Employee_Attrition_Analysis.ipynb

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      "source": [
        "#load libraries\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import plotly as py\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
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        {
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import cufflinks as cf\n",
        "cf.go_offline()\n",
        "cf.set_config_file(offline = False, world_readable = True)"
      ]
    },
    {
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      "source": [
        "pd.set_option('display.max_columns',None)"
      ]
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      "outputs": [],
      "source": [
        "data = pd.read_csv('https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Employee%20Attrition.csv')\n",
        "#copy the data to another df\n",
        "empdf = data.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "data": {
            "text/plain": [
              "(1470, 35)"
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      "source": [
        "#look over shape of data\n",
        "empdf.shape"
      ]
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>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>0</th>\n",
              "      <td>41</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1102</td>\n",
              "      <td>Sales</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>4</td>\n",
              "      <td>Single</td>\n",
              "      <td>5993</td>\n",
              "      <td>19479</td>\n",
              "      <td>8</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>279</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1373</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>92</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>3</td>\n",
              "      <td>Single</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>1392</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>56</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>3</td>\n",
              "      <td>Married</td>\n",
              "      <td>2909</td>\n",
              "      <td>23159</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>591</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>3468</td>\n",
              "      <td>16632</td>\n",
              "      <td>9</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
              "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
              "1   49        No  Travel_Frequently        279  Research & Development   \n",
              "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
              "3   33        No  Travel_Frequently       1392  Research & Development   \n",
              "4   27        No      Travel_Rarely        591  Research & Development   \n",
              "\n",
              "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
              "0                 1          2  Life Sciences              1               1   \n",
              "1                 8          1  Life Sciences              1               2   \n",
              "2                 2          2          Other              1               4   \n",
              "3                 3          4  Life Sciences              1               5   \n",
              "4                 2          1        Medical              1               7   \n",
              "\n",
              "   EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
              "0                        2  Female          94               3         2   \n",
              "1                        3    Male          61               2         2   \n",
              "2                        4    Male          92               2         1   \n",
              "3                        4  Female          56               3         1   \n",
              "4                        1    Male          40               3         1   \n",
              "\n",
              "                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
              "0        Sales Executive                4        Single           5993   \n",
              "1     Research Scientist                2       Married           5130   \n",
              "2  Laboratory Technician                3        Single           2090   \n",
              "3     Research Scientist                3       Married           2909   \n",
              "4  Laboratory Technician                2       Married           3468   \n",
              "\n",
              "   MonthlyRate  NumCompaniesWorked Over18 OverTime  PercentSalaryHike  \\\n",
              "0        19479                   8      Y      Yes                 11   \n",
              "1        24907                   1      Y       No                 23   \n",
              "2         2396                   6      Y      Yes                 15   \n",
              "3        23159                   1      Y      Yes                 11   \n",
              "4        16632                   9      Y       No                 12   \n",
              "\n",
              "   PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
              "0                  3                         1             80   \n",
              "1                  4                         4             80   \n",
              "2                  3                         2             80   \n",
              "3                  3                         3             80   \n",
              "4                  3                         4             80   \n",
              "\n",
              "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0                 0                  8                      0   \n",
              "1                 1                 10                      3   \n",
              "2                 0                  7                      3   \n",
              "3                 0                  8                      3   \n",
              "4                 1                  6                      3   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                1               6                   4   \n",
              "1                3              10                   7   \n",
              "2                3               0                   0   \n",
              "3                3               8                   7   \n",
              "4                3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        0                     5  \n",
              "1                        1                     7  \n",
              "2                        0                     0  \n",
              "3                        3                     0  \n",
              "4                        2                     2  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#look at sample of data\n",
        "empdf.head()"
      ]
    },
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          "execution_count": 8,
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      ],
      "source": [
        "#let's see what data types how many features we are having\n",
        "empdf.dtypes.value_counts()"
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          "iopub.status.idle": "2021-01-20T14:50:33.992543Z",
          "shell.execute_reply": "2021-01-20T14:50:33.993092Z"
        },
        "papermill": {
          "duration": 0.081291,
          "end_time": "2021-01-20T14:50:33.993239",
          "exception": false,
          "start_time": "2021-01-20T14:50:33.911948",
          "status": "completed"
        },
        "tags": [],
        "id": "qxt28sBIrkPl",
        "outputId": "b4f4725b-fbb5-4374-9875-61bc17081399"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "No     1233\n",
              "Yes     237\n",
              "Name: Attrition, dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf['Attrition'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:34.130032Z",
          "iopub.status.busy": "2021-01-20T14:50:34.129289Z",
          "iopub.status.idle": "2021-01-20T14:50:34.133680Z",
          "shell.execute_reply": "2021-01-20T14:50:34.132959Z"
        },
        "papermill": {
          "duration": 0.075112,
          "end_time": "2021-01-20T14:50:34.133816",
          "exception": false,
          "start_time": "2021-01-20T14:50:34.058704",
          "status": "completed"
        },
        "tags": [],
        "id": "sAAPU4o5rkPm"
      },
      "outputs": [],
      "source": [
        "#fig = go.Figure(figsize=(4,5))\n",
        "#trace = go.Pie(labels=empdf['Attrition'], values=empdf['Attrition'].value_counts())\n",
        "#iplot([trace])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:34.625500Z",
          "iopub.status.busy": "2021-01-20T14:50:34.624659Z",
          "iopub.status.idle": "2021-01-20T14:50:34.630511Z",
          "shell.execute_reply": "2021-01-20T14:50:34.629633Z"
        },
        "papermill": {
          "duration": 0.109858,
          "end_time": "2021-01-20T14:50:34.630666",
          "exception": false,
          "start_time": "2021-01-20T14:50:34.520808",
          "status": "completed"
        },
        "tags": [],
        "id": "_7lLILz8rkPn",
        "outputId": "f4feb9a3-32ba-4b71-d0d5-25aeff55de50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# let's see the total Missing values in dataset\n",
        "empdf.isnull().sum().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:35.098360Z",
          "iopub.status.busy": "2021-01-20T14:50:35.097507Z",
          "iopub.status.idle": "2021-01-20T14:50:35.762817Z",
          "shell.execute_reply": "2021-01-20T14:50:35.761892Z"
        },
        "papermill": {
          "duration": 0.787801,
          "end_time": "2021-01-20T14:50:35.762953",
          "exception": false,
          "start_time": "2021-01-20T14:50:34.975152",
          "status": "completed"
        },
        "tags": [],
        "id": "h3Mm3nYnrkPp",
        "outputId": "02df4505-2c3e-4201-b324-d0d3b90d4180"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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8D8gmxNfZxrT+KdC7MlhEjCPrTNWud6trW0TUtW/H9DOAU9KroTb0rGtbRLwD9KqMb2ZmZmbWGrSqjoqZmZmZmTVdNWdUWu0cFTMzMzMzW3I5o2JmZmZm1kY5o2JmZmZmZtaMVHHnX7OG+MNiZmZmS7pWlcJ44aAflf79bL0b7myRc/bQL2uyiVPnlxJ3r23acciZrxce9/rBq3D8Re8XHveKAV24o/0GhccF2POLoh6z800nX9ngHboXycXHLcuBA+cUHhfgxiGrc+zQ4t+/q07pUtq1uPDWBYXHPfUnWeL70LPeKDz2ded+l/NvLv73+vSftSvlc3HjkNUBGHFn8f8m9/2RSrvGB53+z8Lj3nD+avQe9GbhcQHGDVq5lLhAaX+Tx9xbeFgAjtyV0t6/o4e8V3jcUQOX56zrPi88LsC5h36LX13zWeMVF9Jvjvg2A6/+tPGKC2nIUR1KvRbWPNxRMTMzMzNro2rataoET6E8R8XMzMzMzFodZ1TMzMzMzNoo3/XLzMzMzMysGTmjUiBJ84EZuaLfR8SQijo9gQER8eMCj9sT+DwiHknrxwCfRMT1RR3DzMzMzFof1VRv3sEdlWLNi4juLXDcnsBHwCMAETGyBdpgZmZmZs3MQ79ssUjaQ9IsSQ8B++XKB0kakFt/WlK3tHyIpOmSnpL021T2/yQ9JulJSf8raeVU/xjgl5KmSdopH1dSd0l/S7EmSPpOKp8k6QJJkyU9L2mnZrocZmZmZmaNckelWEunzkLtq5ekDsDVwP8DdgK+21gQSZsAZwA/iIgtgH5p00PAdhGxJfB74NSIeAUYCVwaEd0j4sGKcNcDp0XE5mTD0s7ObVsqInoA/SvKzczMzKwNUI1Kf7UUd1SKNS91Fmpf44ENgZcj4oWICOB3TYjzA+CWiHgHICJqnwq1OnC3pBnAKcAmDQWR1BnoEhH3p6LrgJ1zVW5LPx8HutUTo4+kqZKm3n3b1U1oupmZmZnZ4vMcleZR32OUv+TrncUO6afq2Wc4cElETEwT6ActZrtqHzE7n3o+CxExGhgNMHHq/OIfB21mZmZmi6yaJ9NX75m1HrOAtSStk9YPyG17BdgKQNJWwFqp/F7gZ5JWSNuWT+WdgX+m5UNzceYCnSoPHBEfAP/KzT/5BXB/ZT0zMzMzs9bGGZViLS1pWm79rogYKKkPcIekd8jmmWyatt8KHJL2mQI8DxARMyWdB9yfbnn8JNCbLIPyB0n/BP7Gvzs2fwZukbQ3cEJFmw4FRkpaBpgNHFbkCZuZmZlZy6nmu365o1KgiGhXT/ldZHNVKsvnAbvXs891ZHNK8mV/Av5UR93ngc1zRQ/mtk0Dtqtjn5655XeoZ46KmZmZmVlLcEfFzMzMzKyN8hwVMzMzMzOzZuSMipmZmZlZW6XqnaPijIqZmZmZmbU6yp5BaNYk/rCYmZnZkq5VpTD+2a9X6d/PVhs2vkXO2RkVMzMzMzNrdTxHxczMzMysjfJdv8zMzMzMzJqRMypmZmZmZm1UNT+Z3hkVMzMzMzNrdaq2oyJpvqRpudfAguK+ImnFImI14VgdJY2S9JKkmZIekPS9go/RXdKPioxpZmZmZs1DNTWlv1pKNQ/9mhcR3Vu6EYtpDPAysF5ELJC0NrBRwcfoDmwD3FlwXDMzMzOzRVa1GZX6pIzIbyQ9KmmqpK0k3Z2yFsekOj1T9mKCpGckjZT0jWsl6SRJT6dX/1Q2WFK/XJ3zJJ2Ylk+RNEXSdEnn5OocLGlyyvyMktRO0jrA94BfR8QCgIiYHRF3NHDsbpKezsUdIGlQWp4k6YJ0nOcl7STpW8C5QK907F4FX24zMzMzK5FqVPqrpVRzRmVpSdNy6+dHxPi0/I+I2F7SpcA4YAegAzATGJnq9AA2Bl4F7gL2A26pDSZpa+Awss6EgMck3Q9cA9wGDEudm58DPSTtDqyX4gqYKGln4G2gF7BDRHwh6UrgIOB9YFpEzK88sQaO/a9GrslSEdEjDfU6OyJ2k3QWsE1E9G1kXzMzMzOzZlPNHZWGhn5NTD9nAB0jYi4wV9KnkrqkbZMjYjaApJuAHcl1VNL6hIj4ONW5DdgpIi6X9K6kLYGVgScj4t3UUdkdeDLt35Gs47I5sDUwRRLA0sBbwBMNnFudx86dV31uSz8fB7o1UpcUuw/QB2DUqFH06dOnKbuZmZmZWTOo5rt+VXNHpSGfpZ8Lcsu167XXJCr2qVxv6FMxBugNfBcYm6t/fkSM+loQ6QTguog4vaJ8HWALSTW1Q7+acOwv+fpwvg4V22vPdT5NfO8jYjQwuna1KfuYmZmZWTPxAx+XSD0krZWGb/UCHqrY/gCwj6RlJC0L7As8mLZNAPYAtgXuTmV3A4dL6gggaTVJKwH3AvunZSQtL2nNiHgJmAqco5RqkbSepL0bOPabwEqSVpD0beDHTTjPuUCnhbw2ZmZmZmalquaMSuUclbsiYmFuUfwoMATYjKxjMCG/MSKekDQOmJyKxkTEk2nb55LuA96vnWMSEfdI2gh4NPU7PgIOjohnJP0auCd1ir4AjiebG3MkcDHwoqRPgHeBUxo6tqRzgcfI7hY2qwnneR8wMF2r/DweMzMzM2vl0vfKqqQIj+apJKknMCAimpKRqGv/GrI5Jj+NiBeKbFsL84fFzMzMlnStqmfw9q8PK/37Wdf/ubZFzrmaMyotQtLGwO1kk92rqZNiZmZmZq1MSz6QsWzuqNQhIiYBkxZx32eAtYtsj5mZmZnZksYdFTMzMzOzNqqab09cvbkiMzMzMzNrs5xRMTMzMzNrqzxHxQx6D3qzlLjjBq3Mvn2Lv+/AhBHr0X/4R4XHveyEjpx85ceFxwW4+LhlS4kLcEf7DQqPuecXz3Hs0PcLjwtw1SldSnv/Dj/nrcLjjj17JQ4+47XC4/7uvFUB2L/f7MJj3zJsbQ458/XC414/eBWOueBfhccdedp3ADhi8NuFx77mzK4cetYbhce97tzvlva52PPIpwuPC3DHmE1LiQtwxtjPGq+0kM47/NulvHeQvX99L/mg8LgjTurM0UPeKzzuqIHLl3otyvgeMG7Qym0qbm1sax7uqJiZmZmZtVGeo2JmZmZmZtaMnFExMzMzM2ujsueMV6fqPTMzMzMzM2uz2lxHRdJ8SdNyr4EFxX1F0opFxGrisW7Nre8vaVxBsQdJGlBELDMzMzNr5WpU/quFtMWhX/MiontLN6IA20jaJCJmtnRDakkSoIhY0NJtMTMzM7MlW5vLqNQnZSl+I+lRSVMlbSXpbkkvSTom1ekp6QFJEyQ9I2mk6hjYJ+kkSU+nV/9UNlhSv1yd8ySdmJZPkTRF0nRJ5+TqHCxpcsr8jJLULneYi4Bf1XHsr2VEUhu6pdcsSWNS2Q2SdpP0sKQXJPXIhdlC0v+l8qNysb7RzhT3WUlXAk8A/7HQF9/MzMzMWoRqakp/tZS22FFZumLoV6/ctn9ExPbAg8A4YH9gO+DcXJ0ewMnAZsA6wH754JK2Bg4Dvpf2PUrSlsA1wKGpTg3wc+AGSbsD66W43YGtJe0saSOgF7BDygDNBw7KHepmYCtJ6y7Eua8LDAM2BzYEDgR2BAbw9U7P5sCewPbAWZJWra+dqf4GwPURsWVEvFpxPfqkjt/U5x//7UI01czMzMyWFJL2kPScpBfrm5qRkgbTJM2UdH9jMatt6NfE9HMG0DEi5gJzJX0qqUvaNjkiZgNIuonsi/4tuRg7AhMi4uNU5zZgp4i4XNK7qdOyMvBkRLybOgC7A0+m/TuSdQg2B7YGpmQjqlgayD9lbj4wFDgd+EsTz/3liJiR2jUTuDciQtIMoFuu3p8iYh4wT9J9ZJ2THetp59+BVyPib3UdMCJGA6MBeg96M5rYTjMzMzNrBq3hOSpp1NAVwA+BOWTffydGxDO5Ol2AK4E9IuLvklZqLG5b7Kg0pPaRtwtyy7Xrteda+WW7cr2hd3sM0Bv4LjA2V//8iBj1tSDSCcB1EXF6A/F+S9ZRyc9T+ZKvZ7o65JYrzyl/vvn3sq5zrK+d3YByHrNuZmZmZkuCHsCLuWTA74G9gWdydQ4EbouIvwNExFvfiFKhLQ79Wlw9JK2Vhm/1Ah6q2P4AsI+kZSQtC+xLNpQMYAKwB7AtcHcquxs4XFJHAEmrpR7ivcD+tb1FSctLWjN/oIj4ArgU6J8rfgXYKu2zFbDWIpzj3pI6SFoB6AlMaaCdZmZmZtZWqab0V34qQHr1qWjFasA/cutzUlne+sB3JE2S9LikQxo7tbaYUVla0rTc+l0RsTC3KH4UGEI2R+UBss7HVyLiCWW3Cp6cisZExJNp2+dpKNX7ETE/ld2T5qM8moZ4fQQcHBHPSPo1cE/qFH0BHA98bQ4I2dyXX+fWbwUOSec4BXh+Ic6t1mTgDmANYHBEvAa8Vlc7yYagmZmZmVkb1BxDv/JTAeprRl27VawvRTYtYleyKRGPSvpbRNT7XbfNdVQiol095d1yy+PIJtN/bVv6gv5JRPSiQsX+lwCXVNZJHY7tgJ9W7DuMbJJ7ZczxwPhGjvUZsGpufR7ZXJK6bJqr1zu3/ErttogYVM++9bYzH9fMzMzMbCHN4et3jl0deK2OOu+keeAfS3oA2IIG/lN+SRz6tUgkbQy8SDaB/YWWbo+ZmZmZGTU15b8aNwVYL02v+BbZ3XEnVtT5E7CTpKUkLUN2h91nGwra5jIqiyMiJgGTFnHfZ4C1i2yPmZmZmVlbFxFfSupLNie6HTA2ImYqPcswIkZGxLOS7gKmk90IakxEPN1Q3CWqo2JmZmZmVk3S1IYWFxF3AndWlI2sWB9K9niOJlGEH41hTeYPi5mZmS3pWkfPIJk77OTSv5916ndxi5yzMyrWZEcPea+UuKMGLs/xF71feNwrBnThkDNfLzzu9YNX4cCBcwqPC3DjkNVLiQtw7NDir/FVp3ThjvYbFB4XYM8vnmO/E18sPO5tl69L70FvFh533KCVOfK8dwqPO+aMFQE4/JxGbze/0MaevVJpcX854qPC417atyMAx1zwr8JjjzztOxxw6t8Lj3vThWuUFrfMv8llKet35IjBbxceF+CaM7ty8BmV84EX3+/OW5UTLvuw8LjD+y9XyjWG7DqX9W91Wf8+lfH3DbK/ca1K0+aQtEnVe2ZmZmZmZtZmOaNiZmZmZtZGNcdzVFqKMypmZmZmZtbqOKNiZmZmZtZWqXrzDtV7ZmZmZmZm1mY1uaMiab6kabnXwEU5oKRjJB2yKPs2F0ndJf0ot76ypNslPSXpGUl3NrJ/F0nH5dZXlXRLI/ucKOlZSTcsQnv7pyd81q7fKanLwsYxMzMzszamRuW/WsjCDP2aFxHdF/eAlQ9+qSVpqYj4cnHjF6Q7sA3/fmjNucBfI2IYgKTNG9m/C3AccCVARLwG7N/IPscB/x0RLy9Ce/sDvwM+Scf7UcPVzczMzMxat8Ue+iXpFUnnSHpC0gxJG0qqSeVdcvVeTJmJQZIGpLJJkn4j6X6gn6RdJT2Z4oyV9O36jpHKB0m6TtI9qc5+ki5Mde6S1D7V21rS/ZIel3S3pFVyx79A0mRJz0vaSdK3yDomvVLmqBewCvDVgzMiYnrav6Oke3Pt2jtVGQKsk/YfKqmbpKfTPpuk402TNF3SepJGAmsDEyX9UlIPSY+ka/GIpA3Svu0kXZSONV3SCZJOBFYF7pN0X+56rZiWT5L0dHr1T2XdUvbmakkz0/VbenE/C2ZmZmbWvKSa0l8tZWGOvHTF0K9euW3vRMRWwFXAgIhYAPwJ2BdA0veAVyKirqesdYmI/wSuAMYBvSJiM7Jsz7H1HSNXvg6wJ7A3WVbhvrT/PGDP1FkZDuwfEVsDY4HzcvsvFRE9yLISZ0fE58BZwPiI6B4R41PbrpF0n6QzJK2a9v0U2De1axfgYkkCBgIvpf1PqTjfY4BhKTu1DTAnIo4BXgN2iYhLgVnAzhGxZWrLb9K+fYC1gC0jYnPghoi4PLfvLvkDSdoaOAz4HrAdcJSkLdPm9YArImIT4H3gJ9RBUh9JUyVNfXbydXVVMTMzMzMrXFFDv25LPx8H9kvL48m+ZF8L/Dyt16W2fAPg5Yh4Pq1fBxwPXNbAMUSxNW8AACAASURBVAD+EhFfSJoBtAPuSuUzgG4p7qbAX7M+BO2A/OPK83G71dXAiLhb0trAHsB/A09K2pTsC/5vJO0MLABWA1au5zxrPQqcIWl14LaIeKGOOp2B6yStBwTQPpXvBoysHSIXEY09lnhHYEJEfAwg6TZgJ2Ai2bWe1oRzHw2MBjh6yHvRyPHMzMzMrDlV8XNUiro98Wfp5/xczEeBdSV1BfYB/qeefT9OPxu7ynUd46vyiFgg6YuIqP0yvSDVEzAzIrZfyLhfkzoFNwI3Srod2BnoBHQFtk6dpVeADg2dRETcKOkxsizQ3ZKOjIj/q6g2mCwztK+kbsCkVC6yjktTNXRNP8stzwc89MvMzMzMWo3SBp2lDsME4BLg2Yh4t5FdZgHdJK2b1n8B3F9AU54DukraHkBSe0mbNLLPXLJOCGmfHyjdVUtSJ7LhZn8ny3y8lTopuwBr1rV/XsrMzE5DtiYCdU3M7wz8My33zpXfAxwjaakUa/lGjvcAsI+kZSQtSzYU78EGztvMzMzM2hDV1JT+aimLM0dlSBP2GQ8cTP3Dvr4SEZ+Szaf4QxrGtQCo8w5hCyPNOdkfuEDSU8A04PuN7HYfsHFuLs7WwFRJ08kyRWMiYgpwA7CNpKnAQWSdLVKn7OE0gX1oRexewNOSpgEbAtfXcfwLgfMlPUw2VK3WGLIO0vR0Lgem8tHAX2on0+fO/QmyeT+TgcdSu59s5NzNzMzMzFpck4d+RUS7esq75ZanAj0r1lVRf1BuuWfFtnuBLalQ3zHysdJ6x3qOM41sqFZl3Hxb3yHN00jDvLatqF7Z4ajdp84hZRFxYEXRpqn8fOD8Oup3yy0/Cqyf23xmKv8SOCm98vsOJ7thQF2xLiHLauXrv1LbnrR+UV3nYGZmZmatnDxHxczMzMzMWpsWHJpVtuo9MzMzMzMza7OcUTEzMzMza6uqeOiX/n03X7NG+cNiZmZmS7pW1TP45LpzS/9+tsyhZ7XIOTujYk3W95IPSok74qTOpcQecVJn+g2bW3jcYf06cezQ9wuPC3DVKV1KiQvQf/hHhce87ISO7Hfii4XHBbjt8nW5o/0Ghcfd84vnOPnKjxuvuJAuPm5ZDhw4p/C4Nw5ZHYA9j3y68Nh3jNmUQ858vfGKC+n6wauUFhfK+Vs04qTO9B70ZuFxxw1amSMGv1143GvO7MqR571TeFyAMWesWEpcgNNGzys85gV9li7lGkN2ncv6XBx+zluFxx179kocfMZrhccF+N15q3LQ6f9svOJCuuH81Ur721nmtWhNWvL2wWWr3jMzMzMzM7M2yxkVMzMzM7O2StWbd6jeMzMzMzMzszbLGRUzMzMzs7aqplXN7S+UMyqLQNJHFeu9JY0oK34d23tK+kDSk5JmSWr0yfKS9pG0cVFtNDMzMzMrkzsqrYikdgtR/cGI2BLYEvixpB0aqb8P4I6KmZmZWRWRakp/tRR3VAomaU1J90qann6ukcrHSdo/V++j9LOnpPsk3QjMqIj1W0l759ZvkLRXvk5EzAOmAaulOkdJmiLpKUm3SlpG0veBvYChkqZJWie97pL0uKQHJW1Y0iUxMzMzM1to7qgsmqXTF/5pkqYB5+a2jQCuj4jNgRuAy5sQrwdwRkRUZjzGAIcBSOoMfB+4M19B0neA9YAHUtFtEbFtRGwBPAscERGPABOBUyKie0S8BIwGToiIrYEBwJVNPXkzMzMzayVqVP6rpU6txY7cts1LX/i7R0R34Kzctu2BG9Pyb4EdmxBvckS8XFkYEfcD60paCTgAuDUivkybd5I0HXgDuD0i3kjlm6YMyQzgIGCTyriSOpJ1ev6QOlqjgFXqapikPpKmSpo689FxTTgVMzMzM7PF57t+lS/Szy9JHUNJAr6Vq9PQY7J/S9bh+DlweK78wYj4saT1gYckTYiIacA4YJ+IeEpSb6BnHTFrgPdTJ6vhxkeMJsu+0PeSD6KR6mZmZmbWnPwcFVsIj5B1KiDrYDyUll8Btk7LewPtmxhvHNAfICJmVm6MiOeB84HTUlEn4HVJ7dPxa81N24iID4GXJf0Uso6TpC2a2B4zMzMzs9K5o1K8E4HD0rCsXwD9UvnVwH9Kmgx8j4azKF+JiDfJ5ppc20C1kcDOktYCzgQeA/4KzMrV+T1wSrql8TpknZgjJD0FzCTrPJmZmZlZWyKV/2ohHvq1CCKiY8X6OLLMBxHxCvCDOvZ5E9guV3R6Kp8ETKovvqRlyCbL35Tb/rV90p2/VkurV6VX5fEf5pu3J96jsp6ZmZmZWWvgjkorJmk3YCxwSUR80NLtMTMzM7NWpqZ6B0i5o9KKRcT/Amu0dDvMzMzMzJqbOypmZmZmZm1VFd/1yx0VMzMzM7O2qgUfyFg2RfjRGNZk/rCYmZnZkq5V9Qw+/ePlpX8/67DPiS1yzs6oWJP1Gza3lLjD+nViwFWfFB73omOX4egh7xUed9TA5Tn5yibdXXqhXXzcsqXEBTj8nLcKjzn27JXoPejNwuMCjBu0cinX+eLjluWO9hsUHnfPL57j0LPeKDzuded+F4CDz3it8Ni/O29VDjnz9cLjXj94ldLiAhw79P3CY191SpfS2nzAqX8vPO5NF65B/+EfFR4X4LITOjZeaRH1vaT4+8KMOKlzKX/fIPsb19Z+R8r4WwHZ34uy/k096PR/Fh73hvNX44TLPiw8LsDw/suVEneRVfHQr+o9MzMzMzMza7OcUTEzMzMza6ta8IGMZXNGxczMzMzMWh1nVMzMzMzM2qoqfuBj9Z5ZIqne2YaSekq6vZ5tr0haseC2TJK0TZExF5ek/pKWael2mJmZmZnlVX1HxRrVH3BHxczMzKwtksp/tZAloqOizFBJT0uaIalXbvNykiZIekbSSOnr93iT1E3Ss5KuljRT0j2Slpa0kaTJFfWmp+VdJT2ZjjVW0rcrYh4r6cLcem9Jw9PywZImS5omaZSkdqn8I0kXSHpc0v9K6pEyNLMl7ZXqtEvnOUXSdElHp/Keqe4tkmZJuiFdkxOBVYH7JN1X6EU3MzMzM1sMS0RHBdgP6A5sAewGDJW0StrWAzgZ2AxYJ9WttB5wRURsArwP/CQingW+JWntVKcXcLOkDsA4oFdEbEY2D+jYini3VBynFzBe0kZpeYeI6A7MBw5KdZYFJkXE1sBc4H+AHwL7AuemOkcAH0TEtsC2wFGS1krbtiTLnmwMrJ2OcTnwGrBLROzSwPUzMzMzs9ZINeW/WsiS0lHZEbgpIuZHxJvA/WRf5AEmR8TsiJgP3JTqVno5Iqal5ceBbmn5ZuBnabkXMB7YINV/PpVfB+ycDxYRbwOzJW0naYW0z8PArsDWwBRJ09J6bUfoc+CutDwDuD8ivkjLte3ZHTgk7fsYsAJZJ6v2POdExAJgWm6fBknqI2mqpKlPP3JtU3YxMzMzM1tsS8pdvxoaXBeNrAN8llueDyydlscDf5B0GxAR8YKk7k1s03iyTs4sYEJEhCQB10XE6XXU/yIiatu2oLZNEbFAUu37KOCEiLg7v6OknnWcQ5Pe+4gYDYwG6Ddsbl3XxszMzMxaiu/61eY9APRKczi6kmU4aueX9JC0Vpqb0gt4qKlBI+Ilsi/9Z5J1PCDreHSTtG5a/wVZBqfSbcA+wAG5fe8F9pe0EoCk5SWt2dT2AHcDx0pqn/ZfX9KyjewzF+i0EMcwMzMzMytdVWdUUqbhM2ACsD3wFFnG5NSIeEPShsCjwBCyOSoPpLoLYzwwFFgLICI+lXQYWaZlKWAKMLJyp4j4l6RngI0jYnIqe0bSr4F7UsfpC+B44NUmtmUM2ZCuJ1J25m2yzlBDRgN/kfS656mYmZmZtTFV/GT6qu6oAJsAL6UhU6ek11ciYhIwqa4dI6JbWnwH2DRXflFFvYuAyrJ7ySavV8bsWbH+4zrqjOffGZZ8ecfc8qC6tqX5J79Kr7xJ5M4zIvrmlocDwyuPZ2ZmZmbWkqq2oyLpGOBEsjtdmZmZmZlVnxa8K1fZqrajEhEjqWPIlZmZmZmZtX5V21ExMzMzM6t6VTxHpXpzRWZmZmZm1mbp34/mMGuUPyxmZma2pGtVKYxP772+9O9nHXY9pEXO2UO/rMk+nXhFKXE77HU8R573TuFxx5yxIkf95t3C4179qxW48NYFhccFOPUn5SU5Dz7jtcJj/u68VUt57yB7/w4cOKfwuDcOWZ1Dz3qj8LjXnftd7mi/QeFx9/ziOQCOGPx24bGvObNraW0++cqPC4978XHZY6FG3VN4aI7eHXoNaOqd4Jtu/EVrlva71/eSDwqPCzDipM6lxAU45oJ/FR5z5Gnf4a8rb9p4xUXwwzefLu39++WIjwqPe2nfjnw6/sLC4wJ06HUqbz8zufGKC6nrxj149cXnCo+75rob8PqsaYXHBVhlw6Y+29sWlzsqZmZmZmZtVFTxHBV3VMzMzMzM2qoqvj1x9Z6ZmZmZmZm1Wc6omJmZmZm1Vc6oWFEk1Tt7TlJPSbfXs+0VSSuW1KbekkaUEdvMzMzMbFE4o2JmZmZm1kZV82R6Z1RagDJDJT0taYakXrnNy0maIOkZSSOl+vN5krpKulXSlPTaQVJNyr50ydV7UdLKddUv9UTNzMzMzBaRMyotYz+gO7AFsCIwRdIDaVsPYGPgVeCuVPeWeuIMAy6NiIckrQHcHREbSfoTsC9wraTvAa9ExJuSbqysD2xU0jmamZmZWdk8R8UKtiNwU0TMj4g3gfuBbdO2yRExOyLmAzeluvXZDRghaRowkSwb0wkYD9RmaX6e1huqXy9JfSRNlTT1mrsfWvgzNTMzMzNbBM6otIyGBhNGI+t5NcD2ETHva8GlR4F1JXUF9gH+p5H69TcmYjQwGuDTiVc01BYzMzMza26eo2IFewDoJald6kzsDExO23pIWivNTekFNJTGuAfoW7siqTtARAQwAbgEeDYi3m2ovpmZmZlZa+OMSjOStBTwGVknYnvgKbKMyakR8YakDYFHgSHAZmQdmgm5ENMlLUjLNwMnAldImk72Xj4AHJO2jwemAL1z+zdU38zMzMzamprqzTu4o9K8NgFeShmPU9LrKxExCZhU144R0a2emL3qKoyIqVQMMYuId+qqHxHjgHENtNvMzMzMrFm5o9JMJB1DltHo39JtMTMzM7PqUM3PUXFHpZlExEhgZEu3w8zMzMysLXBHxczMzMysrfJzVMzMzMzMzJqPsnndZk3iD4uZmZkt6VrVpJCP/jax9O9nHbfbq0XO2UO/rMkOP+etUuKOPXsl+g2bW3jcYf06ldLmsWevxKFnvVF4XIDrzv1uKXEB9u83u/CYtwxbu9TPxZ5HPl143DvGbMrBZ7xWeNzfnbcqRwx+u/C415zZFYA72m9QeOw9v3iutN+RI897p/C4Y85YEYB9jnu+8Nh/vHJ9jr/o/cLjXjGgS2nXoi3+HTp6yHuFxxw1cPlSfqch+70u63ekrL9DZf5NLutvXFnX4sCBcwqPC3DjkNVLiWvf5I6KmZmZmVlbVcV3/fIcFTMzMzMza3WcUTEzMzMza6Oiiu/65Y6KmZmZmVlb5aFf1UHSRw1s6ynp9nq2HS5phqTpkp6WtHcjx9lH0sa59XMl7dZA/a6SHpP0pKSdmnIuuX27S/pRbn0vSQMXJoaZmZmZWWvjjEojJK0OnAFsFREfSOoIdG1kt32A24FnACLirEbq7wrMiohDF6GJ3YFtgDvTsSYCExchjpmZmZm1NVU89Kt6z6weygxNmZEZknrlNi8naYKkZySNlFQDrATMBT4CiIiPIuLlFOsoSVMkPSXpVknLSPo+sBcwVNI0SetIGidp/7TPkBR/uqSLJHUHLgR+lOovLekqSVMlzZR0Tq7t20p6JB1vsqTOwLlAr7RvL0m9JY1I9deUdG861r2S1kjl4yRdnmLNrm2bmZmZmVlrsSRmVPYjy0JsAawITJH0QNrWA9gYeBW4K9WdALwJvCzpXuC2iPhzqn9bRFwNIOl/gCMiYrikicDtEXFL2kb6uTywL7BhRISkLhHxvqSzgG0iom+qd0ZEvCepHXCvpM2BWcB4oFdETJG0HPAJULlv79y5jgCuj4jrJB0OXE6W7QFYBdgR2JAsA3PLYl5XMzMzM2tm4TkqVWVH4KaImB8RbwL3A9umbZMjYnZEzAduAnZMy3sA+wPPA5dKGpTqbyrpQUkzgIOATRo59ofAp8AYSfuRdTTq8jNJTwBPppgbAxsAr0fEFICI+DAivmzkeNsDN6bl36Zzr/XHiFgQEc8AK9cXQFKflN2Z+tzU6xs5nJmZmZlZMZbEjkpD3c6oaz0ykyPifODnwE/S9nFA34jYDDgH6NDQgVPHogdwK1lm465vNE5aCxgA7BoRmwN3pLiqo30LK7//Z/nDNtDm0RGxTURss8E2hyzm4c3MzMysUKop/9VClsSOygNkczraSeoK7AxMTtt6SForzU3pBTwkaVVJW+X27042NAygE/C6pPZkGZVac9O2r0kT8TtHxJ1A/xSr0nLAx8AHklYG/juVzwJWlbRtitVJ0lL1HSt5hKxjRWrfQ/XUMzMzMzNrVZaYOSrpS/1nZHNOtgeeIsswnBoRb0jaEHgUGAJsRtahmQD8B3CRpFXJhm29DRyTwp4JPEbWcZnBvzsMvweulnQi2ZCxWp2AP0mqzZD8srKdEfGUpCeBmcBs4OFU/nma+D9c0tLAPGA34D5goKRpwPkV4U4Exko6JbX7sIW6aGZmZmbWqkWDg4XatiWmo0I21+OliAjglPT6SkRMAibVsd+rwA/qChgRVwFX1VH+MNm8klq9c8s96qg/jmwYWe1678o6qXwKsF0dm7atWB+X6r9CHW2vjB8RHes6npmZmZlZS1kiOiqSjiHLLvRv6baYmZmZmRUlqvg5KktERyUiRgIjW7odZmZmZmbWNEtER8XMzMzMrCpVcUZF2ZQNsybxh8XMzMyWdK1q9vr70yaV/v2sS/eeLXLOzqhYk31615hS4nbY40iOHvJe4XFHDVyeQ858vfC41w9ehfNvnl94XIDTf9aulLhAadfi8HPeKjwuwNizVyqtzWXFvaP9BoXH3fOL5wBKuc5jz16ptDb3H/5R4XEvOyG778eYewsPzZG7woED5xQe98Yhq5f2eTvhsg8LjwswvP9ypcQFOOo37xYe8+pfrcB9621ReFyAXV54ioNO/2fhcW84fzX6DZtbeNxh/Trx6Z9GFB4XoMPefZnz/NOFx119/U15+aUXC4+71jrrMvullwqPC7D2OuuUEndR+cn0ZmZmZmZmzcgdFTMzMzOzNipUU/qrKSTtIek5SS9KGthAvW0lzZe0f311armjYmZmZmZmi0xSO+AK4L/JniV4gKSN66l3AXB3U+K6o2JmZmZm1lZJ5b8a1wN4MSJmR8TnwO+BveuodwJwK9CkiZfuqJiZmZmZ2eJYDfhHbn1OKvuKpNWAfVmIZxtWfUdFUkj6bW59KUlvS7p9EeN1kXRcbr1nfbEkTZK0TSPxir81jpmZmZktEZpjjoqkPpKm5l59KppRV9ql8rbJlwGnRUSTb526JNye+GNgU0lLR8Q84IfA4txrsAtwHHBlEY0zMzMzM1tU0QyPdYmI0cDoBqrMAf4jt7468FpFnW2A3ysbSrYi8CNJX0bEH+sLWvUZleQvwJ5p+QDgptoNkpaX9EdJ0yX9TdLmqXyQpLEpKzJb0olplyHAOpKmSRqayjpKukXSLEk3SF8fzCfpCEmX5taPknRJRZ2e6VjfiJPujvCIpKckTZbUSVIHSddKmiHpSUm7pLq90/n8WdLLkvpKOinV+Zuk5VO9dSTdJelxSQ9K2rCoi21mZmZmS5QpwHqS1pL0LeDnwMR8hYhYKyK6RUQ34BbguIY6KbDkdFR+D/xcUgdgc+Cx3LZzgCcjYnPgV8D1uW0bAv9FNkHobEntgYHASxHRPSJOSfW2BPqT3eVgbWCHOo6/V9of4DDg2jra+Y046c0eD/SLiC2A3YB5wPEAEbEZWefrunR+AJsCB6Z2nwd8EhFbAo8Ch6Q6o4ETImJrYAD1ZIjyqb5r7nygripmZmZm1kJaw+2JI+JLoC/Z3byeBW6OiJmSjpF0zKKe25Iw9IuImC6pG9kX+jsrNu8I/CTV+z9JK0jqnLbdERGfAZ9JegtYuZ5DTI6IOQCSpgHdgIdyx/9Y0v8BP5b0LNA+ImY0Mc4HwOsRMSXF+jBt3xEYnspmSXoVWD/FuS8i5gJzJX0A/DmVzwA2l9QR+D7wh1zy59t1nVg+1ffpXWMqxxqamZmZmRERd1LxPTsi6pw4HxG9mxJzieioJBOBi4CewAq58oYm/3yWK5tP/derKfXGkGVsZlF3NqW+OOKbk5Gg7nbXFWdBbn1BilkDvB8R3RuIYWZmZmatXdNuH9wmLSlDvwDGAufWkcl4ADgIsnkiwDu1WYt6zAU6LezBI+IxsklGB5KbI9MEs4BVJW2b2thJ0lIV7V4fWAN4rolt+RB4WdJP0/6StMVCtMnMzMzMrFRLTEYlDakaVsemQcC1kqYDnwCHNhLnXUkPS3qabJL+HQvRjJuB7hHxr6buEBGfS+oFDJe0NNn8lN3I5pSMlDQD+BLoHRGf/X/27jzerun+//jrLU0FIUHM/ZLWWIIg1FSSL/Wtaqv9mUoMMU9BEDVEIkNjilkIEZGooYZqS7VoSUwRhIxqKtLWLEKISpB8fn/sddg5zh0S++Tec/N+Ph7ncfdeZ63PXnvfc+Msn7X2VuNH1d2BYZLOBlqTraOZ3PhTMTMzM7OmFi0479DiByoR0bZC2VhgbNqeSYUnZ0ZE/7L9TrntA8qqj8291zO33bWs3o7ApfmCUv/yfaoQ52lg2/I+Aj0q9HsUMCq337HSexHxGvDjCjHNzMzMzJpcix+oNAeS2gNPAZMj4sGm7o+ZmZmZtQzRgteoeKCyGETEh3x1Ry4zMzMzM2uABypmZmZmZjWqMc85qVWK8KMxrNH8YTEzM7MlXbOaa/X2CxOr/v1s9Y22aJJzdkbFGu2Ey+q7a/Oiu7LXCuz/638XHvfWC9euSp+v7LUCB5zxeuFxAW45/ztViQtwzAWNvtlco11z+oqcPHR24XEBLu3ZloP7vlV43BsHrVG1uKde/UnhcS8+bjkAjhg8o/DYI/p0oNeVxf/+LjuhLfe23rDwuHt8nt2B/dghHxYee9hp7TlswLuFxx15zqr06P9O4XFH9V+tKv2FrM/Vsl/vfxUe87aL1qnqf5+q9bd39PkzC4977Rkr0f3MNwqPC3DzeWtxSL+3C487euDqVbvG+546vfC4ALdf3LEqcRdVNK9xU6Fabq7IzMzMzMxqljMqZmZmZmY1qiWvUWm5Z2ZmZmZmZjXLGRUzMzMzsxrVkp+j4oxKHSSFpN/m9r8l6T1Jf17EeO0lHZfb71pXLEljJXVpIN48SZMkTZN0T3qoZH31O0v6yaL03czMzMxscfNApW6fAJ0kLZP2fwR8k1tptAeOa7BW430aEZ0johMwEzi+gfqdAQ9UzMzMzFqQQFV/NRUPVOr3V2CPtL0/cGvpDUkrSfqjpCmSxkvaLJX3lzQyZUVelXRianI+sG7KggxJZW0l3SnpBUk3Swvm7iQdLunS3P6Rki6p0M8ngLVSnW0kjZM0Mf3cUNK3gYHAfun4+0laLvXz6VR3z29+uczMzMzMiuGBSv1+B/xKUhtgM+DJ3HsDgIkRsRlwFnBj7r2NgP8DtgHOkdQaOAN4JWVBTkv1tgB6ARsD3wN2qHD8n6f2AIcCN+QrSGoF7ALcnYpeAHaKiC2AfsC5EfFZ2r4tHf82oA/wUERsDXQDhkhabuEuj5mZmZk1pdBSVX81FQ9U6hERU4COZNmUv5S9vSPw21TvIWBlSe3Se/dGxNyImAG8C6xWxyGeiojXI2I+MCkdK3/8T4CHgJ9K2ghoHRFT09vLSJoEvA+sBPwtlbcD7pA0DbgU2KSOY+8GnJFijAXaAGuXV5J0lKQJkiZMG3dD+dtmZmZm1oQ89WvJdjdwEblpX0ml31qkn3NzZfOo++5qjak3AujB17Mpn0ZEZ2Ad4Nt8tUZlEDAmrV35GdkApBIBe6UMS+eIWDsinv/aCUUMj4guEdGl0/aH1hHKzMzMzKxYHqg0bCQwMJfJKHkE6A7ZHbyAGRHxUT1xPgaWX9iDR8STwP8AB/D1wRIRMQs4Eeidpoi146tF/z3qOf79wAmldTGStljYvpmZmZlZ0/LUryVYmpp1eYW3+gNdJE0hWyh/SANx3gceT7cTHlJf3QpuBx6PiA/qiD0RmAz8CrgQOE/S40CrXLUxwMalxfRkmZfWwJQ0TWzQQvbJzMzMzKxq/MDHOkRE2wplY8nWcxARM4Gv3SkrIvqX7XfKbR9QVn1s7r2eue2uZfV2JFtvUmf/IuJnud0Nctt9c/3duizu0eX9NzMzM7Pa0ZRrSKrNGZVmLD0k8iWy9SgPNnV/zMzMzMwWF2dUmrGI+JAFsyNmZmZmZl9qyjUk1dZyz8zMzMzMzGqWMypmZmZmZjWqJa9RUUQ0XMss4w+LmZmZLema1cjg1Vdeqfr3s++tu26TnLMzKtZoU15+typxN1t/VU4eOrvwuJf2bMv+v/534XFvvXBthv6lOv8m9PxJ9f4dOHzQe4XHvL7vKhxzQcW7Zn9j15y+Ij0vmVV43KGntOPYIR8WHnfYae259oHCw3L0btnPXxz3UuGx/3j1Boyowm06jtiFql1jgHtbb1h47D0+f5Ee/d8pPO6o/qtxSL+3C487euDqnD7808LjAlxw1DJViQtw0uUfFx7z8pOW58GpcwqPC7DLpm346ZH/KDzun6/buGqftx1/9nDhcQEeu2dndt1/QuFx/35rF/Y9dXrhcW+/uCM7/fKxwuMCPPKHHasSd1GFmtW4qVBeo2JmZmZmwMYtkwAAIABJREFUZs2OMypmZmZmZjUqwhkVMzMzMzOzxcYZFTMzMzOzGhUtOO9QyJlJCkkX5/Z7S+pfROwU72BJ0yQ9J+kfknoXFbsIkrpIumIR2l0qqVdu/35JI3L7F0s6ZSHijZXUZWH7kdqOkrT3orQ1MzMzMytaUUOwucD/k9ShoHhfkrQ70AvYLSI2AbYEir8V0DcQERMi4sRFaDoO2B5A0lJAB2CT3PvbA483JpCkVotwfDMzMzOrYYGq/moqRQ1UvgCGAyeXv1H+f+olzU4/u0p6WNLtkl6SdL6k7pKekjRV0rqpyZlA74h4EyAi5kTEdSlGZ0njJU2R9AdJK6bysSlb8Yik5yVtLekuSS9L+k2q01HSC5JGp/Z3Slo2vddP0tMpizNcyu77luJekPr4kqQf5s7lz2l7OUkjU/uJkvZM5ZukdpPS8dYnG4Rsn85zE2Aa8LGkFSUtDXwfmChplxRraoq9dIo5PfX1MWCf3DVeKp3XbyS1kjQk9WeKpKNTHUkamjJU9wKrfoPfv5mZmZlZoYqc1HYV0F1Su4VoszlwErApcBCwQURsA4wATkh1OgHP1NH+RuD0iNgMmAqck3vvs4jYCbgG+BNwfIrVQ9LKqc6GwPDU/iPguFQ+NCK2johOwDLAT3Nxv5X62KvseCV9gIciYmugGzBE0nLAMcDlEdEZ6AK8ngZfX0ham2zA8gTwJLBdqjOF7Hc0CtgvIjYlW1d0bO54cyJix4j4Xal/wM3ASxFxNnA4MCv1Z2vgSEnfBX6Zzn9T4Ei+GjCZmZmZWY1wRqURIuIjsoHDwkyBejoi3oqIucArQOlxaVOBjvU1TAOi9hFRerLRaGCnXJW7c7Geyx3nVeB/0nv/iYjS1KqbgNITfLpJelLSVOB/WXA61l3p5zN19HE34AxJk4CxQBtgbbJByFmSTgfWiYjSk7pKWZXSQOWJ3P44ssHEaxFRetpb+XneVnb8a4FpETE415+DU3+eBFYG1k8xbo2IeWnA9FCFc0HSUZImSJpw5+9urFTFzMzMzKxwRd/16zLgWeCGXNkXpAFRmkL17dx7c3Pb83P783N9ew7Yijq+SNcjH6v8OKXY5Y8XD0ltgKuBLhHxn3RTgDYV4s6j8vUTsFdEvFhW/rykJ4E9gPslHRERD/HVOpVNyaZ+/Qc4lSzDMzLFq88nZfvjyAZaF0fEnNT+hIi4f4FOSj/h6+f/NRExnGxaH1Nefrc6j2M3MzMzs0XSlBmPaiv0fmYRMRO4nWy6Ucl0soEGwJ5A64UMex5woaTVASQtLenEiJgFfFBaJ0I2dezhuoLUYW1J26Xt/YHH+GpQMkNSW2Bh74R1P3BCbl3LFunn94BXI+IKsmzPZqn+42RTy2am7MZMoD3Z9K8ngBeAjpLWS/UbOs/rgb8Ad0j6VurPsZJap35skKaiPQL8Kq1hWYNsmpqZmZmZWbNQjeeoXAz0zO1fB/xJ0lPAg3w9A1CviPiLpNWAv6cv/0GWaQA4BLgmLYJ/FTh0Ifv6PHCIpGuBl4FhEfFfSdeRTRmbDjy9kDEHkWWWpqT+TicbiOwHHCjpc+BtYGCqP5Xsbl+35GJMBdpGxAwASYfy1cDjabJ1N3WKiEvS1LjfAt3Jpqg9m/rzHvAL4A9k09qmAi+x8IM8MzMzM2tiLTmjUshAJSLa5rbfAZYt2982V/3MVD6WbA1HqV7X3Hb5ezew4HSyUvmkstiNidUVsrt+AfMj4pgK7c8Gzm4g7gzSGpX8MdLak6MrtD2PLDtUXj4PWKGsrEfZ/oPAFhXadqynf/mF/melV7meFcrMzMzMrEZEtNyBSst9lKWZmZmZmdWsakz9qgkRMZ3sdsVmZmZmZjWpJU/9ckbFzMzMzMyanSU2o2JmZmZmVutackZFEX40hjWaPyxmZma2pGtWI4Np/3y76t/POq23epOcszMq1mhHnvt+VeJed9bK9LxkVuFxh57SjlOvXqi7YTfKxcctxyH93i48LsDogatXJS5QlT6PHrg6+//634XHBbj1wrXp0f+dwuOO6r8aB/d9q/C4Nw5ag/16/6vwuLddtA4Ax1/0YeGxr+rdngPOeL3wuLec/x0OG/Bu4XFHnrMqQNU+F/e23rDwuHt8/iJj1t+88LjdXp7MPie/VnhcgDsu/W5V4gIcff7MwmNee8ZKHNjnzcLjAtw0eM2qfZYPH/Re4XGv77sK+546vfC4ALdf3LFqfe5+5huFx735vLVq8r/Vi6IlZ1S8RsXMzMzMzJodZ1TMzMzMzGqUn6NiZmZmZma2GDmjYmZmZmZWo+Z7jUrTkzRP0iRJ0yTdIWnZJuhDV0nb5/Y3lDQ29et5ScMbaN9R0rSC+vK1WJL6S+qdtgdK2jVtT5fUoYjjmpmZmZktDrWUUfk0IjoDSLoZOAa4pKFGkr4VEV8U1IeuwGxgXNq/Arg0Iv6UjrVpQcchxWsVEfMWpW1E9CuyL2ZmZmbW/PiuX83Po8B6kpaTNFLS05ImStoTQFKPlHW5B3hAUltJN0iaKmmKpL1Svd0kPSHp2VS/bSqfLmlAKp8qaSNJHckGRyenDMoPgTWAL+/tGRFTU/uOkh5N7Z/NZ2FK6qqTsjZjJN0CTJU0SNJJuXaDJZ3Y0AWSNErS3mVly0i6T9KRdV07MzMzM7PmoJYyKkCWIQF2B+4D+gAPRcRhktoDT0n6e6q6HbBZRMyUdAEwKyI2TTFWTFOhzgZ2jYhPJJ0OnAIMTO1nRMSWko4DekfEEZKuAWZHxEUpzqXAQ5LGAQ8AN0TEh8C7wI8iYo6k9YFbgS5lp1JfnW2AThHxWhog3QVcLmkp4Ffp/eWBdSVNysVcHbiojkvXFvgdcGNE3Cjp3ErXLiKKf/CImZmZmVWF7/rVPCyTvpRPAP4NXA/sBpyRyscCbYC1U/2/RUTpyVK7AleVAkXEB8C2wMbA46n9IcA6uePdlX4+A3Ss1KGIuAH4PnAH2bSw8ZKWBloD10mamt7buELz+uo8FRGvpWNMB96XtEU634kRUXry4isR0bn0Aq6p1M/kT2QDqRvTfn3X7kuSjpI0QdKEF54aXU94MzMzM7Pi1FJG5cs1KiWSBOwVES+Wlf8AyGcGBERZPJENZvav43hz08951HOdIuJNYCQwMi1u7wT8DHgH2JxsMDinQtOT66lTntUYAfQgy5iMrKsvDXgc2F3SLRERZOf/tWtXLiKGA8MBjjz3/fJraGZmZmZNyGtUmq/7gRPSgIWUdajkAaBnaUfSisB4YAdJ66WyZSVt0MDxPiabclWK82NJrdP26sDKwBtAO+CtiJgPHAS0qhCrMXVK/gD8GNg6nfOi6Ae8D1yd9ht77czMzMzMFrtaH6gMIptCNSVlMwbVUe83wIrp1saTgW4R8R5ZluJWSVPIBi4bNXC8e4Bf5hbT7waUYt4PnBYRb5MNBg6RNB7YgK9nSGhkHQAi4jNgDHD7ot4FLOkFtJF0IY2/dmZmZmbWTEWo6q+mUjNTvyKibYWyT4GjK5SPAkbl9meTrUEpr/cQWZaivLxjbnsC2foTIuIlYLNc1UfJFuCXt3+5rN6ZqXw62dSw+uqMJVsz8qW0iH5bYJ/cMb6MlSvrn9vuUel8gENz21+7dmZmZmZmzUGtZ1RaPEkbA/8EHkyDGzMzMzMzIFujUu1XU6mZjMqSKiL+AXyvqfthZmZmZs2Pb09sZmZmZma2GDmjYmZmZmZWo+Y3dQeqSNkjNcwaxR8WMzMzW9I1q7lW41+YVfXvZ9tu1K5JztkZFWu0IwbPqErcEX06VCX2iD4dOOGyjwqPe2WvFeh+5huFxwW4+by1qhIX4MA+bxYe86bBa7L/r/9deFyAWy9cm8MHvVd43Ov7rlKVPt964dpVu8ZQnb+/EX06cHDftwqPe+OgNejR/53C447qvxoAh/R7u/DYoweuzpj1Ny88breXJ3Nv6w0Lj7vH5y9W9W+vWqr1OT7y3PcLjwtw3VkrV+1vpFrXopr/re515ezC4152QluOHfJh4XGHndaenpfMKjwuwNBT2lUl7qLyGhUzMzMzM7PFyBkVMzMzM7Ma1ZS3D642Z1TMzMzMzKzZcUbFzMzMzKxGeY3KYiZpnqRJkqZJukPSsgvZfoik5yQNqVYfq0XSWEkvSpos6WlJnRuo317Scbn9NSXdWf2empmZmZlVT7McqACfRkTniOgEfAYc05hGkkoZoqOBLSPitIVs11x0j4jNgauBhgZb7YEvByoR8WZE7F3NzpmZmZlZ8xCo6q+m0lwHKnmPAutJWk7SyJRlmChpTwBJPVLW5R7gAUl3A8sBT0raT9I6kh6UNCX9XDu1GyXpEkljgAvS/jBJYyS9KmnndLznJY0qdSbVmZAyNgNy5dMlDZD0rKSpkjZK5W0l3ZDKpkjaK5XvJumJVP8OSW0rnPsTwFq5OA/m4u+Z6pwPrJsyUEMkdZQ0LXdt7pJ0n6SXJV2Y6+/hkl5KGZzrJA0t5tdlZmZmZvbNNbdMwgJSpmN34D6gD/BQRBwmqT3wlKS/p6rbAZtFxMzUbnZEdE7b9wA3RsRoSYcBVwC/SO02AHaNiHlpMLIi8L/Az4F7gB2AI4CnJXWOiElAn4iYKakV8KCkzSJiSoo3IyK2TFOxeqe2fYFZEbFp6s+KkjoAZ6djfyLpdOAUYGDZJfgx8Me0PQf4ZUR8lNqPT4OyM4BOufPtWBajM7AFMBd4UdKVwLzUry2Bj4GHgMkN/T7MzMzMrHmZ34Ifx91cMyrLSJoETAD+DVwP7AackcrHAm2A0lOp/lYapFSwHXBL2v4tsGPuvTsiYl5u/56ICGAq8E5ETI2I+cBzQMdUZ19JzwITgU2AjXPt70o/n8nV3xW4qlQhIj4Atk3tHk/ncwiwTi7OzZJeB04HrkxlAs6VNAX4O1mmZbU6zjnvwYiYFRFzgH+k42wDPBwRMyPic+COuhpLOiplkCa88PSNjTicmZmZmdk311wzKp+WMgQlkgTsFREvlpX/APhkIWLnx53l7eamn/Nz26X9b0n6LlmmZOuI+CBlYdpUaD+Pr66tyo5ZKvtbROxfRx+7k2U4zicb5Py/VLYKsFVEfC5petmx65I/j1K/Gj3ZMCKGA8MBjhg8owWP2c3MzMxqj5+j0jzcD5yQBixI2qKR7cYBv0rb3YHHvkEfViAb3MyStBrZtLSGPAD0LO1IWhEYD+wgab1UtqykDfKNUqbjbGBbSd8H2gHvpkFKN77KwHwMLL+Q5/EUsHOahvYtYK+FbG9mZmZmVlW1NFAZBLQGpqTF4oMa2e5E4NA0Zeog4KRF7UBETCab8vUcMBJ4vBHNfgOsqOxWy5OBbhHxHtADuDX1azywUYXjfQpcTJbFuRnoImkC2YDrhVTnfbIpZNPUyNsxR8QbwLnAk2TTyP4BzGpMWzMzMzNrPiJU9VdTaZZTvyLia3fASl/aj65QPgoYVVf7iJhOtkC+vF2PuvZTm051vLdAu1x5x9z2BKBr2p5NtgalvP5DwNYVyruW7V+c292ujmMfUFbUKZWPIndtIuKnuTq3RMTwlFH5A1nmx8zMzMysWWiWAxVbLPpL2pVsncsDfHV3MTMzMzOrEdGCVxB7oLKEiojeTd0HMzMzM7O6eKBiZmZmZlaj5rfgu34pWnK+yIrmD4uZmZkt6ZrVyODvU+ZW/fvZrpst3STnXEt3/TIzMzMzsyWEp35Zo50+/NOqxL3gqGU4+vyZhce99oyVOPLc9wuPe91ZK9Oj/zuFxwUY1X+1qsQF2OOIaYXHvHdEp6r87iD7/R0xeEbhcUf06UCvK2cXHveyE9rS85Li7/I99JR2ABzS7+3CY48euDonXPZR4XGv7LUChw14t/C4I89ZFajOv0UXHLUM+5z8WuFx77j0u+z/638XHvfWC9fm3tYbFh4XYI/PX2y40iLa+6RXC4955+Xfq8rfHmR/f9X6d+j4iz4sPO5VvdtXJW4p9sF93yo87o2D1qjav8mHD3qv8LgA1/ddpSpxF1VLnhzljIqZmZmZmTU7zqiYmZmZmdWoaF5LZgrljIqZmZmZmTU7zqiYmZmZmdWo+V6jsiBJ8yRNkjRN0j2S2jdQv7+keh8wKOkXkjbO7Q9MT04vhKSukv5cx3sj8seuo862kp5M5/28pP4N1O8s6Se5/Z9LOqOBNrdKmiLp5PrqVWjXXtJxuf01Jd25MDHMzMzMzJqTRc2ofBoRnQEkjQaOBwZ/w778Avgz8A+AiOj3DeM1WkQc0Yhqo4F9I2KypFZAQ7db6Qx0Af6SjnE3cHddlSWtDmwfEes0rtcLaA8cB1ydjvUmsPcixDEzMzOzGhLhNSr1eQJYC0DSupLuk/SMpEclbVReWdKRkp6WNFnS7yUtK2l74OfAkJSxWFfSKEl7pza7SJooaaqkkZKWTuXTJQ2Q9Gx6b6NUvnOKMym1Wz4dvq2kOyW9IOlmSUr1x0rqkrZnS7o4xXxQUukedKsCbwFExLyI+Eeqv42kcek44yRtKOnbwEBgv9SH/ST1kDQ0tdknZaMmS3okxX8AWDXV/2Gl65TaribpD6l8crp25wPrprZDJHWUNC3VbyPphnR9Jkrqlsp7SLor/b5elnRhAZ8FMzMzM7NCfKOBSsos7MJXmYLhwAkRsRXQm/R/+MvcFRFbR8TmwPPA4RExLsU4LSI6R8QruWO0AUYB+0XEpmRZoGNz8WZExJbAsHRM0s/jU9bnh0DppvtbAL2AjYHvATtU6N9ywLMp5sPAOan8UuDFNEg4OvUL4AVgp4jYAugHnBsRn6Xt29L53FZ2jH7A/6Vr8PNU9nPglVT/0UrXKdW7Ang4lW8JPAeckWt7WtmxjgdI125/YHSu752B/YBNyQZV/1N+MSQdJWmCpAmTHrm+wuUyMzMzs6YSUf1XU1nUgcoykiYB7wMrAX+T1BbYHrgjvXctsEaFtp1StmUq0B3YpIFjbQi8FhEvpf3RwE659+9KP58BOqbtx4FLJJ0ItI+IL1L5UxHxekTMBybl6ufNB0oDi5uAHQEiYiDZVK4HgAOA+1Kddumcp5ENZho6n1L/Rkk6EmhVR526rtP/kg3KSpmdhp5ytSPw21T/BeBfwAbpvQcjYlZEzCGbcve1aWcRMTwiukREl847HV7+tpmZmZlZVSzqQKW0RmUd4Ntk/9d+KeDD9H/1S6/vV2g7CuiZ/g//AKBNhTp5DU28m5t+ziOtuYmI84EjgGWA8bkpaHNz7b6s34Avx5ER8UpEDCPLIm0uaWVgEDAmIjoBP6Ph8yEijgHOBv4HmJTilBvFwl2nutR3/RblepiZmZlZMzEfVf3VVL7R1K/0f/NPJJtq9SnwmqR9AJTZvEKz5YG3JLUmyxSUfJzeK/cC0FHSemn/ILIpWXWStG5ETI2IC4AJwNfWytRjKb5aiH4A8FiKuUdpTQuwPtkX+w/JMipvpPIejTifUv+eTDcMmEE2YClX13V6kDT1TVIrSSvUdyzgkVJ7SRsAawMv1lHXzMzMzKxZ+MaL6SNiIjAZ+BXZF+LDJU0mWzuxZ4UmfYEngb+RDUJKfgeclhZ8r5uLPwc4lGx61VSyqVnXNNCtXqXF6mQDqL8uxCl9Amwi6RmyaVYDU/lBZGtUJpFNpeoeEfOAC4HzJD3OgtO4xgAblxbTlx1jSFrcPo1sIDG5Qj/quk4nAd3StXgG2CQi3gceT+c8pCzO1UCrVP82oEdEzMXMzMzMal5LXqOySFN9IqJt2f7Pcrs/rlC/f257GGmNRVmdx8kWuZf0yL33INlC+PI2HXPbE4CuafuECt0em16l+j1z213L4vYlGyjky35VISYR8QRfrfmg1C4iZgJbl1Ufld77fxVCTQc65eLWdZ3eocIAMCIOKCvqlMrnsGCmp1R/VKk/af+nFfpkZmZmZtYkirg9sZmZmZmZNYEIVf3VGJJ+LOlFSf9UhYecS+qu7MHmU9IjPSotEVmAByplyrNFZmZmZmZWt/TIkquA3clmSO0vaeOyaq8BO0fEZmQ3oxreUFzf5cnMzMzMrEbNb8I1JDnbAP+MiFcBJP2ObKnCP0oV0nMTS8YD32koqKIpV8hYrfGHxczMzJZ0TXe/3gr++PS8qn8/+8XWreo9Z0l7Az+OiCPS/kHAD/Jrwsvq9wY2KtWvizMqZmZmZmY1anHkHCQdBRyVKxoeEfmpW5UGMhV7JqkbcDjpoer18UDFzMzMzKxGxWJI8KRBSX1rSl5nwecCfgd4s7ySpM2AEcDu6fEa9fJiejMzMzMz+yaeBtaX9F1J3yZ7vuLd+QqS1gbuAg6KiJcaE9QZFTMzMzOzGtUcFtNHxBeSegL3kz0AfWREPCfpmPT+NUA/YGXgakkAX0REl/riejG9LQx/WMzMzGxJ16wW09/5ZPWHKnv/YKkmOeclauqXpD6SnksPmpkk6QeSeklatsBjTJfU4Ru07yFpaNrun+6KUFh8MzMzM2s5Iqr/aipLzNQvSdsBPwW2jIi56cv+t4HbgJuA/zZRv1pFxLwmOO63IuKLxX1cMzMzM7PGWJIyKmsAMyJiLkBEzAD2BtYExkgaAyBpmKQJKfMyoNQ4ZTIGSHpW0lRJG6XylSU9IGmipGvJpQMl/VHSMynWUbny2ZIGSnoS2E7SoZJekvQwsENjT0jSKZKmpVevVNZR0rRcnd6S+qftsZLOTcc5SdI+qe1kSY8s9BU1MzMzsybljErL8ADQT9JLwN+B2yLiCkmnAN3SwAWgT0TMlNQKeFDSZhExJb03IyK2lHQc0Bs4AjgHeCwiBkragwXvMX1YirUM8LSk36dbsS0HTIuIfpLWAG4BtgJmAWOAibkYJ0s6MLe/JoCkrYBDgR+QDY6eTAOQDxq4Du0jYucUYyrwfxHxhqT2jbmIZmZmZmaLwxKTUYmI2WSDgaOA94DbJPWoUHVfSc+SDRY2ATbOvXdX+vkM0DFt70Q2dYyIuJcFBwonSpoMjCe7t/T6qXwe8Pu0/QNgbES8FxGfkU1Fy7s0IjqXXnx1T+odgT9ExCfp3O4CftjghVgw/uPAKElHkt2h4WskHZUyTBOGD6/v9tlmZmZmtrjND1X91VSWpIwKaS3IWGBsyiYckn9f0nfJMiVbR8QHkkYBbXJV5qaf81jw2n0tKSapK7ArsF1E/FfS2FysOWXrUhYlqVbXp+YLFhyAtil7/5MvDxpxjKQfAHsAkyR1Ln/4TtkDfnzXLzMzMzNbLJaYjIqkDSWtnyvqDPwL+BhYPpWtQPZFfpak1YDdGxH6EaB7OsbuwIqpvB3wQRqkbARsW0f7J4Guaa1La2CfRp7SI8AvJC0raTngl8CjwDvAqine0mQ3EKhI0roR8WRE9ANmsOATRc3MzMysmfMalZahLXBlWovxBfBPsmlg+wN/lfRWRHSTNBF4DniVbGpUQwYAt6bpYg8D/07l9wHHSJoCvEg2/etrIuKttNj9CeAt4FnqmIZV1u7ZlPF5KhWNiIiJAJIGkg2AXgNeqCfMkDR4E/AgMLmh45qZmZmZLQ5+4KMtDH9YzMzMbEnXrB74eMtj1f8yf8CO8gMfzczMzMzMYMma+mVmZmZm1qLMb8HzXZxRMTMzMzOzZscZFTMzMzOzGhVN+JyTanNGxczMzMzMmh1nVKzRTh46uypxL+3ZllOv/qThigvp4uOWo/ew/xYe96Jjl+X4iz4sPC7AVb3bVyUuQJ+RcxuutJAGH7Y0RwyeUXhcgBF9OnD68E8Lj3vBUcvQ85JZhccdeko7jrngg8LjXnN69mimo8+fWXjsa89YiSPPfb/higvpurNWZr/e/yo87m0XrQPASZd/XHjsy09avmrXuBp/IyP6dGDvk14tPC7AnZd/rypxAe5tvWHhMff4/MWq/O4g+/1V6++6Wn97xw6pzn+fhp3Wvmr/Jlejz8NOa1+Vf+sh+/e+OWnJN/B1RsXMzMzMzJodZ1TMzMzMzGqU7/plZmZmZma2GDmjYmZmZmZWo7xGpZmS1EfSc5KmSJok6QeSekladhHj9ZfUu0K5JJ0t6WVJL0kaI2mTRsTrIWnN3P4ISRsX2beiSKrOSnkzMzMzq5qI6r+aSs1mVCRtB/wU2DIi5krqAHwbuA24CSjydk/HA9sDm0fEfyXtBtwtaZOImFNPux7ANOBNgIg4osA+mZmZmZm1WLWcUVkDmBERcwEiYgawN7AmMEbSGABJ+0uaKmmapAtKjSX9WNKzkiZLerA8uKQjJf1V0jLA6cAJEfHfdKwHgHFA91R3tqSLU7wHJa0iaW+gC3BzyvYsI2mspC4N9Gu2pMGpX+MlrVbfRZB0mqSnU1ZpQCq7QNJxuTr9JZ1aV30zMzMzq03zo/qvplLLA5UHgP9JU7GulrRzRFxBlr3oFhHd0rSrC4D/BToDW0v6haRVgOuAvSJic2CffGBJPYGfAb8AWgPLRcQrZcefAJSmfy0HPBsRWwIPA+dExJ2pTveI6BwRX958vK5+5WKNT/16BDiyrguQMjvrA9ukOFtJ2gn4HbBfruq+wB311DczMzMza1ZqdqASEbOBrYCjgPeA2yT1KKu2NTA2It6LiC+Am4GdgG2BRyLitRQr/6Sog4DdyQYx9T0hT0BpjDmfbMoZZNPOdmyg+3X1C+Az4M9p+xmgYz1xdkuvicCzwEbA+hExEVhV0pqSNgc+iIh/11W/vo5KOkrSBEkTpj4+soHTMjMzM7PFyWtUmqmImAeMBcZKmgocUlZFdTTNDzLKTSPLNnwHeC0iPpL0iaTvRUT+McCl7EnFrjXQ9br6BfB5xJcfiXnU/zsScF5EXFvhvTvJpsKtTpZhaah+RRExHBgOcPLQ2S34vhJmZmZm1pzUbEZF0oaS8tmAzsC/gI+B5VPZk8DOkjpIagXsTza4eCKVfzfFWikXZyJwNNli+dIdu4YAV6T1KkjalSxrckt6fymyQQHAAcBjaTvfl7xWfv8BAAAgAElEQVS6+rWw7gcOk9Q29WstSaum934H/Cr1685G1DczMzOzGjN/fvVfTaWWMyptgSsltQe+AP5JNg1sf+Cvkt5K61TOBMaQZRP+EhF/gmxKE3CXpKWAd4EflQJHxGPpVsD3SvoRcCWwIjBV0jzgbWDP3LqTT4BNJD0DzOKr9SGjgGskfQpsl4v/Vl39asDZknrl4nxH0veBJyQBzAYOBN6NiOckLQ+8ERFvpfoP1FW/Ecc2MzMzM1tsanagEhHPkN0yuNyV6VWqdwtfZT7y7f8K/LWsrH9u+36yDETJgPSqqz99gb5lZb8Hfp8r6tqIfrXNbd9JyoakvvWvUP9y4PI6+rRpY+vnj2tmZmZmtcEPfDQzMzMzM1uMajaj0pw4G2FmZmZmTcEZFTMzMzMzs8XIGRUzMzMzsxrVlE+OrzZFS84XWdH8YTEzM7MlXX3Pw1vsrvpr9b+fHb9705yzMyrWaNOP2LMqcTuO+BPHDvmw8LjDTmvPkee+X3jc685amREPFh4WgCN2qU5cgEP6vV14zNEDV+fwQe8VHhfg+r6rVCX29X1X4bABxd+Re+Q5q/K31ToVHvdH70wD4MA+bxYe+6bBazJm/c0Lj9vt5cmccNlHhce9stcKADw4dU7hsXfZtE3VrnG1/h3qecmswuMCDD2lXVXiAhx9/szCY157xkrc23rDwuMC7PH5i+zX+1+Fx73tonU4YvCMwuOO6NOBGf0OLzwuQIeB1/PfR+8oPO6yP9yHmVMfa7jiQlpp0x2r0l/I+tycLJ6kQ9OMzbxGxczMzMzMmh1nVMzMzMzMalRLXsXhjIqZmZmZmTU7zqiYmZmZmdWo+fObugfVUzMZFUkrS5qUXm9LeiO3/+2yur0kLduImGMldZF0kqTLcuXXSvp7bv8ESVcsRF9HSdq7QvkISRs3Nk6uXRtJL0jaNFf2a0nXLGwsMzMzM7NaUDMZlYh4H+gMIKk/MDsiLqqjei/gJuC/jQw/Duie2+8MLCWpVUTMA7YH/tiYQJLqvKYRcUQj+1Pebo6kXsDVknYC1gSOBrosSjzI+hkRXyxqezMzMzNrel6j0kxJ2kXSRElTJY2UtLSkE8m+yI+RNCbVGyZpgqTnJA2oEGoisIGkZSS1IxvgTAJKGYztgXGSOksaL2mKpD9IWjHFHyvpXEkPAyeV9XFQyrAsVcrgpPLZkgZLmpxirpbK1037T0saKGk2QETcB7wFHAxcCvQHviXp96nu05J2SDG2kTQuXZtxkjZM5T0k3SHpHuABSWtIeiRlpaZJ+mEhvxgzMzMzWyzmR/VfTaWWByptgFHAfhGxKVl26NiIuAJ4E+gWEd1S3T4R0QXYDNhZ0mb5QCmzMAnYGtgWeBIYD2wvaU2yB2P+B7gROD0iNgOmAufkwrSPiJ0j4uJSgaQLgVWBQyOifAbhcsD4iNgceAQ4MpVfDlweEVun88jrBQwGVomI36a6l6a6ewEjUr0XgJ0iYgugH3BuLsZ2wCER8b/AAcD9EdEZ2DxdAzMzMzOzJlfLA5VWwGsR8VLaHw3sVEfdfSU9S5Y52QSotE7kcbLMyfbAE+m1PbADWTalHdlg5OE6jndbWby+qf7RUflJPJ8Bf07bzwAd0/Z2QOkJRbfkG0TEm8BDwLBUtCswVNIk4G5gBUnLA+2AOyRNI8u+bJIL87eIKD1x62ng0DSVbtOI+Li8k5KOStmoCbe8ML3CaZiZmZlZU4mo/qup1PJA5ZPGVJL0XaA3sEvKhNxLlo0pN45sYLId2SDlebIBzfZkg5iF7c/TwFaSVqqj/ue5Acw8Gr9eaH56Qfb72y4iOqfXWmmwMQgYExGdgJ+x4Pl+2c+IeIRssPUG8FtJB5cfLCKGR0SXiOhywEYdG9lFMzMzM7NvppYHKm2AjpLWS/sHAaVsx8fA8ml7BbIv57PSOpDd64g3jmza1yoR8W4aRLwH7AmMi4hZwAe5dRz541VyH3A+cG/KcjTWeLJpXAC/aqDuA0DP0o6kzmmzHdngA6BHXY0lrQO8GxHXAdcDWy5EP83MzMysicX8qPqrqdTyQGUOcCjZFKepZFmG0u16hwN/lTQmIiaTTfl6DhhJHdmRiPiAbGDyXK74CbI1JpPT/iHAEElTyO4MNrC+DkbEHcB1wN2SlmnkefUCTpH0FLAGMKueuicCXdLi/n8Ax6TyC4HzJD1ONkWuLl2BSZImkg2OLm9kH83MzMzMqqpmbk+cFxH9c7tbVHj/SuDK3H6POuJ0LdvfpGy/P9ndtUr7k8iyLg3F6ZHbHkk2QIJsYFAqb5vbvhO4M+2+AWwbESHpV8CEemLPAPar0J8ngA1yRX1T+SiyGxCU6o0mW2tjZmZmZjWoKe/KVW01OVBp4bYiWyAv4EPgsCbuj5mZmZnZYueBSjMTEY+S3SrYzMzMzKxefuCjmZmZmZnZYuSMipmZmZlZjZrfghepqPKzCM0q8ofFzMzMlnRq6g7knXf7vKp/Pztz31ZNcs7OqFijHdjnzarEvWnwmhwxeEbhcUf06cC+p04vPO7tF3ek+5lvNFxxEdx83lpViQvQ85L67nS9aIae0q6qn4se/d8pPO6o/qtxcN+3Co9746A1qnItbhq8JgCHDXi38Ngjz1m1Kp/lm89bq2p/0wA/PfIfhcf+83UbV+0aV+vzVo1rDF9d52o45oIPCo95zekrsl/vfxUeF+C2i9bh3tYbFh53j89f5ITLPio87pW9VuDIc98vPC7AdWetXLW/kcMHvVd43Ov7rlKV/kLW5+akJeccvEbFzMzMzMyaHWdUzMzMzMxqlDMqZmZmZmZmi5EzKmZmZmZmNWp+C06pVCWjImllSZPS621Jb+T2v91A2y6SrmjEMcYtYt8OzfXlM0lT0/b5kgZK2nVR4jby2GMldfmGMc5KPyXpMUm7597bV9J937SfZmZmZmZNrSoZlYh4H+gMIKk/MDsiLiq9L+lbEfFFHW0nABMacYztF7FvNwA3pH5MB7pFRHVunVIdZwHnRkRIOga4Q9IYoBUwGPjxogaW1Coi5hXUTzMzMzOrspjf1D2onsW2RkXSKEmXpC/VF0jaRtI4SRPTzw1Tva6S/py2+0samTIRr0o6MRdvdq7+WEl3SnpB0s2SlN77SSp7TNIVpbgN9HHvtD1d0rmSnpA0QdKWku6X9EoaIJTanCbpaUlTJA1IZctJulfSZEnTJO1XzzE7SnpU0rPptX0qX0PSIynbM03SDyWdDyyTym6OiGnAPcDpwDnAjcDb6Zo9na7tng0cp6ukMZJuAaYuzO/UzMzMzKxaFvcalQ2AXSNinqQVgJ0i4os03epcYK8KbTYCugHLAy9KGhYRn5fV2QLYBHgTeBzYQdIE4Np0jNck3boI/f1PRGwn6VJgFLAD0AZ4DrhG0m7A+sA2ZA//uVvSTsAqwJsRsQeApHb1HONd4EcRMUfS+sCtQBfgAOD+iBgsqRWwbEQ8KqlnRHTOtR8APAt8ltqdAzwUEYdJag88Jenv9RyH1P9OEfHaIlwjMzMzM2siLfnh7Yt7oHJHbmpRO2B0+tIcQOs62twbEXOBuZLeBVYDXi+r81REvA4gaRLQEZgNvJr78n0rcNRC9vfu9HMq0DYiPgY+ljQnDQJ2S6+JqV5bsoHLo8BFki4A/hwRj9ZzjNbAUEmdgXlkgzmAp4GRkloDf4yISZUaR8Qnkm4jm143Nw2efi6pd6rSBlibbBBX6TiQXb+KgxRJR5Gu2za7X8D6WxxYz6mYmZmZ2eI0vwVP/VrcA5VPctuDgDER8UtJHYGxdbSZm9ueR+U+V6qjRe7l1+POLzvG/NwxzouIa8sbStoK+AlwnqQHImJgHcc4GXgH2JxsKt4cgIh4JGVn9gB+K2lIRNxYR4z56UXq014R8WJZf/pXOk6S/70sICKGA8MBDuzzZssdspuZmZlZs9KUz1FpB7yRtntUIf4LwPfSIAigznUi38D9wGGS2gJIWkvSqpLWBP4bETcBFwFb1hOjHfBWRMwHDiJbFI+kdYB3I+I64PpcjM9TlqW+Pp2QW6ezRX3HMTMzM7PaFRFVfzWVpnyOyoVkU79OAR4qOnhEfCrpOOA+STOAp6pwjAckfR94Io0LZgMHAusBQyTNBz4Hjs01u1dSaY3NE2R38fq9pH2AMXyV3egKnJbqzgYOTuXDgSmSno2I7hW6NQi4LNURMB34KXB1HccxMzMzM2t2qj5QiYj+dZQ/wYLrJPqm8rGkaWDlbSOiU267bXn9tN8z12RMRGyUvrBfRdltjyOiY9l+j0rvRcQossX0ld67HLi87PReIctsLCAiupaXJZvlts9MdUcDoyvEOJ3sLl/5sv657U+Boyu0e7mO44yl7ml3ZmZmZtaMzW/BE/ObcurX4nBkWlz/HNnUp6+tJTEzMzMzs+anKad+VV1EXApc2tT9MDMzMzOrhmjBKZWWnlExMzMzM7Ma1KIzKmZmZmZmLVkLft4jaslPs7TC+cNiZmZmS7ointVXmLOun1v172fnHr50k5yzMyrWaAf2ebMqcW8avCb7njq98Li3X9yRA854vfC4t5z/HY4+f2bhcQGuPWOlqsQFqtLna89YiRMu+6jwuABX9lqBwwa8W3jckeesysF93yo87o2D1uDkobMLj3tpz7ZAdf7+bhq8Jidd/nHhcS8/afmqfd4AevR/p/DYo/qvxuGD3is87vV9V+GIwTMKjzuiTweOv+jDwuMCXNW7fVXiAhx57vuFx7zurJWrco0hu87V+Dfuyl4rcG/rDQuPu8fnL3LMBR8UHhfgmtNX5NfXfFp43AuPWaZq/15U83PRnMz3GhUzMzMzM7PFxxkVMzMzM7Ma1ZKXcTijYmZmZmZmzY4zKmZmZmZmNSrmN3UPqqdFZFQkXSqpV27/fkkjcvsXSzqlkbHGSupSoXy6pA5lZT+XdEbaXkXSk5ImSvphA/FflDRJ0vOSjlrUPpmZmZmZtVQtJaMyDtgHuEzSUkAHYIXc+9sDvSo1zJPUamEOGhF3A3en3V2AFyLikEY07R4REyStBLwiaVREfLYwxzYzMzMzm+81Ks3e42SDEYBNgGnAx5JWlLQ08H2gfcp2TJU0MpWXMiX9JD1GNtghlS8labSk39R1UEk9JA2V1Bm4EPhJypQsI2k3SU9IelbSHZLaVgjRFvgEmJfiDZM0QdJzkgbUccyKddJ5DEjHmyppo1TeVtINqWyKpL1SeWP6Z2ZmZmbWJFrEQCUi3gS+kLQ22YDlCeBJYDugC/ASMALYLyI2JcskHZsLMScidoyI36X9bwE3Ay9FxNmNOP4koB9wW0R0BpYDzgZ2jYgtgQlAfurZzZKmAC8CgyJiXirvExFdgM2AnSVtVuFw9dWZkY43DOidyvoCsyJi04jYDHgoTWGrr39fknRUGhhNeHniTQ1dCjMzMzNbjCKi6q+m0iIGKkkpq1IaqDyR238DeC0iXkp1RwM75dreVhbrWmBaRAxexL5sC2wMPC5pEnAIsE7u/e5p0LA20FtS6b19JT0LTCTLDG1cIXZ9de5KP58BOqbtXYGrShUi4oNG9I9c/eER0SUiuqy/xYGNOXczMzMzs2+spaxRgWydyvbApmRTv/4DnAp8BDwL/Kietp9UiNVN0sURMWcR+iLgbxGxf32VIuK9NOj4QVpb0xvYOiI+kDQKaLNAUOm7DdSZm37O46vfrYDyoXCj+mdmZmZmzZufTF8bHgd+CsyMiHkRMRNoTzb96wago6T1Ut2DgIfriXU98BfgDkmLMpgbD+xQOp6kZSVtUF5J0rLAFsArZIv/PwFmSVoN2L1C3MbUKfcA0DN3zBUb2z8zMzMza94iqv9qKi1poDKV7G5f48vKZkXE68ChZAOPqcB84Jr6gkXEJWSZmN+mbAfAFEmvp9cl9bR9D+gB3JrWoowHNspVuTlNuXoGGBURz0TEZLLpXM8BI8kGXuVxG6xTwW+AFSVNkzQZ6NaI/pmZmZmZNakWM/UrLUhfoaysR277QbLsRXm7jmX7XXPb5+TeWqBezqhUd1RpO+0/BGxd4Xhdy8sq9beePtVVp2NuewLQNW3PJluDUl6/Yv/MzMzMrHaEp36ZmZmZmZktPi0mo2JmZmZmtqTxAx/NzMzMzMwWIzXlQ1ys5vjDYmZmZks6NXUH8npeMqvq38+GntKuSc7ZU7+s0Z7751tVibvJemuwX+9/FR73tovW4ejzZxYe99ozVqLf6M8Kjwsw8JBvVyUuwCH93i485uiBq3PE4BmFxwUY0acDB/Z5s/C4Nw1es2px59x2YeFx2+z3awAOG/Bu4bFHnrMqc/40tPC4bfbsSfcz3yg87s3nrQXAjj+r7+7yi+axe3Zm31OnFx739os7VuVvZESfDhx/0YeFxwW4qnf7qsQFOHZI8X0edlp7ZvQ7vPC4AB0GXs+R575feNzrzlqZYy74oPC415y+Ive23rDwuAB7fP4ij2+xVeFxd5j4DDN/c3ThcVc6+1oe/n7nwuMC7Pz8pKrEta/zQMXMzMzMrEb5rl9mZmZmZmaLkTMqZmZmZmY1qgUnVJxRMTMzMzOz5scZFTMzMzOzGuU1Ks2YMo9J2j1Xtq+k+6p0vFUkfS7p6FxZe0nHldXbQNJfJP1T0vOSbpe0WjX6ZGZmZmbW0tT8QCWyB8EcA1wiqY2k5YDBwPGLEk9Sqwaq7AOMB/bPlbUHvhyoSGoD3AsMi4j1IuL7wDBglUXpk5mZmZlZJRFR9VdTqfmBCkBETAPuAU4HzgFuAvpI/5+98w6Xqyrf9v3QDIQmgkgPgqCIELoUFRSsIKD0Ik2KBUQEpSgEEKkqgkpv0qQp0qQTOoQQUgBBQPATafID6aHl+f5Ya3L2mcw5SWavTU7wva9rrjN7z8yz1+wzs2e96226R9J9kjYEkDRE0q2SRuXbGnn/2pJuknQeME7SYElXShoj6X5Jm1cOtyXwI2BhSQvlfUcAS0gaLeloYCvgTtuXV8Z4k+37szF1hqRxeWzr5DFsL+lSSZdLelzS9yXtlZ9zl6R58vOGSzpW0h15bKvm/avmffflv0tXdP8k6WpJj0g6Ku/fSdKvW+OTtLOkXxX+1wRBEARBEAT/A0j6sqSHczTRvh0el6Tj8uNjJa04Oc33haGSOZhkIHwFGATcaHsVYB3g6OxpeQ5Yz/aKwObAcZXXrwocYHsZ4MvAU7aXt70scDWApEWAj9geAVyYNQD2BR6zPdT2PsCywL19jPN7ALY/RTJ6zsoeGPLrtspjOQx43fYKwJ3Atyoag22vQfLinJ73PQR8Nj//QOAXlecPzWP9FLB5fh9/BL4uaeb8nB2AM9oHK2kXSSMljbzoj+f08ZaCIAiCIAiCacGECW78NjlyRNLvSPPwZYAtJS3T9rSvAB/Lt11I0Ub98r5Jprf9mqQLgFeBzYANJO2dHx4ELAo8BfxW0lDgXWCpisQI24/n++OAYyQdCVxh+9a8fwuSgQJpon8aMLVeiLWA4/OYH5L0z8o4brL9CvCKpJdIXqLWeJaraJyfX3+LpDklzQ3MQTJ6PgYYmLny/BtsvwQg6UFgMdv/knQjsL6kvwEz2x7XPljbJwMnAzzw6NPv32ytIAiCIAiCoFtWBR61/Q8ASX8ENgQerDxnQ+APOW3jrpzjvYDtp/sSfd8YKpkJ+Sbgm7Yfrj4oaRjwLLA8yZs0vvLwa607tv8uaSXgq8Dhkq61fQjJAzK/pK3zUxfMhsHbbeN4APhcH2NUP+N/s+29vFm5X/1ftRsMBg4lGTobSxoCDO9D992K1qnA/iRvzCTelCAIgiAIgmBgMy1zSCosBPyrsv0ksNoUPGchoE9D5f0U+lXlGmB3SQKQtELePxfwtO0JwLZAx8R5SQuSwq7OAY4BVsw5H4NtL2R7iO0hwOEkL8srJI9Gi/OANSR9raL5ZUmfAm4Bts77liJ5enoZVFPA5vn1awEvZW/JXMC/8+PbT4mI7buBRUjhZudP5RiCIAiCIAiC/wGqqQD5tkv7Uzq8rN2CmpLn9OL95lFpcShwLDA2GytPAOsDvwcukbQpcBMVL0obnyLltUwgeUu+Q/Km/LnteZcAf7R9qKTbJd0P/NX2PpLWB46VdGzWGAv8II/hREnjgHeA7W2/mW2qKeVFSXcAcwI75n1HkUK/9gJunAqtC4Ghtl+cmgEEQRAEQRAE0573oo9KNRWgD54kLX63WJiUcjG1z+nF+8pQsT2ssrlrh8cfoXeux355/3AqoVK2ryF5ZaqM7KA3lpQwhO2t2h57iJSU34ntO2idCZxZ2R7S12PAJbb3a3v9nfTOuflZH7rrtx16LeDXBEEQBEEQBEF33AN8TNLipAifLUgRO1UuA76f81dWI0UF9Rn2Be8zQyWYcnIC/ghgjO0bpvV4giAIgiAIgqlnIHSmt/2OpO+TFvpnBE63/YCk3fLjJwJXkfK/HwVeJ1Wc7ZcwVKYzbK9dSOe/9PbABEEQBEEQBNMZEwZGMj22ryIZI9V9J1bum6lsyP5+TaYPgiAIgiAIgmA6RgOkpFkwfRAfliAIgiAI/teZqgpITbPdgc80Pj8765CPTJP3HKFfwRRzxah3GtFdf8WZ2PWIF4rrnrTvPHzvmP8W1/3d3nOz/2lvTv6JXfCLnT7QiC7A9sOeLa555rD5GznHkM7z1vv9e/JPnErOPXyhxj5v/3lwRHHd+ZZZFYCdDv1Pce3TfjYfT/79/uK6Cy+1LNsd+Exx3bMO+QgA6245SW2T2lx//sqNneM9j3+1uO6xu8/Ot37Wbw5q1/zh0AUa0QX4yclvFNc8cpdZef3Wi4rrAsz2mU3Z8eDniuueftCH+fGJ5c/FUbvNyu0rrFRcF2DN++7lypmXLq77tbcf5o1zflFcd9Zt9ueBDT9fXBfgk3+ZmuKqQR3CUAmCIAiCIAiC6ZT3c3RU5KgEQRAEQRAEQTDgCI9KEARBEARBEEynTBgA5YmbIjwqQRAEQRAEQRAMOAakoaLEbZK+Utm3maSrGzjWcEkrT8HzviXpfkkPSHpQ0t6lxzIFY9i/bftdSaPzuC7PTRz7e/2ZkjZpdpRBEARBEATBe4UnuPHbtGJAGiq5IcxuwK8kDZI0GDiMqWwS00LSjHXGkw2mPYEv2v4ksCLw0lS8fqb+tqeC/du237A91PaywAt0eX6CIAiCIAiCYKAxIA0VANv3A5cDPwEOAs4BDpB0j6T7JG0IIGmIpFsljcq3NfL+tSXdJOk8YJykwZKulDQmeyA2bz+mpFclHZafc5ek+fND+wF7234qj2287VPyayZ6ZCTNK+mJfH97SRdJuhy4tsP2YEmnd3g/20v6k6SrJT0i6ai8/whg1uxBObfDKbsTWCg/d2ge/1hJf5b0wQ7vdSVJN0u6V9I1kpqrRxkEQRAEQRA0gu3Gb9OKAWuoZA4GtgK+AgwCbrS9CrAOcHT2tDwHrGd7RWBz4LjK61cFDrC9DPBl4Cnby2cPRKcwssHAXbaXB24Bds77lwXu7WL8qwPb2f58h+0D+ng/AEPze/kUsLmkRWzvS48HZevqQbLH6AvAZXnXH4Cf2F4OGEcy9KrPnxk4HtjE9krA6SSPVRAEQRAEQRAMCAa0oWL7NeAC4GxgPWBfSaOB4STDZVFgZuAUSeOAi4BlKhIjbD+e748D1pV0pKTP2O4UuvUWcEW+fy8wpOZbuM72C31sf7GP9wNwg+2XbI8HHgQW60N/1vz6/wPmAa6TNBcwt+2b83POAj7b9rqlScbXdfn1PwUW7nQASbtIGilp5NV/OmXK3nUQBEEQBEHwnuAJExq/TSumh/LEE/JNwDdtP1x9UNIw4FlgeZLhNb7y8GutO7b/Lmkl4KvA4ZKutX1I27Hedo9/6116zs8DwEpAp1ak79Bj8A1qe+y1frb7ej+rAdW259VxtPOG7aHZOLmClKNyVh/P7XUY4AHbq0/uibZPBk4GuGLUO+/f+ndBEARBEATBgGJAe1TauAbYXZIAJK2Q988FPG17ArAt0DFxXtKCwOu2zwGOISXETymHA0dJ+kjW+oCkPfJjT5CMGICpqajV1/vpj7dz2FYvsndoD2Bv4HXgRUmfyQ9vC9zc9pKHgfkkrZ6PPbOkT07F2IMgCIIgCIIBwIQJbvw2rZgePCotDgWOBcbmyf0TwPrA74FLJG0K3MSkXowWnyLlgUwA3ga+M6UHtn1VTqy/Ph/bpLwOSEbPhZK2pbPHZWrfT3+cnJ8/qj1PxfZ9ksYAWwDbASdKmg34B7BD23PfymWKj8vemJnyWB6YivEHQRAEQRAEQWMMeEPF9rDK5q4dHn8EWK6ya7+8fzgp96P1vGtIXoz2169duT975f7FwMWV7TOAMzq8/qG24/807z8TOLPyvPbtN/p4P+3PW79y/yekKmiTjDdvb1DZ/HQH7e0r90czae5KEARBEARBMB0xLatyNc30FPoVBEEQBEEQBMH/CAPeoxIEQRAEQRAEQWemZef4pgmPShAEQRAEQRAEA47wqARBEARBEATBdMr72aOi93MCTlCc+LAEQRAEQfC/jqb1AKp8Y49HG5+f/em4JafJew6PSjDFXDXq7UZ0v7rizGy+9z+L615wzGLsesQLxXVP2nce9j1l/OSf2AVH7NzeM7Qc2w97trjmmcPm5ztH/7e4LsAJ+8zNVvs+WVz3vCMWZuv9/l1c99zDF+Kfjz48+SdOJYstuTQA2xzwVHHtcw5bkMcfe7S47uJLLMm3D3u+uO6pB8wLwGY/eqK49oW/HNLY56KJ78gJ+8zNnse/WlwX4NjdZ5/8k7qkqXPxwrjbiusCzPOptdjp0P8U1z3tZ/M19vv0ws8nKShahHl+ehJvnPOL4rqzbrM/V868dHHdr739MOMv+11xXYBBX/9eI7rBpIShEgRBEARBEATTKe/n0K9Ipg+CIAiCIAiCYMARHpUgCGG5GxkAACAASURBVIIgCIIgmE4Jj0oQBEEQBEEQBMF7SL+GihK3SfpKZd9mkq4uPRBJ60u6T9IYSQ9K2jXv303Stwof60xJm9TU2FPSbJXtJySNy+O/VtJH6o90iseykaRlKtuHSFr3vTp+EARBEARBMG2w3fhtWtGvoeI0st2AX0kaJGkwcBjQVbkDSTP2sX9m4GRgA9vLAysAw/MYTrT9h26O1zB7ArO17Vsnj38ksH/1gWz0NeXB2giYaKjYPtD29Q0dKwiCIAiCIAgaZ7ITZ9v3A5cDPwEOAs4BDpB0T/aAbAggaYikWyWNyrc18v61Jd0k6TxgnKTBkq7Mnof7JW0OzEHKl/m/fMw3bT+cXz9M0t75/nBJR0oaIenvkj6T988o6Zjs0Rgrafe8fyVJN0u6V9I1khbo631Kml3SDXns4yrva5LxStoDWBC4SdJNHeRuAZbM5+Rvkn4PjAIWkXR01hmX33vrHN0s6cL8vo6QtHV+n+MkLZGft1ge49j8d9F8nr8OHC1ptKQlqh4jSV/I/6dxkk6X9IG8/wlJB1fe78cn91kIgiAIgiAIBhYTJkxo/DatmNJk+oNJE+23gCuAG23vKGluYISk64HngPVsj5f0MeB8YOX8+lWBZW0/LumbwFO2vwYgaS7bL0m6DPinpBvyMc633enMzGR7VUlfJRlO6wK7AIsDK9h+R9I82UtzPLCh7f9ko+AwYMc+3uN4YGPbL0uaF7grj+nLfYx3L5IHpVOzgPWBcfn+0sAOtr+b3/tQYHlgXuAeSbfk5y0PfAJ4AfgHcGp+nz8Adid5cH4L/MH2WZJ2BI6zvVEe5xW2L85jJP8dBJwJfMH23yX9AfgOcGw+5vO2V5T0XWBv4Nt9nJsgCIIgCIIgeE+ZolAk268BFwBnA+sB+0oaTQrPGgQsCswMnCJpHHARlVAkYITtx/P9ccC62TPyGdsv5WN8G/gCMII0aT69j+H8Kf+9FxiS768LnGj7naz1AslAWBa4Lo/1p8DC/bxNAb+QNBa4HlgImL+v8fbBTflYcwKH533/tH1Xvr8WyQB71/azwM3AKvmxe2w/bftN4DHg2rx/XOV9rg6cl++fnfX6Y2ngcdt/z9tnAZ+tPN7pXPZC0i6SRkoa+dc/nTqZwwVBEARBEATvJZ7gxm/TiqkpTzwh3wR8sxWa1ULSMOBZkmdgBpKHosVrrTt5ZX8l4KvA4ZKutX1IfmwcKTzsbOBxYPsO43gz/323Mn4B7WdRwAO2V5/C97c1MB+wku23JT0BDOpvvB3o5WHJHqfXKo+rn+O/Wbk/obI9gb7/T5P75PR3vOoxq+ey9wHsk0n5Q1w16u33b/27IAiCIAiCYEDRTXL3NcDuyvFFklbI++cCns7hWtsCfSXOLwi8bvsc4BhgxZwfsnblaUOBf07FmK4FdpM0Uz7GPMDDwHySVs/7Zpb0yX405gKey0bKOsBifY03P/8VUm7N1HALsHnOqZmP5N0YMRWvvwPYIt/fGrhtMmN5CBgiacm8vS3JixMEQRAEQRC8D7AnNH6bVnTT8PFQUo7D2GysPEHKyfg9cImkTYGb6O1JqPIpUuL3BOBtUs6EgB9LOgl4I792+6kY06nAUnlMbwOn2P5tTig/TtJcpPd6LPBAfs1Jklq5Gv8CNgAulzQSGE2a5Pc1Xkhehr9Ketr2OlM4zj+TwrfGkLwhP7b9zFQksu8BnC5pH+A/wA55/x9JYXd7ABPLLud8oR2Ai7IRdw9w4hQeKwiCIAiCIAimGVNsqNgeVtnctcPjjwDLVXbtl/cPJ5caztvXkLwy7Xx1cse1vXbl/vPkvIqcm7JXvlVfO5reORmt/dt3OhbJiGjniU7jtX08KVm/tT2kw3OeIOXJtLYN7JNv1ecNp/c5WrvTY1nv8x2Oczu9c4K2rzx2A6ncc/trhlTujwTWbn9OEARBEARBMLCJzvRBEARBEARBEATvId2EfgVBEARBEARBMAAIj0oQBEEQBEEQBMF7SHhUgiAIgiAIgmA6ZcI0rMrVNEr53UEwRcSHJQiCIAiC/3Um16fuPeVL241ufH52zVlDp8l7Do9KMMVces+7jehutMqMbLXvk8V1zztiYb5z9H+L656wz9wceNZbxXUBDtlulkZ0AbYf9mxxzTOHzc+OBz9XXBfg9IM+zDYHPFVc95zDFmT3Y18urnv8nnPy9EOji+su8PGhAI19R/7x2GPFdT+6xBJs9qMniute+MshAHx249v6f2IX3PLntdjuwGeK6551yEf4/q9eKq77273mYqdD/1NcF+C0n83XiC7Q2Ll4/daLiusCzPaZTRu5xp1+0If59mHPT/6JU8mpB8zLzZ8YWlwX4HN/G80DG05SeLQ2n/zLjYy/7HfFdQd9/XtcOfPSxXUBvvb2w5N/0nvI+zlHJQyVIAiCIAiCIJhO8YT3b+hXJNMHQRAEQRAEQTDgCI9KEARBEARBEEynvJ9Dv8KjEgRBEARBEATBgKOIoaLEbZK+Utm3maSrS+hXNDeUdGllez9Jj1a2N5B0maQFJV2c9w2V9NXKc4ZJ2rsP/Y9I+qOkxyQ9KOkqSUvVHPOZkl6XNEdl328kWdK8dbSDIAiCIAiC/23sCY3fphVFDBWnGse7Ab+SNEjSYOAw4Hvd6EmasY+H7gBWr2yvDrws6cN5ew3gdttP2d4k7xsKfJXJIEnAn4HhtpewvQywPzB/t+OubD8KbJj3zQCsA/x7SnXfK/o570EQBEEQBEHwnlIs9Mv2/cDlwE+Ag4BzgAMk3SPpPkmtifoQSbdKGpVva+T9a0u6SdJ5wDhJgyVdKWmMpPslbW77P8BLkpbMh10IuIRkoJD/3pGPcb+kWYBDgM0ljZa0eX7eMpKGS/qHpD3yvnWAt22fWHlPo23fmsd2RWu/pN9K2j7ff0LSgZJuAzZt384vOR9oHXtt4HbgnYrepZLulfSApF0q+1+VdFg+B3dJmj/v30DS3fm8Xl/ZP5+k6/J5PUnSP1teG0nbSBqRz8NJLaMkH+MQSXfT2wgMgiAIgiAIBjgTJrjx27SidI7KwcBWwFeAQcCNtlchGQFHZ0/Lc8B6tlckTd6Pq7x+VeCA7M34MvCU7eVtLwu0wsjuANaQtDTwCHBX3p4JWA64pyVm+y3gQOAC20NtX5Af+jjwpXy8gyTNDCwL3Nvl+x5vey3bf+xj+xFgPkkfBLYE/tj2+h1trwSsDOwh6UN5/2DgLtvLA7cAO+f9twGftr1C1vpx3n8Q6ZyvSPIOLQog6ROkc72m7aHAu8DWlWPcb3s12+WbEwRBEARBEARBFxSt+mX7NUkXAK8CmwEbVPJBBpEmzk8Bv5XUmjBXc0BG2H483x8HHCPpSOAK27fm/beTPCczAncCI0jGyArAw7bHpyiufrnS9pvAm5KeYyrCu/rggslsA/wJ2AJYDdi17bE9JG2c7y8CfAz4P+AtoOXJuRdYL99fGLhA0gLALEDrnK0FbAxg+2pJL+b9XwBWAu7J52ZWksEI6X9wSV9vLHt4dgH4zn4n8MWNd+7rqUEQBEEQBMF7zPu5j0oT5Ykn5JuAb9ru1b5T0jDgWWB5kkdnfOXh11p3bP9d0kqk/JLDJV1r+xCSR2V3kqFyiu1XJA2iJ6RqSnizcv9d0nl4ANik89N5h97ep0Ftj782mW1Ino9RwFm2J7SMKUlrA+sCq9t+XdLwiv7bOf+nOk6A44Ff2b4sv35Y3t+XhaZ83P06PDbedp8t522fDJwMcOk9775/698FQRAEQRAEA4omyxNfA+yek9SRtELePxfwtFMJgW1JBsckSFoQeN32OcAxwIr5oQeBBYHPAPflfaNJyfx3dJB6BZijw/52bgQ+IGmiy0DSKpI+B/yTlNfyAUlzkTwUU4Xt/wccAPy+7aG5gBezkfJx4NNTIDcXPcn421X230byZCHpi8AH8/4bgE1aRQckzSNpsal9D0EQBEEQBMHAwhPc+G1a0aShcigwMzBW0v15G9JEfTtJd5HCvjp5HwA+BYyQNJo0wf85TKwwdjfwvO2383PvBD5KZ0PlJpKRUU2mn4SsuzGwnlJ54gdInoqnbP8LuBAYC5xLj4E0Vdg+yfZjbbuvBmaSNJZ0ju6aAqlhwEWSbgWer+w/GPiipFGkPKGngVdsPwj8FLg2H+c6YIFu3kMQBEEQBEEQvBcUD/2yPayy2Z6Lge1HSEnvLfbL+4cDwyvPu4bklel0jK+1bZ8JnFnZfoKUHI/tF4BV+hnvspX7T5E9Eh2e92N6ktar+4dMZnv7PvSqz/tKH8+ZvXL/YuDifP8vwF86vOQl4Eu235G0OrBOzsUhFxKYJHemeowgCIIgCIJg+mJa9jlpmiZyVIJpx6LAhblXy1v0VAkLgiAIgiAIgumKMFTeR2Rv1QqTfWIQBEEQBEHwvmBa5pA0TZM5KkEQBEEQBEEQBF0RHpUgCIIgCIIgmE55P/dRwXbc4lb8BuwyPelOj2OOcxHnIs7F+0t3ehxznIs4F3Eu4tbkLUK/gqbYZTrTbVJ7etNtUnt6021Se3rTbVI7dJvXnt50m9Se3nSb1J7edJvUbnLMQZeEoRIEQRAEQRAEwYAjDJUgCIIgCIIgCAYcYagETXHydKbbpPb0ptuk9vSm26T29KbbpHboNq89vek2qT296TapPb3pNqnd5JiDLlFOIAqCIAiCIAiCIBgwhEclCIIgCIIgCIIBRxgqQRAEQRAEQRAMOMJQCYIgCIIgmAokzSDp/mk9jqlF0jGSPjmtxzGtyf+/Nab1OILJE53pgwCQNNj2a9N6HJND0qa2L5rcvi50BSxs+1+1BjidI+kG21+Y3L6p0Dse6DMR0PYe3ei2HeMbwJHAhwHlm23PWVe7aSTNCixq++FpPZYpYXob73uBpA8Ci9geO63H0heS1gRG235N0jbAisBvbP+zW03bEySNkbSo7f9XbLDN8xBwsqSZgDOA822/VFdU0vzAL4AFbX9F0jLA6rZPK6C9FLAPsBiVeavtz3ermf9/vwRWrzu+oFkimT4oQsMXqdmAH5EmCDtL+hiwtO0rCmivAZwKzG57UUnLA7va/m5N3TWBYfRcWFuTx4/W1B1le8XJ7etS+17bK9XV6aD7AeCbwBB6/8gcUlN3KeAEYH7by0paDvi67Z93oTUImA24CVib9P8CmBP4q+1PdDnG7fp73PZZ3ei2HeNRYAPbf6ur1Yd+sfPcprsBcAwwi+3FJQ0FDrH99S71vtHf47b/1I1uRb/oeNu0j+uw+yVgpO2/1NReiEkneLfU1BwOfD1rjgb+A9xse68u9V6hx6BvffdMIaNb0lhgeWA54GzgNOAbtj9XU/dGYBVgBDBxoavQZ6Kx372svzSwA7AlcDtwiu2bauj9lWT4HGB7+WwI3Wf7UwXGOgY4EbgXeLe13/a9NXUPBsYCf3JMhgcs4VEJSnEm+SKVt/8OXED6QajLGaQLVGvl40ngIqDEBfvXwJeAywBsj5H02QK6pwE/pO3C2i2SvgJ8FViobVIzJ/BOXf3MXZJWsX1PIb0WfyFNuu4F3iyoewpple0kANtjJZ0HdDOB3hXYE1iQNM7WZOll4HfdDrDdEGnIc/dsU0ZKpuR5rjIMWBUYnnVHSxpSQ2+D/PfDwBrAjXl7nXyMWoYK5cdbZRDwcdJ1DZJh/wCwk6R1bO/ZjaikI4HNgQfpuQ4ZqGWoAHPZflnSt4EzbB+UjYGusD1HzfFMjndsW9KGJE/KaZNbRJhCDi6g0ReN/e5JmpH0efs48DwwBthL0q62t+hSdl7bF0raD8D2O5Jq//Zl3rF9QiGtKnsBg4F3Jb3BdOSN/l8iDJWgFE1epJawvbmkLbP2GzlUqQi2/9UmV2LcL9n+awGdFk8BI0mrmNVVpFdIBlEJ1gF2k/QEaXWwddFerqbuwra/XHdwHZjN9oi2/11XRpvt3wC/kbS77eOLjK6CpNVJxuvsQDHPXWakpAuAS6kYgnU9CBWKnec23rH9Uqmvsu0dACRdASxj++m8vQA1jM0KRcfbxpLA522/AyDpBOBaYD1gXA3djUir8CUXCABmyud1M3oWp4ogaS3gY7bPkDQvMIftx2vKvpJ/m7YBPpsn6jPXHavtm+tq9EMjv3uSfkX6HbkB+IXtEfmhIyXVCWl8TdKHyJ4xSZ8mLVCV4HJJ3wX+TO9r3At1RN8DAzkoQBgqQSmavEi9lWPDW9pLUG5l/l85/MuSZgH2AEqsTt8k6WjSKm71wjqqGzHbY4Axks6z/XaB8XXiKw3p3iHpU7brTLg68Xz+LLQ+F5sAT9cRtH18/jwMoXeozB/q6ALH0oznDpJX7XXgi5V9pr4HoUXx85y5X9JWwIw5rGUP4I4CukNaRkrmWWCpArpNjRdgIdLKbuuaOZgURvuupDrXun+QJuSlDZWDgWuA22zfI+mjwCN1RSUdBKwMLE3yKMwCnAOsWVN6c2ArYCfbz0haFDi6pmbrd+544BOksc4IvFZoRb6p3737gZ/afr3DY6vW0N2LdH1bQtLtwHzAJjX0qrS8X/tU9hmoG0otYGtgcduHSloEWKBivAUDgMhRCYogaUXSBXtZ0oVwPmCTEgmWkr5IWrVbhrTKuCawQ5142or2vMBvgHVJHoRrgR/Y/r+aup3G5jrJf1m3kdyXin51NXM+Uu5OrdVMSQ+SVowfJ/3QFvHU5MnRyaQwnxez/tZ1EmQlnQ0sQYq7nxgq45pJ75Lutr2apPtsr5D3jbG9fB3d94I+zvM2tp+oqTsb6XvdMrCuAQ6tu/ov6bfAx4DzSZOZLYBHbe9eU7d9vNeSclRqTx4l7QT8lBRWJuCzpJy/84Fhtvfp+9X96l5Cys24gd4LJl1/nrM3Yg/bv+5Wox/t0cAKwKjK92RsnWtFHu81ttctNMyq9kjS5+sikoH1LdL1c/8C2uuRPhPV373tbQ8voF08bynrzkQyMgU83OCiWhGy53ICyZv5CaXCENfaXmUaDy2oEIZKUIwmL1LZW/PprH2X7edLaU9PSHqIDrkvdQ2rrD1xNdP2UpIWBC6yXWs1U9JinfbXMSiy7uK2H5c0GJjB9iutfTU0/0YKGyp6YZR0MfAr4Lekz/EewMo14sGr2guTFgnWJE3MbyMZ20/W1W47zsTzXEivkQp2WWdj0mQf4Bbbfy6guZPbioNIOsL2vnW1s9YCpBVtASNsP1VAs2MeRnvuVBe6N9lep45GH7ojbK+qXCAkf+buLLCocRmwrQtUt2rTHWl75aoxJekO20XK3jbxuyfpCJJx1StvyQO0iEU+xszAd+j5Tg8HTqo7x6h8zqa7BaT/JSL0KyhCh4vVUpJeAsbZfq6mdqs87JUd9tVC0uLA7kwa6lOraoukuYCD6Lmw3kxafa37Q1k696XKxuTVTADbT0mqHcNr+585J+MzedetOZStLpcAK7p3cvrFQJ3KZfcDH6FMaFOV3Uieu4VISbHXAt8rpH0GcB6wad7eJu9br4S4pLlJK8VDSLkJQJHSyvvRkzze375uGAW8Yvt6SbNJmqOAgbWJpPG2zwWQ9DtSEnwpZiBVz5oJWFLSknVXuW2flUNaW6FvpRaQ7sieqwvoXe2qq9DWChdKOgmYW9LOwI6kYg51GQ+Mk3Qdvcdb9zP8ej6/oyUdRbpuDK6pCUw0tm+0fWXenlvSRrYvrSm9MWXzljbo57FSIagnkEIYf5+3t837vl1T9+3scWuF181H8rAEA4gwVIJS7ESqTtIKeVobuItksBxi++ypFVRPydh5s0u2WjJ2wdojTlxKSnK+nLIXqNNJk97N8va2pMljv6tPU0DR3Jc23rJtSa2Ldqkf3B8AO9Pzg3WOpJPdZdK6pI8DnwTmajOQ56T+xHFe4EFJI+h9fuuWG5XtrWtq9MV8ts+obJ8pqasqUX1wFem7PI4C3xE1XMEuT3B3AeYhhfEtRCptWndh4xvAZZImkPK5XrBdxNhUT3WuB+g5x7Wrc0laGzgLeIJ0/VxE0nYFwnxaHoNqiXEDtUJbbR+TQ55eJnnnD7R9XR3NzJVUFroKsi3JwPw+ydO9CKliWwkOqnoCbf83e73rGipF85aci1g0zCptXo4blUoW1+U4UoL+hyUdRsqp+WkB3aAgYagEpZgAfML2s0Crr8oJwGqkH9upNlRoqGRsG+Ntd+phUJclbFd/sA7O8dd1WS3/Xbmyr/YEIdPUauZOwGotz0eelN1JClfqhqWB9YG56b2a9wrJIKrDsJqv74s7JD1OWoG+xPZ/C2o/r9TE7vy8vSVQOxSwwiB32R+jD5quYPc9UgjV3QC2H5H04W7FJM1T2fw2aaJ4O3CIpHlcs/JQpqnqXL8EvujcoFKpJ8751PM60kTYF4CkH5LCTUsYJxOpG+rWj+4/lRLeF7BdulTxDB32lZizvU7yABXLW4JGowgglQ9ewvZj+VgfpUB1TtvnSrqXtIghYCM3W+o96ILIUQmKIGmcK42dlOJDxjk1iJsY/9mldiMlY7P2VqTE22sp6KGQdCewj+3b8vaawDG2B3QX3LyaOTFZuMSEQdI40orY+Lw9CLjHNRuBSVrd9p11x9dBdzFSQuz1SgnUM5bIy5C0Kik2fCNSfPgfbZ9TQHdRUu7L6iSj9Q5SjkqtHKCK/g+BV0n9G4qVBpU0cxPJtmorXJBz50Z1m+eQDcyJzQfpWTCBQoUslJrlbWr71bpabbqTJKJ32teF7oGd9rt+E9eDSF7oF4A/Ahe3Fr9q6n4MOJyUmD7R61r3f6dmm4CeDvyXtChnUojyB21vX1O3qbylS0hRBC2dbYHlbdeNIkDSF0gRCf8gff8Wo0BBnbZFiBavNHFdCronDJWgCJJ+DyxK74ZlT5LKCV5RdwVO0rJM+iNTt2Qskg4nXVAfoxJy4frVuYaSLthzkS6sL5AqttRyVzc1Qajof4S0Gm2SMfFMAc29SOUlW2EMGwFn2j62pu4gkrfmk/T+XOxYQ3Ni2JDtJfIE58QS+VCVY8xLSqzf2vaMpXSbQtL3gMNIk6bWD0btCXqDk8ejSGP9Fmly913gQdtF+32URA1U58q6p5P+Zy2P9tbATHXDdST9qLI5iOTh/Fud716b/nKkULhvAk+6ZsUuSbeRVvt/TfLC7kCa/xxUU/dekjd7uAtVKatoDwZ+Ru+KlD93+YaxRZA02vbQye2rof8Beor1PFTC+6jUM2wRUjVDkbz0TwPPATu7Zuf7oAwR+hWU4nukGO618vYIkjv8NVIjwa7Jq2xrkyY0V5Hiw28DahsqpMTCj9p+q4DWRGyPBpaXNGfefrmQdPVHauIEoYSwUpfpA0kdvQUcn/OLTq+ja/tXkoaTPhsirYTdV3e8pMnXQ6T+JIeQJmF1z0XRsKEW+XOwMcmjsgTJaKvTswBJP7Z9lKTj6TEgJlIgUbjFXsCSLl9p7wx6Jo/rkCePBXT3JRmw40jho1fZ7jqEUdLnbd+oPqobuUxjzcvyrTTfIX2m9yCd21voSUjuGtu/rG5LOoay438OeIYUwlj7+wfMavsGScqexmGSbiV9/urQWBPQ/NtZpKJclaYWCIA3JK3VFkXwRh3Bfr57S0gq8d27Gviz7Wvy8b4IfBm4kPQ9Wa2f1wbvEWGoBEXISdiPkb7Ym5F6LVxSSH4T0mrjfbZ3yPkvpxbSHkNaRalVmayFpG1sn5O9CNX9QJq019FveIKwD7CCc6ljpdKYd5AKA0w1kua0/XJ2rz+Rb63HSsT2L2l7U0kbOlU3Oo/Ui6MOb9p+q/X/ymFDJdzOY0i5DYcUDFdrGWUjC+n1xQOkuPbSNDV5HGb7QHJ+laQZJZ3r7osZfI5kvHeqblSkqlGDORRvkrx3ta47U8Bs1Gy+ByDpOyRPynykCn47236wri4wXtIMwCOSvg/8mzIGUGNNQHM+0d5MWpGybj5iUwsE3wHOyrkqrSiCjmFmU0HT372Vbe82UdC+VtIvbO+VPTjBACAMlaAW+WK6BT0JvBeQXOolky3fsD1B0jt5Zfo5CvwoZuYHHpJ0D2WqPLUqZXUq69tEnGWRCULmSVJCc4tXgH/V0DuP5PG5l97vvRXrX3fcrTji/+bQwGdIP+p1uFnS/sCsOV/nu6SKcHX5aDbmi1RSA7DdGtfr7tCPpNRxSEmro5WamBYLS6K5yeOikvazfbhS6diLgK49eK3woLrhUp2QdKHtzXIeVyevWLd5NY3oVvSrujOSDIsS4aeLAXtmj3RJ9iRdK/cADiVN0LueREu6inRt2J3UBPRNUpGCa7J+CS4iVas7lQKJ4xUaWSBojyIgLW5sDnTd9LkSmneI2/pjKbUWqMsLkn5CyoeCNN4XlUoWR5niAULkqAS1UCrVeSuwk+1H875/lEgwrRzj98D+JIPoR6TE3tElJg6SPtdpv+2ba+quafv2ye3rQrfjBMH2b2totrw/Q4FPAX/Jx9iQ1Hhut75eOy3JoWqXkMZ8JjA78DPbJ9XQnIEUNvRFkkF1DXCqa14oJa1OKoM9u+1FlfrK7Gr7u3V0s/Yo2ytObl8N/aaSb1cheYXmJk3u5gKOtH13TV0B55JCv9YB/uoaXdTbvaPt1PGSSlrA9tMq3BS1Kd2KflX3HeBZ27VLS2fttUjFLM5Q6msxe/sktYb24BI5HpI2A35OykM8qonka0n32q5Vna0P3dtJPa0uJnkr/g0cYXvpLvXmJIUXLkT67bg+b+8NjLG9YYExd7rG1T4/SvmCB9ETlnwbcDDwErBoa04TTFvCUAlqodSUagtSXf2rSSsTp9ousdrR6XhDgDltd71K00FzfmCVvDnCNRtUZs1GJo9NTBByDlCfuGbZTXVoztlpXwkkLVZ3EtYEku4mhTBeVkm6vd/2sjU0W/1INiN5MlvMCSxju1YOTNuxmmga2H6MmYDNnRsqdvH66ndrZuAkUhnh06D7Sn5t349ds+5E6n4/rFvOrQAAIABJREFU8jGOtP2Tye0bKLpZp9rE9ZYS1+R8rlcmlWpeStKCpHLFa9bULb5QkL2jB5JyGs6msgJfN8Q36w8jRQ/8mbLV9jotEBxl+64u9f5CSka/k1Tm94PALKTKg7U8Y+rpmXUUKTS5xZykqpqfrKMfTB+EoRIUIV+0NyKFgH2etNL0Z9vX1tSdkVSS8fm8PQuwPfBD25+oNWgmrowdDQwnrah8hnQBvLhLvdVJRtuepBjgFnMCG7t306quaGKC0ATqadh5E6kYQrVh51/r/P/yeV6I9P6fU6oStC/wGduL1NBdn/TjvRgpNFakFKw5+33h5HV7lczN+8bU+Tzkz8FQUshNtRrcK8BNtl+sM+bKcdamrWkgsJ27bBrYtgJ7GXAdBVZgc2haX7hAbD+qWWq9H91OCxslygg3pdvexHVjoOsmrhXd0cAKpHLSxapoNbRQMAvpmrMVaaGgaqiUMF47eZFcKlohfw/tmqXXVWlNkH+vnyd5I0qUdN+QNK/4Or1zMV8hlXevlQ+UPXY/ZtLKkSX6kgWFiByVoAjZnX4ucK5S8vSmpIt414aKpC1Iq5evSXqE1IzvbOAeUoWnEhxA6vHxXD7mfCTXdVeGCmklaXbSd6uap/Iy6YeyFh0mCOeqRpf3Nu2VSeejNUkHasWzN9KwU9LRpNyX0cBPJF1Bihf/BalJZR2OJVWvG1c33KuNf0laA3Ce4OxBzQplTqWux0j6M/Ca7Xdh4mShZCJo6aaBZ9OzAvtt0krpLKRma12vwNpeJ4fubWr7gsm+oMvDlBRTSh7/LvBRSdUFhzlI3qC6ukt00C2R7F26iWuLt2xbkrNuyZyuf6l3da6u8z4kfZlUoOAyYEXbxYtNNBiVsDIpoX6OvP0SsKO7L8U70btq+11Jj5cwUrLeX4C/qKGeWaQ5ywWk35PdSHlL/2ngOEEdbMctbgPyRmoetWS+vyLJ/b1x4WOMa9ueoX1fl7qLNXROxgKDK9uDgbGFtB8mrVwtTjJWFivxPoDdC5+DB0nd0iGFGbxBimkvoX0TMEMD/7d5ST+Kz5LCOc4h9WopoX0XKaSltT07cEfBsU/y+arzmat+v0h5Vi8CcxQc7y2l/38V7VGF9eYiFYA4v/qdq/vZaEq3+j9sfQfz9qBC1829SYtT/yAtyNwJ7FFA92KSp3sUySjem7Qi363ercAnm/qcVY6zLCm081utWwHNsSTPc2t7rZrf53dJi0+v5Ns7le2XC52HhUkhcM/la+glwMIFdO9tnZPKvpub/r/Gbepu4VEJBjJvOSez2R6VV2r+PLkXTSVXS7qG9IMOqerHVQV0X88r/6VdyqL3SuC7lCktCfAf2030cpggaW7b/wWQ9EFgS9vd9nN4w7nLve0XJT1s+5FCY/0xcJWkm+kdF163rPTztHkBlUpL711HNzPIlY7mtl+VNFsB3RYjJZ1G76aBdRqhNbYCm7lO0t6kldKJidPuMrZfPQUsRG8PRSsssOuwJNsvkRJ3t8zH+jDpejG7pNlt/786upJ+A7zQOr+S5pC0mmsWLCCtyN+dvXmQwnNOq6mJ7WOUqu29TGrud6Dt67rVUyrEsi9ptfw3pHDDJ0me/u/VGOdnsv7ZtrdtO+Yk+7pBzfUPe8X2ra0N27dJ6vr75/emae0ZpCqSrWqG2+R969XUbV2Lnpb0NeApklEUDCAiRyUYsEh6kt71//eqbtedPFaO801gTdLE45YSxpCka0kTpb2puJRdPzm2kS7vWfsLpAlTe3fsWrXq1bljcdex/pL+S2pcBz15RRPzJdx9aenW/+1V0opx0ZjzDsf6f7YXLaBzO8lrNSpvrwT81vbqdbWz3gdIk7pWZZxbgN+7y87Qkt4lGRAtA3tWUinTUvlARWP71UflrIpw7eINkjYgXdsWJK0aL0bq9F4rWVjSfaTQpFYo1QzASBeoCJeLF0z8TLhME9dOx+n6eyLpxyTPzEG2zys7sklzgHLY5TjbyxTQHkdP/7DllfuH2e7UU2RqdH9Nyh08n2SAb07yal4CtYpONGm0TZLP1+l3pQvd9UnesUVIYYtzAgc3tGAXdEkYKsGARQ1Xo2oS5dKJ1URQSTfb7lgOeSq1G5kgSDoH+DipwV9rkm7btfI+8gr08pXJ0owkV3tXkzD1UVK6hWuUlpY00vbK3b5+Ko/1L9dI/K/orEKqtvdU3rUAqXpWHa9HVX8wMN5tOTBuIC5/IKPU6+WclmewsPYYUhGS622vIGkdktdxl5q6nRYJuk5Oz/mHfdKt12oyx6z1PZG0EMkI/BCpL0l1AaKrRRhJ+5FK5reMbEjX47dIRQX263a8lWOMsL2qpHtJZbZfAe4vYLw2UnSig9E2E+k6X8Jou55Ugr4V+bAlsIMbqBwZDDwi9CsYsLQMEZXpYt6L7OruZKUXWdWlsEs5T0bntf3XvOLVWj3/uqQZCk1Kl3eu3lKYa4ALJZ1IOue7kUpZd0XLEMmrYVfZLtmY63pJX3TNanUt+pnYiUIhe7bvUSrjuXTWfMhlywffAKxL8jRBmpxdS4r575oc+na6y3Qer+rOTOqS/dm8azhwUoFz8hFSGNwo4HTgmpbxXYC3bf+fpBny9/mmnKBel39I2gM4IW9/l5T/0S2t5q0iGcQt47hUE9dO1DrHtv8t6UrgMFKH84mLMHTZ2dz24cDhkg4vYZT0wUhJcwOnkM77q8CIuqIu24y5l9Em6eXWbrLRVugwOwK/JVXSNKkgRN3CKa3iOTuT8rmqBWRqawflCI9KMOBRqvg1mhST+teCk4OWfvGSo6VdypKGA9vbfqJt/5KkFbwSpVdPAX7dwMRxBlIFsC+QfsCuJYUw1Oq2nD1Aq5NCFs6wXauKVtZ8hVSg4K18q2W45jCk1sSuna7DkTocZ1lSLHs1H6puLHtLu9OqfImwi28DO5AmCGcA5+fcilpIOpXUR6XVkHJb4F3b3y6gLVIz0B1I/T4uBE6z/VhN3etJYZyHkwovPEeqRljXGPwwcBzJW2OS0bmny/SKKnbdVN9NNQUcYLtfT04/up8kGWlPkUraP93lEPvSX5PUfPg1SduQir78pkQ4YNtxhlCof1g2fr7FpJPzPWrqNmm0NYKkO0i/0/dSyf20fck0G1QwCWGoBAOePDlYl7SCsiop9+NM238vpF+si3dTqFKrvsNjtfpxVHT+BiwBPE7KUamdLNw0Sr0AtiRNHE3PhLdkcvaApq+kW9u1y2Fn/aZzYJYm/f+2JJXkPcV2f+Epk9PrFM9e5DuStZYnjffLpCpxnwaus/3jGpqDgfGk79zWpKpd5zQRSlWKktfNpsJ88zXtB6U8pB30x5LySJYjFZs4DfhGnRBf9W5cOgnd5pBU9O8gVQpsz8M7q88XTbn2Qkxa3r6rfkttuosDuzOpcdV1PmLWrb3gEjRPGCrBdEWO3T6HtOo9BtjXNeurN2GoSDqL9ANZrXT1y25dypIetb3k1D42lcfomDRcd3VQ0sdIK8XtK/6lvAnzkqrA7EnqTbIkcJy76C2TjeKtgcVtHyppEWAB27VDLiR9g5RbZOBW25fW1cy6jSTdVvQby4HJ+S7rkyb+i5A8FGuR+sJs0aXmKFIvlcfy9keBi+t+x3MI1XakhnanApfafjt7DB+xvUQd/bZjfRz4ke2da+oMIvU8aa8+WCJsZnpY4PmAuyz6MIX6o2yvKOlA4N+2T6t7XprKIanoN/J/k3QEsAWpfHzLO+G6xkTWHkMyAtuNq67zEbPuz0ml3EtU+gwaInJUggGPpA+RJqLbkmqo705qtDUUuAiY6sZYedLYYu627dqVroDlXEm6dSqjWydM4npJhwE/rYa+SToYuLGGbpWmVi3OAA4ixRevQ5qU1s7PUKqUtCPJC3Q2sKpTl/rZSAZLN83nfk/6Ifw8qUP9q6TmlKvUHOvvSQZUKxl0N0nr2e66RGqFN2xPkPRO9jA9R8FcgaZyYCT9itS35wbgFxVj8EhJD9eQ3ge4SdI/SONdjPSZq8uHSKvlvQz3fO7X70ZQ0nLAMaRqX5eSPrO/B1YjNdqsy9nAQ8CXgENIRnjXIZJtIVofbg/Zcs1KjKVzBlpGijrnJL4EjCQZhN3m7bySczS2AT6bDe+Zu9QCyueQdOBsSTsDV9C7umNd793GwNINGYbjbR/XgO4PgP0lvUnKKy2VoxoUJAyVYHrgTtIP7ka2n6zsH6mUoN0N1dXmm9u2u06yrDCDpA/afhEmJlXX+b79iLSK+6ikVvfu5Uk/tLVj7zNX0pNPMYhkAD5MWo2tw6y2b5CkPMkbJulWkvFSh01JOTW9Qgtsvy6p2xXj1fIK6X1Z60WlTvJ1+RywbMvIzB63cQV0oaGk2zZWoWfyuIKkEjkw95MM707Vw1adWjFJe5JCx24GPkZvw6rW5Cl7Tb5pu+NntkZ+1CmkHIo7SaFko0j9IrZ27hVUkyVtbyppQ9tnSTqPVNyiW+ao3D+lbbsEfyHlDFxPjc7xHfgVySN4HukzsQWpOMLDpMIIa3epuzmwFbCT7WckLQocXXu0mYZyz94ijfEAeoy3EoUQ/kEy0powVH6TwwOvpbdxVSsMznbpz2/QABH6FQx48gR3uvqgSvoWsB+pIzKkSfVhts/u+1VTpPtRegyHB2qsBE7JsVYEdrW9a02d20m9Ti4meX/+DRxhe+n6oyyLpLtJ1azuyQbLfMC1dZOGJf2JlMz7z7y9GOkcbFlDc03bt1fDW0om3VaOczbJazWa3iEdXSXfNhWDr1RFbA1Sie2xpMpAtwN3lsj1kHQusJ+7bMLYh2avGHlJ/wKGuGahiYpeq8TtLaSKX88AI0qFXZamqZwBSXfbXq1t3122P10yf6kkTeWeSXqMtCDzfO1B9ta9hLR41t6Hq1aSftY+nBRR8Ri9S+eXKCLzQdLCRtUYrJ1XE5QjPCrBgEXS5eQVn5Q60JtCsa+NVECx/QdJI0khRCKFjJSopvU4qTnl4rYvzyt4HymRQ9GO7VE5P6Eue5IajO1BCqdahxTr3xV9hHFAGbf9caRmmh/OoXabAD/tVqzyGZ4L+Juk1v9pVdJEug7HASuRVuNXBHBbVbhCrAwsU3CxoL+QJpO+M1ON7b0BsgdsZZLRsiNwiqT/un4/hwWAB/L/sNrxvs51aFAOCW1d4F4Flsu5UrVXjIGT80Tsp6Rw2dmBn3UrJqnf8JsCk9IrJH21gZyBCZI2o2fhqDrZ7/pzLenTpHC9TwCzADMCr9qeq1vNCpvQk3u2Qyv3rIDuA/T0finJZfnWBBsDH7X9VklRpcqDPyC1DhhNKoxxJ11eg4JmCEMlGMgc8x4c4yo6VECpSzYgXqVy4Za0aIHV2GoOxSGkJmCXUDOHAiaJP5+BNPn9Tw29VlfiNWzfQzoftXMFmnTX2z5XqcFaq5TyRjXCeqDZz/Dbks4AFuo0gSyxkpm5nxQmU6S063sQgz8rqRz4XPn2FGXC7JpoMPs0KSypxTOV7a6NNkk/sP0bUnf7F4FbKJO31CqgsCZppf+CvL1p5bE6tHIG3qKnF1WJnIGtgd+Qrp8mXfO3kTQr8P0aur8lhZFdRDKOv0VanS9BU7ln7wKjc9J+Mc9HDi2cFVjUdp0cs06MAeYmnYOS/ID023mX7XVyLt6AbST9v0oYKsGApW5FjylkkO2+avjXoZXvAWniVCrfo6kcCugdb/4O6T3UqSe/Ug5x2lHSH2hLoK8TjpNzBsbaXrbG+Drp/ga4wPbvSuhVP8N5RbRlUI5w/V4W65PKdn+eMpPEvpgXeDB7EqoTmxIezTWY1JvZVQy+pJNJ369XgLtJHqtftfLE6tLE9ahBo20H0sT8eLK3rQTOJWwlbQ+s0yqqkHMFa5cAbmoRIofI9lUF77aa2o9KmjGH652hVP63BE3lnl2ab0XJxU2OIXmWFpc0FDikxHUCmB94SNI9lL0Gjbc9XlKrQtxDSuXSgwFEGCrBgEXShbY3Uyq/Ool73mX6ezRSAcVtPU9a+R51NDNv58oyrZC4+SjkCXKXvQr64URSB/qPkn5oq4ZKreTNvNI4ppCXqsoo4KeSliKFgF1ge2Rd0Rx2cjSpS7qA4yXtY/vifl/YDznG/I+S/mZ7TN0x9sOwJkT7yn0Buk0WXhT4APAIKQ/qSeC//b5iKugjzOe1Aqv9KFWq24u0Gr2LUknvpW1f0aXk3yQ9Acyn1Otj4qEo0xtpQdLCRus6OXveVxtJXwc+mzeH1zgHVc2mOpC/nheKRks6iuQhG1xTEwDb3813T5R0NYVyz7LnYxZgqbzrYReo4ke6TqxKusZhe7RS/5MS1C280hdPZmPwUuA6SS/SU4Y9GCBEMn0wYJG0gO2n1VB/j3yM7wGHkSY0EyugNJFsqgL16yVtTao0sxJwJjmHwvZFNTTPoO84bdveqVvtrH+C7e/U0ehD90aSh6JkzkBLex7gm6SwjkVt1wrnUOoDsF7Li5InTteXSOLNE6SfA2+QDMPlSd3Hz6mr3SRKzfhK5r60+uB8kpSfsgawLGkyfaf7qNg1Fdoj6RDmY3v/WoNO2heQjPlv2V42h9DcWSexXNJHSBW+Jvk+1L12StqBNDFt9fz4HDDMNZsGKvXiWAU4N+/aErjX9r41dRvpQJ5/m54lGa4/JIUa/s65h08N3ZmAd21bqZfTasBjtu+ro5u11wbOAp4gGa6LANvVTSBvFSyQdJ9z8RFJYwstKDbhkW7X/xzp/3d16VyYoB5hqAT/0zRYAaVTvseHbH+pgPbH6cmhuKFmDgWSvtlh96KkJPgZbS9cU38J4Enbb+YfyeWAP7jSZ6ZL3Y7dn0uE6EhalWQQbgQ86JrNEyWNq3rZcujamHbPW5fao20PlbQxabw/BG6qawSpp2iB6G3IFuk1IOkiYA/bRXJf2rQXJuVRrEEKkfuQ7blrao60vXJ18iXpDttrFBhvS7s6yStajSon1S9Sd1U+f3Y/TSpH26qkdbftZ2oOkez9GWp7Qt6ekZRMXmuyq/ewA7mkC2xvXuP1OwNHkkK9DiX1BRoFrACcbvvImuO7F9iqlUeSvcfn216ppu5ppIpf+5IWefYAZra9Wx3drN3ukf4M0LVHOi9E9UndiIqgLBH6FQx4mgy5oLkKKKXzParMC7xu+wxJ80la3Pbj3YpVVxWVyh/vTwq9OILUDbgulwArS1oy611G6mfw1TqiTeQMSDoS+AapDOaFwKF1DarM1ZKuoafh4xbAXwvoQk+Dua+SJhwvqEOVvKmlqXwB9VRCm4OCuS9KnePXIBkob5NLE5P6ZJRIpm8szAd4K3tRWiGdS1CgH4Wk4SSPykykELv/SLq5Tl5eDrv8pe3VSX1PSjM3PSFlJapnQXPVxDqxes3X70kKiZyD1JxzMdvP5/DAe0hGTB1mdiXZ3fbfJdVqUpnZndSb5U3S9f0akqe3BAcAq7R7pOmp4ja1PE8KDX0nbxcLSw7KE4ZKMD3QqbLKkoW0m6qA0kjlEKXa+iuTmtmdQZqknkOanNXR/QTpx2AF0srVbrbf6f9VU8wE2+/kFf9jbR+vXAygDupdpngW0rmoa8A+Dqxe2sNmex9J3yD9nwScaLtUQuvlkh4ihX59N/+Il2gWCEzsT3K6y5TXhuYqoQ0hTVx+2ISXhtTHYUZSlagfkkJmOnkju+EgUtjeIkr9WtYEti+gO5ftl5XKsJ5h+6C2nJVuuTZ7Yv9UMnQPOBy4L1+PRVow2a+A7vTUgfwtpwIQL0p6tHUtcmpkWyIkaWT2frR6em1DzWIc2fN1me11Sb8jpZmhLdTr/0iRCt1yPKlHze2kxaPbCn+Og4JE6Fcw4Gk45KJjP49uY60rq8UdqZs/odSVfgVgVKk44ByCszJpAnkhbR2h67rBlZooHkv6AdvA9uOS7nf5il0bAat2kzOg5poQVo2pdjfHeJLn5gDbN3SjXznOB4GXbb+bV17nLBGKk7W/TaoiNRPJOD7f9ksFdI+0/ZPJ7etCt5FQw6aR9CFSSJVI5VJrG8tKhUi+SMpJOMD2PSXyBvLnejBpRXo8BSf+khYg5SKIQiFlpenneiHgCtsL1NB+iJSbMwNpEWqrrCvgHNuf6FY7638A+B6wVta8GTjBuWlsDd3LgG1LXBs6aB9N+h63PNKbk6o+dn2tyPlsa5PO9aqkqnUn1IlOCJohDJVgwKPUVXldUrOrZ0ghF9uXit9WwQoolbyJb5B6T7QSmrcEnuhmEt2m3+o0PcqpTPFgUtJtHUPlCSqFBGhzg7tmYQFJywC7kcZ5vlIlmM1tH1FHt49j3WX701287qZ+HrYLdEDucMwZScne53ZjtEn6vO0bs6dmEmz/qe4Y2463NMlg2ZK0EnmK7f7O2+T0JikuUWgSPZpkeA8hhZ9cRqqg1VWoofqoOtii7njzMTYGbmxN8pQqEa1d1+smaVNSg8fbbH83h3YebbuUJ6gIkj7uVBq2owFQY6GgKd1+P/euUXa6Ke3saZ2v3TMqaVngWdtd98zKOheSDO3r6F3cpFZ0QjYoFiYZry3j6hb///bOO0yyqtrivzUjOalPVAwIIgYkiIIiYEBAwchTophQQQwIKvjMIKgoZlGCguhDEUFFQBFQMgww5IyCKD4xID7CSHgysN4f+1y6uqZ6hrnn3O7qqfP7vv666lbXvmdqquqefc7ea9nH5cTtif9IomJjP+Bjtr9TIm6lHDVRqQw9GqyscpDtGwvEfindKKCcbfvFCzrWIu6ehKHY5kSZxNuBo2wfmBN3OtI3QZ9BTE5fkmrnpw2S3mX70BbP+3Qq5TliwMN2vvRq77lmEk3pOxGfkWOIScPdtrdfyFjvBt5D1IH3qiMtB8yyvWPmWJskfi/CJ+FA9TSpt4g3UHWwwWXUB+dp9s4Z82SQdvFWB5ZsjrX93pT0bYcs86BJeuuFgq7iTkckHU3sGJzVd/wVxDXvjZnxi1Yn9MW+xJnN/n3xlgFeR+zMrAj8jJCi/59S56iUoyYqlWlBWg0id9VnQNyuFFCuA17lMBoj7SKclLttn2JtTpRzCDjF9q9zY6a4IhycV7W9n6SVgcfbbmUypo59cPom6HOJZPM7zpCtTE2l76bHxwE4NGeXrWs0QExh0LGM+F8hGrJPAw7vfT9I+q3thTJIk7QC8Cgi0e6VnZ2TW2aY4k9KqWFJBu0kqU8pbiHjfdj2AZIOZPBnL3eV+51E38eTiCb9DYgd06yJv6Qlbd+3oGMt4sp9k51Ccbch5GznSPoEoe64n8vICBf11pF0je2BhsO5n4+0kHGKo0elOJK+BXzP9kWF4t1N+C39CLiRvs9I6d3oSh61mb4ytKSJ895E86qAGZLmAgfa3rfQabpSQPkAcKakm9L9VShg+CjpfUSpUJHkpI+DCPPIlxHb4HMIxa715/ek+bB7+v3q/KHNi+2dOgh7MNGUf1C6/+Z07J0dnKsUP2Ve9/GfEF47Jbia8OoZpI73/IUNlsqb7gR2SBOcxxHXomUlLet8A8+diFLDz6YkZVXGSjBbo27VBy9OCeG3iEnTbuQ1ODeS5dlmpROwO/G9cIHtTRSS6SUERGYx73t50LGF5XBi9xl4aEX9BELmPYdP2j5W0sbAK4g+v0MYk23O4QjiPdD0Yv6ZEJRpa4A5v+ta1jXP0Rt3j6QVuuhRATYBdk1lyndDtnHpscTn7JnppxcTOyyVIaEmKpVhZg9C/Wb9ZnU41VgfLOkDtr9a4ByXaLwCyo5kKqAA2D45rYA1X4LXO7NZMfF44CJJlxKyq6f0rxRm8IJUMnMZgO3bU/9OKzymvPQeD2iaBnKbpp9ETBw3Ii4u5wK72/5zRtj1Pb736XSFWePQkSaHzwZW6CuDW56ecpyM+M3k8HLgmeqTPLZ9ac6kJCXd+xBlnQ82YYmm2dbYvlbSfxFeQKTvjhL9UF2qD+5G9JL8mJiEnUo0PLfC9onp5j3uM4NNuwC53Gf7PklIWiL1gSzUzlrfmB4PPBFYStK6jPXJLQ8sXWC8tygZz6aStV8CJXoRGuGRVxFlVcdL2qdAXIDVbG8naQcA2/eq/0O4cNygARLNkrYkPHFyuQ+4SlLRHpXElgViPITttyn8gLa2fUzJ2JXy1NKvytCSJsybu0/9JpWBnVqiflvzKqCcTfS/tEoqmpKLdHub3kmCpM+5jIu1iNKvnYgJ0zFESU6uG/KFxOrdRSlhKfI6q7um6V8Tev29Mps72t48I+alwDbNa5kS45/0j38YkPQ6wuDxtcTqcMMc4GjbszLjdyowIOlGIjn+Z06cAXFfQ6xsL257VUnPAfZ1vuJeZ+qDXTHBZ2+eYy3iHkd8/+xB7MDeTuxOtxUseCshx7we43eB5hAlP9kr3GlxZAVip/HzznSlTzF/AdxCiL08j5AIn+0CQi+SZhE7Puel7+PViLLkhd7FTPGeTuzGzGJsMW49wvfl1bZ/lzne4j0qkh5L+Ho9jfBC2t/2XW3jDYif3Tda6Z6aqFSGlvnVzZaoOU8rKleWrF3vnQT0TwhKTBB6Yq1DTBS2AM4gqa3Y/nBGzB2J5sLnAd8DtiZKfo6d3/PmE29+TdPn2X5T27Gm+IMakLMcqCVtSpRc3EQkrk8BdnKGulXXSHqh7fOnehwLS0qENnc5v54m7iXE5PlMj0l4t+736InbmfpgmkTuSZSIPlTp0DYZTKvkrwS2JXZpGpYH1mg72Z3gXC8hEoCTbWf5fEh6Q4kEoide706jiF2r2YRnTXYvQuoj2QK4yvYNCmnltWyfmhM3xd4c+ASwBrHDthHxfjszI+YShNxxc827hhBjKea7VBJJJxNJ1dlECfFytt9WMP4nieTyx4zfBarO9ENELf2qDDPzu+hlG185HJavkLRygbr4Bk1we9D9hQ8e7ttvJZx1DwP2sn1/SrpuAFonKrZ/mCZ5m6axbmX7ugU8bX4cRbivd9I0Ddwm6U3vIbgxAAAgAElEQVSMaevvQBiBtcb2aU3TKvEalCrZ65LLJL2XKAPrVWAqqfq1IfNOov87M+xNRB/XLxlvtvqVzLhzbd/ZVyVTYkXuzYS6XBeGj8cSvQ2H0edj1JK/EDsTr2V8KescYuytkPToAYevSr+XZcxRvi1nSvoGscPdlHPum7Hr9pq++5cR/RivoUAvgsOE8VZivDcQoh435MSUtJHt84jJ+esZ89bZvb+6oMV4/08h2fyhvnOW8C/6A4OFG3Lk7R9vuzGQPCXteJek+Y7sLbOszvRDRk1UKsPMOpIGbfOKAjX4iZWAayTNZvyKStsyEU9we9D9NjwGeL37JFFT0lWiaf0xRF37EZJWVIZ6lHuapuGhbfwlKdc0/Xaib+CrxGs7i56G2TakfpSjgWNyS+kmkSOB64lm3n2JPqucBHMcko4EViN6VZpJtIHcROVP6Wfx9FOKqyW9EZiZks73E++NLJrPnKQHiFK7W5yhMNfHXNsHF4qF7SuAKyQd5bKKdZcw5rW0MlHyJeCRxP/lqpnxjyYm6E0CuCOx2t1KTcr2TgrBhvcX6mkch6S9ifKpZxA7sYsRwg0bZYT9BrGrfX7agf9l7jj72Jx5+wO3HHBsYVmv5/aSwDbAoMR2YVDqKWpWHWb23s9Z8EqLex+x/eMF/nFlSqmlX5WRRNIjbM/VmEHjONynNb8QcR9gTJVkKaBRShKwpO1sRbFU9vWidPecNCnJpveia/vpkp4AHGs756Lb9Ax8BXgCcCtRTnWdJ5DKnEoUnhnbpZ8HiUnSMQV33Iqj5LfR9E4oVOtOye0h6Yl/HVEu1MnFQtJyRM/LvwrFW5qQJn5IwpuQjG1V3iLpEEJp8BqFtPL5RML2aGBP2z+ab4CHd459iM/GcYzfXcraoUiJ2v5E+VDvbluuieshwAlOjdmp1Gyz/pX6FnHn8ctoeoMy457hDBPG+cS9HFgXuLSnzDCr/07SBcRCwysZX7YHtG9OX0ApbrZ/0QTnPNf2xhnP/yPxPTyoGsEF3se1R2UaUHdUKqPKbOC5ts+SdKDt3UoEtT2zRJyJSKVfuzBWsvADhalZCcPH/yRddAFs/yVNInP5DFG+8Js0od6EtMvSBk3gDdHQ9kKennszcABwQJrkfRL4AiFFO6w0K+Z3KFym/0aUaZXiakJt7q8L+sOFIY31SNKqq6TbgLfYviYnrkNG+ePppwQvsr1rur0T8DvbWymUqn7FWOlhDk0j8l49x0qUoBxBSLx/lZB43YkCJaiEOl7zmmD7V5L2KxD3DEnbEwIhEH1yJXYUZkn6JvP2IuSWEv3btiUZaGSPc3k1sYP0MgooUPbQaSmuxlQCYcyAN+v6YXuVnOc/DH6tMFGuPSpDTE1UKqNK78U6a8dgknknoZR0NzykZHM+IdObSxcXXYD7bf9T0gxJM2yfkcbdll5VoE8TE7FiSFqFaELejlg5b933M0l8O5VDfJIoSVo23c5C0onEZHk54NpUHtm72p+logV8G/hgI1Qg6aWEZGwrFS1JX7O9R8+4x5Ex3t5+uM2JfhJs/01ZarFj2M4tmZqIpVLflVISvo+kc8j/zNymMDj8AfFav4nM/rDEuwiTw8b3ZgZwt6QPEivobT1rmvdUr/+WiWQgh2MkHQo8UtLOROlpluxx6kM5WtJ1pXbLU9xxpbgACiWx90ja3vmiMl/uud0Y8G6bGRMASafZ3nRBx1pQe1SmATVRqYwq07XmUYxvtn2AMiuk0MFFN3GHpGWJ2vMfpubT1kpP7pG7lLSHM+Qv+1FINC9GTEa3sV3CX6BTbB+Wbp5F2QvslwrGGsQy7lFTs31mZnLcyFSXHvcdqf/rL8SixjsgykeJ8s4ipB2m/hKt3D6g+xqhDYVvzS3AYzNjQkx29yZK1SA+2613SRtsl9jBHRS3eNlXivslhTrXXUSfyqecacarMYn7dzaLRn3nzPIlUSiTbUeof61N7LCU+L/rorRuSWAZ4DF9vSrLE6XEWXS4QFApSO1RqYwkku4BbiS++FZLt0n3nVNj3CVpZfGtjE0QtiJ8Br5WKP7m9NT25150U8xlCAnIGURz7ArADzOUfHpjF5N8TvGeafv6UvG6JPX+XNnT5P0pogn5ZkIhqJUIwoDzzKMIVEgl6DiizLDXB2c921vlxC2NQjr4G0T529dsfy8dfwXw8ty+jBRrb+ClRKJyEtHcfK7trTPjrk/0OzwS2I/47B1g+4KsAXeAwlx2R0K9zsC1xPdEtsJj6i3aG2j6Ec4i1MSKuKhLWp7xing5Td6vsX2iBvuSuG3ymhafdgCeRJTWHQMcnztZT9ekO20f3nd8N2BmzrVJ0u6EV88TiCS7SVTuAr5j+5ttY6f4bxl0vMACQaUgNVGpjCSpaXpC3KeqNUykWuCHDCptX1Yo7vuIicHtJeINiP8fxEThT7aL1F53kKgsQUz2V2H8xGPfiZ4zVUi6EtjAIZH6akKwYAeiz2gb268odJ6uDDsfRZTu9Zqt7pP7/pO0EeF4/xTi/7BZfMhtvN3Y9rn953JIyWYh6SpgHeAy2+tIehxwmO1+ed2cczwKuKOEKILK+76sQZQtnkf0ZQh4LrGD9brcviVJPyV6rZrd1zcD69h+/cTPelhx30WUk93LWNN39nttPuf7ku09Wz7330SZ8IdsX5yO3VTgc3E10e/5777jSxDmwdmLfpJ2K9SH2R+3N+aShDT/pbkLBJWy1NKvykjSm4ikpGV127+RtBRD+LnQeP+CP6afhx4r1Pz3eOAihVb9d4kdldaTGoVr80dsX53KDS4l+kueKuk7bVfaJM1hrHRvaY1JWDeThLZ17ADHE3Xcl9DTjzGkODWOQ/gtHJ4SwEskvSc3uHpUglJS1LAcZeR+byekg0tzOOEVcgllPEkavkFMnns5cMCxNtzrkBifm1bnbyWjjC/trh1j+/o0YTyZSITmSnqj7d9kjre078uBwLv7d3AlbUZIkOeWFa1mu9fz5tMKxa5c9gSe7Ux/k4Vg23TONjyBkAz+SkqEjyHKXHPxoF0vh2dLqbLkv0lazvac1Bv1XOAzuWII7hPRSTtvR07w55UpYugmZJXKZJK2w3chlIdWI7bFDyFWVoaJXv+Cfoo0/9n+hMKp9+WEOtA3JR1DTIDbeIqsavvqdHsn4Ne236JQEjsPaJWodFXHnniS7S06jF8Spd6fe4j360E9j5XwGepEJUjSCfN7vECT/p22f5UZ4yEkvZBoxl4xlbk0LE85NbiLJT2S6Am7BPgXoUzYlu2IUi8YUxRbEXg6sauQm6gU9X0BnjiozDQtHpVYSb+3d0cs7brdWyDu7xmToJ8MWk/8UzJ1MHCwpCcB2wO3KuTHj7P9sdaDkh5n++/9x9rGG8AnbR8raWPCL+pLxL/lBQXPAfF/uXrhmJVMaqJSGXXeCzwfuBDA9g0KY8KhYrKa/mxb0t8Iidu5wKOAn0j6te2FVb/qNZrblNSYn1bFHiwy4PLMkrSW7asW/KdTztcIE8a7CF+appxjXQpICbtHJUhhmvc44pqRa9j5QuB/CFnfCyknBtFwhqQvEhLevSplbVdfFyeU1B7BeLnVuwj53GxsNztgh0g6GVje9pXze84C+HfPbugrgKNtPwBcl0QAcjkx7dqV8n2ZIWkJ2+N2MVMzdYnxvhv4floxF/C/jCVwOXyU+M64kPGvQ+udwr7d83EPUeizYvvPxGT/S6mML6eZ/ovALyV9iCRtTxhWHkA5YYtm1+5VwMG2j1d4D2Wh8QqBM4gesWMmfkZlKqg9KpWRRtKFtl+gMdO8RxA1qkPZTA8g6fVEXb8Jw8efF4r7fuLifRtR0vFz2/crqQbZXm0h450InEo0QR5O7LDckcrrLvZwGj5eCzwN+AMx8Rh2cYUnEipOV9h+MB1bCVgsI5HoP8f7iJ6PvxN1+JDxmqSkZ3NicrQ24ZPxo9w+hJ74Zww47Lb9Ez1xn9Jl75qktZm35+NnEz5h/rEuIKTM/w78Fniek7iCpOttPzNzrIOEGlr3ZqRyng2A99n+Yzq2ClFud3HbHjFJXyN2b2fZviWV1WH7rvk/82HHnw2cC1zF2GdjnDJhi5h/YOLd8+xFK0nbACeXLKNSGH5+BGgkjq8GPl9qZzOVEd9C+Ms8j9gNm217ncy4vYbPc4GbUxJXGSJqolIZaSQdANwBvAXYjajJv9Z2KbO4okg6iJhINyZz2wG/t/3eiZ/1sGPvS5R5zTMZk/Qs29ctZLzHEo2mjwcOsn1qOr4JMXHqWv52odEEIgtdTlBL0Je8nmv7uAU8ZWFi30h495TwyeiPvQSRsHyRUGEq3jBbitIN5H2xv0skbdcwPhl8+8TPmm+8FxAlXisSSmX7peOvBN5sO1uOtjQpIf4wsHQ6dDfwpZz3RIq5IWM+KrNIiQs9yX1G/Fm2W/n+tDyfcvoGU4wrba+dyqj2J3Y9Pma7dBlVMSQtDWwBXJWqHlYC1mquKS3iPQ14nPuEMCS9CPhLy1LnSkfURKUy0qTdgnfQI8lLqO0M5QdD0jXAms340vivKrU7IWkd4EXp7jkuYDgmaRvbxy7o2FQyn3ILYLidirtMXlP8M4DNbbf2vhkQcwmijGMHYuJ/AvBd27cUiv8qQua215MkS7lN0hVE/9q4Jn0XULCTdK3tNXLjTCbqwPcllXotlmLNSceKiIWkye1GRNLyOmBF5wlvIOmzhBz4iZQpgeuNva/tT/XcnwEcaXvHzLhN9cD+xLXjqOZYZtwVgZ2ZN5FvlWxPcI7HMv791mrXOO3QfKy/vFLSesDeLqi2V8mn9qhURp2liAnSd+ChspSlmNwGyYXht8DKxMUR4MlATi37Q6TSr12I2n6AH0j6doFV7o+S3LwXcGwq6VysoENewvjk9ftEKUopbgLOlPRLxk/GvtImWBrfmkSj/qc9JrhQBEmHEKvymxAljFuT15jeULqBvJfzJa1h+9qSQRWS4HvTs9tG7Fxl7Y5pAt8XINd/4qeEHPHcdJ6VgF8Q5T5txypgLSJB2SiN+YYCY4UwTYT4Pmso9X2xsqSP2t4/JfbHMtYDksMtCmPfzYAvpNgzCsQ9HjiHEGooqbaHpNcSzvdPIBTxVgauJxYj2rDKoB4w2xenksPKEFF3VCojTarl3sz2v9L9ZYFTJ3M7f2GQdBawPmMTr/UJbfx7IE8xSSFB+0Lbd6f7ywDnZ/QibAm8kpDU/HHPQ8sDa9h+ftuxVsaQ9DPgAx4zfnwKUR9epLwnTUrnwfanW8Z7kCjrgbFGVigjL91b2tL8Xhb4me2XZ8bdh5gklWog7439YmJV/m8U7I2S9GvCn+YH6dCOwEttb5YZtxPfF4UK46sIL6MnEztte2aU+Pya+L65HLgAuGBhS1inipRg/ZBYdNgE+JXtrxaIW7SMqifu5bafkzu+CWJfAbwM+E3aDdoE2MH2Li3j3Wj7aQv7WGVqqDsqlVFnySZJAbD9r/RFPqx8asF/0hoxfiXsAfJUZv5C+Ka8ltixaJhD+FwMJV2JFZRGY4o1KxBqTk3y+nwK+Jw0NAmJQlbavZ+XlvFKrN7Oj0Z29h5JTwD+CZRQzWtUovbqOVZq9fy7hAnhuKbsAjy66U9JfEbSVgXiFvV9abD9HYVD/c+JEqJ32c55L99EJFSrE++D2yT9w5m+J5JeZvv09F0xD24pgpBi9/ryfB04lOirOUvSczOb3mcQTehN0zu2/0oBlUDgF5JeafukArH6ud/2PyXNkDTD9hmSvpAR7yJJOzeVFA2S3sH4a1VlCKiJSmXUubv3y19SoygylNg+CyBNDnrrgEv0UBwBXCipacTeilDrakXqb7lC0lG271/gE4aAAf0eu0ravFS/R2EmRYwg9SIcSXgNIek24C0upNLVAb9QeJJ8kSiVMUkaOwd3KxH+J9vz9ZdpyRmStmdMcnVrQmUtl6K+LxrvTyNiN+VyYANJG7QtM7T9rhR/eUJVbEPgvamf4mrbbSWKXwycDgzaQTJj5bNt+HLf/duJcrUvp9itxRtScnmFpJXb9nfMh92Bj0n6P0KavsgOaeKOtDN6NvBDSbcSKl1t2QM4TtKOjCUm6xFS5P+ZNdJKcWrpV2WkkbQ+cDSx+g+wErBdiQbZLpC0C2Hkdi+x8tpcDIr0UKTVvI1T3LNtX1Yg5kaEvO1TiOSq6JhL0rVYQVek0pv1093Ztm8tGHsW8HHbZ6T7LwU+N6zlkb2k+vslHZ4wubGWBj4IrGx7F0mrA8+w/YsCsQ8CHsm8Tdk5E14kzQGWYWx3dAY9ZXclJpGppj/L92Wi8sKGtmWGPfGXID4fTTP9BsCtttdqGe9zzjBIfBjxZwDb2P7xAv944WOfzlj5cPNeKGG02hmpDPle4v27I7GL/IPcBbpUQtbsLl1j+/SsgVY6oSYqlZFH0mLAM4gL+fXDvPov6QaijySrdKEvZqeKV5KuJ0q9+tWSisvd5tJ1v0cXSNqW2D04k3gPvwjYy/ZPCsW/wn1+BYOODQsK5aj3ML6B/GDb92XG/THxHn6L7TUVfkDnl6jLl3TEgMMuqZhUEkmn2d50QcemGklfJRKT1YkdmlnNj+07MuJeavu5C/7L9kg62/aLO4j7kkHHm936FvHm+zrklKot4Lzn2d6oQJxeM1ugvZpYpRtq6VelEqtLqxCfh3UlZctsdsjvKa9I1rXi1Z0uZPzVFZPV79ERHwfWb3ZRUlnLb4AiiQpwk6RPEuVfAG8iDDGHlf8m+qAatbodiLFvkxl3NdvbSdoBwPa9qeE5izRRus32Xgv844cf85m2r59oEtl28piSwKWBx0h6FGPfGcsTikxZpOb3bZokIp3jaNuvaBnyD0RD+mW2SypRzez794+jUCnuryXtSQiR9O58ZMVum5DMh/5StXGnI6NUbQGsnBtA0m6EKt44M1vC06gyJNREpTLSSDoSWI1YbWsuZKaMdGUXfBSYJelCxpeIvL9twI5r7yHq5L9I1G33jrmTlbaWDJ355EIwo6/U65+UkRtteDvwaeL/T0Sd+E4F45fmGX27PWck1aBc/p12UZqywNXoeT+3xfYDC1qVbsEHCanxQZPInMnju4j6/icwr0DGt1rG7GXF3p0O27crvDPacm76vc6gnDLjO+iZxL+/SznzZjettz8uO7akDYgk/llET8ZM4O62ZYC2N8kZTwYlyoF2J74vhm53vzJGTVQqo856hFTudKmBPJRo4iytDgR0pnjVOB6v13Osy5W2haZ3lbHLfo+OOFnSKYwJAGxPeJQUwfbtQOtEeAq4LDVgXwCgcGk/bwHPeTjsDZwMPFnSD4l+h7cViAtwuaQTCK+M3tXzVj0qjWxrB5PIWURj/ta2D5T0VkJK+I/AUQXiP9Db6J1KL3O+m7ta7b/WmQaJC6LDBaRvEt8RxxLfyW8hSuOykLQNcLLtOZI+ATwX2C+nz3EiVTUiQVyqbdwe/gfI7l+rdEvtUamMNJKOBd6fJBqHHkmzumpiHqB4VdThfDrQdb9HV6QL+kaMiSBkJ5hp4jwhw9p8K+k6ouesqTNfGbiOSOztDG8ShYHiBsTrfEGpXrEue1Qkbci8buGtdowlXUr4Tv2vwvvlaGA34DnAs2xvnTnWLYBvA83CwYuBXWyfkhO3NCrg5P4wzrEY8G7iNYD4Tjo0t4dS0sW211PyGUrHsq8rGvMt2hjYn9il/pjtFyzgqfOLOehz8RC2W+3s9qjMPZv4rihiZlvphpqoVEYaSWcQF9nZjP+iGtZJ2GcJV/p+daASpnOdKF6lHYrPAU+wvaWkNQhBgNbSx12RSoQ27+/3GMbG8aTo1HyB95eg3Ef0M33c9mkt4/+DWHH8EXBh/zk6qHUvQlqFn5BGKKFF3P8ETm8UxBTyvC8ttOvYCROVtrYtFe0VUZD0LeAftvdJ94sY/kl6DJEMQtlkcE1C5nfJ5lhGwvY2298rMa75nOMwYDHg++nQm4EHbL8zM+7ZhCv9YYTB6F+Bt+V+xzXJm6T9ievGUZOR0LVhASpztr3vpA2mskBqolIZaUoroHSNpEFNzHYBqd+uFK8k/YrwaPm4w8X6EURzaytp0C6RdFXvuFKydsUwjnV+pAbtNYEfusfcrUWMzYlm9LWJVccfeXj9Ux4iNTo/mfG7CFk9UYMm4qUmYpKeRPQNbMSYUtnutv+cGfc6Cpa2SroaeI7tuUnNbxfbZzePtX2v9Z3jtfTsIriM/PPewEuJROUkYEvg3AI7QI0IRy93Eka3hzpDaa4rtb30vf53oj/lA4SAyEG2b8yM+wvgFiIJavzIZpdY5OlqsUvSNraPXdCxytRSe1QqI82wJiQT0XHj+38wXvFqfeD8pgQoY5fpMbaPkfTRFGeupJIKPCXptN9jskgKR1dIOnCBfzz/GCcTr8kSRMJypqR9bbeO2zWS9iN6R37P2CSyRE/UIIGCUtfQI4gej0aZ7E3p2OaZca8GHk8Z53GIz8VZCtPPe4FzACQ9jQK1/pI+T3zv/DAd2l3SRrY/mhl6a8Kh/jLbO6WJ72GZMQFuAlZkfLns34GnE2aYb86I/YCk1Wz/HkDSU+mRd2+L7ZuTKMRKzvSn6WNbYAvgS7bvkLQSUErJ7nukxa50/3eEGlrurvxHiV6dBR2rTCE1UamMNKUVULpC0odtH5Buj1vxUTnzsU8ViDGIu1Ntf1NStgFD2sBoe6++fo9Dhrm0Z0HYPjTn+SlBeRWRpKwCfIM81+3JYFtCSvjfheNeLOkrhLqVid6MUsawK9rurcf/nqQ92gbrWelfDrg2LT5kl7ba/qyk0whj3FN7dmpmEK9HLq8kdmweBJD0feAyYvKYw70OV/a5Cpf6WymjzLWux3udnKjkf5JKaRea9P9+HvAR4PSeXfRVGFMCa42k1xD9I4sDq0p6DrBvTrlz2nme3bujlvo+SyXIRRe7JG1JvNeeKOkbPQ8tT57jfaUDaqJSGXU6UUDpgO2BA9Lt/hWfLYDsRKXZXUoX8t6Smdz+lw8CJwCrSTqPWIHMKrkozXz6PXaWlN3vMR1Jk8Q1iR2lT9u+eoqH9HC5mnB5L63WthvwSWIlF+BU4BOFYt8m6U2MrczvQMhMt6UzuW0nNbW+Y78reIpHAs13zgqFYl6ceoq+QySX/yL6EnNZUeNVylYGHpMea5soPwn4OrF49jvitbgEOML2XzLHC7AP4Q91JoDtyyWtkhMwJYFX9L4WhSm92PUXojzvtcwrs/2BjLiVDqg9KpWRpisFlNL01sL318UXrJPfBdiPKOl4kJiwl+p/eQShriLgt7nKNZNJiX6P6YikBxmTyu29UDTvi6HadWyQtB5wPJGwFBHISO+BU2xvlj/CgfFXJhZNXki81rOIHpVWjf89cZdhbDfh6YT/x6+G9fOnMNP8PHAG8T57MaEc9aP5PnHhzrEKsLztKwvEeiVwCLGQIWBV4D1EErCz7a9lxF6cWDzbkHhfvBC4w/YamWO+0PYL+q4pD13/MuKeTpTtzWa8xHa2MI3CZ+hA4nv4atJiV+7/oaRH2K47KENO3VGpjDr3pAvC5ZIOILaql5niMQ3CE9wedL8tewHPLqWy05Amea9kTCL15ZKmjQRkiX6P6YjtkqaRk8n3gS9Q0GvIYcp4j6QVnFS/SiDpC7b/C3hBiQndAM4GXpTEBU4jVpG3A3bs4FzZ2P6RpDOJCa+A/7L9t9y4kk6zvWk6xx/7j7XF9kmSVicSQAHX9zTQt05SEksRpUgrpJ+/EO/pVkg6iTCPvFrSG4GZaezvJxLjXEr2u4zD9qUK4Zsii12SjrG9LeG5NM/1Mzdpq5Sl7qhURpoJFFC+1TQwDgupHvdueMjo6p7mIWBJ24sVOMfJwOtt37PAP164uCcRcrnjJo6FGzkrFQAknWV7oJpfZtxjCNncXzN+xbi1GaakqwhjvAttl3anR9Kltp8raTdgKdsHqJCMcBcMSh5yEgpJSwJLEzs0L2WsrHN5YmfpWRnDbc5RzKcmxfs24e8xh5AFv4CQab49c5zbAp8BjiSuIY1QwymEMeP/TfTcqUbSe4kd7TvS/UcBO9g+qGW8lWz/VRNImefuZFbKUndUKqPOVra/TkykPw0gaXeiRnhosD1zEk7zUWCWpAsZXzKT60r+pLpCVZlELlF4OZzA+PdxljwxIc/8y8wY/ZwM3AYsI+kuUlld87tAeZ0kvZDYQXlHOjYZ3yULRU9C8Zg0Ce1NKJ6QEfpdwB4pRu///12EKEIWmsCnBmidqBAGpUsANxByv38G7siIF4OKZvRfEqIpWxAJS7NS/V4ga4e7Y2GanW0/9P9l+3ZJOwOtEhXgEEnnEjtJF3UgvFEpSN1RqYw0zYpj37GhNKnqmqQMdC7z7nx8f8InPby4XwBOs31q3ggrlQWjMHHtx7Zz5YlJsq4r2/5tbqy+uMfbfl3JmCnui4E9gfNsf0EhcbtHgcWHoqTFoSahuKXnoTnAd2x/MzP+bu5AUluFfWp64orYVdkw/axJNNWfb3t+ZoULirs4oSb2RuBoesqGc3e4JV3MAGGaEoqUkq4E1mle51ROfKVbmhFLejVjr+3awPWE0tosYJbtv+eOuVKOmqhURpLUtPlGYGOSF0BieWBuV02zw0xXIgIKR+8fEBKm9zPkzdiVyiB6ZV1tF5F1TXE7bdSfDkhan9g52Nr2gZLeCrwB+COwT67yYJqg70qPkSRhyJglKiDpWOD9SYq3OAoj0I2ICfWrgf+w/ciWsbYgdk1OIN63pUt8OxOmkfRForzuECK52hX4H9sfKhB7JrAuURq4K7DqJFUwVB4mNVGpjCSpNnVVYH9ihalhDrFSM3JKIJI+C9wMnMj4kpncScJNwFbAVaVXHiuVfiStAOzN2KT0LGJiltUEL+kSwjTyzB61pKtsr5UTN8U5AXhzyUb9FPfpxI7KKozvocjeXSqJpEuBzWz/b3wcCHQAAA6+SURBVNoFOpqQg34O8CznO8gfBixGCC1AGDE+YPudmXHPSGMs4lOTYr6fSEw2IhZ2zgPOT7+vcvKYaRH3HGBX2638XR5G/LMJV/rDgL8RwjRvcxln+hlEGd+mxELXqcBhSeikbczHMLarsgGwJFHCd35uFUGlLDVRqYw0002+s0s0ZizWi50pT6xwet+y7QW2UlkYJP2UkDDtnZSuY/v1mXE7kXVNcYo36qe4VxCr0JfQ42puu5RRZREkXdFMaCV9C/iH7X3S/dbN/438bG/8QefMGPdA0QYnT6qWMb9ClCCd19VOTRdMIExzkO0bp3RgA5B0A+HD8lNCrOAi2/+a2lFVJqI201dGnWkl39kltlftKPRfgTMl/Yrxq47TQp64Mu1YzfYbeu5/WtLlBeJ2JesK3TTqQ5SxHtxB3NLM1JinxabALj2P5cxTZhOqag9IWq1Rc0y9Oq1X4xtyEpL5xPxg6ZiTge2bUw/XSqUVHSVtRBhVPoV4P+R6fH2XWBh4A7AWsKak84HLcnZpKt1QE5XKqCPb90h6B3Bgku+8bKoHNZlI+rDtA9LtbWwf2/PY5wo0Q/4h/SyefiqVLrlX0sa2z4WHJjn3Foi7G/BxItk+ipB1/UyBuNj+fkeN+idKeg9wHAXLOTvgR8BZkm4j/q/OAZD0NPIcyBv1sD2BM1IZKkQp3E6tg0rn2t5Y0hymkRlql/T2cAHFergShxO7NON2Bttie//mdqqk2BDYmVi0/Ic7kDevtKeWflVGmpSUvAf4KvAO29eUqjufLvQqn/WroA1SRatUhhlJ6xDysCukQ7cTtfJXtIy3JNFk+zRCEe/w0j1sHTbqd1LO2QVJ3nYl4FTbd6djTweWdUtpaUl/Zkx2dymSZC7Rj3Bv3dUtxwQ9XKVKIy+0/YLcOAPiPpWxfqANCdW5C22/uvS5Ku2pOyqVUWcPwj/kuJSkPJUwBxslNMHtQfcXPvg0aeitLBqkhGQdScun+3dlhvw+0dR8DrAl4ROxR2bMfvYBnk+oUWH7cknZpZgdlnMWx/YFA479LjPsTGBZxn+PLZt+L5cZG3hINepxjP9u+1OJ2NOMubbvDGXl4pyRlL9+RgFvJEnHEaVfdzImVHCg7WsLjLVSmJqoVEaaVGN8Vs/9m4ja81HCE9wedL8NxxINvYdRYNu+UhmEpA8Cd9o+HMYSFIUr+0zbX2sZeo1mh1XS4UTfQ2kGTfKyP3uSFgPeTWFZ3mnEX23v21Xw9N7am2gib8RCTHhzjASSTiIMI7vs4Wp2U9brOWZiB6cNRxAmkrdljaoyKdTSr8pIIulrtveQdCIDJgSF6mqnBZIeIMohRJRHNPr6Apa0vVhm/EtsPy9vlJXK/JF0NfBc97lMS1qCUPVpNXmcjHLIlACdRkilv4GY5C1me9fMuJ3I8k4X1LF5r6QbgRfY/mdX5xh2JG1L9GodSVw/Nk8PnQLsZ/v/JnruVCNpG+Bk23MkfYIQXvhM252aSjfURKUykkh6nu1LupCXrIxH0j7ArQx/Q29lGjO/3rKcvrOeRB7GJ/PFGqclLU006r88HTqFmDDdlxm3E1ne6YKkR3f5PZN8VDYv3bM03Ugy/58CtiASlmZi6Zw+IElvsv2DtFs6D7k9Rk0PjaSNCU+1LwEf66IfptKeWvpVGUkaHwHbZ0laMd3+x9SOapHlren3Xj3HDAxdQ29leiPpcbb/3n8sJ6Y7dKke0Kj/wsKT3k5keacLk7AYchMhvf5LRlt6/X4imV+C6AEqtQK+TPpdpJ9oAM1n4VXAwbaPTwtrlSGiJiqVkURRDL438D5iZXSGpLlEQ11nNc2jyHRq6K1Ma74I/FLSh4CmdON5wAHESukw0nWj/l6MyfKK8KFoLctbmYc/pZ+RlV6XtAWhrHYCUXp5zwKe8rCxfWj6XdSXpYdbJB0KbAZ8IZWJzujoXJWW1NKvykgi6QPAK4FdbP8hHXsqcDBRs/rVqRzfokRt6K1MFpK2JPo81kyHrgY+b/tXUzeqiektSZP0CGB2B/0vSwDPIBKV64e5Z6Ay/ZB0DrCr7Ws6iL0zIXd8Q1pcPJzo4boZeKvtLM+zVHK5BXBVOsdKwFq2T80de6UcNVGpjCTJP2XzftWPVAZ2apcNmKPGqDf0VioT0VWjvqSX2T5d0usHPW77Z7nnGGWqGMvkkAQy1rV9f1IU+xDRx7UusLftF2XEngFcaXvNBf5xZUqppV+VUWWxQdKEtv+RdgAq5Vi/r3n3dEmtzPcqlQWR/Ed2Y17fnmGcPK4jqfF5EbBUup/bqP8S4HTgNQMeM+FHUWnPken3sJYULirM7dl5fzXw30lh7TeSDsgJbPtBSVdIWnlEfW+mDTVRqYwq/275WGXhGemG3sqk83OiRORExrwthpKuGvVt751u7tuUtjaUMJIcdXrFWKZ6LIs4D6ZyrNuBTYHP9jy2VIH4KwHXSJrNmLLfsC5qjCw1UamMKr0rmb0IWHKyB7OIsye1obcyedxn+xtTPYgh4aeEN0QvPyFEBiqZJGPD/YE16Llu2K6KhmX4FHAxMBM4oemDSbYCNxWI31WTfqUgNVGpjCRdSo5WxpA0E1gHWJ3a0FuZHL4uaW/gVMZLxo6MiZukZwLPBlbo61NZnroQU5IjCPXIrwKbEAswmtIRLULY/oWkpwDL2b6956GLgO0KxK87YtOAmqhUKpXOsP2ApNcmFbUrp3o8lZFgLUKw4WWMlX453R8VnkHU9D+S8X0qc4Cdp2REiyZL2T5NkmzfDOyTVLD2XtATKw8P23MlbSap30F+PyBX9WsD4EBCFnxxYufm7hImrpVy1ESlUql0zSxJ3wR+zPg64JFZ4a5MKv8JPNX2yPaa2T4eOF7SC22fP9XjWYS5L6lH3SDpfcAtwGOneEyLIp+0fWxykH8FIWJwCJDrIP9NYHvgWGA94C3E7n9liKiJSqVS6ZoN0+9eI81RW+GuTB5XEDsJt071QIaAyyS9lygD6+2hePvUDWmRYg9gaeD9xAr/JsBbp3REiyadOcjbvlHSTNsPAEdImlUibqUcNVGpVCqdYnuTqR5DZaR4HHC9pIsY36Myiko+RwLXE6vQ+wI7AtdN6YgWEVL/3ba29wL+RRUI6ZKuHOTvkbQ4cHmSO/4rsEyBuJWCVMPHSqXSCZLeZPsHkj446HHbX5nsMVUWfZIi0DyMYuOspMtsryvpSttrJ4+oU2zX3cwMJD0i9U6cDmzqOpHqlK4c5FOj/t+J/pQPACsAB9m+MXfMlXLUHZVKpdIVzcrUclM6ispIMYoJyXxozPLukLQm8DfCCLOSx2yiofsyohfoWMb331VDzUKkHqDZvQ7ytv9K7H5kYftmSUsBK9muUsVDSk1UKpVKJ9g+NN08yPY/pnQwlZFB0hyiBwpipXQxRlfJ59uSHgV8EjgBWJbwpqiU4dHAP4l+OxPSxAZqolKILh3kJb2GaMxfHFhV0nMIk9RRLBMdWmqiUqlUumaWpD8Qql8/69PDr1SKYnvcDp6krYDnT9FwphTbh6WbZwHVhLAcj00lrVczlqA01DKw8nTlIL8P8d1wZop3uaRVMmNWClMTlUql0im2V5f0fEIG8uOSrgWOtv2DKR5aZQSw/XNJH5nqcUwmE/WFNdT+sGxmErtTg8wda6JSnq7KsubavlOqHp3DTE1UKpVK59ieDcyW9DngK8D3gZqoVIrT58Q+g/BHGLXJY+0L65a/2t53wX9WKUHpvjNJJwHvBa6W9EZgpqTVCZnpKk88ZNREpVKpdIqk5QkTvu2B1YDjGNFSnMqk0OvEPhf4I/C6qRnK1FAbgzunLsFPIh04yH8POIWQ716TkDE/Kh3bL3e8lbJUeeJKpdIpqT/l58Ax1SW7Upk8JD0dOBh4nO01Ja0NvNb2Z6Z4aNMaSY+2/b9TPY5RQdLFDHCQt/2xjJjLEMISWxAJSzMZdi2NHC7qjkqlUumap1afgUrXSJqfmpVtj+JK6XeAvYBDAWxfKekooCYqGdQkZfLpwEH+fqIxfwmi36heo4aUmqhUKpWuWV3SnoR/w0PfOdV0rlKYuwccWwZ4B/AfjGZJx9K2Z/c1C8+dqsFUKi0p6iAvaQuiV/IE4Lm27ykzzEoX1ESlUql0zbHAIcBhwANTPJbKIortLze3JS0H7A7sBBwNfHmi5y3i3CZpNdJqsaStKWCUV6lMMm8mhDHeRzjIPxl4Q0a8jwPb2L6mwNgqHVN7VCqVSqdIusT286Z6HJVFH0mPBj4I7Egoy319lH17JD0V+DawIXA78AdgR9s3T+nAKpWFJDnIr2z7t1M9lsrkMmOqB1CpVBZ5TpT0HkkrSXp08zPVg6osWkj6InARMAdYy/Y+o5ykANi+yfZmwIrAM4GXAhtP6aAqlYUkOchfDpyc7j9H0glTO6rKZFF3VCqVSqck1a9+bLs6ZVeKIelBQmZ0LuMbY0W839pKmU47kiT4e4EnAscDv0n39wSusD1Scs2V6Y2kS4CXAWfaXjcdu9L22lM7sspkUHtUKpVKp9hedarHUFn0sV0rBMY4kij1Oh/YGfgw4T+xle3Lp3JglUoLqoP8CFO/2CuVSidI+nDP7W36Hvvc5I+oUhkZnmr7bbYPBXYgvCdeXZOUynRC0kmSVqXPQV7SgVQH+ZGhJiqVSqUrtu+5/dG+x7aYzIFUKiPG/c2N5DvxB9tzpnA8lUobvke4xf+R8Q7ydxKqfpURoCYqlUqlKzTB7UH3K5VKOdaRdFf6mQOs3dyWdNdUD65SeTjYPgZYlzBkfBXwY0Ju/Hai56oyAtQelUql0hWe4Pag+5VKpRC2Z071GCqVQlQH+RGnJiqVSqUr1kmrtwKW6lnJFbDk1A2rUqlUKsNOdZCvQJUnrlQqlUqlUqkMGZLOAXatDvKjTU1UKpVKpVKpVCqVytBRm+krlUqlUqlUKpXK0FETlUqlUqlUKpVKpTJ01ESlUqlUKpVKpVKpDB01UalUKpVKpVKpVCpDR01UKpVKpVKpVCqVytBRE5VKpVKpVCqVSqUydPw/gdp3f0+sXj8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 936x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(13,9))\n",
        "sns.heatmap(empdf.corr(),vmax=0.8,linewidth=0.1,cmap='coolwarm')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.124458,
          "end_time": "2021-01-20T14:50:36.268169",
          "exception": false,
          "start_time": "2021-01-20T14:50:36.143711",
          "status": "completed"
        },
        "tags": [],
        "id": "ytvP_qhOrkPq"
      },
      "source": [
        "# EDA\n",
        "\n",
        "### Univariate and Bivariate Analysis\n",
        "**Categorical Features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:36.529221Z",
          "iopub.status.busy": "2021-01-20T14:50:36.528137Z",
          "iopub.status.idle": "2021-01-20T14:50:36.533731Z",
          "shell.execute_reply": "2021-01-20T14:50:36.533111Z"
        },
        "papermill": {
          "duration": 0.140728,
          "end_time": "2021-01-20T14:50:36.533859",
          "exception": false,
          "start_time": "2021-01-20T14:50:36.393131",
          "status": "completed"
        },
        "tags": [],
        "id": "S-oNh0IJrkPr",
        "outputId": "5ad06e0d-8b4a-4f7c-d631-211e111bfca4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total categorical_features 9\n"
          ]
        }
      ],
      "source": [
        "categorical_features = empdf.select_dtypes(include=[np.object]).columns\n",
        "print(\"total categorical_features\",len(categorical_features))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:36.717074Z",
          "iopub.status.busy": "2021-01-20T14:50:36.716211Z",
          "iopub.status.idle": "2021-01-20T14:50:36.719495Z",
          "shell.execute_reply": "2021-01-20T14:50:36.720198Z"
        },
        "papermill": {
          "duration": 0.103811,
          "end_time": "2021-01-20T14:50:36.720356",
          "exception": false,
          "start_time": "2021-01-20T14:50:36.616545",
          "status": "completed"
        },
        "tags": [],
        "id": "eMPazQCrrkPs",
        "outputId": "2a909bcf-ee3c-4432-836d-6e38079be9cc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Female</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Male</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Married</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>Research &amp; Development</td>\n",
              "      <td>Other</td>\n",
              "      <td>Male</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Female</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</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>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Attrition     BusinessTravel              Department EducationField  Gender  \\\n",
              "0       Yes      Travel_Rarely                   Sales  Life Sciences  Female   \n",
              "1        No  Travel_Frequently  Research & Development  Life Sciences    Male   \n",
              "2       Yes      Travel_Rarely  Research & Development          Other    Male   \n",
              "3        No  Travel_Frequently  Research & Development  Life Sciences  Female   \n",
              "4        No      Travel_Rarely  Research & Development        Medical    Male   \n",
              "\n",
              "                 JobRole MaritalStatus Over18 OverTime  \n",
              "0        Sales Executive        Single      Y      Yes  \n",
              "1     Research Scientist       Married      Y       No  \n",
              "2  Laboratory Technician        Single      Y      Yes  \n",
              "3     Research Scientist       Married      Y      Yes  \n",
              "4  Laboratory Technician       Married      Y       No  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf[categorical_features].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:36.965110Z",
          "iopub.status.busy": "2021-01-20T14:50:36.964253Z",
          "iopub.status.idle": "2021-01-20T14:50:36.969557Z",
          "shell.execute_reply": "2021-01-20T14:50:36.970217Z"
        },
        "papermill": {
          "duration": 0.131003,
          "end_time": "2021-01-20T14:50:36.970364",
          "exception": false,
          "start_time": "2021-01-20T14:50:36.839361",
          "status": "completed"
        },
        "tags": [],
        "id": "a4JeTthlrkPs",
        "outputId": "f6d621a1-f520-4b86-adf8-d445dc91b0e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Travel_Rarely        1043\n",
              "Travel_Frequently     277\n",
              "Non-Travel            150\n",
              "Name: BusinessTravel, dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Business Travel\n",
        "empdf['BusinessTravel'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:37.135323Z",
          "iopub.status.busy": "2021-01-20T14:50:37.133664Z",
          "iopub.status.idle": "2021-01-20T14:50:37.287446Z",
          "shell.execute_reply": "2021-01-20T14:50:37.286855Z"
        },
        "papermill": {
          "duration": 0.237964,
          "end_time": "2021-01-20T14:50:37.287551",
          "exception": false,
          "start_time": "2021-01-20T14:50:37.049587",
          "status": "completed"
        },
        "tags": [],
        "id": "FAp2DkSYrkPt",
        "outputId": "44d6c2a7-8dad-4779-af2a-7b1c665bd8fc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='BusinessTravel',hue='Attrition', data=empdf, palette='plasma')\n",
        "plt.title(\"Attrition wrt BusinessTravel\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:39.048457Z",
          "iopub.status.busy": "2021-01-20T14:50:39.047319Z",
          "iopub.status.idle": "2021-01-20T14:50:39.054468Z",
          "shell.execute_reply": "2021-01-20T14:50:39.053677Z"
        },
        "papermill": {
          "duration": 0.156804,
          "end_time": "2021-01-20T14:50:39.054614",
          "exception": false,
          "start_time": "2021-01-20T14:50:38.897810",
          "status": "completed"
        },
        "tags": [],
        "id": "xVuZ_JwprkPv",
        "outputId": "d9fe1556-368f-4f6d-8810-bbace70b5e5f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Research & Development    961\n",
              "Sales                     446\n",
              "Human Resources            63\n",
              "Name: Department, dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Department\n",
        "empdf['Department'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:39.340379Z",
          "iopub.status.busy": "2021-01-20T14:50:39.339515Z",
          "iopub.status.idle": "2021-01-20T14:50:39.502894Z",
          "shell.execute_reply": "2021-01-20T14:50:39.502391Z"
        },
        "papermill": {
          "duration": 0.30881,
          "end_time": "2021-01-20T14:50:39.503013",
          "exception": false,
          "start_time": "2021-01-20T14:50:39.194203",
          "status": "completed"
        },
        "tags": [],
        "id": "m81ERl7jrkPv",
        "outputId": "5a86b8a5-ec0a-4e67-caee-b72ebc16ef0c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Department wrt Attrition\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='Department',hue='Attrition', data=empdf, palette='rocket')\n",
        "plt.title(\"Attrition w.r.t Department\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:39.692038Z",
          "iopub.status.busy": "2021-01-20T14:50:39.691093Z",
          "iopub.status.idle": "2021-01-20T14:50:39.695489Z",
          "shell.execute_reply": "2021-01-20T14:50:39.695980Z"
        },
        "papermill": {
          "duration": 0.103087,
          "end_time": "2021-01-20T14:50:39.696124",
          "exception": false,
          "start_time": "2021-01-20T14:50:39.593037",
          "status": "completed"
        },
        "tags": [],
        "id": "v6ncNZvbrkPw",
        "outputId": "288dd81d-6979-4e46-d425-eaad40b3c6d5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Life Sciences       606\n",
              "Medical             464\n",
              "Marketing           159\n",
              "Technical Degree    132\n",
              "Other                82\n",
              "Human Resources      27\n",
              "Name: EducationField, dtype: int64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#EducationField\n",
        "empdf['EducationField'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:39.885001Z",
          "iopub.status.busy": "2021-01-20T14:50:39.884148Z",
          "iopub.status.idle": "2021-01-20T14:50:40.127418Z",
          "shell.execute_reply": "2021-01-20T14:50:40.127966Z"
        },
        "papermill": {
          "duration": 0.339192,
          "end_time": "2021-01-20T14:50:40.128104",
          "exception": false,
          "start_time": "2021-01-20T14:50:39.788912",
          "status": "completed"
        },
        "tags": [],
        "id": "2mIeuViGrkPw",
        "outputId": "0cb7011f-f03b-4c1d-c365-050f70e9372e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Department wrt Attrition\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='EducationField',hue='Attrition', data=empdf, palette='seismic')\n",
        "plt.title(\"Attrition w.r.t EducationField\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:40.320880Z",
          "iopub.status.busy": "2021-01-20T14:50:40.320232Z",
          "iopub.status.idle": "2021-01-20T14:50:40.328009Z",
          "shell.execute_reply": "2021-01-20T14:50:40.327310Z"
        },
        "papermill": {
          "duration": 0.107161,
          "end_time": "2021-01-20T14:50:40.328143",
          "exception": false,
          "start_time": "2021-01-20T14:50:40.220982",
          "status": "completed"
        },
        "tags": [],
        "id": "6tW_m86JrkPx",
        "outputId": "046c62c8-d9bd-40b7-f702-dc912d467c57"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Male      882\n",
              "Female    588\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Gender\n",
        "empdf['Gender'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:40.619616Z",
          "iopub.status.busy": "2021-01-20T14:50:40.618718Z",
          "iopub.status.idle": "2021-01-20T14:50:40.759955Z",
          "shell.execute_reply": "2021-01-20T14:50:40.760802Z"
        },
        "papermill": {
          "duration": 0.292302,
          "end_time": "2021-01-20T14:50:40.760989",
          "exception": false,
          "start_time": "2021-01-20T14:50:40.468687",
          "status": "completed"
        },
        "tags": [],
        "id": "cKNQ6VulrkPx",
        "outputId": "2c79bff9-c771-44ff-9bed-c0429b91c193"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# most male of female employes Attriate\n",
        "# Department wrt Attrition\n",
        "plt.figure(figsize=(7,5))\n",
        "sns.countplot(x='Gender',hue='Attrition', data=empdf, palette='plasma')\n",
        "plt.title(\"Gender w.r.t Attrition\")\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:41.056311Z",
          "iopub.status.busy": "2021-01-20T14:50:41.055303Z",
          "iopub.status.idle": "2021-01-20T14:50:41.106745Z",
          "shell.execute_reply": "2021-01-20T14:50:41.107485Z"
        },
        "papermill": {
          "duration": 0.204206,
          "end_time": "2021-01-20T14:50:41.107660",
          "exception": false,
          "start_time": "2021-01-20T14:50:40.903454",
          "status": "completed"
        },
        "tags": [],
        "id": "YOm78hVurkPy",
        "outputId": "877b90b0-5fe1-4c09-9838-1373236d7557"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>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>117</td>\n",
              "      <td>210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Married</th>\n",
              "      <td>272</td>\n",
              "      <td>401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Single</th>\n",
              "      <td>199</td>\n",
              "      <td>271</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Gender         Female  Male\n",
              "MaritalStatus              \n",
              "Divorced          117   210\n",
              "Married           272   401\n",
              "Single            199   271"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# let's also see, how any male and female are Married\n",
        "pd.crosstab(empdf['MaritalStatus'],empdf['Gender'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:41.403219Z",
          "iopub.status.busy": "2021-01-20T14:50:41.402161Z",
          "iopub.status.idle": "2021-01-20T14:50:41.408905Z",
          "shell.execute_reply": "2021-01-20T14:50:41.408121Z"
        },
        "papermill": {
          "duration": 0.157125,
          "end_time": "2021-01-20T14:50:41.409090",
          "exception": false,
          "start_time": "2021-01-20T14:50:41.251965",
          "status": "completed"
        },
        "tags": [],
        "id": "yQvE25yMrkPz",
        "outputId": "aca70f1b-015c-4e89-d9c4-3118de7a2411"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#JobRole\n",
        "empdf['JobRole'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:41.722794Z",
          "iopub.status.busy": "2021-01-20T14:50:41.707881Z",
          "iopub.status.idle": "2021-01-20T14:50:41.934714Z",
          "shell.execute_reply": "2021-01-20T14:50:41.933065Z"
        },
        "papermill": {
          "duration": 0.38134,
          "end_time": "2021-01-20T14:50:41.934837",
          "exception": false,
          "start_time": "2021-01-20T14:50:41.553497",
          "status": "completed"
        },
        "tags": [],
        "id": "PSHvyZ0HrkPz",
        "outputId": "c6659171-7de1-43ff-fdc3-7b54f4502105"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# let's see at which post most people are leaving the jobs\n",
        "# JobRole\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='JobRole',hue='Attrition', data=empdf, palette='plasma')\n",
        "plt.title(\"JobRole w.r.t Attrition\")\n",
        "plt.legend(loc='best')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:42.139156Z",
          "iopub.status.busy": "2021-01-20T14:50:42.138291Z",
          "iopub.status.idle": "2021-01-20T14:50:42.144732Z",
          "shell.execute_reply": "2021-01-20T14:50:42.143950Z"
        },
        "papermill": {
          "duration": 0.111996,
          "end_time": "2021-01-20T14:50:42.144873",
          "exception": false,
          "start_time": "2021-01-20T14:50:42.032877",
          "status": "completed"
        },
        "tags": [],
        "id": "5XJhLT43rkP0",
        "outputId": "1ba8e8b0-0d1b-443a-efe5-b8cd7c8349bf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "No     1054\n",
              "Yes     416\n",
              "Name: OverTime, dtype: int64"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#OverTime\n",
        "empdf['OverTime'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:42.454093Z",
          "iopub.status.busy": "2021-01-20T14:50:42.453119Z",
          "iopub.status.idle": "2021-01-20T14:50:42.629791Z",
          "shell.execute_reply": "2021-01-20T14:50:42.628988Z"
        },
        "papermill": {
          "duration": 0.337992,
          "end_time": "2021-01-20T14:50:42.629947",
          "exception": false,
          "start_time": "2021-01-20T14:50:42.291955",
          "status": "completed"
        },
        "tags": [],
        "id": "fF89Jpa5rkP1",
        "outputId": "8e708438-45b3-4353-8100-d76f406a48a9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "sns.countplot(x='OverTime',hue='Attrition', data=empdf, palette='plasma')\n",
        "plt.title(\"OverTime w.r.t Attrition\")\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:42.851303Z",
          "iopub.status.busy": "2021-01-20T14:50:42.841611Z",
          "iopub.status.idle": "2021-01-20T14:50:42.867440Z",
          "shell.execute_reply": "2021-01-20T14:50:42.866634Z"
        },
        "papermill": {
          "duration": 0.133968,
          "end_time": "2021-01-20T14:50:42.867551",
          "exception": false,
          "start_time": "2021-01-20T14:50:42.733583",
          "status": "completed"
        },
        "tags": [],
        "id": "AwfDZY3vrkP1",
        "outputId": "7c5ad4b4-6bc1-440a-ed70-b1cd6733b297"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>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>408</td>\n",
              "      <td>646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>180</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Gender    Female  Male\n",
              "OverTime              \n",
              "No           408   646\n",
              "Yes          180   236"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# how many male and female do overTime\n",
        "pd.crosstab(empdf['OverTime'], empdf['Gender'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.148433,
          "end_time": "2021-01-20T14:50:43.359838",
          "exception": false,
          "start_time": "2021-01-20T14:50:43.211405",
          "status": "completed"
        },
        "tags": [],
        "id": "1KdMVL3irkP3"
      },
      "source": [
        "**Numerical Features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:43.663829Z",
          "iopub.status.busy": "2021-01-20T14:50:43.663014Z",
          "iopub.status.idle": "2021-01-20T14:50:43.670565Z",
          "shell.execute_reply": "2021-01-20T14:50:43.669640Z"
        },
        "papermill": {
          "duration": 0.162969,
          "end_time": "2021-01-20T14:50:43.670703",
          "exception": false,
          "start_time": "2021-01-20T14:50:43.507734",
          "status": "completed"
        },
        "tags": [],
        "id": "HVUoV5NSrkP3",
        "outputId": "d1c6f811-dc7e-475f-e8e0-590a26913f36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total numerical_features 26\n"
          ]
        }
      ],
      "source": [
        "numerical_features = [feature for feature in empdf.columns if empdf[feature].dtype != 'O']\n",
        "print(\"Total numerical_features\", len(numerical_features))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:44.003824Z",
          "iopub.status.busy": "2021-01-20T14:50:44.001455Z",
          "iopub.status.idle": "2021-01-20T14:50:44.010365Z",
          "shell.execute_reply": "2021-01-20T14:50:44.009366Z"
        },
        "papermill": {
          "duration": 0.181285,
          "end_time": "2021-01-20T14:50:44.010497",
          "exception": false,
          "start_time": "2021-01-20T14:50:43.829212",
          "status": "completed"
        },
        "tags": [],
        "id": "MqGZt_NHrkP4",
        "outputId": "35a0157f-90a8-4118-eb32-97740fa958b8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>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>41</td>\n",
              "      <td>1102</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5993</td>\n",
              "      <td>19479</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>279</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1373</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>92</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>1392</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>56</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2909</td>\n",
              "      <td>23159</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>591</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3468</td>\n",
              "      <td>16632</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  DailyRate  DistanceFromHome  Education  EmployeeCount  EmployeeNumber  \\\n",
              "0   41       1102                 1          2              1               1   \n",
              "1   49        279                 8          1              1               2   \n",
              "2   37       1373                 2          2              1               4   \n",
              "3   33       1392                 3          4              1               5   \n",
              "4   27        591                 2          1              1               7   \n",
              "\n",
              "   EnvironmentSatisfaction  HourlyRate  JobInvolvement  JobLevel  \\\n",
              "0                        2          94               3         2   \n",
              "1                        3          61               2         2   \n",
              "2                        4          92               2         1   \n",
              "3                        4          56               3         1   \n",
              "4                        1          40               3         1   \n",
              "\n",
              "   JobSatisfaction  MonthlyIncome  MonthlyRate  NumCompaniesWorked  \\\n",
              "0                4           5993        19479                   8   \n",
              "1                2           5130        24907                   1   \n",
              "2                3           2090         2396                   6   \n",
              "3                3           2909        23159                   1   \n",
              "4                2           3468        16632                   9   \n",
              "\n",
              "   PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0                 11                  3                         1   \n",
              "1                 23                  4                         4   \n",
              "2                 15                  3                         2   \n",
              "3                 11                  3                         3   \n",
              "4                 12                  3                         4   \n",
              "\n",
              "   StandardHours  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0             80                 0                  8                      0   \n",
              "1             80                 1                 10                      3   \n",
              "2             80                 0                  7                      3   \n",
              "3             80                 0                  8                      3   \n",
              "4             80                 1                  6                      3   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                1               6                   4   \n",
              "1                3              10                   7   \n",
              "2                3               0                   0   \n",
              "3                3               8                   7   \n",
              "4                3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        0                     5  \n",
              "1                        1                     7  \n",
              "2                        0                     0  \n",
              "3                        3                     0  \n",
              "4                        2                     2  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf[numerical_features].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
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          "status": "completed"
        },
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        "id": "sNzHAS3arkP5",
        "outputId": "b4b5d47e-ab7e-45ea-eb3f-b673782b996b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# distribution of age\n",
        "sns.distplot(empdf['Age'],hist=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:44.777037Z",
          "iopub.status.busy": "2021-01-20T14:50:44.776334Z",
          "iopub.status.idle": "2021-01-20T14:50:44.782793Z",
          "shell.execute_reply": "2021-01-20T14:50:44.783542Z"
        },
        "papermill": {
          "duration": 0.167558,
          "end_time": "2021-01-20T14:50:44.783687",
          "exception": false,
          "start_time": "2021-01-20T14:50:44.616129",
          "status": "completed"
        },
        "tags": [],
        "id": "qLMKqH0grkP6",
        "outputId": "667ac36c-2249-465d-87d0-3cdcfa532cd7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "43"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf['Age'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.153316,
          "end_time": "2021-01-20T14:50:45.090107",
          "exception": false,
          "start_time": "2021-01-20T14:50:44.936791",
          "status": "completed"
        },
        "tags": [],
        "id": "iTjvNsl8rkP6"
      },
      "source": [
        "* Age column is very well normalized, most of employees are age between 25 to 40.\n",
        "- we are having some of the numerical columns which are lebel encoded for us, they are ordinal labels, so let's have a look at them first"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:45.408584Z",
          "iopub.status.busy": "2021-01-20T14:50:45.407513Z",
          "iopub.status.idle": "2021-01-20T14:50:45.421405Z",
          "shell.execute_reply": "2021-01-20T14:50:45.420493Z"
        },
        "papermill": {
          "duration": 0.179257,
          "end_time": "2021-01-20T14:50:45.421530",
          "exception": false,
          "start_time": "2021-01-20T14:50:45.242273",
          "status": "completed"
        },
        "tags": [],
        "id": "j6tzCG3mrkP7",
        "outputId": "76b89726-12dc-4082-8042-786af087151e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</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>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</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  EnvironmentSatisfaction  JobInvolvement  JobSatisfaction  \\\n",
              "0          2                        2               3                4   \n",
              "1          1                        3               2                2   \n",
              "2          2                        4               2                3   \n",
              "3          4                        4               3                3   \n",
              "4          1                        1               3                2   \n",
              "\n",
              "   PerformanceRating  RelationshipSatisfaction  WorkLifeBalance  \n",
              "0                  3                         1                1  \n",
              "1                  4                         4                3  \n",
              "2                  3                         2                3  \n",
              "3                  3                         3                3  \n",
              "4                  3                         4                3  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ordinal_features = ['Education','EnvironmentSatisfaction','JobInvolvement','JobSatisfaction',\n",
        "                    'PerformanceRating','RelationshipSatisfaction','WorkLifeBalance']\n",
        "empdf[ordinal_features].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:45.754403Z",
          "iopub.status.busy": "2021-01-20T14:50:45.753398Z",
          "iopub.status.idle": "2021-01-20T14:50:45.757131Z",
          "shell.execute_reply": "2021-01-20T14:50:45.757960Z"
        },
        "papermill": {
          "duration": 0.186942,
          "end_time": "2021-01-20T14:50:45.758152",
          "exception": false,
          "start_time": "2021-01-20T14:50:45.571210",
          "status": "completed"
        },
        "tags": [],
        "id": "b3ef7d3erkP8",
        "outputId": "90fa4765-0d0c-4935-da06-ce374d5a901b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3    572\n",
              "4    398\n",
              "2    282\n",
              "1    170\n",
              "5     48\n",
              "Name: Education, dtype: int64"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Education\n",
        "empdf['Education'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:46.083845Z",
          "iopub.status.busy": "2021-01-20T14:50:46.083049Z",
          "iopub.status.idle": "2021-01-20T14:50:46.315811Z",
          "shell.execute_reply": "2021-01-20T14:50:46.316386Z"
        },
        "papermill": {
          "duration": 0.399283,
          "end_time": "2021-01-20T14:50:46.316531",
          "exception": false,
          "start_time": "2021-01-20T14:50:45.917248",
          "status": "completed"
        },
        "tags": [],
        "id": "obebusn-rkQC",
        "outputId": "102d5f4b-a053-458f-e409-c851abd95341"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "edu_map = {1 :'Below College', 2: 'College', 3 :'Bachelor', 4 :'Master', 5: 'Doctor'}\n",
        "plt.figure(figsize=(7,5))\n",
        "sns.countplot(x=empdf['Education'].map(edu_map), hue='Attrition', data=empdf, palette='prism')\n",
        "plt.title(\"Education W.R.T Attrition\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:46.537901Z",
          "iopub.status.busy": "2021-01-20T14:50:46.534359Z",
          "iopub.status.idle": "2021-01-20T14:50:46.544571Z",
          "shell.execute_reply": "2021-01-20T14:50:46.543602Z"
        },
        "papermill": {
          "duration": 0.122124,
          "end_time": "2021-01-20T14:50:46.544741",
          "exception": false,
          "start_time": "2021-01-20T14:50:46.422617",
          "status": "completed"
        },
        "tags": [],
        "id": "c7_vTXyfrkQD",
        "outputId": "c1b4145b-d914-4fc9-d9bd-8ff9fcdc3e80"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3    453\n",
              "4    446\n",
              "2    287\n",
              "1    284\n",
              "Name: EnvironmentSatisfaction, dtype: int64"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#EnvironmentSatisfaction\n",
        "empdf['EnvironmentSatisfaction'].value_counts()   # it is very nice response from employees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:46.827220Z",
          "iopub.status.busy": "2021-01-20T14:50:46.826318Z",
          "iopub.status.idle": "2021-01-20T14:50:46.835329Z",
          "shell.execute_reply": "2021-01-20T14:50:46.834710Z"
        },
        "papermill": {
          "duration": 0.132273,
          "end_time": "2021-01-20T14:50:46.835470",
          "exception": false,
          "start_time": "2021-01-20T14:50:46.703197",
          "status": "completed"
        },
        "tags": [],
        "id": "iLl9t9lNrkQE",
        "outputId": "d9ea157c-e762-47b8-e487-db00bbc69693"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3    868\n",
              "2    375\n",
              "4    144\n",
              "1     83\n",
              "Name: JobInvolvement, dtype: int64"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#JobInvolvement\n",
        "empdf['JobInvolvement'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:47.486565Z",
          "iopub.status.busy": "2021-01-20T14:50:47.485882Z",
          "iopub.status.idle": "2021-01-20T14:50:47.497841Z",
          "shell.execute_reply": "2021-01-20T14:50:47.496889Z"
        },
        "papermill": {
          "duration": 0.125288,
          "end_time": "2021-01-20T14:50:47.498016",
          "exception": false,
          "start_time": "2021-01-20T14:50:47.372728",
          "status": "completed"
        },
        "tags": [],
        "id": "bmRxvC8WrkQG",
        "outputId": "7c8189ae-33b5-4356-ce93-5c68139a92af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total numerical_features 19\n"
          ]
        }
      ],
      "source": [
        "numerical_features = [feature for feature in empdf.columns if empdf[feature].dtype != 'O' and feature not in ordinal_features]\n",
        "print(\"total numerical_features\",len(numerical_features))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:47.813820Z",
          "iopub.status.busy": "2021-01-20T14:50:47.806135Z",
          "iopub.status.idle": "2021-01-20T14:50:47.820874Z",
          "shell.execute_reply": "2021-01-20T14:50:47.820275Z"
        },
        "papermill": {
          "duration": 0.16342,
          "end_time": "2021-01-20T14:50:47.821016",
          "exception": false,
          "start_time": "2021-01-20T14:50:47.657596",
          "status": "completed"
        },
        "tags": [],
        "id": "NhLkIaHyrkQM",
        "outputId": "4fedf307-d315-43e7-bd23-3148a7ccfba1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>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>41</td>\n",
              "      <td>1102</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>94</td>\n",
              "      <td>2</td>\n",
              "      <td>5993</td>\n",
              "      <td>19479</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>279</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1373</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>1392</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>2909</td>\n",
              "      <td>23159</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>591</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>3468</td>\n",
              "      <td>16632</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  DailyRate  DistanceFromHome  EmployeeCount  EmployeeNumber  \\\n",
              "0   41       1102                 1              1               1   \n",
              "1   49        279                 8              1               2   \n",
              "2   37       1373                 2              1               4   \n",
              "3   33       1392                 3              1               5   \n",
              "4   27        591                 2              1               7   \n",
              "\n",
              "   HourlyRate  JobLevel  MonthlyIncome  MonthlyRate  NumCompaniesWorked  \\\n",
              "0          94         2           5993        19479                   8   \n",
              "1          61         2           5130        24907                   1   \n",
              "2          92         1           2090         2396                   6   \n",
              "3          56         1           2909        23159                   1   \n",
              "4          40         1           3468        16632                   9   \n",
              "\n",
              "   PercentSalaryHike  StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
              "0                 11             80                 0                  8   \n",
              "1                 23             80                 1                 10   \n",
              "2                 15             80                 0                  7   \n",
              "3                 11             80                 0                  8   \n",
              "4                 12             80                 1                  6   \n",
              "\n",
              "   TrainingTimesLastYear  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                      0               6                   4   \n",
              "1                      3              10                   7   \n",
              "2                      3               0                   0   \n",
              "3                      3               8                   7   \n",
              "4                      3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        0                     5  \n",
              "1                        1                     7  \n",
              "2                        0                     0  \n",
              "3                        3                     0  \n",
              "4                        2                     2  "
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf[numerical_features].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:48.053834Z",
          "iopub.status.busy": "2021-01-20T14:50:48.053188Z",
          "iopub.status.idle": "2021-01-20T14:50:48.201425Z",
          "shell.execute_reply": "2021-01-20T14:50:48.200586Z"
        },
        "papermill": {
          "duration": 0.271442,
          "end_time": "2021-01-20T14:50:48.201570",
          "exception": false,
          "start_time": "2021-01-20T14:50:47.930128",
          "status": "completed"
        },
        "tags": [],
        "id": "H3Epw5iqrkQM",
        "outputId": "3660fb6b-7da2-4a3a-e2b9-85ae6ef72950"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#MonthlyIncome\n",
        "sns.distplot(empdf['MonthlyIncome'],hist=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:48.540856Z",
          "iopub.status.busy": "2021-01-20T14:50:48.537368Z",
          "iopub.status.idle": "2021-01-20T14:50:48.656557Z",
          "shell.execute_reply": "2021-01-20T14:50:48.655713Z"
        },
        "papermill": {
          "duration": 0.293902,
          "end_time": "2021-01-20T14:50:48.656674",
          "exception": false,
          "start_time": "2021-01-20T14:50:48.362772",
          "status": "completed"
        },
        "tags": [],
        "id": "trhdORZ_rkQN",
        "outputId": "38b5e02d-1335-4a95-8fd9-60d5c1c3589c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6,5))\n",
        "sns.boxplot(empdf['MonthlyIncome'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:49.449235Z",
          "iopub.status.busy": "2021-01-20T14:50:49.448380Z",
          "iopub.status.idle": "2021-01-20T14:50:49.453840Z",
          "shell.execute_reply": "2021-01-20T14:50:49.453225Z"
        },
        "papermill": {
          "duration": 0.14288,
          "end_time": "2021-01-20T14:50:49.453973",
          "exception": false,
          "start_time": "2021-01-20T14:50:49.311093",
          "status": "completed"
        },
        "tags": [],
        "id": "2x49iwVfrkQQ",
        "outputId": "94cd622e-fe27-4158-f923-6df70ac7e0e5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Age\n",
              "35    78\n",
              "34    77\n",
              "31    69\n",
              "36    69\n",
              "29    68\n",
              "Name: EmployeeCount, dtype: int64"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# employee count in age group and whicg age employees are more\n",
        "empdf.groupby('Age')['EmployeeCount'].sum().sort_values(ascending=False).head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:49.705864Z",
          "iopub.status.busy": "2021-01-20T14:50:49.704895Z",
          "iopub.status.idle": "2021-01-20T14:50:49.712664Z",
          "shell.execute_reply": "2021-01-20T14:50:49.712050Z"
        },
        "papermill": {
          "duration": 0.138798,
          "end_time": "2021-01-20T14:50:49.712771",
          "exception": false,
          "start_time": "2021-01-20T14:50:49.573973",
          "status": "completed"
        },
        "tags": [],
        "id": "MfXoLGQArkQW",
        "outputId": "697ae8af-9ebe-4f14-9087-0eff287d02b4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1    543\n",
              "2    534\n",
              "3    218\n",
              "4    106\n",
              "5     69\n",
              "Name: JobLevel, dtype: int64"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#JobLevel\n",
        "empdf['JobLevel'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:49.975011Z",
          "iopub.status.busy": "2021-01-20T14:50:49.974111Z",
          "iopub.status.idle": "2021-01-20T14:50:50.185699Z",
          "shell.execute_reply": "2021-01-20T14:50:50.184620Z"
        },
        "papermill": {
          "duration": 0.344935,
          "end_time": "2021-01-20T14:50:50.185828",
          "exception": false,
          "start_time": "2021-01-20T14:50:49.840893",
          "status": "completed"
        },
        "tags": [],
        "id": "14O3pbn4rkQX",
        "outputId": "86621182-9512-44b5-b9ed-e214cdd200b8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# JobLevel can have some effect on Attrition of Employees\n",
        "sns.countplot(x='JobLevel',hue='Attrition',data=empdf)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:50.444460Z",
          "iopub.status.busy": "2021-01-20T14:50:50.443705Z",
          "iopub.status.idle": "2021-01-20T14:50:50.606351Z",
          "shell.execute_reply": "2021-01-20T14:50:50.605702Z"
        },
        "papermill": {
          "duration": 0.294006,
          "end_time": "2021-01-20T14:50:50.606467",
          "exception": false,
          "start_time": "2021-01-20T14:50:50.312461",
          "status": "completed"
        },
        "tags": [],
        "id": "9TX0sFZtrkQY",
        "outputId": "726d4a73-1e55-48b1-e63e-6aa0bf4430f2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#NumCompaniesWorked\n",
        "sns.countplot(x='JobLevel',hue='Attrition',data=empdf)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:50.864822Z",
          "iopub.status.busy": "2021-01-20T14:50:50.863986Z",
          "iopub.status.idle": "2021-01-20T14:50:50.869563Z",
          "shell.execute_reply": "2021-01-20T14:50:50.868775Z"
        },
        "papermill": {
          "duration": 0.140374,
          "end_time": "2021-01-20T14:50:50.869714",
          "exception": false,
          "start_time": "2021-01-20T14:50:50.729340",
          "status": "completed"
        },
        "tags": [],
        "id": "oWjDFOQarkQZ",
        "outputId": "df454477-5c63-4b67-9ab9-db7e09ab2a0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    631\n",
              "1    596\n",
              "2    158\n",
              "3     85\n",
              "Name: StockOptionLevel, dtype: int64"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf['StockOptionLevel'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:51.254832Z",
          "iopub.status.busy": "2021-01-20T14:50:51.253753Z",
          "iopub.status.idle": "2021-01-20T14:50:51.258950Z",
          "shell.execute_reply": "2021-01-20T14:50:51.258362Z"
        },
        "papermill": {
          "duration": 0.199878,
          "end_time": "2021-01-20T14:50:51.259064",
          "exception": false,
          "start_time": "2021-01-20T14:50:51.059186",
          "status": "completed"
        },
        "tags": [],
        "id": "x3fWclWtrkQa"
      },
      "outputs": [],
      "source": [
        "#drop the columns which have save val in whole dataset or which are unrelevant for Attrition prediction. \n",
        "empdf.drop(['EmployeeCount','EmployeeNumber','StandardHours'],axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.122492,
          "end_time": "2021-01-20T14:50:51.506935",
          "exception": false,
          "start_time": "2021-01-20T14:50:51.384443",
          "status": "completed"
        },
        "tags": [],
        "id": "AhQ6kozorkQa"
      },
      "source": [
        "## Categorical Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:51.771858Z",
          "iopub.status.busy": "2021-01-20T14:50:51.771210Z",
          "iopub.status.idle": "2021-01-20T14:50:51.776709Z",
          "shell.execute_reply": "2021-01-20T14:50:51.777253Z"
        },
        "papermill": {
          "duration": 0.148075,
          "end_time": "2021-01-20T14:50:51.777394",
          "exception": false,
          "start_time": "2021-01-20T14:50:51.629319",
          "status": "completed"
        },
        "tags": [],
        "id": "Pm4wIRURrkQf",
        "outputId": "2caaa554-640f-4eaa-db79-c1c886f8544a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Female</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Male</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Married</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>Research &amp; Development</td>\n",
              "      <td>Other</td>\n",
              "      <td>Male</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Female</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</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>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Attrition     BusinessTravel              Department EducationField  Gender  \\\n",
              "0       Yes      Travel_Rarely                   Sales  Life Sciences  Female   \n",
              "1        No  Travel_Frequently  Research & Development  Life Sciences    Male   \n",
              "2       Yes      Travel_Rarely  Research & Development          Other    Male   \n",
              "3        No  Travel_Frequently  Research & Development  Life Sciences  Female   \n",
              "4        No      Travel_Rarely  Research & Development        Medical    Male   \n",
              "\n",
              "                 JobRole MaritalStatus Over18 OverTime  \n",
              "0        Sales Executive        Single      Y      Yes  \n",
              "1     Research Scientist       Married      Y       No  \n",
              "2  Laboratory Technician        Single      Y      Yes  \n",
              "3     Research Scientist       Married      Y      Yes  \n",
              "4  Laboratory Technician       Married      Y       No  "
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf[categorical_features].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:52.033803Z",
          "iopub.status.busy": "2021-01-20T14:50:52.030648Z",
          "iopub.status.idle": "2021-01-20T14:50:52.038892Z",
          "shell.execute_reply": "2021-01-20T14:50:52.038244Z"
        },
        "papermill": {
          "duration": 0.1383,
          "end_time": "2021-01-20T14:50:52.039031",
          "exception": false,
          "start_time": "2021-01-20T14:50:51.900731",
          "status": "completed"
        },
        "tags": [],
        "id": "tB5EPCFurkQg"
      },
      "outputs": [],
      "source": [
        "# Target Variable(Attrition)\n",
        "empdf['Attrition'] = empdf['Attrition'].replace({'No':0,'Yes':1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:52.308183Z",
          "iopub.status.busy": "2021-01-20T14:50:52.307521Z",
          "iopub.status.idle": "2021-01-20T14:50:52.311158Z",
          "shell.execute_reply": "2021-01-20T14:50:52.311947Z"
        },
        "papermill": {
          "duration": 0.143975,
          "end_time": "2021-01-20T14:50:52.312125",
          "exception": false,
          "start_time": "2021-01-20T14:50:52.168150",
          "status": "completed"
        },
        "tags": [],
        "id": "-LEO_ZJurkQh"
      },
      "outputs": [],
      "source": [
        "#encode binary variables\n",
        "empdf['OverTime'] = empdf['OverTime'].map({'No':0,'Yes':1})\n",
        "empdf['Gender'] = empdf['Gender'].map({'Male':0,'Female':1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:52.715242Z",
          "iopub.status.busy": "2021-01-20T14:50:52.710809Z",
          "iopub.status.idle": "2021-01-20T14:50:52.723278Z",
          "shell.execute_reply": "2021-01-20T14:50:52.722477Z"
        },
        "papermill": {
          "duration": 0.21673,
          "end_time": "2021-01-20T14:50:52.723423",
          "exception": false,
          "start_time": "2021-01-20T14:50:52.506693",
          "status": "completed"
        },
        "tags": [],
        "id": "cArHEd3crkQi"
      },
      "outputs": [],
      "source": [
        "#encode categorical columns which are ordinal, use labelEncoding\n",
        "cat_cols = ['BusinessTravel','Department','EducationField','JobRole','MaritalStatus']\n",
        "for col in cat_cols:\n",
        "    map_dict = {k:i for i, k in enumerate(empdf[col].value_counts().index,0)}\n",
        "    empdf[col] = empdf[col].map(map_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:53.112889Z",
          "iopub.status.busy": "2021-01-20T14:50:53.112251Z",
          "iopub.status.idle": "2021-01-20T14:50:53.118722Z",
          "shell.execute_reply": "2021-01-20T14:50:53.118212Z"
        },
        "papermill": {
          "duration": 0.203624,
          "end_time": "2021-01-20T14:50:53.118832",
          "exception": false,
          "start_time": "2021-01-20T14:50:52.915208",
          "status": "completed"
        },
        "tags": [],
        "id": "uxWCuXzzrkQi"
      },
      "outputs": [],
      "source": [
        "# drop the Over18 column\n",
        "empdf.drop('Over18',axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.127359,
          "end_time": "2021-01-20T14:50:53.374494",
          "exception": false,
          "start_time": "2021-01-20T14:50:53.247135",
          "status": "completed"
        },
        "tags": [],
        "id": "QXhW1jFlrkQj"
      },
      "source": [
        "### Making a Corelation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:53.665868Z",
          "iopub.status.busy": "2021-01-20T14:50:53.665222Z",
          "iopub.status.idle": "2021-01-20T14:50:53.680407Z",
          "shell.execute_reply": "2021-01-20T14:50:53.679717Z"
        },
        "papermill": {
          "duration": 0.152947,
          "end_time": "2021-01-20T14:50:53.680631",
          "exception": false,
          "start_time": "2021-01-20T14:50:53.527684",
          "status": "completed"
        },
        "tags": [],
        "id": "4JhFntGwrkQk",
        "outputId": "65cab041-941f-4100-c93d-3bfc6d7a09e7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Attrition                   1.000000\n",
              "OverTime                    0.246118\n",
              "DistanceFromHome            0.077924\n",
              "Department                  0.077351\n",
              "EducationField              0.062320\n",
              "NumCompaniesWorked          0.043494\n",
              "MonthlyRate                 0.015170\n",
              "MaritalStatus               0.011195\n",
              "PerformanceRating           0.002889\n",
              "BusinessTravel             -0.000074\n",
              "HourlyRate                 -0.006846\n",
              "PercentSalaryHike          -0.013478\n",
              "JobRole                    -0.027930\n",
              "Gender                     -0.029453\n",
              "Education                  -0.031373\n",
              "YearsSinceLastPromotion    -0.033019\n",
              "RelationshipSatisfaction   -0.045872\n",
              "DailyRate                  -0.056652\n",
              "TrainingTimesLastYear      -0.059478\n",
              "WorkLifeBalance            -0.063939\n",
              "EnvironmentSatisfaction    -0.103369\n",
              "JobSatisfaction            -0.103481\n",
              "JobInvolvement             -0.130016\n",
              "YearsAtCompany             -0.134392\n",
              "StockOptionLevel           -0.137145\n",
              "Age                        -0.159205\n",
              "MonthlyIncome              -0.159840\n",
              "YearsInCurrentRole         -0.160545\n",
              "JobLevel                   -0.169105\n",
              "TotalWorkingYears          -0.171063\n",
              "Name: Attrition, dtype: float64"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf.corr()['Attrition'][:-1].sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:53.943690Z",
          "iopub.status.busy": "2021-01-20T14:50:53.942865Z",
          "iopub.status.idle": "2021-01-20T14:50:54.681682Z",
          "shell.execute_reply": "2021-01-20T14:50:54.680301Z"
        },
        "papermill": {
          "duration": 0.871301,
          "end_time": "2021-01-20T14:50:54.681800",
          "exception": false,
          "start_time": "2021-01-20T14:50:53.810499",
          "status": "completed"
        },
        "tags": [],
        "id": "u2xg9D8zrkQk",
        "outputId": "f4de7f1a-472e-4faa-9953-af6fd2a51946"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(13,9))\n",
        "sns.heatmap(empdf.corr(),vmax=0.8,linewidth=0.1,cmap='coolwarm')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:54.943274Z",
          "iopub.status.busy": "2021-01-20T14:50:54.942590Z",
          "iopub.status.idle": "2021-01-20T14:50:54.963493Z",
          "shell.execute_reply": "2021-01-20T14:50:54.964023Z"
        },
        "papermill": {
          "duration": 0.15222,
          "end_time": "2021-01-20T14:50:54.964159",
          "exception": false,
          "start_time": "2021-01-20T14:50:54.811939",
          "status": "completed"
        },
        "tags": [],
        "id": "68JpOd7XrkQo",
        "outputId": "84d5c32d-f630-45e6-8c7b-bb08aca9de37"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>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>41</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1102</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5993</td>\n",
              "      <td>19479</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>279</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1373</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1392</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>56</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2909</td>\n",
              "      <td>23159</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>591</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3468</td>\n",
              "      <td>16632</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Attrition  BusinessTravel  DailyRate  Department  DistanceFromHome  \\\n",
              "0   41          1               0       1102           1                 1   \n",
              "1   49          0               1        279           0                 8   \n",
              "2   37          1               0       1373           0                 2   \n",
              "3   33          0               1       1392           0                 3   \n",
              "4   27          0               0        591           0                 2   \n",
              "\n",
              "   Education  EducationField  EnvironmentSatisfaction  Gender  HourlyRate  \\\n",
              "0          2               0                        2       1          94   \n",
              "1          1               0                        3       0          61   \n",
              "2          2               4                        4       0          92   \n",
              "3          4               0                        4       1          56   \n",
              "4          1               1                        1       0          40   \n",
              "\n",
              "   JobInvolvement  JobLevel  JobRole  JobSatisfaction  MaritalStatus  \\\n",
              "0               3         2        0                4              1   \n",
              "1               2         2        1                2              0   \n",
              "2               2         1        2                3              1   \n",
              "3               3         1        1                3              0   \n",
              "4               3         1        2                2              0   \n",
              "\n",
              "   MonthlyIncome  MonthlyRate  NumCompaniesWorked  OverTime  \\\n",
              "0           5993        19479                   8         1   \n",
              "1           5130        24907                   1         0   \n",
              "2           2090         2396                   6         1   \n",
              "3           2909        23159                   1         1   \n",
              "4           3468        16632                   9         0   \n",
              "\n",
              "   PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0                 11                  3                         1   \n",
              "1                 23                  4                         4   \n",
              "2                 15                  3                         2   \n",
              "3                 11                  3                         3   \n",
              "4                 12                  3                         4   \n",
              "\n",
              "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0                 0                  8                      0   \n",
              "1                 1                 10                      3   \n",
              "2                 0                  7                      3   \n",
              "3                 0                  8                      3   \n",
              "4                 1                  6                      3   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                1               6                   4   \n",
              "1                3              10                   7   \n",
              "2                3               0                   0   \n",
              "3                3               8                   7   \n",
              "4                3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        0                     5  \n",
              "1                        1                     7  \n",
              "2                        0                     0  \n",
              "3                        3                     0  \n",
              "4                        2                     2  "
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "empdf.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.128062,
          "end_time": "2021-01-20T14:50:55.223075",
          "exception": false,
          "start_time": "2021-01-20T14:50:55.095013",
          "status": "completed"
        },
        "tags": [],
        "id": "QiM6QhagrkQp"
      },
      "source": [
        "## FeatureSelection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:55.481237Z",
          "iopub.status.busy": "2021-01-20T14:50:55.480604Z",
          "iopub.status.idle": "2021-01-20T14:50:55.489490Z",
          "shell.execute_reply": "2021-01-20T14:50:55.488910Z"
        },
        "papermill": {
          "duration": 0.139501,
          "end_time": "2021-01-20T14:50:55.489610",
          "exception": false,
          "start_time": "2021-01-20T14:50:55.350109",
          "status": "completed"
        },
        "tags": [],
        "id": "3-BoURe5rkQp"
      },
      "outputs": [],
      "source": [
        "x = empdf.drop('Attrition',axis=1)\n",
        "y = empdf['Attrition']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:55.798741Z",
          "iopub.status.busy": "2021-01-20T14:50:55.797696Z",
          "iopub.status.idle": "2021-01-20T14:50:56.490491Z",
          "shell.execute_reply": "2021-01-20T14:50:56.491277Z"
        },
        "papermill": {
          "duration": 0.872079,
          "end_time": "2021-01-20T14:50:56.491445",
          "exception": false,
          "start_time": "2021-01-20T14:50:55.619366",
          "status": "completed"
        },
        "tags": [],
        "id": "WLqhlF3XrkQq"
      },
      "outputs": [],
      "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_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:56.885741Z",
          "iopub.status.busy": "2021-01-20T14:50:56.884615Z",
          "iopub.status.idle": "2021-01-20T14:50:57.167456Z",
          "shell.execute_reply": "2021-01-20T14:50:57.168004Z"
        },
        "papermill": {
          "duration": 0.482746,
          "end_time": "2021-01-20T14:50:57.168154",
          "exception": false,
          "start_time": "2021-01-20T14:50:56.685408",
          "status": "completed"
        },
        "tags": [],
        "id": "x95EDXaTrkQq",
        "outputId": "7df4c2e2-362c-437f-f4ac-7c2fdb3066b8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plotting a feature importance\n",
        "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.12897,
          "end_time": "2021-01-20T14:50:57.427439",
          "exception": false,
          "start_time": "2021-01-20T14:50:57.298469",
          "status": "completed"
        },
        "tags": [],
        "id": "kY6Vw3NLrkQr"
      },
      "source": [
        "**OBSERVATIONS:** ****Factors Responsible for Employee Attrition****\n",
        "\n",
        "- OverTime has highest relationship with Attrition, Employee who do OverTime, changes or leaves the Job early. we have seen this above also during categorical variable analysis.\n",
        "- Age is 2nd factor responsible, it can be that who are senior, are retiring or the employees who are bachelor has more expectations more organization.\n",
        "- MonthlyIncome is also a greatest factor for employee to Attritate\n",
        "- StockOptionLevel, we have seen can be determing factor, because employees are in chance that they should be given priority and power and consider to have involvement to buy Company stocks.\n",
        "- EnvironmentSatisfaction, JobSatisfaction, DistanceFromHome, WorkLifeBalance, JobLevel is at same level affects equally to the organization employees base."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.128327,
          "end_time": "2021-01-20T14:50:57.684714",
          "exception": false,
          "start_time": "2021-01-20T14:50:57.556387",
          "status": "completed"
        },
        "tags": [],
        "id": "KDNuQNghrkQr"
      },
      "source": [
        "### Feature Selection with Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:57.959017Z",
          "iopub.status.busy": "2021-01-20T14:50:57.958317Z",
          "iopub.status.idle": "2021-01-20T14:50:57.965302Z",
          "shell.execute_reply": "2021-01-20T14:50:57.964751Z"
        },
        "papermill": {
          "duration": 0.150308,
          "end_time": "2021-01-20T14:50:57.965429",
          "exception": false,
          "start_time": "2021-01-20T14:50:57.815121",
          "status": "completed"
        },
        "tags": [],
        "id": "ehmhizkUrkQr"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "min_max = MinMaxScaler()\n",
        "\n",
        "x_scaled = min_max.fit(x).transform(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:58.236565Z",
          "iopub.status.busy": "2021-01-20T14:50:58.235841Z",
          "iopub.status.idle": "2021-01-20T14:50:58.551037Z",
          "shell.execute_reply": "2021-01-20T14:50:58.550332Z"
        },
        "papermill": {
          "duration": 0.45397,
          "end_time": "2021-01-20T14:50:58.551159",
          "exception": false,
          "start_time": "2021-01-20T14:50:58.097189",
          "status": "completed"
        },
        "tags": [],
        "id": "boK4iXFOrkQs",
        "outputId": "05ce9cb2-c5da-4912-ce5e-5be0c0454a3d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Age                         0.042077\n",
              "BusinessTravel              0.024245\n",
              "DailyRate                   0.030778\n",
              "Department                  0.022886\n",
              "DistanceFromHome            0.036665\n",
              "Education                   0.027269\n",
              "EducationField              0.030718\n",
              "EnvironmentSatisfaction     0.040646\n",
              "Gender                      0.021108\n",
              "HourlyRate                  0.032182\n",
              "JobInvolvement              0.033192\n",
              "JobLevel                    0.035605\n",
              "JobRole                     0.031268\n",
              "JobSatisfaction             0.037526\n",
              "MaritalStatus               0.026702\n",
              "MonthlyIncome               0.044151\n",
              "MonthlyRate                 0.031825\n",
              "NumCompaniesWorked          0.031300\n",
              "OverTime                    0.066854\n",
              "PercentSalaryHike           0.030019\n",
              "PerformanceRating           0.013645\n",
              "RelationshipSatisfaction    0.032518\n",
              "StockOptionLevel            0.038713\n",
              "TotalWorkingYears           0.040938\n",
              "TrainingTimesLastYear       0.029355\n",
              "WorkLifeBalance             0.034646\n",
              "YearsAtCompany              0.035716\n",
              "YearsInCurrentRole          0.032537\n",
              "YearsSinceLastPromotion     0.028893\n",
              "YearsWithCurrManager        0.036026\n",
              "dtype: float64"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ExtraTree = ExtraTreesClassifier()\n",
        "\n",
        "ExtraTree.fit(x_scaled, y)\n",
        "\n",
        "feature_importance = pd.Series(ExtraTree.feature_importances_, index=x.columns)\n",
        "feature_importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:58.822466Z",
          "iopub.status.busy": "2021-01-20T14:50:58.821520Z",
          "iopub.status.idle": "2021-01-20T14:50:59.058441Z",
          "shell.execute_reply": "2021-01-20T14:50:59.057738Z"
        },
        "papermill": {
          "duration": 0.377246,
          "end_time": "2021-01-20T14:50:59.058561",
          "exception": false,
          "start_time": "2021-01-20T14:50:58.681315",
          "status": "completed"
        },
        "tags": [],
        "id": "YlSQ7DnwrkQs",
        "outputId": "0d75cfce-7f65-43fd-8c13-88752521277a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,9))\n",
        "feature_importance.nlargest(20).plot(kind='barh')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.132581,
          "end_time": "2021-01-20T14:50:59.326312",
          "exception": false,
          "start_time": "2021-01-20T14:50:59.193731",
          "status": "completed"
        },
        "tags": [],
        "id": "XZvxc74ErkQt"
      },
      "source": [
        "- Only a little bit of changed occured due to scaling the features that, can be proved strong from modelling part."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.132575,
          "end_time": "2021-01-20T14:50:59.594093",
          "exception": false,
          "start_time": "2021-01-20T14:50:59.461518",
          "status": "completed"
        },
        "tags": [],
        "id": "EMqzfD_PrkQu"
      },
      "source": [
        "## Modelling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:50:59.875353Z",
          "iopub.status.busy": "2021-01-20T14:50:59.874143Z",
          "iopub.status.idle": "2021-01-20T14:50:59.879151Z",
          "shell.execute_reply": "2021-01-20T14:50:59.878426Z"
        },
        "papermill": {
          "duration": 0.150148,
          "end_time": "2021-01-20T14:50:59.879263",
          "exception": false,
          "start_time": "2021-01-20T14:50:59.729115",
          "status": "completed"
        },
        "tags": [],
        "id": "G69GucHcrkQu"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "#metrics\n",
        "from sklearn.metrics import accuracy_score, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:00.158890Z",
          "iopub.status.busy": "2021-01-20T14:51:00.158169Z",
          "iopub.status.idle": "2021-01-20T14:51:00.166306Z",
          "shell.execute_reply": "2021-01-20T14:51:00.166822Z"
        },
        "papermill": {
          "duration": 0.15233,
          "end_time": "2021-01-20T14:51:00.167001",
          "exception": false,
          "start_time": "2021-01-20T14:51:00.014671",
          "status": "completed"
        },
        "tags": [],
        "id": "GQjG73pFrkQv",
        "outputId": "1315c12d-7850-48c1-afc8-18a25591481b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "training shape:  (1102, 30)\n",
            "testing shape:  (368, 30)\n"
          ]
        }
      ],
      "source": [
        "#split data into train and test set.\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=42)\n",
        "print(\"training shape: \",x_train.shape)\n",
        "print(\"testing shape: \",x_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:00.441308Z",
          "iopub.status.busy": "2021-01-20T14:51:00.440692Z",
          "iopub.status.idle": "2021-01-20T14:51:00.445596Z",
          "shell.execute_reply": "2021-01-20T14:51:00.444972Z"
        },
        "papermill": {
          "duration": 0.144294,
          "end_time": "2021-01-20T14:51:00.445702",
          "exception": false,
          "start_time": "2021-01-20T14:51:00.301408",
          "status": "completed"
        },
        "tags": [],
        "id": "BCwwxMn5rkQw"
      },
      "outputs": [],
      "source": [
        "#models\n",
        "log_clf = LogisticRegression()\n",
        "svc_clf = SVC()\n",
        "knn_clf = KNeighborsClassifier()\n",
        "dt_clf = DecisionTreeClassifier()\n",
        "rf_clf = RandomForestClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:00.721364Z",
          "iopub.status.busy": "2021-01-20T14:51:00.720502Z",
          "iopub.status.idle": "2021-01-20T14:51:01.242517Z",
          "shell.execute_reply": "2021-01-20T14:51:01.241835Z"
        },
        "papermill": {
          "duration": 0.664743,
          "end_time": "2021-01-20T14:51:01.242633",
          "exception": false,
          "start_time": "2021-01-20T14:51:00.577890",
          "status": "completed"
        },
        "tags": [],
        "id": "R9ewtKhArkQw",
        "outputId": "92ac27ea-54f1-4322-d2e6-01828528ef13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LogisticRegression   0.8722826086956522\n",
            "SVC   0.8695652173913043\n",
            "KNeighborsClassifier   0.8396739130434783\n",
            "DecisionTreeClassifier   0.8070652173913043\n",
            "RandomForestClassifier   0.875\n"
          ]
        }
      ],
      "source": [
        "# first check the accuracy score on without scalled features\n",
        "for clf in [log_clf, svc_clf, knn_clf, dt_clf, rf_clf]:\n",
        "    clf.fit(x_train, y_train)\n",
        "    \n",
        "    pred = clf.predict(x_test)\n",
        "    \n",
        "    print(clf.__class__.__name__, \" \", accuracy_score(y_test,pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:01.521987Z",
          "iopub.status.busy": "2021-01-20T14:51:01.521039Z",
          "iopub.status.idle": "2021-01-20T14:51:02.261948Z",
          "shell.execute_reply": "2021-01-20T14:51:02.261162Z"
        },
        "papermill": {
          "duration": 0.885076,
          "end_time": "2021-01-20T14:51:02.262093",
          "exception": false,
          "start_time": "2021-01-20T14:51:01.377017",
          "status": "completed"
        },
        "tags": [],
        "id": "wWwe2vHyrkQx",
        "outputId": "6a0f162f-eb4a-454a-e643-ba695209cfc7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "acuracy:  0.8722826086956522\n"
          ]
        }
      ],
      "source": [
        "# let's try out VotingClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "voting_clf =VotingClassifier([('lgclf',log_clf),('svc',svc_clf),('knn',knn_clf),('dt',dt_clf),('rf',rf_clf)])\n",
        "\n",
        "voting_clf.fit(x_train,y_train)\n",
        "\n",
        "y_pred = voting_clf.predict(x_test)\n",
        "print(\"acuracy: \",accuracy_score(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:02.545439Z",
          "iopub.status.busy": "2021-01-20T14:51:02.544532Z",
          "iopub.status.idle": "2021-01-20T14:51:02.552587Z",
          "shell.execute_reply": "2021-01-20T14:51:02.553260Z"
        },
        "papermill": {
          "duration": 0.152544,
          "end_time": "2021-01-20T14:51:02.553447",
          "exception": false,
          "start_time": "2021-01-20T14:51:02.400903",
          "status": "completed"
        },
        "tags": [],
        "id": "jUWjLk67rkQx",
        "outputId": "3062c2c0-95e6-419c-a0e9-72d1c985dfb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "training shape:  (1102, 30)\n",
            "testing shape:  (368, 30)\n"
          ]
        }
      ],
      "source": [
        "# if we do same thing on scaled features\n",
        "#split data into train and test set using scaled data.\n",
        "x_train_scaled,x_test_scaled,y_train_scaled,y_test_scaled = train_test_split(x_scaled,y,test_size=0.25,random_state=42)\n",
        "print(\"training shape: \",x_train_scaled.shape)\n",
        "print(\"testing shape: \",x_test_scaled.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:02.963573Z",
          "iopub.status.busy": "2021-01-20T14:51:02.962446Z",
          "iopub.status.idle": "2021-01-20T14:51:03.466604Z",
          "shell.execute_reply": "2021-01-20T14:51:03.465674Z"
        },
        "papermill": {
          "duration": 0.715873,
          "end_time": "2021-01-20T14:51:03.466783",
          "exception": false,
          "start_time": "2021-01-20T14:51:02.750910",
          "status": "completed"
        },
        "tags": [],
        "id": "xYHFMPdurkQy",
        "outputId": "20b60090-33ab-4c49-e76f-8c54e4b88c86"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LogisticRegression   0.904891304347826\n",
            "SVC   0.8831521739130435\n",
            "KNeighborsClassifier   0.875\n",
            "DecisionTreeClassifier   0.7907608695652174\n",
            "RandomForestClassifier   0.875\n"
          ]
        }
      ],
      "source": [
        "# Now check the accuracy score on scalled features\n",
        "for clf in [log_clf, svc_clf, knn_clf, dt_clf, rf_clf]:\n",
        "    clf.fit(x_train_scaled, y_train_scaled)\n",
        "    \n",
        "    pred = clf.predict(x_test_scaled)\n",
        "    \n",
        "    print(clf.__class__.__name__, \" \", accuracy_score(y_test,pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:03.746753Z",
          "iopub.status.busy": "2021-01-20T14:51:03.744201Z",
          "iopub.status.idle": "2021-01-20T14:51:04.248209Z",
          "shell.execute_reply": "2021-01-20T14:51:04.246614Z"
        },
        "papermill": {
          "duration": 0.647374,
          "end_time": "2021-01-20T14:51:04.248354",
          "exception": false,
          "start_time": "2021-01-20T14:51:03.600980",
          "status": "completed"
        },
        "tags": [],
        "id": "6Snz_MINrkQy",
        "outputId": "e3c67f22-7f8f-4914-a68e-6f2b1b5f54e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "acuracy:  0.8913043478260869\n"
          ]
        }
      ],
      "source": [
        "# try votingClf with scaled data\n",
        "\n",
        "voting_clf =VotingClassifier([('lgclf',log_clf),('svc',svc_clf),('knn',knn_clf),('dt',dt_clf),('rf',rf_clf)])\n",
        "\n",
        "voting_clf.fit(x_train_scaled,y_train_scaled)\n",
        "\n",
        "y_pred = voting_clf.predict(x_test_scaled)\n",
        "print(\"acuracy: \",accuracy_score(y_test_scaled,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.163346,
          "end_time": "2021-01-20T14:51:04.548619",
          "exception": false,
          "start_time": "2021-01-20T14:51:04.385273",
          "status": "completed"
        },
        "tags": [],
        "id": "ZspAigoWrkQz"
      },
      "source": [
        "**This is reason why Feature Scaling is used. by scaling down the features we are able to achieve better accuracy**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.136897,
          "end_time": "2021-01-20T14:51:04.826207",
          "exception": false,
          "start_time": "2021-01-20T14:51:04.689310",
          "status": "completed"
        },
        "tags": [],
        "id": "Nll6_NN7rkQz"
      },
      "source": [
        "**ADABOOST:** Adaboost is a boosting algoritm, which works on concept of errror correction using the concept of SAMME(Stagwise Adaptive Multimodelling using Multiclass exponential loss function) algorithm. It works sequentially by building a sequential model that works on a error generated by previous model. Repeatedly it do this process untill iterations are over of error becomes 0. Thus, it exploits the dependency between the models and different from Bagging(RandomForest).\n",
        "\n",
        "**XGBoost:** Xgboost is an extension to GBM, for better speed and performance. It is trained in additive manner. It prevents the overfitting, and outliers. for xgboost we also no need to impute missing values, it takes care of that too.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:05.106952Z",
          "iopub.status.busy": "2021-01-20T14:51:05.106332Z",
          "iopub.status.idle": "2021-01-20T14:51:05.402370Z",
          "shell.execute_reply": "2021-01-20T14:51:05.401456Z"
        },
        "papermill": {
          "duration": 0.438303,
          "end_time": "2021-01-20T14:51:05.402514",
          "exception": false,
          "start_time": "2021-01-20T14:51:04.964211",
          "status": "completed"
        },
        "tags": [],
        "id": "2BGQCDierkQ0"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:05.824019Z",
          "iopub.status.busy": "2021-01-20T14:51:05.823193Z",
          "iopub.status.idle": "2021-01-20T14:51:07.511373Z",
          "shell.execute_reply": "2021-01-20T14:51:07.512291Z"
        },
        "papermill": {
          "duration": 1.901918,
          "end_time": "2021-01-20T14:51:07.512491",
          "exception": false,
          "start_time": "2021-01-20T14:51:05.610573",
          "status": "completed"
        },
        "tags": [],
        "id": "KLXjWVHWrkQ0",
        "outputId": "923519c5-992b-4de3-d622-c00485a3c0f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "accuracy: 0.8722826086956522\n"
          ]
        }
      ],
      "source": [
        "boost = AdaBoostClassifier(base_estimator = DecisionTreeClassifier(max_depth=1), n_estimators=500, algorithm='SAMME',learning_rate=0.01)\n",
        "\n",
        "boost.fit(x_train_scaled,y_train_scaled)\n",
        "\n",
        "predictions = boost.predict(x_test_scaled)\n",
        "\n",
        "print(\"accuracy:\",accuracy_score(y_test,predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:07.929502Z",
          "iopub.status.busy": "2021-01-20T14:51:07.928575Z",
          "iopub.status.idle": "2021-01-20T14:51:08.081779Z",
          "shell.execute_reply": "2021-01-20T14:51:08.081069Z"
        },
        "papermill": {
          "duration": 0.363197,
          "end_time": "2021-01-20T14:51:08.081892",
          "exception": false,
          "start_time": "2021-01-20T14:51:07.718695",
          "status": "completed"
        },
        "tags": [],
        "id": "TLgWl8p4rkQ1",
        "outputId": "5147ac27-71af-4368-b2c0-c77e4f7caed8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "training accuracy: 0.837568058076225\n",
            "testing accuracy: 0.8722826086956522\n"
          ]
        }
      ],
      "source": [
        "#training accuracy\n",
        "print(\"training accuracy:\",boost.score(x_train_scaled,y_train_scaled))\n",
        "print(\"testing accuracy:\",boost.score(x_test_scaled,y_test_scaled))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:08.363522Z",
          "iopub.status.busy": "2021-01-20T14:51:08.362886Z",
          "iopub.status.idle": "2021-01-20T14:51:08.526248Z",
          "shell.execute_reply": "2021-01-20T14:51:08.527140Z"
        },
        "papermill": {
          "duration": 0.307925,
          "end_time": "2021-01-20T14:51:08.527301",
          "exception": false,
          "start_time": "2021-01-20T14:51:08.219376",
          "status": "completed"
        },
        "tags": [],
        "id": "oih1O-UTrkQ1",
        "outputId": "5005ec9f-2bd3-4483-861d-d4fd033537be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "accuracy:  0.8804347826086957\n"
          ]
        }
      ],
      "source": [
        "# XGBOOST\n",
        "xgb = XGBClassifier()\n",
        "\n",
        "xgb.fit(x_train_scaled, y_train_scaled)\n",
        "\n",
        "prediction = xgb.predict(x_test_scaled)\n",
        "\n",
        "print(\"accuracy: \",accuracy_score(y_test,prediction))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.141115,
          "end_time": "2021-01-20T14:51:08.845128",
          "exception": false,
          "start_time": "2021-01-20T14:51:08.704013",
          "status": "completed"
        },
        "tags": [],
        "id": "WcBhJjA9rkQ2"
      },
      "source": [
        "## Hyperparameter(Performance) Tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:09.126637Z",
          "iopub.status.busy": "2021-01-20T14:51:09.126026Z",
          "iopub.status.idle": "2021-01-20T14:51:09.131999Z",
          "shell.execute_reply": "2021-01-20T14:51:09.130544Z"
        },
        "papermill": {
          "duration": 0.149103,
          "end_time": "2021-01-20T14:51:09.132126",
          "exception": false,
          "start_time": "2021-01-20T14:51:08.983023",
          "status": "completed"
        },
        "tags": [],
        "id": "WtLR5ltcrkQ3"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:09.410781Z",
          "iopub.status.busy": "2021-01-20T14:51:09.410169Z",
          "iopub.status.idle": "2021-01-20T14:51:09.423762Z",
          "shell.execute_reply": "2021-01-20T14:51:09.424522Z"
        },
        "papermill": {
          "duration": 0.156213,
          "end_time": "2021-01-20T14:51:09.424699",
          "exception": false,
          "start_time": "2021-01-20T14:51:09.268486",
          "status": "completed"
        },
        "tags": [],
        "id": "INZjdpZurkQ4",
        "outputId": "18119486-1547-4b01-a601-5de61f69c9ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_depth': [5, 10, 15, 20, 25, 30], 'criterion': ['gini', 'entropy'], 'min_samples_split': [2, 5, 7, 10], 'min_samples_leaf': [2, 5, 8], 'max_features': ['auto', 'sqrt']}\n"
          ]
        }
      ],
      "source": [
        "# let's first try hyperparameter tuning with RandomForest\n",
        "\n",
        "#number of trees in forest\n",
        "n_estimators = [int(x) for x in np.linspace(100,1200,12)]\n",
        "#max depth of tree\n",
        "max_depth = [int(x) for x in np.linspace(5,30,6)]\n",
        "#quality of split\n",
        "criterion = ['gini','entropy']\n",
        "#min no. of samples to consider for splitting a internal node\n",
        "min_samples_split = [2,5,7,10]\n",
        "#min number of node can be as leaf node\n",
        "min_samples_leaf = [2,5,8]\n",
        "# The number of features to consider when looking for the best split:\n",
        "max_features = [\"auto\",\"sqrt\"]\n",
        "\n",
        "random_grid = dict(n_estimators=n_estimators, max_depth=max_depth, criterion=criterion,\n",
        "                  min_samples_split=min_samples_split,min_samples_leaf=min_samples_leaf,\n",
        "                   max_features=max_features)\n",
        "print(random_grid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:09.715615Z",
          "iopub.status.busy": "2021-01-20T14:51:09.714977Z",
          "iopub.status.idle": "2021-01-20T14:51:58.230548Z",
          "shell.execute_reply": "2021-01-20T14:51:58.229497Z"
        },
        "papermill": {
          "duration": 48.665113,
          "end_time": "2021-01-20T14:51:58.230684",
          "exception": false,
          "start_time": "2021-01-20T14:51:09.565571",
          "status": "completed"
        },
        "tags": [],
        "id": "Ru-lySGYrkQ5",
        "outputId": "67179ad4-362f-49a6-9887-a369c66f1101"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   39.8s\n",
            "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:   44.4s finished\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
              "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
              "                                        'max_depth': [5, 10, 15, 20, 25, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [2, 5, 8],\n",
              "                                        'min_samples_split': [2, 5, 7, 10],\n",
              "                                        'n_estimators': [100, 200, 300, 400,\n",
              "                                                         500, 600, 700, 800,\n",
              "                                                         900, 1000, 1100,\n",
              "                                                         1200]},\n",
              "                   random_state=42, verbose=1)"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_clf = RandomForestClassifier()\n",
        "rf_random = RandomizedSearchCV(rf_clf, param_distributions=random_grid, cv=5, n_iter=10,\n",
        "                               random_state=42, n_jobs=-1,verbose=1,)\n",
        "\n",
        "rf_random.fit(x_train_scaled, y_train_scaled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:58.726998Z",
          "iopub.status.busy": "2021-01-20T14:51:58.726223Z",
          "iopub.status.idle": "2021-01-20T14:51:58.730148Z",
          "shell.execute_reply": "2021-01-20T14:51:58.730797Z"
        },
        "papermill": {
          "duration": 0.283022,
          "end_time": "2021-01-20T14:51:58.731024",
          "exception": false,
          "start_time": "2021-01-20T14:51:58.448002",
          "status": "completed"
        },
        "tags": [],
        "id": "nCHwLqY2rkQ6",
        "outputId": "ace1f2fe-097e-41d9-fc6e-8f01d45ba571"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8538831756478815\n",
            "{'n_estimators': 700, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'auto', 'max_depth': 30, 'criterion': 'entropy'}\n"
          ]
        }
      ],
      "source": [
        "print(rf_random.best_score_)\n",
        "print(rf_random.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:59.094975Z",
          "iopub.status.busy": "2021-01-20T14:51:59.094146Z",
          "iopub.status.idle": "2021-01-20T14:51:59.097777Z",
          "shell.execute_reply": "2021-01-20T14:51:59.098411Z"
        },
        "papermill": {
          "duration": 0.226199,
          "end_time": "2021-01-20T14:51:59.098539",
          "exception": false,
          "start_time": "2021-01-20T14:51:58.872340",
          "status": "completed"
        },
        "tags": [],
        "id": "TJXEiPs9rkQ7",
        "outputId": "30f09631-858c-4991-97d6-acee2d7665b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000], 'max_depth': [6, 12, 18, 24, 30], 'learning_rate': [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01], 'min_child_weight': [1, 2, 3, 4, 5, 6, 7, 8, 9]}\n"
          ]
        }
      ],
      "source": [
        "# hyperparameter tuning for XgBoost\n",
        "\n",
        "n_estimators = [int(x) for x in np.linspace(100,1000,10)]\n",
        "\n",
        "max_depth = [int(x) for x in np.linspace(6,30,5)]\n",
        "learning_rate = [0.001,0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.009,0.01]\n",
        "min_child_weight = list(range(1,10))\n",
        "\n",
        "xg_grid = dict(n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate, \n",
        "               min_child_weight=min_child_weight)\n",
        "print(xg_grid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:51:59.391081Z",
          "iopub.status.busy": "2021-01-20T14:51:59.390404Z",
          "iopub.status.idle": "2021-01-20T14:52:22.485119Z",
          "shell.execute_reply": "2021-01-20T14:52:22.485883Z"
        },
        "papermill": {
          "duration": 23.244134,
          "end_time": "2021-01-20T14:52:22.486075",
          "exception": false,
          "start_time": "2021-01-20T14:51:59.241941",
          "status": "completed"
        },
        "tags": [],
        "id": "Oa83FjAlrkQ8",
        "outputId": "04184f89-a83b-481b-d637-872e960152cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   18.5s\n",
            "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:   22.1s finished\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5,\n",
              "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None, gamma=None,\n",
              "                                           gpu_id=None, importance_type='gain',\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate=None,\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           n_estimators=100,...\n",
              "                                           subsample=None, tree_method=None,\n",
              "                                           validate_parameters=None,\n",
              "                                           verbosity=None),\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={'learning_rate': [0.001, 0.002, 0.003,\n",
              "                                                          0.004, 0.005, 0.006,\n",
              "                                                          0.007, 0.008, 0.009,\n",
              "                                                          0.01],\n",
              "                                        'max_depth': [6, 12, 18, 24, 30],\n",
              "                                        'min_child_weight': [1, 2, 3, 4, 5, 6,\n",
              "                                                             7, 8, 9],\n",
              "                                        'n_estimators': [100, 200, 300, 400,\n",
              "                                                         500, 600, 700, 800,\n",
              "                                                         900, 1000]},\n",
              "                   random_state=42, scoring='accuracy', verbose=1)"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xgb = XGBClassifier()\n",
        "xgb_random = RandomizedSearchCV(xgb, param_distributions=xg_grid, cv=5, random_state=42,\n",
        "                               n_iter=10,scoring=\"accuracy\",n_jobs=-1,verbose=1)\n",
        "\n",
        "xgb_random.fit(x_train_scaled,y_train_scaled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-20T14:52:22.826636Z",
          "iopub.status.busy": "2021-01-20T14:52:22.825729Z",
          "iopub.status.idle": "2021-01-20T14:52:22.828961Z",
          "shell.execute_reply": "2021-01-20T14:52:22.829842Z"
        },
        "papermill": {
          "duration": 0.157488,
          "end_time": "2021-01-20T14:52:22.830080",
          "exception": false,
          "start_time": "2021-01-20T14:52:22.672592",
          "status": "completed"
        },
        "tags": [],
        "id": "TQyAanhkrkQ9",
        "outputId": "98c3f15c-f7b5-4b0b-9d95-b4902f0ae5c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "score:  0.8629452900041136\n",
            "best_params: \n",
            " {'n_estimators': 600, 'min_child_weight': 7, 'max_depth': 24, 'learning_rate': 0.004}\n"
          ]
        }
      ],
      "source": [
        "print(\"score: \",xgb_random.best_score_)\n",
        "print(\"best_params: \\n\",xgb_random.best_params_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.144603,
          "end_time": "2021-01-20T14:52:23.119773",
          "exception": false,
          "start_time": "2021-01-20T14:52:22.975170",
          "status": "completed"
        },
        "tags": [],
        "id": "2aP2udWmrkQ-"
      },
      "source": [
        "### **If you like this Notebook, Please Upvote. It gives motivation for making a new notebooks and positively move ahead with a Data science journey**\n",
        "\n",
        "**Thank You!..** "
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "papermill": {
      "duration": 121.040833,
      "end_time": "2021-01-20T14:52:23.373865",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-01-20T14:50:22.333032",
      "version": "2.1.0"
    },
    "colab": {
      "name": "Employee Attrition Analysis",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}