Churn Modelling-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb063d08",
   "metadata": {},
   "source": [
    "## Standard libraries for data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fea5895",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445fd6eb",
   "metadata": {},
   "source": [
    "Load the input dataset into the python notebook in the current working directory. From the head & column methods, we get an idea that churn dataset where each record details the nature of tenure, balance, number\n",
    "of products etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48bf2b48",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Churn_Modelling.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a57a3e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c70ce9",
   "metadata": {},
   "source": [
    "## EDA Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fcef8fe",
   "metadata": {},
   "source": [
    "Exploratory Data Analysis Preprocessing(EDA):- The goal of this step is to understand the data, visualize data, identify the missing values & outliers if any outlier point then use quantitative methods to get a sense of the story it tells.\n",
    "\n",
    "Identification of number of rows and colums using shape,columns function(14 columns and 10000 rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c09a6dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c27bcd",
   "metadata": {},
   "source": [
    "Identification of all columns name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48eb818c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',\n",
       "       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
       "       'IsActiveMember', 'EstimatedSalary', 'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e5668b",
   "metadata": {},
   "source": [
    "info() - null and non null values also data type of each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "260b4f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd76a67",
   "metadata": {},
   "source": [
    "describe() - it will descibe how much mean vlaue, standard devaition, skewness, minimum, maximum values of each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48ada0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.89568</td>\n",
       "      <td>7.193619e+04</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.556570e+07</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.75000</td>\n",
       "      <td>1.562853e+07</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.25000</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.581569e+07</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RowNumber    CustomerId   CreditScore           Age        Tenure  \\\n",
       "count  10000.00000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
       "mean    5000.50000  1.569094e+07    650.528800     38.921800      5.012800   \n",
       "std     2886.89568  7.193619e+04     96.653299     10.487806      2.892174   \n",
       "min        1.00000  1.556570e+07    350.000000     18.000000      0.000000   \n",
       "25%     2500.75000  1.562853e+07    584.000000     32.000000      3.000000   \n",
       "50%     5000.50000  1.569074e+07    652.000000     37.000000      5.000000   \n",
       "75%     7500.25000  1.575323e+07    718.000000     44.000000      7.000000   \n",
       "max    10000.00000  1.581569e+07    850.000000     92.000000     10.000000   \n",
       "\n",
       "             Balance  NumOfProducts    HasCrCard  IsActiveMember  \\\n",
       "count   10000.000000   10000.000000  10000.00000    10000.000000   \n",
       "mean    76485.889288       1.530200      0.70550        0.515100   \n",
       "std     62397.405202       0.581654      0.45584        0.499797   \n",
       "min         0.000000       1.000000      0.00000        0.000000   \n",
       "25%         0.000000       1.000000      0.00000        0.000000   \n",
       "50%     97198.540000       1.000000      1.00000        1.000000   \n",
       "75%    127644.240000       2.000000      1.00000        1.000000   \n",
       "max    250898.090000       4.000000      1.00000        1.000000   \n",
       "\n",
       "       EstimatedSalary        Exited  \n",
       "count     10000.000000  10000.000000  \n",
       "mean     100090.239881      0.203700  \n",
       "std       57510.492818      0.402769  \n",
       "min          11.580000      0.000000  \n",
       "25%       51002.110000      0.000000  \n",
       "50%      100193.915000      0.000000  \n",
       "75%      149388.247500      0.000000  \n",
       "max      199992.480000      1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c0fe5f",
   "metadata": {},
   "source": [
    "missing values - there are no null values, all are 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "723cbae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b795037",
   "metadata": {},
   "source": [
    "Graph to check there is any non null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c6ad0ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Counts')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.isnull().sum().plot(kind='bar')\n",
    "plt.title(\"No Null Values\")\n",
    "plt.xlabel(\"Attributes\")\n",
    "plt.ylabel(\"Counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef93e96",
   "metadata": {},
   "source": [
    "check unique values of Geography column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2da14aac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['France', 'Spain', 'Germany'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Geography\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f70ec3",
   "metadata": {},
   "source": [
    "Storytelling - Visualization Ratio of Active to not Active, Exited to not exited, Credit card owners to non credit card owners, Female to men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "728431da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = df.IsActiveMember.value_counts()\n",
    "labels = ['Not Active', 'Active']\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (4, 3), dpi = 100)\n",
    "explode = (0, 0.09)\n",
    "\n",
    "patches, texts, autotexts = ax.pie(values, labels = labels, autopct = '%1.2f%%', shadow = True,\n",
    "                                   startangle = 90, explode = explode)\n",
    "\n",
    "plt.setp(texts, color = 'grey')\n",
    "plt.setp(autotexts, size = 8, color = 'white')\n",
    "autotexts[1].set_color('black')\n",
    "plt.title(\"Pi-Chart For Active & Not Active Customers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d07c875f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = df.Exited.value_counts()\n",
    "labels = ['Not Exited', 'Exited']\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (4, 3), dpi = 100)\n",
    "explode = (0, 0.09)\n",
    "\n",
    "patches, texts, autotexts = ax.pie(values, labels = labels, autopct = '%1.2f%%', shadow = True,\n",
    "                                   startangle = 90, explode = explode, colors = ['darkblue','deeppink'])\n",
    "\n",
    "plt.setp(texts, color = 'grey')\n",
    "plt.setp(autotexts, size = 8, color = 'white')\n",
    "plt.title(\"Pi- Chart For Exited & Non Excited Customer\")\n",
    "autotexts[1].set_color('black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69a9ce99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = df.HasCrCard.value_counts()\n",
    "labels = ['doesnt has CrCard', 'HasCrCard']\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (4, 3), dpi = 100)\n",
    "explode = (0, 0.09)\n",
    "\n",
    "patches, texts, autotexts = ax.pie(values, labels = labels, autopct = '%1.2f%%', shadow = True,\n",
    "                                   startangle = 90, explode = explode, colors = ['tab:cyan' , 'tab:olive'])\n",
    "\n",
    "plt.setp(texts, color = 'grey')\n",
    "plt.setp(autotexts, size = 8, color = 'white')\n",
    "autotexts[1].set_color('black')\n",
    "plt.title(\"Pi-Chart For Customers Having Card or Not\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47b9f888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pwHedFuEGktbQlN+vgC8xbFgeM2bMclqPkPRYMurZc5LW0IDPARdy2ml+UlIkUKPgNOnAfU6LGOwkpaEpv38EcDWTJ09k1ChpagoDhUsKfQXznBYxmElKQwMuBY5h9mwZXRIGGvc7LWAwk3SGpvz+6cBZ5OZmkp09wWk9gpDAqYW+giudFjFYSSpDswcCLsLjGUFu7qlO6xGEdiiQiBzdI6kMDZgBzGH27BFkZOQ4LUYQ2mE6cKvTIgYjSWNoyu9PAS4hLS2TadPmOK1HEA7DPYW+Agks2kWSxtAwkxdncMop40lNzXJajCAchtHA9U6LGGwkhaEpvz8TmE92diqTJ5/stB5B6CS3y2bFXSNZ3qy5wDF87nNj8Hiks1UYLBwFzHdaxGDC9YZmT6K9CI+nngkTZjksRxC6yp1OCxhMuN7QgDnAZHJzs0hLk05WYbBxTqGvYIbTIgYLrjY05fenAvOAeqZOPclpPYLQTe50WsBgwdWGBpwATGPs2GaGDZM1m8Jg5auFvoIxTosYDLjd0E4HvBx3nFTZhcFMBnCN0yIGA641NOX3TwROJDW1HJ9vltN6BKGHfMVpAYMB1xoacAowghkzJuH1SrwzYbCTV+grmOy0iIGOKw3Nnkg7D6hm8mQZDBDcgEJqaYfFlYYGzAImMXp0I0OGjHdajCD0EmJoh8F1hmaHCJoHRJk+/Sin9QhCLzKn0FcwxWkRAxnXGRowETgWKMXnk00nBDehgC87LWIg40ZDOxYYxtChIbKzpRNVcBvS7OwANxraLKCF6dOPQinltBhB6GVOkThp7eMqQ1N+/2jADxzA55vutB5B6AO8wGlOixiouMrQMGY2Aqhk+PBpDmsRhL5irtMCBipuNDSYNMlHauoQh7UIQl8hhtYOrjE05fenYXZDr2LSJGluCm5mTqGvIM1pET3FsqwSy7Lu7M0yU3qzMIc5AhOHfQ8jR8o3mOBmMoCTgfc7e4FlWU8C17Vx6uhAIFDcS7ocxzU1NMzWXxlAA1lZY50WIwh9THe+tF8Dxie8tvWmKKdxUw0tFwiRmZlOWtoIp8UIQh9zRjeuaQ4EAqWJiZZlXQL8GDge2AMsBH4WCATC9nkN3AJcApwLbAduAPYDf8IEglgHXBMIBLbY10wHHgTygCFAEfCDQCCwpD1xlmUNB+4HLsNUTj4G7goEAms7+4CuqKEpv98LHAnUMm6cD5l+JrifE3qjEMuyzgeeBn4DHAfcDHwd+J+ErPcAf8bM89wIPAs8DNyLaf4C/C4ufzbwKvB5YDbwOrDYsqw2l25ZlqWAV4BxwEXAScAqYKllWZ3eFNwtNbTRwFCgmlGjjnZajCD0A0cU+goy88ryG7twzXzLsurijv8FjAV+HggEFtppWy3Luge4D7Di8j4RCASeB7As6xfAB8BPA4HA63baQ8ATscx2rSq+ZvVDy7IuB77EwcYX4xxgBuALBALNdtp3Lcu6DLPc65HOPKBbDG0s5hthD8OGSf+ZkAx4MNOU1nThmreAW+OO64Fi4BTLsuJrZF4gw7KsrEAg0GCnrYs7v8/+uT4hLcOyrGGBQKDGsqwhQACzDd8EjNdkAu0trj8J8xkut6x4HyUT0z/eKdxkaB4gTHa2GJqQLOTSNUOrTxzRtCzLgzGef7SRvynu91Dc77qDtFg31v3A+cB3MabZCLwAtDfdxAPsBc5u41xVO9ccglsMbTyxNzQz0+esFEHoN3pjNcwqwN8HUzfmAk8GAoFFAJZlZWP6uTvSMQ4IBwKBku7e1C2GNg1oYPToERJuW2iLx+qX8qeGf/P0yO8wPWUs36r6E/siVQxR5t/lwowTuTqr/YFDrTW3Vz9OcbiU10ab1lmjbuHbVY/RosMAjPIM5e6hlzLeO5KwjvA/Nc+xJ1LJRG8O/zvsKlKUl2Yd4s7qJ/nFsGsY5sns6WP1xk5mPwFetixrJ/A3IIqZoD4jEAj8sAflFgMLLMtajKls/JSOByGXYPrlXrQs624giGmqXgS8GAgEPu7MTQf9KKfy+4dgnL2O0aNlqy/hEIKh3WwI7WScZ8RB6Xdlz+fPObfz55zbOzQzgBeaChnvHXlQWjop/Gb4DTyVcztP5dxOXtrRPFT3KgCFLZsZ6snkqZzbyfZkUNiyGYDH69/iioy83jAz6AVDszv15wNfAD4CCoH/wkzN6Al3AZWYyb+LMaOcqzrQoTHm9Q7wOLAJ+AumVrevvesScUMNzYeZ57KXzMyJTosRBhYtOswDdYuxhl3JbVWPdauMneEDvNm0jnuGXsHy5qLWdI/ytNbwtNbU62Y8mClDsdoYQLMOkaq8FIdL2RE5wK3ZX+zhU7XSaUMLBAJf7+Dc6xjDae+8SjguARLTlsWn2XnOTSjq9wnXHJlwXAt8x351CzcY2ljMSEgjaWm98rUnuIdH65dwfsYsJngPncr0u/rX+GP9G0xN8XHrkC8ysY08UR3l3roX+e7QS0hR3jbvcXvV42wJlzLSM4RfD78egFNTp/NW8wa+VvFbjk+dzEmp0/iv6oX8cOgVvfl4nZ6flSy4wdBGYNromrS0LIe1CAOI9aEdFIV3860h5x9yLjD0y4z1jkBrzQtNhXy3+s88l3PnIfmebXyXWalHckzKBPZGKtu8z29H3EBUR3myYRlPNrzF94Zeikd5+MHQy1vz/KXhPealH0eEKD+q+SshHeGKzDmcnNajOAoS6DGBQd+Hhqmdmaqu1NCEOFaHtrE9sp8FFQ9wefn97I/WcGf1E3zQHGSsdwQASim+knkaeyKVVEcb2iijhFebVnF5+f3cXPUItbqRy8vvpyZ68HxWj/JwaeYpvNa85pAy9kYq+aBlEwsyTuWR+iVclnEKPxx6BQ/WvdzTR/QU+gqye1qIm3BDDS2T2JSNlBQxNKGVa7PO4tqss1qPLy+/nweGX8sR3tFUROvI8RgveKt5AzmebIZ7Dq3g/3L4ta2/741Ucn3lH1g06nsAVETr8OJpve7NpnVM9447pIxf173CHdkX4VEeGnULoPAoZf/eY4YCdYfNlSS4wdCGAxEAUlPF0ITDEiLCf1cvpEVH8KAY7snivuHXtJ5/pH4Joz1DWZA5p8NyyiLV3Fu3iIiOooFJ3hx+POzgPUxeb1rLUSnjmJZi5nt/LWseP699kRARrs86pzceZxhmQqqAOwxtKLEZy1JDEzogVrMCeGLkbe3mu2nI59tMH+8d2ToHDeDY1IksHPntDu95fsbMg46PT53MUzm3d0ZuZxnWm4UNdtzQhzYMMDMbxdCE5EMMLY5BbWj2LunZxGpoXq8YmpBsyEhnHIPa0DALXdOJ1dA8Hjc0oQc9YSJhpzUkERGnBQwkBruhZWL6AU0NLRqVD1L/0OH/zbrQ9l1RHZUPWv/QlXhorscthmaMTAytvzi1o5O1uqnlQLS2p2sBhc4hhhbHYDe06EFHWofaySf0LkcryzomIS02F8oLsC1Strl/JSUtYmhxDHZDC2P6EMwiu2hUDK3/uDjhuBgTXSEHYGXL1mC/K0pOxNDiGOyd6CFMLc0Ycy80ObfdcQdN4TBNYVPUve++y/OffNJ6/tqZM1l42WXMf/ZZXtl8aCXkiOHDKf7Od9hQVtaadsXzz7O1spLc0aN59orPFiePyMhgWHo6o+67jxSPh7995StMHTGCLZWVXPm3vxHRmnSvlze+9jUu/ctfqGpqOuR+DjIf+FXsYHlzUeXc9NwgZgeg/dsiZZV10abybE/GKMcUJgeHrtdKYtxmaL1SQ/vy88/zyf79h6RPHDqUm086iQ927uzw+qqmJmY//PAh6UUHDhyU/tsLL2yNW3z+9OlUNDZy+V//ymNf+hIXHHUUr2zezI/OOovfrVgx0MwMYK6yrGE6EKiJS1sHnB472Bkp35TrmXha/0tLKqSGFocbmpxRYk3OSKRPm5yPXHIJd73+Os2Rng/gpXm9fHXGDB5bZWLehaJRslJTAchKTaUlEuEEnw//qFH87dNPe3y/PiAVSAzstQnTlzYU4NPwzk39LSoJEUOLww2GFqEXm5wAzyxYwLpbbuHRSy5hdJZZeHzLySfzyf79rNi9+7DXD0tPZ8WNN7Lyppu4Z948PG3sE7ogN5dtlZWs3WeCcb65ZQu1LS2suflmqpub+fe2bTz4xS9yx2uv9cYj9RXzE453AruBUQArW7buCOlI8yFXCb1FfV5Zfq3TIgYSg7rJqYNBrfz+Zswuy73S5Jz3xBPsrKkhxePhf889l4WXXcZtr77KN088kTMef/yw1++tq2PSgw+yv6GBkRkZ/PXLX+a/TzuN+99//6B8N8yaxWOrV3/2LMBNixe3Ht8xZw4vBoOkeDw8s2AB6V4vv//oI94qKenpI/YmFyrLUtqET2Z5c1F0bnruSuAYgBCRaGmkasvklFHHOarSvcjUmAQcq6FZlnWkZVnasqxZPSwqRKzJ2dLS4zAqO2tMl1A4GuXXhYXMnTKF0yZNYsLQoRTddhvb7riDvEmTeOxLX+LGE0885PqWSIT9DaaftrKpicfXrGHulIO3IpwyfDinT57Ms+vXH3J97PxFRx/NHz/6iJ+ecw6PrFzJ1196id9eeGFPH6+38XHonLTNmJpzGkBxZK80O/uOEqcFDDS6VEOzLOtJ4Drg4UAgcEvCuT9gNjFd2FH88j6giZgxNzRU9aSgrNRUUj0eqptNK+nqE05gdWkpz23YwHMbNrTme+u663jg/ffbHOUck5VFZVMT4WiUNK+XBccey+rS0oPyXD9rFos2bmy9TyIPXXABd73+OhoYkpaGBqJaMyStvS0NHeVi4MO442LgAKbZufejli2bz047XivVRrtb6CklTgsYaHSnybkTuMqyrLsCgUAjgGVZGcDVwI7eFNdJGok9R11dVU8KGjtkCH+/8kq8HrPVxdbKSq5dtOiw11lnn82e2loeXrmSM6dM4SfnnEMkGiXF4+HfJSX8bPnyg/J/fdYsrn/ppTbLuvqEE1i7bx+f2qOsP3/3XR695BLSvF5++s47PXm8vmI+8KPYwfLmooa56bnrgM8De/dHaxoqdf3uHJU9yTGF7qXEaQEDje4Y2irMPpgLgGfstAUYo9say2RZ1gXAD4ETMB33HwB3BAKBLe0VbFnWccADwDzMNvVvAHcFAoEDHejZR2w35urqqm48Tyvbqqo48ZFHDpvvnIULDzoOLFvW+vuijRtZtHFjh9dPfeihds/F1wQBPtqzh1ltTAEZQMxWljVRBwLxoyWfYrZF8wDR7eH9m3PSxND6gBKnBQw0utuH9gRwfdzxDZi99OIZAjyImWh5HmZ6xSJ76/lDsCxrPPA2Zmv7k4ELMDs6PX8YLRXE9hQoL297Fwuhr7ko4XgTUIPZwIa1oe3Sj9Y3lDgtYKDR3VHOp4B7Lcs6EjNAdwZwFXB2LEMgEPh7/AWWZX0DKAOOAw6uhhhuBVYFAoH8uGtuAHZalnVMIBBo70NR1fpbY2MzoVA9qalDuv5IQg+YDzwad7wf2AbkAhWfhHeWNuqWmkyVJsEIe5dtTgsYaHTL0AKBwAHLsl7BDBAo4BU7rTWPZVnTMdu/5wGj+aw2OIW2De0k4BzLstoaqZyO+dZvi0pM7c9E3Whs3C+G1u+cpywrXQcCzQDLm4v03PTc1cDsWIbdkYrNR6WMO8kxhe5jd15ZfkddMUlJT6ZtPA58HWNqbU3QWowZ6fomMMd+Qay/q20ti4FZCa+jMdvDt0clZmDARKttaDh0zZLQ1wwBEnf82IT5u2QBBEN7pNnZu3x4+CzJR08m1r7GZ+Z00DbylmWNwjQ3bg4EAsvttDMPU94q4AqgJBAIdGXG/wHMAt0soJba2v2MO3QrMaHPmY/5n4hRApRivtQaVoQ2b7sgY1bYqySqcC9R6LSAgUi3/7kCgUDEsqzc2O8JpyuBcuAmy7L2YpqZPz9Mkb/H1OaesyzrfoxRHYXpm/tmG/cAQAeDzcrvL8U0S6GioqytfEKfczHQugXS8uai0Nz03FXYI+ANuiW0P1pTMs474ijHFLoLqaG1QY9WCgQCgZrAwdEWYulRjBGdhOkv+xXwvcR8CdfswQwueDE1vg3AQ0A1iYEcD2U7sSbnjh27JXKtIxypLOv4hLRNfNa/ydbwPml29g4R4GOnRQxElNb68LkGOMrvvwC4kdhgw/z51zFs2JFOakpS7taBwH2xg7npucOA+zCmVjbFO3r47dkX3umUOBexJq8sf/bhsyUfgz3aRoy9mG8tE3+nslKGs53hoOgby5uLaoAi7Ci2OyIHqmuijdIl0HOkudkObjG0Esx8tBEA7NkjhuYMpyvLGpmQth7zRaMAdkYOyF4DPUcGBNrBFYamg8FqzCTDEYDpR+vjYI9Cm3gxKzzi2QTUYu/wvSEkQR97gtY6CvzLaR0DFVcYms0nmE2HIRKJUlfnxEJ54dCgj3uAXdhBH1eHtu1s0WGJstpNlFIf5pXl73Nax0DFTYa2jbg4XJSXS7PTGS5QluWNHSxvLooCK7En2EaI6r2RymKnxLmAF50WMJBxk6GVYOa/jQCkH805coDEjVE2YQJxpgNsDkvQxx5w+HhWSYxrDE0Hg3WY4IKmU3rXrr2EwwNuq6QkIbHZuQWzYH0UwIqW4uKoG+YL9T9FeWX5MqjSAa4xNJsiYk3OaFRTWSmb3TrDQZsQL28uasKEhRoBUKnrmyqidR3vBSi0xYtOCxjouM3QtgGfbZqybdsaJ8UkMScoyzoiIW0jZuqGB6AkUibNzq7TdphjoRW3GVoJZg1oDgDFxSU0N1c5qCeZuTjheBNmruBIgDWhEjG0LqC13g6scFrHQMdVhqaDwUbMHz2nNXHfvrWOCUpuEvvRyjF9nKMAguE9+xui8mXTWZRSj+SV5Uu/42FwlaHZrMY0O80OwcHgWqT/2QnOUZaVFTtY3lykgbXE5goCuyIV0sHdCbTWLcCfnNYxGHCjoQUxTU8TFG3//krq6mRD1v4nA7OXRDybMLHrsgE2hndJs7NzLMory5c1sJ3AdYamg8Ew8D4miqrZPGXXLml2OkNis3M7JpBAbPrGtrCWJWqHQyn1e6c1DBZcZ2g2azBx1EYAUFT0iaztdISDdoNa3lwUwUQmHgrQTDhSFq3Z2taFgiGqo5/mleUvP3xOAdxraHswsdHGAtDU1EJFRZGjipKTScqyZiWkBYkL9bQlXCrNzg7wKI/UzrqAKw1NB4MaEzPKSyzM+MaNEnLFGRKbnZsxe6nmAHzUskUGBtohqnU9ZstIoZO40tBs1mH2AR0DwM6de6mslNpA/5O4aqAOU3vOAdgbraytitaXOiFsoKPgobyy/FqndQwmXGtoOhisxQTCG9OauGHDMqf0JDGnKssak5D2CabmrAB2RA7IF00CER2tVUrd77SOwYZrDc1mOWZ2+mggVkuT9Z39iwe4MCFtE2bQZjjAhtAOMbQEIkR/mVeWX+W0jsGGqw1NB4MlwHvAhNbE9euXOaMmqUnsR9sL7MCevrEmVLK7WYfq+13VACWsI5VpKuUBp3UMRlxtaDZLia+l7dpVKrW0fueLyrJSYwf2qoFV2Ks5NLBHgj62EiFakFeWLwbfDVxvaHYt7V0Sa2myHKo/GQ6cmZC2GbNELRNgU3iPNDuBkI6UpavU3zqtY7DiekOz+Tcmmu1ntbSqqo2OKko+EpudWzGj0DkAK1qKt0R19HAbSrsejQ7kleU3O61jsJIUhhbXlza+NXHdumVI1NT+JHH6RjNxQR9rdGNzebQ2qdfcNunQ5jSV8qjTOgYzSWFoNgf3pe3evY99+z52UlCS4VeWdVRCWmz1hhdgWxIHfdRa66iOXpdXlh9xWstgJmkMTQeD20kc8fzgg6WEQnWOiUo+2lo1UIkd9HFVS0nSrhqo1HV/P/eA9YHTOgY7SWNoNm9iItqapmdjYzPB4OuOKkouEpudFZg5aaMAtkRKy+uiTRVOCHOSZh2q8eD5ptM63EBSGZoOBncCr2KanWYawbp1G6iu3uKkriRinrKsoQlp64htbAPsipQnXbOzLFL9nQsO/KzKaR1uIKkMzeYNTN/NtNaUwsLFRCItjilKHtKALySkbQLqsEMKfZpkQR8PRGre+krFgwud1uEWks7QdDDYAPwdE8LG7OFZXl5NcfEbTupKIhL70XZiwj2NAvi4Zcv2kI4kxbSFJh2q2x+t+U+ndbiJpDM0mzXAMmAy9ggbK1eupLpagg32PRcpy1KxAzvo40rssNwhItF90SrX/x2iWust4dJvXV/5h71Oa3ETSWlodry0RZjJnVNbT3zwwT+JJEftwEHGAicnpG3G1JjTAIqTIOhjcXjv09+o/KPEOutlktLQAHQwWA68gHkPRgBQUVHN+vWLZFlUn9PW9I3W/VQ/aine7OY5z3siFRsfbVh6s9M63EjSGprNh5hlUVOINT0//TTIjh3LHNSUDCRO32jAjHbmAJRFa+qrdP1uJ4T1NbXRxqo3m9Z/eXlzUaPTWtxIUhua3fT8B2YD3KNbT7z33ttUVMhaz77jRGVZ4xPSPsX8P3oAtofdF/QxrCOR91qCt/+x/vVPnNbiVpLa0AB0MFgBPAnUYmpqhmXLFtHYuN8hWW5HkVBLIyHo47rQdtetGlgd2vZ/S5vXP+O0DjeT9IYGoIPBT4HnMKFscgCzU9S77z5HONzkpDYXk2hoZZgNokcBrA/v2NukW1wTTz8Y2vP2840f/LcdC07oI8TQPuMt4BVgInaMLvbvr2Tt2hckKkef8HllWemxA/uDvprYew/sjlS4opZWHC7d8KeGpVfaEUaEPkQMzcbuT3sBs+v6UcQGCYLBLWzbtsRBaW4lGzgrIW0T0IgdyTbogqCPJeGy7Qvrl12zvLmozGktyYAYWhw6GGwCFmKmEfhbTxQWvk9Z2WqndLmYxOkb2zBNz1EAK1qKt0Z0dNCG09kdqSj9c8Pbt77ZvG6t01qSBTG0BHQweAB4HLMZ7pGtJ5YuXUxZ2RpnVLmWxOkbIcyqgeEA9bo5dCBaU+KArh5TFqkuf6rh7TtqddNrTmtJJsTQ2kAHg5uAZzAROXwmUWuWLv2nmFqvMk1ZVm5C2iYgir3j/dbw4Av6WBmtq3m6Yfn3yqN1L8ggQP8ihtY+72L61HKIN7UlS16S5mevktjs3ERc0MePQ1sGlaGVR2urn25Y/qO90cqnljcXJf0eCf2NGFo72IMELwHPYz5cvtaTS5b8U0yt10hsdlZjwjvlAOyIHKiqjQ6O+YB7IhX7H6lfEtgROfDH5c1FYaf1JCNiaB2gg8Eo8CLwN+JraiCm1nucoSxrRELaekxzXwHsiAz8VQPF4dLdf6x74+cV0bpHljcXSWw9hxBDOwxxpvY8Ymp9QQpwQULawUEfQ7sG9Hy0tS0lWx6tX3JfE6FHZI2ms4ihdYI4U/sLbZnarl3vOaPMNSSuGtgN7MKevrEytHVHix6YKzbebd64/unG5QVR9MPLm4tkwx2HEUPrJLap/ZO2TO2dd5awYcPfiUZDDskb7FyoLKv1f9HuTF8FDAGIENWlkapip8S1RURHI/9qWl34UtNHPwX+LKsABgZiaF0gwdRGYpZJGdat28C77z5OS0u1Q/IGM6OAvIS0TUAISAfYHN47YPrRaqINNY81LH31380bLOAFGQAYOIihdZE4U1uIWR51DLH3cdeuUl5//RFqarY5p3DQkjh9oxjYz2erBoqjA2BNbXG4dMeDdS8v2hwu/QXwuswzG1iIoXUDHQxGdTD4GvAQUAocj12ToLa2gVdffZo9ez50UOJg5CBDW95c1ASsxZ6PVqHrGit13S4nhAFEdDT8RtPaNQ/Xv/mPet38q+XNRe+JmQ08xNB6gA4G1wIPYPp7/MAwAKLRKMuWvcann75INCrNkc4xQ1nW5IS0WJBND0BJeL8jzc7aaGP1o/VL3n6zed2zwC+WNxfJ2swBihhaD9HB4B5MTe01YBKxXdkB1qxZy3vvPS6BIjtNW6sGqrH3fFgTKulXQ9NaUxTateWBusWvbonsexj4zfLmotL+1CB0DTG0XkAHg3XAY5h+tXRMOG+zVdvOnXtZvPhhdu16V+KqHZbE6RsHgC3Y/Wgbw7vLGnRzvwy61EQbyhc2vP324w1vLW7QzQ9iOv9lJHOAo+Qz1rsov/9E4FrMnp/FmPhehiOOmMiJJ15KZuYYh+QNdBqBUToQaH3P5qbnXgjciFk9wE1DPn/R0SnjT+krAREdDX/UUrzqxaaPdkeIFgJPLW8ukr0zBwlSQ+tldDC4CtOv9iEwDdMMNbW17dt3s3jxw2zf/pb0rbVJJnBeQtomoAF7TtrG0O4+WzWwN1JZ8lDdq0v+3vThugjRp4GHxMwGFylOC3AjOhjcpfz+X2E+nJdhRkG3AI2EwxHee+8dxoxZz6mnXszw4dOd1DoAuRh4Oe54O2YkeRRQv6KleNuFGbNDKcqb2ls3bNDN1Uub1q99p6WoFPgY07yUqTeDEGly9jHK7z8C+A/MbuH1wE5MvC/DjBnHc8wx55GePtIZhQOOnToQmBKfMDc992rgCmADwH9lz796vHfkMT29UaNuqf6wZfOq15rWVEeIlmCWt70nE2UHL2Jo/YDy+1Mx8fMvxawu2AFUtWbweDzMnDmTadPmirEBMFMHAutiB3PTc08E7sb0SYYuzTjlpDPTj00cEe00jbql5qOWLYX/alpdEybSiNkg55/Lm4vKe6xccBRpcvYDOhgMAUuU378OY2rzgHGY5lQj0WiU1atXs3btWmbO/BzTps1LcmObj9lJPcZmTEj0HGDfxy1bNp+ZfmyXC23ULTUrW7YWvtK0qjJMBMzmxouADTJJ1h1IDa2fUX6/AmZijC0X0/zcSfxoqKmxJbOxfaADgdPjE+am534bmIsJ/sgPh15xy3BP1tjOFFYZrdu9NrR93RtNa2tCRJRdxmvASnsfA8EliKE5hN0MPQk4H2NsGtMUPdTYpk6dS0ZGjiNCnSEKjNWBwIFYwtz03HOAb2P60fS1WWedOyN1ytz2CgjpcNO2cNm65S0bN2wM787ErLvdiDGyj8XI3Ik0OR3CboYWKr9/JcbYvggcR7yxmaboGlavXsNRRx3J1KmzyMk5Dm/vjfANUDzAhcBTcWmbgBrM8rLq9aEdm9oytPJo7c71oR0rlzat39VEaAwmSGTMyD4SI3M3YmgOk2BsJ2JqbDFj24mZgwXFxSUUF5eQnv4KubnHMWnSTIYOnYpSTknvay7mYEPbgzH6qUD12lDJ7iv0nIZ0lZpVFa0v3R7ev/HD0OZNm8OlqZilUkMxW+K9i2laSljsJECanAMMuykaM7ZjMUupyjEb8B48nSAnZzjHHjuTceNmurBJWgWM0YFA6zPPTc+9FLMKYz3AGWn+aTsiB8p3Rsq9wFjMBObdwHKMme2Qzv7kQgxtgKL8/hRMBI/ZwBzMB1ZjjK3C/v0zjjxyEhMnTicnZypDhkzC4/H2s+Tephi4QAcCW2IJc9Nzc4F7MDXXdGA0ZnVBNcbk3gfWSVz/5EUMbRCg/P5s4HPAqcAMTJOqATODvv6QC9LSUpgyZTLjxh1JTs5UsrIm4vEM7GVukUgzlZWl7N9fzOTJt+n77w8mZpmbnpsO/AKzo30FsA0TumkdsEtqY4IY2iDCnvIxDjPt4wxMf1Im0ITZnLcaE7b6YNLTUzniiCmMG3ckw4ZNJDNzDKmp2f2nPA6toaWlkvr6Umpr91FZuY+yslIOHKgGxgDDgZ/oYPAQQwOYm557AmY/h43AXjExIR4xtEGK8vu9mDBFx2DWih6Jqbl5MVM/qjAG1/YynqysDHy+0YwcOYbs7BwyM4eTkTGCtLQRpKYO7fZgg9aaSKSJcLix9dXUVEVNzT4OHCiltLSMpqYWTH9XJsbAYrrrMJ3/v9LBoMSQE7qMGJpLUH7/CIypTcUY3BSMUXgwBleHaaY2gJkm3y5er4eMjDRSU1NJTU0hJSWF1NRUUlJSSEkxP73eFDweD83NTTQ1NdLQ0EBDQyP19U1txH1LwUTLyLZfsWknjRjT/QRT49oC7LL3bRCELiOG5kLspulIPjO4XGACkGW/YtWvEKa5Gnu1YCa1RjGm15l/jhSMQcVeseM0TA1M2WXVY8yrBLPka5/92quDQenEF3oFMbQkQfn9sVHB0Zi+qlGYkdNxmJpcBsaEvJhanQdjRh39gyhMkzYU9zPEZzWv7cBejHGVAuU6GOy4digIPUAMTYiZ3QhMszC+lpWS8HvsZxhjWg32z4N+tycLC0K/I4YmCIJrGNhzkwRBELqAGJogCK5BDE0QBNcghiYIgmsQQxMEwTWIoQmC4BrE0ARBcA1iaIIguAYxNEEQXIMYmiAIrkEMTRAE1yCGJgiCaxBDEwTBNYihCYLgGsTQBEFwDWJogiC4BjE0QRBcgxiaIAiuQQxNEATXIIYmCIJrEEMTBME1iKEJguAaxNAEQXANYmiCILgGMTRBEFyDGJogCK5BDE0QBNcghiYIgmv4/4+MWIFrEDfdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = df.Gender.value_counts()\n",
    "labels = ['Male', 'Female']\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (4, 3), dpi = 100)\n",
    "explode = (0, 0.09)\n",
    "\n",
    "patches, texts, autotexts = ax.pie(values, labels = labels, autopct = '%1.2f%%', shadow = True,\n",
    "                                   startangle = 90, explode = explode, colors = ['teal','mediumvioletred'])\n",
    "\n",
    "plt.setp(texts, color = 'grey')\n",
    "plt.setp(autotexts, size = 8, color = 'white')\n",
    "autotexts[1].set_color('black')\n",
    "plt.title(\"Pi-chart for Gender\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab11ef1",
   "metadata": {},
   "source": [
    "Comparing each feature in terms of number of people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74e32250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 2, figsize = (9, 8))\n",
    "\n",
    "sns.countplot('Geography', hue = 'Exited', data = df, ax = ax[0][0])\n",
    "sns.countplot('Gender', hue = 'Exited', data = df, ax = ax[0][1])\n",
    "sns.countplot('Tenure', hue = 'Exited', data = df, ax = ax[1][0])\n",
    "sns.countplot('NumOfProducts', hue = 'Exited', data = df, ax = ax[1][1])\n",
    "sns.countplot('HasCrCard', hue = 'Exited', data = df, ax = ax[2][0])\n",
    "sns.countplot('IsActiveMember', hue = 'Exited', data = df, ax = ax[2][1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61edbad2",
   "metadata": {},
   "source": [
    "Explanation:- Number of French customers is more than german and spanish. Number of spanish customers is in the middle but they exited the least. Maybe they have the most satisfaction and products meet their needs more. Number of female customers is less but also they have more number of exited. Maybe they are hardly satisfied. With the rise in tenures, number of exits rises. Number of exited customers that baught more products is more. \n",
    "\n",
    "One probablity of this would be dissatisfaction. this can be a coincidence, but the probablity of exit increases, when someone has credit card. When a customer is active, she/he hardly exits. Most Costumers are in their 30s. It seems that estimated salary for both women and men in France, Spain and Germany is the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "53481599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FEaHlfSgDXfvX7wYg5NQlrDRdmdka2uxfv5t6rRqmK7tJ+6b8uekAAEIIhIDCVkUAKFrciui7USa/mi7vPen0GFOzSW2ObP0TgH0GGjKyL1q8KG82rMnuNTsASH6axGNtpCY71PduwD4t/8sG9WhK32FN3971u6mv6cvIvnzVClw+dYnEJ4mkJKdw8cgFGrRuBMDtkFuEhZr3pBxz2pOrUVu0KlmMUtloiw3facIhrW6z21dedJus08yZG39f58Zf1wB4GPMAmY0F55Wdq3L3erjeJ0c2H8C1Vf20/mxVn4M/79X8eRmrEsUoVcYagH+OXuRRbPpr+/P7z5CSnKLZXMLGwc5sTaZ4FfrNXq1+Mus3tZrU4fDWgwDsXb+L+q10fSAuMpYrZ0NI0kYNTVHr7TqE3wjn/u17GaZ5oaSk5PyVR8kLtxg5BJQHEEI4CyEOCyHOCiF+EULYCCHKCiFOaJ/XFUJIIcRr2vsrQggrLZ8VwIdCCFvDzI1H0oQQgUKIidr/e4QQc4QQ+4QQfwkh6gshfhZCXBZCGA5rFRBCfKvp+im1TCGEmxBirxDihBDiDyGEo0G+04QQe4HML4dfMWwc7IgKi9S/jwqLxMbB1iiNLZF37v+bJjwSG3vbTG1dW9YnOjxSf4I3RX2fxly/EEpSYsYnj9zSB+DdrS3Tfp9Nn5mDsCpZDICjWw+R8DiB4GPLmXtoCVuXbDT5Q2HjYEtk2P00edsa/VjYONgRdcegfL2ujG1LlbbW/3DHRERTqnSpdGU3aufOnxv3A5CclMyKsYv54o+5LDi2nPLVKrB77c50NgC2Jvxka5/WlyVsSvAo7pH+RzAyLBJbzWcZ2Zd9zYG4yFj6z/qUz7fOps/0QRQuWlifrnW3d5j++1z6zRxMMc3PpjCux8gM9D020BdloC8j+5uXbvBmgxoUty5BoSKFcPFyxa5c6Qx1ZKzPvLYYZcJH5ti+0aAGsfdjuHst/ZN7zOsrL7ZNOjiVQ0rJqFXjmfrrLN7t1yFDbSb12hv5KiwKG3u7dGnS9e1sBGVN/9OCc3tOZUuXMS+r35ivz86o3d/H1siPxv3GUJ85vN2+KQc37cu2ttxCypQcv/IqLzWIE0JYAi3QPVMMYBUwUkpZBzgHTJBSRgBFhBAlgabAcaCpEOJ1IEJKmbp44SG6QC67QVOilLIZsAjYiG5UsBbQXQiR2rLfAJZouuKAgUKIgsB84AMppZtW9lSDfK2llB5Syi+zqSdPI0wdlEZpRPpUUsoMbQsVKUT7wZ34afaaDMstX60inUd1ZUVQ5uv9ckMfwI7Vv+PfbCBj2gYQExFNl3HdAajsXI2UlBSGNOiNv/sAfPq0p0xFexO6TJeZVpep8qVZthlRxbkaCfEJ3Lp0AwDLApa0/KQNo30CGFS/Fzf/vs57g0xPH5n2U7pEGabJyN7S0gKnWlXYvvo3gnz8SXj8hPYDOwGwY/VvDG3Wn1Ft/YiOiOaTcRk/mSa39N0OucWmRb8w9ruJjF41gesXr5GclP2F0RlUp5E889uisW3j9u4c1kbhDClfrSIfjurKN1n2lRfbJi0LWPJG/bdYMHQOn3UaTf02jaj5du1MbdJqMaNMU4LN7CvtBnUiJTmZPzfkLPh4Gf0mp/qMBZqTJiMKFCxAvZYNOPTrwWxryzXy8Ujcy9rYUFQIcRqoBJwAtgshSqELfPZqab4FftT+/xN4G2gGTAPaoDtH7jfKdx5wWgiRncApNYA8B1xIfSCtECIUqAjEADellKktcjXwKfA7umBvu9bgLQHDS+K1GRUohOgL9AX4+ssp9O6W6dM68hRR4ZHYOv571WbraJduOi4qLDLNyIWtgx0xEdEUKFTApG3Z1x0oU9Geab/N1h+f8ussJrw3kth7Mdg62DFsyUgW+c8j4sbdF64PIO5+rP747h+2E7BiDABN3mvK2T2nSE5KJi4ylksn/qZynSrcu5lWZ1R4JHaOpU3mbajLtpxB+Q52REdEU6BQwQxtY+/HYF3WhpiIaKzL2hBroBOgcTt3Dm36t5u8XsMJgIgb4QAc3nKQ9gZrgLy7taV551YAhJ69nM5P0RFpNT+IiqNYyWJYWFqQkpyCnYG2SBN+jo6IQkpJVFgkV05fBuDI1kO8p2kw1L/rh+2M0PycSqtubWmh6btipM8uA31WBvoMfWfcDgztd6/dwe61ummrzsM/ISo8kuxiqi3GmKxzYx+ZbouGthaWFtRr04hx7w5Pk5+Ngx1Dl4xksZl95UW2yaiwSP46fIEH0Q8AOL37BE61qnDh4LlMdRrqTeMrR1tijOo7OlxXp5cN9WawXMCQtzt54tzCjekfTzRLizEvu99kRetuPrTs7A1AyNkQo3ZfmigjfXFG/cbO0Y6ou9FmleXs6crV81fSnYsUucNLXRMHvA4UIus1cfvRjcK9jm60rC7gDqS5ZJJSxgDfAwMNDieR9nsWMco7QfubYvB/6vvUINf4EkSiCyIvaGv7nKWUtaWUrQzSZLhYQUq5REpZT0pZ71UK4ABCz4Tg4ORImYplsSxYgEbt3Dm5/ViaNCd3HMO9kycAVVyq8/jBY2IiojO0vfXPDQa59cDPvT9+7v2JCotk7DuBxN6LwaqkFQHfjGHdjNVcPv73S9EHpFkzUq91Q279oxvZirx9n5pNdKMJhYsWpqpLde5cuZ1O15Uzl9Pk3bidOyeMdJ3YcYymnbwAqOpSnXhNV2a2Jw1smnby4sT2o/r8hBBp1kyB7oewfLUKlLAtCUDtpnXTLI7evuo3/aaC49uO0FTzU1WX6jx+8Eg/TWbIhUPnaOjTBIBmBhpO7jhq0j72XgyRYff1OyxrvV2HW5dvpvNz/dYNuan5OZVtq35jpI8fI338OLbtCM20/Ktlou/ioXM00vR5dPLiuKbv+I6jGdqXtNNNAdqVK02DNo04uDH7ozPP2hZjs2iLADXd6xJ25TbRBsGlVUkrArPRV150mzy79xSvvfU6hYoUwsLSgrca1tTXuzlcPROCfSVHSlfQldmwnTunth9Pk+bU9mO83dFD82c14h88JvZeTKb51vZw5p3+HZjb+wsSnySarceQl91vsuKPVVv1mw6ObTuMh1Y/mfWbC4fO0cjnbQA8OjXn2PYjZpXl3r4ZBzYZj6+8ZPLxxgZh7rTMcy1UiIdSyuLa/y7oArMq6KZKB0sp92vr1kpJKf2EEJXQBWz7pJSfCCG2ohsFqyuljNbSPpRSzhJClAaOAY5SyiLatGcYuinRh8Be4Hcp5UQhxB4gUEp5XAjhqf3/rqZrDxCIbsPEVaCJlPKQEGIp8De6qdSLQFfteEGgupTygmG+Wfni6f3Q51IBwyd8wbFTZ4mJicPO1pqBvbrSqV3rHOfbwy0w3bG6Xq58krrNf91ONgWvp3kXXfy667ttAPhO7kMdDxcStdsmXNVuxWDK1pg5BxYxrt1wHkY/4L0hH9BuYEfuXv13kHN610nERWZ8lZcb+vrP+ZTXazghpeT+rXusGL2ImIhoClsVoe+swZSvVgEhBPt+3MWvizem0ZOiXQM4e7nSdbzulgx71u1kY/BPtOiiq6Od3/0BQPfJfanr4UJCfAKLA+frdZmyBShuXYJPvw6kdLnS3L9zn68GzNSvyXurUU06j+zKhPdHpdHToktr2vR8l+SnSdy/fY9FAfN5EGP6FiM9Jvelroerpmee/pYaI1aOY+mIYKIjoilb0Z4hwQEUty7BtQuhLBg2R78WKyP712s40Xf6IAoULMDdG3dZHDiPR3GPGDhnmG60UEru3Ypg2eiFxEREk9EptKeWf+qtGFLzH7VyHIsN9A010DffQF9G9hN/nEYJmxIkP01i1ZRvOH/wLKALLHt81oeStqV4FPeI6xevMq3bZwAUMnFNXNfLVX9LjH2ZtMXaWltcatQWjW1T6TtrMCGnLunzAPR9Jdygr8ww6ivJRtejL7pNvv2+B+8N7IiUupG4Hz5flUZPQZH5uEIdT1e6jO+h+WQXmxesx0vz527NF10n9aaOpnfZ8AVc0/QOmOfHm41qUtymBHH3Y/llzlr2rdvJjD3BFChUkIcxuhHCK6cu8e2YJRlqSJRZT62/6H5jSFK6MYf09JrcD2etzS0InE/ouRAAglaOY9GIBURHRFG2oj1+wYEUty7B1QuhzBs2m6TEJKzLWPPF5i8pWtwKmZLCk8dP8Gs5mPiH8RQqUohFh5czuGk/Hj/I+DYtP17faHL1Sm6R8PfeHP/OFn7T44VqNpeXHsRp7zcD69BNaS4CrIBQoIeUMlpLcwOYIqVcIoQYDXTW1qhhGMRp72cDflJKob3/FN0U6FXgNnAtm0HcVnRBZBPgMrrA7bEQwhndFG4pdKN2c6WUS19GEJdbmAriFNkjxYyT6stG5nGNefc6+F9MBXF5DeMgLq+RVRCXFzAniHuZmBPEvWxeeBD31+6cB3FveakgTpEeFcTlf1QQl3NUEPd8UEFczlFBXM554UHchZ05D+JqtsiTQVze7zEKhUKhUCgUinSox24pFAqFQqHIv+ThjQk5RQVxCoVCoVAo8i95+D5vOUUFcQqFQqFQKPItMo+vY8wJak2cQqFQKBQKxSuIGolTKBQKhUKRf1Fr4hQKhUKhUCheQdSaOIVCoVAoFIpXEDUSp1AoFAqFQvEKkpJ/NzaoIO4lk9efiPDNiVkvW0KW5HUfWpInb/Sdhid5/JkIFq+ADwuIvK/xSR7fpWch874PCwnLly0hc/J4HSueLyqIUygUCoVCkX9R06kKhUKhUCgUryBqY4NCoVAoFArFK0g+HolTN/tVKBQKhUKheAVRI3EKhUKhUCjyL2o6VaFQKBQKheIVRAVxCoVCoVAoFK8eMh/fdkUFcQqFQqFQKPIv+XgkTm1sUCgUCoVCoXgFUSNxCoVCoVAo8i/5+BYjKojLY9TxcKHrhJ5YWFqwZ80ONi/8JV2arhN74ezlSkJ8AksCg7l2PtQsW5++7/HxGF/6O/vyMPoBtdzr8uGoTyhQsABJT5P4Ydq3XPzzfK59t7HTZrPv4FFsbazZsHpRrpWTGz7sOOxDPD9qyYPIOADWzfyOM7tPUrluVXp9PkCXqRD8Mnctx/84kqXG2kblbMlAY10Djdc1jRnZvvZWJbpP60cRqyLcvxXB10Pn8uRhPKUrlGH6znmEXbkDQMipS6wcszibXoUeE/vg6uVGQnwCCwK/4qqmx5CyFcsybP5wilsX5+r5UOb7zSHpaRLlqpRn0KxPcapZhR9mrWbzkg16mwUHlvDkUTwpySkkJ6cwql1AtrUBdJ/YGxdN38LAeSb1lalYlqHzA/X6gv3mkqzpGzBrCE41q7Bm1mq2LNkIgJ1jaQbNGYp1GWtSUiQ7v9/Gb99seSZ9tTyc+Xi8rt72rd3JVhN1/vGEntTxciUxPpHlgfO5fuEqAD1nDKRu83rERcYyrrWfPv2AYH8cKpcDwKpkMR7HPWKCT84eQ5db9QxgYWHBF1u+JCo8ki96TjFLT2705wY+jeno9yHlqlZgQvuRXD13BYAmHZrxTt/39PlWfOt1xr4TyI2L18zSClDbw5kuWj3vXbuTX03o7TKhJ3W1el5qUM+9ZgzEWavnMQb13GHYf/Hs3JK4KN3556cZ33N2z0mzNQH4TuyNs5cbiVr/uJZB//h0fiDFrItz7XwoC7T+kZm9Vcli9J0+iArVXwMki4cHc/nkP3w82hfXFvVJfprE3evhLBo+n8dxj7Kl+bmhplNzFyGEgxBijRDiihDiohBiqxCiejbz6CCEqJFbGjMpd48Qop6J492FEMHZysvCAt/JfZjhO4URLYfSqH1TylWrkCZNXS9XHJwcCfAYxPKgRXSf0tcsW1tHO2q51+H+rXv6Yw+i4/iy5zSCWvux2H8+/ecMzd6XzyYdfLxZNNu8E/ezkps+/H35Fsb4BDDGJ4Azu3Un0Fv/3GBcu+GM8Qlgpu9kekzrj4Vl5t0qtZyZvlMY2XIojTPQaO/kSKDHIFYELaKHkUZTtr2mD2TdF/9jdGs/jv9xhHf6ddDnF3H9LmN9AhjrE/BMAZyLlxuOTo4M8ejP4qAF9JkywGS6LqN82bJ8E596DuBh7EOaf9gSgIcxD1kxYSmbl24waTex81iG+/g9cwDn7OWGg5MjQz0GsDToa3pN6Z+hvq3LNzHMcyCPjPStnLAsnb7k5GT+N+Ub/FsMYWyHEbTq1pbyRnVlDsLCgq6T+jCn+1TGeA+jYXt3ylVNm08dT12dj/IczMrRC+k6ta/+swM/7WG27+R0+S4cPJsJPoFM8Ank+G+HOfF71hcQmZHb9ezT811uh9w0W09u9edbl27wVb8Z/HPkYpq8/tywT9/HF/p9xf1bEdkK4ISFBd0m9eHL7lMJ8h5Gowzq2cHJkRGeg/lm9EJ8jep5lol6Bvhj+RbG+wQy3icw2wFcav/wy6J/fKz1D3+tf3hp9ZqZve+EXpzZe5LAFoMZ2caP2yG3ADi3/wwjWn3KyDbDCLt6h/cGdsqW5ueKTMn5K4/y0oM4IYQAfgH2SCmrSClrAKMB+2xm1QF4oUGcEM/3SchVnKty91oY927eJflpEoc3H8DNu0GaNG7eDTiwfg8AV05doljJYliXtcnS9pPxPVnz+f+QUuqPXb9wlZiIaEB3UitYuBAFCuXe4Gw959qUKlki1/KH3PWhKRKfJJKSrOvgBQsXBAP/5kSjq5FGq5LFKJWFRsfK5fhb+1E6v/8M9ds2ylKLudT3bsDe9bsBuGzgM2NqNanD4a0HAdi7fhf1W+k0xEXGcuVsCEnaVf3zpr53A/Zp/spMX80mtTm89U9N327qt2qYRl/y07S72GIiovUjUU8ePeF2yC1s7e2yra+yc1Uirofr6+3o5gO4tKqfJo1Lq/r8+fNeAEJPXcaqRDFKlbEG4NLRizyMfZhpGQ3eacKRTQeyrc2Q3KxnWwc7XJvXY+ea7Wbrya3+fCfkNmGhdzItu0n7phzKpj8rO1flrkE9H9l8AFejenZtVZ+DWj1fMarnf45e5FEW9fwsuHk3YL/moxDtfJJR/zii9Y9963dTT+sfGdkXLV6UNxvWZPeaHQAkP03Sj7ad239af268fOofbB2z32+eGykpOX/lUV56EAd4AU+llPr5NSnlacBSCKGftxBCBAshumv/f6GN2J0VQswSQjQB2gMzhRCnhRBVhBDOQojDWppfhBA2mu0eIcQcIcQ+IcRfQoj6QoifhRCXhRBTDMr7RAhxVMtvcWrAJoR4KISYJIQ4AjQ2/CJCiB5CiEtCiL3A29l1hI2DHVFhkfr3UWGR2DjYGqWxJfLO/X/ThEdiY2+bqa1ry/pEh0dy469rGZZd36cx1y+EkpSYOz+yL4rc8iGAd7e2TPt9Nn1mDsKqZDH98SrO1fhi+1w+/2MO34xZrD9x5VRjlJFG2yw03rp0A1dv3Q9Gg3eaYOtYWp+uTMWyTN46izFrJ1O9/luZ6jOFrYNdGp9Fht9PF8yUsCnB47hH+u8fGRaJrdH3yoixqz9j+pYvaflRq2xrg/R1Gqn5KzN9UdnQB1CmQlmcalYm5PSl7OuzN6rPsChsjPxnbZQmOjwSGwfzfviqN6hB7P0Y7l4Ly7Y2Q3KznntM6M3qad+SkpL1hU4qudmfs6Jhu7c5tDF7QZw59Wxjb0KvGfXcwrctU36bTa8ZA9Ocf8zB1oSPTPWPRxnUa0b2ZV9zIC4ylv6zPuXzrbPpM30QhYsWTle+539bciabo4cK88gLQVwt4IS5iYUQtsD7QE0pZR1gipTyT2ATMFxK6SylvAKsAkZqac4BEwyySZRSNgMWARuBQZqO7kIIOyHEW8CHwNtSSmcgGeii2RYDzkspG0op9T1cCOEIfIYuePMmk1FBIURfIcRxIcTxyw+v/nvcVGKj851u4NIoiZQZ2hYqUoj2gzvx0+w1GcmhfLWKdB7VlRVBubdO7UWRGz4E2LH6d/ybDWRM2wBiIqLpMq67PsmV05cZ5T2M8e1H0G5gR92IXDY1Gg/gZUdjqu3S4Qto2a0tk7bMpGixovrRkJiIaIY17ss4n0C+m/wNA+f5UaR40Uw1ptNsQo+xaHPSmGJsx1GMfMefqb6TaN3Nh7caZH9A3bS/0iV6FnkAFLYqgv+ikXw7aTnxD+Ozrc902Vn7zzhNRjRs757jUbiMNDyPenZtXo/YyBhCz1/Jnh5TB59Df86KKs7VSIxP4NalG+YZZKHFKJEJXZkL27X6D4Y3G8Q4nwBiImL4aKzvc9CVLlGGaTKyt7S0wKlWFbav/o0gH38SHj+hvdG0aYfBH5CSlMyBX/ZmS/NzJR9Pp76KGxvigCfAMiHEr0C6VcZCiFKAtZQytdV8C/xokGST9vcccEFKGabZhQIVAXfADTimNd6iQIRmkwysN6GrIbop4XtaXmsBk+v6pJRLgCUAn7zeUd+VosIj0ww52zraEX03Ko1tVFgkduX+HWGxdbAjJiKaAoUKmLQt+7oDZSraM+232frjU36dxYT3RhJ7LwZbBzuGLRnJIv95RNy4a0ruK0Vu+BAg7n6s/vjuH7YTsGJMurLvhNwmIf4JFaq/pl8oba7GGBMabY00RmegMdU27MptZnSdBICDkyN1m7sBkJSYxMNE3RTNtfOhRFwPx9GpXKYaAVp386FlZ28AQs6GpPGZnUNpoiLSao6LisOqZDEsLC1ISU7BztGOqLvRmZYBEK3lExcZy9E/DlPVuTp/Hb2YhRW06taWFp11I3dXzl420menzzeVB0b6TLUNU1gWsCRg0UgObNjL0d8PZ5neFNHhRvXpaEuMkT7jNDYO6duFKSwsLXBr3ZDP2g1/Jm0vop7frPcW9Vo2wMXTjUKFC1G0hBVD5voxf9icTO1yqz9nRaN27tmeStXrNaOe7cqV5rKB3qx0GZ5/9q7Zjt/y0Vlq8e7WluZa/wg16h+2GfSPYkb1mqor0oSPoyOikFISFRbJldO6b3Nk6yHeG9hRn65ZJy9cWtRj6kfjs9Sbq+Th6dCckhdG4i6gC5iMSSKtviIAUsokoAG6QKoD8PszlJmg/U0x+D/1fQF0F4DfaqN6zlLKN6SUE7U0T2TGt382f57ABKFnQnBwcqRMxbJYFixAo3bunNx+LE2akzuO4d7JE4AqLtV5/OAxMRHRGdre+ucGg9x64OfeHz/3/kSFRTL2nUBi78VgVdKKgG/GsG7Gai4f/zsn0vMMueFDIM36kXqtG3LrH90VepmKZfUbGezKl8Gxcnnu3YogM55VY2wWGkvalQJ0V83vDfkPu777A4AStiURFhaaXnvsnRzNCtj/WLWV4T5+DPfx49i2w3h08gKgmkt1Hj94pF9PaciFQ+do5KNbSeDRqTnHtme+0L5w0cIUKVZU/3/dZi7c/Od6ltoAtq36jZE+foz08ePYtiM00/yVmb6Lh87RyKeJps+L49uPZllO/xmDuR1yi1+XbcoybUZcPRNC2UqOlK6gq7cG7dw5tf14mjSnth+jSUcPACq7VCP+wWNi78VkmXcN9zqEhd4mOty8AMWYF1HP38/4H/0b9WKQe1/mDJnF+T/PZhnAQe7158wQQtDwnSbPFMRdPROCvUE9N8ygnt/W6rmKmfWcumYOwK11Q7NGCLev+o0gHz+CfPw4vu0ITTUfVc2iXhtq/aNZJy9OaP3j5I6jJu1j78UQGXYfR22HdK2363Drsm7jSl0PF9oN6MisXtNIfJKYpd5c5QWsiRNCtBFC/COECBFCjDLxeSkhxGYhxBkhxAUhRI/n8dXywkjcLmCaEKKPlHIpgBCiPmAJ1BBCFEYXwLUADgghigNWUsqtQojDQIiWzwOgBICUMlYIES2EaCql3A90BbIzlrsT2CiEmCOljNCmcEtIKTP7dTkCfCWEsEM3Wvgf4Ew2yiQlOYVvxy9jxKrxuu3p63Zy+/JNmnfRXU3t+m4bp3edoK6XK1/u+5pEbTt9ZraZ4e3rg30lBzoM+Q8dhvwHgOldJxEXGZup3bMyfMIXHDt1lpiYOFp0+ISBvbrSqV3r51pGbvmwc1BXXq/hhJSS+7fusWK0buq5er23aDfwfZKfJiOlZOXYJTyMfpClxlXjlzFcK2efCY1ndp3A2cuVWZrGpQYaTdkCNGrvTstubQE4/vth9q3bBcAbDWvQyb8zKUkppKSksHL04mwvnj656wQuXvWYv28RifEJLAicr/8saOU4Fo1YQHREFKs//xa/4EA+CuzC1Quh7FqrW8RuXcaaLzZ/SdHiVsiUFN7p2Q6/loMpYVOS4UuCAN2I14GN+zi991S2tAGc2nUCFy83vtL0LQycp/9s1MpxLB4RTHRENN99voqhwQF8GNiFawb6SpWx5vPNszR9Ep+e7QhoOYTX3qxEs05eXP/rGtO36oKOH2au5vRus1eAALp6+278MgJWjcPC0oL963Zx5/JNPLU63/PdNs7uPkkdL1em711AYnwCy4cv0Nv3m+fHm41qUtymBF8eWsKGOWvZv24nAA3bPZ+pVMi9en6mKWhyrz/Xa92Qbp/1poRtSQK/GcP1i1eZ0U23K/TNhjWICovk3s3sz0ykJKfwv/HLGK7V8751u7h9+SZemt7d323jjFbPM/cuICE+gWUG9TzAoJ7nHFrCL3PWsm/dTj4M6sZrNSqBhPu3IvhmdPaWvpzadQJnLzfm7ltEQnwCiw36x4iV41iq9Y8fPl/FkOAA/qv1j91avWZmv3LCUgZ/5U+BggW4e+Ou/rPuk/pSsFBBRq/+DICQU/+wfMyrv2THFNqa+QXollLdQjeLt0lKaTilMAi4KKVsJ4QoA/wjhPhOSpmjCFeYu+YiNxFClAPmohuRewJcA4YBfYD3gMtAIrpp0D/QrWMrgm7EbJaU8lshxNvAUnQjax+gC+gWAVZAKNBDShkthNgDBEopjwshPLX/39V0GH72IRCEbjTwKTBISnlYCPFQSlncQLuhTQ/NJgw4DVhKKQdn9t0Np1PzIt+cmPWyJWRJD7ec3RcrtzG5NieP8YS8/WxBi1fAi1bPd7N6rvBQ5u2NS4XJ+z60NLWmLQ+R+Ao8J/SH6xteqBPjt8zO8e9s0Xf9M9QshGgMTJRSttbeBwFIKT83SBOEbrnWIKASsB2oLmXOFtzlhZE4pJR3gP+a+GiE9jIm3X0fpJQHSb+ZIN09FqSUngb/7wH2ZPDZWmCtCfviRu8Nbb4BvjGhV6FQKBQKxcsg99fElQcMp75uoVsnb0gwuoGoO+gGmT7MaQAHeWNNnEKhUCgUCkXu8Bx2pxreVUJ79TUoweSNA4zet0Y3Q1cOcAaChRAlc/rV8sRInEKhUCgUCkWu8BxG4gzvKmGCW+imSlOpgG7EzZAewBdSt4YtRAhxFXgTyHp3VSaokTiFQqFQKBSKZ+cYUE0I4SSEKAR05t9bmaVyA90GTYQQ9sAb6Nbr5wg1EqdQKBQKhSL/kss365VSJgkhBqPbeGkJrJBSXhBC9Nc+XwRMBlYKIc6hm34dKaW8n2GmZqKCOIVCoVAoFPmXF3CzXynlVmCr0THDx4neAZ7tuYKZoII4hUKhUCgU+Zd8/MQGFcQpFAqFQqHIv+SB++HmFmpjg0KhUCgUCsUriBqJUygUCoVCkX9R06mK/6/k9UdaQd5/NFg3N/+XLSFLCubxQflX4bFbr8LPRIE87sdCIm+3Q4AnefyxVgmvREt8waggTqFQKBQKheIVJJdvMfIyyfuXPQqFQqFQKBSKdKiROIVCoVAoFPkXNZ2qUCgUCoVC8QqSj28xooI4hUKhUCgU+Rc1EqdQKBQKhULxCpKPgzi1sUGhUCgUCoXiFUSNxCkUCoVCoci/5ONbjKggTqFQKBQKRb5FpqiNDYoXRB0PF7pO6ImFpQV71uxg88Jf0qXpOrEXzl6uJMQnsCQwmGvnQzO17TjsQzw/asmDyDgA1s38jjO7T2JZwJLe0wdSqVZlLApYcmD9HjZ//XOe0li5blV6fT5Al6kQ/DJ3Lcf/OJJNr5rH2Gmz2XfwKLY21mxYvShXysgI34m9cfZyIzE+gYWB8/T+MqRMxbJ8Oj+QYtbFuXY+lAV+c0l+mpSlvbCwYNqWWUSFRzKz59QMNdTxcKHbhF5YWFqwe80ONi9M3xa6TeylL2dR4Pw09WrKtlip4ny6IIAyFcpy71YE8wbO4lHcIywLWNJn+iAq1aqMZQFL9q/fzSajthewLIiyrzkwstXQDPU+73YI4N3dh1bd2pKcnMzpXSdY8/n/nks7rO3hTJfxujL3rt3Jryb0dpnQk7periTGJ7I0cD7XL1wFoNeMgTg3r0dcZCxjWvvp038Y1A3nlvVITkwi4kY4y4YH8zjucbZ05Vbbm3dgCfGP4klJTiElOZkx7XRPf+k0rDPNP/ImTuvra2eu5vTuE2ZpreXhzMfjeyAsLdi/didbF25Il+bjCT2p7eVCYnwiywODuXHhKjaOdvSePYRSZayRKZK9P2xnxzdb9TYtfNvSolsbkpNTOLvrBD9+sTpbPoSM+4YhZSqWZcj8AIpbF+fq+VC+9vtK78eM7PvOHIyLVvem+sI7fd+jy5ju9HPuxoPoB2Zp7TWxD65e9UiITyA4cC6hJrSWrWiP//xAiluX4Or5K3zlN4ekp0k06+BBh/6dAHjyOJ4lYxZy7a9rlKtcnoDg4Xp7+9ccWDP7e7as2GSWplxFrYnLXYQQDkKINUKIK0KIi0KIrUKI6s+Y10ohxAfa/8uEEDW0/0cbpRsjhLgghDgrhDgthGiY82+SM4SFBb6T+zDDdwojWg6lUfumlKtWIU2aul6uODg5EuAxiOVBi+g+pa9Ztr8v38IYnwDG+ARwZvdJABq804QChQoS1NqPce8E0vzjVpSuUCZPabz1zw3GtRvOGJ8AZvpOpse0/lhY5k6z7eDjzaLZU3Il78xw9nLDwckRP48BLA36ml5T+ptM9/EoX7Yu34S/50AexT7E68OWZtm37fkut0NuZapBWFjQY3JfZvhOZnjLT2nS3p3yRvXq7OWKg1M5/D0GsixoIT2n9MvStv3Ajpw/eA5/z0GcP3iOdgM7AtDwnSYULFSAUa2HMeadAFp83DpN26vfphFPHj/JVG9utMO3GtfCzbs+QW38GOU9jK1LdD9AOW2HwsKCbpP68GX3qQR5D6NRe3fKVU2rt46nTu8Iz8F8M3ohvlP76j878NMeZvlOTpfvhQNnGNNqGGPb+hN+9Q7vav41l9xue1M6jyXIx08fwKWydfkmgnz8CPLxMzuAExYWfDKpN3O6T2Wstx8NTfiwtqcL9k6OBHkO4dvRi+im+TAlKZm1U75lbMthTH0/iOZd2+ht32xcExfv+oxvG8C4Vn78vjT7QUdGfcOYj0Z147flm/H3HMSj2Ed4fdgiS/t9P+5iuu8kk/nZOtpR270u925FmK3V1csNR6dyDPLox6KgBfSdMsBkuq6jfNm8fBODPfvzMPYhLT70BuDuzbuM+28Q/m0+5cd5a+n/+SAA7oTeJsBnGAE+wxj+rj8J8Qkc+eOQ2bpyFZmS81ce5aUHcUIIAfwC7JFSVpFS1gBGA/YGaSyfJW8pZW8p5UXtrT6IE0I0Bt4FXKWUdYCWwM1n/AqpeeZ4VLOKc1XuXgvj3s27JD9N4vDmA7h5N0iTxs27AQfW7wHgyqlLFCtZDOuyNmbZpkNKClsVxsLSgkJFCpH0NIn4B/F5SmPik0RSknUdqGDhgrl6v596zrUpVbJEruWfEW7eDdiv+Svk1CWsNH8ZU7NJbY5s/ROAfet3U69VwyztbR3scGlej91rtmeqoapzNe5eCyNCq5tDGdTr/vW705WTma2hzX4DzVJKClsV0dpe4TRtr7BVEXx6t2fD/B8z1Jtb7bDlJ63Z/PUvJCXqRkfiImOBnLfDys5VuXs9XF/mkc0HcG1VP00a11b1OfjzXk3vZaxKFKNUGWsA/jl6kUexD9Ple37/Gb2uK6cuYeNgly1dudn2njeVnasScT2cezcjNB8exNnIhy6t6vPnzzo9oacuY1XCilJlrIm9F8MNbVTzyaMnhF25jbWDLQBeXVqzdeG/dZ46G5AdMuobxhj6cX86P5q2//voRR7GmB5h6zq+J99/vgqy0RwbeDdkj1bWpVP/UKxkMWxMaK3dpA6Hth4EYPf6XTTQtP5z4m8exT3S2Z/8BzvH0ult367D3Rvh3Lt9z3xhimfipQdxgBfwVEqpn7+SUp4GLIUQu4UQ3wPnhBCWQoiZQohj2uhZP9AFgUKIYG0E71egbGo+Qog9Qoh6QogvgKLaiNt3gCNwX0qZoJV3X0p5R7OpL4T4UwhxRghxVAhRQghRRAjxjRDinBDilBDCS0vbXQjxoxBiM7BNCFFMCLFC03hKCPFedhxh42BHVFik/n1UWCQ22onm3zS2RN65/2+a8Ehs7G2ztPXu1pZpv8+mz8xBWJUsBsDRrYdIeJxA8LHlzD20hK1LNpr8oXiZGgGqOFfji+1z+fyPOXwzZrH+Ryu/YGvCX7b2aX1awqYEj+Ie6b97ZFgktprvMrPvNqEX30/7lpQs1oTYONgSGWaQR1gktkYBgY2DHVF3DOpPX68Z25YqbU1MRDQAMRHRlCpdCkhte0/4+tgK5h1awq9LNujb3n8CPuLXpRtJiE/IRG/utEMHp3K80eAtJm74gjFrJ1O5TlV9upy0Qxt7W6IMtYRFYWNvly5NOr3ZCMqa/qcF5/acMjs95G7bk0iCVk9k6pYvaf5RqzR5tu72DtN/n0u/mYMpZtDXM8PayIfRYbr6NMTG3riNRqXzoV2FMrxWoxKhpy8DYF/ZkWoN3mLshs8ZufYzKtWpYpaeNOVm0DcMSe/H+3pt5tgb49qyPtHhUdz461q2tNo62HH/zr/BVWR4JLZGbdFUnduZaIstO3tzak/6kVT39s3Yv2lftnTlKiky5688Sl4I4moBGY2nNwDGaKNzvYBYKWV9oD7QRwjhBLwPvAHUBvoATYwzkVKOAuKllM5Syi7ANqCiEOKSEOJrIYQHgBCiELAWGCqlrItuhC4eGKTlUxv4CPhWCFFEy74x4CulbA6MAXZpGr2AmUII885QgDB10Kjt6AYu032/TG13rP4d/2YDGdM2gJiIaLqM6w5AZedqpKSkMKRBb/zdB+DTpz1lKtqbyumlaQS4cvoyo7yHMb79CNoN7KgbCclHmPZXukQZpsnIPnUdzdXzV7LWYKJ2pJEIE8WAlGbZGlNFa3uDGvRimHt/fPq8R9mK9rxeoxIOlRyzXG+WW+3QooAlxUoVZ2KHUfww7VsGfx2gT5KTdpiRFqNEJnSZ9+PRblAnUpKT+XND9n44c6vtAUzsOIrR7wQw3XcSrbq15c0GNQDYsfo3hjbrz6i2fkRHRPPJuB450Grsw/R2hmkKWxVh0MJAfpi0kicPdSO/FpaWFCtZnCkdglg37X8MWOBvlp602kwcNKN+U7WZZW9AoSKF6DD4A36c/UM2VGYow0Rfz9rXtRrXpsWH3qz6/Ns0xwsULED9lg3489eD2daWa6Sk5PyVR8nrGxuOSimvav+3AuqkrncDSgHVgGbAD1LKZOCOEGJXVplKKR8KIdyApuiCrbVCiFHogskwKeUxLV0cgBDCHZivHftbCHEdSF2zt11KGWWgsb0QInUBSBHgNeAvw/KFEH2BvgANbJ2pVtwJ0K5iHf+92rF1tCP6bpShKVFhkdiV+3f42tbBjpiIaAoUKpChbdz9WP3x3T9sJ2DFGACavNeUs3tOkZyUTFxkLJdO/E3lOlW4d/Nuhr570RoNuRNym4T4J1So/hpXz2UdmORlvLu1pXln3ehE6NnL6fwVHZHWpw+i4ihWshgWlhakJKdgZ+C7SBP+jo6IoqFPY1xb1sfZ042ChQtStIQVg+YOY8Gwuen0RIVHppkWyahebcsZ1J+DHdER0RQoVDBD29j7MViXtSEmIhrrsjbEavXc5L1mnDFqe051qlDCpgROtavw1YHFWBSwoJRdKcaumcy0zuPT6c2NdhgdFsnx3w8DEHomBJkiKWFbkgdR/06xPUs7jAqPxNZQi6MtMUZ1HB2u03vZ0L9G38kUb3fyxLmFG9M/nmiWlhfR9gCitRHYuMhYjv1xhCrO1fj76EV9GwDY9cN2Rpjo66aINvKhjaOdfpQ3bRrDNmpLjKbVsoAlgxYFcnjDfk4aXCREh0dyQnt/NYM6N4V3t7Z4ddatEws9G2KybxiS3o+l9doy6lsZYf+6A2Uq2vPFb3N06R3tmPrrl4x7bwRP7kWmS9+mmw/eWp2HnL1M6XJlSP1ZsjNR53Em6jzKoC2+/mYlBk4fzGTfz9JN9bp4uhF6/gqx92My1P/CycNBWE7JCyNxFwC3DD57ZPC/AIZoo2nOUkonKeU27bNsj3VKKZOllHuklBOAwUAnrQxTeZm8eM9AYycDja9JKf8yNpBSLpFS1pNS1ksN4ED3o+Hg5EiZimWxLFiARu3cObn9WBrbkzuO4d7JE4AqLtV5/OAxMRHRmdoars2o17oht/65AUDk7fvUbFIbgMJFC1PVpTp3rtzO5Ku+eI1lKpbVLyC3K18Gx8rls7WIN6+yfdVv+oXdx7cdoanmr6ou1Xn84FG6HyeAC4fO0dBHN9DcrJMXJ7YfBeDkjqMm7dfMWM3gRr351L0v84Z8yYU/z5oM4ACunLmcpm4at3PnhFG9nthxjKadvPTlxGv1mpntSQObpgaaI2/fM9n2dqz+g0ENejHUvR+ffTCasKthTOk8Lp3e3GqHx7cdoYamy8HJkQIFC/AgKi7H7fDqmRDsKzlSuoKuzIbt3Dm1/XiaNKe2H+Ptjh6a3mrEP3hM7L2YTPOt7eHMO/07MLf3FyQ+STRLy4toe4WLFqZIMd1kReGihanTzFnfpw37ev3WDbmpHc+K9D58m9NGdX56+3GadNTpqexSjccGPuwxfSBhIbfYtnxLGptT247xVuNaANgb1HlWbF/1G6N9/Bnt46/5MX3fMObiofN6Pzbt5MVxzY8Z9a2MuPnPDQa4dWeoez+GuvcjKiySMe8EZNhefl+1Vb/p4Oi2I3hqZVV3eYPHDx6bDBjPHzpHY5+3AfDq1Jxj23WBbulypRmxOIiv/OYQdvVOOrum7ZtyIC9NpeZz8sJI3C5gmhCij5RyKejWpQEeRun+AAYIIXZJKZ9qu1dvA/uAfkKIVejWw3kB35so56kQoqBm+waQIqVMveh1Bq4DfwPlhBD1pZTHhBAl0E2n7gO6ALu0cl8D/gFcTWgcIoQYIqWUQggXKaXZi1RSklP4dvwyRqwar7sNwbqd3L58k+ZddFdQu77bxuldJ6jr5cqX+74mUbttQma2AJ2DuvJ6DSeklNy/dY8Vo3XLD7ev+o2+swbzxfa5CCHY9+Mubv59PU9prF7vLdoNfJ/kp8lIKVk5dgkPzdxGn12GT/iCY6fOEhMTR4sOnzCwV1c6tWudK2UZcmrXCZy93Ji7bxEJ8QksDpyn/2zEynEsHRFMdEQ0P3y+iiHBAfw3sAvXLoSye+32LO3NJSU5hZXjlzJq1QTdbTe0umnRRff9d373B6e1cubsW6iVMz9TW4BNX//Mp18H4vVhC+7fuc9XA2YCsG3Vb/SfNYQZ278CM9uesd7caId71+2i78xBfL5NdwuNxQE6X+a0HaYkp/C/8csYvmocFpYW7Fu3i9uXb+Kl6d393TbO7D5JHS9XZu5dQEJ8AsuGL9DbD5jnx5uNalLcpgRzDi3hlzlr2bduJ10/602BQgUZvlo3Unnl1CW+HbPEbF251fZKlbbGf8koQDcCdnDjPs7s1Z0KPw7y5fUaTiAl925FsGz0QrN9uHr8MvxXjcXC0oID63Zx5/ItPDUf7vluG2c1H36xN5jE+ARWDP8agGr13qRJJw9u/nWdiVt1bXD9jO85t+cU+9ftoueMgUz6YzbJT5NYFhBstv9Syahv6Pw4liUjFhBj4Mf/BH7M9QtX2bN2R5b2g+f581bjmpSwKcn8w0tZP2cNe9buzLbGVE7sOo6rlxtf71us3WLk3zofs3I8X48IJjoiiv99vhL/4OF8HPgJVy+EskOr8/8O7UwJmxL0nazbiZycnMyIdrplB4WKFKJuU2cWjf76mfXlCrm4Ie5lI7Jau/JCRAhRDpiLbkTuCXAN2AC8J6V8V0tjAUwB2qEb8boHdADi0E11NgcuaVmullL+JITYAwRKKY8LIaYD7YGTwGzNxhpIAkKAvlLK+1oAOR8oii6Aa6mlWaTpSwL8pZS7hRDdgXpSysGaxqLa92iiabyWqj8jPnm948uvgFecb07MetkSMqWbW/bX2LxoTK1ry0tY5HF9AJYmFzblLRJl8suWkClFc77JP9d5ksd9+IS8rQ/g5+ubXmhneTy7T45/Z638l+bJDp4ngrj/z6ggLueoIC7nqCAu56ggLueoIC7nqCAuPY9n9c55EBe4LE928LzfYxQKhUKhUCielTx8s96ckhc2NigUCoVCoVAosokaiVMoFAqFQpF/ycM3680pKohTKBQKhUKRb5H5+D5xKohTKBQKhUKRf1EjcQqFQqFQKBSvIGpjg0KhUCgUCoUiL6FG4hQKhUKhUORf1HSqQqFQKBQKxSuI2tigyC1SyNtXCJavwJ3y8/oTEVadmP2yJWTJJ3nch1bC8mVLyJLIlISXLSFLCuVxP5am4MuWkCU3SXrZEjKl7xOrly0h75GPR+LUmjiFQqFQKBSKVxA1EqdQKBQKhSL/ko93p6ogTqFQKBQKRf4lH0+nqiBOoVAoFApFvkU9sUGhUCgUCoXiVSQfj8SpjQ0KhUKhUCgUryBqJE6hUCgUCkX+RY3EKRQKhUKhULyCyJScv7JACNFGCPGPECJECDEqgzSeQojTQogLQoi9z+OrqZE4hUKhUCgU+ZdcHokTQlgCCwBv4BZwTAixSUp50SCNNfA10EZKeUMIUfZ5lK2COIVCoVAoFPkWmfvTqQ2AECllKIAQYg3wHnDRIM3HwM9SyhsAUsqI51GwCuLyAHU8XOg2oRcWlhbsXrODzQt/Tpem28ReOHu5kRifwKLA+Vw7H5qpbbFSxfl0QQBlKpTl3q0I5g2cxaO4R7zdoRnv9O2gz/e1t15nzDsBXL94jcbt3Xlv0AcgJdF3o/h62FweRz9Mp6W2hwtdJ/TEwtKCPWt2sGXhL+nSdJ3Yi7periTEJ7AkMJjrmt6MbF97qxLdp/WjiFUR7t+K4Ouhc3nyMJ7SFcowfec8wq7cASDk1CVWjlmcbR/7Tuyt99/CwHl6/xlSpmJZPp0fSDHr4lw7H8oCv7kkP03K0l5YWDBtyyyiwiOZ2XNqtrVlh7HTZrPv4FFsbazZsHpRrpaVGd0n9sbFy40EzR9XM/Dn0PmBFLcuztXzoQRr/ixXpTwDZg3BqWYV1sxazZYlG3Osp6aHMx+N74GFpQX71+7kt4Ub0qX5aEJPanu5kBifyIrAYG5cuIqNox29Zg+hVBlrUlIk+37Yzs5vtgJQsUYlPpnal4KFC5KSlMJ345Zy9UxIjnT2+qwvbprf5gd8Rej5K+nSlK1oT0DwcIpblyD0/BW+GjabpKdJNPBuyEeBXZApkuTkZFZ8toy/jul+IwbP/JR6LeoTGxnLUO/BOdKYG3Vr51iaQXOGYq35eef32/jtmy3Z1lbdoy7vje+GsLTg6Nrd7Fm4KV2a9hN8edPLmafxiawLXMjtC9cAaNqrLfU/bA5SEv7PTdYNX0RSwlO8h3WiQefmPIqKA+D3GWv5e8/pbGvLjXOMY+VyfBo8XG9f9jV7fpr9A7+t2MzHo31xbVGf5KdJ3L0ezqLh83kc9yhbmkt71eWtKb5gacGt73ZxdX5afzp2epvKg9sDkPQogYsjlvHg4g0AXu/TlgqfNAfg1ne7uL7kt2yVnc8oD9w0eH8LaGiUpjpQUAixBygBfCWlXJXTgtWauCwQQrwvhJBCiDdzJX8LC3pM7ssM38kMb/kpTdq7U75ahTRpnL1ccXAqh7/HQJYFLaTnlH5Z2rYf2JHzB8/h7zmI8wfP0W5gRwAObtjHaB9/Rvv4s9BvLvdvRXD94jUsLC3oNqE3UzuPY1QbP278fZ1Wvj4m9fpO7sNM3ymMbDmUxu2bUs5Ib10vV+ydHAn0GMSKoEX0mNI3S9te0wey7ov/Mbq1H8f/OMI7/Tro84u4fpexPgGM9Ql4pgDO2csNBydH/DwGsDToa3pN6W8y3cejfNm6fBP+ngN5FPsQrw9bmmXftue73A65lW1dz0IHH28WzZ7yQsrKiFR/DM3Cn100fw7T/Nlc8+fDmIesnLCMzUs3PBc9wsKCLpN6M7f7VMZ5+9GgvTuOVdO2ydqeLpR1cmS05xBWjV7EJ1N1bTIlKZl1U75lXMthTHs/CK+ubfS2H4zqyuavfmSSz3A2zl7DB0Fdc6TT1cuNcpXKMbBZPxaOWkC/qQNMpusW1J3NyzYyyKMfj2If0uJDbwDOHjyDX+tP8W87lODAeQycPkRvs+vHnUzqNjFH+iD36jY5OZn/TfkG/xZDGNthBK26tU13nssKYSF4f1IPlnefzpfegTi3b0LZquXTpHnT05nSTg7M8PRj/eilvD+1FwAl7W14u3sb5rUbzezWIxAWFtRt11hvt3/5Vub6BDHXJ+iZArjcOseEhd4hyMePIB8/Rr8bQGJ8Asf+OAzAuf1nGNHqU0a2GUbY1Tu8N7BT9kRbCGp80ZPjH3/BgaYBOL7/NsWqp/Vn/PV7HOkwiYNeI7ky+2dqfqnrN8XfrECFT5pzqM0Y/mw+kjLerlg5OWSv/BdJiszxSwjRVwhx3ODV16AEUw8ZNx7+KwC4Ae8ArYFxQojqOf1qKojLmo+AA0Dn3Mi8qnM17l4LI+LmXZKfJnFo8wHcvBukSePm3YD963cDupEoq5LFsC5rk6mtoc3+9bup18r4ogCatG/Kn5sOACCEQAgobFUEgKLFrYi+G5XOpopzVe5eC+OeVuZhE3pdvRtwYP0eAK5oekuVtcnU1rFyOf4+ohtVOL//DPXbNnomf5pC5wudHkP/GVOzSW2ObP0TgH0GPsvM3tbBDpfm9di9Zvtz05sZ9ZxrU6pkiRdSVkbU927APs0fl09dolgm/jys+XPv+t3U1/wZFxnLlbMhJD9Nfi56nJyrEnE9nPs3I0h+msTRzQdxblU/TRrnVvU59LNOc+ipy1iVsKJUGWti78Vw48JVABIePSHsym1sHGwBkEiKFi8KQNGSVsSY6A/ZoUGrRuxevwuAS6f+oVjJYtiY8FvtJnX4c+tBAHb/tJOGrXV94cnjJ/o0RawKg/z3N+Li0Qs8iHmQI32Qe3UbExGtH9F78ugJt0NuYWtvly1tFZ2rcv96OFE3I0h+msyZzYeo2apemjQ1Wrlx8uf9ANw4FULRElaUKGMNgIWlJQWLFMLC0oJCRQsRdzc6W+VnRm6eY1Kp9XYd7t4I5/7tewCc23+alGTdgvvLp/7B1jF7/rR2rcrjq+HEX49APk0mfMOf2LdJ68+Y45dIitWN7sWcuEwRR13fKFatPDEnLpMSn4hMTiH6z7+w96mfrow8Q0pKjl9SyiVSynoGryUGJdwCKhq8rwDcMVJxC/hdSvlISnkf2AfUzelXU0FcJgghigNvA73QgjghhIUQ4mttd8kWIcRWIcQH2mduQoi9QogTQog/hBCOWZVh42BLZNh9/fuosEhsHeyM0tgRdSfy3zThkdjY22ZqW6q0NTERupNUTEQ0pUqXSld2o3bu/LlRd8JLTkpmxdjFfPHHXBYcW075ahXYvXanCb12RIUZaAmL1P/oGX6nqDsGusIjsbW3zdT21qUbuHrrTgIN3mmCrWNpfboyFcsyeessxqydTPX6b6XTlBW2DrZEmtBjSAmbEjyKe6Q/KUaGRWKracvMvtuEXnw/7VtS8vEWdmNsjPwRmYE/Hxv4M8rAn89dj70t0QZ6osN0/cMQa/u0fSg6PApro35mV6EMr9WoROjpywCs/ewbPgjqyow/F/Gf0d1YP+O7HOm0c7BL018jw9P39RI2JXkU91Dvt/thkdgZpGnYuhHzdy1kzMoJBA//Kkd6TPEi6rZMhbI41axMyOlL2dJWyt6GWIM6jA2LpKS9jVEaW2IM0sSER1HKwZa4u9HsXbqF0X8GM/boQp48eMzl/ef06Zr4tsbvt+n8Z0Y/ipYsli1dkLvnGL3G9u78uWm/yfI9/9uSM3tOZktzYQdb4g189eROFIUzqccKH3txb9dpAB7+fRPbRm9R0KY4FkULUaalM0XKZy+IfKE8h5G4LDgGVBNCOAkhCqGLF4zn+jcCTYUQBYQQVuimW//K6VdTQVzmdEAXOV8CooQQrkBHoBJQG+gNNAYQQhQE5gMfSCndgBVAlgukhIlRWCnTNhhhcqBWmmWbEVWcq5EQn8CtS7r1DZYFLGn5SRtG+wQwqH4vbv59nfcGdTSh16QUI72mdWVmu3T4Alp2a8ukLTMpWqwoSdo6kZiIaIY17ss4n0C+m/wNA+f5UUQbHTEX03rSJcowTUb2Ls3rERcZy1UT65ryMzn1Zy4IMlGWeX0olcJWRRi4MJC1k1by5GE8AJ6ftGbt5JWMaNKftZNX0n36wOcq21ydhmmO/HGYIc0H8EXvqXwU+Mlz15PbdVvYqgj+i0by7aTlxGt+zoa49MfM0iYpWrIYNb3r8UXTT5nScCAFrQrj0sEdgEOrdzC92VDm+owiLiKad8dm36+5dY5JxbJgAdxaNuDIrwfTpesw+ANSkpI58Es271hhqk+kc6gO27drUOFjLy5N/h6AR5fvEBq8iXrrxlDvhyDiLlxHJuXfR1tlhZQyCRgM/IEuMFsnpbwghOgvhOivpfkL+B04CxwFlkkpz+e0bLWxIXM+AuZq/6/R3hcEfpRSpgDhQojd2udvALWA7VqHtATCTGWqzaX3BejZqhtenVvqP7N1tEs3jRkVFoltuX+vcmwd7IiOiKZAoYLYGYxYGdrG3o/BuqwNMRHRWJe1IfZ+bJo8G7dz55DBVd3rNZwAiLgRDsDhLQdpPzB9EBcVHplm2N7W0S7dNJNOr4Euvd4CGdqGXbnNjK6TAHBwcqRuczcAkhKTeJio21xx7XwoEdfDcXQqx9VzmQdO3t3a0rxzKwBCz17GLp2etJofRMVRrGQxLCwtSElOwc7Al5FhkSbtG/o0xrVlfZw93ShYuCBFS1gxaO4wFgybm6m2V5FW3drSQvPnFSN/2mXgTysDf5pq18+L6PBIbAz02Dja6UehDdMY9iEbB1t927MsYMmARYEc3rCfk38c0adp3MmDHz5bAcDxXw/h+4XpNWyZ0babD94ftQYg5OzlNP3VziG9T+Ki4ihWsrjeb6Ud7Ygy4beLRy/g8JojJWxK8iA6Ltu6DHlRdWtZwJKARSM5sGEvR38/nG2dseFRlDKow1KOdsQZ1XNseCTWBmmstVG4qu61iLoZwaMo3ZTz+d+P8bpbdU5tOMBDg3Pj0TW76LF8hFl6XsQ5JhVnT1eung9Ndx5v1skLlxb1mPrReLM0G5IQFkVRA18VKWdLQnj6KebiNV6j1ux+HP/oC54abHS7/f1ubn+v+/mrNrozTwxG9fIcL2CmREq5FdhqdGyR0fuZwMznWa4aicsAIYQd0BxYJoS4BgwHPiSD6xft+AUppbP2qi2lbGUqoeHcesLlhzg4OVKmYlksCxagcTt3Tmw/lib9iR3HaNrJC4CqLtWJf/CYmIhorpy5nKHtSQObpp28OLH9qOF3o+E7TTikrYcDXXBWvloFStiWBKB207omF+uHnglJU2ajdu6cNNJ7cscx3Dt5AlDFpTqPHzwmNiI6U9uSdqX02t4b8h92ffcHACVsSyIsdM20TEV77J0cibhxN4Mq+Jftq37TLwg+vu0ITTU9VV2q8/jBo3Q/8gAXDp2joU8TQHdyTPXZyR1HTdqvmbGawY1686l7X+YN+ZILf57NlwEcwLZVvzHSx4+RPn4c23aEZpo/qmXiz4uHztFI86dHJy+OG7TB58m1MyHYV3KkdAVdu2rQ7m3OGLXJ09uP07ijTnNll2rEP3hM7L0YAHynDyQs5Bbbl6fdLRkbEc0bjWoC8GaT2kRcM3lNlim/rdqKf9uh+LcdypE/DuPVSbebr7rLGzx+8JhoE347f+gsTXzeBsDrgxYc3aYLLB1e/3d1RuVaVShQqECOAzh4cXXbf8Zgbofc4tdl6XeUmsOtM1coXckBmwplsCxoSd12jbm4/URaXdtP4tqxKQCvuVQl/sFjHtyLIebOfV5zqUbBIoUAqPp2LSJCbgPo18wB1Gpdn/BLNzGHF3GOSUW3fnlfmrzqerjQbkBHZvWaRuKTRLM0GxJ76gpWlR0o+loZREFLHDo0IeKPtP4sUt4OlxX+nB20gMehadt/odIl9WnsfeoT9suf2dbwopBS5viVVxF5WdzLRAjRD3CVUvYzOLYX2IFuLrs9UAbd0GlfdPPfF4GuUspD2vRqdSnlhczK+fj196Wzlytdx+tuE7Jn3U42Bv9Eiy66q/edWjDTfXJf6nq4kBCfwOLA+fqRKFO2AMWtS/Dp14GULlea+3fu89WAmTyK1V1FvdWoJp1HdmXC+2lvKt2iS2va9HyX5KdJ3L99j0UB84mPSX+LkbpernQZr7tNyL51O9kUvJ7mXXTx6q7vtgHgO7kPtT1cSIxPYGlgsF6vKVuAVj3eoWW3tgAc//0w66avBqBe20Z08u9MSlIKKSkp/Dx7Dad2Hk+jJ4msh/F7TO5LXQ9XzX/zCNX0jFg5jqUjgomOiKZsRXuGBAdQ3LoE1y6EsmDYHJISkzK1T+WtRrV4t+97Jm8xsurE7Cz1mcvwCV9w7NRZYmLisLO1ZmCvrnRq1zrH+X7i5p+t9D01f6TeDiHVH6NWjmOxgT+HGvhzvubPUmWs+XzzLIoWt0KmSJ48jieg5ZBMp9dKiMwnDWp7uvChdouRg+t28euCn/HQ2uRerU1+PKk3tTycSYxP4JvhX3P93BWq1nuTUT9N4dZf10nR7sr+y4zvObfnFFXrvclHE3pgUcCSpwlP+W7sUv2tckwRmZKQpd/6Tu6Pi6euHc0P/IorZ3W3LBm7cgILRs4n+m4U9q/ZExA8Qnf7jguhzBn6JUmJSbw/oBOenZqT/DSJxCeJfDvtG/0tRvznB1KzcW1K2pQk5n4Ma2Z/z8616TfbFBKWWWrMjbp97c1KTFr/Odf/uqa/Z9cPM1dzenfaoOF1kflSiTc9nWk3vhsWlhYcW7eHXQs20KiLbibj8Hc7AOgwqQdveNQlMT6BH4cv5tY5XZ15+31A3XcbkZKUwu0L1/hp1BKSE5P4cPZAytV4HSRE37rH+tHLeKAF+Ka4KU2309w6xxQqUojgw8sY2rQ/8Q8e68ubs3chBQsV5EG0bnQx5NQ/LB+zCN8nRTL1oSGlWzjz1mRfhKUFt37YTejcDVTspvPnzVU7qDm7Lw7vNCD+lm69nkxK5lDrMQA02DiRQjbFSUlK5u8J/yNqv/kzg23ursloMCRXiOvTKseBTsml216oZnNRQVwGaPdy+UJK+bvBsU+Bt9CNujUDLgGFgdlSyu1CCGdgHlAK3VT1XCnl0szK+fj19/N0BVhmOPCYdzAniHuZPM8gLrfIbhD3oskqiMsLmBPEvWzMCeJeJlkFcXmBjIK4vEJ2griXxQsP4np55zyIW749T/4Y5v0z40tCSulp4tg80O1alVI+1KZcjwLntM9PowvuFAqFQqFQKHIVFcQ9G1u056AVAiZLKcNfsh6FQqFQKBQmeAGP3XppqCDuGTA1SqdQKBQKhSIPooI4hUKhUCgUileQvL1sOkeoW4woFAqFQqFQvIKokTiFQqFQKBT5FrUmTqFQKBQKheJVRAVxCoVCoVAoFK8g+XhNnAriFAqFQqFQ5Fvy83Sq2tigUCgUCoVC8QqiRuJeMpK8fYXw5BUYhy6Yx69F8vojrQBW5/FHg70KPixpUehlS8iSvP4YvRD56GVLyJJiefxnc3mRvP1YMIA2L7rAvP8z9szk7daoUCgUCoVCkQPy83SqCuIUCoVCoVDkX9RInEKhUCgUCsWrh8zHQVzeXkykUCgUCoVCoTCJGolTKBQKhUKRf8nHI3EqiFMoFAqFQpFvyc/TqSqIUygUCoVCkX/Jx0GcWhOnUCgUCoVC8QqiRuIUCoVCoVDkW9R0qkKhUCgUCsUriAriFC8U34m9cfZyIzE+gYWB87h2PjRdmjIVy/Lp/ECKWRfn2vlQFvjNJflpUqb2ViWL0Xf6ICpUfw2QLB4ezOWT/9BpWGeaf+RNXGQcAGtnrub07hNm6+0xsQ+uXm4kxCewIPArrprQW7ZiWYbNH05x6+JcPR/KfL85JD1NolyV8gya9SlONavww6zVbF6yQW+z4MASnjyKJyU5heTkFEa1C8hQQx0PF7pN6IWFpQW71+xg88Kf06XpNrGX3i+LAufr/ZKRbbFSxfl0QQBlKpTl3q0I5g2cxaO4R1gWsKTP9EFUqlUZywKW7F+/m01fpy0vYFkQZV9zYGSroWb7MZXuE3vjovlzYeA8k/4sU7EsQ+cH6v0ZrNV/uSrlGTBrCE41q7Bm1mq2LNmY7fJzythps9l38Ci2NtZsWL3ohZWbG36zcyzNoDlDsS5jTUqKZOf32/jtmy3PpK+2hzNdxvfEwtKCvWt38uvCX9Kl6TKhJ3W9XEmMT2Rp4HyuX7gKQK8ZA3FuXo+4yFjGtPbTp+/o3xlX7wakyBQe3I9laWAwMRHRz6QPoJaHMx+P74GwtGD/2p1sXbghXZqPJ/SktpcLifGJLA8M5saFq9g42tF79hBKlbFGpkj2/rCdHd9sBeB9/844e9dHyhTi7sexIocae03sg6tXPRLiEwgOnEuoyfONPf7zAyluXYKr56/wlXa+adbBgw79OwHw5HE8S8Ys5Npf1wB4t1d7WnZuBVJy/e/rBA//iqcJT83SVNvDha4TdHW7Z80Otpio264Te1HXy5WE+ASWBAZzXdOdke2g4AAcK5cDdOfux3GPGOsTgGUBS3pNH0ilWpWxKGDJwfV72Px1+vNdZuTWORvAwsKCL7Z8SVR4JF/0nJItXblFfg7i8v2aOCGEnRDitPYKF0LcNnif5x526OzlhoOTI34eA1ga9DW9pvQ3me7jUb5sXb4Jf8+BPIp9iNeHLbO0953QizN7TxLYYjAj2/hxO+SW/rOtyzcR5ONHkI9ftgI4Fy83HJ0cGeLRn8VBC+gzZYDJdF1G+bJl+SY+9RzAw9iHNNf0Pox5yIoJS9m8dINJu4mdxzLcxy/TAE5YWNBjcl9m+E5meMtPadLenfLVKqRJ4+zlioNTOfw9BrIsaCE9p/TL0rb9wI6cP3gOf89BnD94jnYDOwLQ8J0mFCxUgFGthzHmnQBafNya0hXK6Muq36YRTx4/Mc+BRqTW39As6r+LVv/DtPo39OfKCcsy9OeLoIOPN4tmv9iTd275LTk5mf9N+Qb/FkMY22EErbq1Tde2zEFYWNBtUh++7D6VIO9hNGrvTrmqafOp4+mKg5MjIzwH883ohfhO7av/7MBPe5jlOzldvluXbGRsW3/G+wRyetcJ3hv6n2xrM9T4yaTezOk+lbHefjQ0obG2pwv2To4EeQ7h29GL6KZpTElKZu2UbxnbchhT3w+iedc2etvflmxkQtsAJvoM5+yuE7TLgUZXLzccncoxyKMfi4IW0DeD803XUb5sXr6JwZ79eRj7kBYfegNw9+Zdxv03CP82n/LjvLX0/3wQALb2trzTox0j3vVnWKshWFha4N6uqVmahIUFvpP7MNN3CiNbDqVx+6aUM2ojdb1csXdyJNBjECuCFtFjSt8sbRcM/pKxPgGM9Qng2O+HOf77YQAavNOEgoUKMrq1H+PfCcTr41Zpzj9ZkdvnbJ+e73I75KbZel4IUuT8lUfJ90GclDJSSukspXQGFgFzUt9LKROfZ1lCCMuc5uHm3YD96/cAEHLqElYli2Fd1iZduppNanNk658A7Fu/m3qtGmZqX7R4Ud5sWJPda3YAkPw0icdxOX/YdH3vBuxdvxuAy6cuUSwDvbWa1OHw1oMA7F2/i/qtGgEQFxnLlbMhJGmjiM9CVedq3L0WRsTNuyQ/TeLQ5gO4eTdIk0bnF51OQ79kZmtos9/Ax1JKClsVwcLSgkJFCpP0NIn4B7qHThe2KoJP7/ZsmP/jM32X+t4N2KfVX2b+rNmkNoe1+t+7fjf1NW2p/kx+mvxM5T8P6jnXplTJEi+0zNzyW0xEtH6U4smjJ9wOuYWtvV229VV2rsrd6+Hc09rZkc0HcG1VP00a11b1OfjzXgCunLqMVYlilCpjDcA/Ry/yKPZhunyfPPz3YeeFrQpDDh4RWdm5KhHXw7l3M0LTeBBnI40urerz5897AAg9dRmrElaUKmNN7L0Ybmijhk8ePSHsym2sHWzTaSxkVRjks4ts4N2QPVqfvHTqH4qVLIaNiXqu3aQOh7Tzze71u2ig1fM/J/7mkXbeu3TyH+wcS+ttLC0tKFSkEBaWFhQuWpiou1FmaariXJW718L0dXvYxPnH1bsBB7T2eUU7/5Qqa2OWLeguHA9tOgCknn8Ka+efQmnOP+aQm+dsWwc7XJvXY+ea7WbrUeSMfB/EmUII4SaE2CuEOCGE+EMI4agd3yOEmC6EOCqEuCSEaKod7y6ECDaw3yKE8NT+fyiEmCSEOAI0FkJ8otmfFkIszm5gZ+tgS+Sd+/r3UeGR2NrbpklTwqYEj+IekZKsGyOODIvEVjthZmRf9jUH4iJj6T/rUz7fOps+0wdRuGhhfbrW3d5h+u9z6TdzMMVKFsuGXrs05UWG30/3I1fCpgSPM9CbFWNXf8b0LV/S8qNWGaaxcbAlMszgO4dFYutgZ5TGjqg7kf+mCY/Ext42U9tSpa310z4xEdGUKl0KgKNbD5Hw+AlfH1vBvENL+HXJBv0P7H8CPuLXpRtJiE8w6/uZ/C5p/Gm6/g39GZUNf+ZXXoTfylQoi1PNyoScvpR9ffa2RBn2y7AobIz6iY19+r5r45B1wNgp8GNm/7mYxu814+fZa7KtLRVrI43RYbo+klajcT+KSqfRrkIZXqtRidDTl/XHOgZ+xKw/F9HovaZsmL32mTXaOthx/849/XtdPac/3xifH+1M+LFlZ29O7dHNOkTdjWLjkg0sPrSc5ce+5fGDR5zZf9osTTYOdkSFGfgkLBIbo3Zl42BU/1r7NMf2jQY1iL0fw91rYQAc23qIhMcJzD+2nLmHlvDbko0mA/yMyM1zdo8JvVk97VtS8tgD52VKzl95lf+PQZwA5gMfSCndgBXAVIPPC0gpGwDDgAlm5FcMOC+lbAhEAh8Cb2sjf8lAl3QChOgrhDguhDge8vCa8WfpCkh34ZpJmozsLS0tcKpVhe2rfyPIx5+Ex09oP1C3NmTH6t8Y2qw/o9r6ER0RzSfjemT1nTPVayzYnDSmGNtxFCPf8Weq7yRad/PhrQY1TGvA1Hc21mDCUEqzbI2p4lyNlJQUBjXoxTD3/vj0eY+yFe15vUYlHCo5cvyPI5naZ0ZO6///K7ntt8JWRfBfNJJvJy0n/qH5ox6Z65PGidIbmiFw/azv8W/Sj0Mb99HSt222tf1bvDka09sZpilsVYRBCwP5YdLKNCNwP8/6gcAm/Tm8cT/NfdvkQGPm5evSZP09ajWuTYsPvVn1+bcAFCtZjAatGjLAvQ+9G3SncNEiNHvf0zxNJo6lr1rTmsyxbdzencPaKBxAZe3882mD3vi7D6Btn/aUqWhvltaMtDyPc7Zr83rERsYQev6K2VpeFDJF5PiVV/n/uLGhMFAL2K41VEsgzODz1BWiJ4BKZuSXDKzX/m8BuAHHtLyLAhHGBlLKJcASgI9e7yC9u7WleWfdSFPo2cvYlft3iN/WwY7oiLTD+g+i4ihWshgWlhakJKdg52hHtDb0HxkWadJeSklUWCRXtKvjI1sP8Z62xiv2fqw+/a4ftjNixZhMv3Drbj607KxbYxJyNiRNeXYOpYky0hsXFYeVkd6ou1kvbE793nGRsRz94zBVnavz19GL6dJFhUemmRaxNfCHPk1YJLbl/r3a1PklmgKFCmZoG3s/BuuyNsRERGNd1kbvpybvNePMnlMkJyUTFxnLpRN/41SnCiVsSuBUuwpfHViMRQELStmVYuyayXzWeWym37NVt7a00Or/ilH922VQ/4b+NPV9/z/wovxmWcCSgEUjObBhL0e1dUnZJSo8ElvDfuloS4yRvuhwXd9NHb+ydchevR7aeAD/FaP5Zc6zjXRFG2m0cbRLtwFBl8awH9kSo2m0LGDJoEWBHN6wn5MZXMgc2bifoStGs3HOOrN1tenmg7dWzyFnL1O6XBngL8B0PceZOD8aTo2+/mYlBk4fzGTfz3gY8wCAOu7O3L15l7go3eauI78f4k23N9n3y54s9UWFR2LraOATRzu9T/RpwozqX3/+KZCprYWlBfXaNGLcu8P1x5q815SzJs4/927ezVDjizhnv1nvLeq1bICLpxuFCheiaAkrhsz1Y/6wOZnavQjy8khaTvn/OhJ3wWBdXG0ppeFcXeo8WDL/BrlJpPVVEYP/n0gpUxfSCOBbg7zfkFJOzErQ9lW/6TcVHN92hKadPAGo6lKdxw8emdzJdeHQORr6NAGgWScvTmw/CsDJHUdN2sfeiyEy7L5+t1Ott+tw67Ju8anheoj6rRty858bmer9Y9VWhvv4MdzHj2PbDuPRyQuAalnobeTzNgAenZpzbHvmo1WFixamSLGi+v/rNnPh5j/XTaa9cuYyDk6OlKlYFsuCBWjczp0T24+lSXNixzGaajqrulQn/sFjYiKiM7U9aWDT1MDHkbfvUbNJbb22qi7VuXPlNjtW/8GgBr0Y6t6Pzz4YTdjVMKZ0Hpfp9wTYtuo3Rvr4MdLHj2PbjtBMq7/M/Hnx0DkaafXv0cmL45q2/0+8KL/1nzGY2yG3+HXZpmfWevVMCPaVHCldQdfOGrZz59T242nSnNp+jLc7egBQxaUa8Q8eE3svJtN87Ss56v93aVmPsCu3n6PGtzlt1I9Obz9Ok46eAFR2qcZjA409pg8kLOQW25an3b1btpKD/n/nlvUJz6bG31dtJcBnGAE+wzi67QieWp+s7vIGjx88JtpEPZ8/dI7G2vnGy+B8U7pcaUYsDuIrvzmEXb2jT3//zj2qu7xBoSK6vW61367LLTMX54eeCUlzDmnUzp2TRn47ueMY7lr7rOJSXee3iOgsbWu61yXsym2iw/+dcr1/+z41jM4/WdX7izhnfz/jf/Rv1ItB7n2ZM2QW5/88mycCOAApRY5feRWR1dRRfkIIMRF4DPQFukopDwkhCgLVpZQXhBB7gEAp5XEhRGnguJSykhDCHZgBuAPlgQtAeynlHiHEQyllcS3/GsBGdNOpEUIIW6CElNJ09IFuJM74WI/JfanroduKvjhwHqHndMPTI1aOY+mIYKIjoilb0Z4hwQEUty7BtQuhLBg2h6TEpEztX6/hRN/pgyhQsAB3b9xlceA8HsU9YuCcYbxewwmk5N6tCJaNXqjv1ElmrJTuNbkfzh4uJMYnsCBwPqHnQgAIWjmORSMWEB0RRdmK9vgFa1v+L4Qyb9hskhKTsC5jzRebv6RocStkSgpPHj/Br+VgStiUZPiSIEB3hX9g4z5+Dja9WaAgFjh7udJ1vO42IXvW7WRj8E+06NIagJ3f/QFA98l9qevhovllPlc1v5iyBShuXYJPvw6kdLnS3L9zn68GzORR7EMKWxWh/6whul2KQrDvx11sWbwhjabSFcowfMVYRrYaSnI2V5v31Oov9RYxqfU3auU4FhvU/1CD+p+v1X+pMtZ8vnmW5k/Jk8fxBLQckuUU4OoTs7OlMTOGT/iCY6fOEhMTh52tNQN7daVTu9Y5yvMTN/8s0+SG3157sxKT1n/O9b+uIbV1Pj9kcAueIlksf63j6UqX8T2wsLRg37pdbF6wHq8uuuvH3d9tA6DrpN7U0drosuELuKZ9hwHz/HizUU2K25Qg7n4sv8xZy751Oxm8cDiOlcshUyT3b9/j2zGLMx29szQ5gfcvtT1d+EjTeGDdLrYs+BlPTeMeTeMnk3pTy8OZxPgEVgz/mmvnrlCt3psE/TSFm39dR2rDHutnfM+5PacYuDAQB01j5O17rBqzJN1IVSoxZuw16zO5Hy7a+S04cB5XtPPNmJXj+XpEMNERUdhXtMc/eLj+fDN32JckJSYxcPpgGrVtwr1bugmS5ORkRmg73z/0+4i3321KSnIyoRdC+XrkfP051ZBiJiaw6nq56m8fs2/dTjYFr6e55rddmt98J/ehtnaeXBoYrD//mLJNpe+swYScuqTPA3RT1n1nDaZctQoI7fyzdfG/txJ6QtabmnLjnG14jqnRqBbt+3bI8BYjP17f+EKjotuNm+c40Cl/aFeejOT+PwZxD4EdwDygFLrRtrlSyqWZBHECWA04A+cBe2CicRCnlfEhEIRu5O4pMEhKmeEcjKkgLi9hThD3simYxweUsxvEvQyeZxCXG5gTxL1ssgri8gJZBXEvG3OCuJeNqSAuL2FOEPeyedFB3K2GOQ/iKhzJm0Fc3m6Nzxmjqc1mJj73NPj/PtqaOKmLdNNtUNA+K270fi3w7NuvFAqFQqFQPDfy8saEnPL/KohTKBQKhULx/4v8POGYt+ehFAqFQqFQKBQmUSNxCoVCoVAo8i1qOlWhUCgUCoXiFUQFcQqFQqFQKBSvIPl5TZwK4hQKhUKhUORb8vNInNrYoFAoFAqFQvEKooI4hUKhUCgU+ZYX8dgtIUQbIcQ/QogQIcSoTNLVF0IkCyE+eB7fTU2nKhQKhUKhyLdoT4LLNYQQlsACwBu4BRwTQmySUl40kW468MfzKlsFcS+ZXG5bOcYijz+mB/K+RqtX4HFMef2xVnn9sWAARcs1fdkSsuR9x3ovW0Km5PVH6AFYiLx9vqmZ9iFCCiAl9x9g3wAIkVKGAggh1gDvAReN0g0B1gP1n1fBKohTKBQKhUKRbzFnOjSHlAduGry/BTQ0TCCEKA+8DzTnOQZxef+yR6FQKBQKheIlIoToK4Q4bvDqa/ixCRPjG5vMBUZKKZOfpy41EqdQKBQKhSLf8jxuMSKlXAIsyeDjW0BFg/cVgDtGaeoBa4RuOr404COESJJSbsiJLhXEKRQKhUKhyLe8gJv9HgOqCSGcgNtAZ+DjtBqkU+r/QoiVwJacBnCggjiFQqFQKBT5mNy+2a+UMkkIMRjdrlNLYIWU8oIQor/2+aLcKlsFcQqFQqFQKBQ5QEq5FdhqdMxk8Cal7P68ylVBnEKhUCgUinzLC7jFyEtDBXEKhUKhUCjyLS/gFiMvDRXEKRQKhUKhyLe8gI0NLw0VxOVBuk/sjYuXGwnxCSwMnMfV86Hp0pSpWJah8wMpbl2cq+dDCfabS/LTpEzt2/Z4lxYfeYMQ7PphO1tXbAagkU8TPvDrTPmqFRjTfjih5668ML3lqpRnwKwhONWswppZq9myZCMAdo6lGTRnKNZlrElJkez8fhu/fbPFLD11PFzoOqEnFpYW7Fmzg80Lf0mXpuvEXjh7uZIQn8CSwGCuaZozs/Xu7kOrbm1JTk7m9K4TrPn8f1SuW5Venw/QJRCCX+au5fgfR7Llv5oeznw0vgcWlhbsX7uT3xZuSJfmowk9qe3lQmJ8IisCg7lx4So2jnb0mj2EUpqP9v2wnZ3f6JZkVKxRiU+m9qVg4YKkJKXw3bilXD0Tki1dxuS1en5Wxk6bzb6DR7G1sWbD6lxbb5wt5syeRNs2zXkcH0+vXn6cOn0+XZoli2fh5lYXIeDy5av07DWMR48eP3ctPSb2wVWr5wWBX5ms57IVyzJs/nB9Pc/3m0OSVs+DZn2KU80q/DBrNZuXbNDbLDiwhCeP4klJTiE5OYVR7QJyrDU32mROqe3hTJfxunPI3rU7+dXE+afLhJ7U9XIlMT6RpYHzuX7hKgC9ZgzEuXk94iJjGdPaT5++o39nXL0bkCJTeHA/lqWBwcREROdYa1WPOrSZ0BULSwtOrtnDgYWb03xeuooj783qh2PNSuyatY4/l+jOLyUdbXl/zgCKlymFTJGc+H4XR755bk+Seu7k5+nUfHOzX+2BsqeFEGeEECeFEE3MsHn4IrRlB2cvNxycHBnqMYClQV/Ta0p/k+m6jPJl6/JNDPMcyKPYhzT/sGWm9hWrv0aLj7wZ3X44I9oMw7VFPRwqOQJw89INvuz3BX8dMX5CSO7rfRjzkJUTlrF56YY06ZOTk/nflG/wbzGEsR1G0KpbW8pXq5ClHmFhge/kPszwncKIlkNp1L4p5Yzs6nq54uDkSIDHIJYHLaL7lL5Z2r7VuBZu3vUJauPHKO9hbF2yCYBb/9xgXLvhjPEJYKbvZHpM64+FpfndSlhY0GVSb+Z2n8o4bz8atHfHsWpavbU9XSjr5MhozyGsGr2IT6bq9KYkJbNuyreMazmMae8H4dW1jd72g1Fd2fzVj0zyGc7G2Wv4IKir2ZpMkdfqOSd08PFm0ewpuVpGdmjbpjnVqjrxZg13BgwYyYLgz02mCwiciFs9b1zdvLl54zaDBvZ47lpcvNxwdHJkiEd/FgctoM+UASbTdRnly5blm/jUcwAPjep5xYSl6eo5lYmdxzLcx++5BHC51SZzgrCwoNukPnzZfSpB3sNo1N6dckb9uY6n7vwzwnMw34xeiO/Uf+8Ze+CnPczynZwu361LNjK2rT/jfQI5vesE7w39z3PQKvCZ3J3vfGewoOUIarVvTJlq5dOkiY95xG8TVvHn0l/THE9JTmHblO9Y0GIEyzpMoEE373S2ihdDvgnigHgppbOUsi4QBJg+E+Zx6ns3YN/6PQBcPnWJYiWLYV3WJl26mk1qc3jrnwDsXb+b+q0aZmpfvmoFLp+6ROKTRFKSU7h45AINWjcC4HbILcJCje9L+GL0xkXGcuVsCMlP097EOiYiWn9V/eTRE26H3MLW3i5LPVWcq3L3Whj3bt4l+WkShzcfwM27QZo0bt4NOKBpvmKgOTPblp+0ZvPXv5CUmKTXDej9CVCwcMFsj9s7OVcl4no4929GkPw0iaObD+LcKu0TWZxb1efQzzq9oacuY1XCilJlrIm9F8MN7Qo+4dETwq7cxsbBFgCJpGjxogAULWlFzN2obOkyJq/Vc06o51ybUiVL5GoZ2aFdu9b877ufADhy9CSlrEvh4FA2XboHD/695ixStAgyF+aI6ns3YO/63UDm9VyrSR0Obz0IwN71u6jfSncuSa3nJG1WIDfJrTaZEyo7V+Xu9XD9OeTI5gO4GvVn11b1OfjzXgCunLqMVYlilCpjDcA/Ry/yKDb92MKTh/H6/wtbFU7/LIBnoLxzFaKu3SX65j2SnyZzfvNh3vB2S5PmUWQcd86GkmLko4cRMYSdvwZA4qMn3Au5Qwn79L7PK0gpcvzKq+SnIM6QkkA0gBCiuBBipzY6d04I8Z5x4ozSCCEqCSH+EkIsFUJcEEJsE0IU1T6rKoTYYTDyV0U7PlwIcUwIcVYI8Vl2hds42BJ5577+fWR4JLb2tmnSlLApweO4R/rgISosElvtxzsj+5uXbvBmgxoUty5BoSKFcPFyxa5c6ezKe+56zaFMhbI41axMyOlLZuixIyosUv8+KixSH9hkpDkqPBIbe9tMbR2cyvFGg7eYuOELxqydTOU6VfXpqjhX44vtc/n8jzl8M2ax/nuag429LdEGWqLDdFoMsba3I+rOv7qiw6Owdkgb6NhVKMNrNSoRevoyAGs/+4YPgroy489F/Gd0N9bP+M5sTSZ15rF6zk+UL+fArZv/XkTdvhVG+XIOJtMuWzqb2zdP8+YbVQlesOK5a7F1sDOq5/vpgmrjeo7MRj2PXf0Z07d8ScuPWuVY64tok9nWZG9LlOG5JSwKGyP/2dibOP84ZH3h0inwY2b/uZjG7zXj59lrcqy1pIMtcQbnu7iwKEo6ZD8Qs65QGsear3P7dPaW4bxIpMz5K6+Sn4K4otp06t/AMiB1TPoJ8L6U0hXwAr4U2nMvDMgsTTVggZSyJhADdNKOf6cdrws0AcKEEK209A0AZ8BNCNEsO18ivTQTDSiTNBnZ3w65xaZFvzD2u4mMXjWB6xevkZyU8yvQnOrNisJWRfBfNJJvJy0n3uBqNEM9pg4alWVas8zU1qKAJcVKFWdih1H8MO1bBn/973TQldOXGeU9jPHtR9BuYEfdiJy5ZKAliyRpHFjYqggDFwaydtJK/RW75yetWTt5JSOa9Gft5JV0nz7QfE0mZeates5PZNQeTdG7jz8VX3flr78v89//tH8hWowr0Zw0phjbcRQj3/Fnqu8kWnfz4a0GNZ5VZoY6nmebfBbMqstn9N/6Wd/j36Qfhzbuo6Vv22eVmCnZHd0tZFWY/y4axu+T/kdCHu63KVLk+JVXyU8bG+KllM4AQojGwCohRC10v+vTtGAqBSgP2APhBrYZpQG4KqU8rf1/AqgkhCgBlJdS/gIgpXyildsKaAWc0tIXRxfU7TMUqj04ty+Am21dBgwcQIvOuivTK2cvpxkhs3OwIzoi7VTYg6g4rEoWw8LSgpTkFGwd7YjWpsuiwiIztN+9dge71+4AoPPwT4gKj+RZaNWt7XPTmxmWBSwJWDSSAxv2cvT3w2ZpiwqPxNbx36taU2UZ+8jWwY6YiGgKFCqQoW10WCTHNQ2hZ0KQKZIStiV5EBWnT38n5DYJ8U+oUP01rpq5OSQ6PBIbAy02jnbpFixHh0diW+5fXTYOtvrpUcsClgxYFMjhDfs5abChonEnD374TDdSc/zXQ/h+YXptU2bk5Xp+1RnQ35devboAcPz4aSpULKf/rHwFR+6E3c3QNiUlhR9/3ESA/wC+XbUux1pad/OhZWdvAELOhhjVc2mijOo5zqie7RztiLqb9SL71PYSFxnL0T8OU9W5On8dzd463BfVJp+VqPBIbA3PLY62xBhpig7XnX8up6ZxyJ6mQxsP4L9iNL/MWZsjrXHhUZQ0ON+VdLTlwd0Ys+0tCljy30XDOLfhIH/9fjxHWnKbvDwdmlPy00icHinlIXQPmC0DdNH+umlB3l2giJFJZmkSDNIlowt8M2oRAvhcW5vnLKWsKqVcbkLfEillPSllvSrFK7Ft1W+M9PFjpI8fx7YdoVknTwCquVTn8YNHJnchXTx0jkY+ur0bHp28OL79KADHdxzN0L6kXSkA7MqVpkGbRhzcuC9dvubwPPVmRv8Zg7kdcotfl20yW1vomRAcnBwpU7EslgUL0KidOye3H0uT5uSOY7hrmqu4VOfxg8fERERnant82xFqNKkNgIOTIwUKFuBBVBxlKpbVb2SwK18Gx8rluXcrwmy9186EYF/JkdIVdGU2aPc2Z4z0nt5+nMYddXoru1Qj/sFjYu/FAOA7fSBhIbfYvjztjs7YiGjeaFQTgDeb1CbiWpjZmlLJy/X8qrNw0bfUq9+KevVbsWnTH3Tt8gEADRu4EhcbR3h4+jZUpUol/f/vvuPNP//kbLdxKn+s2spwHz+G+/hxbNthPDp5AZnX84VD52jk8zYAHp2ac2x75juyCxctTJFiRfX/123mws1/rmdb64tqk8/KVaP+3LCdO6e2pw1wTm0/xtsdPQCoYtSfM8Je24QG4NKyHmFXbudY650zodg5OWBdsQyWBS2p1a4R/2w/Ybb9ezP6cD/kNoeW/ZZjLYpnR+TG4tiXgRDioZSyuPb/m8ABdKNpg4GqUsohQggvYBfgJKW8lmojhBhqKo2W9RYpZS0t30CguJRyohDiMPCFlHKDEKIwuueluaObxm0hpXwohCgPPJVSZvir/uHrHdJVQM/Jfanr4Uqitm0+9ZYfo1aOY/GIYKIjoilb0Z6hwQEUty7BtQuhzB82R7/oPiP7iT9Oo4RNCZKfJrFqyjecP3gWgPqtG9Ljsz6UtC3Fo7hHXL94lWndzF/OlxO9pcpY8/nmWRQtboVMkTx5HE9AyyG89mYlJq3/nOt/XUOm6Fz0w8zVnN6d/iRT0OhapK6XK5+kbvFft5NNwetp3kV39b7ru20A+E7uQx0PFxK1W4ykjpyZsgWwLFiAvjMH8VoNJ5KfJvH91JVc/PM8b7/vQbuB75P8NBkpJb98tY4T29L+SBQRmV8r1fZ04UPtFiMH1+3i1wU/46Hp3avp/XhSb2p5OJMYn8A3w7/m+rkrVK33JqN+msKtv66TInVrfn6Z8T3n9pyiar03+WhCDywKWPI04SnfjV3KdRO3X0jlgcx6IfrLrOfVJ2Znqc9chk/4gmOnzhITE4edrTUDe3WlU7vWOc63aLmmz2w776uptG7lyeP4eHr39ufESV3f3LxxFX37Dyc8PIK9u3+hRMniCCE4e/YigwYHpdnsYA7vO9bLMk2vyf1w1vrGgsD5hJ7TBYtBK8exaMQCoiOiKFvRHr/gQIpbl+DqhVDmDZtNUmIS1mWs+WLzl1o9p/Dk8RP8Wg6mhE1Jhi8JAnQjrwc27uPn4B/TlW2R4fWxaXKjTWY1nV9EWGb6eR1PV7po/Xnful1sXrAeL60/79b6c9dJvanj4UJCfALLhi/gmqZ7wDw/3mxUk+I2JYi7H8svc9ayb91OBi8cjmPlcsgUyf3b9/h2zOIMR++cpPEYRcZU86pLm/FdEZYWnFq3l/3BG6nXpQUAx7/bSfEypei7eQqFixdFpqSQ+DiBBS1HYP9mRXqun8Ddv27o++3OmWu5vPuMWeVOvP7dCx0aO1KuY44DnYZ3fs6Tw3n5KYhLBs6lvgVGSyl/FUKUBjYDBYHTwNtAW6MgzmQaLa+MgrhqwGJ0I35Pgf9IKUO1gLC3ZvsQ+ERKmeHcmqkgTpE9jIO4vEZWQVxewJwg7mXyPIO43CInQdyLwpwg7mWS3SDuZZBVEPeyyU4Q97J40UHc4ecQxDXKo0FcvlkTJ6U02bOklPeBxhl8VjyrNEAtg/SzDP6/DDQ3kedXwFdmC1coFAqFQpFr5OWNCTkl7w8RKBQKhUKhUCjSkW9G4hQKhUKhUCiMyc+7U1UQp1AoFAqFIt9i/u3XXz1UEKdQKBQKhSLfksGt3PMFKohTKBQKhUKRb0nJx/eAUBsbFAqFQqFQKF5B1EicQqFQKBSKfEuKmk5VKBQKhUKhePVQa+IUCoVCoVAoXkHU7lRFrlEojy9LLCDy/hVMXu+gkSkJL1tClpS0KPSyJWTKq/BIq/g7+1+2hCzp4Rb4siVkSt4/2+R9jsuYly0hz5GfR+LydgShUCgUCoVCoTCJGolTKBQKhUKRb8nrszU5QQVxCoVCoVAo8i0qiFMoFAqFQqF4BVFr4hQKhUKhUCgUeQo1EqdQKBQKhSLfkpJ/B+JUEKdQKBQKhSL/op7YoFAoFAqFQvEKIl+2gFxEBXEKhUKhUCjyLfl5d6ra2KBQKBQKhULxCqJG4vIYtT1c6DqhJxaWFuxZs4MtC39Jl6brxF7U9XIlIT6BJYHBXD8fmqntoOAAHCuXA8CqZDEexz1irE8Atdzr8t9Rn1CgYAGSniaxZtq3XPzzfLb01vJw5uPxujL3rd3JVhN6P57QkzperiTGJ7I8cD7XL1wFoOeMgdRtXo+4yFjGtfbTpx8Q7I+Dkd4JPs/+uKDaHs500TTuXbuTX01o7DKhJ3U1jUsNNPaaMRBnTeMYA40fBnXDuWU9khOTiLgRzrLhwTyOe/zMGnt91hc3LzcS4hOYH/AVoeevpEtTtqI9AcHDKW5dgtDzV/hq2GySnibRwLshHwV2QaZIkpOTWfHZMv46dhGAwTM/pV6L+sRGxjLUe/Az68sNH3b074yrdwNSZAoP7seyNDCYmIjoZ9aYGXNmT6Jtm+Y8jo+nVy8/Tp1O386XLJ6Fm1tdhIDLl6/Ss9cwHj169jrNCWOnzWbfwaPY2lizYfWiXCunjtE5Y3MG5xtng/PNNe18k5HtBwEf4epdH5kiiYuMZXHAfGIioqnlXpcPDc43P5h5vsmNc+Jrb1Wi+7R+FLEqwv1bEXw9dC5PHsYDUPHN1+nxeX+KFi+KTJFMaD+CpwlPzfZpbvSVVNr2aU/nMb4McunOw+gHZmsypu9n/ajnVY+E+ATmBszhionzjX1Fe0YEj6SEdXFCzl9h9rAvSXqapP+8Wp1qzNr4JTMGTefg1oMAvNerA60+agVScu3v68wNnJMt3+UWKa/A4yOflWyPxAkhpBDiS4P3gUKIic9LkBCirxDib+11VAjhbvBZUyHEBSHEaSHEW0KIeO3/i0KIRUKIZx5ZFEJcE0KUfga7SkKIj5+13DR5WVjgO7kPM32nMLLlUBq3b0q5ahXSpKnr5Yq9kyOBHoNYEbSIHlP6Zmm7YPCXjPUJYKxPAMd+P8zx3w8D8CA6jtk9pzG6tR9L/OfTb87QbOvtOqkPc7pPZYz3MBq2d6dc1bR663jq9I7yHMzK0QvpOrWv/rMDP+1htu/kdPkuHDybCT6BTPAJ5Phvhznx+5Fs6TLW2G1SH77sPpUg72E0ykCjg5MjIzwH883ohfgaaZxlQuOFA2cY02oYY9v6E371Du8O7PjMGl293ChXqRwDm/Vj4agF9Js6wGS6bkHd2bxsI4M8+v1fe+cdX0Xx9eHnJPROQklQlGpDIITehFAlCqLoa0FABBFBpAUQUEBBRUVQAUEEQazgDwsoSO+9V5XeQ0sIoYSS5Lx/7Cbc3Nw0Qrg3cR4+98Pd3Znd752dnZydOXOGyxcu0fjZpgDsWL2d3s3foE+LnowL+ZxuH/aIz7Pk58W8237YLWuDjCvDuZN+560WfRgSHMK2JZt5oucz6dKZFC0ebUT5cqV54KF6vPbaAMaP+8Blur4hw6harSmBVZty7OgJunfrmCF6UkPr4KZMHD0iQ68R12Z81GEE/Zv0pFYS7Y1faX/6NujOlIETecmpvXGV988vf2PQo30YHNyXrYs38WTP/wOs9uaTl99nYPPefNlnLF1T0d5kVJvY6cNuzBz5LYOa92bT/PU89mprALy8vej6aU+mDfqSgU178f6zbxN9IyZNZZoRzwqAj78vFepX5tzxs6nW44pqQdUoUaoEXR55hXFvjqXbe91dpntpYEd+n/wbXRp04fKFSzR9tln8MS8vL14a2JGty7fE7/Mt7kvLji3p/Vgvujftjpe3F4+0bJAurbcLvQ0fT+VWjJ5rwFO3YvCkhIg8DrwK1FPVB4CuwA8i4mcnaQuMUtUAIAo4YH+vBDwEtHY6353oaSwF3BYjrmxAOU4fDuXssdPE3Ihm3ZxVVG1aI0GawKY1WDVrGQAHtu4lT4G8FCxWOFV5AWo+Voe1s1cBcGT3ofiej+N7j5I9Zw6y5Uh9kZUJKMeZI6fir7lhziqqNKueIE2VZtVZ88tyAA5u3Uee/HkpWLQQAHs37OHShUvJXqPGY3VYb+u9FcoElOO0g8b1c1YR6KQxsFl1VtsaDzhp/HfDHi670Lhr5XZiY2LtPHsp7Od7yxprNKvF0llLANi79V/yFshL4WKFE6WrWKcSa+w33qX/W0zN5rUAuHrlanyaXHlygt5scvZs2M3FiFt/Y4eMK8O4ng+AnHlyZlhL2bJlc779/n8ArN+whYKFCuLnVyxRuosXb2rMlTsXqu5ruqsFVKRggfwZeo3UtBlVndqbvAXyUiiF9iYqwX29WY630t5kVJvoX6YE/6y3eqt3rdxO9RbWs1TxkQCO/XOEo38fBuBSxCU0NvUeVRn1rAC88HZHZnwwHU3ng1KzWS2W2O3Nv8m0N5XqVGLVXKvtXfy/xdS22xuAxzu2ZM281USEXUiQxzubNzly5cDL24ucuXMSfjosXVpvF7G34eOp3IoRFw1MAhL19YrINBF52mH7kv1/QxFZLiIzRWSviIwUkbZ2T9tOESlrZxkA9FPVcwCqugX4BuguIp2B/wOGiMj3jtdV1WhgDVBORF4SkZ9FZA6wQER8ROQ3EdkhIutEpJKtyVdEFojIVhH5Eqw5yHbPWnwfv2NPo4iUE5FFIrJdRLbYukcC9e0ewd4iUsH+Xdvsa5ZPbcEW9vMlPPRmpQ8PDaOwn49TGh/CT567meZUGD7FfVKV9/4aD3HhXASnD4cmunb14Noc2X2Q6OvRiY4lqbe4k5bQcAoXT2jMFHJKc/5UWKoNnvuS0Xs7NRYu7kOYU5mmxSir/0xjdi7bessaff18CQu9ef2wU2H4OF0/f+ECXI68FG84ngsNw9chTc3mtRi7ZAKDpw1lXL/PblmLKzKyDNuEvMDoNV9S+4lH+GX0T7dPtAN3lfDj+LGT8dsnjodyVwk/l2knfzWaE8e28cD95Rg3/usM0eMppLa9SXRfU9HePNPvBT5bO4k6rR9hlov7mtr2JqPaxON7jxLY1DKuajxWBx9/q0/Cr3QJVJV+099m+J+j4nvoUktGPStVmlTj/Olwjv19JE16XOHr58u50Ju9eWGnziVoSwAKFC7A5cjLDu3NzTS+xX2p3bw2876blyBP2Okwfp30C1PXTePbTd9xJfIyW1feert4O4mV9H88lVsdfhwPtBWRgmnIUxnoCVQE2gH3qWoNYDIQN/5TAdjslG8TUEFVJwOzsYy8to4JRCQP0BjYae+qDXRQ1UbAO8BWVa0EDAKm22mGAqtUtYp93ntS8Ru+B8aramWgDhAKvAmsVNUAVR2D1Xv4md1DWA04norzWr/DxT7nzgBxMbav6npREee8tVvVY52LXq27ypfk2TfbMXVgGn1vktCSMEnKaZKiZqt66eqFS/X1XflLpFJjy+5tiI2JYc1vK25FXpIkLsfk06yfv44ejV5jZOf3eD7kxduqJSPLcNaoH+hT51XW/r6CJh1a3KrEZElLHez8Sh9K3hvI3//s4/+eaZUhejwFl3+X0tHeOOb9+eMf6Fm7C2t+W0FTp/t6V/mSPPdmO75ORXuTUW3iV/3G06R9C97942Ny580d7+vlnc2b+6s/yISenzK8zSCqPlqTh+pWTFFnSlqcEiUtzAU5cuWg5ettbttLjrgomUSXT0biK8O6MO2DqcQ69VDmLZiPmk1r0anuy7Sv3o6ceXLR8Mmg26LZkDS3ZMSpaiSWMfRGGrJtVNVQVb0GHAAW2Pt3Yg1JJoWQ9EBLWRHZBqwG/lTVuFeDhaoabn+vB3xr614C+NrG5yPAd/b+P4FkPapFJD9wl6r+aue5qqquvJ7XAoNEZABwr6pGOSew/f42icimfZcOxe8PPxWGj//NNyIff18iTocnyBseGoZPiZsj2T5+vpw/cz7FvF7eXlR7tBbr5qxOcL7Cfr70nDSAL/t8zpmjp5MrgkScP+Wkxd+HiDPhyaYp7Jf4N7nCy9uLqs1rsuGP1SmmTY7wVGr0dS7TVGis26YhAY2rMrHnp2nW1aJ9MKPnfcboeZ9x/kw4vv43r+/r4vqR4ZHkLZAPL2/rkS3i70u4C417NuzG7x5/8hcukGZNSZGRZRjH2t9XUe3RWiknTCWvde3Apo0L2LRxASdDT3F3yRLxx+6625+ToUnX9djYWH7+eTZPPfnYbdPjibhqM5zvWXho4vsakUR74+p+r/l9JdVb1E6Qv9ekAUxMZXuTUW1i6IETfNTuXYY83o+1s1dy5sgp+1zn+Gfdbi6dv8j1q9fZvnQLpR4uk6LOBHpv87NS7F4/it5dnOHzPmHUqgn4+Pny7h8fxw/BpobH2j/G5/PG8vm8sYSfCaOIf9H4Y75+RRINe1rtTV6H9uZmmnIVy9F/3ACmrP6ausF1eW1EN2o1q0VAvQBOHztNZHgkMdExrP1rDQ9WfTDVGjOSWCTdH08lPSFGPgU6AXkd9kXHnVOsV5IcDseuOXyPddiO5eYs2T1AVafrBNr7XXHA7gGroqrDHPZfdvju8oXM6X9H4n+DTa5kzpP4xKo/AK2wfPbmi0gjF2kmqWo1Va1WPl/p+P0Ht+/Hr7Q/RUsWwzt7Nmq1rMeWhRsT5N2yaCP12jQEoGyV+7hy8QoXzpxPMW+FepUJPXCC86duPqx5CuQhZOpgZn70Hfs2/ZOan5eAQ9v3U6yUP0Xutq5Zo2U9ti7clCDN1oUbqfOU5dxapkp5oi5e4cLZiBTP/VC9SoQePMH5U6k3BJLSWNxBY80kNNa1NZZNpcaKDQJ4rGtrPu08kutXr6dZ17zpc+nToid9WvRk/fx1BLWxqsl9Ve7nysUrnHcxS3PX2h3UCa4LQNDTjdmwwJrw4Xevf3yaMg+XJVuObFw8H5lmTUmRUWVYvNRN3VWaVCP0wInbpnnCxG+oVr0Z1ao3Y/bs+bRra3l51KwRSOSFSE6dOpMoT9mypeK/P/5YU/79d/9t0+OJ3Gp7E5FCe+N4XwObVo+/r3kK5KFvGtubjGoTC/hag0giwhM9nmHJ9/MB2LF8GyUfLBXv1/VAzYc4sS/VgykZ8qwc//coPaq9TEi91wip9xrhp8IY8ni/VLWjcfw5/U/eaNGDN1r0YO38dTSy25v7q9zPlYuXXbY3O9fupF6wNa+w8dONWWe3N53rdaJT3ZfpVPdlVs9dzYS3vmDdgnWcPXGW+wPvJ2eunABUrluZY/uPpVpjRpKVJzbcsuO/qoaLyEwsQy7OeeQwlhE2E3gCyJ7G034EfCgij6pqmIgEAC8BNW9VJ7ACa0LEcBFpCJxT1UgRids/QkRaAHGenaeBYiLiC1wCHgf+svMcF5HWqvqbiOQEvIGLQLwHsoiUAQ6q6uf290rAktQIjY2JZfqQyfSbPsQK2TFzMSf2HaNRW2tW0JLvF7B9yWYCggIZteILrkdd46uQccnmjaN2y7qsnb0ywfWadgimeCk/Wvd4htY9rJmBH7V7l0gnZ9Xk9H4/ZDJ9p7+Nl7cXK2cu4eS+YzS09S77fgE7lm6hUlAgHy4fz/Woa0zpNz4+/6uf9+aBWhXIVzg/n6ydxG9jZrBy5mIAarZM/1BqnMZvh0ymn61xxcwlnNh3jCBb49LvF7Dd1vjx8vFci7rGZAeNrzloHLN2Er+OmcGKmYtp905nsuXITr/vhgCWQ/U3gyfdksbNSzZRNagaE1ZOskKMhNz0aXtr2lDGDxjL+dPhTP9gGn3H9eeFfi9yaPdBFs2wOrNrB9ehYZtGxNyI5vrV63zS/aP4/H3GhlChdkUKFC7AV+un8tPoH1g8Y6FHlOEzA17Ev0wJNFY5d+Is3wz+8pbKLyXmzlvMo4824t+/V3MlKorOnfvEH5vz+3S6dO3HqVNnmDrlU/IXyIeIsGPHHrq/PjBD9KSGfkNHsnHrDiIiImnc+kW6dWpHm5bNb+s1YmNi+WbIZPrbbcZyF+3NtiWbqRwUyCd2ezPJob1xlRfg2TdfxL/MXWhsLOdOnGXqIOu+umpvPkyhvcmoNrFWq3o0aW8N8276ax0rZlpN9JXIy8ybPJt35nwECtuXbmb7EmcPn+TLNCOeldvJpiUbqRZUja9WTrZCjISMiT82bNowPh/wOeGnw5n6wVQGjOvPi/3acXD3QRbMmJ/sefdu+5fVc1fz6dzPiI2J4cDug/z1w7xk89wpPNmnLb1IWmdgicglVc1nfy8OHAI+UtVh9vbvWD1Zi4EeqprPNp5CVPVxO98ye3uTi2OvAb2wjN+LQF9VXWEfmwb8oar/E5FS9veHnfS9BFRT1dftbR9gKlAauAJ0UdUdtpH2I1AEWA48BVRV1XMi8gbWUPEh4ARw2P595YEv7Tw3gGeAY8Bf9r5pWD13L9rHTwEvOAztJqLdvU95spFPtkwQX8eTZw4BRMamvafuTlPAK0fKidzI9yfXuVtCikSdXJlyIjfTseqtx1u8E3h+awNeHt4mhsVeTTmRm/nj6J93tBCn3/Viuv/Otj/xXbKaReRR4DOszp3JqjrS6XhbrMmbYHUQvaaq29OrK81GnOH2Yoy49GOMuPRjjLj0Y4y49OP5rY0x4m4Hd9qIm3YbjLiXkjHiRMQb2As0xZrMuBF4XlX3OKSpA/ytquft0b9hqpqeUUbALLtlMBgMBoMhC3MHfOJqAPtV9aCqXgd+wnIpu6lBdY2qxjkfrgPu5jZglt0yGAwGg8GQZbkDPnF3YblWxXGc5H35OwG3xWHQGHEGg8FgMBiyLLfD5UZEugBdHHZNUtW42WzJRcFwPk8QlhFXz9XxtGKMOIPBYDAYDIZksA22pEIQHAdKOmzfDZx0TmSvGDUZaKGqt2VNMmPEGQwGg8FgyLLcgclvG4HyIlIaK6LFczitqS4i9wC/AO1Ude/turAx4gwGg8FgMGRZNIN94lQ1WkReB+ZjhRj5WlV3i0hX+/hEYAjgC3xhL88WrarV0nttY8QZDAaDwWDIstyJMFSqOheY67RvosP3zkDn231dE2LEYDAYDAaDIRNieuIMBoPBYDBkWTw9IHx6MEacm4nx6KV14arGuFtCimTz8DjvOcTb3RJSxNvDy/BJ/3S7jmQ4nr4aAsDUzaPcLSFZXqzaJ+VE7sazm2zyS1qXLM/6ePgtSxfGiDMYDAaDwZBluQPBft2GMeIMBoPBYDBkWbLycKqZ2GAwGAwGg8GQCTE9cQaDwWAwGLIsWbknzhhxBoPBYDAYsixmYoPBYDAYDAZDJsRMbDAYDAaDwWDIhGTl4VQzscFgMBgMBoMhE2J64gwGg8FgMGRZjE+cwWAwGAwGQyYkNgubccaI8zAqNahC+6Gd8PL2YulPi5gz4ZdEadoP60RAUFWuR11jYshYDu86mGzevAXz8cb4vhS9uxhnj5/h826juBx5GYCSD9xL5w9eI3e+3MTGKm+36seNazfSpLnjsFcIDKrKtahrjA/5jEO2HkeKlSxGr7H9yFcoH4d2HWRs7zFE34imRNm76D7qDUpXKMuPo75jzqTfEuTz8vJi5B+fEH4qjJEvj0iTrg7DOseX04SQz+PLyZGiJYvxxtgQ8hbKx+FdBxnf+1NibkQnm//zVZOIuhxFbEwssTExDG5pLbfUptdzNHq+KZFhkQDM+Pg7ti3dnCqtLw3rTBW7DCeEfO6yDIuWLEbPsSHxZTjO1lqi7F28NqoHpSuU5adR3/HHpN8B8PUvQvcxPSlUtBCxscriHxYwb+ofaSrDOB5uEMALQzoi3l6snLGYuRN+S5TmhaEvUzGoCtejrjMlZBxHdx+isL8vnUf3oGDRQmissvzHhSyaOheAJ/s8R0DT6qjGEnkukq9DxhFx5vwt6YOMq4fjV03iqn2/Y2JiebNl31RrqtSgCu2GvoyXtxfLflrEnAm/JkrTblgnAoICuRZ1jUkh4xI8z67yPt33eQKbVkdjlciwC3zZdywRZ87zcL3KPPvmi2TLno3oG9H8+P437FmzK42lmDreen80K1ZvwKdwIX77bmKGXCMpPPVZSY+u5PK36Pg4jZ9vCiIs+XEhc7+eA8C9D5ai8/tdyZUnN2ePn2Fsz9FEXYpKUl9GtIf+ZUrwxrh+8fmL3VOc/43+kXlfz6FmcB2e7v0cJcrdzdut+nFw54E0leftxPjE3WFE5JLT9ksiMu4Wz3WfiMwVkf0i8reIzBSR4ulNm8prTxORp1Od3suLjsO78FGH4fRr8gZ1WtXjrvJ3J0gTEBSIX+kS9GnQjckDJ/DyiFdTzNuq21PsWr2TPg27s2v1Tlp2ewoAL28vun/aiymDJtK/aU9GPPs20TfStlZqlaCq+Jf2p0eDrnw5cDyvjHjNZbq2b3bgjymzeaPha1y6cIlGzzYB4FLEJb4e+hVzvvrNZb7glx/nxP5jadIEEBBUFb/S/vRu8BpfDfyCTiO6ukz3wpsdmDtlNn0aduPyhUsE2bpSyj/iubcYGNw73oCLY+6U2QwM7s3A4N6pNuDirtUzBa1tba29bK2OZTht6OREZRgTE8O3I6bSp3EP3mrdn2btWySqT6lBvLx48d3OjHnpPd5q2puarepRolzC81RsWIXipf0Z2LAH3wyaSPv3ugAQGx3DjBHf8FaTXrz35EAatXs0Pu+8Sb8ztEVfhgX3Y8eSzbTs+UyatcWR0fVw2HNv0S+4d5oMOPHyosPwV/iowwj6N+lJrVb1KeFU/pWDAvEr7U/fBt2ZMnAiL43okmLeP7/8jUGP9mFwcF+2Lt7Ekz3/D4CL5yP55OX3Gdi8N1/2GUvXMT1TrTWttA5uysTRaXupuh146rOSXl1J5S953z00fr4pg1r1o/+jvQhsXA2/Uv4AvPphd34Y+S39mvdkw/x1tHz1yRT13e72MPTgyfj2btDjfbkedY2N89cBcGzvUUa/OpJ/1u9JdTka0o5HGnG3CxHJBfwJTFDVcqr6IDABKOqULltq0yZzrXSvcl4uoDynD4dy5thpYm5Es3bOKqo2rZEgTdWmNVg5aykA+7fuJU+BvBQqVjjZvI55Vs5aSrVmNQGo9EgAR/85wtG/DwNwKeIiGpu2d5bqTWuw3D73vq17yWvrcebhOpVYN3c1AMtnLaF6s1oARIZd4MCO/UTbb3uO+Pj5EtioGot/WpgmTRD3m5cBCcvJmQp1KrJ+7hoAVjiUTWrz3w6qN63BCvtayZVhhToVWWdrXT5rKdVtrXFlGONkgEecOR//Nn/18lVO7D+OT3HfNOsrE1COM0dOcfbYGWJuRLN+zmoCmlVPkKZKs+qs+cX6DQe37iNP/jwULFqIC2cjOLr7ULyG0AMnKOTnY2079BrkyJMT9NaHPDKyHt4qZQPKcfpwKGftZ3JdEs/zKvveH3DQnVxex96WnHlyoXa5Hdl9KL4n8/jeo2TPmYNsOTJmsKVaQEUKFsifIedODk99VtKrK6n8d5W7m31b93L96nViY2LZs343NZpbdda/zF38vX43ADtXbqdmi9pJ6rsT7eHDdStx+ugpzp04C8DJ/ccJPXgy2XK7U+ht+Hgqmc6IE5GWIrJeRLaKyKK4njIRaSAi2+zPVhHJD7wArFXVOXH5VXWpqu6ye/d+FpE5wIIU0pYSkZUissX+1LGv2VBElorID8BOsRgnIntE5E+gWFp+W2E/H8JCz8Vvh4eG4ePn65TGl/CTYTfTnAqjcHGfZPMWLFIovnGPOHOegkUKAuBXugSqypvTh/Den6N4/NXWaZELWIZW2Mmb1w07dS5R45e/cH6uRF4mNsYyEMNCw/Cx/5AnR8ehnfnu/W+IjU37I+Tj55NAV/ipMHyKJ7xm/sL5uZyEruTyK8rA74bx3h+f0Oj5ZgnO2bz9Y3z416e8+vHr5C2QN1VaCztdKywJrY5lGJ7KMoyj6N3FKF2hDPu37U11njgKFfch3EHf+VCrziX4DcWd62U4hZ3qru/dRbnnoVIc3LYvft9TIc8zas1Eaj1Rn99Gz0iztjgysh4CvPXdO3z4xyc0cbrfyVHYz5fwUIcyCQ2jsNP1nO/9zec5+bzP9HuBz9ZOok7rR5g1+qdE164eXJsjuw8Sff32GaWegKc+K+nVlVT+Y3uP8kCNh8hXKD85cuWgSlAgviWKAFZPVzXbsK/1WB18/YskqS8j28M46rSqx5rZK5MrJrcRexs+noqnGnG5HQyybcC7DsdWAbVUtQrwE9Df3h8CdFfVAKA+EAU8DCQ3plUb6KCqjVJIewZoqqqBwLPA5w7HagCDVfUh4EngfqAi8ApQx9XJRKSLiGwSkU37Lx2+uZ/EEQnVqXdCXAUtVE1VXme8s3lzf/UHGd9zDO+0GUT1R2tRoW7FZPM4I64EJdKcchpnAhtV40JYBAd33ZofhatrJrpkMmmSyz/sqTcZ9FhfPuzwLs3at+CBGg8BsOi7efR8pCtvtujN+TPnefHtjndEa0rkzJOLPhMH8M27U5L1mUmbPqeLu7zFN9PkzJOL7hNC+PHdaQl64H4Z9SMhdbqy7veVNOrwaJq1JafxdtRDgLeeepMBj/XhvQ7v0rx9MA/a9ztFTa52OhdbEmWbUt6fP/6BnrW7sOa3FTTt0CJBsrvKl+S5N9vx9cA766t2J/DUZyWj2psT+48ze+KvvPX9MAZNH8qRPYeJibZ6ESf2G0uz9sF88Mcn5M6bm+gbSfsyZ2R7COCdPRtVm9Rg/Z+rk9TgTmIl/R9PxVMnNkTZxhhg+cQB1ezNu4EZIuIP5AAO2ftXA6NF5HvgF1U97rLRTshCVQ1PhZ7swDgRCQBigPscjm1Q1TgNjwA/qmoMcFJElrg6mapOAiYBvHDvk/GPQvipsARvUz7+vpw/nVBeeGgYPiVu9jD4+Ply/sx5suXInmTeC+ciKFSsMBFnzlOoWGEunLsQf66/1+3m4vmLAGxbupnSD5dl9+qdyRZG8/bBNHmuKQD7d+yPfzME8PUrQviZhJojwyPJUyAvXt5exMbE4uvvS/jp5B3YH6j2INWa1KBKw6rkyJmD3Pnz0OPT3oztNSbJPE3bt6DRc1ZPycEd+xLossopoa6L4ZHkddIVV2ZhoWFJ5j9v92pGhl1g4/z1lA0ozz8b9sSXK8CSHxfS/+vBSWpt1r4FjW2tB5y0+iah1bEMXdUNV3hn86bvxAGs+m05G/5al2J6V5w/FYaPg77C/r6JJiBYaRzrpQ8Rtj7vbN50nxjCut9WsmX+epfXWP/7Snp+PYjfx8xMta47UQ+B+HsRGXaBDfPXUS7gPv7ekLKfT/ipMHz8HcokiefZuZ5FnDlPthzZUswLsOb3lYRMHcwvY2bE5+81aQAT+3zOmaOnU9SYGfDUZ+V26nKuB475l85YxNIZiwB4rt+LhJ+yemhPHjjB++2GAeBfugRVGlVNcL071R4CBDQM5NCugwnaQE8iK89O9dSeuOQYC4xT1YrAq0AuAFUdCXQGcgPrROQBYDdQNakTAZcdvieXtjdwGqiMZUzmSOIckI7h8wPb9+FX2p+iJYvhnT0btVvWY/PCjQnSbF60kfptggAoV+U+oi5eIeLM+WTzbnHIU79NEJsXbgBgx/Kt3PPgveTIlQMvby8erFmB4/tSnkQwf/pc+gX3pl9wbzYuWEcD+9zlq9zHlYuXXc4w3L12J7WC6wLQoE0jNi50/cc8jh8++pautTrRvV4XxvQYxa41O5I14AAWTp8X72S7acF66rdpGF9OyemqGWx1mD7iUDZbFm1wmT9n7pzkypsLgJy5c1LpkQCO/3sUIIGPSPXmNTlm73fFgunzGBDcmwHBvdm4YD2P2NdKrgz3rN1JLVtrgzZBbLK1JkfXj17nxP7j/Dl5doppk+LQ9v0UL+VPkbutulWzZV22OdXLbQs3Uecp6zeUqVKeKxevcOFsBAAdP+xG6P7jLJiScLZfsVJ+8d8DmlTn1IETadJ1J+qhdb9zx3+v/EgVjv17JFX6Dm7fn+CZrNWyHlucym3Loo3Us+992Sr3ccV+npPLW9x2bAcIbFqdULvc8hTIQ9+pg5n50Xfs2/RPqjRmBjz1WbmdujYt2pBk/gK+lvuLb4ki1Hi0Fqt/X5Fgv4jwVI9nWPj9/ATXuhPtYRx1WtVnzewVqSo3w+1FUhpycwcicklV8zlsvwRUU9XXRWQr0FlVN4vIVKC0qjYUkbKqesBO/xswDZgP7AR6quqf9rFHgRNYBls1VX3d3p87mbQvA8dV9RMR6Qh8raoiIg2BEFV93E7/FJZhGYzlD7cHeEVV/5fUb3XsiQNr9mm7IVaYkGUzF/P7uP/RuG1zABbbD+lLw7tQuUEVrkVd48uQsRyyp267yguQr1B+3vgihCIlinDu5Dk+e+1jLl+wJgDXfbIBT3R7ClWrJ+7HD6Yn0HcjFd4AnYa/SkCDKlyPusb4kLEc3LkfgIHT3mZi//GcPxNOsZLF6T0uhHyF8nNo90E+7zWa6OvRFCpaiJFzPiF3vjxobCxXr1yld5PXEwxlPFTrYVp1aZ1kiJFsrgef6Di8C5UbBNrl9Hn8FPf+097mq/7jOH/mPMVKFqfHuL7kK5Sfw7sPMr7XmHg/Ilf5i5UsTp9JbwLWm/vq31fwm13O3cb04t6HSoMqZ4+fYfKgCUScOZ8qf4qX7WvFTd+P0/rmtLf50kFrTwetY22tBYsW4oM5o+wyVK5eiaJvkx7c80Ap3p31AUf+PozafoU/JhH2JK8k3ylfsWEVnh/SES9vL1bNXMIf43+hYVvrLX/Z9wsAePHdzjzcIIDrUdf4ut8XHN55gPLVHmDg/0Zw7O8jqFolMeujH9i5bCvdJoTgV6YEGquEnTjL9MGT4nvvnLmoKYe9yYh6mL9wAfpNGghY93vV7yv4ZdzPLq+fk8RzmyoHBfLiECtMyPKZi5k9bhaN7HJbYpdbh+GvUMnWPSlkXPzz7CovwBsT++Ff5i40NpZzJ84yddCXnD8dzhM9nqZlt6c4fSg0/voftnuXyLCbvSNTN49KsRxTQ7+hI9m4dQcREZH4+hSiW6d2tGnZPN3nfbFqnxTTuPtZyQhdyeUf9vP75C+cn5gb0UwfMZVdq3cAVuiRZu2tofQNf63jxw+/BZLumcmI9hAgR64cjFs3mZ71uxJ18Ur89ao1r8lL77xCAZ+CXIm8zOE9hxjZ/h2rbI/8dkcHKAeXeiHdhs57h3/wyEHVzGjEPQGMwTKu1gHVbSNuLBCENdy5B3hJVa/ZPXKfAmWBG8AOoCfQAgcjzr5OUmkLALOAK8BSoIeq5nNhxAlWT2EjIM4r9ru0GHGeRmqMOHeTlBHnKXh+CaZsxLmb1Bhx7saVEedp3C4jLqNIjRFnSJ7MMLx2p424gbfBiPvAQ404j2y5HQ04e3saVs8aqvo78LuLPD2SONc/gCuP6fhzpiLtaaCSw/ZAO/0yYJlDfgVex2AwGAwGg0eQlX3iPNKIMxgMBoPBYLgdZF0TLnP0vBoMBoPBYDAYnDA9cQaDwWAwGLIsmcEv+VYxRpzBYDAYDIYsi/GJMxgMBoPBYMiEZF0TzvjEGQwGg8FgMGRKTE+cwWAwGAyGLIvxiTMYDAaDwWDIhGgWHlA1RpzBYDAYDIYsi+mJM2QY2cWz3RK91CNXGklADg8vwyJkd7eEFNmvl90tIVmyZwL3Xc9/Ujx/WavvNo92t4QU8fQy3Hn1lLsleBxZeXaq57eMBoPBYDAYDIZEmJ44g8FgMBgMWZas2w9njDiDwWAwGAxZmKw8nGqMOIPBYDAYDFkWM7HBYDAYDAaDIROSlUOMmIkNBoPBYDAYDJkQ0xNnMBgMBoMhy2KGUw0Gg8FgMBgyIWY41WAwGAwGgyETEnsbPikhIo+KyL8isl9E3nRxXETkc/v4DhEJvA0/zfTEeTIVGwTQdsjLeHl7sXzGYv6c8GuiNG2HvkzloECuR13nq5CxHNl9CIBOH3UjoFE1IsMuMLh57/j0zw5sT0CTasRcj+bM0VNM7jeOK5FX0qSrUoMqtBtq6Vr20yLmuNDVblgnAoICuRZ1jUkh4zi862CyeWsE1+ap3s9SotzdDG01gEM7DwBQp/UjPNblifjzlnzwXt56LISjew6nWu/DDQJ4YUhHxNuLlTMWM3fCb4nSvDD0ZSoGVeF61HWmhIzj6O5DFPb3pfPoHhQsWgiNVZb/uJBFU+fG52ncoQWN2z9KTEwsO5Zs5ueR36VakyP3NajME0PaI95ebJixlGUTZidK02poBx4ICuBG1HVmhkzgxO7DANTv1ILqzzYCVU79e4yZ/SYSfe0GTXu1ocZzjbgcHgnAXx/N4J9l225JXxydhr1CYFA1rkVdY1zIpxy076kjxUoWp8/YEPIVys+hXQf4rPcYom9E80jrBrTu2gaAq1eimDR4Aof/tn7D451a0eS5ZqDKkX+OMK7fZ9y4diNdWgFeGtaZKkFVuRZ1jQkhn3PIhd6iJYvRc2wI+Qrl49Cug4zr/SkxN6IpUfYuXhvVg9IVyvLTqO/4Y9Lvt6SholN9/yOJZ6Wyw7NyxNaZVN57HizFS++/Sq48uTh3/Axf9PyUq5eiACj5wL10/KArufPlRmOVoa36p7ksM6LcfP2L0H1MTwoVLURsrLL4hwXMm/pHmnSllbfeH82K1RvwKVyI376bmKHXciaj6l7Xj18n0G7XQ5r1vG16B77Xh/qNa3M16hqD3xjO3zv/TZTm+Zefpl2XZ7mndEnqPdiciPAL8ceq1wlkwPBeZMuWjfPhEXR8sttt05YZEBFvYDzQFDgObBSR2aq6xyFZC6C8/akJTLD/Txep7okTkUupSFNFRFREmqci7UsiUsJhe7KIPJRaPU7nOiwiK532bRORXbdyPhfnnyYiT9+Oc6X6ml5etH/3FT556T0GNu1FrVb1KFHu7gRpKjUMxK+0P/0bvs7UQRPo8F6X+GOr/reMUR2GJzrv7lXbGdysF2+16MOpQyd5vNtTadbVYfgrfNRhBP2b9KRWq/qUKJ9QV+UgS1ffBt2ZMnAiL43okmLe43uP8tmrH/Hv+j0JzrXmtxUMDu7L4OC+TOj9GeeOn0mTASdeXrz4bmfGvPQebzXtTU0X5VixYRWKl/ZnYMMefDNoIu3tcoyNjmHGiG94q0kv3ntyII3aPRqf94HaFajStDpDWvTl7Wa9+eurxIZX6vQJT77bkSkvfcgnTUMIaFWHYuXuSpDmgYYBFCntx0cNezNr0Fc8+V4nAAoUL0zdlx7l85aDGN28P+LlReWWtePzrZwyl0+DB/Jp8MB0G3CBQVXxL12C7g1eZeLA8XQZ8ZrLdO3e7MCcKbN5vWFXLl24RONnmwJw+thp3v6/gfR59A1+/nwGXT/oDoBPcR8e69iS/o/3oVezHnh5e1GvZf10aQUICKqKX2l/ejZ4ja8GfkGnEV1dpmv7ZgfmTplNr4bduHzhEo2ebQLApYhLTBs6mTlf/XbLGuLq+8cdRjCgSU9qJ/GsFC/tT0iD7nw9cCIdnZ4VV3k7fdiNmSO/ZVDz3myav57HXm0NgJe3F10/7cm0QV8ysGkv3n/2baJvxKRJc0aVW0xMDN+OmEqfxj14q3V/mrVvwV1OZXG7aR3clImjR2ToNVyRkXVv+c9L+KDDu7dVb/3GtbmndEmCaz3DsJAPePuj/i7Tbd2wg87PvMGJo6EJ9ucvkI+3Rvbj9fb9aN3gBfq+Mvi26rsdxKqm+5MCNYD9qnpQVa8DPwFPOKV5ApiuFuuAQiLin97fdruHU58HVtn/p8RLQLwRp6qdnazWtJJfREoCiMiD6TjPbcW20NNMmYBynD5yirPHThNzI5r1c1YR2Kx6gjSBzaqz+pflABzYuo88+fNSsGghAP7dsIfLFxLb3btWbic2JtbOs5fCfr5p0lU2oBynD4fG61o3ZxVVm9ZIkKZq0xqsmrUs/hp5C+SlULHCyeY9uf8EoQdPJnvtOq3qs3b2qjTpLRNQjjNHTnH22Bm7HFcT4FSOVZpVZ80vlt6DW/eRJ38eChYtxIWzERy1ezavXr5K6IETFPLzASCobXPmTviV6OvRAFwMi0yTrjhKBpTj3JFThB87Q8yNGLbPWUuFZtUSpHmoWVW2/GK9oxzdup/c+fOQ377PXt7eZM+VAy9vL3LkzkHk6fO3pCMlajStybJZSwHYu/Vf8hbIS+FihROlq1inEmvnrgZg6awl1GhmvWj+u/kfLkda67Pu3fIvvv5F4vN4e3uRw/4NOXPnJPx0eLr1Vm9agxV2HdznUAedqVCnIuvmrgFg+aylVLf1RoZd4MCO/cSk0QhyJDXPSqDTs5KnQF4KpvCs+JcpwT/2y86uldup3qIWABUfCeDYP0c4avdwXoq4hMamzaU7o8ot4sz5+N6oq5evcmL/cXyKp63tSSvVAipSsED+DL2GKzKy7v29YQ+XIlLsT0kTQY8+wuyfrRGGHZt3k79APooUS3xv/tm1l5PHQhPtD36qOYvmLuPUidMAhJ/LmDYoPeht+KTAXcAxh+3j9r60pkkzaTbiRMRfRFbE9XSJSH17vwBPYxlnzUQkl0Oe/iKyU0S2i8hIu1erGvC9fZ7cIrJMRKqJyGsi8pFD3pdEZKz9/UUR2WDn+dLJQJoJPGt/fx740eEc3iLysYhstMeiX7X3NxSR5SIyU0T22tra2tfYKSJlHc7fRERW2ukeT8V5l4rID8DOtJYxQOHiPoSfPBe/HR4aTmGnRq9wcR/CHNOcCkuTUVb/mcbsXLY1bbr8fAkPDXPQFUZh27C5mcaFruI+qcqbHDVb1mXt72kz4go5leP5UEtLAr3FfQk/6aDrVHiicvS9uyj3PFSKg9v2AVC8jD/lazzIW799wIAZ71CqUlluhYLFC3PB4doXQsMoULywUxofIhzSRJwKp6CfD5Gnz7P8qz8YtGYcb22YwNWLV9i38mZ1q9OhOb3nfcgzH71K7gJ5b0lfHD5+vpw7eTZ+O+xUWKI/wvkL5+dy5OX4l4Sw0DB8XdTHJs81ZeuyzQCEnw7n90m/8eXaKUzZ+A1XLl5m+8pt6dIKieugpTfhfc9fOD9XHPSGh4bhk4b6mLKG1D0r4U7Pik8Kz8rxvUcJbGq9iNR4rA4+tkHsV7oEqkq/6W8z/M9R8T10adOc8eVW9O5ilK5Qhv3b9qZZX2bAE+peWijuX5RTJ87Eb58OPUNx/6Kpzl+qbEkKFMzP1F++YMaCabR6pkVGyEwXsWi6PyLSRUQ2OXy6OFxCXFzW2fZLTZo0cys9cS8A81U1AKgMbLP31wUOqeoBYBkQDCAiLYDWQE1VrQx8pKr/AzYBbVU1QFWjHM7/P8BxjO9ZYIbdu/YsUNe+dgzQNol8LYE5Dsc6ARdUtTpQHXhFRErbxyoDPYGKQDvgPlWtAUwGejicoxTQAHgMmGgbqcmdtwYwWFVvdYg40T517tJ1kYaUu30BaNm9DbExMaz5bUXadLnamUiWa+2pyZsUZQPKcz3qGsf3Hk1dhhS0JEzkQpZDmpx5ctF9Qgg/vjst3vfIy9ubvAXyMaL1QGa+/y2vje+TJl0OAl1cPOU0qkruAnmp0LQaI+u/wYia3cieJydVWtcDYO13i/jwkZ58GvwmkWfO8/hbL96avuRkOpVjasr64doVafxsU6Z/8A0AeQvkpUazmrxW7xU613iJnLlz8ciTDdOlNWktiRKlnCY9GlzsS/wIp/5Zicv7Vb/xNGnfgnf/+JjceXMTfcPqDfbO5s391R9kQs9PGd5mEFUfrclDdSumTXMGl1vOPLnoM3EA37w7hahLUSlnyIR4Qt1LC+KitiVqI5PB29ubhyo/QLcX+/Dqcz15tc/L3Fum5O2UmG70dvxTnaSq1Rw+kxwucRxw/NF3A85DS6lJk2ZuZWLDRuBrEckO/Kaq2+z9z2ONA2P/3w74BWgCTFXVKwCqmuxYiaqeFZGDIlIL2AfcD6wGugNVsRwGAXIDZxyyhgPnReQ54G/A0Vu/GVDJwa+tIJZz4XVgo6qGAojIAWCBnWYnEORwjpmqGgvsE5GDwAMpnHeDqh5y9RttC74LQC2fKtyXv3SiNOGnwvApcXPIycffh4gzCYvu/KkwfEsUYV9cGj9fzqdiKKpum4YENK7Khy8MSzGtS13+N3tXfPwTXzM81NIVn8bPl4gz58mWI1uKeZOiVst6aR5KBauMHMuxsL+lJXEaB11+PkTYuryzedN9YgjrflvJlvnrE+TZbG8f2r4fjVXy+xTgYnjahlUvnAqnoMO1C/r7Eumk78KpMAo5pClk98KVq/cw4cfOcDn8IgC7/trIvVXvY+tvq7h07qbT8YafltBxims/l+R4tH0wTZ9rBsD+HfsoUqIo1qMFvn6+nHeqj5HhkeQtkBcvby9iY2Lx9fdNMDR67wOl6Pbh6wzv8A6XIizNleoFcPrYaSLtclv/11oeqPoAK35dlma9zdq3oLGt98COfQnqoCu9F8MjyeOgNy31MTW4elYiXDwrPk7PyvkknpW4vKEHTvBRO8svyq+0P5UbVbXPdY5/1u3m0nmrbLcv3UKph8uwZ3XygwF3qty8s3nTd+IAVv22nA1/rUsxfWbC0+peSjzXsQ1Pv2i5bO3a9jd+dxWLP1bcvxhnTp1LKmsiToeeISL8AlFXrhJ15Sqb123l/grlOXLwWMqZsw4bgfJ2J84J4DmsDi9HZgOvi8hPWBMaLsTZHukhzT1xqroCeMQW+q2ItLeHNdsAQ0TkMDAWaCEi+bFeSNP6jjED+D/7nL+q9VogwDd2z12Aqt6vqsNc5BuPw1CqjQA9HPKWVtU4Y+2aQ7pYh+1YEhq5zr8hTlNS572c1I9ztOhdGXBgGQbFS/lT5O5ieGfPRs2W9di6cFOCNFsXbqTuUw0AKFulPFEXr3DhbERSlwWsGa+PdW3Np51Hcv3q9WTTuuLg9v34lfanaElLV62W9diycGOCNFsWbaRem4a2rvu4cvEKEWfOpyqvK0SEmo/VuSUjLnE51mWb0zW3LdxEnacsvWWqlOeKQzl2/LAbofuPs2BKwpl0Wxds5MHaDwNQvLQ/2bJnS7MBB3B8+wGKlPKj8N1F8c7uTeWWtdmzcHOCNHsWbiHwKcvZ/54q5Yi6eIWLZyOIOHmOe6qUJ3uuHACUq/swZ/afAIj3mQN4uHl1Tu1Ne4P61/S59A3uRd/gXmxYsJ6Gbax3mvuq3M+Vi1c472RsAuxau5PawXUBCGrTiI0LLUO3SIki9P9yIJ/1HkPooZsvn+dOnuW+KveTw/4NFetW5vj+W2v8F0yfx4Dg3gwI7s3GBet5xK6D5avcx5WLlxMZ7wB71u6kVnAdABq0CWLTwg23dG1X3OqzciGFZ6WAb0HAei6e6PEMS76fD8CO5dso+WCpeP/CB2o+xIl9x1PUeafKretHr3Ni/3H+nHxrk4A8GU+reynx09RZPN24PU83bs+Sectp9UwwAJWqVuDSxUucOxOWwhlusvSvlQTWqoy3tze5cuekYmAFDu47nEHKb42MDjGiqtHA68B8rDfdmaq6W0S6ikjczJa5wEFgP/AVcFum8Epqu01F5JKq5hORe4ETqhotIr2whhnnAX1UtblD+m+ARcBZYAjQRFWviIiPqoaLyBxgtKoutdMvA0JUdZOIFAY2A0eAAaq6wZ65+jvWcOoZEfEB8qvqEdtwrIZlgHUDxmBNmvhDVR+2e76CgWdU9YaI3IdlhFa3r/m4Cw0N446JyDSgGPA4UBpYDpQD2qfmvMnRoVSbJG9ApYaBtB3SES9vL1bMXMKc8bMIamu97S393rIV273bmUoNqnAt6hqT+43nsB2a47XPe/NArQrkK5yfyHMX+HXMDFbMXMxHy8aRLUf2+J6QA1v38s3gSa4FADEu6kfloEBejAt9MnMxs8fNopGta4mtq8PwV6jUoArX7bAJcSFDXOUFqNa8Ju3f6Ux+nwJcibzMkT2H+Ki9Nbv2wVoVeHZAO4Y9mSj0DgA5JPl3kYoNq/C8XY6rZi7hj/G/0NDWu8zW++K7nXm4QQDXo67xdb8vOLzzAOWrPcDA/43g2N9HsDphYdZHP7Bz2Va8s2fj5Y+6UfKhUsTciGbGe9P5Z63rydBFyJ6svgcaBtBySHu8vL3YOHMZS8b/Rq221ky1dd8vAqD1ux25v0Flrkdd4+d+X3J8p+Uk3rT301R+vBax0bGc2H2Y/705iZjr0Tw7uhslHroXFM4fP8usQZO5mIyBv1+TfOeI55Xhr1KlQaAdYuRzDuzcD8DgaUP4ov84zp8Jp3jJ4vQZ188KMbL7IJ/2+oTo69F0+/B1arWow9njVud5TEwM/Vv2BeDZ3s9T9/H6xMbEcHD3Qb4YMDZ+wkgc2W/B8+Pl4V2o3CCQ63aYh4N2HXxz2tt82X8c58+cp1jJ4vQc15d8hfJzePdBxvYaQ/T1aAoWLcQHc0aRO18eNFa5eiWKvk16JDsEmMOFxspBgfFhglYk86xUtJ+Vr5yeFee8AM06PkaT9pbf0aa/1jHzw5uhbeo8+Qgtuz0FCtuXbuanD75NoOd6KiJeZUS53fNAKd6d9QFH/j6Mxlptyo8ff8e2pQlfWL7bPDpFfaml39CRbNy6g4iISHx9CtGtUzvatEwxcEKKvFg1ZdeJjKp7b3zeh4dqP0z+wgW4cC6Cn8f8xNIZixJce/fVU2n+TYM/CKFeo1pERV3l7Z4j2L39HwC++H40Q/u8z9nT52jb+f/o2P1FihTzIfzceVYuXsvQPu8D0LFbW1o/9zixGsus72fz3aQZyV5v1+l1Lr1rMopn7n0i3YPVPx/5/Y5qTi23YsR1APoBN4BLWIbMUGCdqk50SN8KeE1VW4gV+K491jDjXFUdJCJtgPeBKKA2liEYoqqb7Px/AA+pahmHcz4LDMTqQbwBdFfVdXFGnKqec0hbiptGnBcwAstXTrAMy9ZAFVJvxJ3HMhSLYxmsf6T2vMmRnBHnCbgy4jyNlIw4d5OSEecJpMaIcye3YsTdaVwZcZ5Gaow4d3I7jbiMIjVGnDu5FSPuTnOnjbin722V7j9k/zsyO3MbcYaMwRhx6ccYcenHGHHpxxhx6ccYcenHGHGJeeo2GHG/eKgR5/mtjsFgMBgMBoMhEWbZLYPBYDAYDFmWrDziaIw4g8FgMBgMWZbY9MfU9ViMEWcwGAwGgyHL4tmeoOnD+MQZDAaDwWAwZEJMT5zBYDAYDIYsi5rhVIPBYDAYDIbMh/GJMxgMBoPBYMiEmNmpBoPBYDAYDJmQrDyxwRhxbua6xrhbQrLkEG93S0iRqx5ehseITjmRm8nr4U2Bl3hksPTMh4d3SHj6agjg+atKdK7Wz90SDHcQz265DQaDwWAwGNKBmdhgMBgMBoPBkAkxExsMBoPBYDAYMiFmYoPBYDAYDAZDJiQr98SZFRsMBoPBYDAYMiGmJ85gMBgMBkOWxUxsMBgMBoPBYMiExBqfOIPBYDAYDIbMR9Y14YxPnMFgMBgMBkOmxPTEGQwGg8FgyLJk5dmpxojLBHQY1pmAoKpcj7rGhJDPObzrYKI0RUsW442xIeQtlI/Duw4yvvenxNyITjZ/ngJ56fJhd+6+7x5A+bLfOPZt+TdN2io2CKDtkJfx8vZi+YzF/Dnh10Rp2g59mcpBgVyPus5XIWM5svsQAJ0+6kZAo2pEhl1gcPPe8elb9/o/Gj7XhMjwSAD+99EP7Fi2JU26ANoP6xT/uyeGjE2y3HqM7Uu+Qvk4tOsgX/T+LL7cksrf5ePXqWLrHtCsZ6JzPtblCdoOfolXA9pz8fzFJPVlxH31L1OCN8bdXHan2D3F+d/oH5n39RxeGNSBwMbVibkRzekjp5jYbyxXIi8nqa9igyq0G2rd22U/LeIPF/e23bBOVA4K5FrUNSaFjOOI/RuSytt9XF/8y5QArPp3JfIybwX3xTubN50+7Eaph8vglc2b1bOWMeeLX5LUlljr7a+HT/V5jsCmNYjVWC6eu8BXIeOIOHM+1ZruhMY4WrzSiucGd6B7lZe4lEydc8VLwzpTJagq1+x6dCiJethzbEj8czLOoR4mlb9Fx8dp/HxTEGHJjwuZ+/UcAO59sBSd3+9Krjy5OXv8DGN7jibqUtQd0Vui7F28NqoHpSuU5adR3/HHpN/j83T9+HUC7TIOcfFc327een80K1ZvwKdwIX77bmKGXy+OjKiHzw5sT0CTasRcj+bM0VNM7jeOK5FX7thvSomsbMSlOJwqIjEiss3h82YyaVuLyEMO2++KSJP0ihSRQiLS7RbyDROREPt7LRFZb/+Gv0VkWAp5G4rIH7co+bYREFQVv9L+9G7wGl8N/IJOI7q6TPfCmx2YO2U2fRp24/KFSwQ92yTF/B2GdmL78i2ENH6dAY/25sT+42nSJl5etH/3FT556T0GNu1FrVb1KFHu7gRpKjUMxK+0P/0bvs7UQRPo8F6X+GOr/reMUR2Guzz3/Cl/MCQ4hCHBIbdkwAUEBeJXugR9GnRj8sAJvDziVZfpnn+zPfOmzKFPw+5cvnCZoGcbp5h/xc9L+LDDuy7P5+PvS8V6lTl7/EwK+jLmvoYePMnA4N4MDO7NoMf7cj3qGhvnrwNg58rt9G/2BgMe7UXooZM80a1NkvrEy4sOw1/h4w4jGNCkJ7Vb1adE+YT3tnJQIMVL+xPSoDtfD5xIxxFdUsw7/vVPeCu4L28F92XjX+vY9JelrcZjdcieIzuDmvdmyGMhBL3QjCJ3F022DB21ZkQ9nDvpd95q0YchwSFsW7KZJ3o+kyo9d1IjWHWuQv3KnDt+Ns264upRzxTqYVu7Hvay62Ejp3ronL/kfffQ+PmmDGrVj/6P9iKwcTX8SvkD8OqH3flh5Lf0a96TDfPX0fLVJ++Y3ksRl5g2dDJzvvotUZ7lPy/hgySe64ygdXBTJo4ecceuBxlXD3ev2s7gZr14q0UfTh06yePdnsrw35IWVDXdH08lNT5xUaoa4PAZmUza1kC8EaeqQ1R1UXpFAoWANBtxTnwDdFHVAOBhYGY6z5cAEcmQXs2qTWuwctYyAPZv3UueAnkpVKxwonQV6lRk/dw1AKyYtZRqzWommz93vtw8ULMCS3+ybk/Mjehke2VcUSagHKePnOLssdPE3Ihm/ZxVBDarniBNYLPqrP5lOQAHtu4jT/68FCxaCIB/N+zh8oVLabpmarF+91Ig9eW2MlG5uc7/z4Y9XIpw3dvRbsjL/PDB9BQ9aTPqvjrycN1KnD56inMnrD/uO1duIzYmFoB9W//Fx983SX1lA8px+nBo/L1dN2cVVZvWSJAmsGkNVtkaDtgaChYrnKq8ADUfq8Pa2asAq5HNmScnXt5e5MiVg+gb0URdTF3vTEbVw6sOvUM58+RMl3d0Rj4rL7zdkRkfTL+lMArVm9ZghX0P923dS95k6uE6ux4un7WU6nY9TCr/XeXuZt/WvVy/ep3YmFj2rN9Njea1APAvcxd/r98NWC8WNVvUvmN6I8MucGDHfmJuxCTK8/eGPVyKyJj2yBXVAipSsED+O3Y9yLh6uGvl9vi25cDWvRT2S7ptcQexaLo/nsotT2wQkZEiskdEdojIKBGpA7QCPrZ7u8qKyDQRedpOf1hE3heRtSKySUQCRWS+iBwQka52mnwislhEtojIThF5wr7cSKCsfd6P7bT9RGSjff13HHQNFpF/RWQRcL+D5GJAKICqxqjqHjt9DRFZIyJb7f8d85BcGhF5SUR+FpE5wAIR+dZBMyLyvYi0utUyBvDx8yHs5Ln47fBTYfgU90mQJn/h/FyOvBz/EIWFhuHj55Ns/mL3+BEZdoGuo97gg7mjeeXD7uTMnTNN2goX9yHc8dyh4RQu7psojfP1U/OAN+7QghHzRtPpo27kKZA3TboACvv5En4yLOF1Uyy3c/HaUpPfmcAm1Tl/Kpyjfx9OUV9G3VdH6rSqx5rZK11ev+H/NWF7Mj2chf18CQ91+P2hYRT283FK43T/bQ2pyXt/jYe4cC6C04dDAdg4dy3Xrlxj7MYpfLp2EvMm/Z5qAz8j62GbkBcYveZLaj/xCL+M/ilVeu6kxipNqnH+dDjH/j5ya7qc6lFYEvXwikM9DHeoh0nlP7b3KA/UeIh8hfKTI1cOqgQF4luiCADH9h6lmm3U13qsDr7+Re6Y3v86GfmsxFH/mcbsXLY1/WINqSI1Rlxup+HUZ0XEB3gSqKCqlYARqroGmA30s3vsDrg41zFVrQ2sBKYBTwO1gLg+7KvAk6oaCAQBn4iIAG8CB+zz9hORZkB5oAYQAFQVkUdEpCrwHFAFeApwfMUYA/wrIr+KyKsiksve/w/wiKpWAYYA77vQnVya2kAHVW0ETAY6AohIQaAOMDfZ0k0B6+cnJFHPbjJpksrv7e1F6YfLsvC7eQwM7sO1K1dplczwWuq1qXOixBlT6Jpe8t18+j3SnbeD+xJxJoLn3+qQJl1JXTbRdZPRn6r8DuTIlYPWrz/Nz6N/TKW+jLmvcXhnz0bVJjVY/+fqROlav/40sdExrPp1edL6XOxLXHyuyy81eWu3qsc6uxcOoExAeWJjY3mjRmf61HuNFq+0omjJ4knqS40Op0Qpi3LBrFE/0KfOq6z9fQVNOrRIlR5XZITGHLly0PL1NukyLjOqHp7Yf5zZE3/lre+HMWj6UI7sOUxMtNX7NbHfWJq1D+aDPz4hd97cRN+4ccf0/tfJyGcFoGX3NsTGxLDmtxW3Ii/D0Nvwz1NJzRBglD0EGY89dHgVmCwifwKp9R2bbf+/E8inqheBiyJyVUQKAZeB90XkESAWuAtw1ZI3sz9x5n4+LKMuP/Crql6xdcZdD1V9V0S+t/O9ADwPNAQKAt+ISHmsAZPsLq6XXJqFqhpuX2O5iIwXkWJYRuQsVY12PpmIdAG6AFTzqUy5fKUSHG/avgWNnmsGwMEd++LfYAF8/Hw5fyY8QfqL4ZHkLZAXL28vYmNi8fX35fxpK01YaJjL/KpKeGgYB7btA2D93LU8kUY/hvBTYfg4ntvfhwgnbedPWdff53j90wnTOBN57kL89+U/LaT3lEGp0tO0fQuCnmsKwMEd+/EpcfPt0frdCZ3SE5dbESJsbeGhYSnmd6T4vX4ULVmckfPGWOn9fXnvz094+4n+RJw9H68vo+9rHAENAzm06yAXHMoS4JE2QVRpXI33nh+S5G8B+946DLf6+PvGl018mlCn+2+XUbYc2ZLN6+XtRbVHa/H24zcnYNR5oj47lm0lJjqGyLAL7N38D6UrleXssdPJ6ozXmgH10JG1v6+iz9eD+HXMjFTnyWiNxe71o+jdxRk+75P49O/+8THvtH6TC2cjkszXrH0LGtv18IBTPfRNoh7mcaiHPg71MNypHjrmXzpjEUtnWO4az/V7kfBTVu/syQMneL/dMAD8S5egSqOqSWq93Xr/62Tks1K3TUMCGlflwxeG3UbFtwdP9mlLL7c0nGobJjWAWVh+cH+lMus1+/9Yh+9x29mAtkBRoKptOJ4GcpEYAT5w8NMrp6pT4uQlo/uAqk4AGgOVRcQXGA4sVdWHgZZJXC+5NM6OZN/av6MjMDUJHZNUtZqqVnM24AAWTp8X75y+acF66rdpCEC5Kvdx5eJllzPkdq/dSc3gOoD1h3rzwg0AbFm0wWX+C2cjCAs9Fz9T8OG6lTi+75gruUlyaPt+ipfyp8jdxfDOno2aLeuxdeGmBGm2LtxI3acaAFC2SnmiLl5J9g8MEO9/AVC1eU2O7z2aKj0Lp89jUHAfBgX3scstCLB+d9TFKy7Lbc/aXfHlVr9NEJvsctu8aGOq8sdx7N+jvFb1JXrWe5We9V4lPDSMwY/1TfBb78R9jaNOq/qsmZ3wbbhygyq0fO0pRnV6n+tXryf5WwAObt+PX2l/ipa07m2tlvXYsnBjgjRbFm2knq2hbJX7uHLxChfOnE8xb4V6lQk9cILzp24OuZ47cY6H6lQEIGfunJSrch+hB04kqzGOjKqHxW1HfLCGLVOr505pPP7vUXpUe5mQeq8RUu81wk+FMeTxfin+rgXT5zEguDcDgnuzccF6HrHvYflk6uGetTupZdfDBg7PyaZFG5LMX8C3IAC+JYpQ49FarP59RYL9IsJTPZ5h4ffz75je/zoZ9axUbBDAY11b82nnkSm2Le4gK/vE3ZIzvojkA/Ko6lwRWQfstw9dxOoNu1UKAmdU9YaIBAH3JnHe+cBwEfleVS+JyF3ADWAFME1ERmL9tpbAl7bmx4C5apnk5YEYIMK+Zlzr/FIyulJKE8c0YANwSlV3p5A2RbYu2UxAUFU+XTGRa1HX+DLk8/hj/ae9zVf9x3H+zHl+/GA6Pcb15f9C2nJ490GWzliYYv5pQ7/i9c/6kC17Nk4fPZ3gWGqIjYnl2yGT6Tf9bby8vVgxcwkn9h0jqK311rz0+wVsX7qFSkGBfLx8PNeirjG53/j4/K993psHalUgX+H8jFk7iV/HzGDFzMU8O7A99zxUChTOHT/D1EFpn36/zf7dY1ZMsH/3WIdye4tJ/ccT4VBuz4S8wJHdh1hm9xwkl//1z/vwYO0K5C9cgLHrvmLWmJ9YNmNxmvRl5H3NkSsHFetXZvKgCQmu+dK7XawZoN9ZLqT7t/7LlMGuyzY2JpbpQybTb/oQ+94u5sS+YzSy7+2S7xewfclmAoICGbXiC65HXeOrkHHJ5o2jdsu6rHXy1Vs0fR5dRr3OBws/RURY8fMSjv2TOj+vjKqHzwx4Ef8yJdBY5dyJs3wz+MtU6bmTGtPL1iWbqRJUlc9WTIwPVRPHm9Pe5ku7Hn7/wXR6juvLs3Y9XOJQD5PK32fiAPIXzk/MjWi+HjKJy/bEqbqt6tOsvTU0veGvdSxLw+9Ir96CRQvxwZxR5M6XB41Vgl9uSd8mPYi6FMUbn/fhodoPk79wAb5YN5mfx/wU35OYEfQbOpKNW3cQERFJ49Yv0q1TO9q0bJ5h14OMq4ft3ulMthzZ6fed1cN/YOtevhk8KUN/i8FCUupmFJEYrOHPOP4CPgN+x+qREmCUqn4jInWBr7B62Z4G3gb+UNX/ichhoJqqnhORl+zvr9vXOAxUs88/B2u4chtQF2ihqodF5AegEjDP9ovrCXS281wCXlTVAyIyGGgPHAGOA3tUdZSI/AQEAleAaGCwqs4XkdpYM1fPAkuAdqpaSkQaAiGq+ngyaRL8Docy+wv4TVVTtD6ev7e155r4QA7xdreEFLmhse6WkCye7E8RRzYPX7zFy6WToiGtXNXEszINaeO7zaPdLSFZOlfrl3IiN/PN4Vl39IGu4lc33Y3w1lOrPbIRStGIM6QNEcmDZfQGquqFlNIbIy79GCMu/Rgj7r+BMeLSjzHi0s+dNuIq+9VJdyO8/dQaj2yEPLvlzmTYgY3/AcamxoAzGAwGg8GQsfzXZ6caUokd2Pged+swGAwGg8FgEZuFRxxNT5zBYDAYDAZDJsT0xBkMBoPBYMiyePJwaHoxRpzBYDAYDIYsS1YeTjVGnMFgMBgMhiyL6YkzGAwGg8FgyIRk5Z44M7HBYDAYDAaDIRNieuIMBoPBYDBkWcxwqiHDiPb0ypUJIrxfw7NXbOhyNY+7JaTIlFxR7paQLBU0n7slpMgmjXC3hBTJL9ndLSFZdl495W4JKeLpKyJM3vSxuyV4HO4eThURH2AGUAo4DPyfqp53SlMSmA74AbHAJFX9LKVzm+FUg8FgMBgMWRYPWLHhTWCxqpYHFtvbzkQDfVX1QaAW0F1EHkrpxMaIMxgMBoPBYMg4ngC+sb9/A7R2TqCqoaq6xf5+EfgbuCulE5vhVIPBYDAYDFkWVbe73BRX1VBLi4aKSLHkEotIKaAKsD6lExsjzmAwGAwGQ5Yl9jb4notIF6CLw65JqjrJ4fgiLH82Zwan8Tr5gFlAL1WNTCm9MeIMBoPBYDBkWfQ2TGywDbZJyRxvktQxETktIv52L5w/cCaJdNmxDLjvVfWX1OgyPnEGg8FgMBiyLLFouj/pZDbQwf7eAfjdOYGICDAF+FtVR6f2xMaIMxgMBoPBYMg4RgJNRWQf0NTeRkRKiMhcO01doB3QSES22Z/glE5shlMNBoPBYDBkWW7HcGo6rx8GNHax/yQQbH9fBUhaz22MOIPBYDAYDFkWdwf7zUiMEWcwGAwGgyHLYpbdMriVjsNeITCoKteirjE+5DMO7TqYKE2xksXoNbYf+Qrl49Cug4ztPYboG9GUKHsX3Ue9QekKZflx1HfMmfQbACXK3EXvcSE389/jx4zRPzD36zmp0tRhWGcCgqpyPeoaE0I+57ALTUVLFuONsSHkLZSPw7sOMr73p8TciE42f54CeenyYXfuvu8eQPmy3zj2bfmXFwZ1ILBxdWJuRHP6yCkm9hvLlcjLqS7DTsNeITCoGteirjEu5FMOuizD4vQZG0K+Qvk5tOsAn9ll+EjrBrTu2gaAq1eimDR4Aof/PkyJMnfRd9zNJXiK3+PHT6N/4I+vZ6dalzNFgirz4IgO4O3F8e+XcGhswnP5t6lLmddbARB9+Rp7+k/m4p6jANz7SgvufrERAMe/X8KRSfNuWYcrMqIexuHl5cXIPz4h/FQYI18ekW6t5RpU4tGh7fDy9mLLT8tYNSFhvS5S1p8nRr2Kf4VSLBk1kzWTLLeUAv4+PDnmNfIVLYjGKpt/WML6qfPTrSeOLu+8SjW7Hn7adwwHdh1IlKZ4yeL0HzeA/IXysX/XAUb3+oRo+7kBKF+pPKN+/4SPun/I6rmrAXiiU2uaPd8MVDn8zxE+DRnDjWs3UqUpI55l/zIleMPh2Sh2T3H+N/pH5n09h5rBdXi693OUKHc3b7fqx8GdicsgtQx8rw/1G9fmatQ1Br8xnL93/psozfMvP027Ls9yT+mS1HuwORHhF+KPVa8TyIDhvciWLRvnwyPo+GS3W9YSR8UGAbQd8jJe3l4sn7GYPyf8mihN26EvUzkokOtR1/kqZCxHdh8CoNNH3QhoVI3IsAsMbt47Pv2zA9sT0KQaMdejOXP0FJP7jeNK5JV0a02Jt94fzYrVG/ApXIjfvpuY4dfLKNw9nJqR/KcmNohIjIPD4DYRcbX0hWP6uSJSyP6k+ekWkWEiEpJyyqSpElQV/9L+9GjQlS8HjueVEa+5TNf2zQ78MWU2bzR8jUsXLtHoWWu286WIS3w99CvmfPVbgvQnD56gX3Bv+gX3ZsDjfbkedY0N89elSlNAUFX8SvvTu8FrfDXwCzqN6Ooy3QtvdmDulNn0adiNyxcuEWRrSi5/h6Gd2L58CyGNX2fAo705sf84ADtXbqd/szcY8GgvQg+d5IlubVKlFSAwqCr+pUvQvcGrTBw4ni5JlGG7NzswZ8psXm/YlUsXLtH42aYAnD52mrf/byB9Hn2Dnz+fQdcPugNWGfYN7kXf4F70e7wP16KusX7+2lTrSoSX8NDIl9n0wkhW1e+L/5N1yXtfwoDdUUfOsr71u6wOGsCB0b9Q4RMrbFG+B+7m7hcbsfbRwaxpNICiTQPJU9pVyKJbI6PqYRzBLz/Oif3HbotW8RKCh7/E9x0+YnyT/jzcqjZFyzuVY8Rl5g2dzpqv/kywPzYmlgUjvmd84/5Mbj2UGu2bJsp7q1QLqkaJUiXo8sgrjHtzLN3e6+4y3UsDO/L75N/o0qALly9coumzzeKPeXl58dLAjmxdviV+n29xX1p2bEnvx3rRvWl3vLy9eKRlg1RpyqhnOfTgSQYG92ZgcG8G2e3LRrt9Obb3KKNfHck/6/ekSmNS1G9cm3tKlyS41jMMC/mAtz/q7zLd1g076PzMG5w4Gppgf/4C+XhrZD9eb9+P1g1eoO8raQrn5RLx8qL9u6/wyUvvMbBpL2q1qkeJcncnSFOpYSB+pf3p3/B1pg6aQIf3boYeW/W/ZYzqMDzReXev2s7gZr14q0UfTh06yePdnkq31tTQOrgpE0en/6XKkHH8p4w4IEpVAxw+I5NLrKrBqhoBFALS/4p2C1RvWoPls5YCsG/rXvIWyEuhYoUTpXu4TiXW2W/ly2ctoXqzWgBEhl3gwI79Cd7kE+WtW4lTR09x7sTZVGmq2rQGK2ctA2D/1r3kSUJThToVWT93DQArZi2lWrOayebPnS83D9SswNKfFgEQcyM6vrdt58ptxMbE2uXwLz7+vqnSClCjaU2W2WW4d+u/5C2Ql8Iu9FasU4m1dhkunbWEGrbefzf/w2Vbx94t/+LrXyRx3rqVOH30FGdTWYauKBRYjiuHThF15Ax6I4ZTv62h+KPVEqSJ2LSX6AuWlojN+8jl7wNA3vJ3EbF5H7FR19GYWM6v+ZviwdVvWYszGVkPffx8CWxUjcU/LbwtWu8KKEv44dOcP3aWmBsx7JqzjvubVk2Q5nJYJCd3HCT2RkyC/ZfORBC66zAA1y9f5ez+k+Qvnvh33go1m9ViyawlAPybTD2sVKcSq+auAmDx/xZTu3mt+GOPd2zJmnmriQi7kCCPdzZvcuTKgZe3Fzlz5yT8dFiqNGXUs+zIw/azEde+nNx/nNCDJ1OlLzmCHn2E2T9bPag7Nu8mf4F8FCmWuF34Z9deTh4LTbQ/+KnmLJq7jFMnTgMQfu58ojRppUxAOU4fOcXZY6eJuRHN+jmrCGyW8DkMbFad1b8sB+DA1n3kyZ+XgkULAfDvhj1cvnAp0Xl3rdwe3/4d2LqXwn6pb//SQ7WAihQskP+OXCsj8YAQIxnGf82IS4SIFBSRf0Xkfnv7RxF5xf5+WESKYE0HLmv33n1sH+snIhtFZIeIvONwvsH2+RYB96dXn4+fL2Enz8Vvh506h0/xhA9w/sL5uRJ5Of4hDwsNw8fPJ9XXqNuqPqtnr0iDJp8EmsJPheFTPOH18hfOz+UkNCWVv9g9fkSGXaDrqDf4YO5oXvmwOzlz50x0/Yb/14Tty7Yk2p+0Xl/OnbxpXIWdCnNZhs56fV00lE2ea8rWZZsT7a/X6hFWpqEMXZHTz4eokzf/+F49GU7OZO7j3S8EcXbJNgAu/XMMn1oPkr1wPrxy56BokwBy3XX7GvqMrIcdh3bmu/e/ITb29jSUBfx8iAy9WY6RoeEU8Eu7IVbo7iL4V7iXE9tufbjPEV8/X86FOtbDc4nqWIHCBRLUw3OhN9P4FveldvPazPsu4TB52Okwfp30C1PXTePbTd9xJfIyW1duTZWmjHqWHanTqh5rZq9MlZ60UNy/KKdO3IyZejr0DMX9i6Y6f6myJSlQMD9Tf/mCGQum0eqZFunWVLi4D+GO5REaTmGn56Rw8cRllhajrP4zjdm5LHX312Chqun+eCr/NSMut9Nw6rOqegF4HZgmIs8BhVX1K6d8bwIH7N67fiLSDCgP1AACgKoi8oiIVAWew1rz7CnAZVeIiHQRkU0isungpcPJCrbi/znhVKFSkyYpsmXPRrUmNVj75+pUpU/qeokul0yapPJ7e3tR+uGyLPxuHgOD+3DtylVaOQ2btn79aWKjY1j16/I06E28z/mhdK0pYZqHa1ek8bNNmf7BNwn2Z8uejepNarAmDWXoWqirna7vo0/dh7j7hSD2Dv8BgMv7TnJw3GyqzRxMtR8HErn7CBp9+9YLzKh6GNioGhfCIjjowjfsdpLWRjhHnpz838Re/PXut1y7FHVbNIiLG5z4uUmcLy7NK8O6MO2DqcTGJryveQvmo2bTWnSq+zLtq7cjZ55cNHwyKHWaMuhZjsM7ezaqNqnB+vQ+Gy5wXZ6pv8/e3t48VPkBur3Yh1ef68mrfV7m3jIl06cpFe1IEg1Sqs7fsnsbYmNiWPNb+l4Y/2vEqqb746n81yY2RKlqgPNOVV0oIs8A44HKqThPM/sT9zqUD8uoyw/8qqpXAETEpYe74/Idz9z7RKLa0bx9ME2es/yx9u/Yj2+Jm8N3vn5FCD8TniB9ZHgkeQrkxcvbi9iYWHz9fQk/nbqhgYCGgRzadYAL5y4km65p+xY0es7yzTm4Y18CTT5+vpx30nQxPJK8TprOn7bShIWGucyvqoSHhnFg2z4A1s9dyxMOvh+PtAmiSuNqvPf8kBR/16Ptg2lq692/Yx9FShQF/gasHhFnvZEu9Iafvpnm3gdK0e3D1xne4R0uRVxMkLdKw6oc3HWAC+ciUtSVHNdCw8ld4uYbea4SPlw7lfg+5nvoHh4e/Sqbnh/JjfM3h15O/LCUEz9YQ57lBz3H1ZOpG1JLijtRDx+o9iDVmtSgSsOq5MiZg9z589Dj096M7TXmlnVHngqngMNwewF/Hy6ejkh1fq9s3vzfxF7s/G01f/+16ZZ1ADzW/jGaP/8oAPt27KWIQ0+Rr1+RRMOezvWwiP/NNOUqlqP/uAHWb/IpQLWgasREx+CdPRunj50mMtxaZnHtX2t4sOqDLPt1qUtNd+JZjsNqXw6m2L6kluc6tuHpF58AYNe2v/G76+Y64sX9i3Hm1LmksibidOgZIsIvEHXlKlFXrrJ53Vbur1CeIwdv3Tcz/FQYPo7l4e9DhFN5nj9lldm+uDR+N8szOeq2aUhA46p8+MKwW9ZnyHr813riXCIiXsCDQBSQmnFIAT5w8K0rp6pT7GPpNtnnT58bP+lg44J1NGhjvVWXr3IfVy5eJuJM4j+Mu9fupFZwXQAatGnExoXrU3Wteq0eYVUqhjoWTp8X76i8acF66rdpCEC5FDTVDK4DWAbY5oUbANiyaIPL/BfORhAWeg7/MiUAy5fm+D6rQa3coAotX3uKUZ3e5/rV6ynq/Wv63PhJBxsWrKehXYb3VbmfKxevcN6F3l1rd1LbLsMghzIsUqII/b8cyGe9xxB6KLEvT/1W9VmVzqFUgAtbD5CnjB+57ymKZPfGr3UdzsxPOHSb6y5fqnzdhx3dx3PlYEI/nxxFCsSnKR5cndBf16RLz52ohz989C1da3Wie70ujOkxil1rdqTLgAM4uf0gvqX9KFSyKN7ZvXm4ZS3+XZh4CDwpnvjoFc7tP8Hayemf3fvn9D95o0UP3mjRg7Xz19GojTV7+P4q93Pl4mWX9XDn2p3UC64HQOOnG7NugVWGnet1olPdl+lU92VWz13NhLe+YN2CdZw9cZb7A+8nZy7L9aBy3cocS2aSyJ14luOo06o+a27DsxHHT1Nn8XTj9jzduD1L5i2n1TNWQPtKVStw6eIlzp1J/YvL0r9WElirMt7e3uTKnZOKgRU4uO9wuvQd2r6f4qX8KXJ3MbyzZ6Nmy3psXZjwRWDrwo3UfcqaeFK2SnmiLl7hwtmIZM9bsUEAj3VtzaedR6aq/TMkJCsPp4oni7vdiMglVc3nYn9fLP+1b4ExQG1VvSEih4FqWIbZFlW9107fDBgONFbVSyJyF3ADuBuYBtTE6uXcAnypqqOS0uSqJ86ZTsNfJaBBFa5HXWN8yFgO7twPwMBpbzOx/3jOnwmnWMni9B5nh8fYfZDPe40m+no0hYoWYuScT8idLw8aG8vVK1fp3eR1oi5FkSNXDiaum8Lr9V/lykXX09WzJRFAuuPwLlRuEMi1qGt8GfJ5fJiA/tPe5qv+4zh/5jzFShanx7i+5CuUn8O7DzK+1xiir0cnm//eh0rT5cPuZMuejdNHT/NlyOdcjrzMmOUTyJ4jOxfPW71g+7f+y5TB1pT3a6Q8bPjK8FepYl9vXMjnHLDLcPC0IXzRfxznz4RTvGRx+ozrF1+Gn/b6hOjr0XT78HVqtajD2eOW/01MTAz9W/YFIEeuHHy17mteq98lyTLscjVPivriKNI4gAeHd0C8vTj+41IOfvobJdtbMwGPTV9EhdFd8HusBlHHrR4HjY5hbXNrVl2N34eRo3A+YqNj+Gfot4Sv3JXq607JlfKQYUbVwzgeqvUwrbq0dhlipAKJHttkKR9UmUeHtEO8vdg6czkrx/1OtbZWwPRN3y8mX9GCdJkzgpz5cqOxsVy/co3xTfpT/IGSvDxrKKf/PoraPnqLP57BvqXbU7zmJo1IMU3X4a9RtaEVpuXTkDHs32GV4bBpw/h8wOeEnw6n+D1+DBjXn3yF8nNw90FG9fw4/rmJo9cnvdm4eEN8iJEX+rSl/uP1iY2J4cDug3ze/7NEeQDyS/ZE+zLqWc6RKwfj1k2mZ/2uRDk8G9Wa1+Sld16hgE9BrkRe5vCeQ4xsb7kV77x6KsUydGTwByHUa1SLqKirvN1zBLu3/wPAF9+PZmif9zl7+hxtO/8fHbu/SJFiPoSfO8/KxWsZ2ud9S3u3trR+7nFiNZZZ38/mu0kzUrxm1dzJz1au1DCQtkM64uXtxYqZS5gzfhZBba2ez6XfLwCg3budqdSgCteirjG533gO22X22ue9eaBWBfIVzk/kuQv8OmYGK2Yu5qNl48iWI3v8KMCBrXv5ZrDrtdgnb/o4FSWXOvoNHcnGrTuIiIjE16cQ3Tq1o03L5uk+b/YiZdK8MkF6KJivbLoNnQuXDtxRzanlv2bExQA7HXb9BXyNtRhtDVW9KCKjgYuqOjTOiFPVcyLyA1AJmGf7xfUEOtvnuQS8qKoHRGQw0B44AhwH9qTXiHMnSRlxnkRqjDh3khYjzl2kxohzJ2k14txBaow4d+PKiPMk0mrEuYOUjDh3czuNuIziThtxBfKWSfff2cjLBz3yj+F/yidOVb2TOPSgQ5o+Dt9LOXx/welcnwGfubjGe8B76dVqMBgMBoMh/XjyxIT0YnziDAaDwWAwGDIh/6meOIPBYDAYDP8tzNqpBoPBYDAYDJmQrDycaow4g8FgMBgMWZasPIHTGHEGg8FgMBiyLFl5ONVMbDAYDAaDwWDIhJieOIPBYDAYDFkWM5xqMBgMBoPBkAkxRpzBYDAYDAZDJiTrmnD/sWW3/guISBdVdb2onofg6Ro9XR8YjbcDT9cHnq/R0/WB52v0dH2QOTT+VzETG7IeXdwtIBV4ukZP1wdG4+3A0/WB52v0dH3g+Ro9XR9kDo3/SYwRZzAYDAaDwZAJMUacwWAwGAwGQybEGHFZj8zgt+DpGj1dHxiNtwNP1weer9HT9YHna/R0fZA5NP4nMRMbDAaDwWAwGDIhpifOYDAYDAaDIRNijDiDwWAwGAyGTIgx4gwGQ7oRixdFZIi9fY+I1HC3LoPBYMjKGCMukyMi3iLS2906sgIikltE7ne3jkzKF0Bt4Hl7+yIw3n1ykkZE6olIR/t7UREp7W5NjohIWRHJaX9vKCJviEghN8vKNIiIl4jscreO5BCRx0XE/P01pBszsSELICLLVLWhu3U4IyJjSWbFE1V94w7KSRYRaQmMAnKoamkRCQDeVdVW7lVmISLFgfeBEqraQkQeAmqr6hQ3SwNARLaoaqCIbFXVKva+7apa2d3aHBGRoUA14H5VvU9ESgA/q2pdN0uLR0S2YWksBcwHZmPpDXajLETkqeSOq+ovd0pLSojI98BAVT3qbi2uEJHvsF56ZgFTVfVvN0uKR0T6JHdcVUffKS2GlDFrp2YNVovIOGAGcDlup6pucZ8kADbZ/9cFHsLSB/AMsNktipJmGFADWAagqttEpJQb9TgzDZgKDLa392KVp0cYccANEfHGNtpFpCgQ615JLnkSqAJsAVDVkyKS372SEhGrqtEi8iTwqaqOFZGt7hYFtLT/LwbUAZbY20FYz43HGHGAP7BbRDaQsE30iJcyVX1RRApg9VxPFRHFer5/VNWL7lVH3PNwP1Ad6yUCrPu/wi2KDElijLisQR37/3cd9inQyA1abgpQ/QZARF4CglT1hr09EVjgRmmuiFbVCyLibh1JUURVZ4rIQAD7j3yMu0U58DnwK1BMRN4Dngbecq8kl1xXVbX/aCIied0tyAU3ROR5oAM3DafsbtQDgKrGDUH/ATykqqH2tj+eN3T+jrsFpISqRorILCA30AvrBaOfiHyuqmPdqOsdABFZAATGGZUiMgz42V26DK4xRlwWQFWD3K0hBUpgvd2F29v57H2exC4ReQHwFpHywBvAGjdrcuSyiPhys6erFnDBvZIsbN+eQ0B/oDEgQGtPGiJyYKaIfAkUEpFXgJeBr9ysyZmOQFfgPVU9ZPvsfedmTY6UijPgbE4D97lLjCtUdbmI3AuUV9VFIpIH8Ha3rjhEpBXWfS4LfAvUUNUzts6/AbcZcQ7cA1x32L6ONcRv8CCMEZcF8HR/KWAksFVEltrbDbCGLz2JHlhDldeAH7B8kUa4VVFC+mANa5QVkdVAUazeLrejqrEi8omq1gb+cbee5FDVUSLSFIjEGi4aoqoL3SzLmaaO/qK2IRflTkFOLBOR+cCPWC8VzwFLk89yZ7EN9C6AD5ahdBcwEeslwxNoA4xR1QTDk6p6RURedpMmZ74FNojIr1j3+UlgunslGZwxExuyACIyD9tfSlUri0g2YKuqVnSztHhExA+oaW+uV9VT7tTjiO3LNV9Vm7hbS3LY9/V+rJ6uf+OGpz0BEXkH2AH8oh7cqNi9WqGqetXezg0UV9XDbhXmQNwkEad98RNGPAHbX+8Re3OFqv7qTj3O2JNDamC1NXETbXZ6QpuYWdobABEJBOrbmytU1RN8Mw0OmJ64rIFH+kvZDYAjx+z/S4hICQ+YeAGAqsaIyBURKaiqHjFE6YyLmYH3icgFYKeqnnGHJif6AHmBaBG5imVoqqoWcK+sRPzMTR9SgBh7X3X3yLmJ7Qf3AlBaRGY7HMoPhLlHVULsofMdqvowlg+kp3JNVa/H+bjaL0Ae8XKRGdobB/IAkao6NS4cj6oecrcow02MEZc18FR/qU+SOeb2iRdOXAV2ishCEs5m85QwKJ2wQhLEDVs1BNZhGXPvquq37hIGoKqeNsMzKbKparyfj/2HPoc7BTmwBggFipDw2bmI1cvpduyh8+0ico+nhu+wWS4ig4Dc9vB5N2COmzU54untTYJwPFgjPdmxfDM9JhyPwRhxWQWP9JdS1SD7zb22qq52t54U+NP+eCqxwIOqehri/SAnYA1Rr8DyX3EbIvKIq/3OPj8ewFkRaaWqswFE5AngnJs1AaCqR4AjItIWOOk05Hs3cNiN8hzx6PAdNm9ivfjsBF4F5gKT3aooIZ7e3kDmCMfzn8f4xGURPNxfaq3t9O7R2D0ycbPsPK0ME/jziDVOtFNVH/YEfykRcezlyIXlj7RZVT2ptxURKQt8jzU7WrCG+Nur6n63CnNARDYBdeJ6DO16uVpV3T7kCyAiDVztV9Xld1pLctjl9gBWr/+/jj2whpQRkQ2qWkNuBvLOC6xV1Uru1ma4iemJywKISC6s4YJ6WA3WShGZGPcm7wEsEJE2eLDTu4g0BL7B6u0QoKSIdPCgnqSVdnyuuDhNbYAVdsMa4TZVNqra0nFbREoCH7lJTpKo6gGglojkw3qJdXdgVVd48pCvxxlrrhCRx7Bmox7Aep5Li8irqjrPvcos7DBGH2AFQc8Vt19Vy7hNVGJchePxpN5MA6YnLksgIjOx/GbiYkk9DxRW1Wfcp+omInIRy+k9BojCA53eRWQz8IKq/mtv34cVPb2qe5VZ2D1vT2EZ6mA5uvuranf3qUoaW+8OT5gN6IhYa5K2wYp3Ff8Sq6rvJpXnTmP7SY11GvJ9Q1U9IjyG7XM7FngQyIEVf+2yhz3P/wCPx/Ww2j2wf6rqA+5VZiEiq4ChwBisgM4dsf4eD3WrMCdsf8JmWG32fA8Mx/Ofx/TEZQ3u14RrVC4Vke1uU+NEJnF6zx5nwAGo6l4RcXuU/DjsVQYOYPnA/R9WcN1Z7lV1E0m4Tq4XEAB4TB104HesST+bsWICeiJdge/FWkovfsjXvZISMA4rNtzPWI7v7YHyblWUmDNOQ+QHAU+YxR1HblVdLCJi+0IOE5GVWIadRyAiH6rqAGChi30GD8EYcVmDrSJSS1XXAYhITcBjJhLYvTJtgdKqOtweavNX1Q1ulubIJhGZws0JAm3xgPVd7R7B57B6V8Ow1ksV9bxVOjY5fI/G6sX0mDrowN2q+qi7RSRHZhjyVdX9IuKtqjFYa396xOomDqF4dovIXGAm1svFM8BGtwlLzFV70tc+EXkdOIG1Jq0n0RRwNthauNhncCNmODUTIyI7sRqo7FiTGuKm/N8D7LFjObkdEZmANbuykao+KCKFgQWe4qgN8cNs3bGGKwVrxucXqurW3hoRiQVWAp0choYOepjvTKZBRCZhDVXudLeWpBCRIa72e8qQr4isAJpg+UedwgqL8pLTaIBbEJGpyRxWVfWI1RBEpDrW8lqFgOFAQeCjuBdxdyIir2H5WJfB8imMIz/WBJsX3SLM4BJjxGVixFobMEnsbnq34zC7KX4WpYhs94RGPw57gsBVu2chLqp6TlW94mZdT2L1xNUB/gJ+Aiaraml36nJGROpiLaV2L1YPf5zfo0cZmyKyByiHNRx9jZs6PWbGnYj0ddjMBTwO/O1BBsi9WOul5gB6YxkgX3jSDF/DrSMiBYHCWBMv3nQ4dFFVw13nMrgLY8RlEezerZIkdNb2iBURRGQ9lhGy0TbmimL1xHnSMkLrgCaqesnezoelsU7yOe8MtpHZGmtYtRHWTNpfVXWBO3XFYTuS98Yago5fLURVPWKlgTiSevHxlBceV9i9xLNVtbmbdRQFiqrqHqf9DwOnVfWse5QlRqzl1XqQeAKLW2PZ2aF4kvyj6259ACJSQFUjRcTH1XFjyHkWxicuCyAiw4GXsLq+4xoIT1oR4XOsJXqKich7WIGI33KvpETkijPgAFT1kojkcacgR1T1MlZ8s+/txvUZrLdkjzDigAueEr4hOeKMNREphkNoBw8nD9bQlrsZixVg2pm7gEFYS4Z5Cr8BU7BWaYh1r5QEjHK3gFTwA1bv72asvyPicEzxjLposDE9cVkAEfkXqOjJwSxF5AGgMVaDsFhV/3azpASItdJFj7jeSxGpCozLDEGKPQERGYkVauIXHGZ9ekpvcBwi0gprSasSWLMV78UaqqzgVmEOOPi6glWmRYF3VXWc+1SBiOxOqpxEZJen+OCC1fuvqjXdrcNgyGhMT1zWYBeWg6wnTaF35jSWg342rPUMAz3sD3wv4GcROWlv+wPPuk9OpiPuD2Y1h32e1Bscx3CgFrBIVauISBDWELUn8bjD92isocpod4lxILmQOx4TjsfmM7HW/lyAB75UZIZgvyLSSVWnOGx7A2+p6jtulGVwwhhxWYMPsMKM7CJhg+V2/wrIFMO9qOpGu7cwbumyf9SDlt3ydDww5ElS3FDVMBHxEhEvVV0qIh+6WxSAgw+Sc0iRAiLiCb5I+0QkWFXnOu4UkRZYcdg8iYpAO6w2Jm441ZPanKncDPYbhB3s162KEtPYXmmnE1AE+Brw+NU6/muY4dQsgIjsBr7EWuw53v/DU5bH8eThXnuq/zFVPWVvt8eK6H8EGOYBfzgzBSJSHHgfKKGqLUTkIaC245u8JyAii7AmiHyA9YfpDFDdEyawiMghbvog3QOct78XAo66e0ayHbPwD2ANN2MoVgNqY62OsNdd2pyxJ9pU8sQ2B6wVYlS1qjisiSwiK1W1vru1OSIizwLjgSvA8x4a+/E/jZe7BRhuC+dU9XNVXaqqy+M+7hblQNxwryfyJRC30PgjwEhgOlZU/0lu1JXZmAbMx/I1A9iLNUTtaTyBtfRbb6yQLQewlj1yO6pa2h5Omw+0VNUiquqLNbz6i3vVWauYYPVwLcea9VnK/l7Jkww4m+14bpsDTsF+7VBCHhXs1x7y7Ym1MsxhoJ0nTfYyWJieuCyAiIzGGkadjWf6f1TDWu7I44Z7HePVich44KyqDrO3t6lqgBvleTwikk1Vo0Vko6pWd4oFaMrvFojrpXHat0lVqyWV505h+0XNV9Um7taSHCKyDKiEtUqDR7U54NnBfuOwezO7q708GNAHeNmTJgEZjE9cViEu3loth32e5P/xDfAhTsO9HoJ3nCGCNXu2i8Mx83ykzAYgELgsIr7YPo9iLZJ+wZ3CHBGRi7iOzxUX7NdjFm8HzonIW8B3WJpfxFpyze2oaoyIXBGRgqrqMffXBR6zBqkrVDVuCbBLItIHiFDP61GpoaqRYD0gwCciMtvNmgxOmD9SWYBM4FR+TlU/d7eIJPgRWC4i57CG2VYCiEg5PMgI8WDinLH7YPUEl7XDtRTFigfoEahqfndrSAPPYxkhv9rbK/CsGbRXgZ0ishC4HLdTVd9wn6SEqOpyO7BzeVVdZA8Dertbl72k2kxV/ccO4vwXUBmIFpEXVHWRexWCiPRX1Y/sgL/PqOrPDoc7YsUENHgIZjg1C5AJ1lr09OHeWlghRRbYQXXjnLjzqupWt4rzcETkODDa3vQCcmIZdteAGFUdnVRed+Ic7FdVjyaT3C2ISAEg1jEItScgIh1c7VfVb+60lqQQkVewetV9VLWs7d81UVUbu1nXbuBhVVUR6YJlnDcB7gO+UdUa7tQHN5dJdP7uatvgfkxPXNbgssP3+LUW3aTFFR493Kuq60TkW1X91WHfXhH5FitMgSFpvIF8JA6P4JEO0EkF+wU8xs9HRCpiTa7xsbfPAR1UdZebdcUtu/WN0/6HseJAehLdgRrAegBV3Wcb7u7musOwaXPgJ7XWa/5bRDzl77Ek8d3VtsHNeEqlMaQDVf3EcVtERmH1enkEmWC4F5z+iNsO3FWTSGu4Sain9PimkswQ7PdLoI+qLgUQkYZYM6XdHQYlMy27dU1Vr1v++NYEHJJZs/QOcs3B6A0CQhyOecqLjybx3dW2wc2YECNZE09ZaxEAESkoIqNFZJP9+URECrpbF4CIDLSd3iuJSKT9uYjVS/O7m+VlBjLbm/kNVQ0D4oP9AgFu1uRM3jgDDkBVlwF53ScnnoquQhep6nysmaCexHIRGYS1OkxT4GesdVTdTS/gf8A/wBhVPQQgIsGAp7huVHZoB53bxYruFmdIiPGJywKIh661GIeIzMIKLxI3DNMOqKyqT7lPVUJE5ANVHehuHZkNEfHJTAGRPTnYbxwi8iuwBfjW3vUiUE1VW7tNFCAie1X1viSO/auq999pTUlhx2DrBDTDetGYD0z2wBmgBkO6MEZcFsCehRWHJ621CLiOF+YpMcRE5AF7pphLZ11PmXxhuD2ISF6sWcheQFus+Fzf271zHoGIFAbeAerZu1YA76jqefepAhH5ExivrpfdekNVW7hHWebBDieSJJ46EcjguRifuCyAqh5xt4YUiBKReqq6CkBE6mL9IfUE+gKvYDm7O+Mxky8Mt4e42cdArG2UhHlS74zti/mzhwbT7Q38ISL/h4tlt9ymygER2ZHccVV197BvXKib+4Hq3PRdbollrBsMacL0xGVinAKYxvkmKZZxnkNVPcJIF5HKWLPt4vzgzmPNtku2wTUYbhd2GJmRQDjW5IZvsYZTvYD2qvqXG+UlwA6o2s4Tg+nasc1eAB62d+0GflDVq+5TdRMR2YbVBv6A5QOX4GXRU154RWQB0EZVL9rb+bGM90fdq8yQ2fCIP/KGW8M5gKndEHQDXuVmoFBPIFJVK9txr7CDSLp1Me84RCRZvzxVdfualYbbwjisGZQFgSVACzu0zANYAZ89xojDg4Ppquo1EZkJTLdXb7gPaCYi81T1hgfoC7Dv6fNYhtwe+/8FnuRiAtyDvWazzXWstWgNhjRheuKyACJSCGvWU3usBmuMh/n4JAoQ6Wp9SHcgIlPtr8WwQjgssbeDgGWeNPnCcOs4+mCKyN+q+qDDsa1qr/fqCXh6MF0R2QzUBwoD64BNwBVVbetWYS4QkWeB8cCHqvqxu/XEISKDgf/DetlW4EmslRzed6swQ6bD9MRlYkSkCJZP17PA10AVTxqCsd+IKwAFnXq8CuAQLd+dqGpHABH5A3hIVUPtbX+sxt+QNXBcs9fZH9Nj3mRFpDXW7PKddugOT0RU9YqIdALGqupHIuIp4TEQkbuA57AMo/NYvnyeNDKBqr4nIvOwjGGAjmZ1GMOtYIy4zM0R4CwwFbgCdIoLbgkeMdPpfiyH50JYjrtxXMSaTOBJlIoz4GxOYy2FY8gaVBaRSCzf0dz2d+xtj3ihEJEvsF561gDDRaSGqg53syxXiIjUxprd28ne5xF/S0RkOdbkgZnAS1g+kAA5PDAcTh4sV5OpIlJURErHxY0zGFKLGU7NxIjIMJLpRVDVd+6cmqQRkdqqutbdOpJDRMYB5bH8oxTrTX6/qvZwqzDDfwYR2YUVPzFGrAXbV3qCy4EzIvII1koDq1X1QxEpA/TyBJ89ETnMzTbRsW0UQFXVI4Kgi8hQrJm996vqfSJSAmtiQ103SzNkMowRlwUQkVyeMjvMFSLyETACaxjrL6AyVqP/nVuFOSEiTwKP2JsrHNdSNRgyGrPY+H8HexZtFWBLnD+miOzwgBAohkyGR3SBG9LNLhE5DazEijW02pN844BmqtrfNpKOA88ASwGPMuKwouRfVNVFIpJHRPLHhQAwGO4ADzjEOROgrL0d14vkEX/gRaQo0B9r6Dd+KFpVPSamolh+JW2B0qo6XETuAfxUdYObpcVxXVVVRBTig1AbDGnGGHFZAFUtZzdS9bF80L4QkQhPWBHBJrv9fzDwo6qGO/rueQIi8grQBfABymIt6j0RaOxOXYb/FIF4ThDs5PgemIHV1nQFOmD55noSX2BNZmmEFRfwIjALK8CuJzBTRL4ECtltz8vAZDdrMmRCjBGXBRCRu4G6WEZcZawAnKvcKiohc0TkH6w/UN3sN3lPG/7tDtQA1gOo6j4RKeZeSYb/GD+oaqCIfKuq7dwtJhl8VXWKiPRU1eVYi80vd7coJ2raZbkVQFXPi0gOd4uKQ1VHiUhTIBJrAtgQVV3oZlmGTIgx4rIGR4GNwPuq2tXdYpxR1TdF5EOsmVgxInIZeMLdupy4pqrX43oIRSQbHhR6wvCfIIcdI66OqyDUHhR4Oi6ob6iIPAacBO52ox5X3LCXMIsbrixKwjAzbkVEPlTVAcBCF/sMhlRjJjZkAexlrephOeXfA+wDlqvqFLcKsxGR9q72q+r0O60lKezJFxFYAZN7YK18sUdVB7tTl+G/g4jUw/Lj+j9urqkZh6rqy3deVWJE5HEs/9uSwFisuI/vqKqzZrchIm2x4mcGAt8ATwNvqerPbhVmk0QAdDOxwZBmjBGXRRCRfFiGXH3gRaxGv5RbRdmIyFiHzVxYfmZbVPVpN0lKhO0I3RlohuVIPh+Y7EmLoxv+G4hIJ095AcvM2MHGG2M9z4tV9W83S0JEXsN6QSwDHHA4lB9rQtqLpiC4JQAACIxJREFUbhFmyLQYIy4LICKbgJxYQUJXYYXH8IiFnl0hIgWBb1W1lbu1AIiIF7BDVR9OMbHBkIHYfpivAw9hDQXuAcar6hm3CiP+ZSy5uJRujxMXh4h8BsxQ1TXu1uKI3fYVBj4A3nQ4dNHDAhEbMgnGJy5r0EJVPW12WHJcwQqs6xGoaqyIbBeRe1T1qLv1GP6biEhdrLWPpwHTsXqQAoENItJWVVe7UR5Ya6TG8Q4w1F1CUsEW4C0RuQ9rya0ZqrophTwZjh366QLwPMQb7bmAfCKSz7Q/hrRieuKyAPbb3VBuBqpdDrzrKbHiRGQON9/gvbB6GWaq6ptJ57qziMgSrPADG4DLcfs9pbfQkPURkXXAa85raIpIAPClqtZ0izAXiMjWuCC1noyI+ABtsFZguUdVPeLlUURaAqOBEsAZ4F7gb1Wt4FZhhkyH6YnLGnwN7MJyiAZoh7WeaqIZbncSESkHFAdGOeyOBryBE24R5YSDRuclyhrgIRoN/xkKuFoEXVW3iUh+dwhKhszy9l8OeAAohTU07SmMAGoBi1S1iogEYffOGQxpwcvdAgy3hbKqOlRVD9qfd7AcZ93Np1i+HssdPquxhlM/dauym3xKYo3LgblAa7cqM/zXEBEp7GKnD6atThMi8qGI7APexYqbWVVVW7pZliM3VDUM8BIRL1VdCgS4WZMhE2J64rIGUSJST1VXQbxvjSdEfi+lqjucd6rqJhEp5QY9rsgMGg3/DcYAC0QkBMunC6Aq8KF9zK2IyEVu9sDlEZHIuENYs+ELuEeZSw4BtVX1nLuFJEGEHVFgBfC9iJzBGqUwGNKE8YnLAtg+M98ABbEa1HCggyvj5A7r2q+q5dJ67E6SGTQa/jvYMdji1iUFqxfpY1Wd4z5VmQcReUBV/xGRQFfHVXWLq/13Gnut1KtY7XVbrLb7e7t3zmBINcaIy0KISNyb8BXgWVX93s16fgSWqOpXTvs7Ac1U9Vn3KEugxeM1GgyG1CEik1S1i4gsdXFYVbXRHReVDHabHT8iZsKMGNKKMeIyMXYD0B1rsfbfgUX2dgiwXVXdurSViBTHmt5/Hdhs764G5ACeVNVT7tIWR2bQaPhvISKlsVYNKUXCP/BmpnQqEZFcqno1pX3uQkRexfLXi8JaDixuSNoTfJkNmQhjxGViROR34DywFisyeWEs46Onqm5zo7QE2DOv4gLp7lbVJe7U44rMoNHw30BEtgNTgJ04rPdpT7gxpIIklrVKtM9d2JMuPNlnz5BJMBMbMjdlVLUigIhMBs5hxUK66F5ZCbFnXrka3vAYMoNGw3+Gq6r6ubtFZEZExA9rZCK3iFTB6uECa33XPG4TlpgDWG4vBkO6MEZc5uZG3BdVjRGRQ55mwBkMhjTzmYgMBRYA1+J2eopTvofTHHgJuBv4hJtGXCQwyE2aXDEQWCMi60l4jz1m6TJD5sAMp2ZiRCSGm6sLCJAb6+3OE6f8GwyGVCAiH2AF7D7AzeFUj3PK92REpI2qznK3jqQQkQ1Y61w7D5l/4zZRhkyJ6YnLxKiqt7s1GAyG286TWK4S190tJBNTVUQWq2oEgB1Eua+qvuVeWfFEq2ofd4swZH5MFHCDwWDwLLYDhdwtIpPTIs6AA1DV80Cw++QkYqmIdBERfxHxifu4W5Qh82F64gwGg8GzKA78IyIbSegvZUKMpB5vEcmpqtcARCQ3kNPNmhx5wf5/oMM+xTOWSzRkIowRZzAYDJ7FUHcLyAJ8BywWkalYxtHLwHT3SrqJqpZ2twZD1sBMbDAYDAZDlkNEHgWaYE30WqCq890sCRFppKpLROQpV8dV9Zc7rcmQuTE9cQaDweBBOC00nwPIDlw2s83Thqr+Bfxlr1P6pIj8qaqPuVlWA2AJ0NLFMQWMEWdIE6YnzmAwGDwYEWkN1FBVT4pz5tGISA6siQwvAI8Cs4BfVHWOW4XZiEhpVT2U0j6DISWMEWcwGAwejoisU9Va7tbh6YhIU+B5rKC/S4EZwFhVLeVOXc4ksSzYZlWt6i5NhsyJGU41GAwGD8LJX8oLqMbN4VVD8swHVgL14nq1ROQz90q6iYg8AFQACjrd5wJALveoMmRmjBFnMBgMnoWjv1Q0cBh4wj1SMh1VgeeARSJyEPgJ8KSg6PcDj2PFAXS8zxeBV9whyJC5McOpBoPBYMhyiEhdrKHVNsA24FdVneRWUTYiUltV17pbhyHzY4w4g8Fg8ABEZEgyh1VVh98xMVkIEfHCCjXyvKp2dLceABH5CBgBRAF/AZWBXqr6nVuFGTIdZtktg8Fg8Awuu/gAdAIGuEtUZkRE6tqhReDmDNVh7lOUiGaqGok1tHocuA/o515JhsyIMeIMBoPBA1DVT+I+wCQgN9ARy6/LLMeUNiYAV0SkMtAfOIIHrdiAFfsPrDAoP6pquDvFGDIvxogzGAwGD8FeCH0EsANr4lmgqg5Q1TNulpbZiFbLV+gJ4DNV/QzI72ZNjswRkX+wZh4vFpGiwFU3azJkQoxPnMFgMHgAIvIx8BRWL9x4Vb3kZkmZFhFZjuVr1hF4BDgLbFPVim4V5oCIFAYiVTXGHvrNr6qn3K3LkLkwPXEGg8HgGfQFSgBvASdFJNL+XBSRSDdry2w8C1wDOtmG0V3Ax+6VBCLS32GziarGAKjqZeAN96gyZGZMT5zBYDAYDHcAx5UanFdtcLWKg8GQEibYr8FgMBiyBCJyEderWwhWmJYCd1iSKx2uvrvaNhhSxBhxBoPBYMgSqKonTV5whSbx3dW2wZAiZjjVYDAYDIY7gIjEYMX/E6wQMlfiDgG5VDV7UnkNBlcYI85gMBgMBoMhE2JmpxoMBoPBYDBkQowRZzAYDAaDwZAJMUacwWAwGAwGQybEGHEGg8FgMBgMmRBjxBkMBoPBYDBkQv4foJKPb6HxYbYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check correlation between features and target\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.title(' Correlation Heatmap', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ae0908",
   "metadata": {},
   "source": [
    "Handling Outlier for Numerical Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ae8d39ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cQBXgiJgSEX9RXdP1vohYFxGnV/sWRMQ/VRf/eaD9T0ylujHAmgx+j9a1ev8DeDMi5tP6J6S9wIHAHwCHw9A1RtYAp5dSFgB/BVybMLO0TT3ZA0hjsJjWpTehdeGhxcAXgLtKKR8Br0TEw9X+/YADgA2tyxEwldalEaXaMcCqtYiYReuC8wdERKEV1ELrqmqjHgI8W0o5fIJGlLabSxCqu9OBO0opv1ZK6S2l7EPrt1e8AZxWrQXvTeuCRND6zQZ7RsTQkkRE/FbG4NK2GGDV3WI+ebb7PVoX/N5E66pbf0nrN6q8XUr5Ba1oXx8RT9K60t4REzatNA5eDU2TVkTMLKVsqZYpfkTrN2+8kj2XNFauAWsyu6+6aP4XgT83vppsPAOWpCSuAUtSEgMsSUkMsCQlMcCSlMQAS1KS/wdlWbc8v6G/owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of Qurtile 1: 32.0\n",
      "Value of Qurtile 3: 44.0\n",
      "Upper Whishker: 62.0\n",
      "Lower Whishker: 14.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(18, 92)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.boxplot(df[\"Age\"])  \n",
    "plt.show()\n",
    "\n",
    "q1 = np.quantile(df[\"Age\"],0.25)\n",
    "q3 = np.quantile(df[\"Age\"],0.75)\n",
    "\n",
    "print(\"Value of Qurtile 1:\",q1)\n",
    "print(\"Value of Qurtile 3:\",q3)\n",
    "\n",
    "iqr = q3 - q1\n",
    "uw = q3+(1.5*iqr)\n",
    "lw = q1-(1.5*iqr)\n",
    "\n",
    "print(\"Upper Whishker:\",uw)\n",
    "print(\"Lower Whishker:\",lw)\n",
    "\n",
    "df.Age.min(), df.Age.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd761568",
   "metadata": {},
   "source": [
    "Explanation:- Handling Outliers - Box plot is used for handling outlier. Outlier is a data point that differs significantly from other observations. To find outlier we used IQR Quartile Rang break it into 25%-50%-75%. \n",
    "where, 50% is median ,25% is q1(quartile 1) 75% is q3(quartile 3/)IQR = q3-q1 Here we are calculating IQR for age column which is a numerical column.\n",
    "\n",
    "Upper Whishker/upper limit = q3+1.5*IQR and Lower Whishker/lower limit = q1-1.5*IQR after 60 there is outlier upto 92 For age \n",
    "\n",
    "Summary: Value of Qurtile 1: 32.0 \n",
    "Value of Qurtile 3: 44.0 \n",
    "Upperr Whishker: 62.0\n",
    "Lower Whishker: 14.0 \n",
    "minimum - 18\n",
    "maximum - 92"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a268eb49",
   "metadata": {},
   "source": [
    "Handling Outlier for Categorical Column and remove it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a04a9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.boxplot(data=df, x=\"Balance\",y=\"Geography\")\n",
    "plt.title(\"Box plot for Geography with outliers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e4e53d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "for make in df[\"Geography\"].unique():\n",
    "    price = df[df[\"Geography\"] == make][\"Balance\"]\n",
    "    q1 = np.quantile(price,0.25)\n",
    "    q3 = np.quantile(price,0.75)\n",
    "    iqr = q3-q1\n",
    "    uw = q3+(1.5*iqr)\n",
    "    \n",
    "    index = price [price > uw].index\n",
    "    if len(index) != 0:\n",
    "        df.drop(index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9781366c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.boxplot(data=df, x=\"Balance\",y=\"Geography\")\n",
    "plt.title(\"Box plot after removing outliers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd31c7d9",
   "metadata": {},
   "source": [
    "Explanation:- The plot is regarding Geography which has 3 states France, Spain and Germany. France and Spain has no ouliers but for Germany there is outlier for both left as well as right side.So outliers are removed in Germany with respect to spain and France. France and spain has balance from 0 to 1250000 thats why we cannot remove outliers for left side in Germany.\n",
    "hence state Germany has removed all outliers from 200000.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f258b42",
   "metadata": {},
   "source": [
    "spliting into categoral and numerical print numerical columns (int, float) \n",
    "\n",
    "numerical columns - RowNumber, CustomerId, CreditScore, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, \n",
    "\n",
    "EstimatedSalary, Exited print categorical columns (string,object)\n",
    "\n",
    "categorical columns - Surname, Geography, Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea3d6290",
   "metadata": {},
   "outputs": [],
   "source": [
    "# spliting into categoral and numerical\n",
    "\n",
    "df_num = df.select_dtypes([\"int64\",\"float64\"])\n",
    "df_cat = df.select_dtypes([\"object\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bee7c845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>850</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId  CreditScore  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          1    15634602          619   42       2       0.00              1   \n",
       "1          2    15647311          608   41       1   83807.86              1   \n",
       "2          3    15619304          502   42       8  159660.80              3   \n",
       "3          4    15701354          699   39       1       0.00              2   \n",
       "4          5    15737888          850   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5419287d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hargrave</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hill</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Onio</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Boni</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Surname Geography  Gender\n",
       "0  Hargrave    France  Female\n",
       "1      Hill     Spain  Female\n",
       "2      Onio    France  Female\n",
       "3      Boni    France  Female\n",
       "4  Mitchell     Spain  Female"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2d2b7bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Sm8bTnfSQWb4JpTQySdbR+zT1GVV1d5LD6N0q/aqqujLJq4DLWPo26KcA6+jdI+rz3c+7LMnrgWc4Y9Fy4IxFGo1nAh/a9x9/Vd1D7wub3tdtv5rerWuWsqmq7qqqB+nd2maufanSI2OwSKMRlr7F+b7tP6D7t9ndbHBV3z57+pb34lkHLUMGizQaNwC/muRw6H3fOfAFenf2BXgZ8LlueSewvls+i963SC7lXnpf2yyNnb/tSCNQVbcneRtwc5K99O5gfAFwRZI3AN8Gfr3b/d3Ax5JsohdI9w3wEpcD1yXZVVXPaN+BNDgvN5YkNeWpMElSUwaLJKkpg0WS1JTBIklqymCRJDVlsEiSmjJYJElN/S/9230vAzGWRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical data plot \n",
    "\n",
    "for col in df_cat:\n",
    "    plt.figure()\n",
    "    sns.countplot(data=df_cat,y=col)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a24511",
   "metadata": {},
   "source": [
    "Handling Categorical Data Any non-numeric ddata is categorical(string, object). Machine learning algorithms can read only numeric data, hence categorical data must be converted into numeric data. \n",
    "The common techniques are- \n",
    "1. one-hot encoding:- One-hot encoding creates a column for each category in a categorical variable panda's get_dummies() function is required.\n",
    "2. Label encoding:- Label encoding is a simple technique of assigning a unique integer value to each category in a categorical variable For Label Encoding in python, LabelEncoder() object is required from sklearn.preprocessing module. \n",
    "The code shows that, gender is a categorical variable which is encoded with LabelEncoder(), which assigns 1 to male and 0 to female."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cfbce8a",
   "metadata": {},
   "source": [
    "Handling Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3a7eafa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Tenure</th>\n",
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       "      <th>EstimatedSalary</th>\n",
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       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
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       "      <td>1</td>\n",
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       "      <td>101699.77</td>\n",
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       "      <td>1</td>\n",
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       "      <td>92888.52</td>\n",
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       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>Walker</td>\n",
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       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>38190.78</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9986 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore  Age  Tenure    Balance  \\\n",
       "0             1    15634602   Hargrave          619   42       2       0.00   \n",
       "1             2    15647311       Hill          608   41       1   83807.86   \n",
       "2             3    15619304       Onio          502   42       8  159660.80   \n",
       "3             4    15701354       Boni          699   39       1       0.00   \n",
       "4             5    15737888   Mitchell          850   43       2  125510.82   \n",
       "...         ...         ...        ...          ...  ...     ...        ...   \n",
       "9995       9996    15606229   Obijiaku          771   39       5       0.00   \n",
       "9996       9997    15569892  Johnstone          516   35      10   57369.61   \n",
       "9997       9998    15584532        Liu          709   36       7       0.00   \n",
       "9998       9999    15682355  Sabbatini          772   42       3   75075.31   \n",
       "9999      10000    15628319     Walker          792   28       4  130142.79   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0                 1          1               1        101348.88       1   \n",
       "1                 1          0               1        112542.58       0   \n",
       "2                 3          1               0        113931.57       1   \n",
       "3                 2          0               0         93826.63       0   \n",
       "4                 1          1               1         79084.10       0   \n",
       "...             ...        ...             ...              ...     ...   \n",
       "9995              2          1               0         96270.64       0   \n",
       "9996              1          1               1        101699.77       0   \n",
       "9997              1          0               1         42085.58       1   \n",
       "9998              2          1               0         92888.52       1   \n",
       "9999              1          1               0         38190.78       0   \n",
       "\n",
       "      Geography_France  Geography_Germany  Geography_Spain  Gender_Female  \\\n",
       "0                    1                  0                0              1   \n",
       "1                    0                  0                1              1   \n",
       "2                    1                  0                0              1   \n",
       "3                    1                  0                0              1   \n",
       "4                    0                  0                1              1   \n",
       "...                ...                ...              ...            ...   \n",
       "9995                 1                  0                0              0   \n",
       "9996                 1                  0                0              0   \n",
       "9997                 1                  0                0              1   \n",
       "9998                 0                  1                0              0   \n",
       "9999                 1                  0                0              1   \n",
       "\n",
       "      Gender_Male  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "...           ...  \n",
       "9995            1  \n",
       "9996            1  \n",
       "9997            0  \n",
       "9998            1  \n",
       "9999            0  \n",
       "\n",
       "[9986 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Label Encoding\n",
    "\n",
    "pd.get_dummies(data=df, columns=['Geography', 'Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fc636410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Female  Male\n",
       "0       1     0\n",
       "1       1     0\n",
       "2       1     0\n",
       "3       1     0\n",
       "4       1     0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for gender 1 hot encoding\n",
    "\n",
    "data=df_cat[\"Gender\"].head()\n",
    "pd.get_dummies(df_cat[\"Gender\"]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e33daac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Label Encoding\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "le.fit_transform(df_cat[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d1dec73e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# label encoding on all columns\n",
    "\n",
    "for col in df:\n",
    "    le = LabelEncoder()\n",
    "    df[col]= le.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f5b9b368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hargrave</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hill</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Onio</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Boni</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Surname Geography  Gender\n",
       "0  Hargrave    France  Female\n",
       "1      Hill     Spain  Female\n",
       "2      Onio    France  Female\n",
       "3      Boni    France  Female\n",
       "4  Mitchell     Spain  Female"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical data\n",
    "\n",
    "df_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "29028c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>850</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId  CreditScore  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          1    15634602          619   42       2       0.00              1   \n",
       "1          2    15647311          608   41       1   83807.86              1   \n",
       "2          3    15619304          502   42       8  159660.80              3   \n",
       "3          4    15701354          699   39       1       0.00              2   \n",
       "4          5    15737888          850   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical data\n",
    "\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e37759",
   "metadata": {},
   "source": [
    "Handling Skweness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2196886f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  0.00025017329801033783\n"
     ]
    },
    {
     "data": {
      "image/png": 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q+kpCQevgZcxCWFZbTkUkaBPWTOBlr6FGEal1n5cDbwdenbTNEnErvkXkSjceGxnKlUim6RhIsqbRSgPGLJSACBc0xjjQmbBupC4vew0tBb4pIkGcE/z3VPVHInIXgKreC7wP+D0RSQMjwK1qv5mzjnQPAbCmMeZzJMYUl7VNMXaf7KdzMElzdZnf4fjOs0SgqruAy6ZYfu+E53cDd3sVQ6E73JUgEgqwvLbc71CMKSpr4k4p+2jPkCUC7M7ivHa4a4jWhgqba9WYBVZfGaEyGuK4jTsEWCLIW4lkmq5EktVxqxYyZqGJCK0NFRztGfI7lLxgiSBPnTjjXKnY3APGeGNVQyW9w2P0j9j9BJYI8tTxM8MEBFrqrH3AGC+0NjgXWcesVGCJIF8dPzPM0ppywnb/gDGecP6/hGNnrJ3AzjJ5KJNV2nqHrVrIGA8FA0JLXcXZathSZokgD3UMjDKWUUsExnispa6cU/2jpLNZv0PxlSWCPHTcGoqNWRQtdRVkskpHf9LvUHxliSAPtfWOUBkNUVsR9jsUY4ra+M2abX2lXT1kiSAPtfeNsLy2zOYfMMZjdRVhKiJB2npLe8YySwR5ZiyTpXNwlGU2rIQxnhMRlteWc9ISgcknp/tHySo2vpAxi6SlrpzOwVFS6dJtMLZEkGfGJ9W2RGDM4mipqyCrcKq/dEsFlgjyTHvfCBWRIDXl1lBszGJYWuOMPnqqf9TnSPxjiSDPnOwbYXltuTUUG7NIasrDlIUDlghMfkhnsnQMWEOxMYtJRFhaU85pqxpaeCJSJiIvishOEdkjIp+bYhsRkS+KyEER2SUil3sVTyHoHEyS1deLqsaYxbG0pozTA6NkS3SCRC9LBEngraq6CbgUuFFErp60zTuBde7jTuAeD+PJe6cHnKLpEksExiyqpTVljGWUM4mU36H4wrNEoI6E+zLsPian25uB+91tnwdqRWSpVzHlu9P9o4QCQkNl1O9QjCkpS2qc6thTA6XZTuBpG4GIBEVkB9AJPKGqL0zaZDlwYsLrNnfZ5P3cKSJbRWRrV1eXZ/H67fTAKM3VZTY1pTGLrKkqSkBKtwupp4lAVTOqeinQAlwpIpdM2mSqM945lXSqep+qblbVzY2NjR5Emh9O94+yxCbSNmbRhYMB4rEop0u059Ci9BpS1T7gKeDGSavagBUTXrcA7YsRU75JJNMkkmlrHzDGJ0tqyuiwqqGFJSKNIlLrPi8H3g68OmmzR4APub2Hrgb6VfWUVzHls/ErEUsExvijqaqM3uExkumM36EsupCH+14KfFNEgjgJ53uq+iMRuQtAVe8FHgVuAg4Cw8AdHsaT18Z7DDVb1ZAxvmiudjppdA0maakrrblAPEsEqroLuGyK5fdOeK7AR72KoZB09I8Si4aIRb3MzcaY6TRXORdhHQOjJZcI7M7iPNE5OEpTlXUbNcYv9bEIoYDQMVB6s5VZIsgDqkpXIkmjJQJjfBMQobEqSudg6TUYWyLIA4PJNKNjWSsRGOOzpqqolQiMPzrdP7zGKmsoNsZPzdVl9I+MMTpWWj2HLBHkgS63KGolAmP81eRejHUOllapwBJBHugcTBINBagqsx5DxvipaUIX0lJiiSAPdA0maaqK2mQ0xvisriJCUITuhCUCs8i6BpPWPmBMHggGhPrKiJUIzOIaSWUYTKatfcCYPBGvitJlJQKzmKyh2Jj80hiLcCaRIpMtndnKLBH4bLx3gt1MZkx+iMeiZFTpGy6d2cosEfisczBJKCDUVUb8DsUYw+sXZaVUPWSJwGddg0nisSgB6zFkTF6Ix5xE0F1CDcaWCHzWOThq1ULG5JHKaIjycJCuEprIPqdEICIPisivi4gljgU0lsnSNzxmDcXG5JnGqmhJ3UuQ64n9HuC3gAMi8jcismG2N4jIChF5UkT2icgeEfnEFNtsEZF+EdnhPj4zx/gLWtdgEsUaio3JN/FYtKSqhnIa00BVfwr8VERqgNuAJ0TkBPBV4NuqOjbF29LAH6vqdhGpAraJyBOqunfSds+o6rvO4zsUrPGbVprsZjJj8kpjVZTtx3sZHctQFg76HY7ncq7qEZEG4Hbgd4GXgX8ALgeemGp7VT2lqtvd54PAPmD5ecZbVDoHkwgQj1mPIWPySaP7P1kq1UO5thE8BDwDVAD/j6q+W1W/q6ofB2I5vL8VZ9rKF6ZYfY2I7BSRx0Tk4mnef6eIbBWRrV1dXbmEXBC6EknqKiOEgtb0Ykw+Ge85VCpDTeQ63OU/qeqjExeISFRVk6q6eaY3ikgMeBD4pKoOTFq9HVilqgkRuQl4GFg3eR+qeh9wH8DmzZuL5na/nkSSxpi1DxiTb+pjEQJiJYLJ/nKKZc/N9iYRCeMkgX9R1Ycmr1fVAVVNuM8fBcIiEs8xpoKmqvQkUjRYtZAxeScUCFBXESmZLqQzlghEZAlOvX65iFwGjN/1VI1TTTTTewX4GrBPVf/PDPvvUFUVkStxElPP3L5CYRocTZPKZM8WQY0x+aWUeg7NVjX0azgNxC3AxJP5IPDpWd57HfBBYLeI7HCXfRpYCaCq9wLvA35PRNLACHCrqhZN1c9MxoucViIwJj81VkU53J0gq1r0d/7PmAhU9ZvAN0XkN1X1wbnsWFWf5fUSxHTb3A3cPZf9Foset8gZr7QSgTH5KB6LMpZR+kfGqKso7gu22aqGPqCq3wZaReSPJq+frsrHzK474Qw2V1MR9jsUY8wU4lXOyb9rMFn0iWC2xuJK92cMqJriYeapeyhFfWWk6IucxhSq8R59pdBzaLaqoa+4Pz+3OOGUjp5E0hqKjcljsWiISChwthq3mOV6Q9nfiUi1iIRF5Gci0i0iH/A6uGKVVaVnyLqOGpPPRIR4ZYSeoeIvEeR6H8GvujeDvQtoAy4E/ptnURW5/uExMlm1EoExea4hFqXbSgRnjbdo3gQ8oKpnPIqnJFjXUWMKQzwWoXcoRTqb9TsUT+U6xMQPReRVnL7+vy8ijcCod2EVt+4ht+uolQiMyWsNsSgK9A5NNcBy8cipRKCqnwKuATa7Q04PATd7GVgx604kiYQCVEVzzcPGGD/ES6Tn0FzORBfh3E8w8T33L3A8JaEnkaShMoJY11Fj8lq80qm+7bFEACLyLeACYAeQcRcrlgjmpSeRYlltud9hGGNmUeHOXzxenVusci0RbAY2lso4QF7KZJXe4RRvaKnxOxRjTA7isUjRVw3l2mvoFWCJl4GUit6hFFm1hmJjCkVDLFr0N5XlWiKIA3tF5EXgbGpU1Xd7ElURG7+yGK97NMbkt3gswo4TfUU9f3GuieCzXgZRSqzrqDGFpcH9Xz3aM8SGJdU+R+ONXLuP/hw4CoTd5y/hTDNp5qgnkaQ8HKTCuo4aUxDGh4o/2j3kcyTeyXWsof8K/AD4irtoOc78wjO9Z4WIPCki+0Rkj4h8YoptRES+KCIHRWSXiFw+x/gLTnciSdzuKDamYIyPAHCke9jnSLyTa2PxR3FmHBsAUNUDQNMs70kDf6yqFwFXAx8VkY2TtnknzmT164A7gXtyjKdgOfMUW7WQMYWiLBwkFg1xpDvhdyieyTURJFX1bLO5e1PZjF1JVfWUqm53nw8C+3BKEhPdDNyvjueBWhFZmnP0BWZ0LEPfyJiVCIwpMA2xCEetRMDPReTTOJPYvwP4PvDDXD9ERFqBy4AXJq1aDpyY8LqNc5MFInKniGwVka1dXV25fmzeOdbj/CFZicCYwhKPRTnSU+JtBMCngC5gN/AR4FHgz3J5o4jEgAeBT7pDWf/S6ineck5JQ1XvU9XNqrq5sbExx5DzzxG3scnmKTamsMQrI3QNJhkcLc7B53LquqKqWRF5GHhYVXO+JBeRME4S+BdVfWiKTdqAFRNetwDtue6/0IwnAht+2pjCMl6KP9YzzCXLi29UgBlLBG6vns+KSDfwKrBfRLpE5DOz7VicEdW+BuybYZL7R4APuZ9zNdCvqqfm+B0KxtHuIWLRUNHelGJMsRq/7+dwkXYhna1E8Emc3kJvVNUjACKyBrhHRP5QVf9+hvdeB3wQ2C0iO9xlnwZWAqjqvThVTDcBB4Fh4I75fY3CcKR7yBqKjSlA9e5IAMV6L8FsieBDwDtUtXt8gaoeducrfhyYNhGo6rNM3QYwcRvF6ZpaEo70DLGyvsLvMIwxcxQJBVhWU1a0iWC2xuLwxCQwzm0nCE+xvZlGIpmmazBpQ0sYU6Ba45VFWzU0WyKYaci94h6Ob4GNX0k02GBzxhSk1nglR4u0C+lsVUObRGRyl09wqnzKPIinaI1fScSrrERgTCFaE6+kb3iM3qEUdUV2QTdjIlBV696yQI52DyFiJQJjClVrQyXgtPUVWyLI9YYyc56OdA+xrKaccNAOuTGFqDXuJIJibDC2s9IiOdw9xJrGSr/DMMbM08r6CgJiicDMk6pypCvB6rglAmMKVSQUoKWuoih7DlkiWARnhlIMjKYtERhT4FYXac8hSwSLYHyMIUsExhS21fFKjnYP49wLWzwsESyC8aLkmnjM50iMMeejtaHCuTk0kfQ7lAVliWARHOkeIhwUlteV+x2KMeY8rG50LuaKbZIaSwSL4EjXEKsaKgkGZhx6yRiT51Y3FGcXUksEi+BI95C1DxhTBJbVlhEOStH1HLJE4LFsVjnSM8QaSwTGFLxQMMDK+gorEZi5ae8fIZXOWonAmCKxOl55tidgsfAsEYjI10WkU0RemWb9FhHpF5Ed7mPWWc8KkXUdNaa4tDY49xJks8XThdTLEsE3gBtn2eYZVb3UffyFh7H45mwisOEljCkKqxsrSaaznB4Y9TuUBeNZIlDVp4EzXu2/UBzucuYpbrQJaYwpCuM9h4qpesjvNoJrRGSniDwmIhdPt5GI3CkiW0Vka1dX12LGd96OdA/RGq9AxLqOGlMMxkv3lggWxnZglapuAr4EPDzdhqp6n6puVtXNjY2NixXfgnC6jtodxcYUi+aqMsrCgaLqOeRbIlDVAVVNuM8fBcIiEvcrHi8k0xnaeoetodiYIhIICK0NxdVzyLdEICJLxK0vEZEr3Vh6/IrHCyfODJNV7B4CY4rM6nglR4poFNLZ5iyeNxF5ANgCxEWkDfhzIAygqvcC7wN+T0TSwAhwqxbZkH6HuqzrqDHFqDVeyU/3dZDOZAkVwayDniUCVb1tlvV3A3d79fn54GBnAoC1TdZGYEwxWd1QyVhGOdk3wqqGwr/QK/xUlscOdAyyvLacyqhn+dYY44Ni6zlkicBDBzoTrGu20oAxxaa1yEYhtUTgkUxWOdiZYJ1VCxlTdOKxCLFoyEoEZmYne0dIprOsa6ryOxRjzAITEbfnUHFMUGOJwCMHOgcBuMBKBMYUpdZ4pVUNmZkdsB5DxhS11fFK2nqHGR3L+B3KebNE4JEDHQmaq6PUlIf9DsUY44G1TTGyCkeL4MYySwQeOdg5aO0DxhSxC90egQc6Ej5Hcv4sEXhAVTnQmbBqIWOK2Op4JQF5vRq4kFki8EB7/yjDqYzdQ2BMEYuGgrQ2VHKgY9DvUM6bJQIPjP9hWNWQMcVtbVPMSgRmauNjDNnNZMYUt3XNMY52D5FKZ/0O5bxYIvDAgY4E8ViEusqI36EYYzy0rqmKdFY5VuA9hywReOBA56A1FBtTAsb/zwu9esgSwQIb7zFk7QPGFL8LGmOIwGsF3mDsWSIQka+LSKeIvDLNehGRL4rIQRHZJSKXexXLYuocTDI4mrYeQ8aUgPKI03PIEsH0vgHcOMP6dwLr3MedwD0exrJoxm8usaohY0rDhiVVvHrKEsGUVPVp4MwMm9wM3K+O54FaEVnqVTyL5dXTAwCsb7aqIWNKwfolVRzpGWIkVbhjDvnZRrAcODHhdZu77BwicqeIbBWRrV1dXYsS3HztPTVAc3WUhljU71CMMYtgw5JqVAu7ncDPRCBTLJty8npVvU9VN6vq5sbGRo/DOj972we4aGm132EYYxbJRUud0v94bUAh8jMRtAErJrxuAdp9imVBpNJZDnUlLBEYU0JW1FVQEQmyr4DbCfxMBI8AH3J7D10N9KvqKR/jOW8HOgcZyygbLREYUzICAWH9kqqCLhGEvNqxiDwAbAHiItIG/DkQBlDVe4FHgZuAg8AwcIdXsSyW8SsCKxEYU1o2LKnmsVdOoaqITFXrnd88SwSqetss6xX4qFef74e97QOUhQOsjlf6HYoxZhFdtLSKB148zqn+UZbVlvsdzpzZncULaN+pAdYvqSYYKLwrAmPM/F28rAaA3Sf7fY5kfiwRLBBVZU97v7UPGFOCNi6tJiDwiiWC0na0Z5iB0TSbWmr8DsUYs8jKI0HWNVVZiaDU7WrrA+BXWmp9jcMY449Lltfwysl+nObPwmKJYIHsPNFPWThwdkJrY0xpecPyaroTKU4PjPodypxZIlggu9r6uHhZDaGgHVJjStEb3Grh3W2FVz1kZ60FkM5k2dM+wK9Y+4AxJWvj0pqCbTC2RLAADnYlGBnLWCIwpoSVR4Jc2FzFyyf6/A5lziwRLICd7i/eGoqNKW2Xr6pjx/E+stnCajC2RLAAth7tpa4izBq7o9iYknbFyjoGk+mCm8PYEsEC2Hqsl82t9QU5xogxZuFcsaoOgG3Hen2OZG4sEZynrsEkR7qHeGNrnd+hGGN8tqqhgobKiCWCUrPtmDMb5+bWep8jMcb4TUS4fFUd249bIigpLx3tJRoKcMky6zFkjIHLV9ZxpHuInkTS71ByZongPG09eoZLV9QSCdmhNMbAVWuc2oHnD5/xOZLceXr2EpEbRWS/iBwUkU9NsX6LiPSLyA738Rkv41lo/SNj7D7Zz1WrrVrIGOP4leU1xKIhfnGo2+9QcublDGVB4B+Bd+DMT/ySiDyiqnsnbfqMqr7Lqzi89NyhHrIK11/Y6Hcoxpg8EQoGuGp1Pf95sHASgZclgiuBg6p6WFVTwHeAmz38vEX3zIEuYtEQl66o9TsUY0weuXZtnKM9w5zsG/E7lJx4mQiWAycmvG5zl012jYjsFJHHRORiD+NZcM8c6ObqNQ2EbaA5Y8wE161tAOAXBVIq8PIMNtXdVZPvu94OrFLVTcCXgIen3JHInSKyVUS2dnV1LWyU83SsZ4jjZ4a5fl3c71CMMXnmwqYqGiojPHvAEkEbsGLC6xagfeIGqjqgqgn3+aNAWETOObOq6n2qullVNzc25kd9/NOvOQnpTZYIjDGTBALClvVNPLW/k7FM1u9wZuVlIngJWCciq0UkAtwKPDJxAxFZIu64DCJypRtPj4cxLZjH93awOl5p4wsZY6b0jo3NDIymeelI/ncj9SwRqGoa+BjwE2Af8D1V3SMid4nIXe5m7wNeEZGdwBeBW7UA5nnrG07x3KEebrxkiY0vZIyZ0vXr4kRCAZ7Y1+F3KLPyrPsonK3ueXTSsnsnPL8buNvLGLzw032dpLPKjRcv8TsUY0yeqoyGeNPaOE/s7eAz79qY1xeN1t1lHn78ymmW1pTZRDTGmBm9Y2Mzbb0j7Gkf8DuUGVkimKP+kTGePtDFr11s1ULGmJndePESwkHh314+6XcoM7JEMEeP7DhJKp3lNy9v8TsUY0yeq6uM8LYNzfz7jpN53XvIEsEcfXfrCS5aWs0ly6v9DsUYUwDee/lyuhMpnjmQH/dATcUSwRzsae/nlZMDvH9zi1ULGWNysmV9E3UVYb73UpvfoUzLEsEcfPv5Y0RCAd5z2VQjZRhjzLkioQDvf+NKHt97muM9w36HMyVLBDk63T/Kg9tOcssVLdRWRPwOxxhTQG6/tpVgQPj6L474HcqULBHk6J+eOUxGlY/ccIHfoRhjCsySmjLevWk5333pBL1DKb/DOYclghx0Dozyry8e592blrGyocLvcIwxBegjb17DaDrD3U8e9DuUc1giyMH/fnQf6Yzyybev8zsUY0yBurC5ivdvXsH9zx3lSPeQ3+H8EksEs3jxyBke3tHOR968hlUNNsCcMWb+/uhXLyQSDPC5H+4hn4ZVs0Qwg/7hMf7oeztoqSvn97es9TscY0yBa6oq47/92nqe2t/F/c8d8zucsywRTCOTVf74+zvoGBjlS7ddRnkk6HdIxpgi8DvXtvKW9Y381aP72Hmiz+9wAEsEU8pmlf/x4C5+uq+T/3nTRVy2ss7vkIwxRUJE+Pwtm2iujvI7//wir3UM+h2SJYLJEsk0H//Oy/xgWxufeNs6br9utd8hGWOKTDwW5V8+fDWRYID3f+U5/tPnuY0tEUzw7IFu3n33szy2+xSfeucG6yVkjPHMyoYKvvuRa2iIRfng11/krx/bRyKZ9iUWTxOBiNwoIvtF5KCIfGqK9SIiX3TX7xKRy72MZyqJZJof7mzn1vue4wNfe4FUOsu3f/cq7nrzBTaekDHGU6vjlTz80et472XL+crPD3PD3z3JF36yn4OdiUWNw7MZykQkCPwj8A6ciexfEpFHVHXvhM3eCaxzH1cB97g/F9zg6BiHuoZo7xuhvW+Ett4RXjnZz862PsYySktdOX/26xfxgatXURa2hmFjzOKIRUN8/pZN3HbVSu556hD/+NRB7n7yIMtry9m4rJoNS6pY2xSjubqMNfFKmqrLFjwGL6eqvBI4qKqHAUTkO8DNwMREcDNwvztP8fMiUisiS1X11EIH8+T+Lv7ggZfPvq6IBLloaTX/73Wr2bK+iatW1xMIWAnAGOOPy1fW8dUPbeZU/wg/3dvB80fOsP/0ID/b10HWveXgIzes4U9vumjBP9vLRLAcODHhdRvnXu1Ptc1y4JcSgYjcCdzpvkyIyP6FCHAf8ND83hoH/G3dOT8Wv38KOXYo4fh/e4EDmY9P/y3xT8//+K+aboWXiWCqy+vJt9Llsg2qeh9w30IEtRBEZKuqbvY7jvmy+P1TyLGDxe83r+L3srG4DVgx4XUL0D6PbYwxxnjIy0TwErBORFaLSAS4FXhk0jaPAB9yew9dDfR70T5gjDFmep5VDalqWkQ+BvwECAJfV9U9InKXu/5e4FHgJuAgMAzc4VU8CyxvqqnmyeL3TyHHDha/3zyJX/JpBDxjjDGLz+4sNsaYEmeJwBhjSlzJJwIR+bqIdIrIK9Os3yIi/SKyw318ZsK6oyKy212+dcLyz4rIyQnvuSlP468VkR+IyKsisk9ErnGX14vIEyJywP3pyfCrHsWe98deRNZPWLZDRAZE5JPuukU59h7Gn/fH3133hyKyR0ReEZEHRKTMXZ73x3+W+Od3/FW1pB/ADcDlwCvTrN8C/GiadUeB+BTLPwv8SQHE/03gd93nEaDWff53wKfc558C/raAYi+IYz9hmyBwGli1mMfew/jz/vjj3LR6BCh3X38PuL1Qjv8s8c/r+Jd8iUBVnwbO+B3HfM03fhGpxvlD/Jq7n5Sq9rmrb8Y50eL+fM95BzoFj2JfNAv0t/M24JCqjk9XtSjHHjyLf9GcZ/whoFxEQkAFr9+/VCjHf7r456XkE0GOrhGRnSLymIhcPGG5Ao+LyDZxhsGY6GPijKj6dS+LlzmaKv41QBfwzyLysoj8k4iMT8rcrO79HO7PJh9iHjfX2CH/j/1EtwIPTHidT8ce5h4/5PnxV9WTwBeA4zjD2fSr6uPu9nl//GeJH+Zz/BejCJfvD6CV6Ytn1UDMfX4TcGDCumXuzyZgJ3CD+7oZp8gcAP4K5x6KvIof2Aykgavc1/8A/C/3ed+kffQWUOx5f+wnrI/gjBvTPGHZoh17j+LP++MP1AH/ATQCYeBh4AOFcvxniX9ex99KBLNQ1QFVTbjPHwXCIhJ3X7e7PzuBf8MZcRVV7VDVjKpmga+OL/fDDPG3AW2q+oK76Q9w6isBOkRkKYD7s3ORwwbmF3uBHPtx7wS2q2rHhGV5cexhfvEXyPF/O3BEVbtUdQxn7Mlr3bcVwvGfNv75Hn9LBLMQkSUizgw1InIlzjHrEZFKEalyl1cCvwq84r5eOmEXvzG+3A/Txa+qp4ETIrLe3fRtvD5E+CPA77jPfwf490UM+az5xF4Ix37CJrdxbrVKXhx7mF/8BXL8jwNXi0iFu/5tOIMRQ2Ec/2njn/fx97LYUwgPnD/kU8AYzpXmh4G7gLvc9R8D9uBU/TwPXOsuX+Mu2+mu/58T9vktYDewC+cPa2m+xe+uuxTY6sb5MFDnLm8AfgYccH/WF1DshXLsK3D+qWsm7XNRjr2H8RfK8f8c8CrOifJbQLTAjv908c/r+NsQE8YYU+KsasgYY0qcJQJjjClxlgiMMabEWSIwxpgSZ4nAGGPy2GyD003a9u8nDDj3moj05fIZlghMUXP7Yn9HRA6JyF4ReVRELpzjPt4jIhu9inGGz31KRM6ZqFxEbheRuxc7HuObbwA35rKhqv6hql6qqpcCX8K52WxWlghM0XJvtvk34ClVvUBVNwKfxrkNfy7eAyxqIhCR4GJ+nslfOsXgdCJygYj82B3n7BkR2TDFW6e6YXFKlghMMXsLMKbO/NgAqOoOICgiPxpfJiJ3i8jt7vO/cUsOu0TkCyJyLfBu4PNucfsCEblURJ53t/m38YG93Cv4vxeRp8WZI+GNIvKQOGPb/+WEz/uAiLzo7u8r4yd9EUmIyF+IyAvANRO/iIjc4Rb1fw5c59UBMwXjPuDjqnoF8CfAlyeuFJFVwGqcMYlm5dnk9cbkgUuAbbluLCL1OLflb1BVFZFaVe0TkUdwxoX/gbvdLpx/wp+LyF8Afw580t1NSlVvEJFP4AxPcAXO1dwhEfl7nAEK3w9cp6pjIvJl4LeB+4FKnAHIxieAGY9rKc6dpFcA/cCTwMvzPSimsIlIDGdsoe+P/40A0Umb3Qr8QFUzuezTEoExrxsARoF/EpH/D/jR5A1EpAZnEpyfu4u+CXx/wiaPuD93A3vUHdJYRA4DK4A34ZzQX3L/ict5fWCzDPDgFHFdhVO91eXu67vAnNo5TFEJ4IySeukM29wKfHQuOzSmWO3BOelOluaX//bLAFQ1jTNa44M47QI/nsdnJt2f2QnPx1+HAAG+Od6gp6rrVfWz7jajM1zB2VgwBnBGJQWOiMgt4LSFicim8fXuYIx1wHO57tMSgSlm/wFEReS/ji8QkTfijNe+UUSi7hX+29x1MZxB1B7Fqeq51H3bIFAFoKr9QK+IXO+u+yAwXjrIxc+A94lIk/uZ9W597kxeALaISIOIhIFb5vB5psCJyAM4J/X1ItImIh/GqU78sIiMD3p584S33AZ8R+cwkJxVDZmi5dbz/wbwf0XkUzjVPkdxTvLfwxmh8QCv17dXAf8uzkTgAvyhu/w7wFdF5A+A9+EMT3yviFQAh4E75hDTXhH5M5yZ7QI4I09+FJh2qkdVPSUin8U5GZwCtuMkM1MCVPW2aVZN2aV0QgkzZzb6qDHGlDirGjLGmBJnicAYY0qcJQJjjClxlgiMMabEWSIwxpgSZ4nAGGNKnCUCY4wpcf8/M247r/UmZykAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  0.0007899236436091528\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  -0.07176680338989944\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  1.011568667627666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  0.010997827730166623\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  -0.14458308124325658\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  0.7475108628507042\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  -0.9016112896027532\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  -0.06131459369408613\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  0.002189736750693993\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcnUlEQVR4nO3de3hV9Z3v8feHkEDCNSEBIoIooGKxokagWltbq1V6UTvTc+qVWi3tWM/0Np46bZ8Ze850xpnTy9QzjpWqFW21N221PdopRS1ewaCoICiKdxASbuEeSL7nj73QGBKyiVl7B9bn9Tz72WutvdZeX0LWJ2v/9m/9liICMzPLjj7FLsDMzArLwW9mljEOfjOzjHHwm5lljIPfzCxj+ha7gHxUV1fH2LFji12Gmdl+ZeHChY0RUdN++X4R/GPHjqW+vr7YZZiZ7VckvdLRcjf1mJlljIPfzCxjHPxmZhnj4DczyxgHv5lZxqQe/JJKJD0p6Q/JfJWkOZKWJ8+VaddgZmZvK8QZ/5eBpW3mrwTmRsQEYG4yb2ZmBZJq8Es6GPgYcEObxWcBs5Pp2cDZadZgZmbvlPYZ/78D/xNobbNsRESsAkieh3e0oaSZkuol1Tc0NKRcpplZdqR25a6kjwNrImKhpFP2dfuImAXMAqirqzvg7xZz2/xX91h23tQxRajEzA50aQ7ZcBLwSUnTgf7AYEk/A1ZLqo2IVZJqgTUp1mBmZu2k1tQTEX8fEQdHxFjgM8B9EXEBcDcwI1ltBnBXWjWYmdmeitGP/2rgNEnLgdOSeTMzK5CCjM4ZEQ8ADyTTa4FTC7FfMzPbk6/cNTPLGAe/mVnGOPjNzDLGwW9mljEOfjOzjHHwm5lljIPfzCxjHPxmZhnj4DczyxgHv5lZxjj4zcwyxsFvZpYxDn4zs4xx8JuZZYyD38wsYxz8ZmYZ4+A3M8uY1IJfUn9JCyQ9JWmJpO8ky6+S9IakRcljelo1mJnZntK89eIO4MMRsVlSKfCQpHuT134YEd9Lcd9mZtaJ1II/IgLYnMyWJo9Ia39mZpafVNv4JZVIWgSsAeZExPzkpcslPS3pJkmVnWw7U1K9pPqGhoY0yzQzy5RUgz8iWiJiMnAwMEXSJOA6YBwwGVgFfL+TbWdFRF1E1NXU1KRZpplZphSkV09EbAAeAM6IiNXJH4RW4CfAlELUYGZmOWn26qmRNDSZLgc+AiyTVNtmtXOAxWnVYGZme0qzV08tMFtSCbk/ML+KiD9IulXSZHJf9L4MfCHFGszMrJ00e/U8DRzbwfIL09qnmZl1zVfumplljIPfzCxjHPxmZhnj4DczyxgHv5lZxjj4zcwyxsFvZpYxDn4zs4xx8JuZZYyD38wsYxz8ZmYZ4+A3M8sYB7+ZWcY4+M3MMsbBb2aWMQ5+M7OMcfCbmWVMmvfc7S9pgaSnJC2R9J1keZWkOZKWJ8+VadVgZmZ7SvOMfwfw4Yg4BpgMnCFpGnAlMDciJgBzk3kzMyuQ1II/cjYns6XJI4CzgNnJ8tnA2WnVYGZme0q1jV9SiaRFwBpgTkTMB0ZExCqA5Hl4J9vOlFQvqb6hoSHNMs3MMiXV4I+IloiYDBwMTJE0aR+2nRURdRFRV1NTk1qNZmZZU5BePRGxAXgAOANYLakWIHleU4gazMwsJ81ePTWShibT5cBHgGXA3cCMZLUZwF1p1WBmZnvqm+J71wKzJZWQ+wPzq4j4g6RHgV9JugR4Ffh0ijWYmVk7qQV/RDwNHNvB8rXAqWnt18zM9s5X7pqZZYyD38wsYxz8ZmYZ4+A3M8sYB7+ZWcY4+M3MMsbBb2aWMQ5+M7OMcfCbmWWMg9/MLGMc/GZmGePgNzPLGAe/mVnGOPjNzDLGwW9mljEOfjOzjHHwm5llTJr33B0t6X5JSyUtkfTlZPlVkt6QtCh5TE+rBjMz21Oa99zdBXw9Ip6QNAhYKGlO8toPI+J7Ke7bzMw6keY9d1cBq5LpTZKWAqPS2p+ZmeWnIG38ksaSu/H6/GTR5ZKelnSTpMpOtpkpqV5SfUNDQyHKNDPLhNSDX9JA4A7gKxHRBFwHjAMmk/tE8P2OtouIWRFRFxF1NTU1aZdpZpYZqQa/pFJyof/ziLgTICJWR0RLRLQCPwGmpFmDmZm9U5q9egTcCCyNiB+0WV7bZrVzgMVp1WBmZntKs1fPScCFwDOSFiXLvgmcK2kyEMDLwBdSrMHMzNrJK/gl3QHcBNybNNF0KSIeAtTBS/fkX56ZmfW0fJt6rgPOA5ZLulrSkSnWZGZmKcor+CPizxFxPnAcueaZOZIekXRx8gWumZntJ/L+clfSMOCzwKXAk8CPyP0hmLOXzczMrJfJt43/TuBI4FbgE8lVuQC/lFSfVnFmZtbz8u3Vc0NEvONLWUn9ImJHRNSlUJeZmaUk36aef+pg2aM9WYiZmRXGXs/4JY0kN7BauaRjebt75mCgIuXazMwsBV019XyU3Be6BwM/aLN8E7mLsczMbD+z1+CPiNnAbEl/FRF3FKgmMzNLUVdNPRdExM+AsZK+1v71tmPwmJnZ/qGrpp4ByfPAtAsxM7PC6Kqp5/rk+TuFKcfMzNKWV3dOSf8mabCkUklzJTVKuiDt4szMrOfl24//9OTuWR8HXgcOB65IrSozM0tNvsG/eyC26cDtEbEupXrMzCxl+Q7Z8HtJy4BtwGWSaoDt6ZVlZmZpyXdY5iuB9wF1EbET2AKclWZhZmaWjn259eJEcv35225zS2crSxqdvD4SaAVmRcSPJFUBvwTGkhvb/79FxPp9rNvMzLop3149twLfA94PnJA8uhqVcxfw9YiYCEwDviTpKOBKYG5ETADmJvNmZlYg+Z7x1wFHRUTk+8bJmP2rkulNkpaSG/DtLOCUZLXZwAPAN/J9XzMze3fy7dWzmFyTTbdIGgscC8wHRuy+kUvyPLyTbWZKqpdU39DQ0N1dm5lZO/me8VcDz0paAOzYvTAiPtnVhpIGAncAX4mIJkldbbL7vWcBswDq6ury/qRhZmZ7l2/wX9WdN09uxH4H8POIuDNZvFpSbUSsklQLrOnOe5uZWffk253zL+R64JQm048DT+xtG+VO7W8ElrYbxfNuYEYyPQO4ax9rNjOzdyHfXj2fB34DXJ8sGgX8rovNTgIuBD4saVHymA5cDZwmaTlwWjJvZmYFkm9Tz5eAKeS+nCUilkvq8EvZ3SLiId6+VWN7p+ZdoZmZ9ah8e/XsiIjm3TPJRVz+wtXMbD+Ub/D/RdI3yd10/TTg18Dv0yvLzMzSkm/wXwk0AM8AXwDuAb6dVlFmZpaevNr4I6JV0u+A30WEr6YyM9uP7fWMXzlXSWoElgHPSWqQ9A+FKc/MzHpaV009XyHXLfOEiBgWEVXAVOAkSV9NuzgzM+t5XQX/RcC5EfHS7gURsQK4IHnNzMz2M10Ff2lENLZfmLTzl3awvpmZ9XJdBX9zN18zM7NeqqtePcdIaupguYD+KdRjZmYp22vwR0RJoQoxM7PCyPcCLjMzO0A4+M3MMsbBb2aWMQ5+M7OMcfCbmWWMg9/MLGNSC35JN0laI2lxm2VXSXqj3a0YzcysgNI8478ZOKOD5T+MiMnJ454U929mZh1ILfgjYh6wLq33NzOz7ilGG//lkp5OmoIqO1tJ0kxJ9ZLqGxp87xczs55S6OC/DhgHTAZWAd/vbMWImBURdRFRV1NTU6DyzMwOfAUN/ohYHREtEdEK/ASYUsj9m5lZgYNfUm2b2XOAxZ2ta2Zm6cjrZuvdIel24BSgWtLrwD8Cp0iaDATwMvCFtPZvZmYdSy34I+LcDhbfmNb+zMwsP75y18wsYxz8ZmYZ4+A3M8sYB7+ZWcY4+M3MMsbBb2aWMQ5+M7OMcfCbmWWMg9/MLGMc/GZmGePgNzPLGAe/mVnGOPjNzDLGwW9mljEOfjOzjHHwm5lljIPfzCxjUgt+STdJWiNpcZtlVZLmSFqePFemtX8zM+tYmmf8NwNntFt2JTA3IiYAc5N5MzMroDTvuTtP0th2i88idwN2gNnAA8A30qrBzAzgtvmvvmP+vKljilRJ71DoNv4REbEKIHke3tmKkmZKqpdU39DQULACzcwOdL32y92ImBURdRFRV1NTU+xyzMwOGIUO/tWSagGS5zUF3r+ZWeYVOvjvBmYk0zOAuwq8fzOzzEuzO+ftwKPAEZJel3QJcDVwmqTlwGnJvJmZFVCavXrO7eSlU9Pap5mZda3XfrlrZmbpcPCbmWWMg9/MLGMc/GZmGePgNzPLGAe/mVnGOPjNzDLGwW9mljEOfjOzjHHwm5lljIPfzCxjHPxmZhnj4DczyxgHv5lZxjj4zcwyxsFvZpYxDn4zs4xJ7Q5ceyPpZWAT0ALsioi6YtRhZpZFRQn+xIciorGI+zczy6RiBn+m7WxpZe7SNTzyYiPPvbmJlRu2UVHWl9qh/Zl00BAOGlpe7BLN7ABVrOAP4E+SArg+Ima1X0HSTGAmwJgxYwpcXnp2tbRy+4JXuea+F2jYtIMBZSVMrB1M/9ISNm7byfI1m3jguQZGV5YzYcRAThhbVeySzewAU6zgPykiVkoaDsyRtCwi5rVdIfljMAugrq4uilFkT3uxYTN/e/uTLFnZxNRDq7j6U0fzwcNr6FvSh9vmvwrA1uZdLHptA/Oeb+DTP36U86eO4dsfO4ryspIiV29mB4qiBH9ErEye10j6LTAFmLf3rfZv9z6ziq//+in69e3Dtecdx/SjRyJpj/Uqyvpy4rhq6g6pYuXGbcyat4L6l9dzw4w6RldVFKFyMzvQFLw7p6QBkgbtngZOBxYXuo5CuvGhl7jstic4YuQg7vnyyXzsvbUdhn5bZX378M3pE7nlc1NYuXEb5/znwyx+Y2OBKjazA1kx+vGPAB6S9BSwAPh/EfHHItRREDc//BL/+w/P8tGjRnL756dRO2TfvrT9wOE1/PayE+nXt4Tzb5jv8Dezd63gwR8RKyLimOTxnoj4bqFrKJQ7n3idq37/LKcfNYL/OO9Y+pd2r51+/PBB/GLmNAb26+vwN7N3zVfupuRPS97kit88zYnjhnHNucfSt+Td/ahHV1W8I/yXrHT4m1n3OPhT8MgLjVx+25McPWoIsy6q6/aZfnujqyq4/fPTqCgrYcZNC3ipcUuPvK+ZZYuDv4ctem0Dl95Sz6HVA7j54hMY2K9nO06NGVbBrZdMpaU1uPDG+axu2t6j729mBz4Hfw967s1NfPanC6ge2I9bL5nC0IqyVPYzfvhAbr54Cuu3NHPRjQvYsLU5lf2Y2YHJwd9DXl27lQtvnE+/vn34+aVTGT64f6r7O2b0UGZdVMdLjVv43M2Ps7V5V6r7M7MDh4O/B6xu2s75Nz5Gc0srt14ytWAXWp00vpprzp3Motc28Dc/e4LmXa0F2a+Z7d8c/O/S+i3NXHDDfNZtbmb2xVM4fMSggu7/jEm1/MunjuYvzzfw9V8/RWvrATG6hZmlyKNzvgtN23fy2Z8u4JV1W7n54hM4ZvTQotTx308Yw/qtO7n63mUMLS/lf531ni6vDDaz7HLwd9PGbTu56KYFLFnZxHUXHM+J46qLWs8XPziO9VuauX7eCiorSvna6UcUtR4z670c/N2wcetOLrxpPktX5UL/tKNGFLskAK4880g2bN3JNfe9QEsEf3f6ET7zN7M9OPj30ZpN2/nczY/z/Jub+fEFx3PqxN4R+gCS+OdPHU1Jibj2/hdZt2Un/3T2JEr6OPzN7G0O/n2w+I2NzLylnnVbm7n+ouP50BHDi13SHkr6iO+ePYnKilKuvf9FNm5r5vufnuzx/M3sLQ7+PN37zCq+9qunGFpRym++eCKTRg0pdkmdksQVHz2SyooyvnvPUlY0PMy15x/HuJqBxS7NrKC2NbewonEzS1c1sX5rM1ubW2hpDVY0bEaCygFlVFWUUTOoHxOGD+LgynL6ZOATsoO/C9t3tvCvf1zGTx9+mWPHDOX6C49n+KB0L87qKZeefBgTRgziq79cxCf/70P886eO5qzJo4pdlllqXl+/lQeXN7LwlfU88/pGlq/ZRPseziV9xOMvr6M1gu0733ntS3lpCYePHMQJh1Qy9bBhTBlbxZCK0gL+CwpDEb2/33ddXV3U19cXfL8PLW/kH+5azIrGLcx43yH8/fSJPTbgWnu7b73Y1nlTe+Zew6s2buNvb3+Sx19ezxnvGck/fOIo38zdDgibd+zisRfX8uDyBh5c3siKZODCYQPKeO/BQzh61BCOGDmYJSs3MrSijAFlJUh669ja1tzC+q3NrNq4jeWrN/P86s0sfmMji17bQHNLKxJMHDmYE8cN4+TDa5gytmq/ajaVtDAi6tov9xl/Bxa/sZEfzHme+5at4ZBhFdx6yRROnlBT7LK6rXZIObd9fhqz5q3gmrnLue+5NXzmhNH8zSnj9vnGMGbF1NIaLH5jIw8ub2De8kaeeGU9u1qD/qV9mHbYMM6fdggfmFDN+OED39GjbeO2nR2+X3lZCeVl5Rw0tJzjD6l6a/n2nS0sem0Dj61Yy/wV67jl0Ve44aGXKCvpwwmHVnLyhBreP76ao2oH75dNQw7+RNP2ncxZsppfL3yNx1asY0h5Kd8440guPmlsamf5hVRa0ocvfWg8nzzmIK69/wVum/8qv1jwGmdNPoizjx3FtMOGufeP9Tq7WlpZsrKJBS+tY/5La1nw0jqatufGpXrPQYO59OTD+MCEao4fW0m/vj13nPYvLWHaYcOYdtgwIPfJYMHL63jw+QYeeqGRq+9dBuQ+WZw0vpqTJ1TzvnHDGDW0fL/oQl2U4Jd0BvAjoAS4ISKuLnQNO1taeXZlE0+8up6HX2hk3vONNLe0MmpoOd+cfiSfmTKGwf0PvLa90VUVXP1X7+VLHxrPdX95kbuefINfL3yd6oH9OO2oEUw5tJK6Q6o4uHL/+AW2A8e25haeX72JpauaWPbmJp5d1cSzK5vYvCMX9IdWD+DMSbWcOH4YJ42vpnpgv4LVVl5WwgcPr+GDh+c++a9p2s5DLzTy4PLc4+6nVgJQNaCMo0flmpgmjRrC+OEDOLiyotedPBa8jV9SCfA8cBrwOvA4cG5EPNvZNt1t439t3VZeXruFdVuaWbu5mdVN23llbW7ZS41b2JEMajZqaDlnTBrJx99by+TRQ4sSeGm28e/N9p0t3L9sDXc/tZIHlze+dZBVVpRyaPUAxlYPYHRlBVUDyqgcUEZlRSmVFWWUl5VQVtKHfn37ULb7UdLnXd9pzPauo+O1s0O4syO7s2O+o6Wdv3cn7xGwY1crO3a10LyrlR27Wmne1cr2nS00bd/Fxm072bhtJ03bdtK4eQcrN2xj5YbtrNywjbVb3h5evKKshCNHDuI9Bw3hhEOrmHpoFSPexYi37Y+vnjy2IoJlb26i/pX1PPP6Bp55o4nnV2+iJflWWYKRg/szuqqCgyvLGZYcS8MGlFFZUcbA/n3pX1pC/74llJeV0L+0D/37llDWtw99S0Rpnz7dbk7qTW38U4AXImIFgKRfAGcBnQZ/d10/70V+9tjb/+FlJX0YXVXO2GEDeP/4ao4dU8lxhwzNdDt3/9ISzjy6ljOPrqWlNXjuzU0sfGUdz65q4qXGLTzywlrebHqjW+8tgch1L9Vb87mFbec7+zu7L6Gzr+HX2Qs98d49EaxZUFFWwqihufb1SaOGMGpof8YPH8jE2sGMrqzYb9rOJTGxdjATawcDhwC5E6qlq5p4Ze3W3GPdFl5bt5XHXlzLuq3Ne/Qm2pufXnxCj18zVIwz/r8GzoiIS5P5C4GpEXF5u/VmAjOT2SOA5/ZxV9VA47sst5Bcb7pcb7pcb7q6W+8hEbFHz5RinPF39Gd8j78+ETELmNXtnUj1HX3E6a1cb7pcb7pcb7p6ut5iNMi+DoxuM38wsLIIdZiZZVIxgv9xYIKkQyWVAZ8B7i5CHWZmmVTwpp6I2CXpcuC/yHXnvCkilqSwq243ExWJ602X602X601Xj9a7XwzZYGZmPcedrs3MMsbBb2aWMQdM8EuqkjRH0vLkubKDdUZLul/SUklLJH25CHWeIek5SS9IurKD1yXpmuT1pyUdV+ga29XTVb3nJ3U+LekRSccUo8429ey13jbrnSCpJbmupGjyqVfSKZIWJb+zfyl0je1q6er3YYik30t6Kqn34mLUmdRyk6Q1khZ38npvO9a6qrfnjrWIOCAewL8BVybTVwL/2sE6tcBxyfQgckNHHFXAGkuAF4HDgDLgqfb7B6YD95K73mEaML+IP9N86j0RqEymz+zt9bZZ7z7gHuCve3O9wFByV7WPSeaH9/J6v7n72ANqgHVAWZHq/QBwHLC4k9d7zbGWZ709dqwdMGf85IZ9mJ1MzwbObr9CRKyKiCeS6U3AUqCQdyZ5a7iKiGgGdg9X0dZZwC2R8xgwVFJtAWtsq8t6I+KRiFifzD5G7rqMYsnn5wvwP4A7gDWFLK4D+dR7HnBnRLwKEBHFrDmfegMYpNyAVwPJBf+uwpaZFBIxL9l/Z3rTsdZlvT15rB1IwT8iIlZBLuCBvQ5uIWkscCwwP/3S3jIKeK3N/Ovs+Ycnn3UKZV9ruYTcGVSxdFmvpFHAOcCPC1hXZ/L5+R4OVEp6QNJCSRcVrLo95VPvfwATyV2U+Qzw5YjIf2CawupNx9q+elfH2n41Hr+kPwMjO3jpW/v4PgPJnfF9JSKaeqK2fHfdwbL2/WnzGtKiQPKuRdKHyP0yvj/VivYun3r/HfhGRLT0gmGn86m3L3A8cCpQDjwq6bGIeD7t4jqQT70fBRYBHwbGAXMkPVjg4yxfvelYy1tPHGv7VfBHxEc6e03Sakm1EbEq+bjW4UdiSaXkQv/nEXFnSqV2Jp/hKnrTkBZ51SLpvcANwJkRsbZAtXUkn3rrgF8koV8NTJe0KyJ+V5AK3ynf34fGiNgCbJE0DziG3PdThZZPvRcDV0euIfoFSS8BRwILClPiPulNx1peeupYO5Caeu4GZiTTM4C72q+QtDveCCyNiB8UsLbd8hmu4m7goqTHwTRg4+4mrCLosl5JY4A7gQuLdBbaVpf1RsShETE2IsYCvwEuK1LoQ36/D3cBJ0vqK6kCmEruu6liyKfeV8l9OkHSCHIj664oaJX5603HWpd69Fgr5rfYPfyN+DBgLrA8ea5Klh8E3JNMv5/cR7mnyX0cXQRML3Cd08mdrb0IfCtZ9kXgi8m0gGuT158B6or8c+2q3huA9W1+nvW9ud52695MEXv15FsvcAW5nj2LyTVP9tp6k+PtT8nv7mLggiLWejuwCthJ7uz+kl5+rHVVb48dax6ywcwsYw6kph4zM8uDg9/MLGMc/GZmGePgNzPLGAe/mVnGOPjNgGSkzkVtHp2O7Jmsf4+kocnjsm7s7ypJf9f9is26b7+6ctcsRdsiYnK+K0fEdHhrzKfLgP9MpyyznuczfrNOJGPLPyfpiGT+dkmfT6ZfllQNXA2MSz4l/J/ktSskPZ6Mm/6dNu/3reT9/kzuilazovAZv1lOuaRFbeb/JSJ+Kely4GZJPyI3FvpP2m13JTBp96cFSacDE8gNYSzgbkkfALaQG+LgWHLH3RPAwhT/PWadcvCb5XTY1BMRcyR9mtyl/fnc8ej05PFkMj+Q3B+CQcBvI2IrgKT2Y9yYFYybesz2QlIfcuPLbwOq8tmE3KeFycljfETcmLzm8VGsV3Dwm+3dV8mNhnkucFMyrHdbm8idze/2X8Dnkns+IGmUpOHAPOAcSeWSBgGfSL90s465qccsp30b/x+Bm4BLgSkRsSkZC//bwD/uXiki1kp6OLlB9r0RcYWkieRumAKwmdwIlU9I+iW5URVfAR4sxD/KrCMendPMLGPc1GNmljEOfjOzjHHwm5lljIPfzCxjHPxmZhnj4DczyxgHv5lZxvx/dwkQwxjzy+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness:  1.4692058955657308\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew\n",
    "for col in df_num:\n",
    "    plt.figure()\n",
    "    sns.distplot(df_num[col])\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"skewness: \",skew(df_num[col]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa1c558",
   "metadata": {},
   "source": [
    "Explanation:- Skewness is a measure of a symmetric. A data has a normalised bell curve and skewness means this bell curve is eiter on left or right side. For RowNumber , CustomerId skewness id 0 which means it has normalized bell curve in midddle.\n",
    "for credit score it is right skewed and for age it is left skewed. from scipy import skew which will plot skeweness graph as well as values of it. The above one is a normal distribution with a few outliers in the right end. to remove skewenwss use log or squrt function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "70f421a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Square root\n",
    "\n",
    "df_num[\"Balance\"] = np.sqrt(df_num[\"Balance\"])\n",
    "df_num[\"EstimatedSalary\"] = np.sqrt(df_num[\"EstimatedSalary\"])\n",
    "df_num[\"Age\"] = np.sqrt(df_num[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8f1741e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.4418382693472694\n",
      "-0.5774578854450731\n",
      "0.593545496035361\n"
     ]
    }
   ],
   "source": [
    "print(skew(df_num[\"Balance\"])) \n",
    "print(skew(df_num[\"EstimatedSalary\"]))\n",
    "print(skew(df_num[\"Age\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f8400b",
   "metadata": {},
   "source": [
    "scaling In a dataframe we store only the numbers, unit is unknown. example- 1 km is much larger than compared to 100 cm, but 100 is larger in terms of numerical value. To solve this problem, we use scaling technique. \n",
    "\n",
    "In python we can use StandardScaler() object, MinMaxScaler() object from sklearn.preprocessing package.\n",
    "\n",
    "a) Standard Scaler:- A standard scaler converts a distribution, such that it has 0 mean and 1 standard deviation.\n",
    "\n",
    "b) MinMax Scaler:- A minmax scaler converts a distribution, such that it has range 0-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ef2c5019",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8610d822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.732131</td>\n",
       "      <td>-0.783071</td>\n",
       "      <td>-0.326346</td>\n",
       "      <td>0.363135</td>\n",
       "      <td>-1.041511</td>\n",
       "      <td>-1.296807</td>\n",
       "      <td>-0.910770</td>\n",
       "      <td>0.646111</td>\n",
       "      <td>0.969813</td>\n",
       "      <td>0.189715</td>\n",
       "      <td>1.975426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.731785</td>\n",
       "      <td>-0.606404</td>\n",
       "      <td>-0.440132</td>\n",
       "      <td>0.267593</td>\n",
       "      <td>-1.387239</td>\n",
       "      <td>0.419296</td>\n",
       "      <td>-0.910770</td>\n",
       "      <td>-1.547722</td>\n",
       "      <td>0.969813</td>\n",
       "      <td>0.352690</td>\n",
       "      <td>-0.506220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.731438</td>\n",
       "      <td>-0.995727</td>\n",
       "      <td>-1.536614</td>\n",
       "      <td>0.363135</td>\n",
       "      <td>1.032856</td>\n",
       "      <td>1.071839</td>\n",
       "      <td>2.527125</td>\n",
       "      <td>0.646111</td>\n",
       "      <td>-1.031127</td>\n",
       "      <td>0.372337</td>\n",
       "      <td>1.975426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.731092</td>\n",
       "      <td>0.144842</td>\n",
       "      <td>0.501188</td>\n",
       "      <td>0.072949</td>\n",
       "      <td>-1.387239</td>\n",
       "      <td>-1.296807</td>\n",
       "      <td>0.808177</td>\n",
       "      <td>-1.547722</td>\n",
       "      <td>-1.031127</td>\n",
       "      <td>0.075081</td>\n",
       "      <td>-0.506220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.730745</td>\n",
       "      <td>0.652698</td>\n",
       "      <td>2.063158</td>\n",
       "      <td>0.457546</td>\n",
       "      <td>-1.041511</td>\n",
       "      <td>0.803301</td>\n",
       "      <td>-0.910770</td>\n",
       "      <td>0.646111</td>\n",
       "      <td>0.969813</td>\n",
       "      <td>-0.163784</td>\n",
       "      <td>-0.506220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId  CreditScore       Age    Tenure   Balance  \\\n",
       "0  -1.732131   -0.783071    -0.326346  0.363135 -1.041511 -1.296807   \n",
       "1  -1.731785   -0.606404    -0.440132  0.267593 -1.387239  0.419296   \n",
       "2  -1.731438   -0.995727    -1.536614  0.363135  1.032856  1.071839   \n",
       "3  -1.731092    0.144842     0.501188  0.072949 -1.387239 -1.296807   \n",
       "4  -1.730745    0.652698     2.063158  0.457546 -1.041511  0.803301   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary    Exited  \n",
       "0      -0.910770   0.646111        0.969813         0.189715  1.975426  \n",
       "1      -0.910770  -1.547722        0.969813         0.352690 -0.506220  \n",
       "2       2.527125   0.646111       -1.031127         0.372337  1.975426  \n",
       "3       0.808177  -1.547722       -1.031127         0.075081 -0.506220  \n",
       "4      -0.910770   0.646111        0.969813        -0.163784 -0.506220  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a) Standard Scaler:-\n",
    "\n",
    "for col in df_num:\n",
    "    ss = StandardScaler()\n",
    "    df_num[col] = ss.fit_transform(df_num[[col]])\n",
    "    \n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9c1bd884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.275616</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.418413</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.709664</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.326454</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.403903</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.577955</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.748240</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.214421</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.418413</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.797720</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.752891</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.542636</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.374341</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.682530</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.688778</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.432752</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.707281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.625991</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId  CreditScore       Age  Tenure   Balance  \\\n",
       "0     0.0000    0.275616        0.538  0.418413     0.2  0.000000   \n",
       "1     0.0001    0.326454        0.516  0.403903     0.1  0.577955   \n",
       "2     0.0002    0.214421        0.304  0.418413     0.8  0.797720   \n",
       "3     0.0003    0.542636        0.698  0.374341     0.1  0.000000   \n",
       "4     0.0004    0.688778        1.000  0.432752     0.2  0.707281   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0       0.000000        1.0             1.0         0.709664     1.0  \n",
       "1       0.000000        0.0             1.0         0.748240     0.0  \n",
       "2       0.666667        1.0             0.0         0.752891     1.0  \n",
       "3       0.333333        0.0             0.0         0.682530     0.0  \n",
       "4       0.000000        1.0             1.0         0.625991     0.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# b) MinMax Scaler:-\n",
    "\n",
    "for col in df_num:\n",
    "    ss = MinMaxScaler()\n",
    "    df_num[col] = ss.fit_transform(df_num[[col]])\n",
    "\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c9f33fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hargrave</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.275616</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.418413</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.709664</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hill</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.326454</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.403903</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.577955</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.748240</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Onio</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.214421</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.418413</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.797720</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.752891</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Boni</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.542636</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.374341</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.682530</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.688778</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.432752</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.707281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.625991</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Surname Geography  Gender  RowNumber  CustomerId  CreditScore       Age  \\\n",
       "0  Hargrave    France  Female     0.0000    0.275616        0.538  0.418413   \n",
       "1      Hill     Spain  Female     0.0001    0.326454        0.516  0.403903   \n",
       "2      Onio    France  Female     0.0002    0.214421        0.304  0.418413   \n",
       "3      Boni    France  Female     0.0003    0.542636        0.698  0.374341   \n",
       "4  Mitchell     Spain  Female     0.0004    0.688778        1.000  0.432752   \n",
       "\n",
       "   Tenure   Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0     0.2  0.000000       0.000000        1.0             1.0   \n",
       "1     0.1  0.577955       0.000000        0.0             1.0   \n",
       "2     0.8  0.797720       0.666667        1.0             0.0   \n",
       "3     0.1  0.000000       0.333333        0.0             0.0   \n",
       "4     0.2  0.707281       0.000000        1.0             1.0   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0         0.709664     1.0  \n",
       "1         0.748240     0.0  \n",
       "2         0.752891     1.0  \n",
       "3         0.682530     0.0  \n",
       "4         0.625991     0.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine both categorical and numerical data using concat function\n",
    "\n",
    "df_new = pd.concat([df_cat,df_num], axis=1)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9e7a843b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>217</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>743</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5631</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>5793</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5699</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>459</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>3696</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3920</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Geography  Gender  Age  Tenure  Balance  NumOfProducts  \\\n",
       "0          228          0       0   24       2        0              0   \n",
       "1          217          2       0   23       1      743              0   \n",
       "2          111          0       0   24       8     5793              2   \n",
       "3          308          0       0   21       1        0              1   \n",
       "4          459          2       0   25       2     3696              0   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1             5062       1  \n",
       "1          0               1             5631       0  \n",
       "2          1               0             5699       1  \n",
       "3          0               0             4699       0  \n",
       "4          1               1             3920       0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop customerID column as it is of no use\n",
    "\n",
    "df1 = df.drop(['RowNumber', 'CustomerId', 'Surname'], axis=1)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3eac73",
   "metadata": {},
   "source": [
    "## MODEL SELECTION\n",
    "\n",
    "##  Classification Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce4f9ce",
   "metadata": {},
   "source": [
    "Generate training and test datasets: decouple the master dataset into training and test set with an 70%-30% ratio. On 'X' drop excited column & plot rest of columns and on 'y'-\"Exited\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0ac02941",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "for col in df:\n",
    "    le = LabelEncoder()\n",
    "    df[col]= le.fit_transform(df[col])\n",
    "\n",
    "X = df.drop(\"Exited\",axis=1)\n",
    "y = df[\"Exited\"]\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c3a580",
   "metadata": {},
   "source": [
    "## 1. Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b459764f",
   "metadata": {},
   "source": [
    "Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4141596f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d3a93e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Naive Byes to the Training set:\n",
    "\n",
    "model1 = GaussianNB()\n",
    "model1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "196fc329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8247496423462088"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trainning score for naive bayes classifier\n",
    "\n",
    "model1.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e8255131",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.97      0.90      2402\n",
      "           1       0.65      0.21      0.32       594\n",
      "\n",
      "    accuracy                           0.82      2996\n",
      "   macro avg       0.74      0.59      0.61      2996\n",
      "weighted avg       0.80      0.82      0.78      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predicting the Test set results \n",
    "\n",
    "y_pred = model1.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a663db8",
   "metadata": {},
   "source": [
    "Evaluation Metrics:-\n",
    "Classification Rate/Accuracy = (TP+TN)/(TP+TN+FP+FN)\n",
    "\n",
    "Recall = (TP)/(TP+FN) :: High Recall indicates the class is correctly recognized ::Recall gives us an idea about when it’s actually yes, how often does it predict yes.\n",
    "\n",
    "Precision = (TP)/(TP+FP) ::High Precision indicates an example labeled as positive is indeed positive ::Precision tells us about when it predicts yes, how often is it correct.\n",
    "\n",
    "High recall, low precision:This means that most of the positive examples are correctly recognized (low FN) but there are a lot of false positives. \n",
    "\n",
    "Low recall, high precision:This shows that we miss a lot of positive examples (high FN) but those we predict as positive are indeed positive (low FP) \n",
    "\n",
    "F-measure = 2/((1/recall)+(1/precision)) :uses Harmonic Mean in place of Arithmetic Mean as it punishes the extreme values more. ::Uses harmonic mean because precision and recall must be good if these two are not good it will not give good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c8c7fccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124 67\n",
      "470 2335\n"
     ]
    }
   ],
   "source": [
    "# confusion_matrix\n",
    "\n",
    "tn, fp, fn, tp = confusion_matrix(y_test,y_pred).ravel()\n",
    "print(tp, fp)\n",
    "print(fn, tn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945f4afe",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5f553504",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "63fbdbcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Logistic Regression to the Training set:\n",
    "\n",
    "model2 = LogisticRegression(random_state=1)\n",
    "model2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "09d0fd9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7881258941344779"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trainning score for Logistic Regression:\n",
    "\n",
    "model2.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1e541aaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.97      0.88      2402\n",
      "           1       0.37      0.07      0.11       594\n",
      "\n",
      "    accuracy                           0.79      2996\n",
      "   macro avg       0.59      0.52      0.50      2996\n",
      "weighted avg       0.72      0.79      0.73      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction on test\n",
    "\n",
    "y_pred = model2.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00adede0",
   "metadata": {},
   "source": [
    "Explanation:- Logistic Regression is a “Supervised machine learning” algorithm that can be used to model the probability of a certain class or event. It is used when the data is linearly separable and the outcome is binary or dichotomous in nature. That means Logistic regression is usually used for Binary classification problems. Binary Classification refers to predicting the output variable that is discrete in two classes. \n",
    "\n",
    "Types of Logistic Regression Simple Logistic Regression: a single independent is used to predict the output Multiple logistic regression: multiple independent variables are used to predict the output \n",
    "\n",
    "Evaluation-\n",
    "1. fit data in Logistic Regression model\n",
    "2. Trainning score is 0.78 \n",
    "3. Testing score is 0.79 \n",
    "4. for 0:- precision=0.81 recall= 0.97 f1-score= 0.88 support=2402 \n",
    "   for1:- precision=0.37 recall=0.07 f1-score=0.11 support=594"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f5791674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "plot_confusion_matrix(model2, X_train, y_train, cmap = 'PiYG')\n",
    "plt.title(\"Confusion Matrix for trainning\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8956d15",
   "metadata": {},
   "source": [
    "confusion matrix, also known as an error matrix. A confusion matrix is a summary of prediction results on a classification problem. The number of correct and incorrect predictions are summarized with count values and broken down by each class. \n",
    "This is the key to the confusion matrix \n",
    "\n",
    "True Positive (TP) : Observation is positive, and is predicted to be positive.\n",
    "\n",
    "False Negative (FN) : Observation is positive, but is predicted to be negative. \n",
    "\n",
    "True Negative (TN) : Observation is negative, and is predicted to be negative.\n",
    "\n",
    "False Positive (FP) : Observation is negative, but is predicted to be positive. \n",
    "\n",
    "TN=5384, FN=1318, TP=125, FP=163"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5f09cce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40 67\n",
      "554 2335\n"
     ]
    }
   ],
   "source": [
    "# confusion_matrix on testing\n",
    "\n",
    "tn, fp, fn, tp = confusion_matrix(y_test,y_pred).ravel()\n",
    "print(tp, fp)\n",
    "print(fn, tn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6492f64",
   "metadata": {},
   "source": [
    "## ROC"
   ]
  },
  {
   "cell_type": "raw",
   "id": "312019e3",
   "metadata": {},
   "source": [
    "Receiver Operator Characteristic To visualize performance of multi-class classification model we use AUC-ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "12067a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "## roc auc\n",
    "\n",
    "prob = model2.predict_proba(X_test)[:,-1]\n",
    "\n",
    "thresholds = [0.5,0.4,0.3,0.2,0.1]\n",
    "tprs = []\n",
    "fprs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7915df91",
   "metadata": {},
   "outputs": [],
   "source": [
    "for th in thresholds:\n",
    "    y_pred = np.where(prob >= th, 1, 0)\n",
    "    tn, fp, fn, tp = confusion_matrix(y_test,y_pred).ravel()\n",
    "    \n",
    "    tpr = tp/(tp + fn)\n",
    "    fpr = fp/(fp + tn)\n",
    "    \n",
    "    tprs.append(tpr)\n",
    "    fprs.append(fpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "630e9376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fprs,tprs,\"x--\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR/Recall\")\n",
    "plt.scatter(fprs,tprs)\n",
    "plt.title(\"ROC Graph\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "55b79ff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.19      0.32      2402\n",
      "           1       0.23      0.95      0.36       594\n",
      "\n",
      "    accuracy                           0.34      2996\n",
      "   macro avg       0.58      0.57      0.34      2996\n",
      "weighted avg       0.80      0.34      0.33      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# final model\n",
    "\n",
    "y_pred = np.where(prob >= 0.1, 1, 0) \n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "92d0c8c4",
   "metadata": {},
   "source": [
    "Explanation:- 1. predict_proba(X_test)[:,-1] - this function use to get probabilty value istead of getting 0 1nd 1 but it will give probabilty for both classes 0 and 1.but we required only \n",
    "1. select all rows and last column.\n",
    "2. By default threshold is 0.5. Here we use loop for thrshold= 0.5,0.4,0.3,0.2,0.1 \n",
    "3. Get tpr and fpr value and append it in list. Graph is plotted for these 5 points \n",
    "4. Previous logistic reression testing result was 0.79. \n",
    "5. By selecting threshold o.1 Recall was increase from 0.07 to 0.95.(Recall good when equal to 1).\n",
    "6. And area under curve is 0.57 which is very low."
   ]
  },
  {
   "cell_type": "raw",
   "id": "d5c28279",
   "metadata": {},
   "source": [
    "Area Under Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4b93047b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5702837422237922"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bfb450c",
   "metadata": {},
   "source": [
    "## KNN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163584cd",
   "metadata": {},
   "source": [
    "K-nearest neighbors (KNN) algorithm can be used for both classification as well as regression. \n",
    "calculating distance by using Eucliedan distance or manhattan distance. \n",
    "By default it is euclidean with p value 2 & for manhattan p = 1. \n",
    "1. we will divide the data into train and test split. \n",
    "2. Next,data scaling will be done.\n",
    "3. for loop used for different values of k so it wil give bst model.\n",
    "4. Next, train the model with the help of KNeighborsClassifier class of sklearn.\n",
    "5. At last we need to make prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bbdef53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "acaf2459",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Whenever we perform data always scale the data\n",
    "\n",
    "for col in X:\n",
    "    ss = StandardScaler()\n",
    "    X_train[col] = ss.fit_transform(X_train[[col]])\n",
    "    X_test[col] = ss.transform(X_test[[col]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "95151bab",
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbors = [3,7,9,11,13,15,17]\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "for k in neighbors:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    train_score = knn.score(X_train,y_train) \n",
    "    test_score = knn.score(X_test,y_test)\n",
    "    \n",
    "    train_scores.append(train_score)\n",
    "    test_scores.append(test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dccbd9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(train_scores, color=\"red\")\n",
    "plt.plot(test_scores, color=\"blue\")\n",
    "plt.title(\"KNN Graph\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5d631e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final model fit data in KNN Classifier:-\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "45b1786e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.94      0.89      2402\n",
      "           1       0.56      0.31      0.40       594\n",
      "\n",
      "    accuracy                           0.82      2996\n",
      "   macro avg       0.70      0.62      0.64      2996\n",
      "weighted avg       0.79      0.82      0.79      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction:-\n",
    "\n",
    "y_pred = knn.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "456e628f",
   "metadata": {},
   "source": [
    "## DECISION TREE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09171800",
   "metadata": {},
   "source": [
    "It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes Automatically select good feature.\n",
    "Decision metrics perform on impure data.\n",
    "Metrics: \n",
    "1. GiniIndex= 1-(P)^2-(q)^2 : gini index low means data is pure and high means data is impure \n",
    "2. Entropy= -(p log p) - (q log q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8ec305c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d597b5",
   "metadata": {},
   "source": [
    "1. Gini index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8ac43ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  fit data for trainning in  Decision Tree Classifier by default it is gini index.\n",
    "\n",
    "model3 = DecisionTreeClassifier()\n",
    "model3.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c11e6ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train score Decision Tree Classifier\n",
    "\n",
    "model3.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "87b1b836",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.85      0.87      2402\n",
      "           1       0.48      0.54      0.50       594\n",
      "\n",
      "    accuracy                           0.79      2996\n",
      "   macro avg       0.68      0.70      0.69      2996\n",
      "weighted avg       0.80      0.79      0.80      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict Decision Tree Classifier:-\n",
    "\n",
    "y_pred = model3.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "da3da4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.get_depth()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3735537",
   "metadata": {},
   "source": [
    "Trainning score is 100% and testing score 79% Calculate/get the depth of tree. The maximum depth of model is 26 and it is overfitting data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62db123",
   "metadata": {},
   "source": [
    "2. Entropy fit data for trainning in Decision Tree Classifier for entropy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "07036b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit data for trainning in  Decision Tree Classifier for entropy\n",
    "\n",
    "model4 = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "model4.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8f526e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train score Decision Tree Classifier:-\n",
    "\n",
    "model4.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "43d14589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.85      0.87      2402\n",
      "           1       0.47      0.52      0.49       594\n",
      "\n",
      "    accuracy                           0.79      2996\n",
      "   macro avg       0.67      0.69      0.68      2996\n",
      "weighted avg       0.80      0.79      0.79      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict Decision Tree Classifier:-\n",
    "\n",
    "y_pred = model4.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca501b1",
   "metadata": {},
   "source": [
    "Pruning Techniques removes the branches of decision tree to overcome the overfitting condition of decision tree.\n",
    "\n",
    "1. max depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9d27afbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=18)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5 = DecisionTreeClassifier(max_depth=18)\n",
    "model5.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4f65a8db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.87      2402\n",
      "           1       0.49      0.52      0.51       594\n",
      "\n",
      "    accuracy                           0.80      2996\n",
      "   macro avg       0.69      0.69      0.69      2996\n",
      "weighted avg       0.80      0.80      0.80      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = model5.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8610e87e",
   "metadata": {},
   "source": [
    "2. min samples leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "34712694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(min_samples_leaf=5)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5 = DecisionTreeClassifier(min_samples_leaf=5)\n",
    "model5.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6181c2fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.89      0.89      2402\n",
      "           1       0.54      0.51      0.52       594\n",
      "\n",
      "    accuracy                           0.82      2996\n",
      "   macro avg       0.71      0.70      0.70      2996\n",
      "weighted avg       0.81      0.82      0.81      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = model5.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6baeae4",
   "metadata": {},
   "source": [
    "1. max depth:- Here depth is 26 we can give depth to Decision Tree Classifier as 18 to grow upto 18 and now test score is improved to 0.79 to 0.80. \n",
    "2. min samples leaf - another way used to improve the score Decision Tree Classifier(min_samples_leaf=5) so test score is improved to 0.80 to 0.82."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7646be5f",
   "metadata": {},
   "source": [
    "\n",
    "## Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0d290c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b1f82c1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.2330797  -0.20584702 -0.2099359  -0.25400641]\n"
     ]
    }
   ],
   "source": [
    "model6 = DecisionTreeRegressor()\n",
    "\n",
    "cv = cross_val_score(model6,X,y,cv=4, scoring='neg_mean_squared_error')\n",
    "\n",
    "print(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e758fd72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Decision Tree to the Training set:\n",
    "\n",
    "model6 = DecisionTreeRegressor()\n",
    "model6.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6a5a1570",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results :\n",
    "\n",
    "y_pred = model6.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "348bff5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:- 0.21228304405874499\n",
      "RMSE:- 0.46074184101158533\n"
     ]
    }
   ],
   "source": [
    "# Evaluate results:\n",
    "\n",
    "mse = mean_squared_error(y_test,y_pred)\n",
    "print(\"MSE:-\", mse)\n",
    "\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:-\", rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554770f2",
   "metadata": {},
   "source": [
    "## Random over sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "90de8837",
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = RandomOverSampler(random_state=1)\n",
    "X_train_rs, y_train_rs = rs.fit_resample(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6a7be6ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5547\n",
       "1    5547\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_rs.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9c3e9d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model7 = DecisionTreeClassifier()\n",
    "model7.fit(X_train_rs, y_train_rs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "64d962ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87      2402\n",
      "           1       0.47      0.48      0.48       594\n",
      "\n",
      "    accuracy                           0.79      2996\n",
      "   macro avg       0.67      0.67      0.67      2996\n",
      "weighted avg       0.79      0.79      0.79      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = model7.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6383fe36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get max depth of model\n",
    "\n",
    "model7.get_depth()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83dd2673",
   "metadata": {},
   "source": [
    "1. Max depth\n",
    "Reduce depth to 20 & fit model for trainning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5b4c2c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=20)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model8 = DecisionTreeClassifier(max_depth=20)\n",
    "model8.fit(X_train_rs,y_train_rs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9a7d9f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87      2402\n",
      "           1       0.48      0.49      0.48       594\n",
      "\n",
      "    accuracy                           0.79      2996\n",
      "   macro avg       0.68      0.68      0.68      2996\n",
      "weighted avg       0.80      0.79      0.79      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = model8.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e949fb",
   "metadata": {},
   "source": [
    "2. min_samples_leaf \n",
    "Again fit data in algorithm with min samples leaf=9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8a68f28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(min_samples_leaf=9)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model9 = DecisionTreeClassifier(min_samples_leaf=9)\n",
    "model9.fit(X_train_rs,y_train_rs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "00de9b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.84      0.87      2402\n",
      "           1       0.48      0.61      0.54       594\n",
      "\n",
      "    accuracy                           0.79      2996\n",
      "   macro avg       0.69      0.73      0.70      2996\n",
      "weighted avg       0.82      0.79      0.80      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = model9.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed23190",
   "metadata": {},
   "source": [
    "## Random Forest "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcedaf9f",
   "metadata": {},
   "source": [
    "A random forest algorithm consists of many decision trees. The ‘forest’ generated by the random forest algorithm. A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3d077cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3563d44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=10, max_features=5, n_estimators=70)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier(n_estimators=70, max_depth=10, max_features=5)\n",
    "rf.fit(X_train_rs, y_train_rs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d6bc6cab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.87      0.89      2402\n",
      "           1       0.56      0.65      0.60       594\n",
      "\n",
      "    accuracy                           0.83      2996\n",
      "   macro avg       0.73      0.76      0.74      2996\n",
      "weighted avg       0.84      0.83      0.83      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predictition\n",
    "\n",
    "y_pred = rf.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b406983c",
   "metadata": {},
   "source": [
    "The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18387ca3",
   "metadata": {},
   "source": [
    "## BOOSTING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ebbb17",
   "metadata": {},
   "source": [
    "## Adaptive Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18c1f52",
   "metadata": {},
   "source": [
    "Boosting is a sequential process where each subsequent model attempts to correct the errors of previous model. Boosting algorithms that are able to convert weak learners to strong learners\n",
    "\n",
    "n_estimators- It controls the number of weak learners \n",
    "\n",
    "learning_rate - It controls the contribution of weak learner in the final combination. \n",
    "\n",
    "base_estimators - It helps to specify different algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "72736d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1d275bee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(n_estimators=100, random_state=1)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model10 = AdaBoostClassifier(n_estimators=100, random_state=1)\n",
    "model10.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "01aa7b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8608011444921316"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train Score\n",
    "\n",
    "model10.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9e462f6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.95      0.91      2402\n",
      "           1       0.67      0.45      0.54       594\n",
      "\n",
      "    accuracy                           0.85      2996\n",
      "   macro avg       0.77      0.70      0.72      2996\n",
      "weighted avg       0.83      0.85      0.83      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = model10.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c7d4922",
   "metadata": {},
   "source": [
    "AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. \n",
    "1. Adaboost selects a training subset randomly.\n",
    "2. It iteratively trains the AdaBoost machine learning model by selecting the training set based on the accurate prediction of the last training.\n",
    "3. It assigns the higher weight to wrong classified observations so that in the next iteration these observations will get the high probability for classification. \n",
    "4. Also, It assigns weight to the trained classifier in each iteration according to the accuracy of the classifier. The more accurate classifier will get high weight.\n",
    "5. This process iterates until the complete training data fits without any error or until reached to the specified maximum number of estimators."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77bd5ced",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f08a24",
   "metadata": {},
   "source": [
    "Instead of using decision stump gradient boosting uses fully grown tree.Unlike in AdaBoost, the incorrect result is not given a higher weightage in gradient boosting. It tries to reduce the loss function by averaging the outputs from weak learners as gradient boosting have greedy nature so it will consider each and every observation in consideration.\n",
    "\n",
    "so it may lead to overfit issue to resolve this kind of things we have xgboost which will reduces the risk of getting overfit and also improve performance by removing unnecessary features as it is using ridge and lasso to regularize our training model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "81172021",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "17344bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.5, n_estimators=200, random_state=1)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model11 = GradientBoostingClassifier(random_state=1, n_estimators=200, learning_rate=0.5)\n",
    "model11.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "53bc665d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.92      0.90      2402\n",
      "           1       0.61      0.48      0.54       594\n",
      "\n",
      "    accuracy                           0.84      2996\n",
      "   macro avg       0.74      0.70      0.72      2996\n",
      "weighted avg       0.82      0.84      0.83      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = model11.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b726b406",
   "metadata": {},
   "source": [
    "## XG Boost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "341f4afb",
   "metadata": {},
   "source": [
    "XGBoost is an implementation of gradient boosted decision trees designed for speed and performance. \n",
    "1. Parallelization \n",
    "2. Cache optimization \n",
    "3. It has in built capability of regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e3b8b768",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e0afad43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:13:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=1,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(n_estimators=100,random_state=1)\n",
    "xgb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "085ddfa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.94      0.91      2402\n",
      "           1       0.69      0.50      0.58       594\n",
      "\n",
      "    accuracy                           0.86      2996\n",
      "   macro avg       0.79      0.72      0.75      2996\n",
      "weighted avg       0.85      0.86      0.85      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = xgb.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b1ab1b",
   "metadata": {},
   "source": [
    "## Hyper parameter tuning using GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee8c864",
   "metadata": {},
   "source": [
    "Using default hyperparameters usually results in non-optimised models that overfit or underfit the dataset. Hyperparameter tuning is the process of finding the set of hyperparameter values that achieves optimal performance.\n",
    "\n",
    "For this purpose, we will first define which hyperparameters we want to experiment with and what values to try out. \n",
    "\n",
    "We will pass this information to Scikit-Learn’s GridSearchCV, which then evaluates all the possible combinations of hyperparameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f490dab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\"n_estimators\":[75,100,125,150],\n",
    "              \"learning_rate\":[0.1,0.2,0.3],\n",
    "              \"min_samples_leaf\":[10,20],\n",
    "              \"max_depth\":[2,3,4]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9cc19ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model\n",
    "\n",
    "model13 = GradientBoostingClassifier(random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6c37121a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid Search CV\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "196fd596",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = GridSearchCV(model13, param_grid, cv=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c28918eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=2, estimator=GradientBoostingClassifier(random_state=1),\n",
       "             param_grid={'learning_rate': [0.1, 0.2, 0.3],\n",
       "                         'max_depth': [2, 3, 4], 'min_samples_leaf': [10, 20],\n",
       "                         'n_estimators': [75, 100, 125, 150]})"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "401d4673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1,\n",
       " 'max_depth': 4,\n",
       " 'min_samples_leaf': 20,\n",
       " 'n_estimators': 75}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "035584e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.2, min_samples_leaf=10,\n",
       "                           n_estimators=150, random_state=1)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final Model\n",
    "\n",
    "final_model = GradientBoostingClassifier(random_state=1,learning_rate=0.2,min_samples_leaf=10,n_estimators=150)\n",
    "final_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7786430c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.96      0.92      2402\n",
      "           1       0.74      0.48      0.58       594\n",
      "\n",
      "    accuracy                           0.86      2996\n",
      "   macro avg       0.81      0.72      0.75      2996\n",
      "weighted avg       0.85      0.86      0.85      2996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "\n",
    "y_pred = final_model.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25cf3547",
   "metadata": {},
   "source": [
    "GridSearchCV hypermater as learning_rate=0.2,min_samples_leaf=10,n_estimators=150 gradient boosting score is improved with accuracy 0.86."
   ]
  }
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