Employee_Attrition_Assignment.ipynb

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  "metadata": {
    "colab": {
      "name": "Employee Attrition Assignment.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOeVaDVIxYlRp2IpVnFrj34",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/saima8/Data-Science-from-scratch/blob/main/Employee_Attrition_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxjwbeAmWBGF"
      },
      "source": [
        "# import all nedded file\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd# data analysis and manipulation\n",
        "import os # assign path\n",
        "import matplotlib.pyplot as plt# data visualization \n",
        "import seaborn as sns #data visualization & heatmap creation\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvj6e3yXXH4d"
      },
      "source": [
        "train_data = pd.read_csv('/content/employee_attrition_train.csv')\n",
        "test_data = pd.read_csv('/content/employee_attrition_test.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rD7vKF5dtl0b"
      },
      "source": [
        "# Remove rows with missing target, separate target from predictors\n",
        "train_data.dropna(axis=0, subset=['Attrition'], inplace=True)\n",
        "y = train_data.Attrition\n",
        "train_data.drop(['Attrition'], axis=1, inplace=True)\n",
        "\n",
        "X = train_data\n",
        "X_test = test_data\n",
        "\n",
        "# Break off validation set from training data\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2,\n",
        "                                                      random_state=0)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "G_h_MKDwXVfE",
        "outputId": "695cb93a-19ac-4dbb-fd5d-71165ff22af6"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>Gender</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>Over18</th>\n",
              "      <th>OverTime</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>40.0</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>523.0</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>1346</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>98</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>4</td>\n",
              "      <td>Single</td>\n",
              "      <td>4661</td>\n",
              "      <td>22455</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>775</th>\n",
              "      <td>34.0</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>665.0</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>138</td>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>3</td>\n",
              "      <td>Single</td>\n",
              "      <td>4809</td>\n",
              "      <td>12482</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>55.0</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>478.0</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>1770</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>60</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>Research Director</td>\n",
              "      <td>1</td>\n",
              "      <td>Married</td>\n",
              "      <td>19038</td>\n",
              "      <td>19805</td>\n",
              "      <td>8</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>3</td>\n",
              "      <td>34</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>42.0</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>647.0</td>\n",
              "      <td>Sales</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>1</td>\n",
              "      <td>1171</td>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>45</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>1</td>\n",
              "      <td>Single</td>\n",
              "      <td>5155</td>\n",
              "      <td>2253</td>\n",
              "      <td>7</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>795</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>157.0</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>1952</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>95</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>1</td>\n",
              "      <td>Single</td>\n",
              "      <td>2867</td>\n",
              "      <td>20006</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Age BusinessTravel  ...  YearsSinceLastPromotion YearsWithCurrManager\n",
              "740  40.0  Travel_Rarely  ...                        8                    8\n",
              "775  34.0  Travel_Rarely  ...                        2                   10\n",
              "886  55.0  Travel_Rarely  ...                        0                    0\n",
              "279  42.0  Travel_Rarely  ...                        1                    5\n",
              "795   NaN  Travel_Rarely  ...                        7                    6\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLDI7DKlveqI",
        "outputId": "a55886de-7a80-40c3-81d7-b356ed11bbf2"
      },
      "source": [
        "# Shape of training data (num_rows, num_columns)\n",
        "print(X_train.shape)\n",
        "\n",
        "# Number of missing values in each column of training data\n",
        "missing_val_count_by_column = (X_train.isnull().sum())\n",
        "print(missing_val_count_by_column[missing_val_count_by_column > 0])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(823, 34)\n",
            "Age                 112\n",
            "BusinessTravel        4\n",
            "DailyRate            24\n",
            "DistanceFromHome     75\n",
            "MaritalStatus         5\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RaSNKi8pYd1b",
        "outputId": "f03e82f8-47c6-451d-f458-aa185e62686f"
      },
      "source": [
        "# find duplicate value\n",
        "X_train.duplicated().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iLAuLvHuZS77",
        "outputId": "b4da9179-f8ad-4d2c-ced2-cfd34ee9c82f"
      },
      "source": [
        "\n",
        "X_train.dtypes.value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int64      23\n",
              "object      8\n",
              "float64     3\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psc-_1nEoKlx"
      },
      "source": [
        "import plotly as py\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RE_EwmrCZzgX"
      },
      "source": [
        "# fig = go.Figure()\n",
        "# fig.add_trace(go.Pie(labels=X_test['Attrition'], values=X_test['Attrition'].value_counts()))\n",
        "# fig.update_layout(autosize=False, width=400, height=350)\n",
        "# fig.show()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xMQiKYDZ6Qe",
        "outputId": "0acee70e-93c9-483e-9676-c42b92ff300f"
      },
      "source": [
        "train_data.isnull().sum().sum()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "268"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "XBH5W-1Pov6N",
        "outputId": "595c2ad7-b11a-4f89-f234-20719c1a986c"
      },
      "source": [
        "plt.figure(figsize=(13,9))\n",
        "sns.heatmap(train_data.corr(),vmax=0.8,linewidth=0.1,cmap='coolwarm')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S+GZmZmZmza1FdVLMzMzMzKzxKjWT0mLnpJiZmZmZ2dLJmRQzMzMzs1bKmRQzMzMzM7MmoJK7+5rVx28WMzMzW9q1qNTFC4fsUfbvZ51uGN/k1+zhXtZo45+YV5a4e2zdlj2OnFl43PHXbE7PAe8UHnfUgNUZ17Zz4XEB9pxX1GN0vqnvlf8pPOZFxy/H/ie+WHhcgNsv35Cjzn2v8LhXn9mBPkM+Kzzupb3bM/CGrwqPe9Yh2cf04We9XXjs0QPXKFub9zz6qcLjjhuZ3VDxotsXFB677/5VHHL6G4XHveG8tTnw5JcLj3vLJR05tP+bhccFuH7QWmWJC3Ds4A8Ljzm838qMfKDwsAAc/UPY5/jiP5fvvLIzRw96v/C4I/uvSr+r6n1G82IbfEw7zr6u+O8B5xzWtmz/Pp01+svC4wIMPPxbZYlrC3MnxczMzMyslapq06ISO4XxnBQzMzMzM2tRnEkxMzMzM2ulfHcvMzMzMzOzJuBMSoEkzQfyM8BviojBJXW6A30jYq8Cz9sd+DIi/pG2jwP+ExHXFXUOMzMzM2t5VFWZOQd3Uoo1NyK6NsN5uwOfAf8AiIhhzdAGMzMzM2tiHu5li03STyQ9J+kJYP9c+QBJfXPbT0mqTuuHSXpS0gxJf05lP5P0mKRpkv6fpNVT/eOAkyVNl7RzPq6krpL+mWLdIem7qXyipPMlTZb0vKSdm+jlMDMzMzOrlzspxVo2dRRqlh6S2gFXAT8DtgHWaCiIpE2BM4DdImJL4KS062Fg+4jYCrgJODUiXgGGAZdERNeImFQS7jrgtIjYgmwo2tm5fctERDegT0m5mZmZmbUCqlLZl+bgTkqx5qaOQs0yFtgYeDkiXoiIAK5vRJzdgFsi4n2AiKh5+tU6wL2SZgKnAJvWF0TSisBKEfFgKhoN7JKrcnv6+ThQXUeMXpKmSpr6t9tHNqLpZmZmZmZLxnNSmtdXLNxRbNdA/SHAxRFxV5osP2AJz/9F+jmfOt4LETECGAEw/ol5sYTnMzMzM7MCVerE+cq8qpblOaBa0gZp+6DcvleArQEkbQ10TOV/Bw6UtErat3IqXxF4I60fnoszB1i+9MQR8QnwUW6+ya+AB0vrmZmZmZm1JM6kFGtZSdNz2/dERD9JvYBxkv4DTOK/HYrbgMMkPQ08BjwPEBFPSxoEPJhuazwN6EmWOblF0kdkHZmaTs1fgVsl7QP0LmnT4cAwScsBs4EjirxgMzMzM2s+lXp3L3dSChQRbeoov4dsbkpp+Vzgx3UcM5psDkm+7E7gzlrqPg9skSualNs3Hdi+lmO659bfp445KWZmZmZmTc2dFDMzMzOzVspzUszMzMzMzJqAMylmZmZmZq2VKnNOijMpZmZmZmbWoih7vqBZo/jNYmZmZku7FpW6eOOkHmX/frb2ZWOb/JqdSTEzMzMzsxbFc1LMzMzMzFop393LzMzMzMysCTiTYmZmZmbWSlXqE+edSTEzMzMzsxalYjspkuZLmp5b+hUU9xVJqxYRqxHnai9puKSXJD0uaaKk7xd8jq6S9igyppmZmZk1DVVVlX1pDpU83GtuRHRt7kYsoZHAy0CniFggqSPQpeBzdAW2BcYXHNfMzMzMbLFUbCalLikTcl7KrkyVtLWke1O24rhUp7ukhySNkzRL0jBJ33itJP1W0lNp6ZPKBtasp+1Bkk5K66dImiLpSUnn5OocKmlyatNwSW0kbQB8HzgjIhYARMTLETGunnNXS3oqF7evpAFpfaKk89N5npe0s6RvAQOBHuncPQp+uc3MzMysjFSlsi/NoZIzKctKmp7bPi8ixqb11yKiq6RLgFHAjkA74ClgWKrTjSxr8SpwD7A/cGtNMEnbAEeQdSQEPCbpQeAa4Hbg0tSx+SXQTdKPgU4proC7JO0CvAf0AHaMiHmSrgAOAT4GpkfE/NILq+fcHzXwmiwTEd3S8K6zI2J3SWcB20bECQ0ca2ZmZmbWJCq5k1LfcK+70s+ZQPuImAPMkfSFpJXSvskRMRtA0hhgJ3KdlLR9R0T8O9W5Hdg5Ii6X9IGkrYDVgWkR8UHqpPwYmJaOb0/WadkC2AaYIglgWeBd4Il6rq3Wc+euqy63p5+PA9UN1CXF7gX0Ahg+fDi9evVqzGFmZmZm1gQq9e5eldxJqc8X6eeC3HrNds1rEiXHlG7XZyTQE1iDLLMCWcbjvIgYnq8oqTcwOiJOLynfANhSUpvasil1+IqFh/C1K9lfc63zaeTvPiJGACNqNhvZDjMzMzNrCn6Y41Knm6SOachWD+Dhkv2TgH0lLSfpO8B+qQzgDuAnwHbAvansXuBISe0BJK0taTXgAeCAtI6klSWtFxEvAVOBc5RSLGnOyZ71nPsdYDVJq0j6NrBXI65zDrD8Ir42ZmZmZmZlU8mZlNI5KfdExKLchngKMBTYEJhA1vH4WkQ8IWkUMDkVjYyIaWnfl5ImAB/XZEEi4j5JmwCPpj7HZ8ChEfGMpDOA+1KHaB7wG7K5MEcDfwRelDQXeB84pb5zSxqYyt8AnmvEdU4A+qXXKj9vx8zMzMxauPS9suIowiN4SknqDvSNiMZkImo7vopsTsmBEfFCkW1rZn6zmJmZ2dKuRfUK3jvjiLJ/P+vwf9c2+TVXcialWUjqAtxNNrG9kjooZmZmZtbCNNfDFsvNnZRaRMREYOJiHvsMsH6R7TEzMzMzW5q4k2JmZmZm1kpV6i2IKzM/ZGZmZmZmrZYzKWZmZmZmrZXnpNjS7qBTXytL3DEXrMueRz9VeNxxIzej96WfFh53SJ8V6HvlfwqPC3DR8cuVJS7AuLadC4+557xZHD3o/cLjAozsvyrHX/hx4XGvPGUlDj/r7cLjjh64BvudUPy9Mu4Y2gmAfY6fVXjsO6/szIEnv1x43Fsu6chR575XeNyrz+wAwMH9Xi889o2D1ynb769cn28/7flk4XEB/jZqi7LEBcr2mdyj76uFxwUYe9F6HHnOu4XHvebs1cr2N1KOvw/I/kbK8TqPvWi9sn0OHXL6G4XHBbjhvLXLEtcW5k6KmZmZmVkr5TkpZmZmZmZmTcCZFDMzMzOzVip7hnjlqcyrMjMzMzOzVqvVdVIkzZc0Pbf0KyjuK5JWLSJWI891W277AEmjCoo9QFLfImKZmZmZWQtXpfIvzaA1DveaGxFdm7sRBdhGUpf0hPoWQZIARcSC5m6LmZmZmS29Wl0mpS4pO3Feyq5MlbS1pHslvSTpuFSnu6SHJI2TNEvSMNUykE/SbyU9lZY+qWxgzXraHiTppLR+iqQpkp6UdE6uzqGSJqc2DZfUJneaPwL9azn3QpmQ1IbqtDwnaZSk5yXdIGl3SY9IekFSt1yYLSU9msqPycX6RjtT3FmSrgOeAr63yC++mZmZmTULVVWVfWkOrbGTsmzJcK8euX2vpSzLJGAUcACwPXBOrk43oDfQBdgA2D8fXNI2wBHA99Oxx0jaCrgGOCzVqQJ+CVwv6cdApxS3K1mGZBdJmwA9gB1Tm+YDh+ROdTOwtaQNF+HaNyTr3GycloOBnYC+wO9z9bYAdgN2AM6StFZd7Uz1OwFXRMSmEbHQTdAl9UqdvqkvzrhxEZpqZmZmZrZ4Km24113p50ygfUTMAeZI+kLSSmnf5IiYDSBpDNmX/FtzMXYC7oiIf6c6twM7R8Tlkj5IHZbVgWkR8UH68v9jYFo6vj3Zl/4tgG2AKdkoKpYF8k+Emg9cCJwO/K2R1/5yRMxM7XoaeCAiQtJMoDpX786ImAvMlTSBrGOyUx3tfA14NSL+WdsJI2IEMALgoFNfi0a208zMzMyaQKU+J6U1dlLq80X6uSC3XrNdc62lX7QX5Yv3SKAnsAZZZgVAwHkRMTxfUVJvYHREnF5PvD+TdVLyjyP+ioUzXO1y66XXlL/e/O+ytmusq53VwL/raaOZmZmZWb0k/QS4DGgDjIyIwbXU+QUwgOy76YyIOLiueK1xuNeS6iapYxqy1QN4uGT/JGBfSctJ+g6wXyoDuAP4CbAdcG8quxc4UlJ7AElrS1oNeAA4IK0jaWVJ6+VPFBHzgEuAk3PFrwBbp2O2BjouxjXuI6mdpFWA7sCUetppZmZmZq2Vqsq/NNSEbN71n4Cfkk2pOEhSl5I6ncj+c37HiNgU6PONQDmtMZOyrKTpue17ImJRbkM8BRhKNr9jAlnH42sR8US6HfDkVDQyIqalfV+m4VMfR8T8VHZfmn/yaBrW9RlwaEQ8I+kM4L7UIZoH/AZYaM4HcDVwRm77NuCwNJzrMeD5Rbi2Gk+ma1sVODci3gTerK2dZMPOzMzMzKwVaiHDvboBL+amVNwE7APk72J7DPCniPgIICLe/UaUnFbXSYmINnWUV+fWR5FNnF9oX/py/mlE7NXA8RcDF5fWSZ2N7YEDS469jCy9VRpzLDC2gXN9AayV255LNnekNpvl6vXMrb9Ssy8iBtRxbJ3tzMc1MzMzM1tEawP/ym2/TnYTqryNACQ9QjYkbEBE3FNXwFbXSWkuKWV1N9mk+heauz1mZmZmZjTBLYIl9QJ65YpGpJsrLYplyG7a1B1YB3hI0uYR8XFdlZcaETERmLiYxz4DrF9ke8zMzMzMWrr83V7r8AYLP2tvnVSW9zrwWJqT/bKk58k6LVNqC7g0Tpw3MzMzM6sIksq+NMIUoFO6OdW3yJ4neFdJnb+QZVGQtCrZ8K/ZdV5XhB99YY3mN4uZmZkt7VrETPUacy77Xdm/ny1/0h8bvGZJewCXks03uSYiBkkaCEyNiLuU9Xb+SHan3PnAoIi4qc547qRYYx148stlebPccklHDjvzrcLjXnfumuxz/KzC4955ZWf2P/HFwuMC3H75hmWJC3D0oPcLjzmy/6qMa9u58LgAe86bxb6/Xpyb29XvL1dsxEGnvlZ43DEXrMuh/d8sPO71g7L7avzs2GcLj/3X4ZuU7bU4bcTcwuOe32tZgLJ9Xhzc7/XC4944eB0OP+vtwuOOHrgGPfqW3iyyGGMvWq/hSoup54B3Co85asDqZXlPQPa+KNff9bGDPyw87vB+K5flsx6yz/tj/vBB4XGv+v0q/OaiWqckLJE/9V2pLH/TADcOXqdldVKGnFL+TkrvC5v8mj3cy8zMzMzMWpSlauK8mZmZmVklaSHPSSmcMylmZmZmZtaiOJNiZmZmZtZaqTJzDpV5VWZmZmZm1mo1upMiab6k6bml3+KcUNJxkg5bnGObiqSu6TZqNdurS7pb0gxJz0ga38DxK0n6dW57LUm3NnDMiZKelXTDYrS3j6TlctvjJa20qHHMzMzMrJWpUvmXZrAow73mRkTXJT1hRAyrrVzSMhHx1ZLGL0hXYFugpjMyELg/Ii4DkLRFA8evBPwauAIgIt4EDmjgmF8Du0fE4twvrw9wPfCfdL496q9uZmZmZtZyLfFwL0mvSDpH0hOSZkraWFJVKl8pV++FlJEYIKlvKpso6VJJU4GTJP1Q0rQU5xpJ367rHKl8gKTRkiZJelXS/pIuSHXukdQ21dtG0oOSHpd0r6Q1c+c/X9JkSc9L2jk9JXMg0CNljHoAawJfdx4i4sl0fHtJD+TatU+qMhjYIB1/oaRqSU+lYzZN55su6UlJnSQNA9YH/ibpZEndJD2aXot/SOqcjm0j6SJJT6Vje0s6EVgLmCBpQu71WjWt/zbVf0pSn1RWnbI2V0l6WtJ9kpZd0veCmZmZmTUtqarsS3NYlLMuWzLcq0du3/sRsTVwJdA3IhYAdwL7AUj6PvBqRNT2FKdvRcS2wJ+AUUCPiNicLMtzfF3nyJVvAOwG7E2WTZiQjp8L7Jk6KkOAAyJiG+AaYFDu+GUiohtZNuLsiPgSOAsYGxFdI2JsatvVkiZI6i9prXTs58B+qV27An+UJKAf8FI6/pSS6z0OuCxlpbYFXo+I44A3gV0j4hLgOWDniNgqteUP6dheQDXQNSK2AG6IiMtzx+6aP5GkbYAjgO8D2wPHSNoq7e4E/CkiNgU+Bn5OLST1kjRV0tTZM8fUVsXMzMzMrFBFDfe6Pf18HNg/rY8l+4J9LfDLtF2bmvLOwMsRUfOI6dHAb4BL6zkHwN8iYp6kmUAb4J5UPpPsC31nYDPg/nndIAUAACAASURBVKz/QBsg/2jafNzq2hoYEfdKWh/4CfBTYJqkzci+3P9B0i7AAmBtYPU6rrPGo0B/SesAt0fEC7XUWREYLakTEEDbVL47MKxmWFxENPS42p2AOyLi3wCSbgd2Bu4ie62nN+LaRwAjoHxPnDczMzOzxVShz0kp6hbEX6Sf83MxHwU2lNQB2Bf4vzqO/fcSnOPr8ohYIGleRNR8kV6Q6gl4OiJ2WMS4C0kdghuBGyXdDewCLA90ALZJHaVXgHb1XURE3CjpMWBPYLykYyPi7yXVziXLCO0nqRqYWF/MxfRFbn0+4OFeZmZmZtYilG2QWeos3AFcDDwbER80cMgsoFrShmn7V8CDBTRlFtBB0g4AktpK2rSBY+aQdUBIx+ymdPcsScuTDTF7jSzj8W7qoOwKrFfb8XkpIzM7DdO6E6htEv6KwBtpvWeu/H7gWEnLpFgrN3C+ScC+kpaT9B2y4XeT6rluMzMzM2tFVFVV9qU5LMmclMGNOGYscCh1D/X6WkR8TjZ/4pY0dGsBUOudwBZFmmNyAHC+pBnAdOAHDRw2AeiSm3uzDTBV0pNkGaKRETEFuAHYNrX3MLK5JKQO2SNpsvqFJbF/ATwlaTrZMLTrajn/BcB5kqaxcHZnJFnn6Ml0LQen8hHAPTUT53PX/gTZPJ/JwGOp3dMauHYzMzMzs2bV6OFeEdGmjvLq3PpUoHvJtkrqD8itdy/Z9wCwFSXqOkc+VtpuX8d5ppMNzyqNm2/r+6R5GWlo13Yl1Us7GzXH1DqMLCIOLinaLJUPJrv7V2n96tz6o8BGud1npPKvgN+mJX/sELKbA9QW62KybFa+/is17UnbF9V2DWZmZmbWwslzUszMzMzMrCVppuFY5VaZV2VmZmZmZq2WMylmZmZmZq1VhQ730n/v2GvWIL9ZzMzMbGnXonoF/xk9sOzfz5Y7/Kwmv2ZnUqzRjvlDQ3eRXjxX/X4Vjjv/o8LjDjvtuxxy+hsNV1xEN5y3Nked+17hcQGuPrNDWeICHH/hx4XHvPKUldj31883XHEx/OWKjRjXtnPhcfecN4uTLptTeNzLTlqenx37bOFx/zp8EwB22e/hwmM/dMdO7H/ii4XHvf3yDTnszLcarriIrjt3TQCOPOfdwmNfc/ZqHNr/zcLjXj9orbK9Fged+lrhcQHGXLBuWeJCef4duer3q5T1tSjX769c/z7td0Jtz4hecncM7cQBJ80uPO6tl63PHkfOLDzu+Gs2L0t7IWtzS9Jctwgut8q8KjMzMzMza7WcSTEzMzMza61UmTmHyrwqMzMzMzNrtZxJMTMzMzNrrapa1Dz+wjiTshgkfVay3VPS0HLFr2V/d0mfSJou6TlJDT4xXtK+kroU1UYzMzMzs3JxJ6UFkbQoma1JEdEV2ArYS9KODdTfF3AnxczMzKyCSFVlX5qDOykFk1Qt6e+SnpT0gKR1U/koSQfk6n2WfnaXNEnSXcAzJbGuk7RvbvsGSfvk60TEXGA6sHaqc4ykKZJmSLpN0nKSfgDsDVyYsi8bpOUeSY+n829cppfEzMzMzGyRuJOyeJZNX/anS5oODMztGwKMjogtgBuAyxsRb2vgpIjYqKT8aqAngKQVgR8A4/IVJH0X6AQ8lIpuj4jtImJL4FngqIj4B3AXcEpEdI2Il4ARQO+I2AboC1zRyGs3MzMzs5aiSuVfmuOymuWsrd/c9GW/axpydVZu3w7AjWn9z8BOjYg3OSJeLi2MiAeBTpI6AAcBt0XEV2n3zpJmAG8A90bE26l8s5QZmQkcAmxaGldSe7IOzy2pkzUcWLO2hknqJWmqpKnPTR7diEsxMzMzM1syvrtX0/mK1ClUNrjvW7l9/67nuOuAQ4FfAkfkyidFxF6SOgL/lHRzREwHRgH7RsQMST2B7rXErAI+Th2sekXECLKsC8f84YNoqL6ZmZmZNSE/J8Ua6R9kHQrIMhmT0vorwDZpfW+gbSPjjQL6AETEM6U7UwZmMHBaKloeeEtS23T+GnPSPiLiU+BlSQcCKLNlI9tjZmZmZlZW7qQUrzdwhKQngV8BJ6Xyq4D/SUO0dqD+7MnXIuIdsrkl19ZTbRiwi6Rq4EzgMeAR4LlcnZuAUyRNk7QBWQfmqNSep4GFJuSbmZmZWSsglX9pBh7utRgion3J9iiyjAcR8SqwWy3HvANsnys6LZVPBCbWFV/ScmQT48fk9i90TLrD19pp88q0lJ7/Eb55C+KffOPizMzMzMyamTspLZik3cnu8HVJRHzS3O0xMzMzsxamqjIHRrmT0oJFxP8D1mvudpiZmZmZNSV3UszMzMzMWqsKvbuXOylmZmZmZq1VMz1ssdwU4UdfWKP5zWJmZmZLuxbVK/j8L5eX/ftZu31PbPJrdibFGu3YwR+WJe7wfivTZ8hnhce9tHd7jjv/o8LjDjvtu2VpL2RtLpfDz3q78JijB67BQae+VnhcgDEXrMtJl80pPO5lJy3PuLadC4+757xZHNr/zcLjXj9oLYCyvM5jLli3bG3e48iZhccdf83mABx17nuFx776zA4cePLLhce95ZKO/OzYZwuP+9fhm3DCxeW5n8rQ365YlrgAx1/4ceExrzxlJY48593C4wJcc/ZqHNzv9cLj3jh4HX7xu1cKj3vzH6vL0l7I2lyuv719f/184XH/csVGrfLf6sVSocO9KvOqzMzMzMys1XImxczMzMystWqmhy2WmzMpZmZmZmbWojiTYmZmZmbWWlXowxwr86pyJNU5a0pSd0l317HvFUmrFtyWiZK2LTLmkpLUR9Jyzd0OMzMzM7MaFd9JsQb1AdxJMTMzM2uNpPIvzWCp6KQoc6GkpyTNlNQjt3sFSeMkzZI0TFr4Pm6SqiU9K+kqSU9Luk/SspI2ljS5pN7MtP5DSdPSua6R9O2SmMdJujC33VPS0LR+qKTJkqZLGi6pTSr/LF3D05L+n6RuKTMzW9LeqU6bVGeKpCclHZvKu6e6t0p6TtIN6TU5EVgLmCBpQqEvupmZmZnZYloqOinA/kBXYEtgd+BCSWumfd2A3kAXYINUt1Qn4E8RsSnwMfDziHgO+JakjqlOD2CspHbAKKBHRGxONu/n+JJ4twH75bZ7ADdJ2iSt7xgRXYH5wCGpzneAv6c2zAH+D/hRijMw1TkK+CQitgO2A47JtW8rsqxJF2D9dI7LgTeBXSNi13pePzMzMzNriVRV/qUZLC2dlJ2AMRExPyLeAR4k+xIPMDkiZkfEfGBMqlvq5YiYntYfB6rT+s1knQrSz7FA51S/5slEo4Fd8sEi4j1gtqTtJa0CbAw8AvwQ2AaYIml62l4/HfYlcE9anwk8GBHz0npNe34MHJaOfQxYhayDVXOdr0fEAmB67ph6Seolaaqkqc9OHt2YQ8zMzMzMlojv7gXRwDbAF7n1+cCyaX0scIuk24GIiBckbdnI894E/AJ4DrgjIkKSgNERcXot9edFRE3bFtS0KSIWSKr5PQroHRH35g+U1L2Wa2jU7z4iRgAjAI4d/GFtr42ZmZmZNRff3atVmwT0SHM2OpBlNmrmk3ST1DHNRekBPNzYoBHxEtkX/jPJOiwAs4BqSRum7V+RZW5K3QHsAxxE1mEBeAA4QNJqAJJWlrReY9sD3AscL6ltOn4jSd9p4Jg5wPKLcA4zMzMzs7Kq6ExKyjB8QdYh2AGYQZYpOTUi3pa0MTAFGApsCExIdRfFWOBCoCNARHwu6QiyDMsyKf6w0oMi4iNJzwJdImJyKntG0hnAfanTNA/4DfBqI9sykmwY1xMpK/MesG8Dx4wA7pH0puelmJmZmbUyFfrE+YrupACbAi+lYVKnpOVrETGRkvkiuX3VafV9YLNc+UUl9S4CSsseIJuoXhqze8n2XrXUGct/szL58va59QG17UvzTX6flryJaampf0JufQgwpPR8ZmZmZmbNpWI7KZKOA04ku6OVmZmZmVnlaaa7b5VbxXZSImIYtQyzMjMzMzOzlq1iOylmZmZmZhWvQuekVGZ+yMzMzMzMWi3999EbZg3ym8XMzMyWdi0qdfH5A9eV/ftZux8e1uTX7OFe1mifjx9Rlrjt9ujFMX/4oPC4V/1+FQ4/6+3C444euAYDb/iq8LgAZx1Svj/J/U54ofCYdwztxKH93yw8LsD1g9biZ8c+W3jcvw7fpCxtvn7QWoxr27nwuHvOmwXAwf1eLzz2jYPXKVub+wz5rPC4l/bObnI4/L7CQ3Psj6FH38be7b3xxl60Hoec/kbhcW84b+2yfG5C9tlZLuV6Le5dZdPC4wL87wdPc9iZbxUe97pz1+TkocX/jVxyQns+H3N+4XEB2h10Gh/NqO2xb0vmu1v+D7NfeqnwuOtvsAGvvVD8vyEA63bapCxxbWHupJiZmZmZtVJRoXNS3EkxMzMzM2utKvQWxJV5VWZmZmZm1mo5k2JmZmZm1lo5k2JFkFTnTDlJ3SXdXce+VyStWqY29ZQ0tByxzczMzMwWlTMpZmZmZmatVKVOnHcmpRkoc6GkpyTNlNQjt3sFSeMkzZI0TKo7hyepg6TbJE1Jy46SqlLWZaVcvRckrV5b/bJeqJmZmZnZYnAmpXnsD3QFtgRWBaZIeijt6wZ0AV4F7kl1b60jzmXAJRHxsKR1gXsjYhNJdwL7AddK+j7wakS8I+nG0vqAb/ZtZmZm1lp5TooVaCdgTETMj4h3gAeB7dK+yRExOyLmA2NS3brsDgyVNB24iywL0x4YC9RkZ36ZtuurXydJvSRNlTT16r89VF9VMzMzM7NCOJPS8kQD23lVwPYR8Xm+UNKjwIaSOgD7Av/XQP26GxMxAhgB8Pn4EfW1xczMzMyamuekWIEmAT0ktUkdiV2AyWlfN0kd01yUHsDD9cS5D+hdsyGpK0BEBHAHcDHwbER8UF99MzMzM7OWxJmUJiRpGeALsg7EDsAMskzJqRHxtqSNgSnAUGBDYEKqW+NJSQvS+s3AicCfJD1J9rt8CDgu7R+bYvXMHV9ffTMzMzNrbaoqM+fgTkrT2hR4KWU6TknL1yJiIllW5RsiorqOmD1qK4yIqYBKyt6vrX5EjAJG1ddwMzMzM7Om4k5KE5F0HFkmo09zt8XMzMzMKkOlPifFnZQmEhHDgGHN3Q4zMzMzs5bOnRQzMzMzs9bKz0kxMzMzMzMrP2VzuM0axW8WMzMzW9q1qEkgn/3zrrJ/P2u//d5Nfs0e7mWN1nPAO2WJO2rA6hw7+MPC4w7vtzJHD3q/8Lgj+6/K4We9XXhcgNED1yhLXIB9jp9VeMw7r+zMz459tvC4AH8dvgm77FffY4IWz0N37MRBp75WeNwxF6zLwf1eLzzujYPXAWBc286Fx95z3ix+8btXCo978x+rOezMtwqPe925awKwx5EzC489/prNy/Z5UY7PzlEDVueQ098oPC7ADeetXZa4UJ5/R0YNWL0s72PI3svl+LwfPXCNsn0mH9r/zcLjAlw/aK2y/f7K9Znco++rhccFGHvRemWJawtzJ8XMzMzMrLWq0Lt7eU6KmZmZmZm1KM6kmJmZmZm1UlGhd/dyJ8XMzMzMrLXycK/WT9Jn9ezrLunuOvYdKWmmpCclPSVpnwbOs6+kLrntgZJ2r6d+B0mPSZomaefGXEvu2K6S9sht7y2p36LEMDMzMzNrSZxJaYCkdYD+wNYR8Ymk9kCHBg7bF7gbeAYgIs5qoP4PgZkRcfRiNLErsC0wPp3rLuCuxYhjZmZmZq1NhQ73qsyrqocyF6aMyExJPXK7V5A0TtIsScMkVQGrAXOAzwAi4rOIeDnFOkbSFEkzJN0maTlJPwD2Bi6UNF3SBpJGSTogHTNY0jMpK3ORpK7ABcA+qf6ykq6UNFXS05LOybV9O0n/SOebLGlFYCDQIx3bQ1JPSUNT/WpJf0/nekDSuql8lKTLU6zZNW0zMzMzM2sJlsZMyv5k2YctgVWBKZIeSvu6AV2AV4F7Ut07gHeAlyU9ANweEX9N9W+PiKsAJP0fcFREDJF0F3B3RNya9pF+rgLsB2wcESFppYj4WNJZwLYRcUKq1z8iPpTUBnhA0hbAc8BYoEdETJG0AvAfoPTYnrlrHQKMjojRko4ELifL8gCsCewEbEyWebl1CV9XMzMzM2ti4TkpFWMnYExEzI+Id4AHge3SvskRMTsi5gNjgJ3S+k+AA4DngUskDUj1N5M0SdJM4BBg0wbO/QnwOXC1pP3JOhm1+YWkJ4BpKWYXoDPwVkRMAYiITyPiqwbOtwNwY1r/c7r2Gn+JiAUR8Qywel0BJPVKWZ2pzz/+5wZOZ2ZmZma25JbGTkp9orbtyEyOiPOAXwI/T/tHASdExObAOUC7eoNnnYpuZFmLvciyNQuR1BHoC/wwIrYAxjUUdzF9kT9tXZUiYkREbBsR2260za/K0AwzMzMzW2yqKv/SDJbGTsoksjkcbSR1AHYBJqd93SR1THNRegAPS1pL0ta547uSDQcDWB54S1JbskxKjTlp30LSpPsVI2I8cDLZkLNSKwD/Bj6RtDrw01Q+C1hT0nYp1vKSlqnrXMk/yDpVpPZNqqOemZmZmVmLsdTMSUlf6L8gm2OyAzCDLFNyakS8LWljYAowFNgQmJDqfg+4SNJaZEO13gOOS2HPBB5LZY/x387CTcBVkk4kGyZWY3ngTkntyLIXvy1tZ0TMkDSNbA7Kv4BHUvmXaZL/EEnLAnOB3VM7+0maDpxXEq43cK2kU1Ibj1ikF83MzMzMWrSoe0BMq7bUdFLI5na8FBEBnJKWr0XERLKsSqlXgd1qCxgRVwJX1lL+CNk8kho9c+vdaqk/imzoWM12z9I6qXwKsH0tu7Yr2R6V6tfa9tL4EdG+tvOZmZmZmTWHpaKTIuk44ESgT3O3xczMzMysKFGhz0lZKjopETEMGNbc7TAzMzMzs4YtFZ0UMzMzM7OKVKGZFGVTNMwaxW8WMzMzW9q1qJnqH0+fWPbvZyt17d7k1+xMijXa5/ePKkvcdj/qybGDPyw87vB+K9Oj76sNV1xEYy9aj4E3NPQczcVz1iHl+5M88OSXC495yyUdOejU1wqPCzDmgnXZ/8QXC497++Ubcmj/NwuPe/2gtRjXtnPhcfecNwuAX/zulcJj3/zH6rK1ufelnxYed0ifFQC4dkLhoTli1/K9xgf3e73wuDcOXoffXPRx4XEB/tR3pbLEBTjk9DcKj3nDeWszoVNtd/Rfcru+MIPDznyr8LjXnbtm2f5GPv/L5YXHBWi374m898zkhisuog5duvHS7NmFx91g/fV5/qXy/Pu00QbrliXu4vIT583MzMzMzJqAMylmZmZmZq1Upd7dqzKvyszMzMzMWi1nUszMzMzMWivPSTEzMzMzMyu/iu+kSApJ1+e2l5H0nqS7FzPeSpJ+ndvuXlcsSRMlbdtAvM8Wpx1mZmZmZqGqsi+NIeknkmZJelFSv3rq/Tx9P6/3O3LFd1KAfwObSVo2bf8IWJJ7IK4E/LrBWmZmZmZmZRao7EtDJLUB/gT8FOgCHCSpSy31lgdOAh5rKObS0EkBGA/smdYPAsbU7JC0sqS/SHpS0j8lbZHKB0i6JmVDZks6MR0yGNhA0nRJF6ay9pJulfScpBukhQcHSjpS0qW57WMkXVJSp3s61zfiSNpO0j8kzZA0WdLyktpJulbSTEnTJO2a6vZM13O/pFcknSDpt6nOPyWtnOptIOkeSY9LmiRp46JebDMzMzNbqnQDXoyI2RHxJXATsE8t9c4Fzgc+byjg0tJJuQn4paR2wBYs3Hs7B5gWEVsAvweuy+3bGPhfshf+bEltgX7ASxHRNSJOSfW2AvqQ9RzXB3YsOf/NwM/S8QBHANfU0s5vxJH0LWAscFJEbAnsDswFfgNERGxO1vEana4PYDNgf2A7YBDwn4jYCngUOCzVGQH0johtgL7AFbW9cJJ6SZoqaerV4ybWVsXMzMzMmkkLGe61NvCv3PbrqexrkrYGvhcR4xoTcKm4u1dEPCmpmuzL/PiS3TsBP0/1/i5pFUkrpH3jIuIL4AtJ7wKr13GKyRHxOoCk6UA18HDu/J9J+juwl6RngbYRMbORcT4B3oqIKSnWp2n/TsCQVPacpFeBjVKcCRExB5gj6RPgr6l8JrCFpPbAD4Bbckmfb9d2YRExgqxDw+f3j4o6rt/MzMzMKpSkXkCvXNGI9B2xscdXARcDPRt7zFLRSUnuAi4CugOrNPKYL3Lr86n79WpMvZFkmZrngGuX8HwNycdZkNtekGJWAR9HRNfFjG9mZmZmLUET3II4/5/WdXgD+F5uex0WngO+PNlIn4npP8jXAO6StHdETK0t4NIy3Auy4VXn1JLBmAQcAtm8EOD9mmxFHeaQvdCLJCIeI/vlHUxuTkwjzALWlLRdauPykpYpafdGwLqpbmPa8inwsqQD0/GStOUitMnMzMzMrMYUoJOkjmmqwi/JEgQARMQnEbFqRFRHRDXwT6DODgosRZ2UiHg9Ii6vZdcAYBtJT5JNij+8gTgfAI9Ieio3cb6xbgYeiYiPGntAmnzUAxgiaQZwP9CObA5JlaSZZHNWeqahaY11CHBUivk0tU9uMjMzM7MWLKgq+9JgGyK+Ak4A/j97dx5v13T3cfzzRZQKouahRJUoQRCKFknr8VTV0MdUosQ8VxCViiFDQyo0phoi4lJDiWpLo2hJSpMYIrMhFGlrFlPFkJL8nj/2OuycnjvkZp9777n5vl+v87p7r73Wb6+9701yV35r7X0/8AxwR0Q8JWmQpL2bc13tfrpXRHSsUDYOGJe23wH2rVBnQNl+19z2IWXVx+WOnZzb7lFW79vAQk/1KvUv36cKcZ4AdijvI9kC/PJ+1wF1uf3OlY5FxEvA9yrENDMzMzNbJBFxL2VrvyPivHrq9mgsXrsfpLQFkjoBjwPTIuLB1u6PmZmZmbUP0QJrUlqDByktICLe44snb5mZmZmZWQM8SDEzMzMzq1FNfI9JzVGEX31hTeYfFjMzM1vStan5Va8/O6Xqv5+ttenWLX7NzqRYkx09ZE5V4o7svxqH9n+18Lg3D1mHn17zceFxLzp+efY8embhcQHGjOzaeKVmOmrwW4XHvP7c1TlrRPH3GOAXxy7PYee+VnjcmwavzfePrPQu1cVz76gt6HPF3MLjXnpK9uyPat2LUy5t6InrzXNFn5UY06FL4XH3/DR7yvqRA98sPPao89fg8PNeLzzujYPW4pB+Lxce99ah61Xl703I/u6slgPPmF14zDsu6cwxF7xdeFyA685etWr/PvX62SuNV1xEt1y4blXuMWT3uRq/B4zsv1rV/kxX8160JdG2xkyFaZ/5ITMzMzMzq1nOpJiZmZmZ1aj2uialfV6VmZmZmZnVLGdSzMzMzMxqVHt9T4ozKfWQFJJuzu0vI+ktSX9sZrxOkk7M7feoL5akcZK6NxJvvqSpkmZKuie9MLKh+t0kfb85fTczMzMza0kepNTvQ6CrpOXT/v8Ai/Mojk7AiY3WarqPI6JbRHQF3gFOaqR+N8CDFDMzM7N2JFDVP63Bg5SG3QvsmbYPBm4rHZD0FUm/lzRd0qOStkzlAySNStmQFyX9JDUZCmyUsh/DUllHSXdKelbSLdLC+TpJR0q6NLd/jKThFfo5EVg31dle0kRJUyRNkNRF0rLAIOCgdP6DJK2Q+vl4qrvP4t8uMzMzM7PF50FKw34D/EjScsCWwGO5YwOBKRGxJXA2cFPu2KbA/wLbA+dL6gD0A15I2Y8zU72tgT7AZsDXgG+Vnf8OYK/UHuAIYFS+gqSlge8Cd6eiZ4GdI2Jr4Dzggoj4T9q+PZ3/dqA/8FBEbA/0BIZJWmHRbo+ZmZmZtabQUlX/tAYPUhoQEdOBzmRZlHvLDn8b+HWq9xCwqqSV0rExETEvIuYAbwJr1nOKxyPi5YhYAExN58qffy7wEPADSZsCHSKi9Ba65SVNBV5P8f+cylcGRkuaCQwHNq/n3LsD/VKMccBywPrllSQdK2mSpEnPPnFT+WEzMzMza0We7rXkuhu4mNxUryaYl9ueT/1PUWtKvZFAb7Isyg258o8johuwASC+WJMyGBib1qrsRTb4qETAfimz0i0i1o+IZ8orRcSIiOgeEd033e6wekKZmZmZmRXHg5TGjQIG5jIYJY8AvSB7UhcwJyL+3UCcD4AVF/XkEfEY8FXgECoMlCLiI+AnwBmSliHLpJQW+Pdu4Pz3A6eU1sFI2npR+2ZmZmZmrcvTvZZQaTrW5RUODQC2lTSdbFH84Y3EeRsYnx4ZPKyhuhXcAYyPiHfriT0FmE42Le0i4EJJU1g4MzMW2Ky0cJ4s49IBmC7pqbRvZmZmZtbq/DLHekRExwpl48jWbxAR7wD7VqgzoGy/a277kLLq43LHTs5t9yir922y9SX19i8i9srtbpLbPifX3+3K4h5X3n8zMzMzqx2ttWak2pxJacPSCyCfI1t/8mBr98fMzMzMrCU4k9KGRcR7LJwVMTMzMzP7XGutGam29nlVZmZmZmZWs5xJMTMzMzOrUe11TYoiorX7YLXDPyxmZma2pGtTo4IXX3ih6r+ffW2jjVr8mp1JsSab/NzbVYm7zSarcsqlDb1ipnmu6LMSPzz5+cLj/u7Kjbn4rgWFxwXo+3/Vm4F5SL+XC49569D1OOzc1wqPC3DT4LU5cuCbhccddf4aHDX4rcLjXn/u6lz7QOFhOW737Ov3jyx/VdPiu3fUFtwwtvCwHNGTqn3vAMZ06FJ47D0/ncXRQ+YUHndk/9XoPeCNwuPWDViTE4a9V3hcgKvP7FSVuAAnXVx8n3/VtxMPP/Vh4XEBdtl8BXY7eFLhcf9yW3d6/eyVxisuolsuXJdv7/XXwuMC/O2eXdmj9/TC4/6pbksOOO2lwuOOHr4hu/zwb4XHBXj4d9+uStzmCrWpMVNhvCbFzMzMam58XgAAIABJREFUzMzaFGdSzMzMzMxqVIQzKWZmZmZmZlXnTIqZmZmZWY2KdppzKOSqJIWkS3L7fSUNKCJ2ineYpJmSZkiaIqlvUbGLIKm7pMub0W64pD65/fsljcztXyLp9EWIN05S90XtR2pbJ2n/5rQ1MzMzMytSUUOvecD/SVqtoHifk7QH0AfYPSK2AHYA3i/6PIsjIiZFxE+a0XQ8sBOApKWA1YDNc8d3AiY0JZCkpZtxfjMzMzOrYYGq/mkNRQ1SPgNGAKeVHyj/H3pJc9PXHpL+KukPkl6UNFRSL0mPp4zJRqnJz4C+EfEqQETMi4jrUoxukh6VNF3S7yStksrHpSzFJEnPSNpO0l2Snpf081Sns6RnJd2S6twp6cvp2HmSnkjZmxFS9my3FPcXqY/PSdo5dy1/TNsrSBqV6kyRtE8q3zyVTU393ZhsALJjus7NgZnAB5JWkfQl4BvAZEnfTbFmpNhfSjFnp/5MBg7I3eOl0n3/uaSlJQ1L1zNd0nGpjiRdKWmWpL8AayzG99/MzMzMrDBFTmL7FdBL0sqL0GYr4HiyX8Z/DGwSEdsDI4FTUp2uwJP1tL8JOCsitgRmAOfnjv0nIroD1wB/AE5KsXpLWjXV6QJcFRHfAP4NnJjKr4yI7SKiK7A88INc3GVSH/uUna+kP/BQqtMTGCZphXSdl0VEN6A78HIaeH0maX2yrMlE4DGygUv3dE1LAXXAQSmTtAxwQu58b0fENhHxm1L/gFuA5yPiHOAo4P2I2A7YDjhG0obAD9P1bwYcls5vZmZmZjXEmZRGRMS/yQYNizLt6YmIeC0i5gEvAKVXoc0AOjfUMA2GOkVE6a1FNwK75KrcnYv1VO48LwJfTcf+FRHj0/bNQOntPD0lPSZpBvAdFp6CdVf6+mQ9fdwd6CdpKjAOWA5Yn2wAcraks4ANIuLjVH8C2QChNEiZmNsfTzaQeCkinqvnOm8vO/+1wMyIGJLrz2GpP48BqwIbpxi3RcT8NFh6qMK1IOnYlJGadNftN1aqYmZmZmZWqKKf7nUpMBm4IVf2GWkwlNZdLJs7Ni+3vSC3vyDXt6eAbannl+gG5GOVn6cUO8rahKTlgKuA7hHxr/QAgOUqxJ1P5fsnYL+ImFVW/oykx4A9gXslHRcRD/HFupQtyKZ7/Qs4gyyzcwONK3/N7gSyQdYlEfFJ6s8pEXH/Qp2Uvt+E2ETECLKpfEx+7u3y+2VmZmZmrai1Mh3VVugzyyLiHeAOsilGJbPJBhkAewMdFjHshWRTptYCkLSspKMj4n3g3dK6ELLpYn+tL0g91pdUWhNyCPA3vhiQzJHUEVjUJ17dD5ySW8eydfr6NeDFiLicbPrZlqn+BLLpZO+krMY7QCeyKV8TgFlAZ0lfT/Ubu87rgXuBOyQtk/pzgqQOqR+bpOlnDwMHpTUra5NNTTMzMzMza3XVeE/KJcDJuf3rgD9Imgbcx3//z3+DIuJeSWsCf0m/+AcwKh0+HLgmLXh/EThiEfs6CzhJ0ijgaeDqiPhI0nVkWY3XgScWMeZgsozS9JQ5eolsEHIg8GNJn6a4F6T6M8ie6nVrLsYMoGNEzAGQdAQwOg06niBbZ1OviPhlmg73a6AX2bS0yen+vQXsC/yObCrb08A/yaaZmZmZmVkNaa+ZlEIGKRHRMbf9BvDlsv0dctXPSuXjyNZslOr1yG2XH7uBClOfImJqWeymxOoB2dO9gM8i4tAK7c8Bzmkk7hzSmpT8OdJak+MqtB0KDK1QPh9Yqaysd9n+g8DWFdp2bqB/+UX9Z6dPuZMrlJmZmZlZjYhon4OU9vmKSjMzMzMzq1nVmO5VEyJiNtkjic3MzMzMalJ7ne7lTIqZmZmZmbUpS2wmxczMzMys1rXXTIoi/OoLazL/sJiZmdmSrk2NCmb+/fWq/37W9etrtfg1O5NiTXbkwDerEnfU+Wtw9JA5hccd2X81TrtybuFxh5/ckV4/e6XwuAC3XLhuVeIC/PDk5wuP+bsrN+aQfi8XHhfg1qHrcWj/VwuPe/OQdTjgtJcKjzt6+IYc1Pcfhce9/eINAKr2Z+TAM2YXHveOSzpz+HmvFx73xkFrAdW7F2M6dCk87p6fzmLsxlsVHrfn89PY67hnCo8LcM+136hKXKjOvyOjzl+jKj9vkP3MVevvof1PfbHwuHde9jUO/uk/C48LcNtF69fU9+/GQWtx2LmvFR4X4KbBa1clbnO110yK16SYmZmZmVmb4kyKmZmZmVmN8ntSzMzMzMzMWoAzKWZmZmZmNWqB16S0LknzJU2VNFPSaElfboU+9JC0U26/i6RxqV/PSBrRSPvOkmYW1Jf/iiVpgKS+aXuQpN3S9mxJqxVxXjMzMzOzaqulTMrHEdENQNItwPHALxtrJGmZiPisoD70AOYCE9L+5cDwiPhDOtcWBZ2HFK/ZfY+I84rsi5mZmZm1PX66V9vyCPB1SStIGiXpcUlTJO0DIKm3pLslPQQ8KKmjpBskzZA0XdJ+qd7ukiZKmpyyMx1T+WxJA1P5DEmbSupMNjA6LWVOdgbWBj5//mpEzEjtO0t6JLWfnM++lNRXJ2VrHpF0N/B0yoj0ybUbIunUxm6QpDpJ+5eVLS/pT5KOqe/emZmZmZm1tlrKpABZdgHYA7gP6A88FBFHSuoEPC7pL6nqNsCWEfGOpF8A70fEFinGKmn60znAbhHxoaSzgNOBQan9nIjYRtKJQN+IOFrSNcDciLg4xRkOPCRpAvAAcENEvAe8CfxPRHwiaWPgNqB72aU0VGcboGtEvJQGR3cBl0paCvgRsD2wIrCRpKm5mGsBF9dz6zoCvwFuioibJF1Q6d5FxIeNfAvMzMzMrI3w071a3/LpF/JJwD+B64HdgX6pfBywHLB+qv/niHgnbe8G/KoUKCLeBXYANgPGp/aHAxvkzndX+vok0LlShyLiBuAbwGiyqWCPSvoS0AG4TtKMdGyzCs0bqvN4RLyUzjEbeFvS1ul6p0TE26neCxHRrfQBrqnUz+QPZIOom9J+Q/fuc5KOlTRJ0qRZk24qP2xmZmZmVrhayqR8vialRJKA/SJiVln5N4HGMgIiG8gcXM/xeenrfBq4TxHxKjAKGJUWsncF9gLeALYiGwh+UqHpaQ3UKe/7SKA3WaZkVL1X1LDxwPck3RoRQXb9/3XvykXECGAEwJED34xmntvMzMzMqsBrUtqm+4FT0mCFlG2o5M/ASaUdSasAjwLfkvT1VLaCpE0aOd8HZNOsSnG+J6lD2l4LWBV4BVgZeC0iFgA/BpauEKspdUp+B3wP2C5dc3OcB7zLFxmlpt47MzMzM7MWVeuDlMFk06amS3oq7Vfyc2CV9PjiaUDPiHiLLDtxm6TpwERg00bOdw/ww9zC+d2BUsz7gTMj4nXgKuDwVL4plbM6TakDQET8BxgL3BER8xvpY0NOJZs2dxFNv3dmZmZm1kZFqOqf1lAz070iomOFso+B4yqU1wF1uf25ZGtOyus9RJadKC/vnNueRLbehIh4DtgyV/URssX25e2fL6t3ViqfTTYdrKE648jWiHwuLZjfATggd47PY+XKBuS2e1e6HuCI3PZ/3TszMzMzs9ZW65mUdk/SZsDfgQfTwMbMzMzMDMjWpFT70xpqJpOypIqIp4GvtXY/zMzMzKzt8SOIzczMzMzMWoAzKWZmZmZmNWpBa3egSpS9MsOsSfzDYmZmZku6NjW/6tFn36/672c7bLpyi1+zMynWZL0HvFGVuHUD1uToIXMKjzuy/2qccum/C497RZ+VOOC0lwqPCzB6+IZViQuw59EzC485ZmRXDj/v9cLjAtw4aC0OO/e1wuPeNHht9jrumcLj3nPtN+j1s1cKj3vLhesC1fnzVzdgTQ7p93LhcW8dul7V4kL17sXYjbcqPG7P56cxpkOXwuPu+emsqvwcQ/azXC3V+jN93NB3Co8LcG2/r3DwT/9ZeNzbLlq/Kv+OjB6+IYf2f7XwuAA3D1mHE4a9V3jcq8/sVLW4Rw1+q/C4ANefu3pV4jaX16SYmZmZmZm1AGdSzMzMzMxqVGs9IrjanEkxMzMzM7M2xZkUMzMzM7Ma5TUpLUjSfElTJc2UNFrSlxex/TBJT0kaVq0+VoukcZJmSZom6QlJ3Rqp30nSibn9dSTdWf2empmZmZlVR5scpAAfR0S3iOgK/Ac4vimNJJUyQ8cCW0bEmYvYrq3oFRFbAVcBjQ20OgGfD1Ii4tWI2L+anTMzMzOztiFQ1T+toa0OUvIeAb4uaQVJoyQ9LmmKpH0AJPWWdLekh4AHJd0NdASelHSQpM6SHpI0XdKDktZP7eokXSPpMeCitH+1pEclvSipRzrfM5LqSp1JdSalTM3AXPlsSQMlTZY0Q9KmqbyjpBtS2XRJ+6Xy3SVNTPVHS+pY4donAuvm4jyYi79PqjMU2Chlnoal652Zuzd3SbpP0vOSLsr19yhJz6X7eZ2kK4v5dpmZmZmZLZ62lkFYSMpw7AHcB/QHHoqIIyV1Ah6X9JdUdRuyzMk7qd3ciOiWtu8BboyIGyUdCVwO7JvarQfsFBHz00BkFWBHYG/gbuBbwNHAE5K6RcRUoH9EvCNpabJB0ZYRMT3FmxMR26TpV31T23OB9yNii9SfVSStBpwD7BYRH0o6CzgdGFR2C74H/D5tfwL8MCL+ndo/mgZk/YCuuevtXBajG7A1MA+YJekKYH7q1zbAB8BDwLTGvh9mZmZm1rYsaKev2m6rg5TlJU1N248A1wMTgL0l9U3lywHrp+0/lwYoFewI/F/a/jVwUe7Y6IiYn9u/JyJC0gzgjYiYASDpKaAzMBU4UNKxZPdubWAzoDRIuSt9fTJ3zt2AH5VOEBHvSvpBajdeEsCyZFmTklskLUuWESqtSRFwgaRdgAVkGZY167nmvAcj4v10HU8DGwCrAX/NDepGA5tUapyu9ViAHX8wjE22/XETTmlmZmZm1nxtdZDycSkzUKLst/n9ImJWWfk3gQ+beZ7ydvPS1wW57dL+MpI2JMuQbJcGG3Vkg6Xy9vNp+N6KbGB1cD3He5ENdIYBV5ANeHoBqwPbRsSnkmaXnbs++etorF//JSJGACMAeg94o52O1c3MzMxqk9+T0vruB05JgxUkbd3EdhP4IpPRiywz01wrkQ1s3pe0JtlUtMb8GTiptCNpFeBR4FuSvp7KVpC0UCYjIoJsStYOaX3LysCbaYDSkywjAtl0rRUX8TqeAHZNU8+WAfZbxPZmZmZmZlVTS4OUwUAHYHqafjW4ie1OAY6QNB34MXBqczsQEdOAKcCzwK3A+CY0+zmwirLHKU8DekbEW0Bv4LbUr4nAphXO9zFwCXAmcAvQPU1FOyz1gYh4m2za2Ew18ZHLEfEKcAHweLqG2cD7TWlrZmZmZm1HhKr+aQ1tcrpXRPzXk67SL+zHVSivA+rqax8R/wC+U6Fd7/r2I2I20LWeYwu1y5V3zm1PAnqk7bnA4RXqPwRsV6G8R9n+JbndHes59yFlRV1TeR25exMRP8jVuTUiRqRMyu/4YoG+mZmZmVmrapODFGsRAyTtRrau5QE8SDEzMzOrOdFOVwx7kLKEioi+jdcyMzMzM2t5HqSYmZmZmdWoBe306V6K9pojsmrwD4uZmZkt6drUqOAv0+dV/fez3bb8Uotfcy093cvMzMzMzJYAnu5lTfbTaz6uStyLjl+eE4a9V3jcq8/sxFGD3yo87vXnrs6h/V8tPC7AzUPWqUpcgD16Ty885p/qtuSgvv8oPC7A7RdvwME//WfhcW+7aH1O/mXxT9y+8vSVOeaCtwuPe93ZqwLQ62evFB77lgvX5aSLi/+z96u+naryZ6T056Naf1/sddwzhce959pvVC3umA5dCo8LsOensxqv1Ez7nvhc4TF/f9UmHDf0ncLjAlzb7ytV+1mupb+HIPu76LBzXys87k2D1+bUyz4oPO5lp65Ylb83Ifu7sy1pr5OinEkxMzMzM7M2xZkUMzMzM7MaFW1riUxhnEkxMzMzM7M2xZkUMzMzM7MatcBrUr4gab6kqZJmSrpHUqdG6g+Q1ODLAyXtK2mz3P6g9Eb0QkjqIemP9RwbmT93PXV2kPRYuu5nJA1opH43Sd/P7e8tqV8jbW6TNF3SaQ3Vq9Cuk6QTc/vrSLpzUWKYmZmZmbUVzc2kfBwR3QAk3QicBAxZzL7sC/wReBogIs5bzHhNFhFHN6HajcCBETFN0tJAY49V6QZ0B+5N57gbuLu+ypLWAraLiK83rdcL6QScCFyVzvUqsH8z4piZmZlZDYnwmpT6TATWBZC0kaT7JD0p6RFJm5ZXlnSMpCckTZP0W0lflrQTsDcwLGUqNpJUJ2n/1Oa7kqZImiFplKQvpfLZkgZKmpyObZrKd01xpqZ2K6bTd5R0p6RnJd0iSan+OEnd0/ZcScMlPSXpQUmrp7ZrAK8BRMT8iHg61d9e0sR0ngmSukhaFhgEHJT6cJCk3pKuTG0OSFmoaZIeTvEfANZN9XeudJ9S2zUl/S6VT0v3biiwUWo7TFJnSTNT/eUk3ZDuzxRJPVN5b0l3pe/X85IuKuBnwczMzMxssS3WICVlFL7LFxmCEcApEbEt0Jf0P/tl7oqI7SJiK+AZ4KiImJBinBkR3SLihdw5lgPqgIMiYguy7M8JuXhzImIb4Op0TtLXk1K2Z2eg9IKPrYE+wGbA14BvVejfCsCkiNgc+CtwfiofDsxKA4TjUr8AngV2joitgfOACyLiP2n79nQ9t5ed4zzgf9M92DuV7Q28kOo/Uuk+pXqXA39N5dsATwH9cm3PLDvXSUCke3cwcGOu792Ag4AtyAZUXy2/GZKOlTRJ0qRpj1xf4XaZmZmZWWuJqP6nNTR3kLK8pKnA68CawJ8ldQR2AkanY9cCa1do2zVlWWYAvYDNGzlXF+CliCi9AepGYJfc8bvS1yeBzml7PPBLST8BOkXEZ6n88Yh4OSIWAFNz9fMWAKVBxc3AtwEiYhDZ9K0HgEOA+1KdldM1zyQbyDR2PaX+1Uk6Bli6njr13afvkA3IShmdxt4G9e10HUTEs8A/gE3SsQcj4v2I+IRsmt0G5Y0jYkREdI+I7lvtfFT5YTMzMzOzwjV3kFJak7IBILL/rV8KeC/9b37p840KbeuAk9P/7A8ElqtQZ1HMS1/nk9bYRMRQ4GhgeWB8btrZvFy7z+s34vPxY0S8EBFXk2WPtpK0KjAYGBsRXYG9aML1RMTxwDnAV4EnU5xydRR7nyppzv0wMzMzszZiAar6pzUs1nSviPgI+AlwBvAR8JKkAwCU2apCsxWB1yR1IMsQlHyQjpWbBXSWVFpQ/mOyaVj1krRRRMyIiF8ATwD/tTamAUvxxaLzQ4C/pZh7ltawABuT/VL/Hlkm5ZVU3rsJ11Pq32Pp4QBvkQ1WytV3nx4kTXeTtLSklRs6F/BIqb2kTYD1ye6pmZmZmVmbtNgL5yNiCjCdbL1DL+AoSdPI1krsU6HJucBjZFOens2V/wY4My3u3igX/xPgCLIpVTPIpmNd00i3+qSF6dOBT4E/LcIlfQhsn6ZvfYdsATxkg6NZaSrbr4FeETEfuAi4UNIUFs5EjAU2Ky2cLzvHsLSQfSYwAZhWoR/13adTgZ7pXjwJbBYRb5NljGZKGlYW5ypgqVT/dqB3RMzDzMzMzGpee12T0qzpPRHRsWx/r9zu9yrUH5Dbvpq0pqKszniyBe0lvXPHHiRb9F7epnNuexLQI22fUqHb49KnVP/k3HaPsrinVzjXjyrEJCIm8sUaD8imcRER7wDblVWvS8f+r0Ko2UDXXNz67tMbVBj8RcQhZUVdU3lpkFdev67Un7T/gwp9MjMzMzNrcV6DYGZmZmZWo/yelCVEeZbIzMzMzMxaljMpZmZmZmY1akErrRmpNkVrrYaxWuQfFjMzM1vStan5Vb9/Yn7Vfz/bd7ulW/yanUkxMzMzM6tR7TXf4EGKmZmZmVmNiraV2CmMF86bmZmZmdlikfQ9SbMk/V1SvwrHT5f0tKTpkh6UtEFD8TxIMTMzMzOrUQui+p/GSFoa+BWwB9l7Dw+WtFlZtSlA94jYEriT7IXo9fIgxczMzMzMFsf2wN8j4sWI+A/wG8pePh4RYyPio7T7KLBeQwGXqEGKpP6SnkpppqmSvimpj6QvF3iO2ZJWW4z2vSVdmbYHSOpbZHwzMzMzaz8iqv9pgnWBf+X2X05l9TkK+FNDAZeYhfOSdgR+AGwTEfPSL/rLArcDNwMfNdS+iv1aOiLmt8J5l4mIz1r6vGZmZmZWWyQdCxybKxoRESOaGetQoDuwa0P1lqRMytrAnIiYBxARc4D9gXWAsZLGAki6WtKklHEZWGqcMhgDJU2WNEPSpql8VUkPpPojyT07W9LvJT2Zjh2bK58r6RJJ04AdJR0h6TlJjwPfauoFpQVIM9OnTyrrLGlmrk5fSQPS9jhJl0qaBJwq6YDUdpqkhxf5jpqZmZlZq2qJTEpEjIiI7rlP+QDlFeCruf31UtlCJO0G9Af2Lv1OXp8lJpMCPACcJ+k54C/A7RFxuaTTgZ5p0ALQPyLeSQuAHpS0ZURMT8fmRMQ2kk4E+gJHA+cDf4uIQZL2JEtflRyZYi0PPCHptxHxNrAC8FhEnCFpbeBWYFvgfWAs2cKiktPSiLNkHQBJ2wJHAN8kGxg9JumvwLuN3IdlI6J7ijED+N+IeEVSp6bcRDMzMzOzMk8AG0vakGxw8iPgkHwFSVsD1wLfi4g3Gwu4xGRSImIu2UDgWOAt4HZJvStUPVDSZLKBwuZkTygouSt9fRLonLZ3IZsuRkSMYeFBwk9StuRRstHlxql8PvDbtP1NYFxEvJUWGt1e1p/hEdGt9AFeTeXfBn4XER+ma7sL2LnRG7Fw/PFAnaRjgKUrVZZ0bMosTRoxollZPTMzMzOrkgWhqn8ak5YQnAzcDzwD3BERT0kaJGnvVG0Y0BEYndaG391QzCUpk0Ja+zEOGJeyCIfnj6fRX19gu4h4V1IdsFyuSiktNZ9G7p2kHsBuwI4R8ZGkcblYn1RxHcpnLDz4XK7s+IeljYg4XtI3gT2BJyVtmzI95OqMAEqjk3b6TlMzMzMzWxwRcS9wb1nZebnt3RYl3hKTSZHURdLGuaJuwD+AD4AVU9lKZL/Evy9pTbJnPTfmYVI6S9IewCqpfGXg3TRA2RTYoZ72jwG7prUtHYADmnhJjwD7SvqypBWAH6ayN4A1UrwvkT0soCJJG0XEY+kH6C0WnktoZmZmZm1cG3m6V+GWpExKR+CKtPbiM+DvZFO/Dgbuk/RqRPSUNAV4luwxauObEHcgcJukp4AJwD9T+X3A8ZKeAWaRTfn6LxHxWlrYPhF4D5jalIuJiMkp0/N4KhoZEVMAJA1K5a+ka6nPsDRwE/AgMK0p5zYzMzMzqyZFaw2PrBb5h8XMzMyWdI0v0mhBt/6t+r/MH/Jttfg1LzHTvczMzMzMrDYsSdO9zMzMzMzalQXtdJ6LMylmZmZmZtamOJNiZmZmZlajognvMalFzqSYmZmZmVmb4kyKNdlxQ9+pStxr+32FPlfMLTzupad05OghcwqPO7L/alW9F9VyyqX/LjzmFX1WoveANwqPC1A3YE2OueDtxisuouvOXpUThr1XeNyrz+xEr5+9UnjcWy5cF6Aq97luwJpV6/OBZ8wuPO4dl3QG4KSLi//+/apvJ44c+GbhcUedvwaHnfta4XFvGrw2+574XOFxAX5/1SZViQswpkOXwmPu+eksDj/v9cLjAtw4aK2q/bwd/4t3C497zVmrcNTgtwqPC3D9uatzxlUfNl5xEV1y4gpV+3uoGv+GQPbvSFvSXh/U60yKmZmZmZm1Kc6kmJmZmZnVKD/dy8zMzMzMrAU4k2JmZmZmVqO8JqUNktRf0lOSpkuaKumbkvpI+nIz4w2Q1LdCuSSdI+l5Sc9JGitp8ybE6y1pndz+SEmbFdm3okgqfuW6mZmZmVVVRPU/raFmMymSdgR+AGwTEfMkrQYsC9wO3Ax8VODpTgJ2AraKiI8k7Q7cLWnziPikgXa9gZnAqwARcXSBfTIzMzMza5dqOZOyNjAnIuYBRMQcYH9gHWCspLEAkg6WNEPSTEm/KDWW9D1JkyVNk/RgeXBJx0j6k6TlgbOAkyPio3SuB4AJQK9Ud66k4Smr86Ck1SXtD3QHbklZnuUljZPUvZF+zZU0JPXrUUlrNnQTJJ0p6YmUTRqYyoZKOilX5/MsTKX6ZmZmZlabFkT1P62hlgcpDwBfTdOvrpK0a0RcTpa16BkRPdNUq18A3wG6AdtJ2lfS6sB1wH4RsRVwQD6wpJPJsjT7Ah2AFSLixbLzTwJKU75WACZFxObAX4HzI+LOVKdXRHSLiI9z8Sv2Kxfr0dSvh4Fj6rsBKaOzMbB9irOtpF3IskkH5qoeCNzeQH0zMzMzszajZgcpETEX2BY4FniL7Jfw3mXVtgPGRcRbEfEZcAuwC7AD8HBEvJRi5d/MdxiwB7B/KUvTBAvIBgaQTTX7diP16+sXwH+AP6btJ4HODcTZPX2mAJOBTYGNI2IKsIakdSRtBbwbEf+qr35DHZV0rKRJkiY98/iNjVyWmZmZmbUkr0lpgyJiPjAOGCdpBnB4AWFnkGUZ1gNeioh/S/pQ0tfKsinbkmVNKnZtMc7/acTnPw7zafh7JODCiLi2wrHRZNPf1uKLAVRD9SuKiBHACIDjhr7TTp8fYWZmZmZtSc1mUiR1kZTPAnQD/gF8AKyYyh4HdpW0mqSlgYPJBhaPArtI2jDF+kouzhTgOLKF8aUncw0DLk/rU5C0G1m25NZ0fCmyAQHAIcDf0na+L3n19WtR3Q8cKalj6te6ktZIx24HfpT6NboJ9c3MzMysxixYUP1Pa6jlTEpH4ApJnYDPgL+TTf06GLhP0qtpXUo/YCxNl79YAAAgAElEQVRZFmFMRPwBsmlMwF2SlgLeBP6nFDgi/pYWmo+R9D/AFcAqwAxJ84HXgX1y60w+BLaXdE6KdVAqrwOukfQxsGMu/mv19asR50jqk4uznqRvABMlAcwFDgXejIinJK0IvBIRr6X6D9RXvwnnNjMzMzNrETU7SImIJ8keC1zuivQp1bsNuK1C+z8BfyorG5Dbvp8s81AyMH3q68/pFcp+C/w2V9SjCf3qmNu+E7gz17cBFepfBlxWT5+2aGr9/HnNzMzMrDb4ZY5mZmZmZmYtoGYzKW2JsxBmZmZm1hqcSTEzMzMzM2sBzqSYmZmZmdWo1nojfLUp2muOyKrBPyxmZma2pFNrdyDvV3+q/u9nJ+3R8tfsTIo12T+P/7+qxF3/mrvoPeCNwuPWDViTQ/u/Wnjcm4esw8gHCw8LwNHfrU5cgIP6/qPwmLdfvAGHnfta4XEBbhq8Ngf/9J+Fx73tovU5cmDxT90edf4a3L/q5oXH/d+3nwLgwDNmFx77jks6M3bjrQqP2/P5aRxzwduFx73u7FUBePipDwuPvcvmK3D4ea8XHvfGQWtx3NB3Co97bb+vVCVuKXa1VOsej+nQpfC4AHt+Oqtqfw9V69+9twcdW3hcgFXPG8HHY28pPO7yPXvx2rNTC4+79qbdmPvYPYXHBej4zb2qEre5Wibh0PLjMq9JMTMzMzOzNsWZFDMzMzOzGtVeV244k2JmZmZmZm2KMylmZmZmZjVqwYLW7kF11EwmRdKqkqamz+uSXsntL1tWt4+kLzch5jhJ3SWdKunSXPm1kv6S2z9F0uWL0Nc6SftXKB8pabOmxsm1W07Ss5K2yJWdKenaRY1lZmZmZtbW1UwmJSLeBroBSBoAzI2Ii+up3ge4GfioieHHA71y+1sBS0taOiLmAzsBf2hKIEn13tOIOLqJ/Slv94mkPsBVknYB1gGOB7o3Jx5k/YyIz5rb3szMzMxan9ektEGSvitpiqQZkkZJ+pKkn5D9Ej9W0thU72pJkyQ9JWlghVBTgU0kLS9pZeDjVFbKXOwEjJfUTdKjkqZL+p2kVVL8cZIulTQJOLWsj4NTZmXpUuYmlc+VNETStBRzzVS+UdqfIennkuYCRMR9wGvAYcBwYACwjKTfSnoifb6VYmwvaWK6NxMkdUnlvSXdLekh4EFJa0t6OGWjZkrauZBvjJmZmZm1iAVR/U9rqOVBynJAHXBQRGxBlhU6ISIuB14FekZEz1S3f0R0B7YEdpW0ZT5QyihMAbYDdgAeAx4FdpK0LtlLL/8F3AScFRFbAjOA83Nhlo2I7hFxSalA0jBgdeCIlJHJWwF4NCK2Ah4GjknllwGXpWt6uaxNH2AIsHpE/DrVHR4R2wH7ASNTvWeBnSNia+A84IJcjG2A/SNiV+AQ4P6I6EaWPSr+QeVmZmZmZouolgcpSwMvRcRzaf9GYJd66h4oaTLZQGRzoNK6kAlkGZOdgInpU9qfkDIsnSLir/Wc7/ayeOcCK0fE8VH5LTv/Af6Ytp8EOqftHYHRafvWfIOIeBV4CLg6Fe0GXClpKnA3sJKkjsDKwGhJM8myLvk3zP05IkpvAHsCOCJNn9siIj4o76SkY1MWatKtT79U4TLMzMzMrLVEVP/TGmp5kNIkkjYE+gLfTRmQMWRZmHLjyQYkO5INUJ4hG8zsRDaAaUz5K5CfALaVVN+rez/NDV7m0/T1QQvSB7Lv3w4R0S191o2IucBgYGxEdAX2YuHr/byfEfEw2UDrFaBO0mHlJ4uIESlD1P2QzTZsYhfNzMzMzJqvlgcp84HOkr6e9n8MlLIcHwArpu2VyH4xfz+t+9ijnngTyaZ6rR4Rb6YBxFvAPsD4iHgfeDe3biN/vkruA4YCYySt2EC9co+STd0C+FEjdR8ATintSOqWNlcmG3gA9K6vsaQNgDci4jqyqWLbLEI/zczMzKyVxYKo+qc11PIg5RPgCLJpTTPIsgvXpGMjgPskjY2IaWTTvJ4lmz41vlKwiHiXbFDyVK54IrAGMC3tHw4MkzSd7EljgxrqYESMBq4D7pa0fBOvqw9wejrH14H3G6j7E6B7Wsj/NNkTvwAuAi6UNIWGMzQ9gGmp3kFka1zMzMzMzFpVzTyCOC8iBuR2t65w/Argitx+73ri9Cjb37xsfwDZU7RK+1PJsi2Nxemd2x4FjEq7PXLlHXPbdwJ3pt1XyKZwhaQfAV0aiD2HbHBR3p+JwCa5onNSeR3ZwwZK9W4kW1tjZmZmZjWotZ6+VW01OUhp57YlWwwv4D3gyFbuj5mZmZlZi/IgpY2JiEfIHgdsZmZmZtYgv8zRzMzMzMysBTiTYmZmZmZWoxa000UpqvyeQbOK/MNiZmZmSzq1dgfyLrxjftV/P/vZgUu3+DU7k2JNdsBp1Xnj/OjhG3Lc0HcKj3ttv6+w74nPFR7391dtwj4nzCo8LsAfru7SeKVmOnLgm4XHHHX+Ghza/9XC4wLcPGQdDjv3tcLj3jR4bQ7p93LhcW8dul7V+gtw+HmvFx77xkFrVa3P1fi5uHnIOgDsdvCkwmP/5bbuVevzwT/9Z+Fxb7to/ar+2auWky5+r/CYv+rbqSr3GLL7PKZD8X8v7/npLE4YVvy9uPrMThw9ZE7hcQFG9l+tav9W9x7wRuFx6wasWZV/9yD7t68taa/5Bq9JMTMzMzOzNsWZFDMzMzOzGuVMipmZmZmZWQtwJsXMzMzMrEYtaKeplKpkUiStKmlq+rwu6ZXc/rKNtO0u6fImnGNCM/t2RK4v/5E0I20PlTRI0m7NidvEc4+T1H0xY5ydvkrS3yTtkTt2gKT7FrefZmZmZmatqSqZlIh4G+gGIGkAMDciLi4dl7RMRHxWT9tJQKOPbomInZrZtxuAG1I/ZgM9I6I6j8KojrOBCyIiJB0PjJY0lux7eQHwveYGbuj7YmZmZmZtTyxo7R5UR4utSZFUJ+kaSY8BF0naXtJESVMkTZDUJdXrIemPaXuApFEpA/GipJ/k4s3N1R8n6U5Jz0q6RZLSse+nsiclXV6K20gf90/bsyVdmLIskyRtI+l+SS+kwUGpzZmSnpA0XdLAVLaCpDGSpkmaKemgBs7ZWdIjkianz06pfG1JD6fzz5S0s6ShwPKp7JaImAncA5wFnAfcBLye7tnj6d7u08h5eqTyu4GnF+V7amZmZmZWDS29JmU9YKeImC9pJWDniPgsTbG6ANivQptNgZ7AisAsSVdHxKdldbYGNgdeBcYD35I0CbgW2CUiXpJ0WzP6+8+I6CZpOFAHfAtYDpgJXCNpd2BjYHuyF/vcLWkXYHXg1YjYE0DSyg2c403gfyLiE0kbA7cB3YFDgPsjYoikpYEvR8Qjkk6OiG659gOBycB/UrvzgYci4khJnYDHJf2lgfMAbAN0jYjqvAjFzMzMzKqivb6YvaUHKaMjYn7aXhm4Mf3CHECHetqMiYh5wDxJbwJrAuVvYns8Il4GkDQV6AzMBV7M/eJ9G3DsIvb37vR1BtAxIj4APpA0Lw0Adk+fKaleR7JByyPAJZJ+AfwxIh5p4BwdgCsldQPmA5uk8ieAUZI6AL+PiKmVGkfEh5JuJ5tSNy8NnPaW1DdVWQ5Yn2wAV+k8kN2/igMUSceS7ts23x3C17Y4uIFLMTMzM7OWtKCdTvdq6UHKh7ntwcDYiPihpM7AuHrazMttz6dyn5tSpzlKcReUnWNBOoeACyPi2vKGkrYBvg/8XNKDETGonnOcBrwBbEU2/e4TgIh4OGVl9gTqJP0yIm6qJ8aC9CH1ab+IWOiV6Glt0H+dJ8l/XxYSESOAEQAHnPZS+xyqm5mZmVmb0prvSVkZeCVt965C/FnA19IACKDedSGL4X7gSEkdASStK2kNSesAH0XEzcAwsulU9VkZeC0iFgA/BpZOsTYA3oiI64CRuRifpuxKQ306JbcuZ+uGzmNmZmZmtSsiqv5pDa35npSLyKZ7nQOMKTp4RHws6UTgPkkfkk2fKvocD0j6BjAxjQnmAocCXweGSVoAfAqckGs2RlJpTc1Esqd1/VbSYcB9fJHV6AGcmerOBQ5L5SOA6ZImR0SvCt0aDFya6iwFvAT8ALiqnvOYmZmZmbUpVR+kRMSAesonsvC6iHNS+TjS1K/ythHRNbfdsbx+2j8512RsRGyasgq/ouzRxhHRuWy/d6VjEVFHtnC+0rHLgMvKLu8FsozGQiKiR3lZsmVu+6xU90bgxgoxzirVyZUNyG1/DBxXod3z9ZxnHPVPtTMzMzOzNmxBO52M35rTvVrCMWkh/VNk053+a+2ImZmZmZm1La053avqImI4MLy1+2FmZmZmVg3RTlMp7T2TYmZmZmZmNaZdZ1LMzMzMzNqzdvouR9Re31JpVeEfFjMzM1vSqbU7kHf29fOq/vvZBUd9qcWv2ZkUa7J9TpjVeKVm+MPVXdjz6JmFxx0zsisHnjG78Lh3XNKZo4fMKTwuwMj+q1UlLsBRg98qPOb1567OcUPfKTwuwLX9vkKvn73SeMVFdMuF61bt5+K0K+cWHnf4yR2B6vz5+8PVXTjl0n8XHveKPitV7XsHVC32/qe+WHjcOy/7Ggec9lLhcUcP35CTf/l+4XEBrjx95arEBTj+F+8WHvOas1ah94A3Co8LUDdgTU4Y9l7hca8+sxNjOnQpPO6en87imAveLjwuwHVnr1q1v+NOurj4e/yrvp04tP+rhccFuHnIOlWJ21wLvCbFzMzMzMys+pxJMTMzMzOrUe116YYzKWZmZmZm1qY4k2JmZmZmVqNiQWv3oDraRSZF0nBJfXL790samdu/RNLpTYw1TlL3CuWzJa1WVra3pH5pe3VJj0maImnnRuLPkjRV0jOSjm1un8zMzMzM2qP2kkkZDxwIXCppKWA1YKXc8Z2A0xoLImnpRTlpRNwN3J12vwvMiIijm9C0V0RMkvQV4AVJdRHxn0U5t5mZmZnZAq9JadMmADum7c2BmcAHklaR9CXgG8DKKcsxQ9KoVF7KkPxC0mTggFJASUtJqpP08/pOKqm3pCsldQMuAvZJGZLlJe0uaaKkyZJGS+pYIURH4ENgfop3taRJkp6SNLCec1ask65jYDrfDEmbpvKOkm5IZdMl7ZfKm9I/MzMzM7MW1y4GKRHxKvCZpPXJsiYTgcfIBi7dgeeBkcBBEbEFWQbphFyItyNim4j4TdpfBrgFeD4izmnC+acC5wG3R0Q3YAXgHGC3iNgGmATkp5vdImk6MAsYHBHzU3n/iOgObAnsKmnLCqdrqM6cdL6rgb6p7Fzg/YjYIiK2BB5K09Ya6t/nJB2bBkWTZj99e2O3wszMzMxaUERU/dMa2sUgJZlANkApDVIm5vZfBl6KiOdS3RuBXXJty3/7vhaYGRFDmtmXHYDNgPGSpgKHAxvkjvdKA4b1gb6SSscOTBmdKWQZoc0qxG6ozl3p65NA57S9G/CrUoWIeLcJ/SNXf0REdI+I7p03O6gp125mZmZmtljay5oUyNal7ARsQTbd61/AGcC/gXHAfg20/bBsfwLQU9IlEfFJM/oi4M8RcXBDlSLirTTg+GZaS9MX2C4i3pVUByy3UFBpw0bqzEtf59Pw97ZJ/TMzMzOzts1vnG/7JgA/AN6JiPkR8Q7QiWzK12+BzpK+nur+GPhrA7GuB+4F7pDUnIHco8C3SueTtIKkTcorSfoysDXwAtlC/w+B9yWtCexRIW5T6pT7M3BS7pyrNLV/ZmZmZta2RVT/0xra0yBlBtlTvR4tK3s/Il4GjgBGS5oBLACuaShYRPySbErVr1OWA2C6pJfT55cNtH0L6A3cltaeTAQ2zVW5JU2zehKoi4gnI2JaOt+zwK1kmaHyuI3WqeDnwCqSZkqaBvRsQv/MzMzMzFpNu5nulRafr1RW1ju3/SBZ1qK8Xeey/R657fNzhxaql1OX6taVttP+Q8B2Fc7Xo7ysUn8b6FN9dTrnticBPdL2XLI1J+X1K/bPzMzMzGpHeLqXmZmZmZlZ9bWbTIqZmZmZ2ZLGL3M0MzMzMzNrAWqtF7RYTfIPi5mZmS3p1NodyDv5l+9X/fezK09fucWv2dO9rMkmzXq3KnG7d1mFfU6YVXjcP1zdhUP7v1p43JuHrEO/65rz+pzGDT1mucYrNdMh/V4uPOatQ9fj6CFzCo8LMLL/avzw5OcLj/u7Kzeu2r345LZfFB53uYPPAqjaz/Inv7+88LjL7fsTDjxjduFx77ikMwDf3quhJ8g3z9/u2ZWDf/rPwuPedtH6VfveHXPB24XHBbju7FWrEhfgqMFvFR7z+nNX5+1BxxYeF2DV80ZU5e+4kf1Xq8r377qzV2VMhy6FxwXY89NZPNR5y8Ljfmf2dD647IzC46546iWM33rbwuMCfGvKk1WJawvzIMXMzMzMrEb56V5mZmZmZmYtwJkUMzMzM7Ma1U4TKc6kmJmZmZlZ2+JMipmZmZlZjfKalDZKmb9J2iNXdoCk+6p0vtUkfSrp+FxZJ0knltXbRNK9kp6XNFnSHZLWrEafzMzMzMzak5ofpET2opfjgV9KWk5SR+AC4KTmxJPUWHbpAOBR4OBcWSfg80GKpOWAMcDVEbFxRGwDXAWs3pw+mZmZmZlVEhFV/7SGmh+kAETETOAe4CzgPOBmoL+kxyVNkbQPgKTOkh5JmY3JknZK5T1S+d3A05JWkDRG/8/eeYfLVZVr/PcSgqGDAkrvRWoITYoKKqgIIgqE3hRRuIAiCIpCABERuEqxAdIFaSIISguEDiGEFKr0K9JEEekt7/1jrUn2mcw5SWavTU7w+z3PPGf2npl3r9lnZs/61teksZLulTS0crjtgO8AC0taJO/7CbC0pDGSjgW2B263/afKGEfYvjcbUmdIGp/HtlEew66S/ijpWklPSPofSfvn59wh6YP5eSMknZCPda+ktfP+tSXdnp9/m6TlK7p/kHRV9ur8NO/fXdLPW+OTtIeknxX+1wRBEARBEATBNPO+MFIyh5OMg88Dg4Drba8NbAQcK2l24Hlg4+zZGApUu5gNAfazvRzwOeBp26vZXhm4CkDSosCCtkcCF2YNgIOBR20Ptn0gsDLQW6efvUkOoFVIBs9Z2fNCft2XgbWAo4DXbK8O3A7sXNGYzfZgkvfm9LzvQeDj+fmHkrxJLQbnsa4CDM3v40Jgc0kD83N2q2hNRNLXJY2SNOoPF5zZy1sKgiAIgiAIpgcTJrjx2/TgfZM4b/tVSRcArwDbkCbgB+SHBwGLAU8DJ0saDLwLLFeRGGn78Xx/PHC8pGOAK2zfnPcPJU3uAX5PmtQfP41D3QA4KY/5QUlPVsZxg+2XgZclvUTyDrXGU23zen5+/U2S5pI0DzAnyeBZFjAwsPL84bZfApB0P7C47b9Juh7YTNIDwEDb49sHa/sU4BSAUQ+9+P7MzAqCIAiCIAj6Fe8bIyUzId8EfMX2Q9UHJQ0DngNWI3mR3qg8/Grrju2/ShoCbAr8SNJw20eQPB8fkbRDfupC2Sh4u20c9wGf7GL8b7a9lzcr96v/q3ZjwcCRJCNnS0lLACN60X23onUa8H2SF+aMLsYbBEEQBEEQTEemV85I07yfwr2qXA3sI0kAklbP++cGnrE9AdgJGNDpxZIWIoVanQscCwyRtBwwh+2FbS9hewngaJLh8jLJk9HiPGA9SV+oaH5C0srAzcAOed9yJA9PD2NqKhiaX78B8FL2kswN/D0/vuvUiNi+E1iUFCZ3/jSOIQiCIAiCIAga4f1qpBxJCncaJ+m+vA2pwtYuksYCK1DxnrSxCjBS0hjgMOBHJGPk0rbnXQJsZ/ufwK05kf1Y268Dm5EMpYdziNVewD/yGGaSNB64ANjV9ptMG29Iugf4NfDVvO+nwNF5/7R4yC4EbrX94jSOIQiCIAiCIJjOeIIbv00P3lfhXraHVTb37PD4w/TM7Tgo7x9BJTzK9tUkb0yVUR30xgEfzfe3b3vsQVICfid266B1JnBmZXuJ3h4DzrX9rbbX307PHJsf9KK7WduhNwCiqlcQBEEQBEHQb3hfGSnB1JOT7UcCY20Pn97jCYIgCIIgCKad92vH+TBSZjBsb1hI59/09LwEQRAEQRAEMxgTInE+CIIgCIIgCIJgciR9TtJDkh6RdHCHxz8g6YL8+J25Gm3veu/XsmVBI8SHJQiCIAiC/3Y0vQdQZZdDn218fnbWER/p8z1LGgD8FdgYeAq4i1Rc6v7Kc/YCVrX9DUnbAlvaHtpRkAj3CqaBy0e924juF9ccwNADniyue8Fxi7PLoc8W1z3riI9w2NntrXHKcPjOA6f8pC5p6hzv8eN/FtcFOPX7H2Kr/R4rrnvxCUvx1SP/UVz3tz+cnxfH3lhcd97VUsulXYc9V1z7zGEf5h/3jyyuO/+Ka/O1o14ornvaIfMB8PldxxXX/suZq7L74c8X1z39sAX45rH/Lq77qwPnYecfPlNcF+DsIxdsRBfgO7/srahm9xy/1+y8fsPviusCzLrRDuz5k38V1/3NwR/k2ye/Ulz3Z/8zB9cvseqUn9gFn3piHFcOXL647hfefog3zj+muO6g7Q7iwa03Ka4LsMJF1zSiO4OzNvCI7ccAJP0e2AK4v/KcLYBh+f7FpAbrci8ekwj3CoIgCIIgCIIZFNuN3yR9XdKoyu3rbcNYGPhbZfupvK/jc2y/A7wEfKi39xWelCAIgiAIgiAIesX2KcAp7+Uxw0gJgiAIgiAIghmUCf2jBPHfgUUr24vkfZ2e85SkmYG5gV5jxiPcKwiCIAiCIAiCOtwFLCtpSUmzANsCl7c953Jgl3x/K+D63vJRoJ8aKUrcIunzlX1bS7qqgWONkLTmVDxvZ0n3Shov6R5JB5Qey1SM4ftt2+9KGpPH9afcoLGv158paatmRxkEQRAEQRC8V3iCG79NcQwpx+R/gKuBB4ALbd8n6QhJX8xP+y3wIUmPAPsDk5UprtIvw71sW9I3gIsk3UAa54+Bz3WjJ2nmfPK6IhtL3wI2sf20pA8AO3d7/Brj+T7pPLR43fbgrHkWsDdwVBe6QRAEQRAEQdA1tv8M/Llt36GV+28AW0+tXr/0pADYvhf4E3AQcChwLnCIpJHZk7EFgKQlJN0saXS+rZf3b5j3Xw7cL2l2SVdKGps9D5PVZZb0iqSj8nPukPTh/ND3gANsP53H9qbtU/NrJnpiJM0n6Yl8f1dJl0u6HhjeYXt2Sad3eD+7SvqDpKskPSzpp3n/T4BZs+ekU63F28lVFCQNzuMfJ+lSSfN2eK9rSLpR0t2SrpbUXM3JIAiCIAiCoBHei+pe04N+a6RkDge2Bz4PDCLFrq0NbAQcK2l24HlgY9tDgKHAiZXXDwH2s70cyQvztO3VbK8MdAodmx24w/ZqwE3AHnn/ysDdXYx/CLCV7U922D6kl/cDMDi/l1WAoZIWtX0w2XNie4fqQXIDnU8zKfbvbOAg26sC44HD2p4/EDgpj2UN4HTCAxMEQRAEQRD0E/q1kWL7VeAC4BxSB8uDJY0BRpCMlsWAgcCpksYDFwErViRG2n483x8PbCzpGEkft/1Sh0O+BVyR798NLFHzLVxr+1+9bG/Sy/sBGG77pewWux9YvBf9WfPrnwU+DFwraW5gHtutrnJnAZ9oe93yJMPr2vz6H5CqMExGtS721X84deredRAEQRAEQfCe4AkTGr9ND/plTkobE/JNwFdsP1R9UNIw4DlgNZLR9Ubl4YmtbW3/VdIQYFPgR5KG2z6i7VhvV6oMvMuk83MfsAZwfYfxvcMkY29Q22PtrXWr2729n3WANyu7quNo53XbgyXNRkpU2ptklEwJAffZXndKT6zWxb581Lv9osZdEARBEARB8P6mX3tS2rga2EeSACStnvfPDTxjewKwEzCg04slLQS8Zvtc4FhS6NXUcjQpHOsjWWsWSV/Ljz1BMmAglVObWnp7P33xdg7V6oHt14B9ge+QDKEXJX08P7wTcGPbSx4C5pe0bj72QEkrTcPYgyAIgiAIgn7AhAlu/DY9mJGMlCNJoV3jJN2XtwF+CewiaSywApN7L1qsAozM4U2HAT+a2gPnagUnA9flY48G5soPHwd8U9I9wHwF3k9fnJKfP1nivO17gHHAdqQa1MdKGkfKbzmi7blvkQyqY/J5GwOsNw1jD4IgCIIgCILG6PfhXraHVTb37PD4w8CqlV0H5f0jSLkereddTfJetL9+w8r9OSr3LwYurmyfAZzR4fUPth3/B3n/mcCZlee1b7/ey/tpf95mlfsHtd5f+3jz9uaVzY910N61cn8Mk+eqBEEQBEEQBDMQ06v6VtPMSJ6UIAiCIAiCIAj+C+j3npQgCIIgCIIgCDozNR3hZ0TCkxIEQRAEQRAEQb8iPClBEARBEARBMIPyfvWk6P2abBM0QnxYgiAIgiD4b0fTewBVvrzvI43Pz/5w4jLv+XsOT0ow1ZxzUzO6O30CNtvj/uK6V5y6Ilt/+/Hiuhf9bEkO+NVrxXUBjvvmbI3oAo2di72P+3dxXYBfHDAPm+4+vrjun09fhS/t9dfiun/85XI89uijxXWXWnppALb77v8V1z7/p4vx6GOPFdddeqml2P3w54vrnn7YAkBzn+VdDn22uO5ZR3yEbx5b/jvyqwPnYb8TXi6uC3DCfnM2oguww/f+Xlzzd0cvzDMPjimuC7DgCoPZddhzxXXPHPbhRq6dvzhgHl4+4TvFdQHm3O943jj/mOK6g7Y7iCsHLl9c9wtvP8TrI84vrgsw64bbNaIb9CSMlCAIgiAIgiCYQXm/hntF4nwQBEEQBEEQBP2K8KQEQRAEQRAEwQxKeFKCIAiCIAiCIAjeA/o0UpS4RdLnK/u2lnRV6YFI2kzSPZLGSrpf0p55/zck7Vz4WGdK2qqmxrckzVbZfkLSeEnjJF0j6SP1RzrVY/mSpBUr20dI+ssg10wAACAASURBVMx7dfwgCIIgCIJg+mC78dv0oE8jxWlU3wD+V9IgSXMAPwb27uZgkjqGl0kaCJwCbG57NWB1YEQew69tn93N8RrmW0B7KaaNbK8KjAK+X30gG3xNea6+BEw0Umwfavu6ho4VBEEQBEEQBI0yxUmz7XuBPwEHAYcC5wKHSBqZPR9bAEhaQtLNkkbn23p5/4Z5/+XA/ZJml3Rl9pjcK2koMCcpP+af+Zhv2n4ov36YpAPy/RGSjsnH/qukj+f9AyQdl/XGSdon719D0o2S7pZ0taQFe3ufkuaQNDyPfXzlfU02Xkn7AgsBN0i6oYPcTcAy+Zw8JOls4F5gUUnHZp3x+b23ztGNki6T9Jikn0jaIb/P8ZKWrpzj6/N7HC5psXyevwgcK2mMpKWrniJJn87/p/GSTpf0gbz/CUmHV97vClP6LARBEARBEAT9iwkTJjR+mx5MbeL84cBo4C3gCuB627tLmgcYKek64HlgY9tvSFoWOB9YM79+CLCy7cclfQV42vYXACTNbfulbMQ8KWl4Psb5tjudlZltry1pU+Aw4DPA14ElgMG235H0weydOQnYwvY/skFwFLB7L+/xDWBL2/+RNB9wRx7T53oZ7/4kz8kLHbQ2A1oNHpYFdrF9R37vg4HVgPmAuyS1uo+sBnwU+BfwGHBafp/7AfuQPDcnAWfZPkvS7sCJtr+Ux3mF7YvzGMl/BwFnAp+2/ddsLH0T+Hk+5gu2h0jaCzgA+Fov5yYIgiAIgiAI3jOmKvzI9qvABcA5wMbAwZLGkEKyBgGLAQOBUyWNBy6iEn4EjLTd6r41Htg4e0Q+bvulfIyvAZ8GRpImzKf3Mpw/5L93kwwTSIbKb2y/k7X+BSwPrAxcm8f6A2CRPt6mgB9LGgdcBywMfLi38fbCDflYcwFH531P2r4j39+AZHy9a/s54EZgrfzYXbafsf0m8ChwTd4/vvI+1wXOy/fPyXp9sTzwuO1W57qzgE9UHu90Lnsg6euSRkkadcPlp0zhcEEQBEEQBMF7iSe48dv0YFpKEE/INwFfaYVjtZA0DHiO5BGYieSZaPFq605e0R8CbAr8SNJw20fkx8YD4yWdAzwO7NphHG/mv+9OYfwC7rO97lS+vx2A+YE1bL8t6QlgUF/j7UAPz0r2NL3ay3PbebNyf0JlewLNlYqe4rm0fQopX4hzbuL9WeMuCIIgCIIg6Fd0k8h9NbCPckyRpNXz/rmBZ3KI1k7AgE4vlrQQ8Jrtc4FjgSE5H2TDytMGA09Ow5iuBfZUTsyX9EHgIWB+SevmfQMlrdSHxtzA89lA2QhYvLfx5ue/TMqlmRZuBobmHJr5SV6NkdPw+tuAbfP9HbJeX2N5CFhC0jJ5eyeS9yYIgiAIgiB4H2BPaPw2Pehmhf5IUk7DOKVqVY+TcjB+CVyiVC74Knr3IKxCSvKeALxNypEQ8F1JvwFez6/ddRrGdBqwXB7T28Cptk/OyeMnSpqb9F5/DtyXX/MbSa3cjL8BmwN/yuFqo4AH+xgvJO/CVZKetr3RVI7zUlLI1ljAwHdtPzsNSev7AGdIOhD4B7Bb3v97UqjdvsDE0so5P2g34KJswN0F/HoqjxUEQRAEQRAE04WpNlJsD6ts7tnh8YeBVSu7Dsr7R5DLCeftq0nemHY2ndJxbW9Yuf8COY8i56Lsn2/V146hZw5Ga/+unY5FMiDaeaLTeG2fREpkb20v0eE5T5DyYlrbBg7Mt+rzRtDzHG3Y6THbTwKf6nCcW+mZA7Rr5bHhpJLO7a9ZonJ/FLBh+3OCIAiCIAiC/k10nA+CIAiCIAiCIHgPaCohOwiCIAiCIAiChglPShAEQRAEQRAEwXtAeFKCIAiCIAiCYAZlwnSqvtU0SrncQTBVxIclCIIgCIL/djS9B1Dls7uMaXx+dvVZg9/z9xyelGCqOe+WZr4D228gNt19fHHdP5++Cl876oUpP3EaOe2Q+Tj0rLeK6wIcscssjegC7PC9vxfX/N3RC7P9wU8V1wU47yeLsNV+jxXXvfiEpfjWSa8U1/35PnPwfw8/UFx3sWU/CsDQA6alddTUccFxi/PXR/+vuO5ySy/GNt95orjuhccvAcAntryluPZNl27Azj98prju2UcuyFeP/Edx3d/+cP5GvtOQvtdNsceP/1lc89Tvf4hX7vxTcV2AOdbZnN0Pf7647umHLcCOhzxdXPfcoxbi1tXXKK4LsP49d/Pg1psU113homt4fcT5xXVn3XA7rhy4fHFdgC+8/dCUn/Qe8n7NSQkjJQiCIAiCIAhmUDzh/RnuFYnzQRAEQRAEQRD0K8KTEgRBEARBEAQzKO/XcK/wpARBEARBEARB0K8oYqQocYukz1f2bS3pqhL6Fc0tJP2xsv09SY9UtjeXdLmkhSRdnPcNlrRp5TnDJB3Qi/5HJP1e0qOS7pb0Z0nL1RzzmZJekzRnZd/PJVnSfHW0gyAIgiAIgv9u7AmN36YHRYwUpzrG3wD+V9IgSXMAPwb27kZPUm9haLcBH6tsrwv8R9ICeXs94DbbT9veKu8bDGzKFJAk4FJghO2lba8BfA/48DSMe0Av248AW+R9MwGfApopy1KD9vEHQRAEQRAEwfSgWLiX7XuBPwEHAYcC5wKHSBop6R5JrUn6EpJuljQ639bL+zfM+y8H7pc0u6QrJY2VdK+kobb/QTJKlsmHXRi4hGSckP/emo9xr6RZgCOAoZLGSBqan7eipBGSHpO0b963EfC27V9X3tNY2zfnsV3R2i/pZEm75vtPSDpG0mhg6/bt/JLfA61jbwjcCrxT0ftj9tzcJ+nrlf2vSDoqn4M7JH04799c0p35vF5X2T+/pGuzzmmSnmx5ayTtmP8XYyT9pmWQ5GMcL2ksyegLgiAIgiAIZhAmTHDjt+lB6ZyUw4Htgc8Dg4Drba9NMgCOlTQ78Dywse0hpIn7iZXXDwH2s70c8Dngadur2V4ZaIWO3QqsJ2l54GHgjrw9M7AacFdLzPZbJIPpAtuDbV+QH1oB+CywNnCYpIHAysDdXb7vf9oeYvv3vWz/FZhf0rzAdiSjpcru2XOzJrCvpA/l/bMDd9heDbgJ2CPvvwX4mO3Vs9Z38/7DSOd8JeBiYDEASR8lnev1bQ8G3gV2qBzjznyeyzcfCIIgCIIgCIJppGh1L9uvSroAeAXYBti8kv8xiDRpfho4WVJrslzN+Rhp+/F8fzxwvKRjgCts35z330bymAwAbgdGkgyR1YEHbb+RIrf65ErbbwJvSnqeaQjp6oULprAN8AdgW2AdYM+2x/aVtGW+vyiwLPBP4C2g5cG5G9g4318EuEDSgsAsQOucbQBsCWD7Kkkv5v2fBtYA7srnZlaSsQjpf3BJb28se3a+DvDVA3/Np7749d6eGgRBEARBELzHvF/7pDRRgnhCvgn4iu0ebTklDQOeI3k9ZgLeqDz8auuO7b9KGkLKJ/mRpOG2jyB5UvYhGSmn2n5Z0iBSGNVtUznGNyv33yWdh/uArTo/nXfo6XUa1Pb4q1PYhmS43A2cZXtCy5CStCHwGWBd269JGlHRfzvn+1THCXAS8L+2L8+vH9bLuFsoH/d7HR57w/a7vb3Q9inAKQDn3eL3Z427IAiCIAiCoF/RZAniq4F9ckI6klbP++cGnnEqFbATydiYDEkLAa/ZPhc4lhQKBvAAsBDJa3BP3jeGlLh/awepl4E5O+xv53rgA205IatK+jjwJCmP5QOS5iF5JqYJ208ChwC/bHtobuDFbKCsQM/CAL0xN5MS73ep7L+V5MFC0ibAvHn/cGCrVoEBSR+UtPi0vocgCIIgCIKgf+EJbvw2PWjSSDkSGAiMk3Rf3oY0Sd8lJ2qvQGevA8AqwEhJY0i5Fj+CiZXE7iTlfbydn3s7sBSdPSk3kAyMauL8ZGTdLYHPKJUgvg84GnjW9t+AC4F78997etPpC9u/sf1o2+6rgJklPQD8hJRjMyWGARdJuht4obL/cGATSfeSkvafBV62fT/wA+AaSeOAa4EFu3kPQRAEQRAEQdA0xcO9bA+rbLbnXmD7YWDVyq6D8v4RwIjK864meWM6HeMLbdtnAmdWtp8gJcJj+1/AWn2Md+XK/afJnogOz/sukxLUq/uXmML2rr3oVZ/3+V6eM0fl/sWkZHhsXwZc1uElLwGftf2OpHWBtXLuDblowGS5MtVjBEEQBEEQBDMW06uPSdM0kZMSTD8WAy7MvVjeYlI1sCAIgiAIgiCYYQgj5X1E9lKtPsUnBkEQBEEQBO8LplfOSNM0mZMSBEEQBEEQBEEwzYQnJQiCIAiCIAhmUN6vfVKwHbe4Fb8BX5+RdGfEMce5iHMR5+L9pTsjjjnORZyLOBdxa+oW4V5BUzTVmr7Jlvcz2pjjXDSv26T2jKbbpHboNq89o+k2qT2j6TapPaPpNqnd5JiDLggjJQiCIAiCIAiCfkUYKUEQBEEQBEEQ9CvCSAma4pQZTLdJ7RlNt0ntGU23Se0ZTbdJ7dBtXntG021Se0bTbVJ7RtNtUrvJMQddoJwsFARBEARBEARB0C8IT0oQBEEQBEEQBP2KMFKCIAiCIAiCIOhXhJESBEEQBEEwDUgaIOnB6T2OaUXS8ZJWmt7jmN5ImknSetN7HEHfRMf5IAAkzWb7tek9jikhaWvbF01pXxe6Ahax/bdaA5zBkTTc9qentG8a9E4Cek38s71vN7ptx/gycAywAKB8s+256mo3jaRZgcVsPzS9xzI1zGjjfS+QNC+wqO1x03ssvSFpfWCM7Vcl7QgMAU6w/WS3mrbflfSQpMVs/1+xwTbPA8ApkmYGzgDOt/1SXVFJHwZ+DCxk+/OSVgTWtf3bAtrLAQcCi1OZt9r+VLeatidI+gWwet3xBc0RifNBERq+QM0GfIc0OdhD0rLA8ravKKC9HnAaMIftxSStBuxpe6+auusDw5h0UW1NHJeqqTva9pAp7etSe7ztVerqdND9APAVYAl6/sAcUVN3OeBXwIdtryxpVeCLtn/UhdYgYDbgBmBD0v8LYC7gKtsrdDnGXfp63PZZ3ei2HeMRYHPbD9TV6kW/2Hlu090cOA6YxfaSkgYDR9j+Ypd6X+7rcdt/6Ea3ol90vG3aJ3bY/RIwyvZlNbUXZvLJ3U01NUcAX8yadwPPA7fa3r9LvZeZZMy3vnumkMEtaRywGrAqcCbpmr+N7U/W1L2JNMkdCbza2l/oM9HY717WXx7YDdgOuBU41fYNNfT+QjJ6DrG9WjaC7inxmyJpLPBr0mft3dZ+23fX1D0OuB34g2My3C8JT0pQijPJF6i8/VfgAqC2kZJ17wbWzdt/By4CSlysfwZ8FrgcwPZYSZ8ooPtb4Nu0XVS7RdLngU2BhdsmNHMB79TVz4yWtJbtuwrptbiMNOG6G3izoO6ppNW13wDYHifpPKCbyfOewLeAhUjjbE2U/gOc3O0A242Qhjx2zzVloGRKnucqw4C1gRFZd4ykJWvobZ7/LgCsB1yftzcCbgNqGSmUH2+VQcAKpOsaJKP+cWA1SRvZ/lY3opKOAYYC9zPpOmSglpECzG37P5K+Bpxt+7BsCHSF7TlrjmdKvGPbkrYATrb9W0lfLaD7wwIavdHY756kAaTP2wrAC8BYYH9Je9retkvZ+WxfKOl7ALbfkVT7ty/zju1fFdKqsiewP/CupNeZgbzQ/y2EkRKUoskL1NK2h0raLmu/lsOTimD7b21yJcb9ku2/FNBp8TQwirR6WV09eplkDJVgHWAHSU+SVgVbF+xVa+ouYvtztUc3ObPZHtn2v+vKYLN9AnCCpH1sn1RkdBUkrUsyXOcAinnsMqMkXQD8kYoRWNdzUKHYeW7jbdsvtel2vZppezcASdcAK9p+Jm8vSFpEqUvR8baxKrC+7XcBJP0KuBnYABhfQ/dLpNX3kosDADPn87oNkxamiiBpA2BZ22dImg+Y0/bjNWVfzr9NOwKfkDQTMLDuWG3fWFejDxr53ZP0M5JBPxz4se2R+aFjJNUJY3xV0ofI3wlJHyMtTpXgT5L2Ai6l5zXuX3VE3wPjOKhJGClBKZq8QL2VY8Fb2ktTbkX+bznky5IGAvuRYnbrcoOkY0mrt9WL6uhuxGyPBcZKOs/22wXG14nPNqR7m6RVbNeZbHXihfxZaH0utgKeqSNo+6T8eViCnuExZ9fRBX5OMx47SN6014BNKvtMfc9Bi+LnOXOfpO2BATmUZV+Sx6Mui7YMlMxzwGIFdJsaL8C8JAO2dc2cHfhgznuoc617jDQZL22kHA5cDdxi+y5JSwEP1xWVdBiwJrA8yZMwC3AusH5N6aHA9sBXbT8raTHg2Jqard+5k4CPksY6AHi10Ep8U79744Af2H61w2Nr19Ddn3R9W1rSrcD8wFY19Kq0wmYPrOwzUDd8WsAOwJK2j5S0KLBgxXALpjORkxIUQdIQ0sV6ZeBe8gWqRDKlpE1Iq3UrAteQfrB2qxM/W9GeDzgB+AzJc3ANsJ/tf9bU7TQ210n0y7qN5LpU9KurmPOTcnVqrWJKuh9YhhS+8iaFPDR5YnQKKbTnxay/Q51kWEnnAEsDY6iEx7hmgrukO22vI+ke26vnfWNtr1ZH972gl/O8o+0naurORvpet4yrq4Ej6676SzoZWBY4P+8aCjxie5+auu3jvYaUk1J74phDj35ACiUT8AlSjt/5wDDbB/b+6j51LyHlYgyn52JJ15/nHCq0r+2fdavRh/YYUo7H6Mr3ZFyda0Ue73W2Nyo0zKr2KGBbUhjWmsDOwHK2v1dAe2PSZ6L6u7er7REFtIvnKWXdmUkGpoCHGlxQK0L2WE4APmX7o0pFIK6xvdZ0HlqQCSMlKEaTF6jspflY1r7D9gultGcklEpeTpbrUteoytoTVzFtLydpIeAi27VWMSUt3ml/HWMi6y5p+3FJswMz2X65ta+G5gOkUKGiF0ZJFwP/S8pvWYfksVuzRvx3VXsR0gJB6/90M8nQfqqudttxJp7nQnqNVKrLOluSJvoAN9m+tIDmV91WCETST2wfXFc7ay3IpJXsu2w/XUCzY+GG9lypLnRH2q6z6t6nrnIxkPyZu73AgsZw4MsuUMWqTXeU7TWrhlR1IaKAfvHfPUk/IRlWPfKU3E8LVuRjDAS+yaTv9AjgN3XnGJXP2Qy3ePTfQoR7BUXocKFaTtJLwHjbz9fUbpWAvbLDvlooJb7uw+ThPbWqs0iaGziMSRfVG0mrrnV/JEvnulTZkryKCWD7aUm1Y3ZtP5lzMD6ed92cw9fqcgkwpC1s4WJgjRqa9wIfoUw4U5VvkDx2C5MSYK8B9i6kfQZwHrB13t4x79u4hLikeUgrxEuQchGAIuWTv8ekRPG+9nXDaOBl29dJmk3SnAWMq69IesP272Cix2bW2iOdxEzAP0jXoWUkLVN3ddv2WZJmAZbLu0otHt2a3/8F9Kxq1VU4a4ULJf0GmEfSHsDupMINdXkFGC/pWnqOt+5n+LV8fsdI+inpulGk/1w2tK+3fWXenkfSl2z/sab0lpTNU9q8j8dKhZ3+ihS2+Mu8vVPe97Waum9nT1srpG5+kmcl6CeEkRKU4qukKiStMKcNSav9S0o6wvY50yqoSWVh58tu2GpZ2IVrjzjxR1JC858oe3E6nTTh3SZv70SaOPa56jQVFM11aeMt25bUumDPXkATSfsBezDpx+pcSae4ywR1SSsAKwFztxnHc5GqJNVhPuB+SSPpeX7rlhSV7R1qavTG/LbPqGyfKamralC98GfgDlICd+3viBquVJcnt18HPkgK3VuYVL607qLGV4DLJU0APgf823aJClHVKlz3Mekc167CJWlD4CzgCdL1c1FJuxQI7Rmc/1bLiBuoFc5q+7gc5vQfklf+UNvX1tHM/IFyOVpVdiIZJf9D8nAvSvqclOCwqgfQ9r+zt7uukVI0T8m5YEXDrNXm3bheqSxxXU4kJeMvIOkoUg7NDwroBoUIIyUoxczAR20/B7T6ppxNCm25CZhmI4WGysK28YbtTj0K6rK07eqP1eE53rou6+S/a1b21Z4cZJpaxfwqsE7L45EnZLeTQpS6YXlgM2Aeeq7ivUwyhuowrObre+NWSU+QVp4vsf3vgtr/VGpQ18rB2A6oHf5XYZC77H/RC01XqtubFDZ1J4DthyUt0K2YpA9WNr9GmiTeSvpOf9A1KwxlmqrCdTywiXPzSaWeN+dTz9tIE/kdAJL2By4oZJhMpG54Wx+6Tyolty9o+/DC8p08MiXmbK+RPD/F8pSg0egBSCWCl7b9aD7WUhSowmn7d5LuJi1gCPiSmy3nHkwjkZMSFEHS/bZXrGwLuM/2inVjdNVQWdisvT0pyfYaCnomJN0OHGj7lry9PnCc7XX7fuX0Ja9iTkwMLjFZkDSetBL2Rt4eRIq5r9XkS9K6tm+vO74OuouTigdcp5QsPaBEHoaktUmx4F8ixYP/3va5BXQXJxl865IM1ttIic1FumBL+jYpXOYKCpb/lDSwicRatRUpUMqVG91tXoOkx6k0FmTSYgkUKlqh1Ahva9uv1NVq050s6bzTvi50D+203/UbtB5G8j7/i2TQX9Ra+KqpuyxwNCkJfaK3te7/Ts02+Dwd+Dfwi7xrb1LFt11r6jaVp3QJKXqgpbMTsJrtutEDSPo0KRLhMdL3b3EKFM9pW4Bo8XIT16WgO8JICYog6ZekMp/VZmRPkUoGXlF35U3Sykz+A1O3LCySjiZdTB+lEmbh+lW4BpMu1nOTLqr/IlVmqeWibmpyUNH/CGkV2iRD4tkCmvuTSki2Qhe+BJxp++c1dQeRvDQr0fNzsXsNzYmhQraXzpObX5fIf6ocYz5SEv0OtgeU0m0KSXsDR5EmTK0fjNqT8wYnjj8ljXVnUr7ZXsD9tov28yiJGqjClXVPJ13XWsbwDiSju+vvSNb9TmVzEMmz+UBd3Yr+qqTwt68AT9n+TE29W0ir/K0eIbuRikB0vJ5Og+7dJC/2iEri9fi6CzBZZ3ZSs8jWe78W+JE7lw6e7kgaY3vwlPbV0P8AyYsOKbeqRFW9J0ghei+SfqfnAZ4llS3fwzU72gf1iXCvoBR7k/ItNsjbo4AP5wtqXQPlMFKOy4qk+PjPA7eQwsnqsjWwlO23CmhNxPYYUrfoufL2fwpJV3+gJk4OSggrdY8+lNSpW8BJOZ/o9Dq6tv9X0ggmfTZ2s31PrcEmzgEeJPUfOYI0Aat7LoqGCrXIn4MtSZ6UpUkGW63qSJK+a/unkk6iQ1PBAknBLb4DLOPyFfXOYNLEcSPyxLGA7sEk43U8KWT0z7a7DluU9Cnb16uXKkYu0zTz8nwrzTdJn+nWZ+FmJiUfd43t46vbko4jlZAuxfOkyeI/gdrfP2BW28Mlyamq4LBsYNQyUmiwwWf+7SxSOa5KU4sDwOuSNmiLHni9jmAf371lJJX47l0LXGz76ny8TUiG8Rmk78k6fbw2eA8IIyUoQk64foxULnFrUi+FSwrJb0VaZbzH9m4536V2mEzmXtLqSa0KZC0k7Wj73Ow9qO4H0oS9jn7Dk4MDgdWdyxkrlb+8jVQEYJqRNJft/2SX+hP51nqsRCz/Mra3lrSFUxWj80iTsDq8afut1v8rhwqVmHSMJeUyHFEwRK1lkI0qpNcbj5Di2EvT1MRxWF4hPxVA0gBJv3P3hQs+STLcO1UxKlK9qMGciTdJXrta152pYDZgkboiSl3FtyH12bqItJp9f11d4E2lLvMPS/ofUoW9OQroNtbgM+cPHcDklSfr5h82tTjwTeCsnJvSih7oGFo2DTT93fuY7Yl5jLavkXSc7T2z5yaYzoSREtQiX0i3y7cXSHHEKpxY+brtCZLeySvSz5NctCWYB3hQ0l2UqebUqojVqXRvE7GVRSYHmX+SkpdbvEy9BOzzSJ6eu+n53lux/XVX7lpxw//O4YDPUn/V9UZJ3wdmzfk5e5Eqv9VlqWzIz1ZACwDbrXG95g79Rkodh+S9G6PUoLRYKBLNTRwXlfQ920crlYe9kNScsytsH5b/Fq9iJOlC29vkvK1O3rBu82ga0a3oV3UHkIyKEiGniwLfyp7okuxHulbuCxxJmpx3PYGW9GfStWEfUoPPN0kFCa7O+iW4iFSV7jQKJIlXaGRxoD16gHTd2JbU4b5bzcPy3SPc1v9KqX1AXZ6RdBDw+7w9FHhOqSxxlCLuB0ROSlALpXKcNwNftf1I3vdYiWTSyjF+CXyfdMH7DimJd0yJSYOkT3bab/vGmrrr2751Svu60O04ObDddbWzitdnMLAKcFk+xhbAuLqJmk2Rw9MuIY35TNIE94e2f1NDcyZSqNAmJGPqauA017xQSlqXVOp6DtuLKfWN2dP2XnV0s/Zo20OmtK+GflOJtmuRvEHzkCZ2cwPH2L6zpq6A35HCvTYC/uIa3dHbvaLt1PGOSlrQ9jMq3PC0Kd2KflX3HeA527XLR2ftDUiFK85Q6lsxR/sEtYb2bLZrewXzIsBRpLzDnzaRaC3pbtu1qrD1onsbKfT2YpKX4u/AT2wv3+cLe9ebixRSuDDpt+O6vP0d0u/HFgXG3OkaV/v8KOUHHsakUORbgcOBl4DFWnOaYPoRRkpQC0lfIhkP6wNXkVYkTrNdYpWj0/GWAOay3fXqTAfNDwNr5c2Rrtl8Mms2MnFsYnKQc356xTVLa6pD481O+0ogaTEXqmpVEkl3ksIWL68k2N5re+Uamq1+I9uQPJgt5gJWdMGO4GqmIWD7MQYA2zo3S+zi9dXv1kDgN6RJx2+h+4p9bd+PPbPuROp+P/IxjrF90JT29RfdrFNt0HpTiWtyPtdrksoxLydpIVKFr/Vr6hZfJJA0Bymx/XOk/LiJK+91w3qz/jBS1MCllK2q12lx4Ke27+hS7zJS4vntpFK+C5AWePar6xHTpJ5YPyWFI7eYi1Q9c6U6meajbgAAIABJREFU+kH/J4yUoAhKlUi2IIV9fYqU1H6p7Wtq6g4A5m0l7ebJ0q7At21/tNagk942wLHACNKF9eOki9/FXeqtC6xH6u9SXb2dC9jSPRtSdUUTk4Mm0KRmnDeQCh9Um3FeZXuFGtrrklbubrL9vFI1oIOBj9vuOhRQ0makH+7FSeGwIqVczdXnC6es26Msbt43ts7nIX8OBpPCbKqhGi8DN9h+sc6YK8fZkLaGgMAu7rIhYNvK6+Wk5NXaK685HK03XCCWH9Usp96HbqdFjRKlgpvSbW/QuiXQdYPWiu4YYHVSyejW96TEeJtYJJiFdM3ZnrRIUDVSShiunbxHLhWlkL+Hds3y6qpUM8u/18+QvBBvFBjjFqRqkF+kZ2GJl0kl3Gvl/2RP3XeZvEJkib5jQQEiJyUoglMlkvOA85S6w28NHETqP9IVkrYlrVq+Kulhknv9dOAuUiWnEhxC6uHxfD7m/CR3dVdGCjALKexoZnrmpfyH9CNZiw6Tg9+pRvf2Nu01SeejNUEHasWvN9KMU9KxpFyXMcBBkq4mNdk7mtSAsg4/J1WpG183xKuNv0laD7CkgaQY+VqVyJzKWY+VdCnwqu13YeJEoWTSZ+mGgOcwaeX1a6RQTpGM+Dq5IxvlcL2tbV8wxRd0eZiSYpK+ScptWEpSdbFhTpIXqK7u0h10SyR2l27Q2uIt25bkrDv7lF4wtdj+m3pW4eo6z0PS50jFCC4HhpQIIWunwWiENUnJ83Pm7ZeA3d19ud2JXlXb70p6qoSBkvUuAy5TQz2xSGGhF5B+T75BylP6RwPHCbrFdtzi1i9vpMpby+T7Q0gu780LH2N82/ZM7fu61F28oXMyDpi9sj07afW5hPZDpBWrJUmGyuIl3gewT+FzcD+pCzrAvKQcpSUKad9A6p9Q+v82H+kH8TlSCMe5pF4sJbTvIIWxtLbnAG4rOPbJPl91PnPV7xcpr+r51v+z0HhHlf7/VbRHF9abm1S96fzqd67uZ6Mp3er/sPo/I61Cl7huHkBamHqMtBhzO6kxaV3di0ke7tGkUMADSCvx3erdDKzU1OescpyVSeGcO7duBTTHkTzOre0Nan6f3yUtPL2cb+9Utv9T6DwsQgp7ez7fLgEWKaB7d+ucVPbd1fT/NW5TfwtPStCfecs5cc32aEkPe1JFo1JclVfiz8/bQ0m9WOryWl7xL+1GFj1XAN+FHh2w6/AP2030apggaR7b/wbInrbtbHfbr+EN55U62y/mz8UThcb6XeDPkm6kZxx43dLRL9Dm/VMqH31AHd3MIFc6ldt+RQWriAGjJJ1Gz4aAdcoeN7bymrlO0gGkFdKJfYXcZSy/JhWrED09E61QwK5DkWy/RErS3S4fawHS9WIOSXO4y/yqlq6kE4B/OYf0SJpL0jquWZyAtBJ/Z/biQQrJ+W1NTWwfp1RV7z+kxn2H2r62Wz1JvyJ59L8BnEAKMfw7ycO/d41xfjzrn2N7p7ZjTravG9Rcf7B3bU8s1W77Fkld5zX6vWlIewYpUqNVtXDHvG/jmrqta9Ezkr4APA106kIfTCciJyXot0h6ip71/fevbtedOFaO8xVS4j/AzbYv7ev5U6l5DWmSdAAVN7LrJ8I20r09a3+aNFlq73pdqxa9Onci7jq2X9K/gVY+RCuPaGJ+hLsvH936v71CWikuGmPe4Vj/Z3uxAjq3krxVo/P2GsDJttetq531PkCa0LUq4NwM/NJddnyW9C7JeGgZ17OS+rCUyv8pGsuvXipkVYRrVcrKx9icdG1biLRSvDipg3utxGBJ95DCkVrhUzORPE21K7/lQgUTPxMu06C103G6/p5IOhD4OnCY7fPKjmzynJ8cajne9ooFtMczqT/Yasr9wWzXmphL+jnpO3c+yfgeCrxBXoRw9wUmmjTYJsvf6/S70oXuZqTr2aKkUMW5gMMbWqwLuiCMlKDfooarTjWJcnnEatKnpLtsrzWl106FdiOTA0nnAisA9zFpgm7btfI88o/tqpWJ0gCSe72rCZh6KRvdwjXKR9dNpp3GY/3NNZL8KzprkarqPU2a6H8EGOruY8zb9Wcnea965Ly4gTj8/oykfYBzWh7BwtpjSQVHrrO9uqSNgB1tf7WmbqcFgq4T0ZUas/ZKt96qKRyz1vdE0sIkA/BDpL4j1cWHrhZgJH2PlEvVMrAhfffeIhUQ+F63460cY6TttZV6mGxECp96wDUKjmTdRgpMdDDYZiZd50sYbMNJnpNWxMN2wG5uoEJk0L+IcK+g39IyQiR9yLkLeikkvUznJNgiq7kUdiPnieh8tv+SV7paq+abSpqp0IR0LXdZK38KXAVcIKlVunXPvK8rWkZIXn2+0nbJplt/lrSJa1ala9HHpE4UCtOzfZdSqc7W/650ieDhwGdIHiZIE7NrSDH+XSPpeOC3LtNRvKo7kNT9+hN51wjgNwXOyYdJoW+jSQU8rm4Z3gV42/Y/Jc2Uv8835BXvujwmaV/gV3l7L1K+R7e0GrMKWJB0XSNvl2jQ2ola59j23yVdSSq8sjmVBRi67Fhu+2jgaElHlzBIemGUpHmAU0nn/RVSjk4tXLbRcg+DTdJ/WrvJBluhw+xO8nT8jPR/uw0o0SdtflLu0xL0LBZTtwBLUIjwpAT9HqXKXmNIKyl/KTgxaOkXLyta2o0s6XrSytGTbfsXB87odvWrTesM4NgGJo0zkQyT1qrXtaReOrW6KGfPz7qkJMrTbT9Ya6BMNF5nJ/3AvkVNozWHHrUmde10HYLU4Tgrk2LXq/lPdWPXW9qdVuNLhFp8jTTRmJm8SppzKWqR82cGksomA+xEisP/WgFtkRp97kbq53EhydB6tKbudaTQzaNJRRaeJy0a1DUEFwBOJHlpTDI4v+UyvaCKXTfVe8NMAYfY7mqBR9JKJAPtaVLZ+me6HGJv+uuTGgu/KmlHUoGXE0qEALYdZwkK9QfLhs/OTD4x37embpMGWyMoNba8mWQETvw9sn3JdBtU0IMwUoJ+T54YfIa0mrIWaWJwpu2/FtIv1p27KfoKFasTvtGm8wCwNPA4KSeldmJw0yjV+t+ONGk0kya7tWr/z0j0lmBru3bJ66zfdM7L8qT/33aksrun2u4rJGVKep3i12v1pGnTWo003s+RqsF9DLjW9ndraM5OygsQqTDB3KT8g+LhU6Uoed1sKrQ3X9P2K+UZ7aA/jpQ3sipwJnAasI3tPkNSp6DZ5zntNmekon8bqSJge97dWb2+aOq1F2byEvZd9VNq010S2IfJDauu8w+zbu3FlqBZwkgJZihyrPa5pNXuscDBrlk/vQkjRdJZpB/HakWr47t1I0t6xPYy0/rYNB6jY4Jw3VVBScuSVojbV/pLeRE+RFot/xap98gywInuondMNoh3AJa0faSkRYEFbY8sMM4vk3KJTMol+mNdzazbSIJtRb+xnJec37IZadK/KGkBYgNS35dtu9QcTeqV8mjeXgq4uO53XKlH0c7AC6TJ6B9tv509hQ/bXrqOftuxlgcOsL1HTZ1BpJ4m7VUGa4ezzCCLOx9wlwUeplJ/tO0hkg4F/m77t3XPS1M5IxX9Rv5vkn4CbEsqEd/ySriuIZG1x5Kqx7UbVl3nH2bdH5HKtZeo6Bk0QOSkBP2ePAndkTQRfY60onI5qdv2RcA0N73KE8YW87Rt165oRUoUn5hg61Qqt05oxHWSjgJ+0Ap3yxPqw4Hr6w11Ik2tWJwBHEaKJ96INCGdqa6opC9mrWVIZTnXduo+Pxvph7KbxnK/JP0IforUef4V4BckD16dsf4yj7OV+PkNSRvb7roMaoXXbU+Q9E72LD1PmvAXoamcF0k/I+UIDAd+XDEEj5H0UA3pA4EbJD1GMqoWp0D8Oqknz5fbjfZ87jfrRlDSqsBxpKpefyR91k4G1iE10azLOcCDwGeBI0gGeNdNRNvCshZoD9NyzYqLpXMEWgaKOucgvkQqpf0d293m6bycczJ2BD6RDdaBXWoB5XNGOnCOpD2AK+hZxbGu125LYPmGjMI3bJ/YgO5+wPclvUnKIy2VkxoUIoyUYEbgdtKP7ZdsP1XZP0rSr7vU3Lxy/8a27a4TKivMJGle2y/CxATqOt+375BWbx+R1OrKvRrpR7Z2rH3mSiblTwwiGX8PkVZh6zCr7eGSlCd4w5Qq1hxaU/crwM/awwlsvyap26pI6+SV0Xuy1ouSZqk5TkhGz0crBuZZpCpqJWgkwbaNtZg0cRwiqUTOyziS0f1qh8fWnlYxSd8iJdTeCCxLT6Oq1sQpe3y2tT2s0+O2u534n0rKmbidFD42hpRLs4PL9I5ZxvbWkrawfZak80gx+N0yZ+X+qW3bJbiMNL7rqNERvgM/B54i9doQacV/aVIBktNJ4ZLdMBTYHviq7WclLQYcW3u0mYZyzd4ijfEQJhluJYoePEYy0JowUk7IIYHX0NOwqhX6Zrv05zcoTIR7Bf2ePLmdoT6oknYmVTy5iPSjuBVwlO1zauouxSSj4b4aK4BTc6whwF51E45zDPQGpK7P15Oaqf3EzVQSq4WkO0lVq+7Kxsr8wDV1E4QlXQHs3VqFz6F1J9vevO9X9qm5vu1bqyEtJRNsK8c5hzShG0PPMI6uEm2birlXao65HqmM9nhSfsttpHCO2rkdki4j5eZ01WCxF80eMfGSHisVBpn1WmVsbyJV9noWGFnyGCVpKkeglzylMbYHl8xXKklTuWbZw7i2U4PZYki6hLRw1t5nq1ZCftY+mhRJ8Sg9y+OXKBgzL2lRo2oI1s6jCcoQnpSg3yLpT+SVnhTZ1JNCsa6NVDqxfbakUaQVdEhhIiWqZj1Oajy5pO0/5ZW7j5TImWjH9mhJ6xSQ2g+YDdiXFEK1EakhZVf0EroBZVz1J5IaZS6Qw+u2An7QrVjlMzwn8ICk1v9pbaDu/+xEYA3SKvwQANtP1NTsxJrAigUXCvoKYzKTvjPThO0DALLna02SwbIbcIqkf7t+v4Z5gfvy/7Dayb7OdWhQDgNtXeDerG7XXSkmvfd5SZ/hy4E5gB92Kyapz5CbAhPSKyRt2kCOwGuStiEtlED6Xrc8VV1/riV9jBRW+lFgFmAA8IrtuWuMtcVWTMo1262Va1ZA9xEm9XYpyeX51gRbA0vZfqukqFKFwf2ARUiLMB8jXU9rGz9BGcJICfozx70Hx/gzHSqd1CUbD69QuWhLWqzAKmw1Z+IIUoOvS6iZMwGTxZvPRJr4Pt3L06dGr9VteD3bd5HOR+3cgCZd9LZ/l0PRPk2aKH6pRigPNPsZflvSKcAinSaPJVYwM/eSkuWLlG99D2LuZyWV/J47354mfb/r0vXkvg+eITUabPFsZbtrg03SfrZPIDX/exG4iTI9TFrFEtYnrfBfkLe3JuWB1aWVI/AWk3pNlcgR2AE4gXT9NOmav6OkWYH/qaF7Mil07CKSYbwzsFy9oU6kqVyzV4ExOUG/mMcjhxPOCixmu05OWSfuBeYhnYOS7Ef67bzD9kY59+7HhY8R1CCMlKDfUrdyx1QyyHZvNfrr0MrvgDRpKpXf0VTOBPSML3+H9B7q1ItfQ9JCwO6SzoaevULqhODkHIH7XLP7cgfdE4Hf2/5FCb3qZzivhLaMyZGu36tiM1Jp7s8yafLYBPMB92cPQnVSU8KTuR6TezG7irnPBttKJMP9TlKo1/+28sLq0sT1qEGDbTfSpPwkspetBM5laiV9E9jA9jt5+9fUy3Vp6TeyAJHDYnsLrbylpvYjkgY49X06I1+bS/QLaSrX7I/5VhSl5rrHkTxKS0oaDBxR4jpBMlAelHQXZa9Bb9h+Q1KrEtyDSpX1gn5CGClBv0XShba3USqxOplL3mX6dzRS6cT2KtXtVn5HHc3M23mC3gqDm59CHiB32YugD35Nik9eivQjWzVSaiVq2n5X0kOFvFNV7gZ+kH+oLiUZLKPqiuZQk2NJ3c8FnCTpQNsX9/nCPsgx5b+X9IDtsXXH2AfDmhDtLdeFVKmtGxYDPgA8TMp7egr4d5+vmAZ6Ce15tcAqP0oV6fYnrUJ/Xals9/K2r+hS8gGlJrgLKfXymHgoyvQ+mpfkrWpdJ+fI+2qjVLXvE3lzRI1zUNVsqrP4a3mRaIykn5I8Y7UrFwLYbv1e/FrSVRTKNcsej1mY5PEpUq2PdJ1Ym3SNw/aYnENZgj776NTgqWwI/hG4VtKLQNFGnEE9InE+6LdIWtD2M2qof0c+xt7AUaTJzMRKJ00klkoa3268dKGxA6mizBqk5mFbkSokXVRD8wx6j8u27W4rZbX0f2X7m3U0etG9CVidlNtRKkegpf1BUvWwbUkTx2Vr6o0FNm55T/Kk6boSCbt5cvQj4HXgKlJjuW/bLhG/3hhKjfZK5rq0ynKvRMpHWQ9YmTSRvt12rYlOzjGbLLTHBbpsS7qAZCDvbHvlbLTcVieJXNJHgKuByb4Pda+dknYjTUpvIBk+nwCGuWZDQKVeG2sBv8u7tgNG1T3HaqizeP5teo5ktH6bFF74C+cePTV0ZwbetW2lXk3rAI/avqeObtbekFRB7gnS/25RYJe6yeKS7rD9MUn3OBcaUaFGw1mrtCe6Xf+TpP/fVaVzX4LuCSMl+K+mwUonnfI7PmT7swW0V2BSzsTwmjkTSPpKh92Lkn50B9hepKb+0sBTtt/MP5CrAme70kemS92OXZ1LhOVIWptkDG5BiuvvugpX1uthoCr1Uxhb12jNWq0qRVuSQsD2B26qawBVChSInkZskV4Cki4C9rVdJNelTXsRUt7EeqRz8iHb89TUHGV7zerEqzohK6RdneAVrTqVE+gXrbsanz+7HyOVnG0V1rjT9rM1h0j2+gy2PSFvDyAljtea6Oo97Cwu6QLbQ2u8fg/gGFJ415Gkvj+jSQsyp9s+pub47ga2b+WNSFoOON/2GjV1f0vynB9MWuDZFxho+xt1dLN2uyf640DXnui8CNUrdSMpgnJEuFfQ72kyzILmKp2Uzu+oMh/wmu0zJM0vaUnbj3crVl1NzO7575NWRn9C6vJbl0uANSUtA5xC6oVwHrBpHdEmcgSyV2JLUqnLC4Aj6xpTmaskXc2kZo7bAn8poAuTmsd9AbjI9kvqUA1vWmkqP0A9K54Vy3WRtC+TPChvk8sPk/pglEicbyy0B3hLKem4Fca5NAX6TUgaQfKkzEzyIjwv6dY6eXhOydy/yMbUZXXH2IF5mBRGVqJKFjRXNawT69Z8/bdIYZBzkhpvLm77hexdu4tkwNRhoCuJ7bb/KqlWA8rMPqTeK2+Sru9Xkzy8JTgEWKvdE82kam3TygukcNB38naxUOSgLGGkBDMCTVZQaarSSen8DmBi7fw1SY3qziBNUM8lrRrX0V2BVKZ0ddKK1TdaSbEFmGD7nbzSf5Ltk3JyaS3UsxTxLKRzUdd4fRRYt7RnzfaBkr7MpP/Tr22XSl79k6QHSeFe38w/4CUaAQIg6Xjgty5TQhuaq3i2BOka8e0mvDOkPg0DSNWgvk3yNnbyQnbDYaRQvUUl/Y70Odm1gO7ctv+jVGr1bNuHteWodMvw7IH9Q8lwPeBo4J58PW6FkR1cQHdG6iz+llOxhxclPdK6Fjk1qS0RhjRK0mlMKme8I6kpcNdkj9eVToUgDqk5vk7M1Bbe9U/qLRCcSCqFfytp4eiWwp/joBAR7hX0exoOs+jYr6Pb2OrKKnFH6uZLKHWbXx0YXSruN4fdrEHqX3EhbZ2e67q+lRok/pz047W57ccl3Wt75Tq6bccQKTTrY7aneVKj5hoMVg2pdvfGGySj6BDbw7vRrxzng8BLTgUFZiMl2dYOv8naXyNVi5qZZBifb/ulArrH2D5oSvu60G0kvLBpJH2IFEYlUknU2oayUtGRTUg5CIfYvqtEnkD+XM9OWol+g4KTfkkL0jP3oMjnuCR9XC8EXGF7wRraD5JycWYiGRLbZ10B59r+aLfaWf8DwN6kBruQylP/yrkhbA3d4aR+YLWvDR20jyV9j1ue6KHAuDrXivybsSHpXK9N6mb/qzpRCUF5wkgJ+j1KCdKfAU4j9RF4Bti1VLy2ClY6qeRJfJnUW6K1WrUd8Jztb3c90KTf6iA92qkU8eykpOA6RsoTVIoG0Ob6ds0iApJWBL5BGuf5kpYEtqkbW93LsboyXvPKbW/YBTobdzjmAFJi9++6Mdgkfcr29dlDMxm2/1B3jG3HW55krGxHWoE81XZf521KeqNtD2nbV2ICPYbkbVyC1AfpMmAl212FF6qX6oIt6o43H2NL4PrWBE+p4tCGdb1tkrYm9Xe5xfZeOZzzWNulPEBFkLSCU/nXjpP/GosETen2+bl3jdLSTWlnD+v87R5RSSsBz9v+Rze6FZ3LSAto19KzkEmtqIRsTCxCMlxbhtXNti+to1vRn4cUqXEk8H3bp5bQDcoQRkrQ71HnCiq/tP1IAe0NaabSySjba05pXxe6BwDLAhuTQiN2B86zfVId3RmRtsn5TKSJ6Sdt140Jf0+RtOf/t3fmYZIVVfp+v2bf1RERdZBFFBFEURQRR1F7xH0DEcGFUXBBBBWc4eewO6DgjiLrIKKIoKIgiCyy7/smIgg6Iygqw9KyKA3f748T2XUrO6u6+0bcquzOeJ+nnsp7s/JkdHbmzTgR53yf7cNaPG6fVL5z9IC77Xx51eZzLUY0oG9HfEZOICYMD9p+9wLG+ighx70msZPUYwVC0WqbzLH2EvjdCB+Eg3N2XjWBumAPl1EZnKuxu9RucVcoGvHXBpbunWt73ZR0uEN6edAEvfUiQVdxF0YkHU98b57fd/4VwEdtvyczftGqhL7Y2cqYffGWI3betwJWBn4MnOCycvaVAtQkpbJQkFaByF3tGRC3K6WTm4E3OkzESLsHp+Vu1adYM4kSDgG/sH1mbswUV4Qz8xq295O0GvBU25e3jNepz03f5Hw2kWge4QxpSkUD6Udp+DQAh+XsrnWNBggnDDqXEf8rhBHe2URvyuWN+26xvUDmZ5JWIjw1DmB8v8Gs3NLCFL/z8sLSDNpBypmYSfqM7QMlHczgz17u6vaHiD6PZxA+NxsTO6VZk35JS9t+ZF7nWsSd4aQYVjjuloRk7SxJ/0moOO7nMlLBRb1zJlsky/18pEWMs3J2kOYR/xjgG7avKBTvQcJP6fj0e9xnpPQudKU9tXG+MrSkSfNeRKPqjHRqNtF8vW+hp+lK6eSTwLkKiWMBzwQ+nBtU0k5EXXKRxKSPQwhjyFcTW9+zCGWujSZ70CTsnH6/KX9oc2N7uw7CfotowD8kHb83nftQB89Vih8xt6v4D4k+oxJcT3jxPDjgvpcsaLBU0nQ/sHWa3KxCfBctL2n5AquZ2xHlhf+VEpQ1gGMzY3atMnilpC8D30zHOxJqXG3pyZJnG5FOwM7EdeFS25sphDf2LxD3YuZ+Lw86t6AcSew6A3NW0k8mpNxz2MP2iZI2JUqSDyJMbF86+cPmi6OJ98Am6fhOQhiirbnlZGp9Wd95jl64xyWt1EVPCvF6bptKkx+EbFPSE4nE5Dnpp4mJnZXKEFCTlMow80lC5Waj3qpwqqn+lqRP2v5Kgee4SuOVTrahwBe77dPTytc66dSvndmYmFgFuELS1YS06i9cbjv0palM5hoA2/emfp1WeExh6WMe0CAN5DZIP4OYNPYUsy4Adrb9h4ywG3l8r9MvFUaMQ0eaGD4PWKmv9G1FGiU4GfF7E8PrgOeoT9bY9tU5ExJJHycMAe8mkmOICULWDpvtX0n6d8KBnnTtKNH/1KXK4E5E78gP0vGZRKLSCtunpJsPuc/oNa3+5/KI7UckIWmp1PexQDtqfWN6KvB0YBlJL2SsL25FYNkC471T0iGpL+eJhCR8id6DnsjIG4HDbZ8qqZTs7lq2t5K0NcxR98rRFr9NA2SYJb2e8LzJ5W/ADZKK9qQksv3Fmtj+gMLvZwvbJ5SMXSlLLfeqDC1psjzTfSo3qfTrjBL12ppb6eQCom63VULRK7NIt7dsThAk7W/7/xUYs4hyr+2IydIJRBlOrsvxZcSq3RUpWSnyOqu7BukzCT3+3ir5tsA2tmdmxLwa2LL3Wqak+If94x8GJL0VeBvhg3Fy465ZwPG2L86M36mYgKTbiMT4npw4A+K+mZA5XtL2GpJeAOzrfGW9zlQGu2KCz95c51rEPYm4/uxC7LzeS+xKtxUneD8hufxixi8SzQK+XaL8RuFtsyKxw/h5Z7rNp5g/I3Y4ZhK7PQ8TimTZoi6SLiZ2ei5K1+O1iFLkBd69TPHWJpKzixnbpXsx4evyJtu/yRxv8Z4USU8hfLueRXgdHWD7gbbxBsTP7hOtdEtNUipDy2R1siVqzFOpyU2215nnH89/zDkTgP7JQInJQSPWBsQkYXPgHKIm/Ezbn8mIuQ3RSPgi4NvAFkSZz4mTPW6SeJM1SF9ke9u2Y03xBzUbZzlLS3oNUWbRLNPbzhkqVl0j6WW2L5nucSwoKQma6XJ+PL24VxET53M9JtNd4nrRmcqgohduV0KRbE6FQ9tEMK2OvwF4F2O7MxCT9HXbTnQneK5XEmImp9vO8vGQ9M4SyUMjXnOHUcRu1eWEJ01270HqG9kcuMH2rQr55PVtn5ETN8WeSXhXrUvI476ceL+dmxFzKULSuPdZuIkQXinmq1QSSacTCdX5RNnwCrY/UDD+5wljxx8wfvenOs4PCbXcqzLMTPaFl21qlepob5G0WoE6+B6a4Pag4wUPLu1MlJn8lZgs7Wb70bR1fSvQOkmx/b00wXtNGuvbbN88j4dNxnGEq3onDdLAPZK2ZUw7f2vC5Ks1ts/uNaimU7cUKtPrkmsk7UiUfjWVlkqqe23C3BPo72SGvZ3o2zqV8UaqX86M+6jt+/sqYx6f6I8XgPcSvXFdmDmeSPQyHEmfT1FL7iJ2JN7C+N6WWcSfFAYmAAAgAElEQVTYW6Hw4+nnhvR7ecac4ttyrqSvEzvbBi4kdsHafq7f3Hd8DdF/8WYK9B6kEqw/E+O9lRDwuDUnpqSX276ImJi/gzHvnJ37qwpajPfvClnmT/c9Zwl/ojsYLNKQI2G/qu2eOeQv0k53SbZKv5ulldVxfoioSUplmNlA0qCtXVGg5j7xROAmSZczfiWlbWmIJ7g96LgNTyIMs8bJntp+XFKJBvUnE3XsR0taWRkqUW40SMOcrfulKdcg/W9ET8pXiNf2YmJ3qTUKN+7vE3KUWeVzU8ixwK+Juu19ib6qnORyHJKOBdYiVJx6E2gDuUnK/6SfJdNPKW6S9B5gsZRwfoJ4b2TR+8xJeowor7vTGUpyfcy2/a1CsbB9HXCdpONcVpnuKsa8lFYjyrwEPIH4v1wjM/7xxOS8l/xtQ6xyv7ZNMNvbpR3zTxTqYRyHpL2IkqnnEDuwSxD9jS+f7HHz4OvEbvYlaef91Nxx9jGTufsBXz/g3ILSLJtaGtiS+L7KIvUQ9VYcFmse5yx2pYW9/7D9g3n+cWXaqOVelZFE0uK2Z2vMfHEcts9rGfcxxtRHlgEe6t0FLG07WzkslXq9Ih1ekCYk2TS/cG0/W9LTgBNt53zh9noEvgw8DfgzUUJ1s+3n5Y65NApPjK3Sz+PEBGmo9fN7fRG9XgmFOt0FtjcuFP9mokSoky8LScsD2P5boXjLEvLDc2S6CVnYViUtkg4lFAVvUsgnX0Ika08CdrX9/UkDzN9z7E18Nk5i/K5S1s5EStIOIEqGmrtsuQatRwAn9ZqwU3nZ22xnKRgOKstTAY8MJRPcnBgTxL2WMDC8ulFamNVvJ+lSQlHvbUTSNg63bESfR/lttj/RBM95lTPk/BVqXo8zuArBBd7HtSdlyKk7KZVR5XJgQ9vnSTrY9k4lgtperESciZD0CWAHxsoUvqswLCth5vh20hcugO27JE0mWzm/fI4oWTgrTaY3I5rcW6EJvB96tP0ST4/9PXAgcGCa4O1BKEN1+v+aSW+l/D5J6xH9Ek8pGP9G4KlED0Yx0liPJa22Svor8D7bN+XEtf0QkaR8dl5/O5+8wvZH0u3tgN/YfptCkernjJUb5tBrOt6tca5E2cnRhIz7V4DNiPHPyIwJsLHt7XsHtn+eGtNzOUPSuwkxEIi+uF8UiHuRpG8wd+9BbvnQP2xbkoGetHEubyJ2jl5Hngx1P52W32pMDRDGzHWz5pi2V895/HxwlsIgufakDCk1SamMKs2VmaydginmQ4Qi0oNAT8r3EqLsKZcuvnAhegTukTRDYap2jqSvZsRrqv/sQ0zCitG3m/IYGX0+U8ThqQRiD6IMafl0OwtJpxAT5RWAX6WSyOYqf5ZaFnA48KmeKIGkVxGysJtM9qBJxvtV27s0xj2OjPE2+99mEv0j2P6TshRhx7CdWyY1EcukPiulBHzv1He2Z2bcuxTmhU3p9rsyYwJsTyiG9eLOAB6U9GFi5bytJ01PTKPpr2VCYCGHEyQdBjxB0vZECWqWtHHqOzle0s2ldslT3HHltwAKxbAdJb27wM72lxq3e+a678qMCYCks22/Zl7nWlB7UoacmqRURpWFtc5RjG+sfYwCDfmJ4l+4iftSSc/5wPdSo+kgY8D5wg1JS0m7OEPish+FDPMSxER0S9sl/AM6xfaR6eZ5lP1y/WLBWINYzg3VNNvnZibGPSnq0uO+L/V73UUsaHwQomSUKOksQtpZ6i/Lyu37+XtPVEPhS3MnkcTmsjWxOHBSOj6fxuS3LbZL7NwOituJE7rtLypUuB4g+lL2dKbRrsZk7D/UWzDqe84s35FUxrsVofK1PrGz8u6cmGlcxV9jSUsDywFP7utNWZHw1cmiw8WBSiFqT0plJJH0EHAbcdFbK90mHTunprhLJH2KKA3pTQ7eRvgI5OxMNOPPpFHLn/uFm2IuR/gHzCBWXFcCvpeh2NOMXUzWOcV7ju1bSsXrktTrc32joXtPouH494QSUCvBgwHPM5fyTyE1oJOI0sKmz82LbL89J25pFPLAXydK3r5q+9vp/OuAf+1XSmr5HHsBryKSlNOIRuYLbW+RGXcjQkThCcB+xGfvQNuXZg24AxTGsdsQKnUQ8rjfc6ascYq9EpFU/Us6dR6hGlbEHV3SioxXvstp6H6z7VM02HfEbRNXSTsQieTTiXK6E4Cf5k7U03fS/baP6jv/QUIyuPV3k0LNchein/FOxpKUB4AjbH+jbewU/32DzhdYHKgUoiYplZEklfRMiPvUs4aJVPs7x3zS9jWF4u4EfNf2vSXiDYj/T8Qk4X9sF6m17iBJWYqY6K/O+EnHvhM9ZrpQKJFt7JBBfRMhTrA10Ve0pe0iLs2DXuPc5uAU44lEuV7TSHXv3PefpJcTTvbPJP4PewsPuU22m9q+sP+5HHKxWUi6AdgAuMb2BpJWIT6LrY1JBzzHE4H7SgggqLyvy7pEqeJFjPVhvIjYuXprbp+SpB8RvVW9Xdf3AhvYfsfEj5qvuB8m3sOPMNbgnf1em+T5vmh715aP/QdRGvxp21emc7cX+FxcRVyHHu07vyRwZYkFP0k7Feq77I/bjLk0Ib9/de7iQKUctdyrMpI0k5CUsKxt+yxJyzCEnwuN9yf4XfqZc1+hRr9VgCsUWvT/TeyktJ7QKNyY/8P2jQqTs6uJfpI1JR3RdoVN0izGyvWW1ZhMdW+C0LZuHeCnRN32VTT6L4YUpyZxCD+Fo1Lyd5Wkj+UGV0MNKCVEPVagjKTvvYQ8cGmOIrxArqKM50iPrxOu4k0OHnCuDQ87ZMRnp1X5PxM+LK1Iu2on2P51SrxPJ5Kg2ZLeY/uszPGW9nU5GPho/86tpNcC3yCa/nNYy3bT02YfhTJXLrsC6znTv2QBeFd6zjasSsgCfymJPpxAlLbmsnh/ggJg+x8q1bQFf5K0gu1ZqRdqQ+BzucIH7hPMkfQEBiiqVaaPoZuMVSpTSeq92IFQGFoLeAbx5ZvbkFeapj9BP0Ua/Wz/p6Q9iHKv7YBvSDqBmPy28QxZw/aN6fZ2wJm236dQDLsIaJWkdFW3nniG7c07jF8SpV6fh4j36yGN+0r4CHWiBiTp5MnuL9CQf7/tn2fGmIOklxHN/Cun0pYeK1JO9e3KNEE6gvis/41Y9W7LVkR5F4wph60MPJvYTchNUor6ugBPH1RamhaOSqygP9zcCUu7bQ8XiPtbxmTmp4LWk/5UXnsocKikZxDvkbsVEuMn2f5/LUPPkLSK7bvHDTR2A0uxh+0TJW1KKJ8dBHwLeGnB54Dolax9KkNETVIqo86OwEuAywBs36owHRwqpqrBz7Yl/YmQsZ1NmF3+UNKZthdU5aq5uvYaUhN+Wg0r4QDeBRdLWt/2DfP+02nnq4TB4gOE70yvhOOFFJALbqoBKQzxViG+M3LNOF8G/C8h3XsZ5YQfepwj6SBCprupRtZ21XVJotl8cWIXqccDhERuNrZ7O1+HSjodWNH29ZM9Zh78o7EL+jrgeNuPATenhv9cTkm7daV8XWZIWsr2uN3L1DhdYrwfBY5JvSkC/o+x5C2H3YlrxmWMfx1a7xD27ZqPu4tCnxXbfyDUuL6kkFrPET04CDhV0qdJ8vVEqd5BlBOx6O3WvRE43Papkj6XG1TjlQBnED1hJ0z8iMpUU3tSKiONpMtsv1RjhniLEzWpQ9k4DyDpHUQdv4melJ8Uirsz8D7gr0QZx09sP6qkDmR7rQWMdwpwBtHweBSxs3JfKqm70sNp5vgr4FnAHcSkY9iFFJ5OeKJcZ/vxdG5VYImMJKL/OT5O9HjcTdTdQ8ZrkhKemcTE6PmEo/b3c/sOGvHPGXDabfslGnGf2WWvmqTnM3ePx48nfMDksS4l5MrvBm4hBAnuSPf92vY6mWMdJMrQuhcjlfBsDOzoMSGI1YkSuyvb9oQppM4vBi6yfWcqpcP2A5M/cr7jXw5cCNzA2GdjnAJhi5h3MPGuefaClaQtgdNLlk4pzDz/A+gZcd4IfL7UjmYqHb6TuG5sSOyCXW57g8y4TTPn2cDvUwJXGRJqklIZaRQGZPcRk/OdiBr8X9kuZQRXFEmHEJPonoHcVsBvbe848aPmO/Y+wH8PmohJeq7tmxcw3lMIX4KnAofYPiOd34yYNHUtcbvAaAJBhS4npyXoS1wvtH3SPB6yILFvI7x5stXYBsReikhWDgL2caZaT5eUbhbvi/3fRMJ2E+MTwX9rGe+lRFnXyoQi2X7p/BuA99rOlgsuTUqGPwMsm049CHwxp2E6xdyEMe+di9PPRTQS+4z41zg5zU8FkpTTJ5hiXG/7+al06nPEZ29P26VLp4ohaVlgc+CGVO2wKrB+7zulRbxnAau4T/QilQH+qWV5c6UDapJSGWnSLsEHacjuAkfmfhF0haRfA8/tjS+N/ybbzy0UfwPgFenwAhcwE5O0pe0T53VuOpmkxAIYbgfiLhPXFP8cYKbt2SXipZhLEaUbWxOT/pOJBPnOQvHfSEjZNj1HshTaJF1H1PSPa8h3AaU6Sb+yvW5unKlEHfi6pPKuJVKsWelcEWEQhT9IL2F5K7Cy80Q2kLQ/IWJyCmXK3pqx97W9Z+N4BnCs7W0y4/aqBg4gJv3HlUi2JK1MmHGuzvgkvlWiPcFzPIXx77dWu8VpZ2b3/rJeSesD+9t+c9ZAK8WoPSmVUWcZYnJ0BMwpRVmGqW2GXBBuA1YjvDAgFIBum/jP5x9JnyBEBHolJt+VdHjOSmZid5JL9zzOTSedCxN0yKsZn7geA/yqYPzbgXMlncr4idiX2wST9B2iLOQ0Yvfkxnk8ZEHjH0qsxm9GlC1uAVxeIHTpZvEml0ha13bJ/7ee7PdeNHbZCH+QrF0xTeDrAuT6S/yIkByenZ5nVeBnRI9D27GKMC3chJA0Xhe4tcBYYayXY/fGuVLXi3+WtLvtA1JSfwJQQm7+ToVp70zgCyn2jAJxf0rIiJ9FWVU9JL2F6KF5GqF8txrwa8Y8dRaUVQb1Hdq+IZUZVoaEupNSGWlS7fZrbf8tHS8PnGF7k8kfOT1IOg/YiLFJ10aErO/9kKeMpJCZfZntB9PxcsAlGb0HrwfeQMhm/qBx14rAurZf0naslTHSqmCzlv+ZwDdKrQamCelc2N6nZbzHiVIeGGtahTIS0s1ylt7v5YGf237FPB88edy9iQlSqWbxZuxXErtJf6JgL5SkMwk3+O+mU9sAr7L92sy4nfi6KNQW30Aklv9MvCa7ZpT1nElcb64FLgUuXdCy1ekiJVffI/pdNgNOcwHT3tKlU42419p+Qe74Joh9HbEYc1baBdoM2Nb2B1vGu9X22hPcd5vtZ2UMt1KQupNSGXWW7iUoALb/li7iw8qe8/6T1ojxK2CPkacmcxeRQL2FMYM2gFmEj8VQ0pUwQWk0pkyzAqHa1EtcX0KZnQNgLBlJk32an5eW8Uqs2k5GT1r2oVTicw/hEZFLTw1qt8a5UqvmRxEGg+MasAuwaq8fJfE5SVsViFvU16WH7SMUJoA/IcqGPmw7x5PndqLXZ23iffBXSX9xpq+JpFfb/mW6VsyFWwoepNhN352vAYcRPTTnS9ows8F9MUIYZo5wgu0/UkANEPiZpDfYPq1ArH4etX2PpBmSZtg+J4kitOVKSdv3Kih6SPoQ47+rKtNMTVIqo86DzQu/pBdRRj+/E2yfB5AmBs263xI9E0cDl0nqNV2/jZg8tSL1s1wn6TgPMPsaRgb0d3xE0sxS/R2FmRLhgdR7cCzhJYSkvwLvcyE1rg74mcJz5CBCEtUk+esc3K0M+F9sT+of05IzJL2bMVnVLYi+u1yK+rpovP+MiHKea4GNJW3ctrTQ9odT/BUJ9bBNgB1T/8SNttvKEP8L8Etg0G6lGSuZbcOX+o7vJUrUvpRitxZqsP2YpFskrda2n2MSdgb+n6S/E/LzRXZGE/elRZLzge9J+jNju7Ft2AU4SdI2jCUlLybkxt+eNdJKUWq5V2WkkbQR4TB7F3FRfSqwVYlm2C6QtAOhmPUIseLa+yIo0jORVvE2TYcX2M6ugU6KKXsDzyQSq6JjLknXwgRdkcptNkqHl9v+c8HYFwOftX1OOn4V0Vw6lCWRTVK9/dIOz5fcWMsCnwJWs72Dwl/iObZ/ViD2IcATmLsBO2eyi6RZwHKM7YrOoFFqV2ICmWr4s3xdJiop7NG2tLARfyni8/FyIlHZGPiz7fVbxtvf7c0P5yf+DGBL2z+Y5x8veOzzgRcSu61zJvo5pcJdk0qPHybev9sAKxHlhVmLc6lsrCebfJPtX2YNtFKcmqRURh5JSwDPSYe3DPOqv6Rbib6RrHKFvpidKlulif8nmVsVqbikbS5d93d0gaR3EbsG5xIT0VcAu9n+YaH417nPj2DQuWFBoRD1McY3i3/L9iOZcX9AvIffZ3u9lLRcXKIOX9LRA067pDJSSSSdbfs18zo33Uj6CpGUrE00nV9ClE5dYvu+jLhX295w3n/ZHklX2n5xB3FfOeh8b5e+RbxJX4ec8rR5PO9Ftl9eIE7TqBZorxpWKU8t96pUYoVtdeLzsKGkbCnNDvkt5ZXHula2ut+FTL26Yqr6Ozris8BGvd2TVMpyFlAkSQFul7QHUfIFsC1R6z+sfIfoe+qp0r2HGPuWmXHXsr2VpK0BbD+UmpuzSJOke2zvmhurEXMd27+eaALZduKYEsBlgSdLeiJj14wVgae3Guz4+GcSOwj3peMnAsfbfl3LkHcQogHX2i6pOLVY379/HIXKb8+StCshOtLc8ciK3TYZmYT+8rRxT0dGedo8WC03gKSdCPW7cUa1RB9TZQioSUplpJF0LLAWUf/c+xIzZeQpu2B34GJJlzG+LOQTbQN2XGsPcI6kg4g67eaYO1lha8nQGUsuADP6yrvuoYykaI9/A/ZhrM7+gnRuWFnP4z1HzpFUQtr3H5KWISmSSVqLxvu5LalPIHtFuI9PEXLigyaQORPHDxP1/E9jbjGMEkacKzd3OGzfq/DGaMuF6fcGg/LJjGvQOsS/v0vJ8p7AQbMfLju2pI2JBP65RA/GYsCDbUv/bG+WM54MSpQB7UyUbA7drn4lqElKZdR5MSGHu7DUPR5GNGyWVgECOlO26jkZN0sXulxhW2Caq4td9nd0xOmSfsFYs/+7gWI7V7bvBVonwdPA1anZ+lIAhfv6lQXi7gWcTvhXfI/ob/hAgbgA10o6mfAOaq6at+pJsb1D+l16Ankx0YS/he2DJb0feCdhaHhcgfiPNZu6U7llzrW5q1X+X7ljp/kOF4++QVwjTiSuye8Dnp0bVNKWwOm2Z0n6T2BDYL+cvsaJ1NOI5HCZtnEb/C9Jvr8ynNSelMpII+lE4BNJhnHoUQFn4Elid+pcvjDQdX9HV6Qv895qfJHkMk2aJ2RYG20l3Uz0mPXqylcDbgFmk+k9ojBH3Jh4b1xaqjesy54USZswtwt4q51iSVcTvlL/J+lfCNGRnYAXEIITW2SOdXPgcOA8xj5/O9guoUhWjC6vw43nWAL4KKEkBnFNOiy3Z7LX66LkI5TOlXCc7/kSbQp8jriO7mn7pfN46GQxB30u5mB7u5Zxe2pyzyOuFUWMaivlqUlKZaSRdA7xBXs54y9SwzoB259YtexXASphKNeJslXamdgfeJrt10tal2j+by1v3BUK07CZ/f0dw9gknpSbehfw/rKTR4j+pc/aPrtl/L8QK43fBy7rf44OatuLkFbfJ6QnitAi7tuBX/aUwhQSvK8qtNvYCROVs7YtD20KJkj6JiGdvHc6LmLmJ+nJRCIIZRPB9Qgp36V75zKStQ/Y/naJcU3yHEcCSwDHpFPvBR6z/aHMuOcDrwWOJMxD/wh8IPca10t0JB1AGEUeNxXJXBvmoSZn2/tO2WAqk1KTlMpIU1rppGsk3THgtF1AzrcrZStJPyc8WD7rcKdenHCqbiX/2SWSbmiOKyVq1w3jWCcjNWOvB3zP9nrz+vtJYswEtiYaSU8Fvu/h9UeZQ2pq/mfG7x5k9UANmoSXmoRJegbRJzBnNwzY2fYfMuPeTMFyVkk3Ai+wPTstauxg+/zefW3fa33P8RYauwcuI/G8F/AqIkk5DXg9cGGBnZ+e4EaT+4nywsOcoSjXlapeuq7fTfSjfJKQ8z3E9m2ZcX8G3ElcMzYkJIMvL7HA09VCl6QtbZ84r3OV6aP2pFRGmmFNRiai4yb3fmWrjQjTtpPTc7fdXXqy7RMk7Z7izJZUUmmnJJ32d0wVScnoOkkHz/OPJ49xOvGaLEUkK+dK2sd2iSbpTpC0H9Er8lvGJpAleqAGiRGU+g49mujp6CmQbZvOzcyMeyPh/VSqnPX7wHkKQ8+HiWQKSc+iQG2/pM8T153vpVM7S9rE+Z4kWwAbEIsj26VJ73czY0Ko3K3M+BLZWUSPxxHE7kdbHpO0lu3fAkhak4aEe1ts/z4JQKzqTP+ZPt4FbA580fZ9klYFdisU+9ukha50/BtC9Sx3N353ojdnXucq00RNUiojTWmlk66Q9BnbB6bb41Z6VM5YbM8CMQbxYKrl75WRbcyQNiva3q2vv+PQYS7nmRe2D8t5fEpO3kgkKKsDXwdOyh9Zp7yLkAv+R+G4V0r6MvDNdLwj4xWucljZdrP+/tuSdmkbTOMltX+VFh6yy1lt/5eks4FVgTMaOzQziN6UXN5A7NQ8DiDpGMLfJPf69rDtxyXNVrjP/5nYactlE9sbNY5PkXSF7Y0ktdpxTP/vFwP/AfyysXu+OgVU9SS9mVAzXBJYQ9ILgH1zSpzTruvVttfpnUt9nqWS46ILXZJeT7zXni7p6427ViR61ypDQk1SKqNOJ0onHfBu4MB0u3+lZ3Pyv8Tn7CqlL/FmmUxuv8ungJOBtSRdRKw8ZpVZlGaS/o4dJGX3dyyMSPoOUTJ2GrCP7RuneUjzy42Ee3tpVbadgD2IFVwDZzJeHjaHeyRty9iK/NaElHRbOpPUdlJN6zv3m4JP8QSgd81ZqVDMK1MP0RFEYvk3wtgxl+U1Xo1sNWD5dF/bJPkZwFeJhbNbidfiHOBHtu/KHC/A3oT/07kAtq+VlLVD75DRvqX5WhSm9ELXXURJ3luYW0r7kxlxK4WpPSmVkaYrpZPSNMfUP76CdfE7APsSTdePE5P1Uv0uixMqKgJuyVWomUpK9HcsjEh6nDE53OYXRe99MVS7jT0kvRj4KZGsFBHDSO+Bs9yRJ0TqEzgYeBnxWl9MqA5mTfgkLcfYLsKzCX+Pnw/r509hlPl5YlIuojdld9vHF3yO1YEVbV9fINYbgEOJRQwBawAfIxKA7W1/NSP2ksTC2SbE++JlwH0e7wHUJu6ltjfu+06Z8/2XEfd84IWECE1TRjtbhEZhSnowcR2+kbTQlft/KGlx23XnZIipOymVUeeh9GVwraQDie3pkkZ4pfAEtwcdt2U3wgiviJpOjzTBewNjMqj/KmmhkXks0d+xMGJ7GD8H88MxwBco6CWUVoofl7SSk7pXCSR9wfa/Ay8pMZkbwPnAK5KQwBnAFUTfxDYdPFc2tr8v6VzGfIr+3fafcuNKOtv2a9Jz/K7/XFtsnyZpbSL5g1iA6TXLt05QEssQ5UcrpZ+7iPd0KySdRuz83STpPcBiaeyfIJLiXPYoEGMgtq9WiNwUWeiSdILtdwHXSJrr+zM3YauUo+6kVEaaCZROvtlrVhwWUv3tgzDHxOqh3l3A0raXKPAcpwPvsP3QPP94weKeRuzOjJs0Fm7arFQA6PUEdBD3p8RK8ZmMXylubXQp6QZCOe0q2xtmD3Lu+Ffb3lDSTsAytg8soRDVFYMSh5xkQtLSwLLEzsyrGCvlXJEwHlxngocuyHMU86FJ8Q4n/DtmEdLflxJSzPdmjnNL4L+AY4nvkJ4owy8I08W/T/TY6UbSjsRO9n3p+InA1rYPaRlvVdt/1ARy5W4pU14pT91JqYw6b7P9NWISvQ+ApJ2Br03rqPqwvdgUPM3uwMWSLmN8mUyu2/gz6spUZQq5QOHVcDLj38dZEsTAj9NPSU4H7iV6Gx4gldL1fhcoqZOklxE7Jx9M54Zuh6yRTDw5TUCbycTTM0J/GNgFeBrQ/P9/gOhHzEIT+NAArZMUwnx0KaIf5U7gD8B9GfFiUPaJCjn4PYg+xmMZ24XfEcja2e5YhGZ72z3BCmzfK2l7oFWSAhya+iMvAq7oQGSjUoi6k1IZaXorjX3nhq4nZSpICkAXMveOxzETPmj+4n4BONv2GXkjrFTmjcKgtR/bzpUgJkm3rmb7ltxYfXF/avutJWOmuP8C7ApcZPsLChnbXQosPBQlLQz1kok7G3fNAo5wpuS1pJ1sFy/XVGEfmkZcEbspm6Sf9YgG+ktsT2ZEOK+4SxKqYe8BjqdRKpy7sy3pSgaI0NjePSduin0D8Pze65xKiK+3/byW8d7E2Gu7AXAzUfJ2EXCx7btzx1wpQ01SKiNJatB8D7ApSes/sSLh6ptVq7ww0lVypnDq/i6xgvsoQ954XakMoindaruIdGuK22lT/sKApI2IHYMtbB8s6f3AO4HfAXvnKgymyflHaJhEEmaLWQICkk4kBA5KSe32x38GIYe+CfAm4J9sP6FlrM2J3ZKTifdt6bLezkRoJB0EPBPoSap/GPhf258uEHsxoozzVcR7ZI0pqlyozAc1SamMJKkWdQ3gAGJlqccsYoVm5BQ/JO1PTApOYXyZTO4E4Q7grcANpVccK5V+JK0E7MXYhPQ8YlKW1fAu6SrCEPLchipSKZf1s4l+sKL+QUnRa1fm7pnI3lUqiaSrgdfa/r+0+3M8Ifn8AuC5zneGPxJYghBVgDBZfMz2hzLjnpPGWMSHJsX8BGOr/I8SK/y9nxucPGRaxL0A+IjtVv4t8xH/fOC1wJHAnwgRmg+U6H+SNINITHqLh2cCRyZRk7Yxn8L7MK0AAA6CSURBVMzY67wxsDRRtndJbvVApRw1SamMNAubRGeXaMw0rImdKUGcvrxe1fbLtVJZECT9iJApbU5IN7D9jsy4nUi3pjjFm/JT3OsIidyraLiV2y5lQlmEZjO/pG8Cf7G9dzq+1vYLWsZd3GH8N5dYQAkBgaQ4NRdOnlMtY36ZsbKjTnZoumACEZpDbN82rQMbgKRbCZ+VHxHCBFfY/tv0jqoyiNo4Xxl1FiqJzi6xnWXoNQm3A+emps3mauNCIUFcWehYy/Y7G8f7SLq2QNyupFuhm6Z8gNm2v9VB3NIspjHPitcAOzTuy5mnXA5sCDwmaa2eamPqzWm9Ct8jJxmZJOanSsecCmz/PvVsrVpauVHSywkTymcS74dcD6//JnZP3gmsD6wn6RLgmpzdmUp5apJSGXVk+yFJHyRWfQ4sNKFZaJD0GdsHpttb2j6xcd/+tnPd7O9IP0umn0qlSx6WtKntC2HOBOfhAnF3Aj5LJNrHEdKtnysQF9vHdNSUf4qkjwEnUbCEswO+D5wn6a/E/9UFAJKeRZ6zeE8lbFfgHEm3p+PVge1aB5UutL2ppFksREanXdLs2QKK9WwljiJ2Z8btCLbF9gG926mCYhNge2BTSX+1PXCHrDL11HKvykgj6RrCIfgrwAdt3yTpBtvrT/PQpoymwlm/2tkg9bNKZZiRtAEhAbtSOnUvURt/Xct4SxMNtc8ilO+OKt2z1mFTficlnF2QJGxXBc6w/WA692xgebeUj5b0B8akdZchZHEhJroP193cckzQs1Xku1TSZbZfmhtnQNw1iQSlJ07wNOAy228q/VyVdtSdlMqoswvhD3JSSlDWJIy/RglNcHvQ8YIHX0iadyuLBikZ2UDSiun4gcyQxxANzBcAryd8IHbJjNnP3sBLCNUpbF+brkVZdFjCWRzblw4495vMsIsByzP3dWxxYIXM2MAcdahVGH9t+58SsRcyHrV9f6gnz6HUKvg5SeHrxxTwPpJ0EvBSwi+nJ0rwdds3FxhrpSA1SamMNKmm+LzG8e1Erfko4QluDzpuw4lE8+6RFNiqr1QGIelTwP22j4Kx5CSVcq5g+6stQ6/bWw2WdBTR51CaQRO8bKEJSUsAH6Ww9O5CxB9t79tVcEk7EUpydzP2/2VgZMxrJZ1GmEF22bPV20V5ceOciZ2bNhxNGET+NWtUlc6p5V6VkUTSV23vIukUBkzEC9XRLhRIeoxQFBJREtHTzxewtO0lMuNfZftFeaOsVCYnlZts3D8BTx4ZV7ZV4ZqKEsiU/JxNyKG/k5jgLWH7I5lxO5HeXVgo5dMxSfzbgJfavqer5xh2JG0J/BfhYL8MMDPd9QtgP9t/n+ix000a++m2Z0n6T0Jk4XNtd2gq5alJSmUkkfQi21d1ISFZGY+kvYE/M/zNu5WFmMkkZXNq4xtJPIxP5Is1SUtalmjK/9d06hfEZOmRzLidSO8uLEh6UpfXmeSTMrN0j9LChqTlgT2AzYlkpTexdE7fj6RtbX837ZLORW5PUU9CXNKmhAjGQcCeXfS/VNpRy70qI0nPJ8D2eZJWTrf/Mr2jWmR5f/q9W+OcgaFr3q0s1MyQtIrtu5snJa2SE9Qduk8PaMp/WeEJbyfSuwsLU7AQ0pNXP5XRllf/B5HIL0X0AJVa/V4u/S7SPzSA3mfhjcDhtk+VVESxr1KGmqRURpa0wv9xYEYcajZwcJc1zKPIwtS8W1moOQg4VdKngV65xovS+S9O26gmp+um/N0Yk94V4TPRWnq3Mhf/k35GVl5d0uaEgtrJwIa2H5rHQ+Yb24el30V9VxrcKekwokTtC5KWIuYDlSGhlntVRpK0ffx6YAfbd6RzawLfImpUvzKd41uUqM27lalC0uuJvo710qkbgc/b/vn0jWpimmVokhYHLu+g32Up4Dnp8JZh7hGoLHxIugD4iO2bOoi9PSFpfKtCVeIoomfr98D7bV+TGX9ZokTthvQcqwLr2z4jd+yVMtQkpTKSJH+Umf3qHqn064wumy1HjVFv3q1UJqKrpnxJr7b9S0nvGHS/7S7c7UeGKrwyNUi6EXih7UeTctinib6tFwJ72X5FRuzFgJtsr1NmtJUuqOVelVFliUHyg7b/klb+K+XYqK9R95eSWhnrVSrzQtIahDv86oz3rhjGieMGkno+LgKWSce5TfmvBH4JvHnAfSb8JirtOTb9HtYywkWF2Y0d9zcB30lKamdJOjAnsO3HJN0iabUR9bVZKKhJSmVU+UfL+yoLzkg371amnJ8QZSGnUMBrpEu6asq3vVe6uW+vnLVHSuIqGTSFV6Z7LIs4j6cSrHuB1xBSxz2WKRD/iYS/y+WMKfgN64LGSFKTlMqo0lzBbCJg6akezCLOrtTm3crU8Yjtr0/3IIaEHxHeD01+SAgKVDJJpoUHAOvS+N6wXZULy7AncCWwGHByr+8lWQfcXiD+HgViVDqkJimVkaRLWdHKGKnudwNgbWrzbmVq+JqkvYAzGC8LOzIGbZLWAZ4HrNTXl7IidRGmJEcTjvNfATYjFl+qOlQhbP9M0jOBFWzf27jrCmCrAvHrTtiQUxvnK5VKp0i63PZLpnscldFA0gGEOMNvGSv3su1XT9+ophZJbwXeBryFkIbtMQs43vbF0zKwRQxJV9l+UZ9K21W2605VQSZwht+vgLrXxsDBhPT3ksSOzYMlDForZag7KZVKpWsukvQN4AeMr/sdmZXtypSyJbCm7ZHtLbP9U+Cnkl5m+5LpHs8izN8lzQBulfRx4E7CzLBSlj1sn5ic4V9LeB8dCuQ6w38DeDdwIvBi4H3AszNjVgpSd1IqlUqnSDpnwOmRWtmuTB2SfkL4H/15uscy3SRH+w8SpV/Nnol/m7ZBLUJI2gi4GXgCsB9RTneQ7UundWCLGJKusf3CtEt6g+3jeucy415p+8WSrrf9/OZzFRl4JZu6k1KpVDrF9mbTPYbKSPEE4NeSrmB8T8ooKvYcC/waeB2wL7ANMamuZJL67bayvSvwN6oYSJd05Qz/kKQlgWuTpPEfC8WtFKLupFQqlU6QtK3t70r61KD7bX95qsdUWfRJyj9zMYpNso0V6OttPz95QF1ge+PpHtvCjKTFbc+WdGl9LbunK2f41JR/N9GP8klgJeAQ27fljrlShrqTUqlUumK59HuFaR1FZaQYxWRkEnpGePdJWg/4E/CUaRzPosLlRPP2NZJOJnoamv121SyzEGnH6uqmM7ztPxK7HlnY/r2kZYBVbe+TG69SnpqkVCqVTrB9WLp5iO2/TOtgKiODpFmEqzrECukSjK5iz+GSnkj4QZxMNHXvOb1DWqRYGrgHeDXxnlP6XZOUQnTpDC/pzcAXievEGpJeQBigjmJp6FBSk5RKpdI1F0n6HaHu9eM+vftKpSi25+zcSRLwVmAkS3JsH5lungdUg8FyPCWVsd7IWHLSo9bQl6crZ/i9gZcA56Z410paIzNmpSA1SalUKp1i+9mSXkJIPX5W0q8Ir4bvTvPQKos4jqbLnyRzx/+Y7vFMFRP1gfWo/WDZLEbsSmnAfTVJKU9XzvCP2r4/1jLmUP//hoiapFQqlc6xfTlwuaT9gS8DxwA1SakUp89hfQbhf/DINA1nuqh9YN3yR9v7TvcgRoXSfWaSTgN2JHZn3gMsJmlt4BNANTodImqSUqlUOkXSisDbiZ2UtYCTiC32SqUL3ty4PRv4HVHyNTLUJuDOGbSDUumIDpzhjwZ+QUh0r0dIlR+Xzu2XPeBKMaoEcaVS6RRJdwA/AU6o7teVytQh6dnAt4BVbK8n6fnAW2x/bpqHtlAj6Um2/2+6xzEqSLqSAc7wtnfPiLk8UUa2OZGs9CbDruWQw0PdSalUKl2zputqSKVjJE2mWmXbo7hCegSwG3AYgO3rJR0H1CQlg5qgTD22b5O0mO3HgKMlXQO0TlKAfxBN+EsR/UX1O2oIqUlKpVLpmrUl7QqsTuOaY/vV0zaiyqLIgwPOLQd8EPgnRrOMY1nbl/c1Bs+ersFUKi0p6gwvaXOiN/JkYEPbD5UZZqU0NUmpVCpdcyJwKHAk8Ng0j6WyiGL7S73bklYAdga2A44HvjTR4xZx/ippLdIqsaQtKGCCV6lMMe8lkpKPE87w/wy8MyPeZ4Etbd9UYGyVDqk9KZVKpVMkXWX7RdM9jsqij6QnAZ8CtiEU5L42yr48ktYEDgc2Ae4F7gC2sf37aR1YpbKAJGf41WzfMt1jqUwdrbfLKpVKZT45RdLHJK0q6Um9n+keVGXRQtJBwBXALGB923uPcoICYPt2268FVgbWAV4JbDq9o6pUFozkDH8tcHo6foGkk6d3VJWpoO6kVCqVTknqXv3YdnXArhRD0uOElOhsxjfBini/tZUrXehIst87Ak8HfgqclY4/DVxve6QkmSsLN5KuAl4NnGv7hencDbbXn96RVbqm9qRUKpVOsb3GdI+hsuhju1YGjHEsUd51CbA9UYMv4O22r53OgVUqLajO8CNKvahXKpVOkPSZxu0t++7bf+pHVKmMDGva/oDtw4CtgXWB19UEpbIwIek0SWvQ5wwv6WCqM/xIUJOUSqXSFe9u3O7Xs998KgdSqYwYj/ZuJF+JP9h+ZBrHU6m0oecM/zvGO8PfT6j3VRZxapJSqVS6QhPcHnRcqVTKsYGkB9LPLOD5vduSHpjuwVUq84PtE4ENCbPFNwI/ICTF7yV6rCqLOLUnpVKpdIUnuD3ouFKpFML2YtM9hkqlENUZfoSpSUqlUumKDdKqrYBlGiu4ApaevmFVKpVKZdipzvCVKkFcqVQqlUqlUhkqJF0AfKQ6w48uNUmpVCqVSqVSqVQqQ0VtnK9UKpVKpVKpVCpDRU1SKpVKpVKpVCqVylBRk5RKpVKpVCqVSqUyVNQkpVKpVCqVSqVSqQwVNUmpVCqVSqVSqVQqQ0VNUiqVSqVSqVQqlcpQ8f8BQVMKEWrpQy0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 936x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-VMoxstUo4Wa",
        "outputId": "5a253911-d8b3-491c-b0a2-ee176e927ecf"
      },
      "source": [
        "categorical_features = train_data.select_dtypes(include=[np.object]).columns\n",
        "print(\"total categorical_features\",len(categorical_features))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total categorical_features 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "WAQXIv7wpDTK",
        "outputId": "9b446bcf-6f62-49fc-f18b-551b6a2b911b"
      },
      "source": [
        "train_data[categorical_features].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>Department</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Over18</th>\n",
              "      <th>OverTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Research Director</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Manufacturing Director</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>Male</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Human Resources</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Manager</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Male</td>\n",
              "      <td>Healthcare Representative</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Attrition     BusinessTravel  ... Over18 OverTime\n",
              "0        No      Travel_Rarely  ...      Y       No\n",
              "1        No      Travel_Rarely  ...      Y       No\n",
              "2       Yes      Travel_Rarely  ...      Y       No\n",
              "3        No  Travel_Frequently  ...      Y       No\n",
              "4        No      Travel_Rarely  ...      Y       No\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8pQ-cbJpHCO",
        "outputId": "04d28200-a426-4397-a0d0-d66f83d1fd0a"
      },
      "source": [
        "train_data['BusinessTravel'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Travel_Rarely        723\n",
              "Travel_Frequently    199\n",
              "Non-Travel           102\n",
              "Name: BusinessTravel, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "FbHUL8QSpOad",
        "outputId": "c1c9c2c0-70f0-43ba-9bf6-bec192f17401"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='BusinessTravel',hue='Attrition', data=train_data, palette='plasma')\n",
        "plt.title(\"Attrition wrt BusinessTravel\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "rRwhHL3upVRK",
        "outputId": "382f75ec-281d-41e7-af25-be2c90bc2a7d"
      },
      "source": [
        "fig = px.histogram(train_data, x='BusinessTravel', color='Attrition', barmode=\"group\",\n",
        "            width=650,height=390)\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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              "                    Plotly.newPlot(\n",
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              "                        [{\"alignmentgroup\": \"True\", \"bingroup\": \"x\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"Attrition=No<br>BusinessTravel=%{x}<br>count=%{y}\", \"legendgroup\": \"Attrition=No\", \"marker\": {\"color\": \"#636efa\"}, \"name\": \"Attrition=No\", \"offsetgroup\": \"Attrition=No\", \"orientation\": \"v\", \"showlegend\": true, \"type\": \"histogram\", \"x\": [\"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Frequently\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Non-Travel\", \"Travel_Rarely\", \"Travel_Frequently\", \"Travel_Rarely\", \"Travel_Frequently\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Non-Travel\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", \"Travel_Rarely\", 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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"width\": 650, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"BusinessTravel\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "                \n",
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              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_vv1HGnpeye",
        "outputId": "1bec20cf-9430-42cd-c22a-64cb27f47645"
      },
      "source": [
        "train_data['Department'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Research & Development    676\n",
              "Sales                     311\n",
              "Human Resources            42\n",
              "Name: Department, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "K0tM2T6zpniW",
        "outputId": "18b4c0d1-9272-4d65-c234-f2f99520ec91"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='Department',hue='Attrition', data=train_data, palette='rocket')\n",
        "plt.title(\"Attrition w.r.t Department\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEvJGhqXpudw",
        "outputId": "ce73464d-7de2-44a5-cd63-f84067442530"
      },
      "source": [
        "train_data['EducationField'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Life Sciences       426\n",
              "Medical             328\n",
              "Marketing           110\n",
              "Technical Degree     82\n",
              "Other                66\n",
              "Human Resources      17\n",
              "Name: EducationField, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "sxr0R0OMp0pP",
        "outputId": "d5aca7f4-ab92-4f2b-fa83-48f0b0d3164a"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='EducationField',hue='Attrition', data=train_data, palette='seismic')\n",
        "plt.title(\"Attrition w.r.t EducationField\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEfn7QROp5m3",
        "outputId": "04a992c5-a9c7-4bd4-c5f0-80dd481e3747"
      },
      "source": [
        "train_data['Gender'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      617\n",
              "Female    412\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "Py-glN_bp_24",
        "outputId": "479a46fe-a480-41de-80ff-714c0060bc3e"
      },
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "sns.countplot(x='Gender',hue='Attrition', data=train_data, palette='plasma')\n",
        "plt.title(\"Gender w.r.t Attrition\")\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "oTY0KD5AqE36",
        "outputId": "e6206b51-1bb3-45e1-fe12-f0f83bd1799b"
      },
      "source": [
        "pd.crosstab(train_data['MaritalStatus'],train_data['Gender'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Gender</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Divorced</th>\n",
              "      <td>79</td>\n",
              "      <td>151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Married</th>\n",
              "      <td>196</td>\n",
              "      <td>278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Single</th>\n",
              "      <td>134</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Gender         Female  Male\n",
              "MaritalStatus              \n",
              "Divorced           79   151\n",
              "Married           196   278\n",
              "Single            134   186"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFJcv6NmqP4W",
        "outputId": "3f182a67-ae42-48df-84a8-2a34e378d5d6"
      },
      "source": [
        "#JobRole\n",
        "train_data['JobRole'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "V6k0waxvqWdN",
        "outputId": "15f73906-3a1f-4596-909d-cc2fca14c1ff"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='JobRole',hue='Attrition', data=train_data, palette='plasma')\n",
        "plt.title(\"JobRole w.r.t Attrition\")\n",
        "plt.legend(loc='best')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvbY3v2LqbBw",
        "outputId": "be08bea3-885e-46d9-9fdf-bee3e0de5c59"
      },
      "source": [
        "train_data['OverTime'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     731\n",
              "Yes    298\n",
              "Name: OverTime, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "T-VkXvogqgf3",
        "outputId": "726825ce-81f3-4c91-ae28-b0767cc9b76d"
      },
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "sns.countplot(x='OverTime',hue='Attrition', data=train_data, palette='plasma')\n",
        "plt.title(\"OverTime w.r.t Attrition\")\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "VRqoMKKlqmxQ",
        "outputId": "65e03405-5cd1-4444-8b21-930476befaeb"
      },
      "source": [
        "pd.crosstab(train_data['OverTime'], train_data['Gender'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Gender</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverTime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>286</td>\n",
              "      <td>445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>126</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Gender    Female  Male\n",
              "OverTime              \n",
              "No           286   445\n",
              "Yes          126   172"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQj1MqrWquU8",
        "outputId": "14b1366d-9282-4276-ef0b-d17b21f362d0"
      },
      "source": [
        "numerical_features = [feature for feature in train_data.columns if train_data[feature].dtype != 'O']\n",
        "print(\"Total numerical_features\", len(numerical_features))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total numerical_features 26\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "m6zoa85Dq7dM",
        "outputId": "bb4eb4bd-21c2-4eca-8e37-981a3707a0eb"
      },
      "source": [
        "train_data[numerical_features].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1126.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>997</td>\n",
              "      <td>4</td>\n",
              "      <td>66</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>17399</td>\n",
              "      <td>6615</td>\n",
              "      <td>9</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>2</td>\n",
              "      <td>84</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4941</td>\n",
              "      <td>2819</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1780</td>\n",
              "      <td>2</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2679</td>\n",
              "      <td>4567</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1246.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>644</td>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>18200</td>\n",
              "      <td>7999</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.0</td>\n",
              "      <td>994.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1118</td>\n",
              "      <td>2</td>\n",
              "      <td>87</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>10445</td>\n",
              "      <td>15322</td>\n",
              "      <td>7</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  DailyRate  ...  YearsSinceLastPromotion  YearsWithCurrManager\n",
              "0  50.0     1126.0  ...                        1                     3\n",
              "1  36.0      216.0  ...                        0                     1\n",
              "2  21.0      337.0  ...                        1                     0\n",
              "3  50.0     1246.0  ...                       10                     7\n",
              "4  52.0      994.0  ...                        4                     0\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "yw5kO-QLrBO_",
        "outputId": "7aa9010f-423f-46e6-fe7a-ddd676f71de8"
      },
      "source": [
        "sns.distplot(train_data['Age'],hist=False)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7ONNq5ArHQl",
        "outputId": "a13f0c93-b3f0-4882-f430-418e68ec8b38"
      },
      "source": [
        "train_data['Age'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "V3wQQmB1rNF5",
        "outputId": "e9112cfc-370e-4027-81a4-742ea2e87130"
      },
      "source": [
        "ordinal_features = ['Education','EnvironmentSatisfaction','JobInvolvement','JobSatisfaction',\n",
        "                    'PerformanceRating','RelationshipSatisfaction','WorkLifeBalance']\n",
        "train_data[ordinal_features].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Education</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Education  ...  WorkLifeBalance\n",
              "0          2  ...                2\n",
              "1          2  ...                3\n",
              "2          1  ...                3\n",
              "3          3  ...                3\n",
              "4          4  ...                3\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6E6kvmfrVgE",
        "outputId": "7052a530-9928-4b2c-84aa-0b8d0c5a705e"
      },
      "source": [
        "train_data['Education'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    386\n",
              "4    279\n",
              "2    194\n",
              "1    134\n",
              "5     36\n",
              "Name: Education, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "tB6VEM1arcVM",
        "outputId": "4f96003f-4c17-494a-eb0c-55f683f78788"
      },
      "source": [
        "edu_map = {1 :'Below College', 2: 'College', 3 :'Bachelor', 4 :'Master', 5: 'Doctor'}\n",
        "plt.figure(figsize=(7,5))\n",
        "sns.countplot(x=train_data['Education'].map(edu_map), hue='Attrition', data=train_data, palette='prism')\n",
        "plt.title(\"Education W.R.T Attrition\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jrUQo1JurkA1",
        "outputId": "826c6b22-6eee-4a1d-d468-67935bb285a7"
      },
      "source": [
        "train_data['EnvironmentSatisfaction'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    310\n",
              "4    300\n",
              "2    212\n",
              "1    207\n",
              "Name: EnvironmentSatisfaction, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhmtxHKDrzeQ",
        "outputId": "2a7715c7-239b-4ca0-dc45-dce8fded2d1d"
      },
      "source": [
        "train_data['JobInvolvement'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    606\n",
              "2    269\n",
              "4     94\n",
              "1     60\n",
              "Name: JobInvolvement, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mg7Nak5ir4vj",
        "outputId": "ca29d09f-f00b-4344-d021-dc79d11a2ec7"
      },
      "source": [
        "numerical_features = [feature for feature in train_data.columns if train_data[feature].dtype != 'O' and feature not in ordinal_features]\n",
        "print(\"total numerical_features\",len(numerical_features))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total numerical_features 19\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "g5qm0j6EsLz6",
        "outputId": "ac67549d-70b0-429e-ab0c-696d16edcc3b"
      },
      "source": [
        "train_data[numerical_features].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1126.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>997</td>\n",
              "      <td>66</td>\n",
              "      <td>4</td>\n",
              "      <td>17399</td>\n",
              "      <td>6615</td>\n",
              "      <td>9</td>\n",
              "      <td>22</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>84</td>\n",
              "      <td>2</td>\n",
              "      <td>4941</td>\n",
              "      <td>2819</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>80</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1780</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>2679</td>\n",
              "      <td>4567</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1246.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>644</td>\n",
              "      <td>99</td>\n",
              "      <td>5</td>\n",
              "      <td>18200</td>\n",
              "      <td>7999</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>2</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.0</td>\n",
              "      <td>994.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1118</td>\n",
              "      <td>87</td>\n",
              "      <td>3</td>\n",
              "      <td>10445</td>\n",
              "      <td>15322</td>\n",
              "      <td>7</td>\n",
              "      <td>19</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  DailyRate  ...  YearsSinceLastPromotion  YearsWithCurrManager\n",
              "0  50.0     1126.0  ...                        1                     3\n",
              "1  36.0      216.0  ...                        0                     1\n",
              "2  21.0      337.0  ...                        1                     0\n",
              "3  50.0     1246.0  ...                       10                     7\n",
              "4  52.0      994.0  ...                        4                     0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "IowyEyZZsQZT",
        "outputId": "c4fa7a11-187a-4a76-d772-e5b3afc78081"
      },
      "source": [
        "sns.distplot(train_data['MonthlyIncome'],hist=False)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z+5b9yGe7Ebihi2N/Dvy8N2065fcD9/spV+C2vsYeydralUPHT3Jp/mi3Q+mV8/NSiY4SXttdwVm5o9wOxxjjh00QMKcoqz5Jc1t7yFzZjBgSy+ycZFbvsUkCxgQrSzbmFAecac/BvHpAZ/NOT2fb4RrKaxvdDsUY44clG3OKEmfac25qaFzZAFw4yTuD8I09lS5HYozxx5KNOcUBTwPxMVFkJCa4HUqv5WcmkTo8ntV7bAq0McHIko05RUllPTmjhhIVFdzTnn1FRb0/Bbqt3VaBNibYWLIxpzjgaQip+zUdLjw9jaqGFjaX2hRoY4KNJRvzAarKgeOh84yNrwvyUokSWG2rCRgTdCzZmA8or22isaWdcUG+AKc/yUPjmDk2mdd22xRoY4KNJRvzASWVzky0ELyyAe8U6C2Hq/HYKtDGBBVLNuYDOp6xyQ3BezbgnQKtCm/stSnQxgQTSzbmA0o89cRGC5kjQmfas69pWSNIGRbHahtKMyaoWLIxH3DA08DYkUOJiQ7NH42oKOGCSWm8vreSdpsCbUzQCM3fKCZgSjz1Qf/CtJ5cOCmN4/XNbD1c7XYoxhiHJRvzHlUN2WdsfF0wKQ2xKdDGBBVLNuY9nvpm6ppaQ3YmWodRw+KYnm2rQBsTTCzZmPcccBbgDMVnbDqbNymNzYeqOFHf7HYoxhh6mWxE5B8icqWIWHIKYyWVoT3t2deFp6fRrvBGkU2BNiYY9DZ5LAFuBPaKyK9E5PQAxmRccsBTT5RAVvIQt0P50GZkJzNyaKxNgTYmSPQq2ajqq6r6KWA2UAK8KiJvi8jnRSQ2kAGawVPiaSBr5BDiYkL/AjY6SvhIXhqv76mwKdDGBIFe/1YRkRTgc8AXgY3A/+BNPq8EJDIz6A546sNiCK3DRyenUVnXzLYjNgXaGLf19p7NM8AbwFDg46p6lao+qapfA4YHMkAzeEo8DSH/jI2vC/JsCrQxwaK3VzYPqWq+qv5SVcsARCQeQFULujpIRBaIyG4RKRKRO/3sjxeRJ539a0Uk12ffXU75bhGZ31ObIjLeaaPIaTPOKc8RkVUislFEtojIFb3sc0Spamim+mRLWF3ZpAyPZ3p2Mqvsvo0xruttsvmZn7J3ujtARKKBB4DLgXxgsYjkd6p2M3BCVScC9wH3OMfmA4uAqcACYImIRPfQ5j3AfU5bJ5y2AX4ILFPVWU6bS3rZ54jSsQBnqD/Q2dm8SWlsOlTFcZsCbYyruk02IjJaRM4EhojILBGZ7XzNwzuk1p05QJGqFqtqM7AUWNipzkLgUWf7aeBiERGnfKmqNqnqfqDIac9vm84xFzlt4LR5tbOtQJKzPQI40kPcEanEE9qvFujKvNM7VoG2oTRj3BTTw/75eCcFZAO/8SmvBb7fw7FZwCGfz6XA3K7qqGqriFQDKU75mk7HZjnb/tpMAapUtdVP/buBl0Xka8Aw4BJ/wYrILcAtADk5OT10Lfwc8DQgAmNHhVeymZ6dzKhhcazeXcHCmVk9H2CMCYhuk42qPgo8KiLXqerfBymmgbYY+D9V/bWInAM8LiJnqGq7byVVfRB4EKCgoCDi5sqWeOrJTEogITba7VAGVHSUcEFeKq85U6CjosTtkIyJSN0mGxH5tKr+BcgVkTs671fV3/g5rMNhYKzP52ynzF+dUhGJwTvM5enhWH/lHiBZRGKcqxvf+jfjve+Dqr4jIglAKmB3jX2EwwKcXfno5HSe3XSELYermTk22e1wjIlIPU0Q6PjtMxxI9PPVnfVAnjNLLA7vzfnlneosB25ytq8HVqqqOuWLnNlq44E8YF1XbTrHrHLawGnzOWf7IHAxgIhMARIAG8Dv5ICnntzU8BpC6/CR96ZA2/8vjHFLT8Nof3T+/HFfG3buwdwOvAREA39S1e0i8hOgUFWXA4/gHdYqAo7jTR449ZYBO4BW4DZVbQPw16Zzyu8BS0XkZ3gfOn3EKf828JCIfAvvZIHPOcnJOGobW6isaw7bK5tRw+KYOTaZVbsr+OYlk9wOx5iI1NMEAQBE5F68059PAi8C04FvOUNsXVLVF4AXOpX9yGe7Ebihi2N/Dvy8N2065cV4Z6t1Lt8BnNddnJHuvWnPYTY5wNe8Sen8dsUePHVNpAyPdzscYyJOb5+zuUxVa4CP4V0bbSLwnUAFZQZXuD5j46tjCvTrNgXaGFf0Ntl0XAFdCTylqrbYVBjpeMYmnJaq6Wxa1ghSnCnQxpjB19tk87yI7ALOBFaISBrQGLiwzGA64KknLTGeYfG9GlUNSVFRwoWT0nhtTwVttgq0MYOut68YuBM4FyhQ1RagnlNXAzAhqsTTEHYrB/gzb3I6VQ0tbC6tcjsUYyJOX/4rOxnv8za+xzw2wPEYFxzw1PORvDS3wwi4C/JSiRJYvauc2Tkj3Q7HmIjS21cMPA78N3A+cJbz1eVqzyZ0nGxu41hNU0Rc2SQP9U6BXr3H7tsYM9h6e2VTAOTb8ynh5+Dx8J+J5uujp6fz61f2UFHbRFqiTYE2ZrD0doLANmB0IAMx7nh/tefISDbzTk8H4HW7ujFmUPX2yiYV2CEi64CmjkJVvSogUZlBc8BJNjkRMIwGMHVMEqnD41m5u5zrzsx2OxxjIkZvk83dgQzCuKfE08CoYXGMGBLrdiiDIipKuGhyGv/eepTm1nbiYnp7cW+M+TB6O/X5NbwrB8Q62+uBdwMYlxkkBzz1Yf0wpz+X5o+mtqmVtfs9bodiTMTo7Wy0L+F9C+YfnaIs4NlABWUGT0llQ1iviebP+RNTSYiN4pUdx9wOxZiI0dsxhNvwLmZZA6Cqe4H0QAVlBkdjSxtHqk9GzEy0DkPiovlIXhqv7jiGTbA0ZnD0Ntk0qWpzxwfnwU77VxriDh1vQBUmpEVWsgG4ND+DI9WNbD9S43YoxkSE3iab10Tk+8AQEbkUeAr4Z+DCMoOhuDKypj37unhyOlECL9tQmjGDorfJ5k68b7fcCnwZ7/tkfhiooMzgKOlINqmRl2xShsdz5riRvGrJxphB0dvZaO14JwTcqqrXq+pDtppA6NtfWU9KBE177uzS/Ax2lNVQeqLB7VCMCXvdJhvxultEKoHdwG4RqRCRH3V3nAkN+yvrGR+BVzUdLs33LophVzfGBF5PVzbfwjsL7SxVHaWqo4C5wHki8q2AR2cCqsRTH5FDaB3Gpw5jYvpwu29jzCDoKdl8Blisqvs7ClS1GPg08NlABmYCq76plWM1TRF9ZQNw+RmjWVPsobKuqefKxph+6ynZxKpqZedCVa0AehzoF5EFIrJbRIpE5E4/++NF5Eln/1oRyfXZd5dTvltE5vfUpoiMd9ooctqM89n3CRHZISLbReSJnuKOBB0LcEZ6srlyeibtCi9uO+p2KMaEtZ6STXM/9yEi0cADwOVAPrBYRPI7VbsZOKGqE4H7gHucY/OBRcBUYAGwRESie2jzHuA+p60TTtuISB5wF3Ceqk4FvtlDnyPC/gie9uzr9IxEJqYP5/ktR9wOxZiw1lOymSEiNX6+aoFpPRw7ByhS1WLngdClnPoq6YXAo87208DFIiJO+VJVbXKG8Iqc9vy26RxzkdMGTptXO9tfAh5Q1RMAqlreQ9wR4f1pz5G1VE1nIsKV0zJZu/845bWNbodjTNjqNtmoarSqJvn5SlTVnobRsoBDPp9LnTK/dVS1FagGUro5tqvyFKDKaaPzuSYBk0TkLRFZIyIL/AUrIreISKGIFFZUhP+7Toor6xmdlMDQuL68GTw8XTk9E7WhNGMCKhLWV48B8oB5wGLgIRFJ7lxJVR9U1QJVLUhLSxvkEAdfSYRPe/Y1KSORSRnDeX5LmduhGBO2AplsDgNjfT5nO2V+6zjrrY0APN0c21W5B0h22uh8rlJguaq2OENye/Amn4hW4mmI6GnPnV05bQzrS45zrMaG0owJhEAmm/VAnjNLLA7vDf/lneosB25ytq8HVjorEywHFjmz1cbjTQ7rumrTOWaV0wZOm88528/ivapBRFLxDqsVD3RnQ0l1QwvH65uZYMnmPVdOH40qvLDVrm6MCYSAJRvn/sntwEvATmCZqm4XkZ+ISMfrpB8BUkSkCLgD7xpsqOp2YBmwA3gRuE1V27pq02nre8AdTlspTts4dT0isgNvQvqOqkb0W7P2eyJ3TbSuTExPZPLoRP652WalGRMIAb07rKov4F2007fsRz7bjcANXRz7c+DnvWnTKS/GO1utc7niTWR39DH8sLW/sg6wZ2w6Wzgzi3te3EVxRR0T0oa7HY4xYSUSJgiYTvZXNhAlkBNhb+jsybWzs4gSeHpDqduhGBN2LNlEoP2V9WSNHEJcjH37fWUkJTDv9HT+8e5h2tptUXNjBpL9tolA3mnPNkzkzw1nZnO0ppE39ob/s1bGDCZLNhFGVb2vFkixITR/Lp6SwcihsTxlQ2nGDChLNhGmoraJuqZWmxzQhbiYKBbOzOKV7ceoauh2+T9jTB9YsokwReXemWiTMhJdjiR43VCQTXNbO8ttGrQxA8aSTYTZ6ySbiel2z6YrU8eMID8ziSfXH8Lefm7MwLBkE2H2lteSlBBDWmK826EEtcVzc9h+pIaNh6rcDsWYsGDJJsLsPVZHXkYi3rcymK5cOyuLxPgYHn27xO1QjAkLlmwiTFF5HXk2hNajYfExXF+QzQtby+w9N8YMAEs2EcRT14Snvtnu1/TSZ8/JpbVdefydA26HYkzIs2QTQTpmouXZTLReGZ86jEunZPDYOweob2rt+QBjTJcs2USQjploNozWe1++8DSqT7bw5PpDPVc2xnTJkk0EKSqvY1hcNJkjEtwOJWScOW4kc3JH8fAbxTS1trkdjjEhy5JNBNlbXsvE9OE2E62PbrtoIkeqG1lWaEvYGNNflmwiyN5jdUxMt/s1fXVBXioF40bywMoiGlvs6saY/rBkEyGqT7ZQXttEXobdr+krEeGOyyZxtKaRv6yxmWnG9IclmwhRZJMDPpRzT0vlgklp3L9iL8frbYFOY/rKkk2EKCqvBSDPhtH67f9dOYX65jZ+++oet0MxJuRYsokQe4/VkRAbRdbIIW6HErLyMhL59Nwc/rLmAFtKbc00Y/oioMlGRBaIyG4RKRKRO/3sjxeRJ539a0Uk12ffXU75bhGZ31ObIjLeaaPIaTOu07muExEVkYLA9Da47S2vY0LqcKKjbCbah3HHZaeTOjye7z69hZa2drfDMSZkBCzZiEg08ABwOZAPLBaR/E7VbgZOqOpE4D7gHufYfGARMBVYACwRkege2rwHuM9p64TTdkcsicA3gLWB6GsoKCqvs8kBA2DEkFh+evUZ7Dpay5JV+9wOx5iQEcgrmzlAkaoWq2ozsBRY2KnOQuBRZ/tp4GLxPgSyEFiqqk2quh8octrz26ZzzEVOGzhtXu1znp/iTUYRuaJiXVMrh6tO2uSAATJ/6miunjmG/1mxh3X7j7sdjjEhIZDJJgvwXeOj1CnzW0dVW4FqIKWbY7sqTwGqnDY+cC4RmQ2MVdV/dResiNwiIoUiUlhRUdHbPoaE3Ue9kwPs7ZwD52fXTCNn1FC+/reNtiq0Mb0Q1hMERCQK+A3w7Z7qquqDqlqgqgVpaWmBD24Q7SyrAWBKZpLLkYSP4fExPPCp2VSfbOFLjxZystke9jSmO4FMNoeBsT6fs50yv3VEJAYYAXi6Obarcg+Q7LThW54InAGsFpES4GxgeaRNEthZVkNiQgzZNhNtQE0dM4L7F89iy+FqvvKXDba6gDHdCGSyWQ/kObPE4vDe8F/eqc5y4CZn+3pgpXpf+r4cWOTMVhsP5AHrumrTOWaV0wZOm8+parWqpqpqrqrmAmuAq1S1MFCdDkY7y2qYkplka6IFwKX5Gfzymmm8tqeCLz1WSJ29isAYvwKWbJz7J7cDLwE7gWWqul1EfiIiVznVHgFSRKQIuAO40zl2O7AM2AG8CNymqm1dtem09T3gDqetFKftiNferuw6Wku+DaEFzKI5Odx7/XTeKqrk2iVvccBT73ZIxgQd8V4UGF8FBQVaWBgeFz8llfXM++/V3HPdND55Vo7b4YS1N/dWctsT79LS1s5dl0/mxrnj7LkmE1FEZIOq+keNfNsAABfmSURBVL1NEdYTBAzssMkBg+b8vFRe+MZHOHPcSP7fc9tZ8NvXeW7TYZpb7eFPY2J6rmJC2c6yGqLEpj0PlqzkITz2hTn8e9tR/vvl3Xxj6SZ+MmwHl03N4CN5aUzPHkFW8hC7f2YijiWbMLf9SA2npQ0nITba7VAihohwxbRMFkwdzet7K3hqQyn/3FzG39Z5HxEbOTSWKZlJjE8dxvjUYeSmDCM3dRg5o4YSF2ODDSY8WbIJY6rKltJqLpwUXs8NhYqoKGHe6enMOz2d5tZ2dpbVsPVwNVtLq9l9rJZ/bS2jqqHl/foC41OHMXdCCueelsI5E1JIGR7vYg+MGTiWbMLY0ZpGKuuamJ49wu1QIl5cTBQzxiYzY2zyB8qrGprZX1nP/sp6Sirr2XakhuWbjvDE2oNECXwkL40b5+ZwyZQMm2xgQpolmzC2pbQagGmWbIJW8tA4ZuXEMStn5HtlrW3tbDtSwys7jvLMu4f58uMbmJA6jDsum8SV0zLtfo8JSTZAHMa2llYTHSX2jE2IiYmOYubYZL4zfzKvf/ej/O7GWcRGR3H7ExtZ9OAaDh1vcDtEY/rMkk0Y23K4mkkZiTY5IITFREfxseljeOEbH+EX10xj+5Ea5v/2df6y5gD2jJwJJZZswpSqsrW0iulZNoQWDqKjhBvn5vDSty5gds5IfvjsNr70WCG1jS09H2xMELBkE6ZKT5zkREOL3a8JM1nJQ3j85jn858fzWbW7gmuXvG3L45iQYMkmTL178AQAs3KSe6hpQo2I8PnzxvP4F+ZQUdfEwgfe4p19HrfDMqZblmzC1LsHTjAsLprTbeWAsHXuxFSeu+080obHc9Of1vHitqNuh2RMlyzZhKnCAyeYmZNMTLR9i8PZuJRhPPWVczgjK4lb/7qBZesP9XyQMS6w30RhqL6plZ1lNZzp8+yGCV/JQ+P4yxfnct7EVL779y08+Po+t0My5hSWbMLQ5kNVtCvMHmfJJlIMjYvhkZvO4srpmfzihV3c++IumxptgoqtIBCGNhxwJgeMtWQTSeJiorh/0SySEmJZsnofVSdb+OnCM2yZGxMULNmEofUHTpCXPpwRQ2PdDsUMsugo4RfXnMHIoU7CaWjmN5+YaQ/2GtfZMFqYaWlrp7DkOGdPSHE7FOMSEeG7Cybzgyum8MLWo3z2kXVUNTS7HZaJcJZswsyW0ioamts49zRLNpHuSxdM4P7Fs9h0qIrr//AOpSdsTTXjHks2YebtIu/DfXPtysYAV80Yw6NfmMOxmkaufuAt3t5X6XZIJkIFNNmIyAIR2S0iRSJyp5/98SLypLN/rYjk+uy7yynfLSLze2pTRMY7bRQ5bcY55XeIyA4R2SIiK0RkXCD77LZ3ij1MyUxi1LA4t0MxQeKc01J45tZzGTEklk8/vJYlq4tob7eZamZwBSzZiEg08ABwOZAPLBaR/E7VbgZOqOpE4D7gHufYfGARMBVYACwRkege2rwHuM9p64TTNsBGoEBVpwNPA/cGor/BoLGljcIDJzjHrmpMJxPTE3nu9vO5fFom9764my8+VkhZ9Um3wzIRJJBXNnOAIlUtVtVmYCmwsFOdhcCjzvbTwMXifTPUQmCpqjap6n6gyGnPb5vOMRc5beC0eTWAqq5S1Y7B6jVAdgD6GhQ2HDhBc2u73a8xfg2Pj+F3i2dx98fzeXtfJZf8+jX+/NZ+2uwqxwyCQCabLMB37YxSp8xvHVVtBaqBlG6O7ao8Bahy2ujqXOC92vl3P/oSElbtKicuOopzLNmYLogInztvPC9/80LOzB3Fj/+5g6t+9yav7jhmD4GagIqYCQIi8mmgAPivLvbfIiKFIlJYUVExuMENkJW7y5k7YRTD4u3xKdO9nJShPPr5s7h/8SxqGlv44mOFXHH/m/xz8xGaWtvcDm9QtbcrDc2t1DS2UN/USmNLm93TCoBA/lY6DIz1+ZztlPmrUyoiMcAIwNPDsf7KPUCyiMQ4VzcfOJeIXAL8ALhQVZv8BauqDwIPAhQUFITcT9oBTz3FFfV85uywnv9gBpCIcNWMMVxxxmiWbz7C71YV8bW/bSR5aCxXzRjDtbOzmZE9Au8odegqr21kZ1ktReV1FJXXcqSqkfLaJipqm6htbKGptf2UY0QgKSGW5KGxpA6PZ3zqMManDmNSRiJzckfZA9P9EMhksx7IE5HxeH/xLwJu7FRnOXAT8A5wPbBSVVVElgNPiMhvgDFAHrAOEH9tOsesctpY6rT5HICIzAL+CCxQ1fIA9tdVK3d5u3bR5HSXIzGhJiY6imtnZ7NwZhZv7K3g7+8e5sn1h3jsnQNkJMVz0eR0LpqcwfkTUxkSF/wrEdQ0trBqVzlvF3lYu99Dief954tGDo0lZ9RQspITmDl2BEkJsQyJi2ZIbDTRUUJbu9KmSmNzG9UnW6g62cLR6kbe2FvB0xtKAW8iys9M4oJJaVw3O5uJ6cPd6mpIkUCO04rIFcBvgWjgT6r6cxH5CVCoqstFJAF4HJgFHAcWqWqxc+wPgC8ArcA3VfXfXbXplE/Am2hG4Z2B9mlVbRKRV4FpQJkT1kFVvaq7uAsKCrSwsHDA/h4Gw2ceWcvhqpOs/PY8t0MxYaD6ZAuv7jjGyl3lvLangrqmVuJjojj3tBQumpLBZfkZZCQluB3mexqaW3lh61Fe2FrGm3sraW5rJykhhjnjRzF3fArTs0cwMX04KcPj+32OuqZWth+u5p1iD+/s87DhwAla25WCcSO5cW4OV80YE/Gv9BCRDapa4Hef3RQ8VaglG09dE3N+sYIvXzCB7y6Y7HY4Jsw0t7azvuQ4K3aWs2LXMQ44Vwozxyaz4IzRzJ86mvGpw1yJbdfRGp5Ye5Bn3j1MbVMrWclDuPyM0Vw+bTQzx44M6CKkFbVN/OPdUp5cf4jiynpOSxvGdxdM5rL8jJAfeuwvSzZ9FGrJ5om1B/n+M1v519fPZ+qYEW6HY8KYqlJUXsdL24/y0vZjbD1cDcDk0YlcOS2Tj80YE/DE09jSxvNbyvjr2gNsPFhFXEwUV5wxmhvnjuOs3JGD/oteVXl5xzHufXEX+yrqKRg3kl9dNz0ih9cs2fRRqCWbGx9aw9HqRlZ8+8KI/R+VcUfpiQZe3n6MF7aWUei82uKMrCQ+Nn0MV07LZOyooQN2ruKKOv669iBPbyil+mQLE9KGceOcHK6bnc3IIFgxo7Wtnac2lHLvi7toaG7jzssnc9M5uURF0CseLNn0USglm4raJub+4lVu++hEvn3Z6W6HYyLYkaqTvLC1jH9uPsLmUu8Vz6yc5PcSz+gRfb/Hc7y+mVd2HOW5TUd4e5+HmChh/hmj+fTccZw9YVRQ/ueqvLaRO/++lZW7yjlvYgq//eQs0hL7f68olFiy6aNQSjYPv1HMz/61k5e/dQGTMhLdDscYAA56Gnh+6xGe31zGjrIaRGDW2GQKckcxOyeZ6dnJjE5KOOV//Sfqm9lcWsXmQ9WsK/Gwpvg4be1KzqihfKIgm0+cNZb0xOCZmNAVVWXp+kP8+J/bGTk0jj9+5kymZye7HVbAWbLpo1BJNqrKxb95jeQhsfzj1vPcDscYv/ZV1PH85jJW7yln++Eamtu8z7XExUQxcmgsQ2KjaWlTqk+2UNfkXQREBPLSh3PJlAyumJbJ1DFJQXkV05PtR6q55bENVNY18avrpnHNrLBdLQuwZNNnoZJs1u0/zif++A73Xj+dTxSM7fkAY1zW1NrG9iM1bD9SQ+mJBqrqWzjZ0kZMtJA8JI7MEQlMzUpiWtYIEhPC48FJT10Tt/71XdbuP843Ls7jm5fkhWTi7I3uko2taxLC/rbuIInxMXxseqbboRjTK/Ex0czOGcnsnJFuhzJoUobH85cvzuWuf2zlf1bspby2iZ8unBpxz+RYsglRZdUneX7LET41dxxD4+zbaEwwi42O4r+un05GUjwPrNpHZV0T/7t4Fgmxwb8iw0CJrNQaRh55Yz/tCjefP97tUIwxvSAifGf+ZH581VRe3XmMTz28lqqGZrfDGjSWbEJQVUMzT6w7yFUzxgzocwzGmMC76dxcHrhxNltLq7n+D+9wuCoyXmJnySYE/f61fZxsaePLF05wOxRjTD9cMS2TR78wh2PVjVy35G12Ha1xO6SAs2QTYg4db+DPb5Zw7axsJo9OcjscY0w/nXNaCsu+cg6KcsPv3+HNvZVuhxRQlmxCzK/+vYuoKPjOfFstwJhQNyUziWduPY8xyUP43J/Xvfcag3BkySaEvLjtKP/aWsat8yb2a+kPY0zwGZM8hKe+eg5nT0jhP57azL0v7qItDN8UaskmRHjqmvjBM1uZOiaJr847ze1wjDEDKCkhlj997iwWz8lhyep9fP7/1ofdTDVLNiGgubWd2554l9qmVn7ziZnERtjDYMZEgriYKH557TR+ee001uzz8PHfvcmW0iq3wxow9lsryKkqP3puG2uKj3PvddM5fbQttmlMOFs8J4cnv3w2rW3KNUve5tcv76a5td3tsD40SzZBrL1d+eGz21i6/hC3f3QiV8/KcjskY8wgmJUzkhe/eQELZ47hf1cWsfCBt9h8KLSvcizZBKnaxhZu/eu7/HXtQb467zS+fdkkt0MyxgyiEUNi+c0nZvLQZwuorGti4QNv8bW/beSg81ruUGOLagWht4sq+f4zWzl04iQ/vHIKN58/PmxXiTXGdO/S/AzOnjCKB18v5qE3inlxWxnXzc7mM+eMC6nXwNsrBvxw4xUDqsqmQ1UsWb2PV3YcY1zKUO69bjpzJ6QMahzGmOB1rKaR+1fs5e/vltLY0s6Z40Zyw5nZXDQlPSheKufa+2xEZAHwP0A08LCq/qrT/njgMeBMwAN8UlVLnH13ATcDbcDXVfWl7toUkfHAUiAF2AB8RlWbuztHVwYr2TS2tLHxYBXvFHt4adtRdh+rZcSQWL54/ni+dMGEiFoR1hjTe9UNLTy14RB/XXuQ/ZX1AMwcm8xH8lKZkZ3MjLHJrryK2pVkIyLRwB7gUqAUWA8sVtUdPnVuBaar6ldEZBFwjap+UkTygb8Bc4AxwKtAx00Lv22KyDLgH6q6VET+AGxW1d93dY7uYu9vsmlvV5pa22lsaaOxtY3GFu92XVMrnrpmTjQ0U1Z1kv2eBg546tl1tJbm1naixPuDcs3sbK6ZlcXweBvdNMb0TFXZWVbLip3HeHXnMbYdqXnvgdDU4XHkjBrKuJRhjElOYOTQOEYOjWPUsDiSh8YyJC6a+Jho4mOiiIuJeu/PuOiofg/bu/XytDlAkaoWO0EsBRYCO3zqLATudrafBn4n3l4uBJaqahOwX0SKnPbw16aI7AQuAm506jzqtPv7rs6hAciyz28t4+t/29htnSiBrJFDyE0ZxmfPHsfZE1I4a/woRgwJj7cSGmMGj4iQPyaJ/DFJfO3iPBqaW9l+pIbNh6ooKq+jxFPP2mIPx2qber0qwZcvmMBdV0wZ8FgDmWyygEM+n0uBuV3VUdVWEanGOwyWBazpdGzHvF9/baYAVara6qd+V+f4wKp3InILcIvzsU5Edve6p320H3hz4JtNpVOfwlik9NX6GV5Cop/fvwe+3//Dx3W1w8ZrHKr6IPCg23H0l4gUdnX5Gm4ipa/Wz/ASKf3sSiCfszkMjPX5nO2U+a0jIjHACLw38bs6tqtyD5DstNH5XF2dwxhjzCAJZLJZD+SJyHgRiQMWAcs71VkO3ORsXw+sdO6lLAcWiUi8M8ssD1jXVZvOMaucNnDafK6HcxhjjBkkARtGc+6P3A68hHea8p9UdbuI/AQoVNXlwCPA484EgON4kwdOvWV4JxO0ArepahuAvzadU34PWCoiPwM2Om3T1TnCUMgOAfZDpPTV+hleIqWfftlDncYYYwLO1kYzxhgTcJZsjDHGBJwlmzAgIgtEZLeIFInInW7H0x8iUiIiW0Vkk4gUOmWjROQVEdnr/DnSKRcRud/p7xYRme3Tzk1O/b0iclNX5xssIvInESkXkW0+ZQPWLxE50/l7K3KOdWXF1i76ebeIHHa+p5tE5AqffXc5Me8Wkfk+5X5/lp1JQWud8iedCUKDTkTGisgqEdkhIttF5BtOedh9TwecqtpXCH/hnSixD5gAxAGbgXy34+pHP0qA1E5l9wJ3Ott3Avc421cA/wYEOBtY65SPAoqdP0c62yNd7tcFwGxgWyD6hXeW5tnOMf8GLg+ift4N/IefuvnOz2k8MN75+Y3u7mcZWAYscrb/AHzVpX5mArOd7US8y2flh+P3dKC/7Mom9L23LJCqNuNdjHShyzENlIV4lx7C+fNqn/LH1GsN3mesMoH5wCuqelxVTwCvAAsGO2hfqvo63lmQvgakX86+JFVdo97fUo/5tDWouuhnV95bjkpV9wMdy1H5/Vl2/md/Ed7lpuCDf2eDSlXLVPVdZ7sW2Il3lZKw+54ONEs2oc/fskCh+EpPBV4WkQ3iXToIIENVy5zto0CGs91Vn0Pl72Kg+pXlbHcuDya3O8NHf+oYWqLv/exuOSrXiEguMAtYS2R9T/vFko0JFuer6mzgcuA2EbnAd6fzv7ywm6cfrv1y/B44DZgJlAG/djecgSMiw4G/A99U1RrffWH+Pe03SzahrzfLAgU9VT3s/FkOPIN3SOWYM6yA82e5U72vyxkFm4Hq12Fnu3N5UFDVY6rapqrtwEO8v3L7QC5HNehEJBZvovmrqv7DKY6I7+mHYckm9PVmWaCgJiLDRCSxYxu4DNjGB5ca6rwE0WedmT5nA9XOEMZLwGUiMtIZsrnMKQs2A9IvZ1+NiJzt3Nf4rE9bruv45eu4Bu/3FAZ2OapB5fw9PwLsVNXf+OyKiO/ph+L2DAX7+vBfeGe87ME7k+cHbsfTj/gn4J15tBnY3tEHvGP1K4C9eF+gN8opF+ABp79bgQKftr6A94ZzEfD5IOjb3/AOIbXgHX+/eSD7BRTg/SW+D/gdzqogQdLPx51+bMH7SzfTp/4PnJh34zPbqqufZednZJ3T/6eAeJf6eT7eIbItwCbn64pw/J4O9JctV2OMMSbgbBjNGGNMwFmyMcYYE3CWbIwxxgScJRtjjDEBZ8nGGGNMwFmyMaYLIqIi8hefzzEiUiEiz/ezvWQRudXn87yu2hKR1SJS0EN7df2Jwxg3WLIxpmv1wBkiMsT5fCkf7mnuZODWHmsZE4Ys2RjTvReAK53txXgfXgTee4fJs85Ck2tEZLpTfrez8ORqESkWka87h/wKOE2873b5L6dsuIg8LSK7ROSvnd9dIiJfEJHf+nz+kojc16nOPOdcp7QjImeJyNsisllE1olIoogkiMifnXembBSRjzp1P+f05xXxvl/odhG5w6mzRkRGOfVOE5EXnUVT3xCRyQP1l23CmNtPldqXfQXrF1AHTMe7tH0C3qfF5wHPO/v/F/hPZ/siYJOzfTfwNt73taTiXdsrFsjlg+97mQdU413/Kgp4B++CpACr8T5JPhzvk+SxTvnbwLSO+LprB+87YYqBs5x6SUAM8G3gT07ZZOCg07/P4X2aPRFIc9r8ilPvPryLToL3Sfk8Z3susNLt75V9Bf9Xx8J2xhg/VHWLs5T8YrxXOb7OB65z6q0UkRQRSXL2/UtVm4AmESnn/SXnO1unqqUAIrIJb0J60+f8dSKyEviYiOzEm3S29rKdaqBMVdc7bdU4+8/HmyhR1V0icgCY5LSzSr3vaakVkWrgn075VmC6s9rxucBTPhdh8V30zZj3WLIxpmfLgf/GewWR0stjmny22+j631pv6j0MfB/YBfz5Q56vJ77ttPt8bnfajML7bpmZ/WzfRCi7Z2NMz/4E/NjPFcUbwKfAe98EqNRO7zbppBbvEFWfqOpavMvR34jPPaNe2A1kishZToyJzjL9vnFPAnKcur2JpQbYLyI3OMeLiMzoQ0wmQlmyMaYHqlqqqvf72XU3cKaIbMF78/8mP3V82/EAb4nINp8JAr21DHhLva8Q7hX1vlr5k8D/ishmvK8eTgCWAFEishV4EvicM+TXW58Cbnba3E74vIbcBJCt+mxMCHCex7lPVVe4HYsx/WFXNsYEMedB0D3ASUs0JpTZlY0xxpiAsysbY4wxAWfJxhhjTMBZsjHGGBNwlmyMMcYEnCUbY4wxAff/AZSwVTPwF07WAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "RWsOvpr5sVzU",
        "outputId": "b0a3cd11-d0b4-49ec-abca-997c8f34e837"
      },
      "source": [
        "plt.figure(figsize=(6,5))\n",
        "sns.boxplot(train_data['MonthlyIncome'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLxG2O45sak4",
        "outputId": "ad1e8603-1b62-4694-9788-0d4439939092"
      },
      "source": [
        "train_data.groupby('Age')['EmployeeCount'].sum().sort_values(ascending=False).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age\n",
              "29.0    49\n",
              "34.0    48\n",
              "36.0    48\n",
              "31.0    47\n",
              "30.0    42\n",
              "Name: EmployeeCount, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFpNGqCtsmi6",
        "outputId": "8d71ea14-32ad-4d9c-dc9f-f78d64121f04"
      },
      "source": [
        "train_data['JobLevel'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    403\n",
              "2    350\n",
              "3    151\n",
              "4     78\n",
              "5     47\n",
              "Name: JobLevel, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "wXxZYSetsuBb",
        "outputId": "3e57a9be-1981-4ce7-9971-16807afa31c0"
      },
      "source": [
        "sns.countplot(x='JobLevel',hue='Attrition',data=train_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "giMJ7hiDsznk",
        "outputId": "7a339741-cf42-416f-e672-387c4c1a9465"
      },
      "source": [
        "train_data['StockOptionLevel'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    432\n",
              "1    417\n",
              "2    111\n",
              "3     69\n",
              "Name: StockOptionLevel, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAU6AaGztG50"
      },
      "source": [
        "#drop the columns which are unrelevant for Attrition prediction. \n",
        "train_data.drop(['EmployeeCount','EmployeeNumber','StandardHours'],axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "7yO9u4l8tPEJ",
        "outputId": "3edb180d-7da3-4e57-853c-edd6aabf8825"
      },
      "source": [
        "# Categorical Encoding\n",
        "train_data[categorical_features].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>Department</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Over18</th>\n",
              "      <th>OverTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Research Director</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Manufacturing Director</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>Male</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Human Resources</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Manager</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Male</td>\n",
              "      <td>Healthcare Representative</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Attrition     BusinessTravel  ... Over18 OverTime\n",
              "0        No      Travel_Rarely  ...      Y       No\n",
              "1        No      Travel_Rarely  ...      Y       No\n",
              "2       Yes      Travel_Rarely  ...      Y       No\n",
              "3        No  Travel_Frequently  ...      Y       No\n",
              "4        No      Travel_Rarely  ...      Y       No\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3TK_oGdtX8A"
      },
      "source": [
        "train_data['Attrition'] = train_data['Attrition'].replace({'No':0,'Yes':1})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yfc4VH3Wtxkv"
      },
      "source": [
        "train_data['OverTime'] = train_data['OverTime'].map({'No':0,'Yes':1})\n",
        "train_data['Gender'] = train_data['Gender'].map({'Male':0,'Female':1})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Upg4nBstt8Ys"
      },
      "source": [
        "cat_cols = ['BusinessTravel','Department','EducationField','JobRole','MaritalStatus']\n",
        "for col in cat_cols:\n",
        "    map_dict = {k:i for i, k in enumerate(train_data[col].value_counts().index,0)}\n",
        "    train_data[col] = train_data[col].map(map_dict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-hpj6nQuFAX"
      },
      "source": [
        "train_data.drop('Over18',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74gTEkT4uNZ_",
        "outputId": "731dbe33-5ab3-44f2-be82-3910ac03da21"
      },
      "source": [
        "# Making correlation\n",
        "train_data.corr()['Attrition'][:-1].sort_values(ascending=False)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Attrition                   1.000000\n",
              "OverTime                    0.273295\n",
              "EducationField              0.092899\n",
              "Department                  0.079797\n",
              "DistanceFromHome            0.065264\n",
              "NumCompaniesWorked          0.061832\n",
              "MonthlyRate                 0.002960\n",
              "MaritalStatus              -0.000983\n",
              "HourlyRate                 -0.007533\n",
              "PercentSalaryHike          -0.011157\n",
              "BusinessTravel             -0.016686\n",
              "PerformanceRating          -0.021510\n",
              "Education                  -0.022094\n",
              "JobRole                    -0.030180\n",
              "DailyRate                  -0.034119\n",
              "RelationshipSatisfaction   -0.036987\n",
              "Gender                     -0.039339\n",
              "YearsSinceLastPromotion    -0.050700\n",
              "TrainingTimesLastYear      -0.051681\n",
              "WorkLifeBalance            -0.054439\n",
              "EnvironmentSatisfaction    -0.082964\n",
              "JobSatisfaction            -0.102101\n",
              "JobInvolvement             -0.129238\n",
              "YearsAtCompany             -0.154422\n",
              "StockOptionLevel           -0.158530\n",
              "MonthlyIncome              -0.171551\n",
              "Age                        -0.175555\n",
              "JobLevel                   -0.181610\n",
              "YearsInCurrentRole         -0.188421\n",
              "TotalWorkingYears          -0.199323\n",
              "Name: Attrition, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "9Nt4YoFjug8T",
        "outputId": "a03f05dc-c10c-4066-a54a-ecff93ed092d"
      },
      "source": [
        "plt.figure(figsize=(13,9))\n",
        "sns.heatmap(train_data.corr(),vmax=0.8,linewidth=0.1,cmap='coolwarm')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "C6EaY_AcumNf",
        "outputId": "9b6d7f1e-e097-4295-96d5-52748baef112"
      },
      "source": [
        "train_data.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>Gender</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>OverTime</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1126.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>17399</td>\n",
              "      <td>6615</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>84</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4941</td>\n",
              "      <td>2819</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2679</td>\n",
              "      <td>4567</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1246.0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18200</td>\n",
              "      <td>7999</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>994.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10445</td>\n",
              "      <td>15322</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  Attrition  ...  YearsSinceLastPromotion  YearsWithCurrManager\n",
              "0  50.0          0  ...                        1                     3\n",
              "1  36.0          0  ...                        0                     1\n",
              "2  21.0          1  ...                        1                     0\n",
              "3  50.0          0  ...                       10                     7\n",
              "4  52.0          0  ...                        4                     0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9zNeEX-gusPQ"
      },
      "source": [
        "# Feature selection\n",
        "x = train_data.drop('Attrition',axis=1)\n",
        "y = train_data['Attrition']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "YR71qSO7wkfM",
        "outputId": "7f319c4a-a093-4178-d044-283fb69651ea"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "\n",
        "extra_tree = ExtraTreesClassifier()\n",
        "\n",
        "extra_tree.fit(x,y)\n",
        "\n",
        "feat_importance = extra_tree.feature_importances_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-145-d46b7b5bd85e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mextra_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExtraTreesClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mextra_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mfeat_importance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextra_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \"\"\"\n\u001b[1;32m    294\u001b[0m         \u001b[0;31m# Validate or convert input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "VzicEWmwwpV1",
        "outputId": "cf2811c2-356b-4680-ea78-1107b789a8fd"
      },
      "source": [
        "plt.figure(figsize=(11,9))\n",
        "feat_imp = pd.Series(extra_tree.feature_importances_, index=x.columns)\n",
        "\n",
        "feat_imp.nlargest(20).plot(kind='barh')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NotFittedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-81-fb0fcbd3f44c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfeat_imp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextra_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfeat_imp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'barh'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfeature_importances_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    425\u001b[0m             \u001b[0marray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mzeros\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m         \"\"\"\n\u001b[0;32m--> 427\u001b[0;31m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m         all_importances = Parallel(n_jobs=self.n_jobs,\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 967\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    968\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNotFittedError\u001b[0m: This ExtraTreesClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8GobFeLw9uM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}