11839</td>\n",
       "      <td>3c49b42b8</td>\n",
       "      <td>285771075</td>\n",
       "      <td>6f323c53f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ord_0</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ord_1</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Novice</td>\n",
       "      <td>Master</td>\n",
       "      <td>Expert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ord_2</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Warm</td>\n",
       "      <td>Lava Hot</td>\n",
       "      <td>Freezing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ord_3</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>j</td>\n",
       "      <td>l</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ord_4</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>P</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ord_5</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>be</td>\n",
       "      <td>RP</td>\n",
       "      <td>tP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>day</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>month</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  dtypes  Missing  Uniques First Value Second Value Third Value\n",
       "0      id   int64        0   200000      300000       300001      300002\n",
       "1   bin_0   int64        0        2           0            0           1\n",
       "2   bin_1   int64        0        2           0            0           0\n",
       "3   bin_2   int64        0        2           1            0           1\n",
       "4   bin_3  object        0        2           T            T           F\n",
       "5   bin_4  object        0        2           Y            N           Y\n",
       "6   nom_0  object        0        3        Blue          Red        Blue\n",
       "7   nom_1  object        0        6    Triangle       Square      Square\n",
       "8   nom_2  object        0        6     Axolotl         Lion         Dog\n",
       "9   nom_3  object        0        6     Finland       Canada       China\n",
       "10  nom_4  object        0        4       Piano        Piano       Piano\n",
       "11  nom_5  object        0      222   0870b0a5d    a5c276589   568550f04\n",
       "12  nom_6  object        0      522   9ceb19dd6    1ad744242   1fe17a1fd\n",
       "13  nom_7  object        0     1219   530f8ecc3    12e6161c9   27d6df03f\n",
       "14  nom_8  object        0     2214   9d117320c    46ae3059c   b759e21f0\n",
       "15  nom_9  object        0    11839   3c49b42b8    285771075   6f323c53f\n",
       "16  ord_0   int64        0        3           2            1           2\n",
       "17  ord_1  object        0        5      Novice       Master      Expert\n",
       "18  ord_2  object        0        6        Warm     Lava Hot    Freezing\n",
       "19  ord_3  object        0       15           j            l           a\n",
       "20  ord_4  object        0       26           P            A           G\n",
       "21  ord_5  object        0      192          be           RP          tP\n",
       "22    day   int64        0        7           5            7           1\n",
       "23  month   int64        0       12          11            5          12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bold('**Variable Description of  train Data:**')\n",
    "description(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Target Feature: Balanced or Imbalanced\n",
    "\n",
    "Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally.\n",
    "\n",
    "For example, you may have a 2-class (binary) classification problem with 100 instances (rows). A total of 80 instances are labeled with Class-1 and the remaining 20 instances are labeled with Class-2.\n",
    "\n",
    "This is an imbalanced dataset and the ratio of Class-1 to Class-2 instances is 80:20 or more concisely 4:1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' #1.Function for displaying bar labels in absolute scale.'''\n",
    "def abs_bar_labels():\n",
    "    font_size = 15\n",
    "    plt.ylabel('Absolute Frequency', fontsize = font_size)\n",
    "    plt.xticks(rotation = 0, fontsize = font_size)\n",
    "    plt.yticks([])\n",
    "    \n",
    "    # Set individual bar lebels in absolute number\n",
    "    for x in ax.patches:\n",
    "        ax.annotate(x.get_height(), \n",
    "        (x.get_x() + x.get_width()/2., x.get_height()), ha = 'center', va = 'center', xytext = (0, 7), \n",
    "        textcoords = 'offset points', fontsize = font_size, color = 'black')\n",
    "    \n",
    "'''#2.Function for displaying bar lebels in relative scale.'''\n",
    "def pct_bar_labels():\n",
    "    font_size = 15\n",
    "    plt.ylabel('Relative Frequency (%)', fontsize = font_size)\n",
    "    plt.xticks(rotation = 0, fontsize = font_size)\n",
    "    plt.yticks([]) \n",
    "    \n",
    "    # Set individual bar lebels in proportional scale\n",
    "    for x in ax1.patches:\n",
    "        ax1.annotate(str(x.get_height()) + '%', \n",
    "        (x.get_x() + x.get_width()/2., x.get_height()), ha = 'center', va = 'center', xytext = (0, 7), \n",
    "        textcoords = 'offset points', fontsize = font_size, color = 'black')\n",
    "         \n",
    "'''#3.Function to create a dataframe of absolute and relative frequency of each variable. And plot absolute and relative frequency.'''\n",
    "def absolute_and_relative_freq(variable):\n",
    "    global  ax, ax1 \n",
    "    # Dataframe of absolute and relative frequency\n",
    "    absolute_frequency = variable.value_counts()\n",
    "    relative_frequency = round(variable.value_counts(normalize = True)*100, 2)\n",
    "    # Was multiplied by 100 and rounded to 2 decimal points for percentage.\n",
    "    df = pd.DataFrame({'Absolute Frequency':absolute_frequency, 'Relative Frequency(%)':relative_frequency})\n",
    "    print('Absolute & Relative Frequency of',variable.name,':')\n",
    "    display(df)\n",
    "    \n",
    "    # This portion plots absolute frequency with bar labeled.\n",
    "    fig_size = (18,5)\n",
    "    font_size = 15\n",
    "    title_size = 18\n",
    "    ax =  absolute_frequency.plot.bar(title = 'Absolute Frequency of %s' %variable.name, figsize = fig_size)\n",
    "    ax.title.set_size(title_size)\n",
    "    abs_bar_labels()  # Displays bar labels in abs scale.\n",
    "    plt.show()\n",
    "    \n",
    "    # This portion plots relative frequency with bar labeled.\n",
    "    ax1 = relative_frequency.plot.bar(title = 'Relative Frequency of %s' %variable.name, figsize = fig_size)\n",
    "    ax1.title.set_size(title_size)\n",
    "    pct_bar_labels() # Displays bar labels in relative scale.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absolute & Relative Frequency of target :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Absolute Frequency</th>\n",
       "      <th>Relative Frequency(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208236</td>\n",
       "      <td>69.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91764</td>\n",
       "      <td>30.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Absolute Frequency  Relative Frequency(%)\n",
       "0              208236                  69.41\n",
       "1               91764                  30.59"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plot and count the target variable in absolute and relative scale'''\n",
    "absolute_and_relative_freq(train.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can see that target variable is a binary and It's seems that balanced in the ratio of 7:3 approximately.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Distribution of categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''#4.Create a function that relative frequency of Target variable by a categorical variable. And then plots the relative frequency of target by a categorical variable.'''\n",
    "def crosstab(cat, cat_target, color):\n",
    "    '''cat = categorical variable, cat_target = our target categorical variable.'''\n",
    "    global ax1\n",
    "    fig_size = (18, 5)\n",
    "    title_size = 18\n",
    "    font_size = 15\n",
    "    cat_grouped_by_cat_target = pd.crosstab(index = cat, columns = cat_target)\n",
    "    pct_cat_grouped_by_cat_target = round(pd.crosstab(index = cat, columns = cat_target, normalize = 'index')*100, 2)\n",
    "       \n",
    "    # Plot relative frequrncy of Target by a categorical variable\n",
    "    ax1 = pct_cat_grouped_by_cat_target.plot.bar(color = color, title = 'Percentage Count of target by %s' %cat.name, figsize = fig_size)\n",
    "    ax1.title.set_size(fontsize = title_size)\n",
    "    pct_bar_labels()\n",
    "    plt.xlabel(cat.name, fontsize = font_size)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Binary Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by bin_0:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by bin_1:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by bin_2:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by bin_3:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by bin_4:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plot the binary variables in relative scale'''\n",
    "\n",
    "bold('**Percentage Count of target by bin_0:**')\n",
    "crosstab(train.bin_0, train.target, color = ['r', 'g'])\n",
    "\n",
    "bold('**Percentage Count of target by bin_1:**')\n",
    "crosstab(train.bin_1, train.target, color = ['r', 'g'])\n",
    "\n",
    "bold('**Percentage Count of target by bin_2:**')\n",
    "crosstab(train.bin_2, train.target, color = ['r', 'g'])\n",
    "\n",
    "bold('**Percentage Count of target by bin_3:**')\n",
    "crosstab(train.bin_3, train.target, color = ['r', 'g'])\n",
    "\n",
    "bold('**Percentage Count of target by bin_4:**')\n",
    "crosstab(train.bin_4, train.target, color = ['r', 'g'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Nominal variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by nom_0:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by nom_1:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7BTg3zQA9caox1AMm4TyRnYYzfz5pP3+7juMznITDaJybn/twnmz/k6T9NR+Pq1rC4654D+Gct2nACJzpCMNxhsvHtf8BZyTDeqAbTiWBUq7jb5+k799wkhonXf31xSnX90xacXlgEM6N+ovAXJxznlY1hrY4572UK+5uruO4y1p7rU+q2+JMLRjpimUSxF8bT+BMURgBDMP5Pt7r2uYxVxnChsD/cJI6I3ASEk+6miS9PobjJHqO4ix6OBnnhjqH6/0JdcMp6TgIpwRn0ooiKZmEc522d70WBEKstUNSaP8BTuInDGeR1gxhre2Hc6w5gVE4pSL34pSK/DyjPtcDnXG+byOAuOlLcV93zsS4RERuCT6xsVc6TVJEROTGZYzxx7lpnGqtfT6z4xHvYoyphVOZYIC1dlRmx5Ma11SDP4APrbVdMzseERG5tWhkgoiI3LTcrX+A89QfUl9gUm4BSa8P1/oPfV1f3gjXxws4C1JOy+xARETk1qM1E0RE5Gb2hTHmEM6T5qzA/ThD1b/j30UE5db1szFmLc70HT/gMaABsMha+2OmRpYKY0xrnHKQfYCvvDnWK+FacNKThStPZtY6DSIi8i8lE0RE5Ga2Emf++X+BXDiL7L0NvKabEQFW4CQQ2uH8TvQbzvoYozMzKA8swFnoMm5dk5vF3cA6D9qV5uoWdxURkXR0vdZMyIGzGvFf/FvWSUREREQEgBUrVuRbunRp1bTa9enT54eqVatevh4xiYjcQrLiLDr9PU657DRdr2RC3KrgXqNRo0asX7/e7b7vvvuOu+66i6CgIA4dOpRoX2BgIMeOeb5A+fLly2nevDm1atXihx9+iN++e/duevXqxY4dOzh16hSBgYE88MADjBgxgqJFiyZ6f8+ePQkPD+fFF19k6NChifofPnw4P/74IytWrPA4JhERERERERE3GuBUJErT9UomlAUOnDlzgZgY76geYe1ewsLOJ9o2atQb7Ny5nd27D+Dr60uNGpWpW7cezz337wLJ2bJlp1q16h59xqVLl6hfvw7//HORokVv55tvNsTv27p1C4sXL6RevbspUqQohw//zpgxoyhQoABr1qzH19eXU6f+pmbNKvTq1ZeSJYPo0eNlZsyYzX333Y+/fx527dpHgwb1WL16HWXKlE2fEyNyjfz983DqVHhmhyHilq5P8Va6NsWb6foUb6VrM/1kyeJDwYJ+AOWAg56853qtmRANEBMT6zXJhOBgk+jriIgIfv55G82aPUGWLFnj4wwICKRmzTqJ2np6DJMmTaBIkaIEBZVm7949id5Xu/ad1K59Z/zXd999D0WKFOPJJ//Lrl07ueOO6nz//VaKFy/BSy/1ACA0dAPr1q3l3nsbAzB06GDatGlHUFAZrzmvIuD5z4hIZtD1Kd5K16Z4M12f4q10baY7j5cl0AKMLmvXfs3Zs2dp0aJluvR35MgfvPvuO6xY8TnTp0/x6D233eYsYBwREeF6jSRnzn+rVuXKlYvISGffli1bCA1dz5Yt29IlXhERERERERFPZcnsALzF8uVLKFq0GPXq3Z1o+4IFc7j9dn/Kli1Op07t+OOPwx71N3ToIJo1a84dd6Q+JSImJoaIiAgOHNjPiBFDqVGjJjVr1gagatU72LNnNxs3buDQod9ZtepTqlWrQWxsLK+88goDBrxK3rz5ru6ARURERERERK6SRiYAFy9e5KuvvqRduw74+PjEb3/ooUeoVasOxYrdzr59lrFjR/H44w+xfv1m8uXLn2J/GzduYN26b9i8Oe1RA//3f0+wbt03AFSrVoP585eQJYuT4ylVKoiQkN60aPEoAE2aPECLFq1YtGg+kZGRtGnT7loOW0REREREROSqKJkArF79BRcuhCeb4vDGG2/F/71evbupU+dOGjeuz4IFc+na9UW3fUVFRTFoUF969OhDYGBgmp/95ptjOHPmDL/+epDx48fwf//3BJ99tpqcOXMC0Lt3fzp2fI6LFy9QokRJwsPDGTlyOIsWLSQqKoqBA/uyatUKAgICGT16PPXq3XUNZ0JEREREREQ8ER0dxZkzJ4mKisjsUDzm65udggULkzXrtacClEwAPvlkKaVLl6F69ZqptqtYsRLlygWzY8f2FNvMmTOLc+fO8dRTbTh37iwAkZERREdHc+7cWXLn9iNbtmzx7cuUKQdArVp1qFfvbmrXrsqyZR8nGnXg7++Pv78/ABMnjqNu3Xo0bNiQkSPfYvfunWzevI11676hS5cOfP/9DnLkyHHV50JERERERETSdubMSXLmzI2fX5FEI9y9VWxsLBcunOfMmZMUKlT0mvu75ddMOH/+HGvXrqF5c88XXkztQjl4cD9Hj/5JlSrlCA4uSXBwSZYtW8KuXTsIDi7J8uVLU3xviRIlKViwIIcO/eZ2/+HDh5g58wOGDBkOwMaNobRs+SQFChSkefOWRERc5uDBAx4fh4iIiIiIiFydqKgI/Pzy3RCJBHDuY/388qXbSIpbfmTC559/xuXLl2nRolWabffs+YUDB/bTvn3HFNt06tSFhx9+NNG2iRPHcfjwIcaOfSdZScqEDhzYz+nTpylZMsjt/mHDBtO583OULFkqftvFi/8AEB0dzeXLEcTGqjSKiIiIiIjI9XCjJBLipGe8t3wy4ZNPllC5clXKl098k79mzZcsWbKIpk0fokiRouzfv4/x48dw++0laN26bXy7RYvmExLyIlu3bqdEiZKUKVOWMmXKJupr4cJ5nD59mvr1G8RvGzp0EL6+vtSsWZv8+fOzb59l8uR3CAoqzX//+0SyOL/7biM//vg9kyb9W2by7rvrM23a+xhjCA3dQJ48eShXLji9To2IiIiIiIiIW7d0MuHUqVOEhq6nf//ByfYVK1acv//+m1df7c+5c+coWPA2GjduwqBBQxOVY4yNjSU6OvqKRwRUr16DDz6Yypw5M7l8+TK3316c//zncV55pRd+fn6J2sbExDB4cH8GDRqaaF+HDs+yZ88vdOvWhcDAQKZOnaH1EkRERERERLxAWFgYa9Z86dEo+GuxbdsPZMuWjapVq2Xo5yTlc52GxQcBv506FU5MjIbhp4fChfNy8mRYZochkoyuTfFmuj7FW+naFG+m61O8VWZfm8eOHaJIkVIp7v/rr6P07RvCnDmLPeovNjaW2NhYsmS5sqUNP/xwKrly5U60iH9q3MWdJYsP/v55AEoDv3vSzy09MsGdvPlykTOH95+WiMjozA5BREREREREUjBlyiT+/PNPOnRoQ82atTh48ABhYeeJioriuedeoEGDRvz111F69+5OjRq12b17B2+++TY//PA/5s6dTaFChShRoiTZsmWjZ89+nDlzhrFjR3L8+HEAunfvSeHCAaxYsYwsWbKwevUX9OjRh2rValyX4/P+u+brLGcOXx7rtSKzw0jTyrebZXYIIiIiIiIikoLnn3+ZX389yKxZ84mKiuLy5Uv4+eXh7NmzdO3agXvuuRdwqvYNGDCU3r378/ffJ5k160NmzJhL7tx+dO/+fPy6eO+8M5Ynn2xLtWrVOXbsGL16vcS8eUto1qzFFY1MSC+3fGlIEREREZGM8t//PkJAQD63f77//n+AM7R5woSxVK9ekZIlA3j88YfYuXNHmn3HxsYyfvwYatSoRIkShbn//gasXft1snZ79+6hVatmlCoVSIUKQfTp04Pw8PBEbT7//DNq176DSpXKMGbMm8n6GDt2FO3bt77KsyAiAFOnTuaZZ1oTEtKNkydPcvr0KQCKFClKlSpVAfjll91Ur16TfPny4+vry333NYl//w8/bGX8+Lfo0KEN/fv35MKFC1y8eCFTjgU0MkFEREREJMOMHj2OsLDzSba9wa5dO6hRoxbglBEfN+4thg4dQbly5Zky5V1atXqc9ev/R2BgYIp9T5w4jrffHk3fvoOoUqUqS5Ysol27p/jss9XxfZ8/f44WLR6lbNlyTJs2izNnTjN8+KscP36M2bMXAM6i5N26PUfPnn0pVaoUPXt2p3btutx33/0A/Pnnn0yb9h5ffrkuI06RyC1h9eovOHv2LB9+OBdfX19atnyMiIgIAHLmzBnfLrU1DWNjY1yL7udMsc31pGSCiIiIiEgGMaZCoq8jIiLYvv0nmjV7Al9fXy5dusTEiePp3r0nnTt3BaB27brUrl2FGTOmMmDAELf9RkRE8M4743jppRC6d+8BQOPGTdi3zzJ27CjmzfsYgJkzP+DSpUvMnbuI/PkLAFCgQEHat2/Nzz9vo3r1mvz441ZKlCgR38/GjaGsX78uPpnQt29f2rRpn6z8uYikLnfu3Fy8eBGA8PBwChYsiK+vL9u2/cCxY3+5fU+lSpWZNGkc58+fJ3fu3Kxfvzb+Z69OnXosXbqYNm3aA7B/vyU42JA7t1+mjFDQNAcRERERketk7dqvOXv2LC1atATg++//R1jYeZo1axHfxs/PjwceeJhvvkk+ZSHO77//Rnh4GPfee1+i7ffeex/r16+Lf+K5a9dOqlevEZ9IAGjUqDE+Pj6sWfMVABERkeTMmSt+f65cuYiMdN7/ww9b+eabb+jVq+81HrnIrSd//gJUrVqNdu2e5MCBfezdu4fOnduxevUXlCoV5PY9hQsH0K5dR7p06UBISDeCgkrj55cHgJCQPuzdu4dnnmnN00+3YvnypQDUr9+ADRvW0aFDG7Zv/+l6HZ5GJoiIiIiIXC/Lly+haNFi1Kt3NwD79+8ja9asyZ76BwcbVqxYlmI/ly9fAiBbtmyJtmfPnp2IiAgOHfqd4ODyXL58KVkbX19fsmTJwv79FoCqVe9gz57dbNy4gRIlSrJq1af06zeI2NhYBg/ux+uvv07evPmu+dhFbkXDhr2RZpukpSObNn2IZs1aEBUVxcCBfahTpx4ABQoUYPjw5GualCxZio8+Wpg+AV8BjUwQEREREbkOLl68yFdffUmzZi3w8fEB4Ny5s/j55SFr1qyJ2hYoUICLFy/GjzBIqlSpIHx8fPj5522Jtv/0048AnDlzBoCgoDLs3r2LyMjI+Dbbt/9EdHR0fJtSpYIICelNixaPUqfOHRhTgRYtWrFo0XwiI6Po1KlT+pwAEfHIjBnT6NChDe3bP0WxYsVo2LBRZofklkYmiIiIiIhcB6tXf8GFC+HxUxzixCUWEopbhM3dPoB8+fLTvHlLxo8fS4UKlahcuQpLly5mw4ZvAcia1Xlm2K5dB6ZPf58BA/rQp88Azpw5Tb9+vciaNWuiBEbv3v3p2PE5Ll68QIkSJQkPD2fkyOFMnTqDqKgoevcOYdWqFQQEBDJ69Hjq1bsrPU6JiLjx0kshmR2CRzQyQURERG4KUVFRTJw4jjvvrE7x4oWoVq0Cr77aP1Gb48eP0b37C9xxhyEoqCiNG9/DkiWL0uzb0xJ8cWJiYmjSpCEBAflYvfqLRPtUgu/W9cknSyldugzVq9eM35Y/fwHCw8OIjo5O1PbcuXPkzp072RSFhF5/fbRrFMGjGBPE5MkT6dGjD+DMuwYIDi7P229P5JNPllC1ajCNGt1FjRq1qFLljvg2cfz9/SlRoiTgVIqoW7ced91VnylTprB79042b95GSEhvunTpwOXLl9PlnIjIjUsjE0REROSm0L37C4SGrqd37/4EB5fnzz+PsG+fjd8fExNDu3ZPcfr0GYYMGU5AQCArV66gW7fnyJUrN//5z2Mp9u1JCb6E5s79iL/+Oppse0ol+J588r8A/PXXUZXgu0mdP3+OtWvX8OKLryTaHhxcnujoaH777VfKlQuO337gwD7KlSufap+FChVi2bLPOHr0T86fP0+5csFMnfoeAQGBlCxZKr5dmzbtaNGiFb/+epBChQrj7++PMUG0bdvebb+HDx9i5swP+OabUADWrVtHy5ZPUqBAQZo3b8mAAb05ePAAlSpVvtrTISI3AY1MEPFCGfl0LaHPP/+MgIB8NG16b6Lthw8fIiAgX7I/Xbp0SPZ+PV0TEW+wdu0ali9fypIln/LMM524++57aNWqNYMGDY1vc/DgAX7++SfeeGM0LVs+RcOGjRgzZjxVq1aLXxHbnaQl+Bo3bsJ7702nYsXKjB07Kln7s2fP8Oabwxk4MHlJv4SyrdspAAAgAElEQVQl+Jo1a0GLFq1Yv/7fxMFrr72qEnw3qc8//4zLly/TokWrRNvr1LmTvHnz8emnn8Rvu3jxIqtXf8H99zfxqO9ixW6nQoWKREVFsWDBHNq0aZesTc6cOalUqTIBAQF8/PFCYmJiaNasudv+hg0bTOfOzyVKSFy8+A8A0dHRXL4cET8NQ0RuXRqZIOKFMvLpWpxLly4xZMjAZEMcExo27A3q1r0z/uvbbvOP/7ueromIN5k/fy733NMQYyqk2CZuAbp8+RKvSp8/f/5Ub4xSK8E3depkIiIiyJ49e/z2UaNep06dejRocG/SrtIswRcaup4tW7Yle5/c+D75ZAmVK1elfHmTaHvOnDnp3r0H48a9Rf78BQgOLs+UKe8SExND587Px7dbtGg+ISEvsnXr9vipCIsXLyAqKopSpYI4cuQPpk59j6xZs9K9e8/494WFnWf8+LHcddfdZM3qy6ZNobz//iTGjZtEwYK3JYvzu+828uOP3zNp0pT4bffeey/jxo3HGENo6Aby5MmTaBSFiNyalEwQ8TJxT9fWrfsuxV+K456uzZmziAcffBiAhg0bsW3bDyxfvtSjZMLkye9QtGhRgoJKs3fvHrdtypUrR+3add3uS/h0DWDjxlDWr18Xn0zQ0zURuZ62bfuBBx98mP79e7F48UKio6O4774mjBo1liJFigJQsWIlatWqzejRbzBu3EQKFw5g1aqVbN26hQULUh6Z4GkJPoDdu3excOE81q7d5LavtErwDRjwqkrw3YROnTpFaOh6+vcf7HZ/9+49iYmJYeLEcZw5c5pq1Wrw8ccrCAj4N+EfGxtLdHR0osRXTEwMkyaN58iRP8ibNx8PP/wogwYNIU+ePPFtsmTJys6d25k7dxaXLl2iQoWKfPDBbB555NFkccTExDB4cH8GDRqKn59f/PYXXniB77/fRrduXQgMDGTq1BnkyJEjPU6NyE0lb75c5MyR/rfYly5HEXb+n3Tv91opmSDiZTLy6VqcI0f+4N1332HFis+ZPn1Kmu3d0dM1EfEmJ04cZ+HC+VSuXIVp02YQHh7O8OFD6NChDV98sRYfHx98fHxYsGAp7dv/H/XqOQvgZcuWjXfeec/tKII4CUvwJUywJi3BBzBwYB86dnyOMmXKcvjwIbd9xZXgA2jS5AFatGjF7NmziYyMcjs8XW58/v7+HD16OsX9Pj4+9OjRJ37xRHdat25L69Zt09yWlJ+fHx9/vMKjOLNkycLatRuTbc+RIwcTJkxmwoTJHvUjcqvKmcOXx3p59vN2JVa+3YwwD9odPnyIN94Yxrlz58ifPz+DB78WP5IpIyiZIOJlMvLpWpyhQwfRrFlz7rijeqrtXnmlG2fOnKFQocI0b96SgQOHkCuXk0DQ0zUR8SaxsbHExsYye/aC+ClZgYFFaNbsYUJD19OwYSNiYmJ48cUunDlzmunTZ1GoUGG+/no1PXq8xG233Ubjxk3d9u1pCb5PPlnCgQP7mTs39fVr3JXgGzhwIO+//yFRUVEMHNhXJfhuERn1FDO9RURGp91IRDLd2LFv0qJFKx588BG++upzxowZycSJV/fg0BPe/6+XyC0mI5+uAWzcuIF1675h8+aURw3kyJGDTp2eo1Gj+8mbNy+bNoXy7rsTOHToN2bPXgjo6ZqIeJf8+QtQqlRQorVd7rzzLrJnz86+fXtp2LARq1d/yZo1X7FlyzbKlCkHQP36DTh69AivvTYkxWQCOCX4unTpEP9v3u23F6dHjz6MGfMmhQsHEBkZyWuvvcrLL4cQExPDuXNnCQtzniNdvHiR8PAw8uTJG9+fv78//v5OrBMnjqN+/frcdVd9pk9/P74E37p139ClSwe+/36HhpTfpDLqKWZ6W/l2s8wOQUTScObMafbt28v48c4IoiZNHmT8+Lc4c+YMBQsWzJDPVDJBxMtk5NO1qKgoBg3qS48efQgMDEwxhsDAIowa9Xb81/XrN6Bw4QD69evJzp07qFr1DkBP10TEe5Qvb9zWvY+NjcXHxxk5sH//PnLnzh2fSIhTpUo1vvrqi1T7T6sE37lzZzl69E+GDBnIkCEDE723S5eOBAWVZuvW7cn6jSvB9/PPPwHO+jMqwSciIlfq+PHjFCoUQNasWQHImjUrhQoV5sSJ40omiNwqMvLp2pw5szh37hxPPdWGc+fOAhAZGUF0dDTnzp0ld26/ZAuMxXnssf+6kgnb45MJoKdrIuIdmjZ9iDFjRnLq1Kn4f5M2b95EZGQklStXBaBEiRJcvHiRAwf2J1qJfseOnz2eU1qs2O0UK3Y7ly5dSlSCz88vD598sipR2xMnjtO1aycGDRrKPfc0dNtfXAm+oKAgTp6MG8mgEnwiIuL9smR2ACKSWNKSUXE8fbp26NBvKfZ98OB+jh79kypVyhEcXJLg4JIsW7aEXbt2EBxcMtU66z4+Polek4p7uvbWW28ByZ+uRURc5uDBAykfuNwQoqKimDhxHHfeWZ3ixQtRrVoFXn21f7J2v/yym7ZtW1G2bHFKly7Ggw82Yvv2n1Lt+9tv19K1a0dq1apCQEA+3nprZLI2ERERDBs2mMcee5CSJQMICHC/Lsfnn39G7dp3UKlSGcaMeTPZ/rFjR9G+fWsPj1puBO3bd6Bgwdt4+ukn+eqrL1i6dDEvvtiFhg3vix8V1aTJAxQvXoJnnvk/li37mPXr1/Hqq/1ZsWIZHTs+F9/XokXzKVq0IH/8cTh+2+LFC5g/fw6bNoWyaNF8HnmkSaISfL6+vtSv3yDRn1q16gBx69zUSRZzXAm+l1/+t4zf3XfXZ9asD1i9+guGDRusEnwiIuKRwMBA/v77BNHRzhon0dHR/P33SQICUh6NfK00MkHEy2Tk07VOnbrw8MOJS0FNnDiOw4cPMXbsOwQHu09kAKxcuRwgxUUb9XTt1tC9+wuEhq6nd+/+BAeX588/j7Bvn03UZufOHTz++EM89NAjTJ8+E4CfftrGP/9cSrXvtWu/5pdfdtOgwb0pJrb++eci8+bNpkaNmtSpcyehoeuTtTl16hTduj1Hz559KVWqFD17dqd27brcd9/9APz111GmTXuPL79cdzWnQLxU3rz5WLp0JYMG9aVr145ky5adhx56hBEj/k0m5cmTlyVLPuWNN15j6NBBhIWFERRUmjFjJtC+fcf4dldbgu9KpFSCr0OHZ9mz5xeV4BMRkStSsOBtlCtXnq+//ooHH3yEr7/+iuBgk2FTHAB8rtMv90HAb6dOhRMT4903E4UL571hFsKJu2GTm0tY2HkaNqxHkSJFCQnpTXh4GCNGDKVcufIsWeJcm+HhYTRsWI9cuXLRq1c//P0L8fXXXzF16nuMGvU2nTo5T9gWLZpPSMiLbN26PcUkw8svP8/evXtYs+bfm7K33hpJeHg4devWI2/evGzevIn33pvI/fc/wMyZc5P18d13G3nhhWf57rsfCQoqwsmTYUydOplp097nzTfHEBq6geXLl/LDDzv1S/ENbO3aNTz99FOsW/ddqqVLH364MaVKBTFlyowr6j8mJoYsWZzRNxUqBNGpUxf69h2YrJ0zSseHDz+cyoABfThx4nyi/atXf8GIEUMJDd0KQJ8+PfDz82PYsNcpXDgvTzzxJEWKFGXYsNevKD6RjFS4cF79v34L0u+dItcms//tPHbsEEWKlIr/OqMqtFy6HEXY+X/SbHfo0O+8/vpQwsLCyJs3L6+++holSwYla5c0boAsWXzw988DUBr43ZO4NDJBxMtk9NM1TwQHl+e99yYxb95sLl36h9tvL063bt3d1r/W07Vbx/z5c7nnnoapJhKs3cuPP/7A8OHJpxakJS6RkJaUptrEiYiIJGfOXPFf58qVi8jICAC2bNlCaOh6tmxJuZqJiIiIyNUIO/8PmZl2K1UqiOnTP7pun6dkgogXKlOmLAsWpLx+QVybDz+cnWqb1q3b0rp121TbTJqUvPZs8+Ytad68ZdqB4twArl27Mdn2HDlyMGHCZCZMmOxRP+L9tm37gQcffJj+/XuxePFCoqOjuO++JowaNZYiRYrGtwE4d+4sjRrdjbV7KF68BCEhvWnbtv11ibNq1TvYs2c3GzduoESJkqxa9Sn9+g0iNjaWV155hQEDXiVvXvdrLcjNJ6OeEqW3iMjozA5BRETkinj//64iAugXYsl8J04cZ+HC+VSuXIVp02YQHh7O8OFD6NChDV98sRYfHx9OnDgOwEsvdeXFF0OoUaMmK1cup0ePlwgMDKRJkwczPM5SpYIICelNixbO+iBNmjxAixatWLRoPpGRkfGr78utIWcO3xtmGLmIiMiNxPvvTEQE0C/EkvliY2OJjY1l9uwF8aVLAwOL0KzZw4SGrqdhw0bExMQA0LbtM7z8cggA99zTkP379/HOO+OuSzIBoHfv/nTs+BwXL16gRImShIeHM3LkcBYtWkhUVBQDB/Zl1aoVBAQEMnr0+PjV/kVERETEMyoNKSIiHsmfvwAVK1aKTyQA3HnnXWTPnp19+/YCUKCAs2Jw/foNEr33nnsaxre5Xvz9/eMXHp04cRx169ajYcOGfPTRh+zevZPNm7cREtKbLl06cPny5esam4iIiLdKqwx0REQEzz77DLVr30HJkgFUrFia1q1bpFkCGpwHE+PHj6FGjUqUKFGY++9vwNq1XydqEx4exrPPPkPZssV5+OHGHDy4P9H+s2fPUKlSGY8+TzKWkgkiIuKR8uXdlw51qitkSbONpwssprfDhw8xc+YHDBkyHICNG0Np2fJJChQoSPPmLYmIuMzBgwcyJTYRERFv0737C0yfPoVu3bqzePFyBg8elmhh4+joaHx8fHjllZ7Mm/cxb789iX/++YcWLR7j999/S7XviRPH8fbbo+nY8Tk++mgBxlSgXbun+OmnH+PbjB8/loMHDzB9+ixKlizFyy+/kKiPMWPepGnTh6hWrUb6HrhcMU1zEBERjzRt+hBjxozk1KlT+Ps7oxM2b95EZGQklStXBaBOnTspUKAAoaHrady4Sfx7Q0PXU6lS1UyJe9iwwXTu/BwlS/5bAuniRae8UnR0NJcvR1xxxRMREZGb0dq1a1i+fGmqZaBz5crF9OmzEm1r2LARFSoE8cUXq3jhhZfcvi8iIoJ33hnHSy+F0L17DwAaN27Cvn2WsWNHMW/exwBs2PAtPXr0pnHjJlSpcgdVqpTjwoUL+Pn5sW+f5eOPFxIa+n36HXQ6KlggB77Zsqd7v1GREZw5632jKJVMEBERj7Rv34EPPpjC008/SUhIb8LDwxgxYigNG94Xv+ZA9uzZ6dWrH8OHDyF//vxUr16Tzz77lM2bN7FixRfxfS1aNJ+QkBfZunV7/FSEP/44zM8/OyUbIyIi2bfPsnLlcnLnzs399z8Q/95vvlnNxYsX2bVrJwArVy4HoHr1mvF9xfnuu438+OP3iaqW3H13faZNex9jDKGhG8iTJw/lygVnwBkTERG5sXhSBtodPz8/cuTIGV+K2Z3ff/+N8PAw7r33vkTb7733PqZOnUxERATZs2cnIiKCnDlzApA7tzMiwunXj1df7c9LL/UgMDDwyg7sOvHNlp0fVycvpX6taj0wBkg7mfDuuxNYv34tf/11lNmzF1KmTLl0jyUhJRNERMQjefPmY+nSlQwa1JeuXTuSLVt2HnroEUaMeDNRu65dXyQmJpYPP5zKmDFvUq5cMB9+OId69e6ObxMbG0t0dHSiEQGbNoXSvfu/Qxk//fQTPv30E0qUKMmPP+6K3963b0/++ONw/NedOzslJydOfD9RKdSYmBgGD+7PoEFD8fPzi9/eocOz7NnzC926dSEwMJCpU2eQI0eOdDhDIiIiNzZPykDHifu//NSpU7z//iSyZs2Samnxy5cvAZAtW7ZE2+MSCIcO/U5wcHmqVavO3LkfUbt2XWbN+pBSpYIoUKAga9Z8ya+/HmT27IXpf+A3iQYNGtGqVWtefPG56/J5SiaIiIjHypQpy4IFS9Ns98ILL6U4zBGgdeu2iW78U9rmTsLEQmqyZMnC2rUbk23PkSMHEyZMZsKEyR71IyIicqvwpAx0nEmTxvP668MAKFSoEPPnL0k2QjChUqWC8PHx4eeft1G7dt347XHrJZw5cwZwKjK1bPk4FSqUxs8vDzNmzCEyMpKhQwcxbNgbegCQimrVql/Xz1MyQURErlnefLnImcP7/0uJiIzO7BBERES8lidloOM89VRbGjZsxPHjx5k5czpPP/0kK1Z8meIUiXz58tO8eUvGjx9LhQqVqFy5CkuXLmbDhm8ByJrVWai5ZMlSfPfdjxw69BtFi95O7ty5ef/9dylatBj/+c9jbNnyHf369eLEiWO0atWKwYNfJ3v29F+nQNLm/b/5iYiI18uZw5fHeq3I7DDStPLtZpkdgoiIiNfKn78ApUoFpVgGOmEyITAwMH7tgvvvb0qDBnWZOHEckydPS7H/118fTZcuHWjR4lEAbr+9OD169GHMmDcpXDggvp2vry9lyzrrGZ06dYp33hnLsmWruHz5Ms8++wxvvDGaRo0a07ZtS+bMmUnnzl3T8zSIh1QaUkRERERERDwqA+2Or68vFStW5tCh31Ptv1ChQixb9hk//7yHDRv+x/ff7yB3bj8CAgITVV1KaOTI4Tz2WHMqVarM/v37iIqKpFmzFuTPX4B27dqxcWOox8cn6UvJBBEREREREaFp04fYs2c3p06dit+WtAy0O5cuXWLHju0pJgSSKlbsdipUqEhUVBQLFsyhTZt2btvt3r2LVatW0L//4PhtERGRREc70xYvXLig8s6ZSNMcRERERERExKMy0MuWfcw336yhceMmFClSlOPHjzFz5gecOHEs0eLL7spAL168gKioKEqVCuLIkT+YOvU9smbNSvfuPd3GM3hwP3r06IO/vzPtoly5YHLlysVrr71KgwYNmTx5Ms8/n/KCz9dbVGSEq4xj+vfriQkTxrB+/TpOnz5FSMiL5MuXn7lzF6d7PHGUTBARERERERGPykCXKxfMkiWLGDJkIOfOnSUwsAg1a9ZmzJgJVKhQMb6duzLQMTExTJo0niNH/iBv3nw8/PCjDBo0hDx58iSL5bPPPuX48WN06tQlflvOnDmZOnUG/fr1ZP78ObRq1ZJnnumcQWfjyp05exm4nGmfHxLSh5CQPtft8zxKJhhjygP3A3WBIkBO4DSwD9gIfG2tzbyzJiIiIiIiItcsrTLQd9xRnfnzl6TZz7WUgQZ49NHHefTRx5Ntv+eehmza9AMAhQvn5eTJMI/6k/SXajLBGNMGeBm4EzgP7AT+Bs4BBYDmQF/gvDFmATDKWns4QyMWERERERERkUyVYjLBGLMbyA7MBZ6z1u5KoV1e4CGgFfCLMaaLtXZ+RgQrIiIiIiIiGStvvlzkzOH9M+IjIqMzOwRXpQufzA7DY+m5YGVqV8gIYLG1Nia1Dqy1YcDHwMfGmJJA8XSLTkRERERERK6rnDl8eazXiswOI00r326WqZ+fJUtWoqOj8PXNlqlxXIno6CiyZMmaLn2lmEyw1i680s5cUxw0zUFERERERERuarly5SEs7CwFCvjj45Mls8NJU2xsDGFhZ8iVK/mCl1fjqsauGGNuA+oAPsD31tpTabxFRERERERE5KaRJ09+zpw5yfHjR4D0mz6QcXzInj0nefLkT5ferjiZYIx5DJgD/Ab4AUWMMU9baz9Nl4hEREREREREvJyPjw+33RaQ2WFkmqsZizEWeNxaW8NaWx4YBYxP37BERERERERExFulmEwwxmw1xtRzsysfsDfB19a1TURERERERERuAalNc5gCLDXGfAv0sdYedW2fBWwwxiwDcgNtgRkZGaSIiIiIiIiIeI8URyZYa2cABjgC7DTGvGqMyWmtHQAMBPK43v+8tbbfdYlWRERERERERDJdqgswWmvDgX7GmGnAGGCvMaavtXYxsOx6BCgiIiIiIiIi3sWjBRittQettS2ATsAgY0yoMaZmxoYmIiIiIiIiIt4oxZEJxhgf4BmgKZAd+B54D6gBdAU+N8asAgZYa09ch1hFRERERERExAukNjJhAvA6sBvYBDwFfGmtjbHWvo+znkIY8Isxpm+GRyoiIiIiIiIiXiG1ZMLTQFdr7Uhr7QTgUeAuY0xpAGvtOWttCHAPcG/GhyoiIiIiIiIi3iC1BRiPAQ2AVa6vGwLRwKmEjay1e4H/ZEh0IiIiIiIiIuJ1UksmPA8sMMY8B1zGKQXZzVp7/rpEJiIiIiIiIiJeKcVkgrU21BhTBqiIswDjXmtt2HWLTERERERERES8UmojE7DWRgDbr1MsIiIiIiIiInIDSHEBRmPMAGNMvivpzBjT0Bij9RNEREREREREbmKpjUxoAPQzxnwCLAE2W2tPJ2xgjMkKVAYexikdeTvwTAbFKiIiIiIiIiJeILU1Ex4xxtQHXsZJJmQ3xhwD/sZZkLEAUBzIAVhgBjDFWhue4VGLiIiIiIiISKZJa82ETcAm13SHBkBNoAiQEziNk0TYZK3dk9GBioiIiIiIiIh3SDWZEMdVDnKV64+IiIiIiIiI3MJSXIBRRERERERERMQdJRNERERERERE5IoomSAiIiIiIiIiV0TJBBERERERERG5IkomiIiIiIiIiMgV8aiagzFmCzADWOiq7CAiIiIiIiIityhPRyYcBMYDfxlj5hljGmdgTCIiIiIiIiLixTxKJlhr2wJFgB5AEPC1MeZ3Y8xrxpjSGRifiIiIiIiIiHgZj6Y5AFhrw4BpwDRjTHmgA9AJGGSM2YAzDeJja+3ljAhURERERERERLzD1S7A+I/rTwQQC+QCpgO/agqEiIiIiIiIyM3N45EJxpicwBM4IxLuA34DPgRmWWuPGmMKA+/ijF4ol/6hioiIiIiIiIg38LSaw1TgKSA7sAxoaq1dl7CNtfakMWYcsDndoxQRERERERERr+HpyITawEBgnrX2XCrtdgNNrzkqEREREREREfFaHiUTrLW1PGwXDnxzTRGJiIiIiIiIiFfzaAFGY0wrY0zPFPb1MMY8kb5hiYiIiIiIiIi38rSawyAgKoV9Ea79IiIiIiIiInIL8DSZEAzsTGHfL0D59AlHRERERERERLydp8mEf4DbU9hXHGd0goiIiIiIiIjcAjxNJnwNDDbG+Cfc6Pp6ELAmvQMTEREREREREe/kaWnI/sAW4FdjzOfAX0BR4BEgDOibMeGJiIiIiIiIiLfxaGSCtfZ3oBowDWf9hOau1ylATWvtoYwKUERERERERES8i6cjE7DWHgf6ZGAsIiIiIiIiInID8HTNBBERERERERERwMORCcYYX+AloAVO9YacSdtYa4ulb2giIiIiIiIi4o08neYwDugGfAFsRqUgRURERERERG5ZniYTngQGWGvHZGQwIiIiIiIiIuL9PF0zISvwc0YGIiIiIiIiIiI3Bk+TCR/gjE4QERERERERkVucp9McjgD/Z4xZA6wBzibZH2utnZ6ukYmIiIiIiIiIV/I0mTDJ9VoSuN/N/lhAyQQRERERERGRW4CnyYRsGRqFiIiIiIiIiNwwPEomWGujMzoQEREREREREbkxeDoyAWNMIaAHUBsoAbS01v5ijHkJ2Gqt3ZpBMYqIiIiIiIiIF/GomoMxpjawH2gDHAMMkNO1uyTQO0OiExERERERERGv42lpyPFAKFAe6Az4JNi3BaiXznGJiIiIiIiIiJfyNJlQG3jXWhuJU7khob+BgHSNSkRERERERES8lqfJhPNAoRT2lQFOpE84IiIiIiIiIuLtPE0mfAoMM8aUSrAt1hhzG9AL+CTdIxMRERERERERr+RpMqEf8A+wB1jr2vYuYIEo4NX0D01EREREREREvJFHyQRr7WmgLk5pyOPAt8BfwBDgLmvt+YwKUERERERERES8i6+nDa21l4Gprj8iIiIiIiIicovyKJlgjMmeVhtrbcS1hyMiIiIiIiIi3s7TkQmXSF4SMqms1xiLiIiIiIiIiNwAPE0mdCF5MuE24AGgPDAyPYMSEREREREREe/lUTLBWvtBCrvGGGOmAsHpF5KIiIiIiIiIeDNPS0OmZgnQIR36EREREREREZEbQHokE2oCkenQj4iIiIiIiIjcADyt5uBuTYTsQEWcdRMmpWdQIiIiIiIiIuK9PF2AsZ2bbZeAI0Av4P10i0hEREREREREvJqnCzCWyOhAREREREREROTGkB5rJoiIiIiIiIjILcTTNRMGXkGfsdbaN68yHhERERERERHxcp6umdAHZ8HFXK6vLwE5XX//B4hI0DYWUDJBRERERERE5Cbl6TSHB4FjQAcgn7U2N5AP6Oja/pC1tqDrz20ZEqmIiIiIiIiIeAVPRya8C4y21s6O22CtDQc+MsbkAiYDtTMgPhERERERERHxMp6OTKiKUwbSncNA5fQJR0RERERERES8nafJhH3AK8aYbAk3GmOyAz1c+0VERERERETkFuDpNIcQ4DPgiDHmK+AEEICzlkJe4D8ZE56IiIiIiIiIeBuPRiZYa9cBBpgHlAaauV7nAca1X0RERERERERuAZ5Oc8Bae8Ra29Na28BaG+x67Wmt/SMjAxQRERERERGJc3Tfd2xa0A9/f39KlCjMXXfVZNy4t4iIiEjU7pdfdtO2bSvKli1O6dLFePDBRmzf/lOqfX/77Vq6du1IrVpVCAjIx1tvjUzWJjw8jGeffYayZYvz8MONOXhwf6L9Z8+eoVKlMml+1o3O02kOABhjygO1gBLAR9ba48aY0sBJV3UHERERERERkQwTeSkM/xJVeat/GyA727b9+P/t3XmcjeX/x/HXjHXsMpbS2LlEJMmXLKmUEhVRvlSWkl9axFeWsmbJWikluySRlCVSUcmSaJJKXLJX1uwMZv39cZ05zqzOYWYM3s/Hw8Oc677OdUm4XGcAACAASURBVH/OuN3nvj/3tTBq1GscOLCfYcNGA/Dbb7/ywAP3cu+9jZk4cSoA69f/zOnTZ1Jt+5tvlvLHHxupV+925s2bm2ydN94YxbZtW5k4cRqzZ3/I888/w+LFS73bR458jbvvvpebbro5bT5wJuVXMsEYkxuYCDwKxOJ6NCwF9gMjgJ3AS+kTooiIiIiIiIhTsmojAJo1e5CDB09Qt259Tp48wZQpE3nttVEEBQXRo8eLNGp0L+PGTfK+78477z5v2wMGDObVV11vhCVLFiVb5/vvv6Nr1+7ceWdDbryxKjfeWI5Tp06RO3dutmyxzJkzixUr1qXBJ83c/B3mMBpowLkJF4N8ti0G7kvbsERERERERET8U7DgNURFuWEO1m4mPPwnnnyyU8DtBAef/xY5MjKSnDlzApArVwiAd999+/biuee6UrRo0YD3fbnxN5nQAuhprV0KRCXathMomZZBiYiIiIiIiKQmJiaGiIgI1qz5gUmT3qNt2ycJCgri559/AuDYsaM0aHAb115bkFtvrcqHH05Pk/3edFM1Zsx4n8OHDzFhwjhKlixFgQIF+frrJWzfvo1OnTqnyX4yO3/nTAjBLQeZnDxATNqEIyIiIiIiInJ+uXPn5uzZswA88sh/GTBgMAAHDuwH4LnnOvHssy9y883VWbhwHl27PkfRokVp2LDRRe23e/detGjxABUrliZ37jxMmfIBUVFR9O//CgMGDCFHjhwX98EuE/72TPgJeDyFbQ8DP6RNOCIiIiIiIiLnt3r1ahYs+JKBA4eyZMlievX6HwCxsbEAtGnTlueff5G6deszfPjr1K1bnzFjXr/o/ZYoUZLVq8P54YdwNm7cyh133MWkSeO59trruP/+pqxZs5rbb6/NDTeU5qWXuiZZZeJK4W8yoR/Q0hizBGgHxAGNjDFTgVbAgHSJTkREJEDxy0V9+e7jLB7Tkm+ndubPNR8TG+NG6UVGRvLUU22pUaMqJUoU4YYbStOqVXO/lm+KjIxk1Khh1Kx5EyVKFKFmzZsYPnyI96lIvM2bN9Gy5YOULFmUihVL8dJLXTl5MuGiR4sXf06NGlWpVKkMI0e+lmRfo0YN44knWl3Eb0JEROTKVr16dWrVqs0zzzzHkCHDmTZtMjt2bKdAgYIA1KlTL0H9unXrs2XL5jTZd9asWSlbtjy5cuXi0KFDjBkzikGDhnH27Fmeeqot3bq9xJo16/ntt1/44IOpabLPzMavYQ7W2uXGmLuB4cAE3ASMQ4B1QCNr7Y/pF6KIiIj/4peLKlOjGdly5Obovj/Z8sMszpw6CrQgJiaGoKAgunTpRqlSpTlx4gTjx79D8+ZNWbZsBaVKlU6x7UGD+jN9+hR69epLlSpV+fXXDQwbNojjx48xZMgIAI4fP0bz5k0oW7YcEyZM48iRw7z6al/279/H9OkfAXDo0CE6d+5It249KFmyJN26vUCNGjW54467ANi7dw8TJrzLkiXfpvvvS0RE5EpQtWo1AHbv3kWFCibZOnFxcX5NsBiooUNfpWnTZlSqVJnff/+N6OgoHnywOQAtW7Zi5coVFzQZZGbn75wJWGu/B2p7loksBByx1p5It8hEREQuQPxyUfFCS1QhOjKCnRsWExcXR0hICBMnTktQp379BlSsWIovvljEM888l2Lbn346h7Ztn/TWqVu3Pnv37mHu3I+9yYSpUydx5swZZsyYTf78BQAoUKAgTzzRil9++Zlq1aoTHr6WsLAwXnihKwArV65g+fJvvcmEgQP70rr1E5QpUzZNficiIiJXurVr1wBuCELx4tdToEABVqxYzp13NvTWWbFiOZUqVUnT/W7c+DuLFs1n1apwb1lkZBQxMTFkyZKFU6ciiIuLS9N9ZhbnTSYYY3ICh4DW1tr51tpTwKl0j0xERCSNZA/JS2xMdIrbc+fOTY4cOb3LOqUkOjqKfPnyJSjLnz8/bvSf8/vvv1Gt2s3eRAJAgwZ3EhQUxNdff0m1atWJjIwiZ84Q7/aQkBDvvn/6aS0rVixnzZqfA/mIIiIiV40f5w4ktORNfPFFdk6cOMvatWsYN24sDz3UnNKlywDwv//15NVX+5E/f36qVavO558v4IcfVjF//hfedmbPnsmLLz7L2rUbCAsrAcBff+3ml1/cd3BkZBRbtlgWLpxHrly5uOuue5LE0qdPT7p2fYlChQoBUK5ceUJCQhg4sC/16tVn6tSJdO78fHr/Si6J8yYTrLVnjDFHgJSvwkRERDKZuNgYYmOiOXZgGzvWf06pm+4lKCjo3Pa4OGJiYjh06BDjxr1NlizBNGvWItU227Rpy/TpU6lXrwGVK9/I77//yrRpk+nQ4WlvnbNnz5AtW7YE78uaNSvBwcH8+acFoEqVqmzatJGVK78nLKwEixYtoGfPV4iLi6NPn5707t2XvHkTJi1ERETEKVCsHH9v/IaWLeeQJUtWSpYsRZ8+/Wnb9klvnU6dniU2No7Jk8czcuRrlCtXnsmTP6BWrdu8deKvBXx7DqxatYIXXnjG+3rBgs9YsOAzwsJKEB7+e4I4Pv98Afv370twHZAzZ07Gj59Cz57dmDnzA5o2fTBBXFcSf4c5TACeN8YssdZqGUgREcn0vni7lXfSxesr3cEN9dsl2P72228wePAAAEJDQ5k58xPvU4mU9O07kDNnTtO06bknE+3bP0X37r28r0uVKsOnn84hKirKm1TYsGE9MTExHDlyBICSJUvx4ovdad68CQANG95D8+YtmT17JlFR0bRundICSiIiImLqtMHUacPC0Q9y8GDKI++feea5VIcvtmrVhlat2py3LCVNmjxAkyYPJCmvW7c+q1b95FcblzN/kwm5gKrADmPMUmA/vn06Ic5a+0paByciInKh6rQaRkz0WTcB45rZZPkmB9DMu/3RR9tQv34D9u/fz9SpE3nssUeYP38JxlRMsc2xY8fwySezee21kVSqdCMbN/7GsGFDKFjwGnr16gPA44+3Y+LEcfTu/RIvvdSbI0cO07Pn/8iSJQtZsmTxttW9ey/at+9IRMQpwsJKcPLkSYYOfZXx46cQHR3Nyy/3YNGi+RQpUpThw9+gVq3a6fa7EhERuRzFxkRRuHDeSx3GeUVHRXLk6NnzV7zM+JtMaAPE4FZxuDuZ7XGAkgkiIpJp5C/qJi+8pnglsofk45clY9i2bRv58hUBoGjRohQtWhSAu+66m3r1avLWW6/zzjsTkm3v0KFDDBs2iGHDRvP44+0AqF27DtmyZad37+48+WQnChcuTPnyFRg9+i369u3N9OlTCA4O5vHH2xMUFEThwkUStFmoUCHvGMu33nqdmjVrUbt2HSZOHMfGjb/xww8/8+23y3j66XasW/crOXLkSI9flYiIyGUpOEs2wr966VKHcV633DMSuEqTCdbasPQOREQuL3u2rGZH+HxOHtlDTNQZQvIV5vobGhAZeZ+3zpQpE1m69EvCw9dx5MgRPvtsUZL1fpPz/PP/x+zZM5OUr1r1E+XLVwDcsj81aiSdjfehh5ozYcI07+vFiz+nX7+XiYg4Sfv2HXnppd4J6o8aNYxff/2F6dNn+fvR5TKUr4ibjGnHjh3cdFORJNuzZs3KDTdUZteunSm2sWvXDqKiorjxxoTHXZUqVYmOjubvv3dTuHBhAFq3fpzmzVuyffs2QkMLU6hQIYwpRZs2TyTb9u7du5g6dRLLlq0A3OoOLVo8QoECBWnWrAW9e3dn27atVKpU+UI+voiIiEia83tpSBERX1FnTlAorAplajQjW47criv5D7Po1q0g/fu/BsCcOR8BQdxxx118+uknAbVfvnwFxox5N0FZcuPZBwwYQs2a//G+vuaaQt6fDx06ROfOHenWrQclS5akW7cXqFGjpnf5vb179zBhwrssWfJtQLHJ5efIns0AlC5dOtntZ86c4ddfNyQ4lhK7/np3/P366wZuvvkWb/mGDb8AEBZWMkH9nDlzem/+Z836kNjYWB58sBnJGTCgD08+2ZESJc61ERFxGoCYmBjOno28YpeVEhERkctTiskEY8wWoIW19lefsg7APGvt4YwITkQyr5JVGyV4HVqiCtGREcyYMYN+/YYSFBTEokVLCQ4OZtOmPwJOJuTKlZsaNWqet165cuVSrBcevpawsDBeeKEr4J72Ll/+rTeZMHBgX1q3foIyZcoGFJtkbvHLReUtFEZQUDCH92xme/h8rjN1KVu2LOPHT2HZsq+5886GFCt2Lfv372Pq1EkcOLAvwSRNiZeLKlKkCPfd14RBg/pz9uwZKlVyqzmMHDmMBx5oRmhoKAAnThznjTdGUbv2bWTJkpVVq1YwbtzbvP762xQseE2SeFevXkl4+Drefvs9b9ltt9VhwoRxGGNYseJ78uTJQ7ly5dP/lyfpLj17dQF88cUihg0bxPbt2yhZshTdu/fioYceTlLv888X8NZbo9m8eRMhISFUq1adKVNmkDt3bkC9ukRE5PxS65lQDsgZ/8IYkwWYCPwCKJkgIklkD8lLZGSk93VwcPAljMatDZwzZ4j3dUhICFFRLr6fflrLihXLWbPm50sVnqST+OWiIo4fICg4mFz5i1Gx7mOUrHov4NZ//uST2fTr9zLHjh2laNFiVK9eg5Ej36RixRu87SS3XNTYse8xatRwJk58j/3791Gs2LU88UR7/ve/Ht46wcFZ+O23DcyYMY0zZ85QseINTJo0ncaNmySJNTY2lj59evHKK/29N3EA7do9xaZNf9C589MULVqU8eOnaL6EK0R69upas+YHOnR4jPbtn2LIkBEsXfoVnTp1IH/+At4kKsCMGe/Tu3d3nn22C/37D+bo0aOsXLmcmBi3Crh6dYmIiD8CHeYQdP4qInI1iYuNITYmmmMHtrFj/ec888wzBAVd/Kliy5bNlClTnMjIs1SrVp2XX+7HbbfVTVKvS5fOHDlyhNDQwjRr1oKXX+5HSIhLIFSpUpVNmzaycuX3hIWVYNGiBfTs+QpxcXH06dOT3r37kjdvvouOVTKX+OWiUlK1ajVmzjz/DVpyS0PlzZuPgQOHMHDgkBTflzt3bubMme9XrMHBwXzzzcok5Tly5ODNN9/hzTff8asduXykZ6+u118fTu3adRg6dCTgliazdhOjRw/3JgIOHTpE3769GTp0pHciUYD772/q/Vm9ukRExB+aM0FELsoXb7ciNiYKgOsr3cHIkSM5dOjURbVZpUpVqlevgTEVOXToX959921atnyQhQu/pHr1GoC72erQoSMNGtxF3rx5WbVqBWPHvsmuXTu83W5LlizFiy92p3lz90S4YcN7aN68JbNnzyQqKprWrR+/qDhFRNJCWvTqOnv2LKtWrWDIkBEJyh966GG6dOnM8ePHyJcvP/PnfwrAo4+2TrEt9eoSERF/nC+ZkNMYkytRXd8yL2ttRJpGJiKXhTqthhETfdZ11V0zm+eee46BA4dfVJtPP905weuGDRtRr15N3nxzNNOnfwRA0aLFGDZs9Lk46tSjcOEi9OzZjd9++5UqVaoC0L17L9q370hExCnCwkpw8uRJhg59lfHjpxAdHc3LL/dg0aL5FClSlOHD36BWrdoXFbtkblqPWjKLtO7VtXOnW20kfsWbeBUqGGJjY9m2bSs333wLP//8E+XKlefDD6fz5pujOHjwAFWr3sSrrw7zTkCqXl0iIuKP8yUTkhsMtyKFulkuMhYRuQzlL+q6uV5TvBLZQ/IxbtwY2rXrROnSZdJsHyEhIdx119189dWSVOs1bfqQJ5mwwZtMAChUqBCFCrlVHt5663Vq1qxF7dp1mDhxHBs3/sYPP/zMt98u4+mn27Fu3a8am34F03rUklmkda+uo0ePApAvX/4E5fnzF0iw/cCB/Wzd+idvvDGSfv1e5ZprrmHs2DG0atWcNWvWU6RIEfXqEhERv6SWTGifYVGIyBUhXxGXQNi9e1eaJhPine+pXfz2lOrt3r2LqVMnsWyZy4muXLmCFi0eoUCBgjRr1oLevbuzbdtW73J+IiLpJT16dUFy57+4BOWxsXGcOnWSyZPf58477wbg1lv/Q/XqNzJlynh69eoLqFeXiIicX4rJBGvt+xkZiIhc/o7s2QxAiRIl07Td06dPs2zZUqpWrZZqvYUL5wGkWG/AgD48+WTHBPFFRJwGICYmhrNnIxPM3C8ikl7SuldXgQKuB8Lx48cSlB875l7nz+96LBQsWBCA2247t9Rk3rz5qFq1GtbaBO9Vr66ry54tq/jnj+84un8b0ZER5Cl4HWVqPETxivW9dc6ePcvgwQP49NM5nDx5gqpVqzFo0GtUq1b9vO3/+OMa+vfvzR9/bKRIkaJ06tSZjh2fSVBn9OjhrF69ivXrwzl58gQ//fRbkmuKtWt/pHv3F9i7dy/Nm7dgyJARZM167pZm9uyZjB//LkuXfn/JV5USudJpAkYRuSA/zh1IaMmbyFsojKCgYA7v2cz28Pk8+uij3ovhX375mb/+2s0///wNwOrVKzl8+BBhYSW8Fx6zZ8/kxRefZe3aDYSFleD48WO0afMILVq4dg4fPsT48e+wb98eJk2a5t3/iBFDOXnyJDVr1iJv3rz88MMq3n33Le6//wEqV74xSbyrV68kPHwdb7/9nrfsttvqMGHCOIwxrFjxPXny5KFcufLp+FsTEUkqLXp1lSpVmmzZsvHnn1sSrHzz559bCA4OpmzZcgCUL1+BoKCgZBKncSneeKlX19Vhe/gCcuUrQuUGHcgeko8DO8JZv/h1Ik8fBx4E4OWXezBv3lz69h1IWFgYEye+R4sWD/Ltt6sICyuRctvbt/Hoo824555GvPLKANavD6dfv5cJCcnFY4+19dabPn0qpUuXoW7deixZsjhJO1FRUXTq1J4mTR6kXr369OjRjcqVq/DEE65D9alTpxg69FXGjZukRIJIBlAyQUQuSIFi5fh74zdEHD9AUHAwufIXo2Ldx/jgg3c5evQMAJMnT2D27Jne94wc6dZQf/TR1t6b+ri4OGJiYrwXttmz56BQoVDeeGMk//57kBw5clKjxq3Mm7c4wZOP8uUr8O67b/Phh9M5c+Y0xYtfT+fOL9C1a9Lx8LGxsfTp04tXXulP7ty5veXt2j3Fpk1/0Lnz0xQtWpTx46foyZqIZLi06NWVI0cO6tSpx4IF82jbtoO3fP78T6lRo6Z3LoV77rmXUaOGsWrV9zRs6JapPH78GBs2/ELnzs8n27Z6dV0daj70CtlDzk2sGVqiKmdOHmZ7+AJgInv2/MPMmdMZPfot77wZdevezq23VuWdd8YkmBQ5sXfeeYtixYrx7ruTyJo1K/Xq3c7ff//FqFHDaNPmCe8wnPXr/yA4OJivvvoi2WTC1q1/cuzYMQYMGEyWLFnYvHkzy5d/600mjBkzmho1aia7lLSIpD0lE0Tkgpg6bTB12iQpz5YtG+CSCW+//V6CngDJadWqDa1anWsnZ86cTJv24Xn336xZC5o1a+FXrMHBwXzzzcok5Tly5ODNN9/hzTff8asdEZGLlV69ugC6detJs2aN6dOnJ/fd14SlS79i6dKvmDXrU+/+q1Wrzr333s+LLz5Hnz4DKFSoEGPHjiFbtqx06NAxSbzq1XX18E0kxMtfpAz7t60FYNOmjcTExFC/fgPv9hw5clCrVm2WLv0q1ba/+eZrmjdvmWA4QrNmLZg2bTKbNv3h7dVyvt4EUVGRZM+ejSxZ3LzvuXKdW7Y0cQ8aEUl/SiaIiIiIZJD06tUFUKtWbSZP/oBhwwYxbdpkSpQoyXvvTeaOO+5KEMO7705k4MC+9O//MqdPn+bWW2sxd+7nFChQMEE99eqSw3s2k6dQGABnzrgVZrJly56gTvbsOfjrr91ERESQK1eS1eM5deoU//zzd5JlS8uXNwBs3brF7yEyZcqUIzIyijlzZlG//h18/PFHNGrUGICBA/vSocNTaT5vk4ikTMkEEUlTsTFRFC6c91KHcV7RUZEcOaql90QkY6VXr654jRs3oXHjJqm+N0+ePIwc+QYjR76Raj316rq6/bt7A/u3reWmRs8BJOg506jRfYBLav3yy8/ExcVx/PixZJMJ8ZOCJl62NH7S0PhlS/2RJ08eBg8eRpcunYmOjubmm6vTseP/sXr1Stat+5G33hoX+AcVkQsWUDLBGHMfUAMIAwZba3cbY+oDW621e9IjQBG5vARnyUb4V0nnLchsbrlnJKBkgoiISGIRx/bz8+LXKVq2JmGVXc+WSpUqU7NmLQYMeIVixYpRvHgY7703lm3btgIQHJwl1TZTWrb5fMs+J9a69eM0afIA//77L6VLlyEuLo4+fXrRp88AcufOzWuvvcqHH35Arly56N9/MPff3zSg9kXEf34lE4wxRYEFwC3ATqA08B6wG2iPS6U/k9L7RURERCRl6tUlmUXk6ROs/WwQIXkLc3Pjrgm2vfXWOJ56qi133307ABUr3kDHjs8wefJ477KjicX3SDh2LGEPhPgeCfHLlgYiX7783nZnzHif7Nmz0bJlK5YsWcynn37C0qXfs3PnTlq3bsGtt/6HIkWKBLwPETk/f3smvA3kASrikgmRPtuWAv3TNiwRERGRq4d6dUlmEBN1lnXzBhMbE0XtRwaTNVvOBNvLlCnLN9+sZOfOHURHR1O2bDl69+5O1ao3eYbqJJU7d26KF7+erVv/TFC+desWAMqVq5Dc2/xy4sRxhg0bzPvvzyQoKIhVq76nceOmFCt2LcWKXUvZsuVYvz7cOyxDRNKWvwuw3gv0sdZuBRKv/fM3UDxNoxIRERERkQwTGxtD+OcjOHV0LzWb9yNHrgIp1i1VqjTlypXn8OHDLFjwGa1bP5Fq23feeTeLFy8kJibGWzZv3lyKF7+eG26odMExjxo1nNtvv4MaNWp6y06fjvD+HBFxSsuWiqSjQOZMiEmhPBQ4nQaxiIiIiIjIJfD7svc4sCOcyg2eIur0SY6ctt5tZ8+63igTJ46jYMFruPba69i+fRtjxrzODTdUpk2bc8mE1atX8vDDTZk7dyG33VYXgGeffYG5cz/m2Wc78thj7Vi//memT5/KiBFvJJgzYfXqlRw69C8bNvwCwLJlXxMaGkqFChUxpmKCeLdv38pHH33A8uVrvGW1a9elS5fO1K1bn127dvH3339RvXqNtP9liQjgfzJhBfC8MWaRT1l8mq8D8E2aRiUiIiIiIhnm4C53A7/xu0lJtu197RFy5y7E6dNnGD9+MPv27SU0tDDNm7eke/deBAef6+yc3LKlZcqUZdasT+nfvzf//e/DFClSlIEDh/DYY20T7GfEiKGsXn1uBZGePbsB0L17L3r0eDlB3f79X6FTp2e59trrvGWNGzchPHwdPXp0JXfuPIwdO17zJYikI3+TCT2BlcDvwGe4REJHY8yNwI1ArfQJT0RERERE0ttdT01McVupUqU4ePAEL7zQlRde6JpiPYA6depx4MDxJOW1atXmyy+/S/W98+Yt9itWgA8+mJ1sed++A+nbd6Df7YjIhfNrzgRr7e+4JSF/Atrhhjw0B/4C/mOt3ZJeAYqIiIiIiIhI5uL3nAmeyRcfT8dYREREREQkk7kcli7VsqUiGc+vZIIxZiAwy1q7KZ3jER97tqzinz++4+j+bURHRpCn4HWUqfEQxSvWT7b++PHv0Ldvb5o0eZApUz5Ite0RI4YyatSwJOWzZs3lzjvv9vz8IS+88Eyy73/88faMHj0GgLVrf6R79xfYu3cvzZu3YMiQEWTNeu7Qmj17JuPHv8vSpd8nGFMnIiIiIpnf5bB0qZYtFcl4/vZM6AT0Mcb8AXwEzLbWbku/sARge/gCcuUrQuUGHcgeko8DO8JZv/h1Ik8fBx5MUPfgwYOMHj2c0NBQv9vPly8/s2bNTVBWoYLx/tywYSMWL16aYPvPP/9Enz69uOsul3CIioqiU6f2NGnyIPXq1adHj25UrlyFJ55oD8CpU6cYOvRVxo2bpESCiIiIiIjIFcLfZMJ1QAPgUeBFYJAxZj0usTDHWrs7fcK7utV86BWyh+Tzvg4tUZUzJw+zPXwBkHCSnCFDBtCwYSP27PnH7/azZs2SYF3exEJDQ5MkJ+bMmUW+fPm9yYStW//k2LFjDBgwmCxZsrB582aWL//Wm0wYM2Y0NWrU9C4NJCIiIiIiIpc/fydgjLXWfmOt7QRcCzQGfgVeAXYYY1am2oBcEN9EQrz8RcoQGXEsQdn69eHMn/9Zus9cGxMTw8KF87n//qbkyJEDgKioSLJnz0aWLFkAyJUrhKioSAB2797F1KmT6N9/ULrGJSIiIiIiIhkr4H7n1toYa+2XwDPAs8A+oHZaBybJO7xnM3kKhXlfx8XF0bt3d557rkuCdXb9cezYMSpWLMV1113DnXfW5fPPF6Ra//vvv+Pffw/SrFkLb1mZMuWIjIxizpxZ7N+/n48//oibbroZgIED+9Khw1OUKFEyoLhEREREREQkc/N7NQcAY0w24F7ccIemQAiwHOiX9qFJYv/u3sD+bWu5qdFz3rKPPprBgQMH6Nz5hYDaKl26DP36DeLGG6tw6tQppk+fQocOjzFlygyaNHkg2ffMmzeX0NDC1Kt3u7csT548DB48jC5dOhMdHc3NN1enY8f/Y/Xqlaxb9yNvvTXuwj6siIiIiIiIZFr+ruYQn0B4CMgHrAR64+ZLOJh+4Um8iGP7+Xnx6xQtW5OwyncBcPz4MYYMGcjQoSMICQkJqL2WLVsleN2o0X00btyQ0aOHJ5tMiIyMZPHiz3n44ZbeIQ3xWrd+nCZNHuDff/+ldOkyxMXF0adPL/r0GUDu3Ll57bVX+fDDD8iVKxf9+w/m/vubBvjpRUREREREJDPxt2fCYmAtMBD42Fq7J/1CksQiT59g7WeDCMlbmJsbd/WWv/nmaIoXL06DBndy7NhRSKAXdgAAHQ9JREFUAKKjo4mOjuLYsaPkyZM3yY1/SoKCgrj//gcYNKgfMTExSd63bNnXHDt2lGbNWib7/nz58pMvX34AZsx4n+zZs9GyZSuWLFnMp59+wtKl37Nz505at27Brbf+hyJFilzIr0JEREREREQyAX+TCWWstTvTMxBJXkzUWdbNG0xsTBS1HxlM1mw5vdu2bv2TX35ZT/nyJZK8r3z5EixY8CW1agU2nUVQUFCy5fPmfcL114dRs+Z/Un3/iRPHGTZsMO+/P5OgoCBWrfqexo2bUqzYtRQrdi1ly5Zj/fpwGjW6L6C4REREREREJPPwK5mgRMKlERsbQ/jnIzh1dC+3tXqNHLkKJNjeu3dfOnXqnKCsT59e5MuXjx49XqZSpUp+7ysuLo7FixdSuXKVJL0SIiIi+PLLJXTo0DHFZEO8UaOGc/vtdyRYcvL06Qiftk4RFxfnd1wiIiIiIiKS+aSYTDDGrAXaWWv/MMasA1K9A7TW1kxtuwTu92XvcWBHOJUbPEXU6ZMcOW29286ePcsNNyRNFuTPn59rrilEnTr1vGWrV6/k4YebMnfuQm67rS4ADz54H02aPEC5chWIiIhgxoxphIev4/33P0rS5pdfLiYi4lSCVRySs337Vj766AOWL1/jLatduy5dunSmbt367Nq1i7///ovq1WsE/LsQERERERGRzCO1ngkbgdM+P+txcgY7uOsXADZ+NynJtr2vPULu3IX8aicuLo6YmJgEPQJKly7D+PHjOHBgH8HBwVSpchMzZ87hrrvuSfL+zz6bS7ly5alSpWqq++nf/xU6dXo2wRKVjRs3ITx8HT16dCV37jyMHTte8yWIiIiIiIhc5lJMJlhr2/v83C5DopEE7npqYorbSpUqxcGDJ5KUz5u3OElZnTr1OHDgeIKyN998x+84pk9P2lshOR98MDvZ8r59B9K370C/9yciIiIiIiKZW7A/lYwxU4wxpVPYVtIYMyVtwxIRERERERGRzMrf1RzaAe8BO5LZFgq0BTqkUUzih9iYKAoXznupwziv6KhIjhw9e6nDEBERERERkTTkbzIBUp4z4UbgYBrEIgEIzpKN8K9eutRhnNct94wElEwQERERERG5kqS2mkMXoIvnZRwwzxiT+K4wJ1AUmJYu0YmIiIiIiIhIppNaz4Q/gLlAENAN+BbYm6hOJLAZ+DhdohMRERERERGRTCe11Ry+Br4GMMacACZZa//JqMBEREREREREJHPya84Ea63W9RMRERERERERIIAJGI0xtYEngQq4uRISsNbWTMO4RERERERERCSTCvankjHmbuB74HqgLm71hpPATUAh4Pf0ClBEREREREREMhe/kgnAq8AY4H7P677W2jtxvRSigO/SPjQRERERERERyYz8TSZUAr4AYnHLROYGsNbuAgYAr6RHcCIiIiIiIiKS+fibTDgDBFtr43DLQ5b12XYcN/xBRERERERERK4C/k7AuAEwuKUilwG9jTH/AJG4IRC/pU94IiIiIiIiIpLZ+Nsz4U3c8AaAl4FTwJfAt0AR4Nm0D01EREREREREMiO/eiZYaxf7/PyPMeYWoBwQAmy21kamU3wiIiIiIiIiksn4O8whAc/cCX+mcSwiIiIiIiIichlIMZlgjOkcQDtx1tpxaRCPiIiIiIiIiGRyqfVMGBtAO3GAkgkiIiIiIiIiV4EUkwnWWn8nZxQRERERERGRq4gSBiIiIiIiIiISEL8nYDTGFAH+B9QAwoBm1tqNxpguwFpr7Q/pFKOIiIiIiIiIZCJ+9UwwxtTErd7wMLATKAvk8Gy+FpdkEBEREREREZGrgL/DHN4AvgUqAJ2AIJ9ta4GaaRyXiIiIiIiIiGRS/iYTqgPvWmtjcSs3+DoEFEnTqEREREREREQk0/I3mXAMKJzCtjLA/rQJR0REREREREQyO3+TCfOBgcaYMj5lccaYUKA78GmaRyYiIiIiIiIimZK/yYRewHHgD+B7T9l7gAVOA/3SPjQRERERERERyYz8SiZYa48AtYBngV3AUmAHLslQx1p7It0iFBEREREREZFMJau/Fa21kcBkzx8RERERERERuUr5O8whRcaYO4wxX6RFMCIiIiIiIiKS+aXaM8EYUwC4FwgDtgMLrLVRnm0tgZ64ZSO3pHOcIiIiIiIiIpJJpJhMMMZUAb4CivoU/2yMeRiYiZtD4Q+gDTA7PYMUERERERERkcwjtZ4JQ3ErODwEbABKAm8D64AcQFtr7Yx0j1BEREREREREMpXUkgk1gC7W2h89r60x5hngT+BpJRJERERERERErk6pTcBYFNiZqCz+9Yb0CEZEREREREREMr/zreYQl0J5dFoHIiIiIiIiIiKXh1RXcwC+NMYklzhYlrjcWlsk7cISERERERERkcwqtWTCwAyLQkREREREREQuGykmE6y1SiaIiIiIiIiISBLnmzNBRERERERERCQBJRNEREREREREJCBKJoiIiIiIiIhIQJRMEBEREREREZGAKJkgIiIiIiIiIgFRMkFEREREREREAqJkgoiIiIiIiIgERMkEEREREREREQmIkgkiIiIiIiIiEhAlE0REREREREQkIEomiIiIiIiIiEhAlEwQERERERERkYAomSAiIiIiIiIiAVEyQUREREREREQComSCiIiIiIiIiAREyQQRERERERERCYiSCSIiIiIiIiISECUTRERERERERCQgSiaIiIiIiIiISECUTBARERERERGRgCiZICIiIiIiIiIBUTJBRERERERERAKiZIKIiIiIiIiIBETJBBEREREREREJiJIJIiIiIiIiIhIQJRNEREREREREJCBKJoiIiIiIiIhIQJRMEBEREREREZGAKJkgIiIiIiIiIgFRMkFEREREREREAqJkgoiIiIiIiIgERMkEEREREREREQmIkgkiIiIiIiIiEhAlE0REREREREQkIEomiIiIiIiIiEhAlEwQERERERERkYAomSAiIiIiIiIiAVEyQUREREREREQComSCiIiIiIiIiAREyQQRERERERERCYiSCSIiIiIiIiISECUTRERERERERCQgSiaIiIiIiIiISECUTBARERERERGRgCiZICIiIiIiIiIByZpB+8kCEBwclEG7uzhFCoZc6hD8kj1nwUsdgl8ul3/3y4GOzbSlYzNt6fhMWzo+046OzbSlYzNt6fhMOzo205aOzbSV2Y9Pn/iy+PueoLi4uPSJJqG6wIqM2JGIiIiIiIiIXJB6wEp/KmZUMiEHcCuwF4jJiB2KiIiIiIiIiF+yANcC64Cz/rwho5IJIiIiIiIiInKF0ASMIiIiIiIiIhIQJRNEREREREREJCBKJoiIiIiIiIhIQJRMEBEREREREZGAKJkgIiIiIiIiIgFRMkFEREREREREAqJkgoiIiIiIiIgEJOulDuBqY4xpDjwHVAdCgF3Ax8CbQB5gB9DUWvv5Re6nHTAVyGutPXkxbYmIiIj/PN/BzwMVgGhgJ/CttbabZ3sRoDMwzVq789JEKSIicnGUTMhAxpjRwIu4m/w3gONAJeD/gMpAK6A2sPlSxShXHmPMAKC/T9Fe4Aegh7V2m59ttEPJKUln57sBE7kcGGN6A4OAEUAvICdwC/AYEH8sF8Gdl7/DHeciABhj4vyodoe19rv0jiUjGWP2AZOstX1SqVMDWAfUttauybDg5Lwu9rg1xlQENgF3W2uXpmVs/jLG/B8wDshmrY2+FDFcjpRMyCDGmKa4i4gnrbVTfDYtN8ZMAO6x1p4FUj05GmNCrLWn0zFUuTIdA+71/FwGd6G7zBhT2Vp76tKFJeL4eQMmcjl4DhhvrX3Zp2yhMWZgeu1Q1wZXlNo+P4cA3wCDgUU+5X9kaEQZozFw4FIHIRfsYo/bnZ42rsRj+4qmZELG6Qr8nCiRAIC1Ngb4whhTikTDHIwxO4G5wFGgE1AUyObZVh8YCNwKxADrga7W2vXJBWCMyQm8CvwX91RkM9DbWrs4rT6kZFrRPln8NcaY3cAK3Jf3nEsXlohXht+AXQhjTDYg1nPeFklOAWBf4kJrbRyA57v+N0/xt8aY+O1BxpjcwHDgbiAM2A8sxn1XH49vy/MU8H9ACaANLmFcLn0+jmQk3yfuxpg8nh+3+fMk3hiT01p7Jt2CS0fW2p8vdQxy4dLouFVvk8uQkgkZwHPxeRsw+gKbaA1sxI2vzOppswHwNfAt0BY4BdQBiuOSCsn5BKiJ61q5DXgEWGCMqWGt/eUCY5PLU7jn71IAxphHgL647uUHgOlA/5S6eRlj1gG/W2vbJyp/H6hira3ueV0VeA83R8g24CVcQut3a207n/elun+fYRZVgddx/5/+Al621n564b8GyURSvQGLZ4wJA8YDd+ButAbjet2EWmsbeOpMA2601tbweV8pkiZr/4cbXlYBOAOsxSVkt/q87zvgX+AroCfu/0wp4C9jzI24G7/6nupLgOettUk+h1xVfgae9yRtP7fWHkq0fS8uAfAh8KynfrxcQBbgFeAgLqHwCi7p2yhROy8B3wOPowm1rzo+XbJvAcYANYB+xphRwCjc8VIaOIx7StzdWnvQ5/37gMm4h1GdcMfep8CzvsMZjTGFgWFAUyAv8BPworU23LN9GO7cmJi11lb01CmK++5uDOTADbX8n++1Z3LDHIwxXXDHeUHcOXjihfyuJPNI5bhdSKJhDsaYJ4EngRuAWNz9TfdEx80s4HpgKDASKIm7xn3aWmt96oXirkfvwz2gHQWUBRrGH6cpxJsLd53xKBCKux/raa39+qJ/GVcIJRMyRiHcyXP3RbTRJFG2+TVgA9DI52J7SUpvNsbcBdwPNLDWLvcUf2WMqYC7UGl5EbHJ5aeU5+99xph7gNm4G/iXcDfsg3DH7f+l8P5JwGhjzPPxFx2eTPTDQG/P61zAl7gbxP/iuq2/gbso+D2+oQD3PxOYgPvCeB6YZYwpY639+0J+CZKpnO8GDGNMEDAf94X+JC4BMBC4BvjzAvZ5PTAWNxFuPtzxtsoYU8Fae8ynXh3cRUdPIAI4ZowpB6zCXVg/jrsBHITrTVEzcRJErirPAvOAaUCcMWYTrofhKGvtcWvtWWPMr566f/g+ufPc7D0T/9oYkxWXBFtpjClhrfW9jthnrX00nT+LZH6zgXeAfrjEQTDunDgYl7gqivtu/coYUz3Ruakd7gauA66Xy3DP+x8HN3wG99AqB2642SHcd+8yY0w5a+2/nn3P82kzryemLZ42goDPgeuALrheND2B74wxN1lrdyX3oYwxj+ImJ38b11X+Ltz3v1wZEh+3ySkJTAG2464hn8CdC2+w1v7lU68c7ngfAEThElcf4R5kxfsQuBnXC/JfoDtu2G+KQ319rjmqeOLciRt6uchz7G7y+9NewZRMyFgXenG5zDeR4OkG+R+gSwAXrA1xN3WrPBcn3rZxXyZyhfP5dy8DvAucAJYCnwHfWWvberYv8XS7fc0YMziFG/WPcCfrlrgeA+B6umTD3fADtMclBGpYa//xxLAN+DFRW68GsP834ocKGWPCcU+mm+CyzXJ5S/UGzFPnPtzFQC1r7Y/gPQ62cQHJBGtt1/ifjTFZcL29DgAP4pJb8QoAN/v2ODDGvIM7p95nrY30lP2KGz7WmITjROUqYq391RhzA3AP7unwnbieV608N3OpTmJrjHkcd+NWHsjts6kCCR9K6BgTcOfI8YnKvL0GPee2cGArbljsWp96WfF5WGWMiQQmGGMGeCZo7oBLpN4Qv+qIMeYbT1tdgL6em7q/PNuCcNcUJ3EJX3Dn0xokPG9/izuWu3naSc4rwDxr7Que118aY67F3czJ5S/BceuZgDEBa20/n+3x39FbcA+oRvhUvQb4T3xiyjOs+yNjTClr7U5jzC248/ED1tqFnjrfAX+TSjIB913eEJ9jF5eUM8DLeJJuVzt1i8sYh4CzuKzvhdif6HVBIAiXcfZXKFAMl7Hz/TMA141SrmyFOPdvbnEJhUdxN07VSTpvwmzc+aE2yfDc3H1CwkRUO2CBzxPlW4Hw+ESC531r8TmePV8Ogez/K5+2Dnnivz65GOXyYq39FdeV8QFcsisIdwP2k8/4y5rAfp8vdTwXD+FcAGNMLWPM18aYQ7jVIyJwS/RWSFQ1PJmhCw1xF82xxpisPk+Qd+IunOUqZq09a61daK19zlpbCXgKlxx4MrX3GWOa4RJZP+CStbWAZp7NORNVT3xtIFenJEklY8wDxpg1xphjuHNb/NCtxOe2JYl6vX6K++69xfO6Ie4BwN8+57kY3JxLyZ3n+uGGnT3sM6SiJvBXovP2ceALoG5yH8hzM3gj7qmwLw1rvHKcNxlqjKlijFlgjDmAO44jcT1rEx/HWxL1cImfxDH++jB+bjnvHHHW2hO4XjepaYj7Tg+PP/49/weWoe95L/VMyADW2ihjzCrcE4oUl7xJReLeB0dwY4euDaCNw8A/wEMXsH+5/B3DnRTjcE9T91hr4zzjGLOR9KI0/vU1qbQ5GddNsazndT1cFjdeMdyY38R8y0ID3P/RRK8jSXqBLZcpz4o2Cz1/4sdLTsLdgI3BHVPJzfZ9ANe11m/GmBK45NRa3HjhPbjjaRH+3bSF4rrqJjdWWAlaScBaO9kYMwJIcWyuR0vgR2tt5/gCY8ztKdTVUBqBROcnY0wdXKJzFjAE952bDTe/RuJzW4LzqbX2iDEminPXl6G4G/6oZPa7MdF+m+Lm5OporV3ns+naxDH6xF0rhc9UDJdQTny+12oPV45Uk6HGmPh5Mnbheq/8hRvaOJ2kx3Fy14b41CsGHE5m4uSDpH5ODsUlL5I7/rUSmoeSCRnnTdxkh22tte/7bjDGBOO632z2pyFr7SljzI/AE8aYsX4OdViGm/n5pLXWr/3IFSXaWvtTMuX/4k6SRRKVF/X8ndI4Nqy13xtj/sRNABqEuxn7yqfKPsAk89bCabF/ufIlcwO2j6THCp4y32XxzgDZE9VJnJi6Fzfh2IPWszyq54lDcgm05M6xh3EX7JOS2fZvMmVylTDGFLHWHkhUVhjIz7kL6MQXu/FCcD0ZfbVJ8yDlSpL4/PQwsNta6z1uPN2yk5PgfOq5gcvGuZ6vh3Fzw7yYzHtP+7zPADOAidbayYnq7cUN9UmsKCl/x+/Dfa7E5/vkzv9yeTrfvUs9XBKgdvwQG/Aeo4HaB1xjjMmSKKFQOKU3eBzG9Th8JJltsRcQxxVJyYQMYq1daIx5HZjsyRrPx40pq4ib9GsnbvlIf/XCjXf/whgzAZchqw38FD9TeSJf4ybD+9oYMxyXUc4HVANyWmt7X9AHk8uatTbGM+a8JW523XiP4E6UP5yniSm4VUYApic6Sa8D2hhjivvMmVCTc4mCtNi/XCH8vAFbB/Q3xvzHZ+xtCdxQmVU+b/0bKGUSLpN2d6JdhuCOMd8VSx7B/+/FZbhuuOGabFES+c0YMx+XXD2Am0SsO24YTfzDhN24m7G2nq7oUZ6E79fAO8aYV3DdyxvjJp4T8VcI55JV8VJKSN2b6DzZHHdejB86tgw33Gy7tTbZG39jTF7cfDd/4CZnTOxHoLdnYtq1Pu+5l4Rz03hZa88YYzbi5luY5rOpeQqfQ648IZ6/vclVY8yduARDoNbhJkm+H1jgaSsvblWo1IaML8NNiHvEM4eIJEPJhAxkrf2fMWY1bibRmbj/KDtxB/YoAuiu7XkqfDdu9vAZuC+O9SScUde3fpwxpjluwpAXcfM3HAZ+wc2UK1ev/riJjabiukVWwR1XE/1YJeF93Ay6WUn4hQ9uYsY+wOfGmIG4430grluZb0b3YvYvVw5/bsAW41axmWOM6YnrgfAqSbu+zvOUTzJumcib8ZmQzOMb3MXFVGPMZKCyZ3+Ju0umZABuiMQiY8wUXG+E4rikxTRr7Xd+tiNXnldxN0Fv4Xq67ANWA49aa3eA92apI+78txz3NDgIt+xpGVy33py45EJrtP66+O9r4P+MMSNxq3zVxy2Bm5xo3Hf0G7jhWSOAWT43TpOAjrghja/jntKG4h5e7bDWvoObkb8M7jit7tMJ4rS1dgPuGjccmGuMeZlzqzkE4SZyTslQYKYx5i3cahANcTd/cnVYhUu4TvYcn6Vwc3IEvPSytTbcGPM17pqgB+77+iXgOKn3MPgcNzxomedB7CbchMzVPe32S+W9Vw0lEzKYtXYubobylAQlql8qlbaWc25988TbppHo5s4zHrm/548IANbar4wxrXA3/m1wN2aj8eM4sdbu8wy5wdpz6/l6XkcYY+7F9TiYjUuc9cBdrBz3qXfB+5crij83YHHGmAdwy4NNwR0rQ3E38KHxDVlrfzfGdMA9UWuOSxx0wKf3grX2N2NMe9xx1gyXpGiJO1bPy1q7xRhTC5dMm4BLlv2De5KxNbX3ypXN5wbrfPU+xC1X5lsWg0tqdU9UPfG1QRAiybDWfmqM6YvrNdgZN1niQySa48Bjms/fIbihW8/6tBXhmbNjEG7+hcK4nmJrgI891SrghpV9kjgUoKLnvN0Elzh421N3DW6p8mSXhfTs+yPP6g3dcROYLsXNb5Nc71u5wlhr/zZuedARuISUxX2PD7rAJtvgVv56F5fQGoN7iFA+lRhiPcduP1zy4XrcpPrrPe8XICguTr0zReTCGGOuwd1APZfMOMnk6pfGLevztLV26vnqi/jDGPMJEGqtbXCpYxERuRwYY/YBk6y1FzIxuMhlzRiTHTdX3dfW2k6XOp7LmXomiEjAPGPNKuG64p4APkqhXm/cxIy7cENreuOGOaTWO0dEREREJE0YY1rjej3+gZuL6Rnc0J5xqb1Pzk/JBBG5ELfg1ufdBTxhrY1IoV4crhv5dbhJdFYA3T1rTIuIiIiIpLcI3OT1ZYFg3NDG+621v1zSqK4AGuYgIiIiIiIiIgEJvtQBiIiIiIiIiMjlRckEEREREREREQmIkgkiIiIiIiIiEhBNwCgiIiKXhDGmHG797lrAjcAKLfEpIiJyeVAyQURERC6VykBjYA2Q/RLHIiIiIgHQMAcRERG5VBZaa8OstS2BjZc6GBEREfGfeiaIiIhcoYwx03DDB3oDo3FrbK8HOllrN3rq5AKGAY8ABYDfgFestV/5tPMd8C+wCOgHFAY+A54CbgLGApWAn4AnrLW7/YnPWht7sZ9RRERELg31TBAREbmylQBGAkOA/wJFgI+NMUGe7ROB9p7tzYC/gEXGmLqJ2qkFtAWeB3rgkg9ve94/BngMKANMSM8PIyIiIpmDeiaIiIhc2a4B6lhr/wQwxgTjehUYT0Lhv0B7a+37nu1fAr8CfYFGPu3kAR601h7z1GsAdARut9Z+7ym7DnjHGJPLWhuRER9ORERELg31TBAREbmy7YxPJHj84fn7euBWIAiYE7/RM/RgDpC4Z8JP8YkEj61AJLAyURnAdWkQt4iIiGRiSiaIiIhc2Y4meh3p+TsncC1wMpleBPuBXMaYHOdp50SieQ982xYREZErmJIJIiIiV6+9QB7PJIy+igIR1tqzlyAmERERuQwomSAiInL1WgfEAS3iCzzzKLQg4fAFERERkQQ0AaOIiMhVylq7yRjzETDWGJMPN+dBR6Ai8Ex679/TI6Kx52VxIJ8xJj6xsViTOIqIiGReSiaIiIhc3ToCw3GrNxQAfgOaWGszomdCEXwmf/SIf10a2JkBMYiIiMgFCIqLi7vUMYiIiIiIiIjIZURzJoiIiIiIiIhIQDTMQURERNKcMSaYVB5aWGujMzAcERERSWPqmSAiIiLpYQoQldIfY0ypSxeaiIiIXCz1TBAREZH0MAAYm8r2PRkUh4iIiKQDTcAoIiIiIiIiIgHRMAcRERERERERCYiSCSIiIiIiIiISECUTRERERERERCQgSiaIiIiIiIiISED+H6PuqC49jMHaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by nom_2:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by nom_3:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by nom_4:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plot the nominal variables in relative scale'''\n",
    "\n",
    "bold('**Percentage Count of target by nom_0:**')\n",
    "crosstab(train.nom_0, train.target, color = ['b', 'y'])\n",
    "\n",
    "bold('**Percentage Count of target by nom_1:**')\n",
    "crosstab(train.nom_1, train.target, color = ['b', 'y'])\n",
    "\n",
    "bold('**Percentage Count of target by nom_2:**')\n",
    "crosstab(train.nom_2, train.target, color = ['b', 'y'])\n",
    "\n",
    "bold('**Percentage Count of target by nom_3:**')\n",
    "crosstab(train.nom_3, train.target, color = ['b', 'y'])\n",
    "\n",
    "bold('**Percentage Count of target by nom_4:**')\n",
    "crosstab(train.nom_4, train.target, color = ['b', 'y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3. Ordinal Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by ord_0:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by ord_1:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by ord_2:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by ord_3:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plot the Ordinal variables in relative scale'''\n",
    "\n",
    "bold('**Percentage Count of target by ord_0:**')\n",
    "crosstab(train.ord_0, train.target, color = ['c', 'm'])\n",
    "\n",
    "bold('**Percentage Count of target by ord_1:**')\n",
    "crosstab(train.ord_1, train.target, color = ['c', 'm'])\n",
    "\n",
    "bold('**Percentage Count of target by ord_2:**')\n",
    "crosstab(train.ord_2, train.target, color = ['c', 'm'])\n",
    "\n",
    "bold('**Percentage Count of target by ord_3:**')\n",
    "crosstab(train.ord_3, train.target, color = ['c', 'm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crosstab(cat, cat_target, color):\n",
    "    '''cat = categorical variable, cat_target = our target categorical variable.'''\n",
    "    global ax1\n",
    "    fig_size = (28, 6)\n",
    "    title_size = 18\n",
    "    font_size = 15\n",
    "    cat_grouped_by_cat_target = pd.crosstab(index = cat, columns = cat_target)\n",
    "    pct_cat_grouped_by_cat_target = round(pd.crosstab(index = cat, columns = cat_target, normalize = 'index')*100, 2)\n",
    "       \n",
    "    # Plot relative frequrncy of target by a categorical variable\n",
    "    ax1 = pct_cat_grouped_by_cat_target.plot.bar(color = color, title = 'Percentage Count of target by %s' %cat.name, figsize = fig_size)\n",
    "    ax1.title.set_size(fontsize = title_size)\n",
    "    pct_bar_labels()\n",
    "    plt.xlabel(cat.name, fontsize = font_size)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by ord_4:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Count of target by ord_5:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GDgwhXAi8A2RijL9KEdNU4O8hhNuBZSTX7+7ANTHGd3M3jDGuDSHcm80DkkJQWj8EjgVuDSHsQjJ826dI5hWJ2fXrFWNcHEK4HLgeeCmE8HOSidLPIOk986mW9m/GZSGEPYFnSY5dH5Ljuz/wd+DmNrQpSZKkDsieIJIkSUotxlhD8nD/SpLhlb5PUgT4IskD2dta0dZjJMWCF0keit5C8lDzPXIe4McY7yWZh+BfJN9G/ynJfB2bA5eTPNBvSy5/JSkifAy4E/glycN+gHNIHrjuSfIw9CSS4sBFedpZkM3jjySFkh+QPFDdn6Qnwcom2xecT4xxBckxP4ak+HFGNr5rSIatuppkWKX67SeS9Bx5HjgTuIGkcHUhyfwkuW1PJynGzM+2dwHw1+wxKNSlJAWGbwMPkBzzwS3uASeQHPdh2bjPzOaxV4wxzZwo62v7TeC6bCw3Q8O18WWSoayuAa4iOY+fyy5LLcYYSc7330mKUdeQFFK+kt2k6fVxNUmB6n2SychvBU4Dumf3z3UmMIHkuP4im0MaN5Ncp9/I/h4IjIkxXtHM9neRFKyWAQ+nfI3c94txJAWGm7K/bwc+G2Nc1oq2biC5znuSvO+MJrlH21qseA5YSnJd30hSeNqY5FjuF2Nc2fyukiRJ6kwqMpnWDssrSZIkaX2yw+0sAMbFGM9o73jUsYQQdgUmAhfHGP+nveNpSXbIuneBu2OMp7d3PJIkSVJr2BNEkiRJKlC++T1IellAyxO/qwtoen1k5ze5IPtnZ7g+vkUyUfwd7R2IJEmS1FrOCSJJkiQV7okQwkySb/ZXAQeQDGn0Eh9N7q2u698hhL+QDPPWGzgC2Af4dYzx/9o1shaEEL4KDAXOB57qyLFCwwTvaSaKn5+dtF6SJEldgEUQSZIkqXB/IJlf4UskcxbMIpm/YqwPWwX8nqTwcSLJ/4NNJ5n/5QftGVQKvySZgL5+3p6O7jMkE52vzwhgRmlDkSRJUkfhnCCSJEmSpE4vhDAQ2DXFphNijB+UOh5JkiR1DBuqCNId2B2YDfhNOEmSJEmSJEmS1FpVwObAP4BVaXbYUMNh7U7ShVqSJEmSJEmSJKkQ+wAT0mxYWaoIKioqRlZUVIyrqKh4taKi4vn99tuPxYuXs3BhbcPPggXLuOyyK9lyy63o2bMne+21N88991KjbRYurOWllyay77770atXL4YM2ZzzzruIefNq1tmu6c/06e/xzW9+kwEDBtKvX3+OOeY43nxzZqNt7r//lwwfPoLBgwdz4YWXrtPGRRddxqGHfmG9r/WPf7zKSSedzE477UxVVRV7773POtuYr/kWmu/xx3+dgQPN13zNtzX5dqScu9p7lvmW7ppu608xj9Ubb7xR0P2f9liddtq31om56bHy/Hbe89tR7v2ulu9LL03kgAMO6DL3r5+xyvv9akNdzxdeeOk6cQ8dOmydY+V/k4p/jv3MUd7vWeZrvubbefNtr88cixcvry9BzE5bqyhlT5AdgcOAlzfaaKPewMfq6jLU1X00/NZPfnIDN9zwQ6688hpGjhzF7bffwpe/fATPP/93NttsMwCWLFnM0UcfwahR23Hffb9kxozpXHXVpdTVreXii69oMYBTTjmJ6dOn8eMf30xlZSXXXHMFJ574Nf7wh6cAWLhwIWeccSrnnHMBw4YN45xzzmLXXXdn//0PAGD27PcZN+5Wnnzy2UZx5zN58mSeeebP7Lrr7qxevRpgnX3M13wLzXfatLe46667WLZslfmar/mmzLcj5dzV3rPMt3TXdFsV81gdeOCBjBwZ2nz/pz1WZ531LXr27MXee+/D6tWrWbVq1TrHyvOb6Iznt6Pc+10t36OPPoKddtqxy9y/fsYq7/erDXU9n3POWdTVZRriXrFiOe+++y4PP/z7sn3OUYjO+B7dUd6z/AxtvuZrvp0l3w7wmSP9tBuZTKYkP0Bl/b/79ev3+Oc+97nMggXLMvPmLc3Mm7c088478zJ9+/bLXHDBJQ3Lpk+fndlkk00yZ599XsOySy+9MtO//4DMtGmzGpZdfvnVmZ49ezZa1vTnT396OgNknn/++YZlTz75lwyQefjh32fmzVuaeeCBX2dC2K5h/UknnZI588yzGv4++uhjG/3d0s+cOUsa/n344UdmPvOZzzZab77mW4x8f//7JzKZTMZ8zdd8W5FvR8q5q71nmW/prum2/hTzWA0YUNj9n/ZYfeMbJzccm8MPPzIzaNDgRsfK89u5z29Hufe7Wr79+w/I1NTUlDzfjnL/+hmrvN+vNtT1fNJJp2S+9a3vNvy9xRZbZbbYYst2Ocf+N6m837P8DG2+5mu+nSXf9vrMsWDBskzW8LS1ipINh5XJZOpaWv+Pf/ydZcuWcuSRRzcs6927NwcffCjjxz/TsGz8+KfZf///om/ffg3Ljjrqy6xcuZKXXvprs+2PH/80gwdvyr777tuwbJdddmPo0OGMH/80AKtXf0iPHj0b1vfs2ZMPP0yqZxMnvsKLLz7PuedekCrfysqWD6X5mm8x8t1rr73N13zNt5X5QsfJuau9Z5lv6a7ptirmsTrkkEM2yP3fq1evhmO1ePEiamqWNDpWnt+PdMbz21Hu/a6W7/77/xf9+nWd+7eznqOOnm8hOuP13LNnT9as+RBIjtXChfPZaqutC4rb/yZ1rPfojvKe5Wdo8zVf8+0s+XaGzxz1SlYEWZ8335xKVVUV22zzsUbLt9028NZbUxv+fuutqYwcOarRNltttTW9evXizTen0py33nqTbbcdtc7yUaNGNbS/884fZ/LkSUyY8AIzZ87gT396jE984lNkMhkuu+xCLr748kYXQSHMN2G+5mu+6zLfRHvlC10vZ/NNlGu+hWjNsdpuu+0abbMhjtWkSa8xdOiwRsfK85teRz+/7Xnvd7V82+Ne8P7tevkWojMcqxC2p7q68QjjnSHuzniO/czhe5b5mm+hzDcZDeq4477Cddddy5w5M5kzZyZXXnkF5557TsPfc+bMpLq6jscff4IRI7ZutDz354gjDuOXv/wlc+bMZNKkNxqW33vvPRx77NHMmTOTT35yR77//WuZMWMqs2ZN40tfOoKTTx7N7Nkz2Wgj+P3vf8/y5YubfY05c95h0aJ59SNOFayUc4K0qKZmCb1796GqqqrR8gEDBrBixQpWr15Nt27dWLJkCf37919n//79B1BTs6TZ9pcsWUK/fvn3mzlzBgDDhg1nzJjzOProwwE48MCDOfroY/n1r3/Bhx+u4fjjTywgw8bMN2G+5lsft/l+xHw/2q898oWul7P5Jso130K05lgNGDBgnf1Lfazq6jJsuulmbY7Z89uxz2973vtdLd/2uBe8f7tevoXoDMdq6NBhLF68qNPF3RnPsZ85fM8yX/MtlPlCbW0NW201lEWLlrDppltTWVnJ3LmL2HjjIQwevGmj/RcuXEbfvhszZMgWedtfvvxDKisrGTJkGNXVlaxZkwwItWpVhtWrVzFkyDAAqqt7MWdOMnf5gAGbMmLENixevIgePVbysY8FKioqms0hk6ljyZIF1NbW0Lfvuv8daK126wkC5E20vrqTu6657Vo6UGn3O++8i5g8eTr/93+v84tf/IYPPviA6667mu99739Ys2YN5503hu23H8HnPrcnL7/8t9S5tSaepuvM13xb0775mq/5Fp5vSzE1XVcuOZtveedbiI58rHbccScymUyjY/Xuu+906Jg9v9775uv9myaepuvKJd9CdPRjlclkmDbtrXWOVUeP23Pse1ZrmK/5po27Ne2bb/vku3JlLT179imozZSv3PCvIUM2Z6edPs4OO+zINtt8jLq6OmbPns2WW25FJpPh3Xff4fXX/8OUKZOpra1tEn8lffsOZOXK2qYv0CbtVgTp338AtbXLWLu28STuNTU19OrVi4022ghIKnI1NTXr7L906dK8FbZ6AwYMYOnSfPvVrFPR22STTdh666EA3HTTj9ljjz3Za6+9ue++u5k06TX+9rd/MmbMeZx22mhWrVrV6lzBfOuZr/kmcZtvLvOt36998oWul7P5Jso130K05lgtWbLuN6FKfaw22WQQc+fOaXSsHnvsEZYtW+r5TaGjn9/2vPe7Wr7tcS94/3b8c9RZ36/a61jNnDmd5cuXNzpWvXv36fBxd8Zz7GcO37PM13zNt3Xy5VtXt5a6ujoqK6sa5hSpqqpe55gArF27dp1eM7mqqqpS71ddXU23bt0BmDdvLr1796ZPnz4sXLiAlStXst12O7DZZpsxc+YM6uoaTzFeVVVNXd26r9MW7VYE2XbbUaxdu5bp099utLzp2GsjR45qNDYbwHvvzWLFiuV5x1r7aL9t847V9uab647tVu+dd2Zy7713ccUVVwMwYcKLHHPMVxgwYCBHHXUMq1evYtq0t1LnmMt8E+Zrvua7LvNNtFe+0PVyNt9EueZbiNYcqylTpjTaZkMdq5qaJY2OVSaToa6uzvObQmc4v+1173e1fNvrXujq929nOEed8f2qvY7VggULGDx4cKNj1atXrw4fd2c8x37m8D2rnvmar/mm01y+q1atonv37g1/d+/enVWrPmi0zerVq6mrW0v37j2abT/ffgAffPBBo/abvvaCBQvYfPNkiK3a2loGDtyY6upqBg7cmEymbp2iz/p637RGuxVBdt/90/Tt24/HHnukYdmKFSv485+f4IADDmxYdsABB/Hss+OprV3WsOzRR39Hz549+cxn9qY5BxxwEPPmzWXChAkNy/79738yc+YMDjjgoLz7XHXVZZxyyqkMHTosJ6aVQFLJWrVqdZsnYzFf862Pu5B8c7vCmW9j5mu+hUyW5XuW+dbHXQ75FqI1x+qpp57qEPd/XV0dvXr19vym0BnP74a697tavs8+O55ly9o/3w11/3bWc9TZ8y1EZ7me67+xWn+sdtxxJz9zpNQZ36O72meOrvYZ2nzNtz7ucs23ri6T7dHy0eTr/fr1Y+nSpMfIsmXL+N3vHmbJksVUVlbSp0/zQ2f169ePDz/8sNEQVitWLGf16lWN2s81e/Z7DBo0iEmTXuO1117NxpT8dzQpMpf2v0VVV111VUkarqio6DV27Ngjx44du0NFRcWh3bt333yTTQYT4xS23nooPXv2BDLceOP19OvXn9raZVxxxcW8//573HzzOHr37g3Adtttz3333cuECS+y2WZDeOGF57j22rGcccaZHHDAwQ2vt8cen2DSpNf5/Oe/AMCWW27FxImvcO+997D55lvy1ltvcsEFZzNqVODiiy9fJ96XXprAnXfezh13/Ixu3boBMG/eHO6++w5GjBjBz39+L2+/PY2LLrqM6up155NfsWIFTz75J6ZOncKzz46npmYJgwcPZupU8zXf4uX74IM/Z9iwoUyaNNl8zdd8U+bbkXLuau9Z5lu6a7qtinms7r//Xl58se33/xZbpDtWd9xxG0cddQzTpr3V8KDrlVdeZsmSRTz11BPMmDGd008/k5tu+rHntxOe345y73e1fO+7716ee+45Bg3atEvcv37GKu/3qw11Pd955+385Cc/Zfz4p5k6dQovvPAcs2e/z8qVK7jzzttYtGgRl112FVVVVX7moHO+R3eU9yw/Q5uv+ZpvKfLNZNZQUVHF0KHDGoas6tGjB4sWLaC2tpba2mXceutP+OQnd2Xw4E0bDf81efIkVq5cSf/+yQTl3bp1o7a2lgULFtC9ew8++GAls2bNokePHg09PXLV1i5j/vwFDBs2nCef/BOLFi1i++13YMGCBXTr1p2FCxdkJ1TffJ3eH7W1NfTp03hi9IqKCnr16gbwE6D5GetzZTKZkvwAw4FMvp+JE1/LzJu3NDN3bk3m4osvz2y++RaZHj16ZD796b0y48e/mJk3b2mjnxdffCXz2c/um+nRo0dm0003y5xzzvmZ2bMXN9pm662HZo477vhGy958853M6NGjM/369c/06dM3c/TRx2QmT56+Tvtz5izJ7LTTxzO33DKu0fJ3352fOf74EzP9+vXPbLvtqMyjjz6+zr71PxMnvpY3V/M132Lm+9WvnpDp3998zdd8W5NvR8q5q71nmW/prum2/hTzWE2aNKmg+z+QJkP8AAAgAElEQVTtsRo79rpmYx4+fETm0Ucf9/x24vPbUe79rpbviy++ktl///27zP3rZ6zyfr/aUNfzLbeMazHucePu2WDn2P8mlfd7lp+hzdd8zbcU+T7zzPjMjBlz1mnrnXfmZl566f8yX/vaCZkdd9wxc9BBh2QuvfTKzNe+dkLm8MOPyHz+84dmbrzx1szEia9lXn11Suaggw7OXHDBJZnDDjs888QT4zNXX31d5rOf3Sdz5JFfypx99nmZiy66NDNv3tJMjDMzp556euaLXzwyc/DBh2R+97s/Zl59dUpmzz33ynzmM3tnDjvsC5kHHvh1Zvz4CZkXXng58/bb7+XN9T//eW2dZQsWLMtkDU9bq6jYQN0ehwPTFy6sLXnXlqYGD+7L/PnL1r9hmTDf8tfVcjbf8ma+5c18y5v5ljfzLX9dLWfzLW/mW97Mt7yZb3kz345jzpyZDBkyrNn1s2e/zwUXjOH++x9izZo1rFr1Ab1792HJkiWcfvpofvWrR5gzZzZf+cqR3HbbPey0084sWDCfM844mXvueYBevXpz1llnMHLktpxzzoVcddWlHHXUsXziE59kzpw5nHvud3jwwd9w993j6NmzF8cff2Kb466srGCTTfoAjABmpGmnuP0dJUmSJEmSJElSpzVu3K28+uq/qKioZP78+SxatBCAIUM2Z6eddgbgjTcmscsuuzYMnbX//gfy7rszAZg48RVmzJje0N7y5ctZsWL5Bs7iI52iCNJvQC+6b1TVpn0/XFtX5GhKz3zT64z5QtfL2XzTM9+Oz3zTM9/ytubDtQwe3LdN+65etYaapSuLHNH6eX7T64zntxBdLV/onDl3tXu4q+VbCK/n8ub57fjMNz3z7fi6Wr4AlVWVVFVVUlFRQXV1JU8++SQ1NUu4774Hqa7eiC996QusXbuGqqpKevbsSXV1ZbJfJTQ3yFQmU8e4cffQvXuPDZhJ8zpFEaT7RlWc+vg/27TvnYftUuRoSs980+uM+ULXy9l80zPfjs980zPf8la9URVXn/vHNu17xQ2HFzmadDy/6XXG81uIrpYvdM6cu9o93NXyLYTXc3nz/HZ85pue+XZ8XS1fgMoKWLi2gqW1tcyoWcE7CxZR3bsfs5Z/yKR//505c2Yza1lSUF69to4ZNSsA6Lf1x/jXv25g6dKl9OrVi+ef/wvbbPMxAHbffU9++9uHOP74bwDw5puRbbcN9OrVu116hFRu8FeUJEmSJEmSJEkdQt9+/Rm1w86cd9qJzJz2Fm+/OYVLvnMKE579M1tsnX8ukY0HDWb06JM57bTRjBlzJsOHj6B37z4AjBlzPlOmTOakk77K179+LI8++lsA9t57H1544VlGjz6eV1/91wbLr1P0BJEkSZIkSZIkSaVx1sVXrXeb6++4v9HfBx98KIcffhRr1qzhkkvOZ/fd9wRgwIABXH3199fZf+jQYdx336+KEm9rWASRJEmSJEmSJEmtctdd43jllb+zevUq9thjT/bdd7/2DikviyCSJEmSJEmSJKlVzjrrbNas6fgTwjsniCRJkiRJkiRJKksWQSRJkiRJkiRJUlmyCCJJkiRJkiRJksqSRRBJkiRJkiRJklSWLIJIkiRJkiRJkqSyVN3eAUiSJEmSJEmSpA2j34BedN+oquHvgQN7F6XdVR+uZemSFevd7p13ZnLttVdRU1ND//79ueyysWy99dCixJCPRRBJkiRJkiRJkrqI7htVcerj/yx6u3cetkuq7a6//vscffSxHHLIYTz11OP86EfXcdNNtxc9nnoOhyVJkiRJkiRJkkpu8eJFTJ06hQMPPASAAw88hKlTp7B48eKSvaZFEEmSJEmSJEmSVHJz585l0KBNqapKhuOqqqpi0KDBzJs3t2SvaRFEkiRJkiRJkiSVJYsgkiRJkiRJkiSp5DbbbDMWLJjH2rVrAVi7di0LFsxn0003K9lrWgSRJEmSJEmSJEklN3DgxowcOYpnnnkKgGeeeYpttw0MHDiwZK9ZXbKWJUmSJEmSJElSh7Lqw7XcedguJWk3jfPPv4Tvfe9K7r33Lvr27cvll48teiy5LIJIkiRJkiRJktRFLF2youHf1dWVzKhZ0cLWzRvevxdr1tS1er9hw4Zz5533tek128LhsCRJkiRJkiRJUlmyCCJJkiRJkiRJksqSRRBJkiRJkiRJklSWLIJIkiRJkiRJkqSyZBFEkiRJkiRJkiSVJYsgkiRJkiRJkiSpLFW3dwCSJEmSJEmSJGnD6N+vJ926f1QaGDiwd1HaXb1qDTVLV653u1tuuZHnn/8Ls2e/z89//iu22WZkUV6/ORZBJEmSJEmSJEnqIrp1r+bqc/9Y9HavuOHwVNvts89+HHvsV/n2t08tegz5WASRJEmSJEmSJEkbxCc+8ckN+nrOCSJJkiRJkiRJksqSRRBJkiRJkiRJklSWLIJIkiRJkiRJkqSyZBFEkiRJkiRJkiSVJSdGlyRJkiRJkiRJG8SNN/6I559/lkWLFjJmzLfp168/DzzwUMlezyKIJEmSJEmSJEldxOpVa7jihsNL0m4aY8acz5gx5xf99ZtjEUSSJEmSJEmSpC6iZunKhn9XV1cyo2ZFm9oZ3r8Xa9bUFSusknFOEEmSJEmSJEmSVJYsgkiSJEmSJEmSpLJkEUSSJEmSJEmSpDKWyWTaO4RWKWa8FkEkSZIkSZIkSSpT1dXdWL58aacphGQyGZYvX0p1dbeitOfE6JIkSZIkSZIklamBAwezePF8amuXrLOusrKSZStXt6nd95d3o66uNBOjV1d3Y+DAwcVpqyitSJIkSZIkSZKkDqeqqppBgzbPu27w4L6c+vg/29TunYftwPz5ywoJbYNwOCxJkiRJkiRJklSWLIJIkiRJkiRJkqSyZBFEkiRJkiRJkiSVJYsgkiRJkiRJkiSpLFkEkSRJkiRJkiRJZckiiCRJkiRJkiRJKksWQSRJkiRJkiRJUlmyCCJJkiRJkiRJksqSRRBJkiRJkiRJklSWLIJIkiRJkiRJkqSyZBFEkiRJkiRJkiSVJYsgkiRJkiRJkiSpLFkEkSRJkiRJkiRJZckiiCRJkiRJkiRJKksWQSRJkiRJkiRJUlmyCCJJkiRJkiRJksqSRRBJkiRJkiRJklSWLIJIkiRJkiRJkqSyZBFEkiRJkiRJkiSVJYsgkiRJkiRJkiSpLFWn2SiEMAo4ANgDGAL0ABYBU4EJwDMxxlWlClKSJEmSJEmSJKm1WiyChBCOB74LfBpYCrwGLABqgAHAUcAFwNIQwi+B/4kxvlPSiCVJkiRJkiRJklJotggSQpgEdAMeAE6NMb7ezHZ9gc8DxwJvhBBOizH+ohTBSpIkSZIkSZIkpdVST5BrgIdijHUtNRBjXAY8DDwcQhgKbFXE+CRJkiRJkiRJktqk2SJIjPFXrW0sOxSWw2FJkiRJkiRJkqR2l2pi9KZCCBsDuwMVwD9ijAuLGpUkSZIkSZIkSVKBKlu7QwjhCOBt4H+Am4DpIYQvFjswSZIkSZIkSZKkQrS6CAJcD3wxxvipGOMokmLI/xY3LEmSJEmSJEmSpMI0WwQJIbwSQtgzz6p+wJScv2N2mSRJkiRJkiRJUofR0pwgtwO/DSE8B5wfY3w/u/xnwAshhN8BvYATgHtKGaQkSZIkSZIkSVJrNdsTJMZ4DxCAWcBrIYTLQwg9YowXA5cAfbL7nxFjvHCDRCtJkiRJkiRJkpRSSz1BiDHWAheGEO4AfgRMCSFcEGN8CPjdhghQkiRJkiRJkiSpLVJNjB5jnBZjPBo4Gbg0hPBiCGGX0oYmSZIkSZIkSZLUds32BAkhVAAnAQcB3YB/AD8FPgWcDjweQvgTcHGMcd4GiFWSJEmSJEmSJCm1lnqC3Ah8D5gE/BU4DngyxlgXY7yNZL6QZcAbIYQLSh6pJEmSJEmSJElSK7RUBPk6cHqM8boY443A4cBeIYQRADHGmhjjGOCzwOdKH6okSZIkSZIkSVJ6LU2MPgfYB/hT9u99gbXAwtyNYoxTgC+UJDpJkiRJkiRJkqQ2aqkIcgbwyxDCqcAqoA9wZoxx6QaJTJIkSZIkSZIkqQDNFkFijC+GELYBtieZGH1KjHHZBotMkiRJkiRJkiSpAC31BCHGuBp4dQPFIkmSJEmSJEmSVDTNToweQrg4hNCvNY2FEPYNITg/iCRJkiRJkiRJanct9QTZB7gwhPAI8BvgbzHGRbkbhBCqgB2BQ4HjgC2Bk0oUqyRJkiRJkiRJUmotzQlyWAhhb+C7JEWQbiGEOcACkonSBwBbAd2BCNwD3B5jrC151JIkSZIkSZIkSeuxvjlB/gr8NTss1j7ALsAQoAewiKT48dcY4+RSBypJkiRJkiRJktQaLRZB6sUYlwJ/yv5IkiRJkiRJkiR1eM1OjC5JkiRJkiRJktSZWQSRJEmSJEmSJEllySKIJEmSJEmSJEkqSxZBJEmSJEmSJElSWbIIIkmSJEmSJEmSylJ1mo1CCC8D9wC/ijEuLW1IkiRJkiRJkiRJhUvbE2Qa8L/A7BDCgyGE/yphTJIkSZIkSZIkSQVLVQSJMZ4ADAHOBoYDz4QQZoQQxoYQRpQwPkmSJEmSJEmSpDZJNRwWQIxxGXAHcEcIYRQwGjgZuDSE8ALJcFkPxxhXlSJQSZIkSZIkSZKk1mjrxOgrsz+rgQzQE7gTeNuhsiRJkiRJkiRJUkeQuidICKEH8GWSHiD7A9OBu4GfxRjfDyEMBm4h6S0ysvihSpIkSZIkSZIkpZeqCBJCGAccB3QDfgccFGN8NnebGOP8EMKPgb8VPUpJkiRJkiRJkqRWStsTZDfgEuDBGGNNC9tNAg4qOCpJkiRJkiRJkqQCpSqCxBh3TbldLTC+oIgkSZIkSZIkSZKKINXE6CGEY0MI5zSz7uwQwpeLG5YkSZIkSZIkSVJhUhVBgEuBNc2sW51dL0mSJEmSJEmS1GGkLYJsC7zWzLo3gFHFCUeSJEmSJEmSJKk40hZBVgJbNrNuK5LeIJIkSZIkSZIkSR1G2iLIM8BlIYRNchdm/74UeLrYgUmSJEmSJEmSJBWiOuV2FwEvA2+HEB4HZgObA4cBy4ALShOeJEmSJEmSJElS26TqCRJjnAF8AriDZH6Qo7K/bwd2iTHOLFWAkiRJkiRJkiRJbZG2JwgxxrnA+SWMRZIkSZIkSZIkqWjSzgkiSZIkSZIkSZLUqaTqCRJCqAa+AxwNbAX0aLpNjHGL4oYmSZIkSZIkSZLUdmmHw/oxcCbwBPA3YHXJIpIkSZIkSZIkSSqCtEWQrwAXxxh/VMpgJEmSJEmSJEmSiiXtnCBVwL9LGYgkSZIkSZIkSVIxpS2C3EXSG0SSJEmSJEmSJKlTSDsc1izgayGEp4GngSVN1mdijHcWNTJJkiRJkiRJkqQCpC2C3Jz9PRQ4IM/6DGARRJIkSZIkSZIkdRhpiyAblTQKSZIkSZIkSZKkIktVBIkxri11IJIkSZIkSZIkScWUticIIYRBwNnAbsDWwDExxjdCCN8BXokxvlKiGCVJkiRJkiRJklqtMs1GIYTdgDeB44E5QAB6ZFcPBc4rSXSSJEmSJEmSJEltlKoIAvwv8CIwCjgFqMhZ9zKwZ5HjkiRJkiRJkiRJKkjaIshuwC0xxg+BTJN1C4BNixqVJEmSJEmSJElSgdIWQZYCg5pZtw0wrzjhSJIkSZIkSZIkFUfaIshjwFUhhGE5yzIhhI2Bc4FHih6ZJEmSJEmSJElSAdIWQS4EVgKTgb9kl90CRGANcHnxQ5MkSZIkSZIkSWq7VEWQGOMiYA/gbGAu8BwwG7gC2CvGuLRUAUqSJEmSJEmSJLVFddoNY4yrgHHZH0mSJEmSJEmSpA4tVREkhNBtfdvEGFcXHo4kSZIkSZIkSVJxpO0J8gGQWc82VQXGIkmSJEmSJEmSVDRpiyCnsW4RZGPgYGAUcF0xg5IkSZIkSZIkSSpUqiJIjPGuZlb9KIQwDti2eCFJkiRJkiRJkiQVrrIIbfwGGF2EdiRJkiRJkiRJkoqmGEWQXYAPi9COJEmSJEmSJElS0aQaDiuEkG/Oj27A9iTzgtxczKAkSZIkSZIkSZIKlXZi9BPzLPsAmAWcC9xWtIgkSZIkSZIkSZKKIO3E6FuXOhBJkiRJkiRJkqRiKsacIJIkSZIkSZIkSR1O2jlBLmlFm5kY4/fbGI8kSZIkSZIkSVJRpJ0T5HySidB7Zv/+AOiR/fdKYHXOthnAIogkSZIkSZIkSWpXaYfDOgSYA4wG+sUYewH9gG9ml38+xjgw+7NxSSKVJEmSJEmSJElqhbQ9QW4BfhBj/Hn9ghhjLXBfCKEncCuwWwnikyRJkiRJkiRJapO0PUF2BmY1s+4dYMfihCNJkiRJkiRJklQcaYsgU4H/F0LYKHdhCKEbcHZ2vSRJkiRJkiRJUoeRdjisMcAfgVkhhKeAecCmJHOF9AW+UJrwJEmSJEmSJEmS2iZVT5AY47NAAB4ERgBHZn8/CITsekmSJEmSJEmSpA4jbU8QYoyzgHNKGIskSZIkSZIkSVLRpC6CAIQQRgG7AlsD98UY54YQRgDzY4y1pQhQkiRJkiRJkiSpLVIVQUIIvYE7geOAOpJhtJ4B5gI/BGYA55cmREmSJEmSJEmSpNZLNScIcAOwHx9NhF6Rs+5x4NDihiVJkiRJkiRJklSYtEWQY4ALY4zPAB82WTcDGFbMoCRJkiRJkiRJkgqVtgjSE5jXzLo+wNrihCNJkiRJkiRJklQcaYsgE4ETm1n3ZeBvxQlHkiRJkiRJkiSpOFJNjA5cAfw5hDAIeBjIAIeEEL4LfBX4XInikyRJkiRJkiRJapNUPUFijM8DBwH9gTtIJka/FtgBOCTG+PeSRShJkiRJkiRJktQGaXuCEGN8AdgrhNAb2ARYHGNcVrLIJEmSJEmSJEmSCrDeIkgIoQewEDg+xvj7GONyYHnJI5MkSZIkSZIkSSrAeofDijF+ACwG1pQ+HEmSJEmSJEmSpOJINScIyTwg3w0hVJUyGEmSJEmSJEmSpGJJOydIL+DjwPQQwjPAXCCTsz4TY7y02MFJkiRJkiRJkiS1VdoiyAnAWqACOCjP+gxgEUSSJEmSJEmSJHUYqYogMcatSx2IJEmSJEmSJElSMaWdE0SSJEmSJEmSJKlTabYIEkKYGkL4eJNlJ4cQNi59WJIkSZIkSZIkSYVpqSfISKBH/R8hhCrgTmB4iWOSJEmSJEmSJEkqWGuHw6ooSRSSJEmSJEmSJElF5pwgkiRJkiRJkiSpLFWvZ32PEEKvJtvmLmsQY1xR1MgkSZIkSZIkSZIKsL4iyLN5lr3YzLZVBcYiSZIkSZIkSZJUNC0VQb65waKQJEmSJEmSJEkqsmaLIDHG+zZkIJIkSZIkSZIkScXkxOiSJEmSJEmSJKksWQSRJEmSJEmSJEllySKIJEmSJEmSJEkqSxZBJEmSJEmSJElSWbIIIkmSJEmSJEmSylJ1azYOIRwK7AZsDXwvxvhOCGFf4K0Y4/ulCFCSJEmSJEmSJKktUhVBQgibAY8BuwIzgBHA7cA7wDeBD4BvlSZESZIkSZIkSZKk1ks7HNbNQB9gu+xPRc66Z4ADihyXJEmSJEmSJElSQdIWQT4PXBZjfAvINFk3C9iyqFFJkiRJkiRJkiQVqDUTo69tZvkgYGURYpEkSZIkSZIkSSqatEWQF4HvhhCqcpbV9wg5GfhLUaOSJEmSJEmSJEkqUKqJ0YELgQnA68AjJAWQU0MIOwE7AXuWJjxJkiRJkiRJkqS2SdUTJMb4OrAbMBEYTTI01tHAu8CnY4xTSxWgJEmSJEmSJElSW6TtCUJ2UvQTSxiLJEmSJEmSJElS0aTqCRJCGBtC2L7UwUiSJEmSJEmSJBVL2onRTwdeDyG8FkK4JITwsVIGJUmSJEmSJEmSVKi0RZAtgIOAl4AxwNQQwsQQwrkhhKEli06SJEmSJEmSJKmN0k6MXhdj/EuM8XRgc+Aw4D/ApcD0EMKEEsYoSZIkSZIkSZLUaml7gjSIMa6NMT4FfAv4NjAH2KvYgUmSJEmSJEmSJBWiujUbhxA2Aj4PHAccAfQEngeuKH5okiRJkiRJkiRJbZeqCBJCqC98fAnoB0wALgYejjHOL114kiRJkiRJkiRJbZO2J8jjwCvAWOChGOP7pQtJkiRJkiRJkiSpcGmLINvEGGeUMhBJkiRJkiRJkqRiSjUxugUQSZIkSZIkSZLU2TTbEySE8AowOsb4RgjhH0CmpYZijHsUOzhJkiRJkiRJkqS2amk4rEnAypx/t1gEkSRJkiRJkiRJ6kiaLYLEGL+Z8+/RGyQaSZIkSZIkSZKkIkk1J0gI4Z4Qwohm1g0LIdxT3LAkSZIkSZIkSZL+f3v3HSdXXS5+/BNaCDUQiighSHssgIKhixDa9SIWkItcVMoPQUDACAjSu3gFsaEIKE0viCjSpMfQO1wRAb8USQSkSUmoCUn298f3TLKZ7G4mO7M7e85+3q9XXps9c2b3efZ7Zs6Z83xLcxoqggC7A8t289gywG4tiUaSJEmSJEmSJKlFGi2CQPdrgqwJvNyCWCRJkiRJkiRJklqm2zVBIuKbwDeLbzuAyyNiSt1uCwPLA+f3SXSSJEmSJEmSJEm91G0RBHgU+AMwBDgIGA88X7fPVODvwO/6JDpJkiRJkiRJkqRe6rYIklK6EbgRICLeAH6ZUnquvwKTJEmSJEmSJElqRk8jQWZKKR3f14FIkiRJkiRJkiS1UkNFEICI2AjYE1iDvBbIbFJK67cwLkmSJEmSJEmSpKbM18hOEbE1cCuwIvBJ4GXgTeBjwAjgb30VoCRJkiRJkiRJUm80VAQBTgB+DHym+P7olNIW5FEh7wE3tz40SZIkSZIkSZKk3mu0CPIR4FpgBtABLAqQUpoIHAcc2RfBSZIkSZIkSZIk9VajRZB3gflSSh3A88CqnR6bTJ4mS5IkSZIkSZIkacBodGH0h4AAbgTGAYdHxHPAVPJUWQ/3TXiSJEmSJEmSJEm90+hIkB+Rp8ECOAJ4C7geGA8sB3yj9aFJkiRJkiRJkiT1XkMjQVJK13T6/3MR8QlgNWAY8PeU0tQ+ik+SJEmSJEmSJKlXGp0OazbF2iBPtDgWSZIkSZIkSZKklum2CBIR+83Dz+lIKZ3ZgngkSZIkSZIkSZJaoqeRIGfMw8/pACyCSJIkSZIkSZKkAaPbIkhKqdFF0yVJkiRJkiRJkgYcCx2SJEmSJEmSJKmSGl4YPSKWAw4GRgMjge1TSo9ExDeBe1NKd/VRjJIkSZIkSZIkSfOsoZEgEbE+8ATwRWACsCowtHh4BXJxRJIkSZIkSZIkacBodDqsHwLjgTWArwNDOj12L7B+i+OSJEmSJEmSJElqSqNFkHWBn6eUZgAddY+9AizX0qgkSZIkSZIkSZKa1GgRZBKwbDePrQK82JpwJEmSJEmSJEmSWqPRIsgVwPERsUqnbR0RsQxwCHBZyyOTJEmSJEmSJElqQqNFkO8Ak4FHgVuLbb8AEvAOcEzrQ5MkSZIkSZIkSeq9hoogKaXXgA2BbwATgZuAp8nFkU1SSm/0WYSSJEmSJEmSJEm9sECjO6aUpgK/Kv5JkiRJkiRJkiQNaI1Oh9WtiBgTEde2IhhJkiRJkiRJkqRW6XEkSEQMBz4NjAT+AVyZUnqveOy/gMOAdYHH+zhOSZIkSZIkSZKkedJtESQi1gJuAJbvtPnBiPgicBF5jZBHgS8Dl/RlkJIkSZIkSZIkSfOqp5Eg3wUmA18AHgJGAT8F7gOGArullH7T5xFKkiRJkiRJkiT1Qk9FkNHAN1NK9xTfp4jYF3gC2NsCiCRJkiRJkiRJGsh6Whh9eWBC3bba9w/1RTCSJEmSJEmSJEmt0lMRBKCjm+3TWh2IJEmSJEmSJElSK/U0HRbA9RHRVcFjXP32lNJyrQtLkiRJkiRJkiSpOT0VQY7vtygkSZIkSZIkSZJarNsiSErJIogkSZIkSZIkSSqtua0JIkmSJEmSJEmSVEoWQSRJkiRJkiRJUiVZBJEkSZIkSZIkSZVkEUSSJEmSJEmSJFWSRRBJkiRJkiRJklRJFkEkSZIkSZIkSVIlWQSRJEmSJEmSJEmVZBFEkiRJkiRJkiRVkkUQSZIkSZIkSZJUSRZBJEmSJEmSJElSJVkEkSRJkiRJkiRJlWQRRJIkSZIkSZIkVZJFEEmSJEmSJEmSVEkWQSRJkiRJkiRJUiVZBJEkSZIkSZIkSZVkEUSSJEmSJEmSJFWSRRBJkiRJkiRJklRJFkEkSZIkSZIkSVIlWQSRJEmSJEmSJEmVZBFEkiRJkiRJkiRVkkUQSZIkSZIkSZJUSRZBJEmSJEmSJElSJVkEkSRJkiRJkiRJlWQRRJIkSZIkSZIkVZJFEEmSJEmSJEmSVEkWQSRJkiRJkiRJUiVZBJEkSZIkSZIkSZVkEUSSJEmSJEmSJFWSRRBJkiRJkiRJklRJFkEkSZIkSZIkSVIlWQSRJEmSJEmSJEmVZBFEkiRJkiRJkiRVkkUQSZIkSZIkSZJUSRZBJEmSJEmSJElSJVkEkSRJkiRJkiRJlWQRRJIkSZIkSZIkVZJFEEmSJEmSJEmSVEkWQSRJkiRJkiRJUiVZBJEkSZIkSZIkSZVkEUSSJEmSJEmSJFWSRRBJkiRJkiRJklRJFkEkSZIkSZIkSVIlWQSRJEmSJEmSJEmVZBFEkiRJkiRJkiRVkkUQSZIkSZIkSZJUSRZBJEmSJEmSJElSJVkEkSRJkiRJkiRJlWQRRJIkSZIkSZIkVZJFEEmSJEmSJEmSVEkWQSRJkiRJkiRJUiVZBJEkSZIkSZIkSZVkEUSSJEmSJEmSJFWSRRBJkiRJkiRJklRJFkEkSZIkSZIkSVIlWQSRJEmSJEmSJEmVZBFEkiRJkiRJkiRVkkUQSZIkSUxX3BwAACAASURBVJIkSZJUSRZBJEmSJEmSJElSJVkEkSRJkiRJkiRJlWQRRJIkSZIkSZIkVVKpiyCv/fMfXHPEPpy3w8Zc9NX/4IFfn8mM6dPn+rzJkydx4IH7svrqK7Hqqiuyzz578uqrr8y2zzXXXM3o0WvzkY+swqmnnjLHzzjttO+x6647tyyXRpiv+XbFfM3XfGdnvn1vsOUL5cw5pb/zxS9+llGjlmettdbge987iektjHmVVVbh+2d+hfF3XTzHz7j5rou56IqT5jnmdrF9B377mm9r8x09em2WW265AZNvM8r4+m1GGfP1eG6c7Vvt9oVytnEzzNd8u2K+5tuOfBdo6U/rR1PemMy1R+7L8JGrsPXRp/PG889yzy9/SEdHB6N33a/H5+611+489dSTnH76T5lvvvk48cRj2G23XbjqqusBeOWVV9hvv7046KBDGTVqFAcddCCjR6/PmDFbAvD88//i7LN/znXXje/zPGvM13y7Y77ma77ma759q4w5v/76a+y44+dYY40PccEFFzNhwtMcd9yRdHTM4PDDj2lJzMccczQP3PYqV954BiNXCFZbeV0AJr/xCnc/eCV77fKDeYq5XWzfgd++5tv6fA866FDWWutD7PrVPdqebzPK+PptRhnz9XhunO1b7faFcrZxM8zXfLtjvubbjnxLWwR57NrfM23KFLY66lQWWmQxWAemvv0WD150FmvvuGve1oX77ruH8ePHccUV17LRRpsAsMIKK/DpT2/BLbeMZ7PNxvDAA/cycuRIDjzwWwDcfvtt3HLL+JmNcfzxR7PLLruyyiqr9k+ymK/5mi+YL5iv+Zpvf+cL5cz5ggvO5d133+X883/D4osvAcAbb7zBaaedwv77j525rZmYDzvsME546WqefuZhnpr4l5k3JW647TzWWXNrRiz1/nmKuV1s34Hfvubb+nwPPPBbLLvs4px68vltz7cZZXz9NqOM+Xo8N872rXb7QjnbuBnma75dMV/zbVe+pZ0O69n772TFT2w02x991U9tw/QpU3j+4Qe7fd64cTey7LLLzWwIgHXXHc1KK63MuHE3AjB16nssvPCwmY8PGzaM996bCsD999/LbbfdwsEHH9rqlHpkvubbFfM1X/M1X/Pte2XMedy4GxkzZovZbj5sv/0Xeeedd7jzzjtaGvOCCw5l+oxpADzzr7/z9D8fYrMN+3/Kst6yfQd++5pvtfNtRhlfv80oY74ez42zfavdvlDONm6G+ZpvV8zXfNuVb2mLIK8/O4ElV1x5tm2LLbcCCwxdmEnPTuj2eU8++QSrr77GHNvXWGMNnnzycQDWWmttHnvsEW6//VYmTpzAn/50JR/72Dp0dHRw1FGHcfjhR3fbo6GvmK/5dsV8M/M1X/PtP4MtXyhnzk8++TirrTb7715xxZEsssgiPPHE4y2Jefz48bw26QUee+JOPrD8anR0dHDtzeewxSZfYeGhi8xzzO1i+w789jXf1ud7++238vTTTw+IfJtRxtdvM8qYr8dz42zfarcvlLONm2G+5tsV883Mt//zLe10WFPenMzQRRefY/tCiy3BlDcnd/u8119/nSWWWHKO7UsuOZyJEycAMGrUyowdewg77LAdAFtttQ077PBfXHLJRbz33jR22eWrrUliHphvZr6zM9/MfM3XfPvPYMsXypnz66+/zpJLdv27J016vSUxb7HFFgCs/sHRrPmhzXjo0T8zY8Y01llz617F3C6278BvX/PNWplv7ZgcCPk2o4yv32aUMV+P58bZvtVuXyhnGzfDfDPznZ35Zubb//mWdiQIAEO62tjR3QOznjZkzsc7Ojpm237IId/hscee5oEH/sZFF/2ed999l+9+9wROOul7TJs2jUMOGcuHP/xBNttsQ+6++67m8miU+WK+XTzNfM3XfM3XfPteCXNu5Hc3E/PLL7/Mt772K76y/bFMmzaVm26/kE9vvhczZkznqpt+xv+c+WV+dsH+THzukYZjbhvbd8C3r/m2Nt/HHnuaCRMmDJh8m1LC129TSpivx/M8sH2r3b5QyjZuivlivl08zXzNtw35lrYIMnSxJZj61ptzbJ/61psMXWzOSlXN8OHDmTx50hzbJ0+eNEcPhhEjRjBy5EoA/OQnp7P++huy0UabcMEFv+KRRx7mrrseZOzYQ9h7792ZMmVKkxn1zHwz852d+Wbma77ma759qYw5Dx8+nEmTuvrdk7vsmdObmJdZZhmGL7EcALfdeykrfeDDrLzimtz/0LW88PLTHLjHWXxqg5249OrvM23ae3ONuV1s34HfvuZbi7t1+Y4YMYJRo0YB7c+3GWV8/TajjPl6PDfO9q12+0I527gZ5puZ7+zMNzPf/s+3tEWQ4SuuzOvPTJht25svv8C0d9+ZY86yzlZbbfUu56Z84ok557Ks+ec/J3Leeb/kmGNOAPKq9TvuuBPDhy/F9tvvyNSpU3jqqSd7nUsjzNd8u2K+mfnOYr7ma76tV8acV1tt1pyrNc899yxvv/1Wl3O0NhPza5Ne5L6HrmHrTfcA4OlnHmbtD23OsIUXY60PfYrp09/jldeem2vM7WL7Dvz2Nd9q59uMMr5+m1HGfD2eG2f7Vrt9oZxt3AzzNd+umG9mvrP0V76lLYKsOHpjnnvwLqa+/dbMbf+49QbmHzqUFdZat9vnbbnl1rz00ouzDaX5y18eZOLECWy5ZddzSh533FHsuederLTSqJnb3n77HQCmT5/OlClT6ejoaDalHpmv+XbFfM23nvmar/m2Xhlz3nLLrRk/fhxvvvnGzG2XX34Zw4YNY+ONN2lpzDfcei7rf3w7llpy+Znb3puWe+rMmDGdadOn0UHft1Nv2b4Dv33Nt9r5NqOMr99mlDFfj+fG2b7Vbl8oZxs3w3zNtyvma771+ivf0hZBPvyfOzLfggty08nf5rn/u4e/X3sZD150Nmt94SsstMhiM/dbbbXVGDv2GzO/X2+9DRgzZkv23//rXH31lVxzzdXsu+/X2GCDjdhsszFz/J4777ydBx64jwMOOGjmto033oTzz/8lN9xwLccddxSLLbYYq622uvmab5/n+7uvfZ4999zTfM3XfM3XfPspXyjnOWm33f4fCy00lN13/wq33DKeCy88j1NPPYV99vkGiy++xMz91l//Y03FPOGZh3n2+cSm6+84c9vKI9fkvoeuIf3jXm649TyGLjSMZZZaca4xt4vtO/Db13z7Jt9bbrllQOTbjMF2Tipjvh7PjbN9q92+UM5rDvM1X/M137Kck+ZmgaZ/QpsMXXwJtv3uL7jzzP/hhhO+xUKLLsaaX9iFdXf5+mz7TZs2jenTp8+27eyzz+Poow9n7NhvMGPGDLbZ5j84+eRT5/gdM2bM4KijvsORRx7LoosuOnP77rt/jccee5T99tub5ZdfnrPOOpehQ4f2TaIF8zVfgBnTp5uv+Zov5ltjvn2fL5TznDR8+FL84Q9Xcfjhh/DVr36JJZZYkn322Y9vf/uI2fab3mQ7XXvzL9nqk7uy0IILz9y+3trb8uLLE7ns2tNZbNGl2HHbQ1hggQXnGnO72L4Dv33Nt2/yHTt27IDItxmD7ZxUxnw9nhtn+1a7faGc1xzNMF/zBfOFauc7kM5JczOkP6aRAFYGnn7llTeZMWPef9+yyy7OXtc82KtffM626/Lyy2/MfccBxHwbV8Z8YfDlbL6NM9+Bz3wbZ77Vtuyyi3PCwVf36rnH/GC7tvytbN/GlbF9mzHY8oVy5jzYXsODLd9meDxXm+078Jlv48x34Bts+UL5cp5vviGMGLEYwAeBCY08p79GgswPOcDeGjFsoV4/t5nf2y7m27gy5guDL2fzbZz5Dnzm2zjzrbYllxrW6+e2629l+zaujO3bjMGWL5Qz58H2Gh5s+TbD47nabN+Bz3wbZ74D32DLF8qVc6ffN3+jz+mvkSCfBG7rj18kSZIkSZIkSZIqbVPg9kZ27K8iyFBgPeB5YPpc9pUkSZIkSZIkSao3P7ACcB8wpZEn9FcRRJIkSZIkSZIkqV/N1+4AJEmSJEmSJEmS+oJFEEmSJEmSJEmSVEkWQSRJkiRJkiRJUiVZBJEkSZIkSZIkSZVkEUSSJEmSJEmSJFWSRRBJkiRJkiRJklRJFkEkSZIkSZIkSVIlLdDuAFopInYA9gfWBYYBE4HfAT9KKf07IjqAA1JKZ7QxzJboKVfg98BmPTx9Ykpp5b6OsRUi4jjg2G4e/mpK6TfdPO/3wDIppc37KLQ+VeS9f0ppmS4eOx9YM6U0OiJ2B84DFk8pvdmvQfaBnvIuu07H8pMppdW7ePxJYFXgeGA0MCqltFY3P+sM4MvA+1JKU/os6Bbo4jX8PHAXcCgwEhg/lx+xR0rp/D4Jrg90ke+LwP3AESmlvxb7rAw8DXw2pXR1f8fYasX70AHAGsA0YAIwPqV0UBvDaqni+mFuxhRfOx/Tk4AEnJJSurzlgQ0AEbE5s+f8OvAIcGxKaVxbgmpC53NsD/tMAEZ18VC31yVlEhFrAg8DY1JKN9fl+x7wD+B/gf9JKU1tS5B9KCI2AY4ANiJfYz8OnAuckVKa3s7YeisijgG+DqwA/JPcnmullP7W1sDarDi2f59SOqTNocyzBt+rhpLPz18mn6OHkN+fzwHOL8Prt6c8I+LXwEYppdXqtt8DrA+MTCk922n7ycBYYMmU0rQ+DbyFGmnrwSQiPg4cTr7fsTTwb+AG4PsppUfbGVurdfUeFRGrALcCLwFbpJReb090fWOwXHNExOrka62jU0qn1j12JfBR4KMppXfbEZ9UVZUZCRIRPwAuJb9JfhXYBvgh8FnyhV5lNJDrfuQPbvX/Dih+xDX9HHKzJtF1Pte1M6gB4k/kv8Xb7Q5EDXkX+GBEzPYhJiLWI1/s1S5yLgbWjIiP1v+AiJgf2BG4bKAXQDrp/Bo+BPg4MA74P7p+bW8EPAS8CdzWhnib1TnfseQbDzdGxNJtjaoPRMThwC+B64EdgF2BK4DPtTOuPtD52Nyi2HZS3fYHO+3/5WLbLsArwGUR8al+i7Y9ajl/hfxedl1xo6KqLmJwXZfU8t0a+C252PvdtkbUByJiF+CW4tv/B2wLXA6cAlwaEaX77FRccxwPnAFsApzY3ojUHyJiGHATcBT5vPx58rn5SvL5a9/2RdcydwKrRsRytQ0RsTCwDvmz0cZ1+28M3FumAohmV3QEvRcYAXwL2Ir82WIZ4I42htYvImIl4M/Aa8A2VSuAdFL5a46U0hPAqcCxETGytj0ivkC+r7e/BRCp9SoxEiQiPgscBOyZUjq300O3RMTZ5CJBJTSSa1c9ICJiUeBC4DHg4H4JtnWmpZTubncQA1FK6WXg5XbHoYa9Rb5RujN5dEDNzuQL2k8U319B/vC2M3B03c8YAyxPLpSURefX8N0R8U9ycWOblNKl9TtHxN7Ax8ijQJ7qxzhbpT7fCeTRL58mX9RXyf7AWSmlIzptuyoijm9XQH2h8zkoIhYr/vtU/bkpImr//Wuth3VE3Aw8Qy4O3NrnwbZP55xvIee8F/CNtkbVd54fZNcmnfO9JSJWBPaJiG+nlBoZKTXgRcQHgLPJvW537vTQ+Ii4m9yJaB/g5+2IrwkfKr7+LKU0uRgdoOo7iTxjwAZ1I35uioifMeu4KLM7i68bk4uVkEdTTym+35g8UwIRsQCwHnnWhEqLiGEppXfaHUerRcT7gQvIn4F2rzv3XBQR27Unsv5R5D+O3NFkq5TSv9scUl+q/DVH4WRyh6kfAzsU9+x+TO7seG1bI5MqqhJFEHIvgAfrigIAFMPWq/QG0ttcf0zuab5+1S6Kisr5WeSbwy+SL/oHhapNh1UvIkaQe318DliKPO3bmSmlMn+A+S1wXO0iLiKGADsBx1AUQVJKb0bE1cCXmLMIsjP5OJ/bNFID2QPF15XrH4h8F/mHwCVlmgZrLh4qvo7sca9yGg68UL+x/gNK0SP1ePIx/T7gX8BvU0qH90eQ7ZRSeruY7q6U7R8RG5GnnRgNLAk8AZyaUvrf7p5TvIc9Thev8bIoeuJ9j5zD/cDeVZtmoyYi9iO38dLkgvxPGnjaA8Ce5N63pemMERGfJN90WA94B7gMOCil9AbwNWBh8lRYs0kpXVsUNMdSoiJIMY3ObsW3k4pCbW3qvmUi4lLgP8nTqpyWUipNbo2IiP2Bw8jH9o3AT8mjI8aklG5uY2gtFxELka8xRwPbkac/+0VXU56llF5lVgGhVOry3AqYzOxFkI3JIwVuJ79H1XwMWJSS5t1Z3d9gC/Ix/QfydJRfJ3eWWrBtATYhInYif/ZZg/y+dCF5es1p5PfohYCDu7oRXsYpZrs5/95A3XtUMdppHHlKuy1SSi/2f7St0WjOdcp6zdFjrimldyPiQODqiNgW2Jw8ymlsm0Ju2txyjohFyNfXO5E/Rz4MHJlSuqFNITelmLnjB+QpGIeSpxw9I6X0s26ms9udkt2/i4gtyO8/H0gp/avYdhc55xG1EWkR8TD53LsH8PWU0gWdfsYQ8kxGf2z3lNmlG9JdLyIWJF/sVHkKAqD3uRbDRvcEvpNSemhu+w9EEbFA/b9i+xByr/k1yTkeBHyTPHyy9LrJe0i74+oPxU3Tm4EvkKdu2JZ8gnl/G8NqhcvIH04+WXy/KbAs8Me6/S4GVo+I2uiQ2nvA9sDvyjoveWHl4utsN8+L/C4iz+27Tz/H1JdWKr4+3dYo+saDwAERsVtRtJxDp/fpfYGfkV/Lx5I/yFReMX3OipS3/UeRp5j4Gnl4/h+A8yLiv7t7QjFt30i6KJCVxCjgdPK5Zxdy8ef6YpqVmiF15+f52xFosyLi8+TX5dXkKe0eJq9/MTcrA1OBV/ssuBaLvNbHOPJxuSP5JsO25A+jAJ8ij2j6Rzc/4nLyeblM1yEnMqtz0Bbk6+Mliu/PIRfptydfb/0sItbv7wD7SkRsTy56XEnO8a/Ar9oaVB8p3pv+SL7Rvyn5PWtRKvb5uD7PlNLjwD3MPu3VRuTRt3cBHy8+T1Ds01FsL60u/gZPFg/tQl4jYz9yh5PSiYhtgEvI15afJ79+DyFP5Qc5v/urMgJiHs6/S5MLXcPIBZB/9VuQLTbIrjkayjWl9Cfy9cXZ5A7Px6eUnunHUFumwZzPId8kP5l8bn4G+FPRSaWMrgSmk0f8f478vrV4WyNqvbvI6/NsClAUsj5Bfk1uUmxbmryOzRXkc9QedT9jc/Lr+DzarAojQUYwq+JWdfOcazG0/xzyfO1l7T0/gvyim01EfBD4CHne1w1TSvcU2x8AniL3Vi2zLvMuPNDN9irZlfxGum5K6S/Ftj+3MZ6WSCm9HhHXkUd03FZ8va7Y3nnXa8k9unZmVnv/B/lCuExTYQEzpyEAWIXci/YN8gV9ZyeRP9RtXvY5bjvlO4r84e0v5IuCqvkG+cL9fKAjIh4j3yQ/LaU0udhnG/Kcvp9PKV3Z6bkX9meg/Wz+4hhYCjiUfDPqx+0NqXdSSr+t/b8oaN1KLursxezvRbWclwaOJC/AXF/cLYtlyMfrnTDbdcXuwC+KfQ4q/tU8R/67lM2R5HNQbX2A6yNiWXLRq7MhRfsuRL6Zvg9wVckK8t8D7kwpzbxBGBHPAeMiLwb/AfK0sd2ZWHz9AHk024CXUnoqImrTSt5XjNLavPj+4pTSSTBz2r7Pkm9a3NvvgfaNI4BrUkq1KfluiIhlqMZaGDMVNyOuJL//fCql9Fwxgg8q9Pm4qzyLh+4EDouIhYpFkzcir1X2N/K0QeuRz1sbA4+mlF7r9+BbpIe/Qc12JV9D4ATg5pRSbfTadcVno1Mi4iTye+//tSu4PtDo+XcPcgFvnZRS2V/Tg+mao9FcIY9+epjcU/6H/RRfX+gx54j4MPDf5CmvLyi2XU/upHA0+V5HaRTXFKsAX0gpPVxsHtfGkPpESumd4rPQpuRC9YbkUZg3Fdv+RO7g20E+J08jX3Ot0qlj0R7AA53+Tm1T+pEgnVRpbsC5aSjX4mbFheTK5G4lnj9xEvkCtv7fv8hDsF6sFUAAUkoTqUaRoLu8SzfUt5e2AP6vUwGkSn4L7Bh5Xu4di+9nUyx6/kdgp+K1DLln10SgbPPQ1wp67wGJfLHwpZTS87UdImIMubfXySml29sSZet0zvdJcqF2hxItZN+wlNJfgQ+Te778nDxS7Wjg/pi1dsYWwKt1BZCq+wu5/V8i3yjfPaWU2htS70TEUhHxk4iYyKzjem/yVBWd1XJ+kTwy87AyTk1ReKlWAIHZris695L/DbOfm7ft1whboBi9sg5zFmgv62L3g8jt+xZwFfmmYmnWeyluHm4E/K5udO3t5Lw+0eMPmF1Zr6frzZx6IqX0HrnzUBkLeXMoju2Pk28Yd1a181BttMfywGZd3BSvyrHaU553kKexWzciVi32uTulNAO4j1mjRDam3FNhza2tx5W5AFK8ZtcF6tcKvIR8z6pW2KvEMT2P59/byAW904qp0EppkF1zzEuukK+rO8jn4FX7MLQ+02DO65E/K858nRfv1Zcya5aMMnmVPJLlFxHxpWLauqq6jWIkCHnk9G3ALXXbHio6QY4j37PaDSAiFid3smn7KBCoxkiQV8iLn600tx0rYF5z/Tb55tN2ZZ43krzI8P1dPRAR7yPfZKr3EuUfhtZl3hHxCrmHbdWNAJ6f617ldCW5l9rJ5A81V3Wz38XkqvlGEVEbGv6zEhY0J5Hnbe4gT0Pyr845RMRS5ILtPeSpO8qulu/85JEtp5EXbNykuNCrlKK4c1Xxj4jYk3x870ke/VDl13J3diaPHFiJPMLpvIi4t6RTGJxP7vFzIvAouefPvuT3o85qOb8GTCzm7y6r7q4rOp97X+zu2qREliV/FqjPt6v8f0N+PU8BJhRraJTJUuT35J/T9ZoeI8mjeUb18DNqj1Xl/ax+xOVU8s3kKqgd2/Vzx5dmLvkGvR9YndyBpPNnvdoN8pWAx/s9qtbrLk/IHYNmkIscLwEppfRK8dhdwMbFFHYrkQsmZdXT3wByB4QyW4a8jkl9HrXvlyYf11W55zMv5997yZ8lLgMujIj/LuFnQRhc1xwN5xoRHydPY3dA8e+n5FH0ZdNIzisAb6aU3q7b50VgkYgYWqZOgymlGcU0fieTp/0aFhF3AAemlKo0ag1yIfLgiBjOrNEftwE/KqZp3LT4nmLd2/OA/xcRx5HXf1mAPO1525V+JEjRc+kOSjZ0qjfmJdeIWJd8w+KnxTyDVfUC0FXFtcpV2MGissWelNJb5BE93yIP7X2rm13/TL4o2Bn4DLmwV7qpsCgKeimlB1JKz3Vx4X42eY7yL5f8xmlNLd97Ukpnk3subQj8V5vj6hcppV+Re8Z8qNhU2ddyDx4pjoHLyFPMLEIeIVMqxUXtZ8iLkp6RUvpzceO/q+vHWs5PVeB13N11RVVufte8TB6yXp9vV/m/WLTvwyW8GQH5hn8HeT2irkbZnkvu0bZWMd1qVz5H7tlWxmLmYFM7tpet217/fdk9Qe4sc1REdJ7m635yD+qqfD7uLk+K96O/kYsgGzP7mh93kUcQbFJ8X+YiSLd/g0IZb4p39m9yz//688/yxddXyWsXjS7mni+7eTn/Uoym3ps8K0App1dlcF1zNJRrMdvDmeT37J8D+wNbR0QZPzM2kvPzwGLF6NzOlgfeLlMBpCal9PeU0hfJi7xvRe5M8qdiTch3yVO6dVbW9687yKN4Niff17gVeAR4E9iSPJLvtk77n0fuYDSGPJ3w5QNlOsrSF0EKPyKfEHerfyAi5ouIT7chpr4y11yLN5WLyNPOHNrfAfaz+4DlI2KD2oaIWIn8IlS5jQPWiYi12x1IHzmT3HP+F93tUMx7ein55vkuwGPF9EOVUYwa2BHYL6VU1oWj5+Y35IuEw9odSKt1Ney3mPt1SWb13hsHLB0R2/VnbANFSukp8siY3Us4THoouff8zA8lxZDmz7Utov6xXETMXGi303VFVdZKAGaeY/7CnKN6dmhDOH2q6GxwNxDFjZX6f/8iL5o9hVkLic9U9PTbAji/pD1wB5Ueju3KvXellH5NvnF2RkR8pdj2DnAWsG9EfKT+ORExvNO6IaXQVZ6d3EEugNQWRa+5mzzCYHfyNIdPUmJz+RuUWvGafYA5OwztRB7pcxf5Pfo98qiIOUTEZ/oyxlbqzfk3pXQuea2jAyLiyD4Mr08MsmuORnPdizzV6n4ppY6U0k3kz/6nR8SifR9p6zSY833kgu2OtQ1FIWhH8vSkpZVSei+l9GfgdHLnv+HAs+Rpozvbur9ja4WigPE3cife6eRp6zvI7XYoeaTH7Z32f4Y87erx5KnOBsRUWFCN6bBIKV0VEacDv4qITcjz0L1J7oW6DzCBPIcmwMcjYse6H/FySumW/oq3GQ3m+nkgyG+qH69bbLn2c8q0psACEbFhF9ufAa4BHgIujYjDyNXWE+h6WKXKoXZz4UJyD/obimF0CfggsEZK6Tttiq1lUko3k3s0zc3F5A882wPH9GFI/a6Yu/lH5A+pT3XzOn82pfRs/0bWWsWQ0O8C/xsRW5KnDALYpOhp39mEkk2x83BEXEG+yHmJPF3MIcDbwAXFPjcC15OnBDsBeJB8cfiplNLX+z/ktvg++Zx8ACUaEZJSmhQR9wHHRMRk8o2I75CnfFuircH1rX8Dv46Io4F3mHVdcX47g+oj3wUui4gzyetQbQZUqfNQZ4eSF0GfAfweeIM8tcpngCNTSo9HxN7ktl+CXLycRP6bHEp+7zqlLZGrN2rH9hnkaUg3Ibc15PeymtIXtVJKZxbrcJ0XEW+mlC4HjiLfXLsjIn7IrFEQG5DPRd9j9oLBgNdNnpDX+tgXeB+dckopvRIRjwP/yZzz1JdSD3+DKjiWvJDyeeT1Etciz2xxTu2zQETsxsWrbgAABvhJREFUDlwcESuSR/A9R14w/Uvk9+oy9bKe5/NvSumUYjrwkyLihWL0dZkMpmuOHnMtFtU+BTgzpfRgp+d9C/g7+XN/2TrQ9ZhzSumxiLiYXMhdgrx25l7ke5ldjXAb0IrOuqeR1y76B3nq1cPIa2O8GhF/BH4aEUeQC0A7AB9tV7wtUFub5/qi6AV59MepwBMppRfq9v8Vuaj3LPl+wIBQlZEgpJQOJp/8ViePgrgROJjcA7XzC2pPckN0/nd8vwbbpAZy/c9i13PIF4Jd/SuTJek6hz2K6uPnyPOUn0u+oXoG5cuxWaX/AFcYRp6TmmJxvy3IoyVOAK4l34QYbNNQ3EUubg6hiwXUS25TYDHykMru3qu+1rboWusS8lQGnUfnfYc5z0f7939oTTkBWBn4CbkQciJ51Mv6tZE9xfv09uRpz8aSX8snkW80DwopL6z9G2C/svXsIo9Ce5pcmP4x8Ifi/1U2kbyu2nHk993JwH+UedHZ7qSU/ki+IfpZ4HLyopZ7tjWoPpJSup28cOOywK/J1xeHkjvVvFjsc1Gxz3zkotd48nFwHblwW7qpGgar4tg+EPgC+dhej1ykh/yahk7XnWWXUjqVfEPttxGxdTEaZCvyXOU7MGvtru3Jhfmz2hVrM+rzLDbXCjxvkq9BOruLfA1d5kXRZ9PN36D0Uko3kKcAHk0+VscCP6DTtXFK6Q/kQt7r5GuSWs/rt8jHe2k0cf4dS+4kd1ZE1Pe6H9AG2TXH3HL9Pnn6qKPqnvcc+fPVtyLiQ5RIg+27F7mj3NHk4vQo8hrGZRwJ8gL5+vFI8ufbnwOPMWvU6dnk+5MHAr8jX2/MMdq4RGrTXd3axbau2u9q8jF+QRpA66IO6eioyr1TafCJiAOAU1NKlVjIMiJ+D3wgpVSqIfqSJKl6ImIBcoF3VWCDLnq5qUQi4ijyzYqlyfN0vwJ8I6VUyoKApGqJiDWBh4ExxawBlTeYch5MudYMxpyVRcS25ELIGgNpOspKTIclDTYRMT+5p+JXyfOnllpEfJS8aNJ25KHQkiRJbZVSmlYsUHovcFVEbJZServdcWnuivWpDieP5nmbPPr0MPIInw2Br5Pntb6mTSFKkiRVSkS8nzxr0feAawZSAQQsgkhltTh5jv2HyR/iyu4Mci/LH5OHNEuSJLVdSukV8jWKymUqeZ7xXclT6z5Pvs78FXlxz8eAzxaLd0qSJKl5e5OneXuQPD3agOJ0WJIkSZIkSZIkqZIqszC6JEmSJEmSJElSZxZBJEmSJEmSJElSJVkEkSRJkiRJkiRJleTC6JIkSZIGjIj4PbBMSmnzBve/Gdisi4eGpZTebWFokiRJkkrIIogkSZKkshsPHFG3bUo7ApEkSZI0sFgEkSRJktSvImLhFo/SeDWldHcLf54kSZKkirAIIkmSJKkpEbETcDSwBvAScCFwbEppWkTsDpwHbAB8v/j6XeDEiBgJnAWMAV4ETur/6CVJkiRVmUUQSZIkSb0WEdsAl5ALH98G1gZOBEYA+3Ta9WLgTOB44PWIGAJcASwD7Am8Wzy2NPDEPIaxTUS8Xfz/NuDbKaW/9iohSZIkSZViEUSSJElSM04Abk4p7VZ8f11EAJwSEZ1HdvwkpfTj2jcRsS2wDrBhSumeYtsDwFPMWxHkFuAC4ElgFHAkcFtEfCylNKF3KUmSJEmqCosgkiRJknolIuYH1gXG1j10CfA/wEadtv2pbp/1gRdrBRCAlNLEohDSsJTSsZ2+vS0ibgL+XsRUH5ckSZKkQWa+dgcgSZIkqbSWARYkr+fRWe37pbvYVvM+8voh9bra1rCU0gvAHeTijCRJkqRBziKIJEmSpN76N/AesFzd9uWLr6922tZRt88LXTyPbrb1Rv3vkyRJkjQIWQSRJEmS1CsppenAA8B/1T20EzADuKuHp98HLB8RG9Q2RMRKNDmCIyKWBzYp4pIkSZI0yLkmiCRJkqRmHAtcHxHnAb8F1gJOBM5JKT1bLJLelWuAh4BLI+Iw4F3yIusNT4cVEWsDpwCXAhOBlYDDyQWYH/UqG0mSJEmV4kgQSZIkSb2WUroB2BkYDVxFXoz8B8D+c3leB/A54FHgXHLR4gx6Hj1S7xVgCLkQcj1wOvAIsHFK6Z/zlIgkSZKkShrS0eFUuZIkSZIkSZIkqXocCSJJkiRJkiRJkirJNUEkSZIkDUgR0dPnlY5iYXZJkiRJ6pYjQSRJkiQNVO/18G9cG+OSJEmSVBKOBJEkSZI0UK3Xw2Nv9FsUkiRJkkrLhdElSZIkSZIkSVIlOR2WJEmSJEmSJEmqJIsgkiRJkiRJkiSpkiyCSJIkSZIkSZKkSrIIIkmSJEmSJEmSKskiiCRJkiRJkiRJqqT/D3BxXDfZ8XaHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2016x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plot the Ord_4 and ord_5 variables in relative scale'''\n",
    "bold('**Percentage Count of target by ord_4:**')\n",
    "crosstab(train.ord_4, train.target, color = ['c', 'm'])\n",
    "\n",
    "bold('**Percentage Count of target by ord_5:**')\n",
    "crosstab(train['ord_5'].head(26), train.target, color = ['c', 'm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Distribution plot of ord_5'''\n",
    "ord_5_count = train['ord_5'].value_counts().reset_index()['ord_5'].values\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = sns.distplot(ord_5_count, bins= 50, color = 'k')\n",
    "ax.set_title(\"Distribution of ord_5 category values\", fontsize=22)\n",
    "ax.set_xlabel(\"Total of entries in ord_5 category's\", fontsize=18)\n",
    "ax.set_ylabel(\"Density\", fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It's seems that ord_5 is normally distributed.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Distribution plot of day'''\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = sns.distplot(train.day, color = 'g', kde = False)\n",
    "ax.set_title(\"Distribution of day category values\", fontsize=22)\n",
    "ax.set_xlabel(\"Total of entries in day category's\", fontsize=18)\n",
    "ax.set_ylabel(\"Density\", fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Distribution plot of month'''\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = sns.distplot(train.month, color = 'r', kde = False)\n",
    "ax.set_title(\"Distribution of month category values\", fontsize=22)\n",
    "ax.set_xlabel(\"Total of entries in month category's\", fontsize=18)\n",
    "ax.set_ylabel(\"Density\", fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Feature Engineering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Binary Encoding - Binary Features\n",
    "\n",
    "A binary variable is a variable with only two values. For example:\n",
    "* 1 / 0.\n",
    "* Yes / No.\n",
    "* Success / Failure.\n",
    "* Male / Female.\n",
    "* Black / White\n",
    "\n",
    "For machine learning models, we need numerical feature. so, we convert bin_3 and bin_4 in 1 and 0 form. In Binary Encoding — convert each integer to binary digits. Each binary digit gets one column. Some info loss but fewer dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Binary encoding of bin_3 and bin_4 of train data and test data'''\n",
    "train['bin_3'] = train['bin_3'].apply(lambda x: 0 if x == 'F' else 1)\n",
    "train['bin_4'] = train['bin_4'].apply(lambda x: 0 if x == 'N' else 1)\n",
    "\n",
    "test['bin_3'] = test['bin_3'].apply(lambda x: 0 if x == 'F' else 1)\n",
    "test['bin_4'] = test['bin_4'].apply(lambda x: 0 if x == 'N' else 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 One Hot Encoding - Nominal Features\n",
    "Nominal variables contain two or more categories without a natural ordering of the categories. For variable likes nom_0, nom_1, nom_2, nom_3, nom_4 we using One Hot Encoding. \n",
    "\n",
    "Categorical variables without any inherent order will be converted into numerical for our model using pandas get_dummies method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Now extract the nominal variables for one hot encoding of train and test data.'''\n",
    "one_hot_train = pd.get_dummies(train[['nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4']], drop_first = True)\n",
    "\n",
    "one_hot_test = pd.get_dummies(test[['nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4']], drop_first = True)\n",
    "\n",
    "'''Droping the variables'''\n",
    "train.drop(['nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4'], axis=1, inplace=True)\n",
    "test.drop(['nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'''Using sklearn bulit funtion for One Hot encoding'''\n",
    "#from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "#for f in ['nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4']:\n",
    "#    if train[f].dtype == 'object' or test[f].dtype == 'object':\n",
    "#        ohe = OneHotEncoder()\n",
    "#        ohe.fit(train[f].values.reshape(-1, 1)) #fiting the data\n",
    "#        ohe_train = ohe.transform(train[f].values.reshape(-1, 1)) #transforming the data\n",
    "#        ohe_train = pd.DataFrame(ohe_train.toarray()) #convert sparse matrix to dataframe\n",
    "#        \n",
    "#        ohe.fit(test[f].values.reshape(-1, 1))\n",
    "#        ohe_test = ohe.transform(test[f].values.reshape(-1, 1))\n",
    "#        ohe_test = pd.DataFrame(ohe_test.toarray())\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Feature Hashing\n",
    "### Nominal Features: High Cardinality Features\n",
    "\n",
    "Implementing feature hashing, aka the hashing trick on Variables like nom_5, nom_6, nom_7, nom_8, nom_9 due to High Cardinality Features.\n",
    "\n",
    "Feature hashing is a very cool technique to represent categories in a “one hot encoding style” as a sparse matrix but with a much lower dimensions. In feature hashing we apply a hashing function to the category and then represent it by its indices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'''Using sklearn's FeatureHasher to encode High Cardinality Features'''\n",
    "#from sklearn.feature_extraction import FeatureHasher\n",
    "#for f in ['nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9']:\n",
    "#    if train[f].dtype == 'object' or test[f].dtype == 'object':\n",
    "#        h = FeatureHasher(input_type='string', n_features=1000)\n",
    "#        h.fit(train[f].values) #fiting the data\n",
    "#        hash_train = h.transform(train[f].values) #transforming the data\n",
    "#        hash_train = pd.DataFrame(hash_train.toarray()) #convert sparse matrix to dataframe\n",
    "        \n",
    "#        h.fit(test[f].values) #fiting the data\n",
    "#       hash_test = h.transform(test[f].values) #transforming the data\n",
    "#       hash_test = pd.DataFrame(hash_test.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'''Another hashing trick'''\n",
    "#I got this solution from @kabure and @Giba\n",
    "#high_card = ['nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9']\n",
    "#for col in high_card:\n",
    "#    train[f'hash_{col}'] = train[col].apply( lambda x: hash(str(x)) % 5000 )\n",
    "#    test[f'hash_{col}'] = test[col].apply( lambda x: hash(str(x)) % 5000 )  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Encoding with the Frequency\n",
    "### Nominal Features: High Cardinality Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_card = ['nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9'] \n",
    "for col in high_card:\n",
    "    enc_nom_1 = (train.groupby(col).size()) / len(train)\n",
    "    train[f'freq_{col}'] = train[col].apply(lambda x : enc_nom_1[x])\n",
    "    enc_nom_2 = (test.groupby(col).size()) / len(test)\n",
    "    test[f'freq_{col}'] = test[col].apply(lambda x : enc_nom_2[x])\n",
    "\n",
    "    '''Droping the variables'''\n",
    "train.drop(['nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9'], axis=1, inplace=True)\n",
    "test.drop(['nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Label Encoding\n",
    "### Nominal Features: High Cardinality Features\n",
    "we use sklearn's label encoder method, it will randomly encode variables. Variables like nom_5, nom_6, nom_7, nom_8, nom_9, ord_5.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#for f in ['nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9']:\n",
    "#    if train[f].dtype=='object' or test[f].dtype=='object': \n",
    "#        lbl = LabelEncoder()\n",
    "#        lbl.fit(list(train[f].values) + list(test[f].values))\n",
    "#        train[f'le_{f}'] = lbl.transform(list(train[f].values))\n",
    "#        test[f'le_{f}'] = lbl.transform(list(test[f].values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I will keep only the frequency encoding to evaluate the results.**\n",
    "\n",
    "**Feel free to change if you would;**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 Ordinal Encoding \n",
    "### Ordinal Feature\n",
    "Then, we will use pandas replace method to manually encode ordninal variables, because we don't want to be lost ordinality. Variables are ord_1, ord_2, ord_3, ord_4. ord_0 contains numerical values so we do not need to encode it again.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"let's begin the manual process of ordinal encoding.\"\"\"\n",
    "# ordinal encoding on train data\n",
    "train.ord_1.replace(to_replace = ['Novice', 'Contributor','Expert', 'Master', 'Grandmaster'],\n",
    "                         value = [0, 1, 2, 3, 4], inplace = True)\n",
    "\n",
    "train.ord_2.replace(to_replace = ['Freezing', 'Cold', 'Warm', 'Hot','Boiling Hot', 'Lava Hot'],\n",
    "                         value = [0, 1, 2, 3, 4, 5], inplace = True)\n",
    "\n",
    "train.ord_3.replace(to_replace = ['a', 'b', 'c', 'd', 'e', 'f', 'g','h', 'i', 'j', 'k', 'l', 'm', 'n', 'o'],\n",
    "                         value = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inplace = True)\n",
    "\n",
    "train.ord_4.replace(to_replace = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I','J', 'K', 'L', 'M', 'N', 'O', \n",
    "                                     'P', 'Q', 'R','S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'],\n",
    "                         value = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, \n",
    "                                  22, 23, 24, 25], inplace = True)\n",
    "\n",
    "# ordinal encoding on test data\n",
    "test.ord_1.replace(to_replace = ['Novice', 'Contributor','Expert', 'Master', 'Grandmaster'],\n",
    "                         value = [0, 1, 2, 3, 4], inplace = True)\n",
    "\n",
    "test.ord_2.replace(to_replace = ['Freezing', 'Cold', 'Warm', 'Hot','Boiling Hot', 'Lava Hot'],\n",
    "                         value = [0, 1, 2, 3, 4, 5], inplace = True)\n",
    "\n",
    "test.ord_3.replace(to_replace = ['a', 'b', 'c', 'd', 'e', 'f', 'g','h', 'i', 'j', 'k', 'l', 'm', 'n', 'o'],\n",
    "                         value = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inplace = True)\n",
    "\n",
    "test.ord_4.replace(to_replace = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I','J', 'K', 'L', 'M', 'N', 'O', \n",
    "                                       'P', 'Q', 'R','S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'],\n",
    "                         value = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, \n",
    "                                  22, 23, 24, 25], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal Feature :High Cardinality Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'''Using sklearn's OrdinalEncoder to encode High Cardinality Features'''\n",
    "#from sklearn.preprocessing import OrdinalEncoder\n",
    "#oe = OrdinalEncoder(categories='auto')\n",
    "#oe.fit(train.ord_5.values.reshape(-1, 1))\n",
    "#train.ord_5 = oe.transform(train.ord_5.values.reshape(-1, 1))\n",
    "#test.ord_5 = oe.transform(test.ord_5.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Then encode 'ord_5' using ACSII values'''\n",
    "# Source:- https://www.kaggle.com/c/cat-in-the-dat/discussion/105702#latest-607652\n",
    "\n",
    "'''Option 1: Add up the indices of two letters in string.ascii_letters'''\n",
    "# train['ord_5_oe_add'] = train['ord_5'].apply(lambda x:sum([(string.ascii_letters.find(letter)+1) for letter in x]))\n",
    "# test['ord_5_oe_add'] = test['ord_5'].apply(lambda x:sum([(string.ascii_letters.find(letter)+1) for letter in x]))\n",
    "\n",
    "'''Option 2: Join the indices of two letters in string.ascii_letters'''\n",
    "#train['ord_5_oe_join'] = train['ord_5'].apply(lambda x:float(''.join(str(string.ascii_letters.find(letter)+1) for letter in x)))\n",
    "# test['ord_5_oe_join'] = test['ord_5'].apply(lambda x:float(''.join(str(string.ascii_letters.find(letter)+1) for letter in x)))\n",
    "\n",
    "'''Option 3: Split 'ord_5' into two new columns using the indices of two letters in string.ascii_letters, separately'''\n",
    "train['ord_5_oe1'] = train['ord_5'].apply(lambda x:(string.ascii_letters.find(x[0])+1))\n",
    "test['ord_5_oe1'] = test['ord_5'].apply(lambda x:(string.ascii_letters.find(x[0])+1))\n",
    "\n",
    "train['ord_5_oe2'] = train['ord_5'].apply(lambda x:(string.ascii_letters.find(x[1])+1))\n",
    "test['ord_5_oe2'] = test['ord_5'].apply(lambda x:(string.ascii_letters.find(x[1])+1))\n",
    "\n",
    "'''Option 4: Simply sort their values by string of ord_5'''\n",
    "ord_5 = sorted(list(set(train['ord_5'].values)))\n",
    "ord_5 = dict(zip(ord_5, range(len(ord_5))))\n",
    "train.loc[:, 'ord_5'] = train['ord_5'].apply(lambda x: ord_5[x]).astype(float)\n",
    "test.loc[:, 'ord_5'] = test['ord_5'].apply(lambda x: ord_5[x]).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 Encoding cyclic features\n",
    "Encoding cyclical continuous features - 24-hour time. Some data is inherently cyclical. Time is a rich example of this: minutes, hours, seconds, day of week, week of month, month, season, and so on all follow cycles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Transfer the cyclical features into two dimensional sin-cos features'''\n",
    "# source:  https://www.kaggle.com/avanwyk/encoding-cyclical-features-for-deep-learning\n",
    "def cyclical_encode(data, col, max_val):\n",
    "    data[col + '_sin'] = np.sin(2 * np.pi * data[col]/max_val)\n",
    "    data[col + '_cos'] = np.cos(2 * np.pi * data[col]/max_val)\n",
    "    return data\n",
    "\n",
    "train = cyclical_encode(train, 'day', 7)\n",
    "test = cyclical_encode(test, 'day', 7) \n",
    "\n",
    "train = cyclical_encode(train, 'month', 12)\n",
    "test = cyclical_encode(test, 'month', 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seting X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Transfer the dtypes of encoded ordinal features into float64'''\n",
    "for col in ['ord_0', 'ord_1', 'ord_2', 'ord_3', 'ord_4', 'ord_5_oe1', 'ord_5_oe2', 'ord_5']:\n",
    "    train[col]= train[col].astype('float64')\n",
    "    test[col]= test[col].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Let's concate one hot encoded, cyclic encoded variables together.\"\"\"\n",
    "train_processed = pd.concat([one_hot_train, train], axis = 1)\n",
    "test_processed = pd.concat([one_hot_test, test], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Updated train data for modelling:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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       "      <th></th>\n",
       "      <th>nom_0_Green</th>\n",
       "      <th>nom_0_Red</th>\n",
       "      <th>nom_1_Polygon</th>\n",
       "      <th>nom_1_Square</th>\n",
       "      <th>nom_1_Star</th>\n",
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       "      <th>freq_nom_9</th>\n",
       "      <th>ord_5_oe1</th>\n",
       "      <th>ord_5_oe2</th>\n",
       "      <th>day_sin</th>\n",
       "      <th>day_cos</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003827</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.000903</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.749279e-01</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.002807</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.866025</td>\n",
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       "      <td>0.003897</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.000927</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.5</td>\n",
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       "</table>\n",
       "<p>3 rows × 46 columns</p>\n",
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      ],
      "text/plain": [
       "   nom_0_Green  nom_0_Red  nom_1_Polygon  nom_1_Square  nom_1_Star  \\\n",
       "0            1          0              0             0           0   \n",
       "1            1          0              0             0           0   \n",
       "2            0          0              0             0           0   \n",
       "\n",
       "   nom_1_Trapezoid  nom_1_Triangle  nom_2_Cat  nom_2_Dog  nom_2_Hamster  ...  \\\n",
       "0                0               1          0          0              0  ...   \n",
       "1                1               0          0          0              1  ...   \n",
       "2                1               0          0          0              0  ...   \n",
       "\n",
       "   freq_nom_6  freq_nom_7  freq_nom_8  freq_nom_9  ord_5_oe1  ord_5_oe2  \\\n",
       "0    0.003827    0.000803    0.000903    0.000063       11.0       18.0   \n",
       "1    0.002807    0.000957    0.000370    0.000043        2.0       32.0   \n",
       "2    0.003897    0.001583    0.000927    0.000097       36.0        3.0   \n",
       "\n",
       "        day_sin   day_cos  month_sin  month_cos  \n",
       "0  9.749279e-01 -0.222521   0.866025        0.5  \n",
       "1 -2.449294e-16  1.000000  -0.866025       -0.5  \n",
       "2 -2.449294e-16  1.000000   0.866025        0.5  \n",
       "\n",
       "[3 rows x 46 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/markdown": [
       "**Updated test data for modelling:**"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <th>nom_1_Star</th>\n",
       "      <th>nom_1_Trapezoid</th>\n",
       "      <th>nom_1_Triangle</th>\n",
       "      <th>nom_2_Cat</th>\n",
       "      <th>nom_2_Dog</th>\n",
       "      <th>nom_2_Hamster</th>\n",
       "      <th>...</th>\n",
       "      <th>freq_nom_6</th>\n",
       "      <th>freq_nom_7</th>\n",
       "      <th>freq_nom_8</th>\n",
       "      <th>freq_nom_9</th>\n",
       "      <th>ord_5_oe1</th>\n",
       "      <th>ord_5_oe2</th>\n",
       "      <th>day_sin</th>\n",
       "      <th>day_cos</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003025</td>\n",
       "      <td>0.001145</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-9.749279e-01</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003035</td>\n",
       "      <td>0.001625</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>44.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-0.866025</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003860</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.000975</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>7.818315e-01</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nom_0_Green  nom_0_Red  nom_1_Polygon  nom_1_Square  nom_1_Star  \\\n",
       "0            0          0              0             0           0   \n",
       "1            0          1              0             1           0   \n",
       "2            0          0              0             1           0   \n",
       "\n",
       "   nom_1_Trapezoid  nom_1_Triangle  nom_2_Cat  nom_2_Dog  nom_2_Hamster  ...  \\\n",
       "0                0               1          0          0              0  ...   \n",
       "1                0               0          0          0              0  ...   \n",
       "2                0               0          0          1              0  ...   \n",
       "\n",
       "   freq_nom_6  freq_nom_7  freq_nom_8  freq_nom_9  ord_5_oe1  ord_5_oe2  \\\n",
       "0    0.003025    0.001145    0.000565    0.000070        2.0        5.0   \n",
       "1    0.003035    0.001625    0.000860    0.000125       44.0       42.0   \n",
       "2    0.003860    0.000760    0.000975    0.000050       20.0       42.0   \n",
       "\n",
       "        day_sin   day_cos     month_sin  month_cos  \n",
       "0 -9.749279e-01 -0.222521 -5.000000e-01   0.866025  \n",
       "1 -2.449294e-16  1.000000  5.000000e-01  -0.866025  \n",
       "2  7.818315e-01  0.623490 -2.449294e-16   1.000000  \n",
       "\n",
       "[3 rows x 45 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\"\"\"Let's look at our final train and test data for modelling.\"\"\"\n",
    "bold('**Updated train data for modelling:**')\n",
    "display(train_processed.head(3))\n",
    "bold('**Updated test data for modelling:**')\n",
    "display(test_processed.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Function to reduce the DF size'''\n",
    "# source: https://www.kaggle.com/kernels/scriptcontent/3684066/download\n",
    "\n",
    "def reduce_mem_usage(df):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        \n",
    "        if col_type != object:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 65.23 MB\n",
      "Memory usage after optimization is: 24.60 MB\n",
      "Decreased by 62.3%\n",
      "Memory usage of dataframe is 41.96 MB\n",
      "Memory usage after optimization is: 16.21 MB\n",
      "Decreased by 61.4%\n"
     ]
    }
   ],
   "source": [
    "train_processed = reduce_mem_usage(train_processed)\n",
    "test_processed = reduce_mem_usage(test_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Seting X and Y'''\n",
    "X_train = train_processed.drop(['id', 'target'],axis = 1)\n",
    "y_train = train_processed['target']\n",
    "X_test = test_processed.drop(['id'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Data Dimension for Model Building:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input matrix dimension: (300000, 44)\n",
      "Output vector dimension: (300000,)\n",
      "Test data dimension: (200000, 44)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Let's have a final look at our data\"\"\"\n",
    "bold('**Data Dimension for Model Building:**')\n",
    "print('Input matrix dimension:', X_train.shape)\n",
    "print('Output vector dimension:',y_train.shape)\n",
    "print('Test data dimension:', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Model Building & Evaluation \n",
    "With all the preprocessings done and dusted, we're ready to train our regression models with the processed data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Importing the auxiliar and preprocessing librarys'''\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV, KFold, StratifiedKFold, cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "'''Initialize all the regression models object we are interested in.'''\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Creating Pipeline and Evaluation Models\n",
    "\n",
    "#### Standardization\n",
    "\n",
    "Numerical features preprocessing is different for tree and non tree model.\n",
    "\n",
    "1) Usually:\n",
    "   * Tree based models does not depend on scaling\n",
    "   * Non-tree based models hugely depend on scaling\n",
    "\n",
    "2) Most Often used preprocening are:\n",
    "    * MinMax scaler to (0,1)\n",
    "    It can formulated as\n",
    "    x <- (x - min(x)) / (max(x) - min(x))\n",
    "    \n",
    "    * Standard Scaler to mean = 0 and std =1\n",
    "    It can formulated as\n",
    "    x <- (x - mean(x)) / sd(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Spot-Check Algorithms'''\n",
    "def GetBasedModel():\n",
    "    basedModels = []\n",
    "    \n",
    "    basedModels.append(('LR',\n",
    "                        Pipeline([('Scaler', StandardScaler()), \n",
    "                                   ('LR', LogisticRegression())])))\n",
    "    basedModels.append(('RC',\n",
    "                        Pipeline([('Scaler', StandardScaler()),\n",
    "                                   ('RC', RidgeClassifier())])))\n",
    "    basedModels.append(('LDA', \n",
    "                        Pipeline([('Scaler', StandardScaler()),\n",
    "                                   ('LDA', LinearDiscriminantAnalysis())])))\n",
    "    #basedModels.append(('KNN'  , KNeighborsClassifier()))\n",
    "    #basedModels.append(('DT' , DecisionTreeClassifier()))\n",
    "    #basedModels.append(('NB'   , GaussianNB()))\n",
    "    basedModels.append(('AB'   , AdaBoostClassifier()))\n",
    "    basedModels.append(('GBM'  , GradientBoostingClassifier()))\n",
    "    basedModels.append(('RF'   , RandomForestClassifier()))\n",
    "    basedModels.append(('ET'   , ExtraTreesClassifier()))\n",
    "    basedModels.append(('XGB'   , XGBClassifier()))\n",
    "    \n",
    "    return basedModels\n",
    "\n",
    "\n",
    "'''Test options and evaluation metric'''\n",
    "def BasedLine(X_train, y_train, models):\n",
    "    num_folds = 7\n",
    "    scoring = 'accuracy'\n",
    "\n",
    "    results, names = [], []\n",
    "\n",
    "    for name, model in models:\n",
    "        kfold = StratifiedKFold(n_splits = num_folds, random_state = 44)\n",
    "        cv_results = cross_val_score(model, X_train, y_train, cv = kfold, \n",
    "                                scoring = scoring, n_jobs = -1)\n",
    "        names.append(name)\n",
    "        results.append(cv_results)\n",
    "        msg = \"%s: %f (+/- %f)\" % (name, cv_results.mean(),  \n",
    "                                   cv_results.std())\n",
    "        print(msg)\n",
    "    \n",
    "    return names, results\n",
    "    \n",
    "    \n",
    "'''Visualization of results'''\n",
    "class PlotBoxR(object):\n",
    "    \n",
    "    \n",
    "    def __Trace(self,nameOfFeature,value): \n",
    "    \n",
    "        trace = go.Box(\n",
    "            y=value,\n",
    "            name = nameOfFeature,\n",
    "            marker = dict(\n",
    "                color = 'rgb(0, 128, 128)',\n",
    "            )\n",
    "        )\n",
    "        return trace\n",
    "\n",
    "    def PlotResult(self,names,results):\n",
    "        \n",
    "        data = []\n",
    "\n",
    "        for i in range(len(names)):\n",
    "            data.append(self.__Trace(names[i],results[i]))\n",
    "\n",
    "\n",
    "        py.iplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.744670 (+/- 0.002599)\n",
      "RC: 0.743953 (+/- 0.002049)\n",
      "LDA: 0.744423 (+/- 0.002449)\n",
      "AB: 0.740723 (+/- 0.001593)\n",
      "GBM: 0.737970 (+/- 0.001223)\n",
      "RF: 0.710073 (+/- 0.001580)\n",
      "ET: 0.709487 (+/- 0.001500)\n",
      "XGB: 0.737820 (+/- 0.001438)\n"
     ]
    },
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "models = GetBasedModel()\n",
    "names,results = BasedLine(X_train, y_train,models)\n",
    "PlotBoxR().PlotResult(names,results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.7 Optimizing Hyperparameters\n",
    "Now let's add Grid Search to all the models with the hopes of optimizing their hyperparameters and thus improving their accuracy. Are the default model parameters the best bet? Let's find out.\n",
    "\n",
    "Note: Though optimizing hyperparameters is time consuming, hyperparameters should be tuned for all the models you try because only then you will be able to tell what is the best you can get out of that particular model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Create a function to tune hyperparameters of the selected models.'''\n",
    "seed = 44\n",
    "def grid_search_cv(model, params):\n",
    "    global best_params, best_score\n",
    "    grid_search = GridSearchCV(estimator = model, param_grid = params, cv = 10, verbose = 3,\n",
    "                             scoring = 'accuracy', n_jobs = -1)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    best_params = grid_search.best_params_\n",
    "    best_score = grid_search.best_score_\n",
    "    \n",
    "    return best_params, best_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimizing our best model performed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed:  9.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR best params:{'C': 100.0, 'penalty': 'l1'} & best_score:0.74468\n"
     ]
    }
   ],
   "source": [
    "'''Define hyperparameters of Logistic Regression.'''\n",
    "LR_model = LogisticRegression()\n",
    "\n",
    "LR_params = {'penalty':['l1', 'l2'],\n",
    "             'C': np.logspace(0, 2, 4, 8 ,10)}\n",
    "\n",
    "grid_search_cv(LR_model, LR_params)\n",
    "LR_best_params, LR_best_score = best_params, best_score\n",
    "print('LR best params:{} & best_score:{:0.5f}' .format(LR_best_params, LR_best_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    5.4s\n",
      "[Parallel(n_jobs=-1)]: Done 120 tasks      | elapsed:   27.3s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:   33.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge best params:{'alpha': 9, 'random_state': 44} & best_score:0.74401\n"
     ]
    }
   ],
   "source": [
    "'''Define hyperparameters of Ridge Classifier.'''\n",
    "RC_model = RidgeClassifier()\n",
    "\n",
    "RC_params = {'alpha':[ 9, 9.2, 9.4, 9.5, 9.52, 9.54, 9.56, 9.58, 9.6, 9.62, 9.64, 9.66, 9.68, 9.7,  9.8],\n",
    "             'random_state':[seed]}\n",
    "\n",
    "grid_search_cv(RC_model, RC_params)\n",
    "RC_best_params, RC_best_score = best_params, best_score\n",
    "print('Ridge best params:{} & best_score:{:0.5f}' .format(RC_best_params, RC_best_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   26.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA best params:{'solver': 'svd'} & best_score:0.74436\n"
     ]
    }
   ],
   "source": [
    "'''Define hyperparameters of Linear Discriminant Analysis.'''\n",
    "LDA_model = LinearDiscriminantAnalysis()\n",
    "\n",
    "LDA_params = {'solver': ['svd', 'lsqr', 'eigen'],\n",
    "              }\n",
    "\n",
    "grid_search_cv(LDA_model, LDA_params)\n",
    "LDA_best_params, LDA_best_score = best_params, best_score\n",
    "print('LDA best params:{} & best_score:{:0.5f}' .format(LDA_best_params, LDA_best_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:  6.7min\n",
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed: 19.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AB best params:{'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 100} & best_score:0.74364\n"
     ]
    }
   ],
   "source": [
    "'''Define hyperparameters of AdaBoostClassifier.'''\n",
    "AB_model = AdaBoostClassifier()\n",
    "\n",
    "AB_params = {'n_estimators': (50 , 100),\n",
    "             'learning_rate': (1.0, 3.0),\n",
    "             'algorithm': ['SAMME', 'SAMME.R']}\n",
    "\n",
    "grid_search_cv(AB_model, AB_params)\n",
    "AB_best_params, AB_best_score = best_params, best_score\n",
    "print('AB best params:{} & best_score:{:0.5f}' .format(AB_best_params, AB_best_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'''Define hyperparameters of Gradient Boosting Classifier'''\n",
    "#GBC_model = GradientBoostingClassifier()\n",
    "\n",
    "#GBC_params = {'learning_rate': [0.02, 0.05, 0.1],\n",
    "#              'max_depth': [4, 6],\n",
    "#              'max_features': [1.0, 0.3], \n",
    "#              'min_samples_split': [ 2, 3],\n",
    "#              'random_state':[seed]}\n",
    "\n",
    "#grid_search_cv(GBC_model, GBC_params)\n",
    "#GBC_best_params, GBC_best_score = best_params, best_score\n",
    "#print('GBC best params:{} & best_score:{:0.5f}' .format(GBC_best_params, GBC_best_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I am not optimizing XGBoost but you can try optimization on all the models for better score.**\n",
    "\n",
    "**Note: Though optimizing hyperparameters is time consuming.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'''For XGBC, the following hyperparameters are usually tunned.'''\n",
    "#'''https://xgboost.readthedocs.io/en/latest/parameter.html'''\n",
    "\n",
    "#XGB_model = XGBClassifier(\n",
    "#            n_estimators=500,\n",
    "#            verbose = True)\n",
    "\n",
    "\n",
    "#XGB_params = {'max_depth': (2, 5),\n",
    "#               'reg_alpha':  (0.01, 0.4),\n",
    "#               'reg_lambda': (0.01, 0.4),\n",
    "#               'learning_rate': (0.1, 0.4),\n",
    "#               'colsample_bytree': (0.3, 1),\n",
    "#               'gamma': (0.01, 0.7),\n",
    "#               'num_leaves': (2, 5),\n",
    "#               'min_child_samples': (1, 5),\n",
    "#              'subsample': [0.5, 0.8],\n",
    "#              'random_state':[seed]}\n",
    "\n",
    "#grid_search_cv(XGB_model, XGB_params)\n",
    "#XGB_best_params, XGB_best_score = best_params, best_score\n",
    "#print('XGB best params:{} & best_score:{:0.5f}' .format(XGB_best_params, XGB_best_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.8 Retrain and Predict Using Optimized Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Instantiate the models with optimized hyperparameters.'''\n",
    "lr = LogisticRegression(**LR_best_params)\n",
    "rc = RidgeClassifier(**RC_best_params)\n",
    "lda = LinearDiscriminantAnalysis(**LDA_best_params)\n",
    "ab = AdaBoostClassifier(**AB_best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.7446 (+/- 0.0022) [LR]\n",
      "Mean Accuracy: 0.7440 (+/- 0.0017) [RC]\n",
      "Mean Accuracy: 0.7444 (+/- 0.0023) [LDA]\n",
      "Mean Accuracy: 0.7436 (+/- 0.0022) [AB]\n"
     ]
    }
   ],
   "source": [
    "'''Instantiate the models with optimized hyperparameters.'''\n",
    "models = {'LR': lr, 'RC': rc, 'LDA': lda, 'AB': ab}\n",
    "\n",
    "# 10-fold Cross Validation after Optimization\n",
    "score = []\n",
    "for x, (keys, items) in enumerate(models.items()):\n",
    "    # Train the models with optimized parameters using cross validation.\n",
    "    # No need to fit the data. cross_val_score does that for us.\n",
    "    # But we need to fit train data for prediction in the follow session.\n",
    "    items.fit(X_train, y_train)\n",
    "    scores = cross_val_score(items, X_train, y_train, cv = 10, scoring = 'accuracy')\n",
    "    score.append(scores.mean())\n",
    "    print('Mean Accuracy: %0.4f (+/- %0.4f) [%s]'  % (scores.mean(), scores.std(), keys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Submission with the most accurate logistic regression'''\n",
    "submission = pd.DataFrame({'id': test['id'],\n",
    "                           'target': lr.predict_proba(X_test)[:,1] })\n",
    "submission.to_csv('submission_lr.csv', index = False)\n",
    "\n",
    "'''Submission with the most accurate Ridge Classifier'''\n",
    "submission  = pd.DataFrame({'id': test['id'],\n",
    "                            'target': rc.predict(X_test)})\n",
    "submission.to_csv('submission_rc.csv', index = False)\n",
    "\n",
    "'''Submission with the most accurate Linear Discriminant Analysis'''\n",
    "submission  = pd.DataFrame({'id': test['id'],\n",
    "                            'target': lda.predict(X_test)})\n",
    "submission.to_csv('submission_lda.csv', index = False)\n",
    "\n",
    "'''Submission with the most accurate AdaBoostClassifier'''\n",
    "submission  = pd.DataFrame({'id': test['id'],\n",
    "                            'target': ab.predict(X_test)})\n",
    "submission.to_csv('submission_ab.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***<font color=\"green\">Give me your feedback and if you find  my kernel helpful please <b>UPVOTE will be appreciated.</b> <br></font>***\n",
    "# --Thank You for Reading"
   ]
  }
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