Python Understanding Exploratory Data Analysis.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dartwinshu/rakamin-digital-festival-data-science/blob/main/Python%20Understanding%20Exploratory%20Data%20Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-29T04:35:22.605758Z",
          "start_time": "2020-08-29T04:35:22.483778Z"
        },
        "id": "FCAHqFN_Wcnb"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ahp0IZElWcnb"
      },
      "source": [
        "## Mengubah parameter default matplotlib\n",
        "Parameter default matplotlib dapat diubah dengan rcParams sebagai berikut (optional: estetika plot)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-29T04:35:23.229059Z",
          "start_time": "2020-08-29T04:35:23.214079Z"
        },
        "id": "ULs8_FOiWcnb"
      },
      "source": [
        "from matplotlib import rcParams\n",
        "\n",
        "rcParams['figure.figsize'] = (12, 4)\n",
        "rcParams['lines.linewidth'] = 3\n",
        "rcParams['xtick.labelsize'] = 'small'\n",
        "rcParams['ytick.labelsize'] = 'small'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFqDHXnkWcnb"
      },
      "source": [
        "## Load dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3j9jQQYWcnb"
      },
      "source": [
        "Untuk hands-on kita akan melihat dataset sintetik prediksi kebotakan. Agar running time-nya tidak terlalu lama, kali ini kita ambil sampel 1000 baris data saja."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "end_time": "2020-08-29T04:35:24.161264Z",
          "start_time": "2020-08-29T04:35:23.966992Z"
        },
        "id": "enaTJCbmWcnb"
      },
      "source": [
        "# change file path if necessary\n",
        "df = pd.read_csv('botak.csv').sample(1000, random_state=42)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGx3fpRKWcnb"
      },
      "source": [
        "## Descriptive Statistics\n",
        "### Memeriksa kolom-kolom dan nilai hilang dengan `df.info()`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5i0I3cfZWcnb"
      },
      "source": [
        "Hal pertama yang biasanya dilakukan dalam EDA adalah mengecek tipe data setiap kolom dan jumlah nilai yang hilang. Hal ini bisa kita lakukan dengan menggunakan `df.info()`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "end_time": "2020-08-29T04:35:25.695391Z",
          "start_time": "2020-08-29T04:35:25.649431Z"
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      "source": [
        "df.info()"
      ],
      "execution_count": 5,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1000 entries, 6255 to 6621\n",
            "Data columns (total 13 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   umur           1000 non-null   float64\n",
            " 1   jenis_kelamin  999 non-null    object \n",
            " 2   pekerjaan      987 non-null    object \n",
            " 3   provinsi       1000 non-null   object \n",
            " 4   gaji           1000 non-null   float64\n",
            " 5   is_menikah     1000 non-null   int64  \n",
            " 6   is_keturunan   1000 non-null   float64\n",
            " 7   berat          1000 non-null   float64\n",
            " 8   tinggi         1000 non-null   float64\n",
            " 9   sampo          1000 non-null   object \n",
            " 10  is_merokok     1000 non-null   int64  \n",
            " 11  pendidikan     1000 non-null   object \n",
            " 12  botak_prob     1000 non-null   float64\n",
            "dtypes: float64(6), int64(2), object(5)\n",
            "memory usage: 109.4+ KB\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5yhcpExWcnc"
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      "source": [
        "Kita dapat melihat bahwa:\n",
        "* Dataframe memiliki total 1000 baris dan 13 kolom\n",
        "* Dataframe masih memiliki *null* values di kolom `pekerjaan` dan `jenis_kelamin`\n",
        "* Target klasifikasi adalah kolom `botak_prob` dengan tipe data `float64`\n",
        "* Berarti sisanya adalah *feature* (predictor)\n",
        "\n",
        "Dari informasi di atas, kita bisa pisahkan kolom kategorical dan numerical sebagai berikut:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "end_time": "2020-08-29T00:32:04.700005Z",
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        "id": "UbKney-6Wcnc"
      },
      "source": [
        "# best practice\n",
        "cats = ['jenis_kelamin', 'pekerjaan', 'sampo', 'pendidikan', 'provinsi']\n",
        "nums = ['umur', 'gaji', 'is_menikah', 'is_keturunan', 'berat', 'tinggi', 'is_merokok', 'botak_prob']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7lgvwtNWcnc"
      },
      "source": [
        "Tentu saja menyimpan nama kolom berdasarkan tipe *numeric* dan *categorical* seperti di atas tidak wajib, tapi ini lumayan akan memudahkan beberapa proses ke depannya."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bOquij0Wcnc"
      },
      "source": [
        "### Sampling untuk memahami data dengan `df.sample()`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "duQ19hmcWcnc"
      },
      "source": [
        "Setelah mengetahui kolom apa saja yang ada di dalam dataset, kita lakukan sampling untuk memastikan apakah isi kolomnya sesuai ekspektasi. Biasakan lakukan ini beberapa kali karena seringkali apabila ada keanehan tidak akan keluar pada sampling pertama.\n",
        "\n",
        "Sampling dapat dilakukan dengan menggunakan `df.sample()`"
      ]
    },
    {
      "cell_type": "code",
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          "end_time": "2020-08-29T04:36:13.934642Z",
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        },
        "scrolled": true,
        "id": "T2c32UrTWcnc",
        "colab": {
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          "height": 206
        },
        "outputId": "62459ab4-cb07-4f7d-fac0-9b1071baa7b7"
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      "source": [
        "df.sample(5)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      umur jenis_kelamin       pekerjaan   provinsi          gaji  is_menikah  \\\n",
              "982   37.0     Perempuan             PNS     Serang  1.255546e+07           0   \n",
              "4478  41.0     Laki-laki             PNS  Palembang  1.276825e+07           0   \n",
              "3317  47.0     Perempuan             PNS      Ambon  7.878717e+06           0   \n",
              "4322  42.0     Perempuan  Pegawai swasta     Serang  1.042874e+07           0   \n",
              "7648  49.0     Laki-laki  Pegawai swasta     Padang  3.077706e+06           0   \n",
              "\n",
              "      is_keturunan      berat      tinggi     sampo  is_merokok pendidikan  \\\n",
              "982            0.0  56.806408  158.727226   Merpati           0         S1   \n",
              "4478           1.0  50.045133  156.674450  Deadbuoy           0         S1   \n",
              "3317           0.0  59.755579  159.074581  Deadbuoy           0         S1   \n",
              "4322           0.0  53.516554  159.321199  Deadbuoy           0         S1   \n",
              "7648           0.0  68.888055  166.111800   Merpati           1         S2   \n",
              "\n",
              "      botak_prob  \n",
              "982     0.267744  \n",
              "4478    0.695412  \n",
              "3317    0.279869  \n",
              "4322    0.272655  \n",
              "7648    0.446233  "
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              "      <th>982</th>\n",
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              "      <td>47.0</td>\n",
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              "      <th>7648</th>\n",
              "      <td>49.0</td>\n",
              "      <td>Laki-laki</td>\n",
              "      <td>Pegawai swasta</td>\n",
              "      <td>Padang</td>\n",
              "      <td>3.077706e+06</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1r-Zeo4Wcnd"
      },
      "source": [
        "Beberapa pengamatan:\n",
        "* Target kita, `botak_prob` benar bertipe float dengan *range* 0-1, dimana 1 melambangkan kemungkinan 100%\n",
        "* Sepertinya tidak ada yang aneh dengan input setiap kolom (sudah sesuai)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-25T01:13:50.998861Z",
          "start_time": "2020-08-25T01:13:50.995862Z"
        },
        "id": "RigrAOnGWcnd"
      },
      "source": [
        "### Statistical summary dengan `df.describe()`\n",
        "Selanjutnya kita munculkan ringkasan statistik dataframe, baik untuk numerical dan categorical. Hal ini dilakukan untuk mengecek secara singkat akan keberadaan outlier dan karakteristik distribusi untuk `feature` numeric."
      ]
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      "cell_type": "code",
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        "outputId": "c03ca8e1-7faa-4800-f432-42398a10609b"
      },
      "source": [
        "df[nums].describe()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              umur          gaji   is_menikah  is_keturunan        berat  \\\n",
              "count  1000.000000  1.000000e+03  1000.000000    1000.00000  1000.000000   \n",
              "mean     40.008000  9.223088e+06     0.033000       0.18000    56.162619   \n",
              "std       9.886642  4.739127e+06     0.178726       0.38438     9.258898   \n",
              "min       8.000000  1.500000e+06     0.000000       0.00000    40.921334   \n",
              "25%      33.000000  5.849544e+06     0.000000       0.00000    50.099311   \n",
              "50%      40.000000  8.107307e+06     0.000000       0.00000    54.102392   \n",
              "75%      47.000000  1.132830e+07     0.000000       0.00000    60.485423   \n",
              "max      72.000000  4.179443e+07     1.000000       1.00000   128.643924   \n",
              "\n",
              "            tinggi   is_merokok   botak_prob  \n",
              "count  1000.000000  1000.000000  1000.000000  \n",
              "mean    157.486031     0.492000     0.390735  \n",
              "std       6.548078     0.500186     0.192807  \n",
              "min     142.554038     0.000000    -0.068044  \n",
              "25%     152.899106     0.000000     0.260230  \n",
              "50%     157.293445     0.000000     0.368024  \n",
              "75%     161.493659     1.000000     0.508568  \n",
              "max     193.484937     1.000000     1.000000  "
            ],
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              "      <th></th>\n",
              "      <th>umur</th>\n",
              "      <th>gaji</th>\n",
              "      <th>is_menikah</th>\n",
              "      <th>is_keturunan</th>\n",
              "      <th>berat</th>\n",
              "      <th>tinggi</th>\n",
              "      <th>is_merokok</th>\n",
              "      <th>botak_prob</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.008000</td>\n",
              "      <td>9.223088e+06</td>\n",
              "      <td>0.033000</td>\n",
              "      <td>0.18000</td>\n",
              "      <td>56.162619</td>\n",
              "      <td>157.486031</td>\n",
              "      <td>0.492000</td>\n",
              "      <td>0.390735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.886642</td>\n",
              "      <td>4.739127e+06</td>\n",
              "      <td>0.178726</td>\n",
              "      <td>0.38438</td>\n",
              "      <td>9.258898</td>\n",
              "      <td>6.548078</td>\n",
              "      <td>0.500186</td>\n",
              "      <td>0.192807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.500000e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>40.921334</td>\n",
              "      <td>142.554038</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.068044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>5.849544e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>50.099311</td>\n",
              "      <td>152.899106</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.260230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>40.000000</td>\n",
              "      <td>8.107307e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>54.102392</td>\n",
              "      <td>157.293445</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.368024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>1.132830e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>60.485423</td>\n",
              "      <td>161.493659</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.508568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>4.179443e+07</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>128.643924</td>\n",
              "      <td>193.484937</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-be2d51dc-2d3f-4d4b-ba5d-06baf7bcf9ba button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-be2d51dc-2d3f-4d4b-ba5d-06baf7bcf9ba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SceQdfMWcnd"
      },
      "source": [
        "Beberapa pengamatan dari *statistical summary* kolom numerik di atas:\n",
        "* Distribusi nilai di kolom `umur` terlihat normal/simetrik (*mean* dan *median* cukup dekat, dan nilai Q2 kira2 tepat di tengah2 Q1 - Q3)\n",
        "* Sedangkan kolom `gaji` tampaknya tidak simetrik, karena mean > median\n",
        "* Ada baris yang memiliki probability `botak_prob` minus. Baris ini harus dihilangkan di tahap pre-processing nanti"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgfogYgjZSCa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7e2acd0-edbf-4979-f721-ff2077137f21"
      },
      "source": [
        "# jumlah baris dengan botak_prob negative\n",
        "df[df['botak_prob']<0].shape[0]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-29T00:32:07.456785Z",
          "start_time": "2020-08-29T00:32:07.423804Z"
        },
        "scrolled": false,
        "id": "01tAVWj5Wcnd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "054e4ed4-fb36-4c5f-f975-f57b9ec62642"
      },
      "source": [
        "df[cats].describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       jenis_kelamin       pekerjaan    sampo pendidikan  provinsi\n",
              "count            999             987     1000       1000      1000\n",
              "unique             2               4        5          6        34\n",
              "top        Laki-laki  Pegawai swasta  Merpati         S1  Denpasar\n",
              "freq             684             409      228        562        41"
            ],
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              "\n",
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              "      <th>jenis_kelamin</th>\n",
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              "      <th>sampo</th>\n",
              "      <th>pendidikan</th>\n",
              "      <th>provinsi</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>999</td>\n",
              "      <td>987</td>\n",
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              "    </tr>\n",
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              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Laki-laki</td>\n",
              "      <td>Pegawai swasta</td>\n",
              "      <td>Merpati</td>\n",
              "      <td>S1</td>\n",
              "      <td>Denpasar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>684</td>\n",
              "      <td>409</td>\n",
              "      <td>228</td>\n",
              "      <td>562</td>\n",
              "      <td>41</td>\n",
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              "</table>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e0c9f690-fe3a-4d58-b82a-32abccce1f10 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e0c9f690-fe3a-4d58-b82a-32abccce1f10');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4bMXuioWcnd"
      },
      "source": [
        "Beberapa pengamatan dari *statistical summary* kolom kategorikal di atas:\n",
        "* Mayoritas data adalah Laki-laki (Top kolom `jenis_kelamin` adalah `Laki-laki` dengan frekuensi 684)\n",
        "* Kolom `provinsi` memiliki jumlah nilai unik yang banyak;ada kemungkinan tidak terlalu berguna untuk menjadi predictor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Juio7NVFWcnd"
      },
      "source": [
        "## Univariate Analysis\n",
        "Setelah melakukan analisis sederhana tentang statistik deskriptif, sekarang kita fokus pada satu-persatu kolom dengan *Univariate Analysis*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQA6nvPjWcnd"
      },
      "source": [
        "### Box plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-29T04:42:08.001602Z",
          "start_time": "2020-08-29T04:42:06.531448Z"
        },
        "scrolled": true,
        "id": "AHFiQSI4Wcnd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c0cdf0c7-cec8-45b7-e9de-1fc35db9c162"
      },
      "source": [
        "for i in range(0, len(nums)):\n",
        "    plt.subplot(1, len(nums), i+1)\n",
        "    sns.boxplot(y=df[nums[i]], color='blue', orient='v')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eM7NVf00Wcnd"
      },
      "source": [
        "Untuk boxplot, hal paling penting yang harus kita perhatikan adalah keberadaan outlier.\n",
        "* Outlier terlihat utamanya pada kolom `gaji`, `berat`, dan `tinggi`\n",
        "* Dari boxplotnya terlihat mana distribusi yang terlihat agak *skewed* (tidak simetrik) yang ditandai dengan lokasi box yang jauh dari daterah tengah sumbu Y: `gaji`, `berat`, `tinggi`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-25T01:31:54.648374Z",
          "start_time": "2020-08-25T01:31:54.645375Z"
        },
        "id": "wIJ4xk-sWcnd"
      },
      "source": [
        "### Dist plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-29T04:46:20.960234Z",
          "start_time": "2020-08-29T04:46:18.699139Z"
        },
        "id": "vTq7jD88Wcnd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "outputId": "5afe9743-c361-49dc-e029-94208a2b2bec"
      },
      "source": [
        "plt.figure(figsize=(12, 5))\n",
        "for i in range(0, len(nums)):\n",
        "    plt.subplot(2, len(nums)/2, i+1)\n",
        "    sns.distplot(df[nums[i]], color='blue')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 8 Axes>"
            ],
            "image/png": 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JapGJiEjZKFoDq5UhS+OAxdGxnmaWWX3pM9Ewph8RvrQki4ULw0KrAAMGhEehmTXuxfrZz8L8r+RDysoKwhwrgD7A8sSxrwFPuvvetHAR6O4T3X2Eu4/o379/UYOFwvVgzZ4N9fUFCUmkRe6+xN3XRZsbgSHAfHff6O4zgH3Ti05ERMpFMXuwWhqylPWYu2+M9m0FvJjtTUt9l70czZ8fl/fcs3jnOf30sLArwJtvwjvvFO9c0m4zgSjBPkcDMxLHjHhY4DJCAyx1ySyCffu27bVbbx0ntli3Dl54oXBxibTGzHYGjgKeJO45Bsi6WIa+t0REaktODSwzu9fMjjOztjTIWhqylPWYmX3CzGYA1wJTs71pqe+yl6NSNbD694eTE/2Ojz9evHNJLJ/65u7PAUvNbDqwN3CPmU2IDv8FGGdmU4D/AX5T6JjbqqEBliyJt9vagwVw8MFx+emn2x+T1JY8v9cws97ALcCZQB1xzzHA5myv0feW1JJ865ZINcn1w38t8BVgoZn9wsyG5PCaloYsZT3m7u+7+2jgJODnOcZWU1aujIdWdeoEgwcX93znnx+XZ89WsosSyae+4e4XuvsYdz89GrJ0drR/pbsf4+5jo+OvFjP4XPz2t7BpUyj36AFdurT9PdTAknZqcz0zs07A7cBl7v4KYX7jnmbWxcxGAfNafAOR2pDXd5hINcmpgeXu/3D304H9CBN7/xFlTfp6MnlFEy0NWdriWJSNKRPPSmBt7j9G7Uj2Xn3qU2ENrGI66CA48MBQrq8Pa2JJceVZ3yrKihVxOZ/eK4CRI+PyU0+1Lx6pPXnWs9OAg4BLoh7hLwJXAVOAy6OHSE2rhe8wkdbk3H1rZtsShkR8E/gncDWh8jya7fmtDFl6CBgYHVvv7k8BnwSmmNkTwHXAxXn9RFWuVMMDk5K9WFOnxgk2pHjaWt8qzcqVcbmtGQQzPv3puOdr4UL48MP2xyW1JY/vtVvcfduoN3isu98RPUa5++HuvqhkwYuUsWr/DhNpTadcnmRm9xGyJd0CfN7dM7Mn7jCzOc29zt0vbLIrM2SpnlDxks99Bzg0t7Brk3v2BlaxM/qdfDJ85zvhonjlSpg7N+7VksLLt75VkkL0YN18c8ig+eabYfuZZ+DYY9sfm9SGWqhnImlQ3RLJvQfrBnffy91/nqkoZtYVwN1HFC06aWT+/PjOf48esPPOpTlvly5w2GHxtpJdFF3V17dkAyvfHixovB6W5mFJG1V9PRNJSZvrlpn1MbNZZrbazIZG+xaa2ZTocWTpwhdpv1wbWNnGlWvWQ4k9muhY32MP6FDC/DxjxoSkGhB6DDK9BlIUVV/fCjFEEMI8xAw1sKSNqr6eiaQkn7q1FjgOuDuxb2ViOK6GFkpFaXGIoJl9AhgAdDezTxPW04GQAbBHkWOTJpo2sEqpd2844IA4mYB6sQqvlupbIYYIQuMerGeeCenfS3njQSpPLdUzkVJqT91y901AnZkld/cys6nAe8B33X151heLlKHW5mAdTZgrtSON185ZBfxXkWKSLDZuhClT4u299ip9DIcfHjew5swJ6eJ32KH0cVSxmqlvhRoiuO22ofH/8cfhMX8+7L13++OTqlYz9UykxApdt0a7+4dmdgZwGfC9pk8ws/HAeICBmdXnRcpAiw0sd78ZuNnMTnL3e0oUk2TxzDOwZk0o9+sXFgEutYEDYbfd4LXXQk/B9dfDT35S+jiqVS3Vt0I1sMxCL9bzz4ftp59WA0taVkv1TKSUCl233D2TG/ZuQjbCbM+ZCEwEGDFihLf3nCKF0toQwa+6+/8Bg8zsgqbH3f03WV4mRZAcHliq9OzZHH54aGBBaGBdfHF+i8TKlmqlvtXXw6pV8XZ7hghCmIeVaWDNnAnf+Eb73k+qW63UM5FSK2TdMrMugLn7BmAM8FrhIhUpvtaGCPaM/u1V7ECkZeXSwBo+PPQ4rFgBdXUweTKMG5dePFWmJurb0qVhyQGArbaCjh3b93677hqXtRC25KAm6plICtpVt8zsEWA4IcX7/cCpZrYG2ACcVZAIRUqktSGCE6J/LytNOJLNypUwa1Yom8GQIenF0rFjWANr8uSwfeutamAVSq3Ut8WL43J7hgdmDBoEXbvChg1hweH334dPfKL97yvVqVbqmUiptbduuXvTlQyvaHdQIinJKd+Wmf3SzHqbWWcze8zM6szsq8UOToInnghzniDMg+qV8n3Xgw6Ky3/9K6xenV4s1aja61uygdXe4YEAnTvDTjvF2+rFklxUez0TSYvqlkju62Ad5e4fA+OAt4DdgAuLFZQEEyeGx1VXxfvSHB6YseOOcfbAdevg/vvTjacKVXV9K3QPFsDgwXF52rTCvKdUvaquZyIpUt2SmpdrAyszlPA44C53X9nSk6WwFiyIy+XQwIIwTDDj1lvTi6NK5VXfzOwKM5tuZreYWecmx75sZo+b2RQzG1nogNvivfficiF6sAB23z0uqwdLcqTvNZHiUN2SmpdrA+shM1sA7A88Zmb9gfXFC0syli8PSQEgDIVKTuhPU7KBNXkyXHll6G2TgmhzfTOzYcAAdx8DLABOThzbATgB+Ky7j3X3p4oXeuuK0YP1qU/FCwzPmwcffVSY95Wqlk8962Nms8xstZkNjfYtjG5cTDGzI0sQt0i50zWj1LycGljufhEwChgRrba9hnDBJkX28stxefDg0MgqB9tuG9bEgjA/LJMmW9ovz/o2CohSjzAJGJ04dgwhC9OjUe9WqrP4itHA6tYtnoflDjNmFOZ9pXrlWc/WEu7K353YtzK6cTHW3R9t5nUiNUPXjCKtp2lP2oOwtkHyNX8ucDzSxPz5cblchgdm7LdfvCbWP/8JY8akG0+VaWt96wssicorgW0Sx7YH+gFHAucA3wV+0fQNzGw8MB5g4MCBeQfemmI0sCDcgHj77VCeNk3ZLSUnbapn0cVinZkld/cys6nAe8B33X15USIVqSy6ZpSallMDy8xuAXYFngM2R7sdVZaiamgoz/lXGcOHw513hvKCBbB2bbrxVIs869sKoHdU7gMsb3LsCXd3M3sMuDjbG7j7RGAiwIgRIzzvH6AVhc4imDF4MPzjH6GseVjSmgJ+r4129w/N7AzgMuB7Wc5VkpsXIuVA14wiufdgjQD2cveiXXTJlt59N06BvtVWMGBAuvE0te22IW38O+/A5s3wwgtpR1Q18qlvM4ELCF9gRwPJQXIziDM4DQfeKESQ+Vi/Hj78MJQ7dAif60LJDFkFmDMH1qyBnj2bf77UvIJ8r7l79InmbuCbzTynJDcvRMqErhml5uWa5OJFQEt3llhyeOAee8ST+MvJpz8dl597Lr04qkyb65u7PwcsNbPpwN7APWaWWfRxHrDIzKYAZwG/K2y4uVuyJC737l3Yz3SvXvHyAfX18PTThXtvqUrt/l4zsy5m1jXaHAO81u6oRCqfrhml5uXag9UPeNnMZhEmywPg7scXJSoBynv+VcZ++4XFhgFefDEME+zRI92YqkBe9c3dm64zcnbi2H8VNMI8FWv+VcbgwfE5pk6Fz3628OeQqpFXPTOzRwg9wUOA+4FTzWxN9B5nFS9ckYqha0apebk2sC7N583N7ApCJpm3gLOiCcKYWUfgBmAwMNfdv29muxCGNzUAq4Gv1PLaCevXxwkkoHwbWJ/4BHzyk6FnYuPGkLL9xBPTjqriXZp2AMVS7AbW7ruHhhXAE08U/v2lqlyaz4vc/dgmu65ofygiVeXStAMQSVtODSx3n2pmOwOD3f0fZtYD6NjSa5Lr8pjZjwjr8twWHR4HLHb3s8zshmjh0wXA5919hZmdDXwLuDLPn6viTZ8OmzaF8vbbwzbbtPz8QmrrelbDh8dDv+69Vw2s9sqnvlWKYiW4yEguOPz005qHJc2r5nomkibVLZEc52CZ2bcIE3gnRLsGEIZGtKSldXm2OObuH7n7imjfRkJPVs2aPDku77VXenHkIjkP68EHQ0+W5C/P+lYR3nsvLhejB6t37zgZTH09/FdZDIyUclTN9UwkTapbIrknuTiX0ED6GMDdFwLbtfKavpnns+W6PM0eM7OtCWv13JTtTc1svJnNMbM5dXV1OYZfeZINrL33Ti+OXAwcGPewrVgBU6akGk41yKe+VYRi92ABDBkSl195pTjnkKpQtfVMJGWqW1Lzcm1gbXD3f/VLRAvHtZZ+s7V1ebY4ZmadgVuBC5pbrNHdJ7r7CHcf0b9//xzDryxLlsC8eaHcsWOYuF/OzBr3Yt13X3qxVIl86ltFSDaw+vYtzjnUwJIcVW09E0mZ6pbUvFwbWFPN7L+A7mZ2JHAX8GArr5kJHBGVm67L09yxicCd7v5kjnFVpUcfjcu77grduqUXS66aNrA2b27+udKqfOpbRShFD9buu4dGP8Dbb4deVZEsqraeiaRMdUtqXq4NrIuAOuAFQurnR4CLW3pBS+vyAA8BA6Nj6939KTMbA5wKfN3MppjZ+W3/carD3/8el8t9eGDGrrvGi8YuXQoXXRSSZbQ1YYYAedS3SlHsLIIQlgkYODCU3WHatOKcRype1dYzkZS1uW6ZWR8zm2Vmq81saLTvFDObaWaPmdmORY9apIByzSLYYGb3A/e7e84Tn5pbl8fd64Ezmzx3OlDz+b4aGhr3YJV7gouMDh1CL1bmYvbZZ8t/aGO5yre+lbtVq8IDoFOn4q6XNmRI6L2CkK79eK2+Ik1Uaz0TSVuedWstcBzwK/jXsMILgMOAA4BLSKztKFLuWuzBsuBSM1sGvAK8YmZ1Zvbj0oRXe557DjK5O7baCnasoHs2yWGC//xnaCxK7qq9vjXtvcoM4yuG5Dysxx8v3nmk8lR7PRNJS3vqlrtvatIYGwzMd/eN7j4D2LdIYYsURWtDBH9AyARzgLtv4+7bAAcBo83sB0WPrgYlswfuuWfoGaoUQ4bEvRIffRT3IEjOqrq+lWL+VcZuu8V1Z968+KaFCFVez0RSVMi6lcw2Dc2so1UrmaWl8rR2+f5vwGnu/mZmh7u/AXwVOKOYgdWqSlr/qqmOHWHYsHj72WfTi6VCVXV9K8X8q4xu3WCXXeLtJ54o7vmkolR1PRNJUSHrVjLbNEDW1Fm1kFlaKlNrDazO7r6s6c6oG7dzcUKqXatWwZOJ/Il77pleLPnab7+4/OyzIcmA5Kyq61spG1gAe+wRlydNKv75pGJUdT0TSVEh69ZCYE8z62Jmo4B5hQhQpFRaa2BtzPOY5GHSJNi0KZSHDSvNRWih7blnnFZ+2TJ4991046kw7apvZnaFmU03s1uiNeWaHr/IzOa0K8J2KOUQQYB99onLf/ub5gTKv+h7TaQ42vsd9ghwFHADcDpwFTAFuDx6iFSM1rIIDjOzj7PsN6ACVmeqLA88EJdPOCG9ONqjc2fYd1+YNStsa5hgm+Rd38xsGDDA3ceY2Y+Ak4HbEse3AvZp7vWl8N57cbkUNw923hl69oQ1a+D990MCmWQPq9Ss9tSzPsCjwF7Awe7+opmdQph7sg74mrvrtpLUqnZdM7r7sVl239HuqERS0GIPlrt3dPfeWR5bubuGUhTQpk3w8MPxdqU2sKBxNkE1sHLXzvo2CsjM4JtEmGicdD7w+0LH3BalbmB16NB4Hbm//a3455Ty1856lkklfTc0SiU9FvgxIZW0SE3SNaNIrIJy1FW3J58MmfcgpGZPNlIqzdCh0KVLKL//Prz8crrx1IhkxqWVwDaZA9Fd933c/amW3qDY2ZiSw0X79i3422eVHCb4yCOlOadUL6WSFhGRXKiBVSaSwwOPP764awQVW5cuoZGVcc896cVSQ5IZl/oAyxPHvg/8rrU3KGY2ps2bS9+DBSETZ6YuPf00LF/e8vNF2kippEVEZAtqYJUBd/i//4u3O3eGiRPTi6cQkj1wamCVxEzgiKh8NDAjcWw34GIzmwQMjuZoldTSpaGRBdCvX9zDWWy9esFBB4VyQ0PjZRBECkCppEVEZAtqYJWBefNCxj0IGfgGD043nkLYZx/oFKVQef55eP31dOOpdu7+HLDUzKYDewP3mNmE6Ni/ufsx7n4MsNDdf1rq+BYtiss77ljac2+3XVy+76OAMvIAACAASURBVL7SnluqnlJJi4jIFtTAKgO33x6X99kn9GBVuu7dG6/jpV6s4nP3C919jLufHs0JOTvLc0akEVty/tVOO5X23Mne1AcfhNWrS3t+qS5KJS0iIq1RAytl7o0bWAcckF4shZZMiX333enFIelL9mCVuoG1ww4wYEAor1sXGlki+XL3Y919B3cf6e43ufsd7j7K3Q9390Wtv0NhzZ8PJ50EV10VRguIiEj61MBK2TPPwFtvhXKPHmFSfrUYNiykygaYPRveeCPdeCQ9yR6sUg8RBBiR6LdL3tAQqWRvvw0jR8K994aG1rXXwty5aUclIiJqYKXs1lvj8qc/XR3DAzN69my8DpEubGtXmj1Y0Lhn+JFHYPHi0scgUmjf+x6sXNl43623wvr16cQjIiKBGlgpWreucfbAahoemJH8mdTAql1pJrkA6N8fdtstlOvr4frrSx+DSCG98kr24a5r1sDMmaWPR0REYmpgpeiee2DFilDu1w+GDEk3nmIYNixkRgR44QV46aV045F0pJnkIuPww+Py9dfDhg3pxCFSCMmlPE44AU47Ld5+8snSxyMiIjE1sFJ0ww1xefToeL5SNenWDT7/+XhbvVi1Z/PmxkPyMgknSm348Lj3rK4O/vSndOIQaS93uOuuePvss+Hgg+Mh5u+9By+/nE5sIiKiBlZqFiyAadNCuWPH0MCqVl/+cly+/fZwcSC14/3340WGt9oK/vzndOLo2DHMWcn48Y+3nL8iUgnmzImH3fbtC0ccEW5m7bNP/Jz7708nNhERKXIDy8yuMLPpZnaLmXVO7O9oZjdGx65K7H/czFaY2bhixlUO/vCHuPz5z0OfPunFUmzHHhsurAFee01ZrmpNJksmhIvBNH3ve/EQxbo6+MlP0o1HJB+PPhqXx42Le66GDcv+HBERKa2iNbDMbBgwwN3HAAuAkxOHxwGLo2M9zWxktP+rhEUbq9o118B118XbaUz6L6Vu3eALX4i3b7stvVik9F5/PS73759eHBAWwP7lL+Pt3/wG/va39OIRyccTT8TlI46Iy8nF3WfM0KLaIiJpKWYP1ihgclSeBIxu7Zi710Ty5JkzYe3aUO7fv3Eq82qVnIB9662waVN6sUhpvfZaXE67gQXwpS/BkUfG21/9aki+MnFi/BApVxs2hMZTxmc+E5f79InnOG7aFA9DFxGR0ipmA6sv8HFUXglsk+OxFpnZeDObY2Zz6urqChJoKW3eDI89Fm8fcUR1Jrdo6ogjYIcdQnnp0uzphaU6JRtY222XXhwQGk833ABHHx1fiC5fHi5S33sv3dhEcjFrVljiA2DXXbfMypnsxZo8GZGKZWaDzKzOzKZEjzK4RSdpqqQbocW8tF8B9I7KfYDlOR5rkbtPdPcR7j6ifzncDm+j++6DZctCuWdPGDUq3XhKpVMn+MY34u1KqBxSGOXWgwVhTuB998VzA+vqwnDB5HpdIuUoOTww2XuVsddecVnzsKQKTHX3sdGj8u6qS80qZgNrJpAZHX40MCPHY1XLHX71q3j7sMOgS5f04im1b3wDzEJ58uTGyQ+keiXnYKXdg5V0wAHhc9g7utWzejX89rfwzjvpxiXSkmQDa+zYLY8PHhwyZkJI1f7BByUJS6RYRkcJ0X5mlrmCECl/RWtguftzwFIzmw7sDdxjZhOiww8BA6Nj6939KQAzuxE4A7jczC4qVmxpefTRMLwDQo9Oti/HapTpzv373+O7q+7wxz+mG1e1aSFr5+fN7Bkze9LMri5lTMuXhweETGfllC1z4kSYNw++852Q/AJgzZqQhObNN9ONTSpHKYcxrV8PTz0Vb2frwerSBXbZJd6eOrVY0YgU3RJgN+BQYDvgi02fUOnTRqR6FXX2j7tf6O5j3P10d9/o7mdH++vd/czo2HmJ55/l7p9y9+Hu/otixlZq7vDf/x1vH3JIeV1slsqYMXH5hhvCBYO0XytZO58HRrv7IcB2ZjaiVHE1zSBYjvcfd9kFfvAD6NEjbK9aFVJfr1qVblxSUUoyjOmpp0KSC4Ddd4/ntTY1ZEhcnjKlWNGIFJe7b3D3Ne7uwL3AsCzPqehpI1K9aiC9QnmYPBmefjqUO3WCY45JN5607LsvbL11KC9dmt6is1Wo2ayd7v6Ou9dHmxuBhlIFVU4JLlqy885w7rmhbkIYWnXeeS2/RiShJMOYko2lbL1XGbvvnv01IpXEzLZKbI4BXmvuuSLlRg2sEnCHSy+Ntw85JP0FV9PSsWPjdVt++cuQWVHardXMnGZ2ALCduz+b7Q2KMdSiHBNcNGe33ULK9oybboK7704tHKkcJRvG1FqCi4xPfSqe36t5WFLBDjGzudF0kgHAX9IOSCRXamCVQLL3qkuX2u29yhgzJh6O9frrYV0sabcWM3Oa2Y6ERby/1twbFGOoxcKFcbncG1gAI0fCgQfG29/5TsgwKNKcUg1jWrs2/h6BlufwdukCBx0Ub6sXSyqRu//N3fePppOckRiJIVL21MAqsoYGuOSSeHv8+Nrtvcro1q3x3deLL9ZcrAJoNjNnNMziduBsdy/pvewXXojLn/xkKc+cv9NOgx13DOW6Ojj//HTjkfJWqmFMM2bEC7TvuSdsv33Lz082wNTAEimuVatgzhyYPz8sxeOedkSSNjWwiuz222H27FDu2hUuqrrciPk58si4R2PRIri6pLntqk8rWTu/D+wC/D7KcnZYKWKqrw9fNhmZhX3LXY8eMGFCvH3bbVoYW1pUkmFMyQXqP/vZ1p+vBpZI8b38Mnz5y+F65oAD4Kqr4Ec/CjeOr7sONm5MO0JJixpYRbRuXeMG1QUXVM5FZrF17944q+JPfqLU2O3VQtbO/3H3AYksZyVJ3LxwYZzxbMCAsLB2pXj33cZDrL79bVixIr14pHyVahhTWxtYI0fG87Dmzw9JhUSkMBoa4LLLYPhwuOOO+LsuY9myMMR8xAhYsCCdGCVdamAV0VVXhd4ZCBnU1HvV2PjxMHRoKK9dG7bVrV495s2Ly/vsk14c+Tr1VNgqGvy1eDFceGG68UjtWr4c5s4N5Q4dcltDsXt3OPjgeFvrYYkUxtq1cPLJIXlZZtguwB57hAQzXbvG+154ITSyJk/e4m2kyqmBVSSLFsHPfhZvX3YZ9O7d/PNrUefOYbHhDtGn8B//gCuvTDcmKZzM0FiA/fZLL4589eoV5mNl/OEP4TMqUmpTpsQ3n0aMiJe6aE2yIZbMQCgi+dmwAU48Ee67L943cmS4ATJ/PvzHf4TrmC9+MW5orVkT1lZMvkaqnxpYReAeuoZXrw7be+8N3/xmujGVqwMPhH//93j7P/9T8wWqxTPPxOXkcLtKsv/+4Ysy45vfDL0JIqWUbNgnl7loTbKBNWmSRgiItEd9PZx+Ojz6aLzvBz+A6dMb30Ts0gWOPjp8Bw4cGPZt2gRf+lLj10p1UwOrCP7yF3jooXh7woR4AVPZ0uWXhztAENbE+sIX4MUX041J2qe+Ph7SBGHyb6X63/+NM3++/Xbo1dLabVIqDQ1w//3x9pFH5v7a0aOhT59QfuutxsN2RSR3DQ1hGsM998T7LrsMfvObsL5nNsOGwZNPxgt/b9oUrm+SNx+leqmBVWCvvw7nnBNvH3oovPQSTJwYPyTI/H/cdBOccEI8hHLFinARoYuByjV7dkjyArDzzpWToj2bBx4Idx4zJk8OPdTqDZBSePppWLIklPv1CwvV56pLFzjuuHhbQ5RE2s4dfvhD+NOf4n0/+EHjJXias9NOoQd6p53C9po18LnPhetCqW5qYBXQqlVw0knhX4Dddgvb0rq+feF734uTCrz/fmicTpuWblySn2TGs8MPTy+OQvn0p8OXYsbEiWGoVkNDejFJbUjeMT/xxLaPhjjxxLic7AkTkdxcfjn89rfx9te/Dr/+NZjl9vqddgpDA/v1C9sffRRuIr/xRuFjlfKhBlaBbNwIp5wCzz8ftjt3DmtgdeuWblyVZOBAePjhuCdr5crwR+jaa9VbUGmSGZNySSldCY4/vvFcsscfD+ufZOZaihSaO9x7b7ydzw27Y46J07U//zy8VpRlkEWq09VXw49/HG+fdFK4wZZr4ypjyJAwDzJzE3nJknB9s3hx4WKV8qIGVgGsXRuGuP397/G+CRPCBHlpm/nz4bzz4kbWxo1w7rnhQlbJBSrD0qVh3DmEL6G2TMovZx06wNe+1ngy8113hXH2f/ubbgJI4U2bFuZOQZhLlU9v8FZbhQn3GclhTiLSvP/9X/j+9+Pto46CW2/Nf079/vuHReszN97feCO857Jl7Y9Vyo8aWO305pswZky4M5ExblyYzKj5VvnZaSf4f/8vHrMMcOedMGhQmGQ6YUJqoUkO7r47bmwMHgx//Wv11IWOHeFb34LPfCbe98YbcOyxYRjh5ZeHoSAffJBejFI9rr02Lp92WtwT1VZnnRWX//hHWL++fXGJVLtrroHvfjfeHj069CYn17jKx2GHhRtzmUbaSy+F6RCZNVOleqiBlaf6+nB3Y9gwePbZeP+4ceEh7dO/f1hP4tBD432rVsENN4SLjldfTS82aZ47XHddvF2J61+1pkOH0KN61llxhjYIw68uuSTckdx++zCvcP/9w4LFs2Y1XpBSpDWvvhpuVmR8+9v5v9dxx8GAAaG8dGlILCQiW6qvD/PBzz8/3jdyJDzyCPTsWZhzjBsHf/5zPMxw/nwYNSosSizVQ8nD22jVKrjjDvjVrxpf5HfuHOZgHXZYerFVm86dw5oTQ4eG1PcrVoT98+aFtcXGjw+ZfXbZJd04JfbAA3F2pJ494eCD042nmA46KB5XP316+GJOWrEi3Hx59tlwx7Jnz3AX9LDDQq/3AQdojqY078IL4yQqRx8dbublq3NnuOCCeM3BSy8NPWLJGwQite6dd+DMMxsvyn3wwWEIeGbaQqGcdlpoYJ1xRrj59u674Tvl6qvDeottneNVzTZvhpdfDjcq77gjzHtety4Mt+zeHbbdNoxwGjw43NQcOLA8/v/UwGrFpk2hITVjRsiM9tBDYc5V0vbbh6wyutAvjmHDwjoS994bZxWsrw89WddfH5IPnHpqyPK29dbpxlrLVq4MqWszvvnN8Mevmm29dejNOuGEkCFq2rTQk/Xss1v2WK1ZE5J/ZBKAdO4cvgzGjAnz1MaMqf7/L8nNjTeGmxUZl13W/vc8++yQCe3dd0Mv1vjxcNttoUdW0pMcPj1+fHpx1LI1a8KQwJ//PM4CDeGm+c03F+/v8pe/HL43vvCFuNEwfjz83//BFVdU9w3K5riH4ZKzZsWPOXPC76ip5nr8+vWDAw8MvYIjR4Zyr17FjTsbNbAimzaFycQvvRQeL74YHvPnN7+oaPfuYTjQkUeGiyUpnu7dQ2/W6NEhbXGm9zCzCOf994cLhT32gOHDYd99Q5r8XXcNj0zmHimOZcvCl9Gbb4btPn3g4osbZ0CrZt27hy+A/fcPjzPPDNmhXn89ZG1buDCk5k3atCmscfT006FHvGvX0Mg66qiQeXHPPdXgqjUNDWEY9Lnnxvu+9a3G2Svz1bNnaGCdckrYvvNO6NEj7NONqXSsXx/ma370UbhB1bt3mGfXv3+Yg7zDDvnPu5OWbdwYRh7cf39o0GRGyEC4lrjkkpA9sNg3II44Iiw8fMopoZcGwo26kSPD4/TTww28HXcsbhxpcA+9hs8/D889B3PnhgbV+++3732XLQtDOh95JGx36BBu1CcbXLvskn+yklyZFzH1lZldAYwC3gLOcvdN0f6OwA3AYGCuu38/2n8+cCrwIfBVd/+4pfcfMWKEz5kzp9G+RYvgww/DxUt9feN/N24MlejDD8Pj/ffDBPU33gi/5FzXtNlhh/CLGjWqcGNyJXfu4Q/RP/4R/0FqTb9+cWNrl13Cdt++4dGrV6honTqFhnKnTuHCdo89sr+Xmc119xGF+4kKo631rSXZ6tZ770FdXbjLtm5d6Ml9993QQLj//nCBkHH77WFx3mpJbtFe7uFvzsKF4fHaa6EXoSVm4Uu1f//wOc1cfGU+o9n+bWiADRvChdv69eF3lHnU14fPdY8e4e/W1lvHjz594nKvXuHc7nGykoaG8Dc089i4Mfv25s2Nz9GjR3h06xa+5Dp0CA3JvfZq7mcuz7rVmubqXjbZ6ta8eaHBc8cdjdOoDx0a6lcu3zNN61pzvSHnnBN6/jP69IEvfhFGjAgLgvftG/897NQpJHYph+E25cw9fPYz1xrJx/r14e/mBx+EOr94cbhOWbQo7G+JGXziE2HI0847h38HDoy/t3r1CvUr83vq2jV8ZrK/V23WrRdegAULwvXekiXh/z9zEz1bspc99giJYEaNyi/efHsk164NQ3d/85vsN/V33DE0EnbeOTS++/dv/Bno2DH+G9v0kfl7Do3/rhe63HR7w4bQI7hqVeihW7Uq1IHM5/+ttxpfN7Rkhx3CjaaGBthmm/A9c/TR4Vrk/ffDzd0XXgi9XR+32HIIOnUK14KDB4f5qf36haGGmffu1i3Up8y/3buHRFbZNFe3itZ+M7NhwAB3H2NmPwJOBm6LDo8DFrv7WWZ2g5mNBBYCxwOHAF8BzgV+3tbzXnRRmK9TSH37hg/0kCGh8g0YoC+cNJmFOVh77x3+YD77bLgD8s47zafKXrYsPJ55JrdzDBwIb79duJiLra31zd2faus5Lroo3OlrzRVXhMaVxMzCH/B+/cIdNAhfNq+/Dq+8EnrKlyxp/JrMUIlqyy41YEBomFeLVupeTs44I15DMWOffcJw0kLfxPvd78JFyc03h+2VK0PqdqVvLz/u4e/CkiW5fXf169d6o62SFKJu/fKXuX1v7bZbmPd41lnF79nIpkePEOtZZ8FPfxqG7yYbWu++W11/N1uy1Vbhhs9BB4XepgMPjJP0JBuwxx+/5WsbGsJNqqefhpkzw+PFF7e8Nqyvj2945qJXr8bDR3NRtB4sMzsHWOPufzaz/YGvu/t3o2NXAA+7+zQzOwnYBXgJOMDdf2Jm2wI3u/sW+fjMbDyQuS8wBHgly+n7AeW0skC5xQPlF1MlxbOzu/cvZTCtaWt9c/crs7xHLnWrJWn9DtP87NTaz1zs85Zd3WpNS3Uv8Zz21q1clPozkcZnsNp/xmKeT3WrNMrtWiZXlRh3ucSctW4Vs53eF8jck10JbNPk2MdNjmXbtwV3nwi0OPDIzOaUU1d4ucUD5ReT4mm3tta3LeRSt1qS1v9Zmr+rWvuZK7BelEJLdQ9of93KRal/N2l8Fqr9Z1T92kJZ1K22qNTfYSXGXe4xF3P63gogk9iyD7C8lWMtPV9EWtbW+iYihaH6JVIcqltSsYrZwJoJHBGVjwZmtHJsNnBoM88XkZa1tb6JSGGofokUh+qWVKyiNbDc/TlgqZlNB/YG7jGzCdHhh4CB0bH17v6Uu9cBD5vZDEKSi2vbcfqy6S6OlFs8UH4xKZ52aGt9K1IYaf2fpfm7qrWfuaLqRSlkq3sphVLq300an4Vq/xlVvxLKqG61RaX+Disx7rKOuahp2kVERERERGqJ1nAXEREREREpEDWwRERERERECqTqGlhmdoWZTTezW8ysc0oxHGhmT5nZNDO7zcw6m9kpZjbTzB4zsx1Tius0M6uLyqnGY2Zjo3M/YWZfMLNDonieNLN9Uoing5ndFH12njSzPdKOqZKkVe/MrI+ZzTKz1WY2tITn3aKOl+i820efyalm9riZfbIU502c/19/Q6S0mqtjZtbRzG6Mjl2V2H++mc0wswfMrHf2d837nJ83s2eiv41XJ/avMrMp0aPNfzNbON9YM1sUve9jhfoZWzjfFxI/xztmdn60f2Fi/5F5nK/Zv1fF/D1KcbS1TpaLttaztOVTb8pBVTWwLLHqN7CAsOp3GhYBh7v7ocBbwAnABcBY4MfAJaUOyMw6AqcAi8ysU5rxmFl34N+Bz7n7Z9z9PuCnwHGEBCdXlDKeyHCga/TZ+U/C/0/aMVWElOvdWsLv6O4SnhOy1/FSWAYc4u6HAX8GvlGi8zb6G1Kqc0rQSh0bByyOjvU0s5Fm1g84HjgEuAM4t8DnfB4Y7e6HANuZWWYtmlfcfWz0eKGA5wO4I3rfz0bPb9fP2NL53P2+zM8BvA7cHx1amfj5Hm3L+SIt/b0qyu9RiqOtdTKNGLNpaz0rE22qNyWNrAVV1cACRgGTo/IkYHQaQbj7EndfF21uJKwuPt/dN7r7DGDfFMI6DbgLaAAGpxzPSGAd8KCZ3Rfdhd/s7h+5+zs0sxBukb0LmJkZYXHDNWUQU6VIrd65+6YoA2lJZanjDSU672Z3z5xrK+ClUpw3kvwbIqXVUh3LduwAYKqHLFb51slmz+nu77h7fbSZ/PzvGvXqXmdm3Qp1vshJ0V3q86Pt9v6Mrf7dMrNPEG68vR3t6hX1Hv/FzNr8ndDK36ti/R6lONpaJ8tFW+tZ6vKoN2Wh2hpYfYGPo3LWVb9Lycx2Bo4CniSOC6BjiePoCJxKuAMGjf+fSh4PsD2wG/B54Abgsibx1JtZlxLHtAzYRLij8zvgt2UQU6Uoq3pXSok6/mAJzznczJ4Bvgs8W6JzNv0bIqXVUh3LdqwQdbLV9zCzA4Dt3D3zOdwt6tVdQtt7W1o63xzCjcrPAseY2f65xNeO82V8kcapwUdHvceTCN9bhVSs36MUR1vrZLloaz0rd2X7f11tDayyWfU7Git9C3AmUEccF8DmEofzVeDOxJ3v5P9TGvGsAGa4+0bgMeDTTeLpFB0rpaOAencfApwE/LoMYqoUZVPvSilZx919U6nO6+7PuftBhKG9/1mi0zb9GyKl1VIdy3asEHWyxfewMHf3KuBrmX3u/mFUvBsYVqjzufvqaMTFRsLNjGGtxdee8yWcTGJYUjt/vnziqcm/rRWirXWyXLS1npW7sv2/rrYGVlms+h3NcboduMzdXwEWAnuaWRczGwXMK3FIewFnmNkkwvDA76Ucz+zo/EaY+/Qy0MnMtjaznUinghiQ+fJcRhh+lXZMlaIs6l0pZanjpTpvshd1JWFseik0+htiZteU6LwStFTHsh2bDRzazPPbfU4z24rw+T/b3T+I9vWMejoBxgCvFfB8yZtdh0Tv3d6fscW/W2a2PYnhgdH3ZdfocD4/Xz7xFOL3KMXR1jpZLtpaz8pd2f5fd0o7gEJy9+fMLLPq9zvAlSmFchpwEHCJmV0CXEe40zcFWE/ijl8puPt/ZMpmNsfdzzGzL6UYzzIzuw+YCjhwFjAAeCTa/k4p44k8CpxpZlOBroQkF51SjqkipF3vzOwRQkN9iJlNcPebSnDaLeq4u5di+NxwM7uS0Ou8nlB3ii7L35DzSnFeCbLVseizfjbwEHBidOyf7v4UgJk9bGYzgI+A0wt8zu8DuwC/D/fJ+G9Cg/9GM1sdnfOMAp7vVDMbD9QTRj9Ma+/P2Mr5YMvhgX2BR8xsDbCBPOte079XwMhi/h6lOPKpk+Ugn3pWDtpab8qBhbmTIiIiIiIi0l7VNkRQREREREQkNWpgiYiIiIiIFIgaWCIiIiIiIgWiBpaIiIiIiEiBqIElIiIFY2Z9zGyWma02s6EtPG+CmU2JHuvMrG8p4xQRESkWNbBERIrEzL5tZmdE5Z+Y2RGtvaYKrAWOI7FAazbufra7jyUsxv6Uu39U/NBERESKTw0syUm0sKqItIG7X+/uf47KP3b3f6QdU7G5+yZ3r0vuM7MzzWy6mc00s8ObvOQU4K7SRSiFZGYz044hw8z+YGZ7ReXVbXjdWDN7qHiRibQun7qUx+d8VFvPIfnRRXMVMLNBwEPuPjTa/iHQCxgL/JOw6nxPwsKP/wnsA9zh7hc391p3v9TMpgDPEVb0vg34dal+JpFyFS0s/FWgDlgEzCUssjoe6AK8Bvybu681s0uB1e5+pZndRKhrLfbsVBsz2xb4MnAo0AN4GHg88ZQvAiekEJoUgLuXzQWbu38z7RhE8lWCujQWWA3k3JAzs07uXl+0iKqYerCq30Z3HwFcD/wVOBcYCpwZXfi0pou7j3B3Na6k5pnZAcBJwDDgc8CI6NC97n6Auw8D5gPfSCnEcrQrsDfwBKFx1T9zILrBs9bdP0glMmm3zB10M/ukmU0zs+fM7EUzG9PSa8zsV2b2kpn9w8wOjObivWFmx0fP6Rg9Z7aZzTOzs6P9Y6Pn3m1mC8zsVjOz6NgUMxvR5Fz9zOwpMzvOzAZFPanPRo/kBW2vbO8pUir51KXEa5Of8/5mdk9Ud2ab2ejob+23gR9E7zvGzG4ys5OznH9sVE8eAF6O6s2Lief9MLp5mKlzV0Tzbl/NxNpcXWup/lYb9WBVvweif18AXnL3JQBm9gawE7CildffUcTYRCrNaOCv7r4eWG9mD0b7h5rZ5cDWhN7jv6cVYBl6A5gHjHN3N7POiWMno+GB1eIrwN/d/adm1pHQW9mcnsDj7n6hmd0HXA4cCewF3Ez43voGsNLdDzCzrsAMM5scvf7ThEb7YmAGoV4+2fQkZrZ99F4Xu/ujZtYDONLd15vZYMLIjBFteU+REmhLXcr2Of8L8Ft3f9LMBkbvtaeZXU80oiJ6XUs3AvcDhrr7m1HjrCWd3P1AMzsW+G/gCOADaryuqYFVHepp3BvZLVHeEP3bkChntju18lqANQWKUaSa3QSc6O7Pm9mZhKEYNcvMHgGGA0OACcDtwFQz20y42XNe9FQND6wes4Ebowb0/e7+XAvP3QhMisovABvcfZOZvQAMivYfBeybuMPeBxgcvXaWu78LYGbPRa9peoHWGXgMONfdpyb2/d7MhgObgd0Tz8/lPUVKoS11Kdvn/Ahgr0THUG8z69XGGGa5+5s5Pvfe6N+5xPW35uuahghWh6XAdma2bXSnb1yJXitSa2YAnzezbtEXVqa+bAUsib4QsaQDGQAAIABJREFUT08tujLh7se6+w7uPtLdb3L3W9z9UHf/jLufl3jeqKYJMaQyufs0wjy794CbLMqe2YxN7u5R+V83/9w9c+MPwIDvufvw6LGLu2d6sJI3CzeT/WZxPeGC7+jEvh8QvvOGEe6md0kcy+U9RYqujXUp2+e8A3Bwou4McPdsyTD+dYPdzDrQuD6syfa8SNMb8Zm6k6w3NV/X1MCqAu6+CfgJMAt4FFhQiteK1Bp3n00YijEP+Bvh7vtK4BLgGUIDrGkdckSqnJntDCx19xuAPxCGGLXH34FzMkNKzWx3M+vZhtc7cBawh5n9R7SvD7Akasj9G9CxnTGKFFwb61K2z/lk4HuJ9xseFVcRbgZmvAXsH5WPJ/Q6ZZPPjfiar2tV2WqsRe5+DXBNC8enAFMS22Nbe23yOSLyL1dGWTZ7ANOAue7+LHBdluduC7wN4O5nli5EkZIbC1xoZpsImcpauuueiz8Qhg49G02CrwNObMsbuPtmMzsNeMDMVgHXAvdEPQKT0BB4KU9jaUNdyvI5Pw/4XzObR7jOn0ZIcPEgcLeZnUBogN0A/NXMnqeF+hAN383ciH+P3G7E13xds7iXXkREWhNNIN6LMEziZnf/eTPP+x/CsI3PufuHJQxRREREUqQGloiIiIiISIFoiKCIiIgUnJk9A3Rtsvvf3P2FNOIRqVSqS5VHPVgiIiIiIiIFoiyCIiIiIiIiBaIGloiIiIiISIGogSUiIiIiIlIgamCJiIiIiIgUiBpYIiIiIiIiBaIGloiIiIiISIGogSUiIiIiIlIgamCJiIiIiIgUiBpYIiIiIiIiBaIGloiIiIiUlJkdaGZPmdk0M7vNzDonjo01s0VmNsXMHkszTpF8mLunHUPe+vXr54MGDUo7DKkxc+fOXebu/dOOo5hUtyQNqlsixVGOdcvMPgmscPd1ZvZzYK673x0dGwuMc/cf5vp+qluShubqVqc0gimUQYMGMWfOnLTDkBpjZm+nHUOxqW5JGlS3RIqjHOuWuy9JbG4EGpo85SQzOwi4292vbu39VLckDc3VLQ0RFBEREZFUmNnOwFHAg4ndc4AhwGeBY8xs/2ZeO97M5pjZnLq6uuIHK5IjNbBEREREpOTMrDdwC3Cmu2/K7Hf31e6+0d03Ehpew7K93t0nuvsIdx/Rv39ZjYCUGqcGloiIiIiUlJl1Am4HLnP3V5oc653YPAR4rZSxibSXGlgiIiLNUKYzkaI5DTgIuCSqQ18yswnRsVPNbJaZzQTec/dp6YUp0nYVneQiTRMnxuXx49OLQ0RalqyroPoqbbYIODyR6ewE4O7E8TvakumsXDWtJ22leiVt5e63EIYHJt0RHfsD8IeSB1UBstVV1b/yox4sERGRZrj7EndfF202l+lsupmdX+LQRESkTKmBJSIi0gplOhMRkVypgSUiItICZToTEZG20BwsERGRZrSW6czdP442DwGuL3V8UjjtnYcGmgsjIoF6sERERJqnTGciItImRe3BMrMrgFHAW8BZmaEVZtYRuAEYDMx19+9H+4cCvwa6AHe6+3XFjE9ERKQlynQmIpVg3Tro2hU6qOukLBTt12Bmw4AB7j4GWACcnDg8DlgcHetpZiOj/T8HTnH3z6hxJSIiIiLSvAULYL/9oEcP6N0bzj0XPv649ddJcRWznTsKmByVJwGjWzpmZp8COgO3mtnfzWyPIsYmIiIiIlKx5syBq66Cf/4zbK9ZA9deC3vuCR9+mG5sta6YDay+QKYNvRLYppVj2wP7Al8F/h34TbY3VbpbEREREall778PN90E7lseW7wYvvQlaGi6ap+UTDEbWCuA3lG5D7C8lWMrCPOxVrr7i0DWXLZKdysiIiIitcodbr0VNkWLRmy/PfziF3DGGfFzHnsMrlde09QUM8nFTOAC4M/A0cCMJseOAKZFx/4ELAT6mVlnQm/WyiLGJiI1ZuVKWL4cVq+GXr3SjkZERCQ/L78Mr74ayh06hOUB+vaF0aPhgw9g0qRw7KKLoL4eunWLX6ulBEqjaD1Y7v4csNTMpgN7A/ckUts+BAyMjq1396fcvR74FfAEcBfwX8WKrRDeeQd+/Wv4/e/jsa8iUn4aGvj/7Z13uFTV1f+/6xbaRSlSpINUKWIBFVDAAhohotForDFGJcZXTXx/aGI0xjRjSSyxojEaXwuxKyiIKB1FmmIBUUGQJkU6l8vlrt8f6xz3nmFm7pRz5syZsz7PM8/dp8zZ68ydNWevvVfB888DN9wgM3ytWwPPPBO0VIqiKIqSHW+/bdrHHw+0bWu2R44EmjpBOdu3A1On5lU0xcHXNO3MPCZu12hnfzWASxKc/xKAl/yUyQtWrZKgwp07ZXvwYGDpUhm4KYpSWLz4YuzDaPt24IILZLbvBz8ITi5FURRFyZT162UFCwCIgOHDY4+XlwMjRgBPOcUlJk0Chg6NXcVS/Eez5WfB3Xcb4woQl6PzzvOmCryipAMR3U5EM4joKcet1t1fSkSPO8fusfb3drJzvktEVwYjdf6ZPTvWuLK57DLRXUVRFEUJC9Otcua9ewPNmu1/zoABgJumYNcuYO7c/MimGNTAypDKSuDf/95//7x54ueqKH6jNebSgxkYY62h9+kD3HmnBAMDkmXpwQeDkU1RFEVRMmXvXmDOHLM9dGji80pLY4/NmOGnVEoi1MDKkOnTgS1bpN2smbgZATJD4AYcKorPaI25NHjlFVnBAuRhc+65UoRx2DBzzl//qhMjiqIoSjiYOtV4UDVuDPTsmfzcAQOAMicQaOVKYMUKv6VTbNTAypBJk0z7sMOkerbLxx/nXx4lkmiNuTS4/XbTHjrUuEsMGCCGFiCZBd96a7+3KoqiKErB8cILpn3kkZJBMBkVFUC/fmZ71qzk5yreowZWBowdC/z3v2a7Vy+gh7UW8OWX+ZdJiSRaY64W3n9fXoDM4J16qjlWVgYcc4zZTuTyqyiKovgLER1NRHOIaDoRPZtOPHGUqa4GXn7ZbB91VO3vGWT5tyxYAOzb571cSmLUwMqAykpg9WppEwFdugCdO5vjK1eKq6Ci+IxbRw5IXmPOPvZ9jTkiaosI1Ji77z7T7t/frFi5DBxo2q++CmzcmB+5FEVRlO9ZBeBEZh4MYAWAUdaxZPHEkWXGDMB1LmnUCDjkkNrf06WLuBICktRp6VL/5FNiUQMrA1askMB5AGjTRlJeVlQArVrJvpoamSFQFD8p9hpzubJmTexK8wkn7H9O69ZAp07S3rtXjCxFURQlfzDzWmbe7WxWAaixDqeKNY4kdojK4Yendg90KSmJXemaN897uZTEqIGVAV99Zdr2zEH79qa9eHH+5FGiCzOPYebjmfkCZq5i5u9rzDHzJc6xa6zzX2Lm45h5ADMXdcLWRx4xiSsGDQI6dEh8nu2b/tpr/sulKIqi7A8RdQAwHMDr1u5Uscb2e4sqdjgVkyebdq9e6b/PftYtXAhUVXknk5IcNbAy4JtvTNsetLVpY9offZQ/eRRFiWXPHuDhh832NdckP/eww0z7zTeB++/XWnaKoij5hIgOBPAUgEuYea91KFWs8fcUU+xwKjZuFOMIkFWpbt3Sf2+nTkBTxzzdtUtTtucLNbAywI2/AmKNKrutK1iKEhzjxgHffivttm2BM89Mfm6LFsa9d+9e4LPP/JdPCR8aiK8o/kBEZQCeA3ArM8dHB6WKNY4c775rQlQ6dgTq10//vUSxE4qvv578XMU71MBKk8pKM3AjkhgOl3gDy1UCRVH8Z+xYeT3yCHCjFV32y18C5eXJ3wfEPnR09VlJggbiK4o/nAfgGAA3E9FUIjo3VTxxYFIWAG+/bdqHHpr5++MNLB2n+o+vBhYR3e7M7D2VzqwfEW13lGwqEfXxU7ZM+ewzSWIBSIHhunXNscaNgQYNpL1tm2QTVBQlv3zyiVllrqgARo+u/T32Q+eTT/Sho+yPBuIrij8w81PMfBAzD3Ve42qLJ44quRpY3bqZcetXXwFLlngjl5Ic3wwsIuoLoI0zs7cEwNnW4WSzfkstRSsoZzvb9c9esQJkRatt28TnKoqSH+yCwZddZnzOU9Gpk2QDBYDvvjOr1IoSjwbiK4oSBF99ZZKsVVSYDLiZUF4O9OxpttVN0H/8XMFKNbOX7Fhnx8/9ISKql+iiQT2oUhlYQKzLoBpYipJfVqww9T1KS4Ff/zq995WWxgYL66yekggNxFcUJSjs1ashQ4Cysuyuo3FY+cVPAyvVzF6yY10cP/e1AK5KdNGgHlS20WQbUy66gqUowWGvXv3kJ8lTsyeiRw/TVgNLiUcD8RVFCRLbwDr55OTn1UafPuJxBQCzZ0tmQsU//DSwUs3sJTzGzJucfS8A6OujbBnz8cemnWgFy81GBmg2MkXJJxs2xBb4HjMms/fb/uxLlgD79nkjl1I0aCC+oiiBUFMDTJlitocNy/5aBxxgarjW1AATJ+Ymm5IaPw2sVDN7+x0jogoiKnX2HQ/gCx9ly4ht20zwfGmppHeO5+CDTXvpUpMQQ1EUf3n7bZOcomdPoG+GUzOtWgEHOtM9u3YBixZ5K58SbjQQX1GUoFi0CNjsLE+0bJlZgeFE9O5t2hMm5HYtJTW+GVjMvAjAemdmrxeAF2uZ9esK4AMimg7gNAD3+iVbpixbZtotWoiRFU/DhjI7AAC7d2smQUXJB5s3i6uDSzaze0SxboK2O4aiKIqiBMXkyaZ98snGxS9b+lj5uSdOBKqrc7uekhxf07Qz8xhnZu8CZq5KNevHzIuY+UhmHszMo5h5q5+yZUK8gZWMli1NW2M5FMV/Hn4YqKqSdtu22aWvBWINLNsdQ1EURVGCwqv4K5e2bU3OgC1bYicoFW/RQsNp8Pnnpp3KwNI4LEXJH3v2APfdZ7aHDct+ds82zGbONEaboiiKogTB7t3A1Klme/16YOzY3K5JBIwYYbbVTdA/1MBKg3RXsOw4LF3BUhR/eeYZeeAAUuy7X7/sr9W0qRQQB+ShNn9+7vIpiqIoSrbMnm1c+A4+GGjSxJvr2gbW+PHeXFPZHzWw0sA2sGw3wHhsA+vdd/2TR1EU4F//Mu0TTsi+NohL166mPWNGbtdSFEVRlFyw469sN/ZcOfFEoG5daX/6qdSRVLxHDaw0SNdF0Daw1q3zTx5FiTpffAHMcvKSlpQAAweaY2PHxr7SpUsX054+3Rs5FUVRFCUb7PirbOOLE1FRIUaWi7oJ+oMaWLWwaRPw3XfSrlNHXJGS0bQpUF4u7e3b5b2KonjPk0+adu/eJs16LtgrWDNnaj0sRVEUJRg2bTL1HYmA7t29u/bYsUCjRmb7oYe8u7ZiUAOrFuJXr1IF0ZeUaByWovgNM/DUU2Z7wABvrtuihTHUtm6NLS6uKIqiKPni3XdNfceOHYH69b29vp2ufelSYOdOb6+vqIFVK+kmuHBRA0tR/OXmm4Gvv5Z2gwaxD4pcINI4LEVRFCV4bPfAnj29v36zZibzdXU18M473vcRddTAqoVcDCxN1a4o3vPhh6bdu7dxy/UC28DSOCxFURQlCPxKcGFjT05qHJb3pGVgEdFLRDSCiCJnkNkugqkyCLroCpaSCVHWrWz56CPT7tvX22vHJ7pwXTSU4kF1TlH8QXXLG776Sl6AxP4fcog//cQbWPq885Z0leBBAOcDWEZEfyMiD8PtCps5c0xbV7AUH4isbmXDN98AK1dKu7QU6NXL2+u3aWMS2axfL9kKlaJDdc5i40bg9deB5ct1gKXkjOqWB0yZYtrduuVegiQZnTuLmz0gz1Z78lLJnbQMLGZ+m5kvAHAkgBUA3iai2UT0MyJK6qBDRLcT0Qwieso+j4hKiehx59g9ce8ZQERMRA2zuyXvYAa+/dZsp7OC1bKlSYSxfDlQWemPbEpxkK1uRZU33zTtbt28D/wtKQHatTPbf/qTt9dXgkd1zvDEE0CHDsDppwN/+xtw112SAVdRsiEb3SKiRkQ0l4h2EFHvuGNDiWgVEU0loimJ3l+M2PFXfrkHAjJJacd3vf66f31FkbSXcYnoIACXALgMwEIA90KUaHKS8/sCaMPMxwNYAuBs6/BIAGucYxVEZOcBuwbA/AzuwTfWrQP27JF2/fpAwzRMvvJy4KCDpM0c62KoKInIVLeijJ14wuvVKxc7DktXsIoT1TkZTF16KbBrl9n3xRfAAw9oiQIle7LQrV0ARgB4Icnxccw8lJlP8ljUgmTfPv/qXyXCdrN//nl/+4oa6cZgvQxgBoAGAH7IzKcz8zhmvhpAMrNjIIC3nPZEAINqO0ZExwH4CMCOTG7CL7780rSbN0+dot3GzcwCaByWkposdSuyuMWFgdh4KS+xr2snuVGKA9U5YNs24IorjEugXUdu+fLYAHuldlavBu6/H7j+emDIkOgmyMlGt5h5LzNvSHHZsxxvp2t9ELngmD8f2LxZ2gcfLG7rfnLYYSZR1Ecf6ZjVS9JdwXqUmXsy823MvBYAiKguADBzvyTvaQJgm9PeCqBpGseuBXB/KkGI6AoimkdE8zZsSKWTubN8uWk3b57++zTRhZIB2ehWJFm71gT+lpfHuvJ5SYcO5oGzcSOwZo0//SiBEXmdu/tu8dAAZELwyy+BM880xydO1Lo46bJ8OXD77cDixVI/b/p04IQTgFdfDVqyQPBat+YB6A7gJACnEtFR8Sfkc0yYDyZNMu3hw9Of2M+WevUkG6/LuHH+9hcl0jWw/pxg35wE+2y2AHDnxRoB2JzqGBENAfAhM6f0AGfmsczcj5n7Nc/E6skCdzAHGLe/dNBEF0oGZKNbkcReverY0b/A37IyoFMns631sIqOjHWumOJEdu0C7rvPbP/tb1ITZ9gwk8hp925g5sxg5AsTlZXAo4+aUAKXmhrgwgslcUDE8PR5xsw7mLmKmasAvA5gv7yx+RwT5gPbwDrllPz0eZRltj73nCa78YqUBhYRHezMGNQnoiOI6EjnNRSyBJyK2QBOdtqnAJhVy7G+AE4iookADgPwZEZ34gO6gqX4RY66FUlsA6tzZ3/7st0EdaBZHOSoc0UTJ/L888YFqVMn4PzzpV1aCpx6qjlv2jQxFJTkvPUWsGmTtBs0AC6/3KTU3rFDXAajgF/PMyKynFdxHICijoq9916TuZpIvDbywWGHmWyCS5YAH3yQn36LndrmgE+BBCu2BfAPa/92ADemeiMzLyKi9UQ0A8BKAHcR0SPMPBrAeABnOMcWMvMcyCzHfQBARFMB/DTz2/EW28Bq1iz999kG1tKl8pAq0aoQSixZ6xYgGTohsYwrAFzKzHud/aUAHgXQFcB8Zv6V9Z4BkMmNA5i5IOIcMyEf8VcudqILXcEqGnJ5nu0FsIGS++ucRUTHAHiBme/NXVT/ePxx0/7FL2JXgvv3B158UdwDN20SLw6/dS2s7NoVm4zgnHOAfv1k5WrIENn33HPALbcA3Ys/WXmuz7M3ABwOoDsRPQJggDNWPIeIrgBQDWAWMxd1dNtnn5lJjXbtgAMOyE+/devK9/eJJ2T78ceBo4/OT9/FTEoDi5mfBPAkEZ3FzC9menFmHhO3a7SzvxqijMneNzTTvvzAdhHMxMBq2FAUY/t2cbVYuVJcmhTFJRfdsjN0EtHvIBk6n3UOuxk6LyWiR4logDOBARRQhs5M2bULWLjQbPtVeNG+PpG4Snz0kcRWNGrkb5+Kv+T6PEuBGycCAK8S0UxmjtEzZ5B4BQC0b9/ew64zY+1aM2FQWgpcfHHs8Tp1gCOOMKu28+apgZWMmTONa2CrVsAxx0h78GDgtNOAN96Q348HHoh1ySxGPBgrnha36wln/2MAHstdwnDwySem7VeW3GTYY9wnn5S4rP/5n/zKUGzU5iJ4odPsSETXxb/yIF9g7NkjmYEAGWg1bZr6/Hjsmlkff+ydXEpxkKNu+ZKhs5CDhefOBaqrpd26NVBR4W9/9eqZJBrMwOzZ/van+I9fz7MwxYm8+qqJrxg8ONbbwqWflYpgwQJ1E0xE/G/CySfHeqlcZ32bnnkGqKrKn2xBEOWxolcwA59+arbzbWB16WLGrZWV6iboBbU5rrnDmIYADkjwKlpWrjQPosaNMw+otzOcLVjgnVxK0ZCLbvmSobNQBoGJsOOg/I6/ctE4rKLDl+dZmOJEXn7ZtO2sgTbduhnXpK1btRZcIr75xsTH1KkjrpU2J5xgxgCbNslqVpET2bGiVyxdamIj69Xz30sjHiJgkDVVO2WKJrvIldpcBB9x/t6aH3EKB9s9MJuxpu0FogaWEk+OupV1hs4UMSQFTT4TXLh06QK88460NQ4r/OT6PAt7nMiWLeb7DACjRiU+r7QUOPJISXIBiItst27+yxcm7Nn9vn0lhsWmpAS46CLgr3+V7SefBM44I3/y5ZsojxW9ws4e2KOH6GG+Oe44YMIE48E1ZYqszirZkW6h4TuI6EAiKieiKUS0wVoSLkrsBBeZpGh36dDBtNXAUpKRpW6FPkNnJtTUmMxKQP5iQux+5s7dPxWzEk6yfZ4x82nM3JqZBzDzE45xBWZ+jJmPZuaBCeKOC4ZJk4yb7VFHxU4CxtOnj2nbcSGK/B7Nm2e241evXH5qpel64w3JKljsRHGs6BW2gdWzZzAyVFQAAwea7TvuCEaOYiHd3HbDmXkbJIB+BYAuAAr2QeIFua5gHXywKVa6ahVQYCEtSuGQsW4x8yIAbobOXgBedGbUAcnQ2d45VsnMc5j5PmY+gZlPhcRhBZ6hMxM++URclQDRq0wSzuRCo0amLtCePbGDKiXURO55BsRmvBs5MvW53bqZGfQ1a2T1SxFWrYpNzZ4sVqZbN0l/DUgMlv35FzGR1K1cqawEpk412/mOv7I56SRT3HjyZHWPz4V0DSzXlXAEgOeZeatP8hQMua5glZYCbduabTsDmqJYZKVbzDyGmY9n5gucAPvvM3Qy8yXOsWsSvG9o2FK02+6Bgwb5X9nexl7F+vvfgbFj89e34huRe54BsQP82tx+6taN/e5/9pk/MoURO2lVnz6p47NHjDDtCRP8k6mAiKRu5crMmZJxGpBJvXxNIiaieXOTERMAbrpJY7GyJV0DazwRLQFwFIApRNQcQKV/YgVPtjWwbGwXjPmhTI6t5IHI6Vam2DNodhBuPrAHmRrsXzRETue++gpYsULaDRvGDqCSYbsp2dnNoo7tMtm7d+pz7ZXCCRMiMVCNnG55QSG4B9qMHGmyYk6bFpnJAc9Jy8Bi5t9A0j/3cwou7gSQJES2OMjVRRCINbDUvUhJRBR1K1PiV7DyiW1gffmlpqwuBqKoc/bq1eDBxn09FfZA77PPImEc1MrOnWZsQFT7YPiYY4wHzNq1xR+PHUXd8gLbwArSPdCleXNJeOFyzTVmhU1Jn3RXsACgB4BziehiSGHT4f6IFDxbtwLffSft8nLgwANTn58Mu7jwrFn6gFKSEhndypQ1a8zMe/36UgQ1n7RoYfR/1y6TmlkJPZHSuSlTTDvdrGBt25p6c9u3A+vXey9X2LANzQ4dZDUwFaWlwKmnmu3Jk/2TrYCIlG7lypo1wOLF0i4tLZyMnaNGmfqvy5cDt90WrDxhJN0sgk8BuAtS46O/8+qX8k0hJt49MNuYj9atJQgWkIfTsmW5y6YUF1HTrUyxV6+OPjq9mXcvIYpdxVIdDj9R0zlmYLqVOP7EE9N7X0lJbEkEdZGNjUVLd6XBNmjtRAbFSNR0ywveesu0u3SRGliFQMOGsUbV7bfr8y9T0i2f2w9AT+ZorMHY7oHZJLhwcR9Q7uzE9OmFMzuhFAyR0q1MsQ0s22Uhn3TpYlx7dJBZFERK5776Cli3TtqNGsWmYK+NLl2kDhYg3/2gdLBQsAeYPXqk956hQ0175kxg7978TxTlkUjplhcUWvyVzWWXAf/6l5QpqaoCLr9caumVZOL7FmHS/Zg+BnBwphcnotuJaAYRPUVE5db+UiJ63Dl2j7V/FhFNc/4G9lXzIsGFS9eupq3FSpUEZKVbUSHI+CuX+BUsHTqEnkjpnK1DAwdmNjjS1VuD7SZZWhobApCKjh1NXcydO4s+4VWkdCtX9u2LdRsthPgrm8ceA4YNi0148dhjwcoUJtL9qW0G4FMimkREr7mvVG8gor4A2jDz8QCWQHxxXUYCWOMcqyCiAc7+E5h5CIDfAbguozvxkPHjTdtLA8t201AUh4x1Kyrs2GHKGxABS5cGkya9bVtJWw1IPSB7AkYJJZHSOTsLZ6YrUB06mNWWjRujXQ/ryy9Nu2NHoE6d9N9rr2IVuZtgpHQrV+bPNzXVWrYE2rQJVp5EtG8PDLei6MaMAb75Jjh5wkS6LoJ/yOLaAwG43qUTAfwMwLPWsQnWsUEA5jBzlbPvAMhMSCBs3GjauRpY7dvLD3FVlQTrf/21mc1SFGSnW5Fg7lyZ4QOAVq1MPGO+KS2ViRK3/s2UKcAhhwQji+IJfwhagHySyypwWRnQqRPw+eeyvWwZ0L+/d7KFCXsFz45NS4ehQ4Enn5T21KnAb37jlVQFxx+CFiAsjB0bm/68U6fCdb0bMUImO9evB7ZtA668EnjttfzWpAwj6aZpnwapyl3utD8AUFvC0SYAtjntrQCa1naMiA4molkAHgQwLdFFiegKIppHRPM2bNiQjvgZ46WBVVYW62ZhBzQqSpa6FQnsgaGtQ0Fgx1vYKa+V8BElndu82dSwKi/PzjjSWnCCfe+Z/h4lisMqRqKkW15g11QrtPgrmzp1gIsuMtvjxwPjxgUnT1hIN4vg5QBeAPCIs6sNgFdqedsWAG6C80YANtd2jJnXMfMgAGcBSJgUkpnHMnM/Zu7XPNsCVSmoqfHWwAJi/WrffDP36ynFQ5a6FQlsAyvTGWOvOfRQ054yRethhZko6dzs2aZ95JHZrQLbbu5RNbD27AFWrjTbmRpYUYmT01aVAAAgAElEQVTDipJu5cru3bHu5vYzphDp2hUYMsRsX3117FhZ2Z90FySvgrjxbQMAZl4GoEUt75kNwE1QegqAWamOEVE5EbnybAWwK03ZPGXdOqC6WtoVFVJ7J1dsA+vtt4t39krJimx0q+jZtw+YM8dsB72C1bo1cMAB0t60Cfjww2DlUXIiMjqXS/yVyyGHGFeg1aujWXB0+XIzqdK6takPlgn2Kta773oiViESGd3Klc8+M9+p9u2zr7eaT848E2jXTtobNwK/+lWw8hQ66RpYe6z4KBBRGYCUubSYeRGA9UQ0A0AvAC8SkTurMR5Ae+dYJTPPAdAKwFQiehfAQwBuyuxWvMGeUcglRbvNwQeb2avt22NnFZXIk7FuRYGPPxZfb0BSS3uli9lSUqJugkVEZHTOiyyc9eqZQRVzbBmTqJCLe6CLPftvG75FRsa6RUSNiGguEe0got5xxxJmnC4GXNddoLDdA23q1wcefthsP/00MHFicPIUOukaWNOI6EYA9YloGIDnAbxe25uYeQwzH8/MFzBzFTOPdvZXM/MlzrFrnH0rmXkwM5/AzCcy86epr+4PXqZodyGKreb+xhveXFcpCrLSrWLHLmnQpUthBNPaLhxqYIWaSOjcnj3ABx+Y7VzKHNhJXexselHBCwPLXkGcNcsk8CkystGtXQBGQFwL40mWcTrUMMcaWIWWnj0Vp50GXHih2b7++qL9LudMugbWbwBsALAYwGgAbyCgFSa/WbHCtL0ysADJwuLy6qveXVcJPZHRrUywSxrYMSBBYhtYM2YAlZXByaLkRCR0buFCMbIAMQpa5OCoZcdARm0Fa9++2HvO1sDq0kVScQPA1q2xCQ6KiIx1i5n3MnOyjGXx2agDqoboLUuXmvTsdeuGLyvtXXcZN9nFi4FnnglWnkIl3SyCNZBAxV8y89nM/GixVur2YwULkPTsbt2MpUuBJUu8u7YSXqKkW+nCHGtgdesWnCw2TZuaQeru3bHuV0p4yEbnwujGZMcwDshx3j/ewIpSkpdvvjGGapMm2bsrE8WuYhWjm6APz7NU2agB5CeztNfYyc4OPVSyTYeFsWNlkcCOKbz5ZqMjiiGlgUXCH4hoI4ClAJYS0QYi+n1+xMs/fsRgAWJc2cvAv/2td9dWwkcUdStdli2TehuADGhatQpWHhvbV15dfcNFjjoXOjcmLw2spk2Bxo2lvWePJLuICl64B7ocf7xp227QYcfH51mqbNQA/M8s7Qe2gRUm90CbYcOAhg2l/fXXsbFZilDbCtavIUuy/Zm5KTM3BXAMgEFE9GvfpQsAv1awAODww0170SJvr62EjsjpVrrYq1fHH19YxRf79DHt8eODk0PJiqx1Llc3piBm2e1kSgMH5nYtoui6CXppYNkrWDNmyGp9keDX8yxVNupQsnMnMM2q8tq7d/JzC5n69SUey+XOO4GqquTnR5Hahi4XATiPmb83O5j5KwAXArjYT8GCoLoaWLXKbHuduaxPHzNYXLEiWrOAyn5ESrcy4V//Mm3XrbZQ6N7d1BL6/HN5KaHBL52r1Y0p37Psq1aZ50vDht4M4qKY6II51sDKNR60b18z6796tcz8Fwk56RYRvQFgOIBHieiSWjJOh5p33zWGSOvWsjocVgYPNunlV68GRo8WF8KxY4OVq1CozcAqZ+b9Sok5M3nl/ogUHKtWmWwojRoB5R7fYUWFDNBcXtHye1EmUrqVCcuWmXahJLhwKS8X1wiXCROCk0XJGL90rlY3pnxjr14dfTRQWpr7Ne0VrKgYWN9+a8pFNGiQu7tyWVmsu2YRxWHlpFvMfBozt2bmAcz8RKqM02GnGNwDXcrLgRNPNNtvvRWt+MzaqM3ASrXgV3SLgX66B7rYboIvvuhPH0ooiJRupcvXX8dmV3Lr7xQSI0eatroJhgq/dK7g3Jjs+Ktc3QNd2rUzk44bN0omvGLHXr3q3Nkbd+UiTXShz7M0YI6N3bVdzsPK4MHyrAaANWuKNjtmVtT2c9GXiLYleG0HUARfjVj8SnBhc8QRpqbP1KnA2rX+9KMUPJHSrXSxA787d/Zm5t1rbL/z6dOjMdAsEnLSuTC5MXmZ4MKlrAzo0MFsR2EVy77HXOOvXIo00YU+z9Jg6VJTCqhu3dhV4bBSURH7nX7rreTnRo2UBhYzlzLzgQleBzBz0bkx5WMFq1Ejk3aaGRg3zp9+lMImarqVLoVY/yqe1q2Bo46SdnU1MGlSsPIo6ZGrzoXFjWn3bmDBArN97LHeXTtqiS5sd2WvDKxjjjFpuT/91KzYhxl9nqVHmNOzp+Kkk8zq7uefx+YyiDIFlJ8reOwiw36tYAHiE++iBdoUxRAGAwuIdRN8+eXg5FCUeObPF8MfkJhfL4PooxSHtW2bxGAB+6/e5UKDBmaCBoiNl1OKm2KKv7Jp2jT2Oz1lSnCyFBJqYFnkYwULEDdB1/Xpgw9k2VhRos66dUYXysqAjh0DFSclP/qRaY8fD1RWBieLotj4EX/lYhtYK1cCe/d6e/1Cwo6/6tjR26RX8enaleKnWNKzJ8NOdvHBB6aWZZTx1cAiotud6vZPEVG5tb+U4irfE1EnZ3saEU0gokZ+ypaIfBlYFRWxwY2PPJL8XEWJCvas1yGHeJ/F00v69DGDzR07gLffDlYeRXGxV0S8ir9yadgQaNlS2tXVYmQVK17Wv4qnSBNdKCmw07P36hXu9OyJOOQQoFMnaVdX67gW8NHAIqK+ANo41e2XADjbOjwS2K/y/RYAP2TmIQBeA3C5X7IlYvduk3CipARo0sTf/gYPNu0nnpD+FSXK2EZKjx7ByVEbY8cCjz4aO5v/0kvByaMoLsz+JLiwseth2UZIseGngTXIKkU9b54+/6OA7R5oJ0oqJuxVrIceAvbsCU6WQsDPFaxU1e33O8bM3zHzFmdfFYC8ZtO3C/41aeJ/9rJDDzUPqu++A/77X3/7U4qLsK0O1wZz7ArWoYcGJ0u6HHmkab/6anG7SynhYPly45pz4IFAz57e9xGFRBeVlSZQnyjWqPSC5s3NJNLevcDcud5eXyksmIHXXzfbP/hBcLL4yVFHAY0bS3vdOh3X+mlgpapun/QYETUGcCWAJxJdlIiuIKJ5RDRvw4YNngmbL/dAl5ISqXrtcuONwMMPawVspXbCtjqcDsuWmQFNvXreBZT7SYcO5mGyeTNw/fWqv0qw2KtXxx7rTd2meOITXTB730fQLF9uCqa2bi1u/V5jp7ZWN8HiZuFC83xr0iT2f19MlJYCQ4aY7XvuKc7fh3Tx08BKVd0+4TFnJv5pANcxs33+9zDzWGbux8z9mjdv7pmw+TawAOCyy8SnHZACbR9+mJ9+ldATqtXhdLBXr7p3L8z6V/GUlEjCGpeFC4OTRVEAYJZV4tgP90AAOPhgoH59aW/fLkWHi43PPzdtr90DXTQOKzq8+qppjxhRPOnZEzF4sImfXrAg9jcpavhpYKWqbp/s2FgA/2XmvP/c5KPIcDxNmwJXXWW2J0yItrWvpE2oVofTISzxV/HEG1g1BWe6KlHCzlLm1yx5SUmsy1wxpmu3Dazu3f3pwzawZs8G9u3zpx8leGwDa9So4OTIBw0bSq03l3vvDU6WoPHNwGLmRQDWO9XtewF4kVJUviei4wGcA+BnRDSViK71S7ZEBGFgAcB115nZwFWrdBVLSYtQrQ7Xxr59kmHJJUwGVteuwAEHSHvbttjfEUXJJ99+K4VrAZlB9msFCyjuelhVVbF67Fc9vk6dxP0QkN+OxYv96UcJlhUrzLiuTh3glFMCFScv2MkuXnopNsdBlPA1TTszj3Gq21/AzFWcovI9M89g5gpmHuq88mr32kWG8+UiCAAtWgBXXmm2X3tNZ8GVWgnV6nBtLFwoiV4AcT9q1SpYeTKhpATo29dsL1gQnCxKtLGLdB99tBS09YtiTnTx5ZdmNalVK0kW4gdEWg8rCtjJLbp2BZ59tvhjddu0AU46Sdo1NcADDwQrT1BooWEH+yGRTwMLAG64AahbV9qrVwMvvJDf/pVwEbbV4dp44w3TPvlkGXiEiXg3QXXzVYLAdg8cOtTfvjp2NHq6enVxpRm33QO7dfO3L010Ufy88opp25Nxxc611ijj4YeBTZuCkyUo1MCC/OPdGfQ6dYBGeU5i3aIFcMIJZvuWW9QfW0lNmFaHa8M2sEaMCE6ObOnRw7j5btoEvP9+sPIo0WTqVNO2M3n5Qb16QLt20maONUrCTj7ir1ziE13o5ExxsX690UuiaBlYI0aYcivbtwN33BGsPEGgBhYkRbRLixbBzKAPHy4PLQBYsgR4+eX8y6Ao+WbDBlMDpqQknP7pZWWxq1hPPx2cLEo02bgR+PhjaZeVAQMH+t+nHSv52Wf+95cP8hV/5dKnj3FBXLNGYziLjRdeMCEfXbqYsh5R4LHHYldo775bvuNRQg0sxM5YtWgRjAwVFbGrWP/4RzByKEo+efNNM2s7cKDUCAkjRx9t2uPGadFhJb/Y8Tv9+/tTtykeuxj4kiX+95cP8hV/5VJaGmsM28l+lPDz7LOm3b9/cHIExRFHmJXuvXuB//3fYOXJN2pgYf8VrHwxdqx5AWJgufUR5syJLRqpKMXI+PGmHUb3QJfu3c3s5IYNsWnnFcVv8uke6NKli3lerV0rsVhhZ+lS0/Y7/srFTQYAAJMn56fPQoKIbieiGUT0lJPt1t0/lIhWOXHDU1JdoxBZudLUgIqvmRgVSkqAs8822889F63vuBpYiDWwWrYMTo5GjWJnOaJcP0ApfnbulNpvLj/8YXCy5EpJSazuqptgcVHog0DboPc7wYVLnTqx2QSnhG4IvD+ffGLa+SoXMXy4aU+eHK34ayLqC6ANMx8PYAmAs+NOGefEDZ+0/7sLm//+17R79PB/NbRQ6dEj1sPj0kujk/BCDSwUhougiz2b9cILEiSpKMXI+PHArl3Sbt1aZvvCnL7Wfoi8/DKwY0dwsijeUeiDwJUrTf2runX9KzCcCNtNMOyrtt9+K58lIBMm9r35SZ8+Up4CADZvjlyph4EA3nLaEwEMijt+ljOxUVCZb9Mh6u6BNj/+sXFb/uYb4Kc/jUY5osgbWMyFs4IFiL+qOyu4bx/w+OPByqMofjFunGn36xecHF7Rrp2p4bVrl7hDKEVBQQ8CJ00y7SFD/K1/FU+8gRXmLHhvvWXahxxiMoP6DVHsKpYtRwRoAmCb094KoKl1bB6A7gBOAnAqER2V6AJEdAURzSOieRs2bPBV2HRZvNgYynXqAIcfHqw8QXPggWJUuUyYAFx3Xbh/L9Ih8gbWunVmprl+faBhw2DlAYDBg037kUei5TKgRIOtW2PTsx+V8NEZLoiAQdbQ+4EHiv8BEhEKehA4caJpn3qqp5eulfbtjUG3dq1ZSQsj9ufYq1d++7YNrDffzG/fAbMFgOs81wjAZvcAM+9wSpBUAXgdQMIk58w8lpn7MXO/5s2b+y5wOjz2mGmfcUZ+Jz0Klb59gTFjzPa99wJ/+1tw8uSDyBtYhZCiPZ6jjjLLqV9/HTtDqSjFwJNPAnv2SLtdO+MiE3YGDjQz34sWAe+9F6w8iicU7CBw795Y17x8G1glJbGxSnbSmjCxb1/sc7Z37/z2P3y4fJYAMHu2uCtGhNkATnbapwCY5R4gIjtq6TgAX+RRrqyprASeespsX355cLIUGrfdFpv04sYbgXvuCU4ev4m8gWWnlw06/sqlvDw2detDDwUni6J4TU2NrO642Cu2YaeiAjjvPLP94IPByaJ4RsEOAmfMALY5a2vt2uUvMYPNYYeZ9quv5r9/L5g5U2qJAeLO1LZtfvtv3tysfjOH11DNFGZeBGA9Ec0A0AvAi0T0iHP4HCKaS0SzAaxm5umBCZoBL7wAfPedtDt1Ak48MVh5ConSUjE+7c/k178GHn44OJn8xFcDK0XmpVIietw5do+1/x0i2kJEI/2Uy2bxYtNu0yZfvdaOHag8YYKsZClKMTBlikksc+CBsckhioFf/tK0//tfSduuhJdCHgS++KJpjxoVjAdGnz6m3/feE7f7sPHSS6Z9xBFmNSmfjBpl2q+8kv/+g4KZxzDz8cx8gbMaPNrZ/xgzH83MA5l5TG3XKQSYY2uY/vznwXyXCpl69YDXXot1p7/ySuCJJwITyTd8+9fXknlpJIA1zrEKIhrg7L8QQF4XDD/6yLTzPWuVipYtTQAxc6xPr6KEFWbgllvM9iWXyA9uMTF/PtCxo7SrqoD77w9UHMUDCnEQWFMj2Spdzjorn70bGjYEunaVNjPw+uvByJEtzPsbWEFgG1iTJ0sZCyVcTJsGLFwo7Xr1gNGjg5Wn0HDrvj79tGQWdJ+TgBijxZYYyk/bOlXmpYTHmHlNbRf1MliYOdbAKqQVLCDWdeqxx8TfXlHCzPPPmwLadeoA14Yu+W562OUW7rtPknooipe8954klgCAZs2A444LTpa+VuSZvaoWBubNk9TRgCQjyFeB4Xi6dDHJNSorw2eoKsBdd5n2T38qeqkkpn594JprTIbFmhrgwgtjJ43Cjp8GVqrMS6mOpcTLYOFvvgG2bJF248ZAkyY5Xc5z+vY1aZ/XrQuvf7uiADJouOEGs33NNZIOuRjp18/EdG7ZEhtzpihe8Pzzpn3GGUBZWXCy2Ks+kyeHy03QLgret6/EiQTFueea9v/9X3ByKJnz/vsSzuHSurVZsVESU1EhRpU7zt23T1a2rr46WLm8wk8DK2nmpVqO5Q179eqwwwojg6BNaSlw2WVmW5NdKGHm3nuBFSuk3awZ8LvfBSqOr5SUAD/4gdn+xz/U5Ufxjr17Yw2DH/84OFkA4KCDjMdFTU14XH2qqmI/x6DjQS+4wLQnTtT4zTBx002m3a9f8WTG9ZsDDpBEF+6E5L59Up7ogw+ClcsL/DSwkmZequVY3og3sAqRyy83QZLvvGPcqxQlTHz7LfCXv5jtYcMkAUQxz+4dc4wMPAFg0ybgoouK+36V/DFhghl8t20b65IaFBddZNp2mupC5s03TfbAtm2DycJoc8ghJvh/377wGKpRZ/JkUy6BCPjhD4OVJ2w0aiSFh12Xyr17JSbRdd0NK74ZWLVkXhoPoL1zrJKZ5wAAET0O4GIAfyai3/glm4ttYPXp43dv2dGuHfCTn5jtYp71V4qXW24Btm+XdqtWsVkyi5XS0ti6RG++aVJqK0ou/Pvfpn3xxcG6tbmcfTZQt660FyyQOnCFTvznWAgZ3y680LQfeUSLlRc6lZXAVVeZ7QEDdPUqG5o0kbABu3D56aeH2/PD15+TFJmXqpn5EufYNdb5lzLzIcx8ODP7XuN5wQLTLtQVrLFjJZug+8P/7rvAG28EK5OiZMIf/iADBZezziqMAWE+GDTIPGw1cF3xgq+/jo31uOSSwESJoXFj4MwzzfZ99wUnSzp89VWsPv70p8HJYnPeeRKbAgCffCLPfKVwue02YNkyaTdqJPGQSna0bAn84hdmvLtwoUx81NQEK1e2FMB8TTBs3Ghq8ZSXx2ZBKjRatIitGXDZZeJypChh4IUXzCzsoYcCvXsHK08+KS2NrVw/Y4YMmhQlW+65R9zHAOCEE0yK9ELgmmtM++mnxTW4ULnnHjNwO+WU4LIHxtOoUazRXOiGapSZNSvW9f0vf5H/n5I93bsD559vtl96Cbj55uDkyYXIGljvvWfaRx4pKSMLmdNPN0GAa9dKMGxlZbAyKUptTJxoDAoiCcYvtGQyftO7d2xNuyuvDO+MnBIs330HPPqo2f5//y84WRJx7LEmUURVVeFmz9y8GXj8cbN93XXByZKI//kf037tNZ2UKUQ2bBBDwJ3sOO44WX1Rcuf444Ff/cps//Wv4cyqGVkDa+ZM0x4wIPl5hcKBB8Zmipo0CRg5ElhTa+UwRQmG6urYAeCgQYVXay4fuIal6/YwY4ZkVFSUTLn7bhOT0KtXbKbKQoAodmD0j38UZia8224zn2OfPpJ0p5Do0QMYMULazOGdwS9WKislCcPKlbLdpIms2EbF9T0fdO8e6+1yySXhq5EVWQNr8mTTtgv6FjKHHWZ+dAFgyhSZGb/9dmDXruDkUpREjB1rZl7r1pVV2KjSpk1swovf/haYPTs4eZTwsWpVbCHTG24ozNXgc84BevaU9o4dsS5UhcCKFbFudzfdVJif45/+ZNovvyx1lpTg2bNH3L7djM5EwH/+A7RvH6xcxUZJiYTDtG4t226NrDAlfomkgbVhg0lwUVoKnHhisPJkwumny8yJy7ZtwG9+I1XgH35Y0lsqStBs3BhbF+TUU9U3fcQIyQoKyEN61Cjgs8+ClUkJD9dfD+zeLe0jjoitmVRIlJaKS4/LAw8UTkZBZllhq6qS7WOOCb6GWDKOOEKMVZfRo/X5HhRuweAHHgD6949NMvOPf4g3keI99etLXKddI+sXv5DM2mFI4R5JA+u110z72GPDN/A77TTxGW/Z0uxbu1ZiO3r3Lo4CbUq4uflmiRcBpLZFobngBEFZmQyS3FofGzeKe7L9e6QoiXjuudiaSHffXRgpxZNx+ummFEN1tbj3uEZNkDz5JPDqq2b7rrsKc/XK5a9/NfHhH34oro1KMOzaBdx/P7B4sdn3298C114bnExRoEkTCTVwJycBqaHZpYtMMj39tGQEtVe1XIPYfgVBAf9E+4f9oGrVKth/QLZ07y61hc4/P9ZA/PxzYOBA4M47NZBeCYb582PTsp9zjmTqVIDmzWWA59b62LpVVrKGD5fyC27AtKK4LF0aGzx/0UXAkCHByZMORMBjjwH16sn2hx9K4oYgXXsWLQKuvtpsX3mlJCYoZDp3Bv78Z7N9662xqydKfti4EbjjDmDJErPvd78T99dCNtCLhUaNZAX/Zz8z+/bsAZ55RurGde4sZSKOOUYmcyZOFH0Puu5kWbDd558vvpDYJZd+/YKTJVdKS+VBO2CAqY9VWSkzhtdfL5XF//Of2JUuRfGTXbvkB88dSJ16auHWmAuKgQMl0cXppwOrV8u+yZPl1b69+J3//OfG91yJLitXyurv1q2y3amTzKKHgW7dgL/9zSS9ePRR+U7fckv+B6XLlklCkB07ZLtrV5mEDAPXXisxWDNnyqTpT34iRcsL3TgsFl5/XVYS7YK3o0bJioqd0VPxlzp1JPPnxRdL/OncubHHt22TffZ+IqBjR3G3PeMM42qYLyK3gnXnnbGDvyZNgpXHC+rUkToeN90kXyaXt96Swe1zz4UnKFAJL8wyS+3O8jVoAPzznzrDl4gjj5QiildcEevqtXIl8Pvfi6F1xhm6qhVlZs+WGdlVq2S7QQNg3DjJKBsWrrlGVtxcbr1VVuPcWLJ88M47Egqwbp1sN24stXXcYr6FTmmp1BLs0EG2d+wQo/uZZ/S57idbt8qK5+mnG+OqrEwmv047TZ9rQTB2rHhp/fznMt4dOVK8Pxo3Tnw+M7B8ueh727bi8TV9ev70hjjEGtqvXz+eN29e2ufPni1+4a7r3OTJ4rtZTOzbB7zyihhXNj17Aj/6kSylVlfLj/SUKbLiVVYGnHSSWPdt28qrTRtx66quFt/5ykrgiSfkIW8Xk4wiRDSfmUO89lk7mepWTQ3w61/HZuf617+ASy8Nn/ttvlm/Xla0Zs+OnSV1adUKOO88mTXt3z9xzb59+0Snd+6UgeOBB4ZzAKC6Jauat90GPPSQeVaVl4trmJ+xjLnq6RVXJN5fVSUJXt5+2+zr2lWy344a5V8s2fLl4l5n17uqV09KnCTLHOzFb1WyzyFXPvlEEnLZxZtHjZJsg3361P5+1a30qKqSmks33ii/zS5NmgCXXy5jKKWwYJYVrHXrJB/B2rWSBOOrrxKHyvTqJRM9F13kTQ6GZLrlq4FFRLcDGAhgBYBLmXmvs78UwKMAugKYz8y/cvZfC+AcAJsAXMjMKT0o01UmZllSv/hiYNMm2XfKKbKvWJd4P/0U+Pe//fFBPeAA+ZHp2VO+qO6rU6fM6kDs2wds2SL/k6oqWYlr0EBmIyoqCneAWAgPqkLRrZoamRG66Sapau9y8cVikBOpgZUue/eK3/j06TJLlwgiMbjc2JZdu4Dt2/c3zMrK5LyOHRO/2rXzJi6uuloGyF4NkgtBtxKRqb6lIpFuffMNMH68uCNNmhS7annQQcDzzwMnnODV3STGLwMLkHiJSy+VVRebDh1k4m/QIHHXb9NGvrvZsGWLGCFz5ogxOn167OCqZUtJKOMWQk5EIRtYAPDll+J588UXsfuPPRY480yZKD388MTP4ajqVjps2gTMmye699xzMkC36dNHYnsaNsz40kqA7Nwp8Z/Tp8uESzwNGsgK2IgREmrTrVt2z8VkuuVbDBYR9QXQhpmPJ6LfATgbwLPO4ZEA1jDzpUT0KBENALAMwOkAjgNwPoCrAGScM+df/wLee08+2J07xcD45JPYYocHHSQpzQt1AO8FPXsCf/yjuBjNnOltnazt22UgGJ96t359mZls2lRm0OvXF+OWWR5027dLZrnNm+W1ZUvypdrSUjG0GjeWmSO3XVFh3uNeO367uloGq3v3iuGWqA2IclVUmFf9+maw2Lx54WZsCkq3/v1vMaK2bTP/yyVLTHyIy9lnix4Ws375QXm5rFD1729Wtd5/P3aShDm94uLV1eJatmqVXCeekhIZzHbsKFkNS0tlYFtaKi9mGRS7r6oq+Q3ZsUNe27fL3z175Hr16xs9athQXm67okKuXVIi127aNDzxL0Dm+sbMczLtY+RIGQjEM2yYDPpt1+8wUreurAoMGQKMGWO+019/LRkR775btktKgIMPFm+KBg1kIsF91dSI4em+qqvlN2jTJnm58VWJOOMMiV0Le6Hzzp0lidD//q8kEXF57z15AaLzbdsGI1+m5EO3nn1W3ESrqsxvmft30yb5PWHHX+sAABHXSURBVLVXqmzatJE07N99p8+zMFJRITHPAwfKxMrDD8vvkDshuWuXeHy98ops16kjcaLNm8tzsX592Ve3riws/POfmfXvZ5KLgQBcR7WJAH4GozgDAUywjg0C0BjANGZmIpoI4MlsOn3nnf1nyWyaN5dZwrA/sNKhfn3grLPEOl+8WOI7tmyRwU69enK8bl3jXrRtm/yQfPedDJqZZdBXp46cX1Ul51VXJ+5v927go4+8kX3fPvPgDIJOnQrXwEJAujV1qiRNSUZZmbhV3HRT9rPQitCypRiqZ54pRuyCBRKo/+23yScl6taVl2sUpaKmxhhgXrB7t7w2bqz93LZtw2VgIXN9y9nAGjJEBtEjRxbPwI5IVndGjQLuuUcyjbqlHFxqamTAm84kQjoMGyYGXTGViTjwQPG8ufJKyWL32mvmmdy9e3iMKwffdWvWrFhjNB1atZL4q6uvlkki9cIIP4cfLgbWHXeIkfXww7Ep9wEZ465YIa94GjcuLAOrCQB3oXUrgKZxx7bFHUu0bz+I6AoA7iL8DiJamolQGzbIcjqAZgDSGA54Sr77zLm/ffsk/ipNV8PQ3V8yli9POrBpBqCDH31mQEHqVnU1mv3xj9j4xz9m8q6CIYjfA69oBmBjOoZVIfDNNzG6Ff+5B61bichU3/YjU92aNk1eeSYnHRg92kNJkpORjG52znwyenRwvyVLl6Y0yKOmW1n/H9aulcnCG29M6/R8/r/z1VfR3VOuerllS+a65aeBtQWAm++oEYDNtRzbAqBLkvO/h5nHAsh5PoGI5uXbHznffWp/vvXZMZ99JqAgdSuI/4dXqOzBEBLZM9W3/fDqueUnYfhfqIxFh2+6la//Qz7/33pPhd+PjZ9p2mcDONlpnwJgVi3HPgAwOMn5iqIYVLcUJX9kqm+KoqSH6pZStPhmYDHzIgDriWgGgF4AXiSiR5zD4wG0d45VMvMcZt4AYAIRzYIE4j/ol2yKEmZUtxQlf2Sqb0HJqShhQ3VLKWZ8DUVn5jFxu0Y7+6sBXJLg/LsB3O2nTBZBuGvku0/trzj63I8C1a2C+GyyRGUPhlDInqm+hZQw/C9UxiLDR93K1/8hn/9vvafC7+d7Ql1oWFEURVEURVEUpZDwMwZLURRFURRFURQlUqiBpSiKoiiKoiiK4hGRM7CI6Dwi2uC0f0xEs4loChF5Xp6PiIY6136XiM4kouOc/mYSUR+P+yohoieIaIZz/R5+9EdEjYhoLhHtIKLezr79Pken/+nO/pO86o+IDiCid5xrv0NEHbzsL9k9Ovs7ENEe67496zMshPmzSfLdbUtErzk6equz72AieouIZhHRhcFKLSSR/Spn31wiOsvZV4iyH01Ec5zvw7NEVO7nb4ZSO0R0u/OseIqIyq39pUT0uHPsngKVcSgRrSKiqUQ0JUD5Ev4WOscK5nOMAsm+K86xHxLR+8446F5r/3bnOzS1tvFRpvpCRNc6v8GvEdGBia+aUT8530MGfSXULx/u6UxL9pVEdK2zf5m1P+0S4dnoY7b3lDbMHJkXgFIALwFYAEnwMQdAHUiF8Ec87qs+gNcB1LH2TYMUz2sP4A2P+zsSwLNO+3hIQJ/n/QEoB9AcwBMAeif7HJ3PuSukjsUsD/urB6C1c+wUAPd72V+iPq39DwB4x93nZZ9heYX5s0kkO4BnAbSJO+9uSHpg97tdr0Bl/8SRsQGA+QUseysA9Z32bQDO9vM3Q1+1/j/6Avg/p/07AOdZx0YB+LPTfhTAgAKUcSiAuwrgc0z4W1hIn2MUXqm+K86+9gDKnPazAPo57Xm5Xj/R/xlSOHcKAAJwAYDfetBPTveQYV/76Zcf9xR33rsAOuR4TxnpY7b3lMkraitY5wF4HkAN5EH+GTNXMfMsAId53NcAALsBvE5ELxNRKwD7mPk7Zl6JJFXJc+AbAEREBDGqdvrRHzPvZUn77ZLsc2zNzMuYeRuAzUTUzIv+mLmSmdc4m1WQ/6Vn/SXqEwCIqBMABrDS2u1Zn2EhzJ9NvOzObFpHAH8nWQ0d6Bw6GsA7LFms5kEM+0BJ9LkD+AoykXMApCgnUJiyr2Xm3c5mFYDu8PE3Q6mVgQDectoTIUZuOsfySW1ynOXMRl+bX7EMSXTSpVA+xyiQ8rNm5pXO7yEQO2bo7KyYP0RE9bK8fqJj/QFMYxnNZ/K/T9qPB/eQdl8O8frl+T25ENHBAOoy89fOroZENI2IniGitMetWehjtveUNpExsIioFMA5AMY5u5oA2GadUupxly0BdAHwQ4jFfGtcf9VEVMfD/jYC2AtgCYB/Qmay/ezPJdnnaH+3tsJjg9K5lz9A7tX3/gDcAOCuuH1+9xkWwvrZNANwOIDrIfXBXNeLcmZ2H2CFKjsATADwGYBFAP7u7CtY2UnceYcDmIkAfjOU77F/s+M/51TH8kkqOeZBjPSTAJxKREflWbZ0KJTPMQqk9VkTUX8ALZh5gbOrCzMPBrAWwFVZXj/RsWz/97W+L4d7yKSvRPrl2z0B+BGAF63tQcw8BGL03JpmP9nI4buORsbAAnAhgP9ag48tEFcUl30e97cF4uZSBVmGPCKuvzLnmFcMB1DNzN0BnAUZcPnZn0uyz7HG2tcIwGaP+x0L4EFmXuZ3f0TUGQCYeUXcIb/vseAJ+WezBcAXzuzgOgB7iajM+ev+Nhak7I6/+JWQFeQeAP7krF4XpOyOvE9B6tpsQDC/GYpg/2bHf86pjuWTpHIw8w5n9bMK4obfNwD5aqNQPseigSS+dGr8yzmc8rMmifO8B8BP3X3MvMlpvoDU36FM9SXb/33K9+V4D2n3lUS/fLknh7Md+d3+s72nTOXwXUejZGD1BHAxEU2EDEquBnAoEdVxXIM+8ri/D5zrE2SW/FMAZUTUmIjawft/JgFwv5gbIW5DfvbnsgyJP8e1RNSZiA4A0JSZN3rVIRHdAuArZh5n7fatP4iS93K+O8MAPOwsx/vZZ1gI7WfjuK1tcnSkAuKmUA3R3aGOsXUUJNap0KiBuCBXQtyB60B+AwpOdkeW5wDcysxLEcBvhhLDbEicHiBxrLPSPJZPksoRF4x+HIAv8ihXuhTK51g0MPM6Zh4a/wLwBlJ81s7vyXMARjPzt86+CserCZCY9VTfoUz15QMAg5PJk00/HtxDJn0l0i/P78npqyUs90DnmVDXOZzpPWUqR7b3lD5eB3WF4QUniA7Auc4H/w6Adj70cxWA6ZBkE52df+Zs5x/Z1+O+yiDuj9MAvAfxOfWlP8gP2hpIoPoliT5HiEE7w9k/zMP+bgZQDWCq87rN6/4S3aO1/wmYJAOe9hmWV5g/mwTf3YGOnO8DGOmc0wrAZEf2i4OWOYXs1zu6PhfALwpVdgAXQSZ/XJ091+/fDH3V+j+50/msn4YY526ikTJHj2cAuK9AZbzM+c7PBnBnwDLG62TBfY5ReMV/V5x97v/iZgCrrd+fIZBJ7wWQ8dmrABplcv3a/s8Afg0Zd42v7dpp9pPzPWTQV0L98vqenGNXArjO2m4JYL5zT5OR4bg8U33M9p7SfZHTiaIoiqIoiqIoipIjUXIRVBRFURRFURRF8RU1sBRFURRFURRFUTxCDSxFURRFURRFURSPUANLURRFURRFURTFI9TAUhRFURRFURRF8Qg1sIoAIupIRB/7cN2hTp0aRYkUTm2sXzrt1kT0Qm3vyaIPX66rKIqiKEqwqIEVcZwioMkYCqkTpChRozGAXwIAM69h5rO97sCv6ypKPiGi2UHLkAgiuoSI7s/g/B1+yqMomZLp5DkR/YqIGqRx3goiapabdGnLFFm9UgOreCgjoqeJ6DMieoGIGhDRUUQ0jYjmE9EkImoFAEQ0lYjuIaJ5AK4loh8S0ftEtJCI3iailkTUEcAvAPyaiBYR0fEB3pui5Ju/AejsfPefdx9yzqDtJSKaSETLiOgO9w1E9HMi+pyI5hLRo+7gjog6E9F7RLSYiP7sPnD8WnlWlHzCzIFNwtUyQagoUeNXAGo1sLyGiErz3WcYUAOreOgO4EFmPhTANgBXAfgngLOZ+SgAjwP4i3V+HWbux8x/BzATwLHMfASA5wBcz8wrADwM4G5mPpyZZ+TxXhQlaH4D4EtmPhzAmLhjhwM4F0AfAOcSUTsiag3gZgDHAhgEoId1/r0A7mXmPgC+8V1yRckj1oRBKyKa7kxKfJxqUo6IdhDRnUT0iTOpd7Qz8fcVEZ3unFPqnPMBEX1ERKOd/UOJaAYRvQbgUyKqR0T/diYwFhLRCQn6G0FEc4ioGRGd55z7MRHdnuDcZs65Izz7kBQlexJNnp/kfNcXE9HjRFSXiK4B0BrAu0T0LgAQ0UNENM/Rs1vjL0xE9YnoTSK6PFHHziTgkvj+nWMriOh2IloA4Mep9IqI7nZkmEJEzT3/hAoUNbCKh1XMPMtp/x+AUwD0BjCZiBYBuAlAW+v8cVa7LYBJRLQYMpjslQd5FSWsTGHmrcxcCeBTAB0AHA1gGjNvZua9AJ63zh9gbT+TX1EVJW+cD2CSMynRF8CiFOdWAHiHmXsB2A7gzwCGATgTwB+dc34OYCsz9wfQH8DlRNTJOXYkgGuZuRtkMpGdCYzzADxJRPXcjojoTMiEyWkA6gC4HcCJkImS/kR0hnVuSwATAPyemSdk/UkoinfET55fB+AJAOc63/kyAFcy830A1gA4gZndSYbfMXM/AIcBGEJEh1nXbQjgdQDPMvOjGfT/S+vYJmY+EsB0JNerCgDzHF2fBuCWbD6EMKIGVvHAcdvbAXzirD4dzsx9mHm4dXyn1f4ngPsdZR0NoB4URUnGHqu9D/KAU5So8wGAnxHRHwD0YebtKc6tAjDRaS+GTE7sddodnf3DAVzsTBC+D+AgAF2dY3OZebnTPg4yqQhmXgLgawDdnGMnArgBwAhm/g5iqE1l5g3MXA3gaQCDnXPLAUyBeHBMzvz2FcUX4ifPTwKwnJk/d/Y9CfMdjuccZ4VpIWTivKd17FUA/2bm/2TY/3HWMXeiPpVe1Vjnxb+/qFEDq3hoT0QDnPb5AN4D0NzdR0TlRJRsZaoRgNVO+6fW/u0ADvBDWEUpcDL97n8AmSFs4sSFnGUde8/a/olH8ilKQcHM0yGDqtUAniCii1OcvpeZ3UnBGjiTFsxcAzNhQQCutiYJOzHzW86xnUiPLyF63K22EwFUA5gP8f5QlEIhfvJ8SzpvclZ7/x+Ak5j5MMjKrD15PgvAqUREGfZvb6erh6muV7SogVU8LAVwFRF9BqAJnPgrALcT0YcQd41kwch/APA8Ec0HsNHa/zqAMzXJhRI1mHkTgFlOEoo70zh/NYC/ApgLeXCtALDVOfwrANcR0UcAulj7FaVoIKIOANY77kaPQdz4cmESgCuJqNy5fjciqkhw3gwAF7jnAGgPeR4Cspp1FoD/OBOMcyETIc2cwPzzIG5LgAz8LgXQg4huyFF2RfGK+MnzeQA6ElEXZ99FMN9he2LwQIgBtNVxff1B3HV/D+A7AA9k2P/MBOek0qsSyFg01fuLEnVtKQKchBQ9EhxahARLx8w8NG77Vchycfx5n0N8dxUlcjDz+Qn2PQHxf3e3R1qHn2Hmsc4K1ssAXnH2r4YkkWEi+gnEp93V296+CK8o+WcogDFEtBfADgCpVrDS4TGIu+ACZ5Z9A4AzEpz3IICHnBjiagCXMPMed2KemZcQ0QWQOMgfQuKx3oWskE1wnn/uufuI6DwArxHRdmZ+MMd7UJRccSfPH4fE/F4D8Yp43nnWfABJSAYAYwFMJKI1zHwCES0EsATAKsjEXzzXAniciO5g5uvT7P+h+BOYeS0RJdOrnQCOJqKbAHwLSRAVCcis0iuKoijZQkR3ATgZ4obxFiQIn53V3/shD54tAC5l5i+Ck1RRFEVRUkNSrmc8M+tEYBaogaUoiqIoiqIoyveogZUbamApiqIoiuI5RPQ+gLpxuy9i5sVByKMoyv4Q0UGQDJrxnOTEIytZoAaWoiiKoiiKoiiKR2gWQUVRFEVRFEVRFI9QA0tRFEVRFEVRFMUj1MBSFEVRFEVRFEXxCDWwFEVRFEVRFEVRPOL/A6gGOed23pS7AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YNv0HtEZWcnd"
      },
      "source": [
        "Untuk distribution plot, hal utama yang perlu diperhatikan adalah bentuk distribusi:\n",
        "* Seperti dugaan kita ketika melihat boxplot di atas, kolom `gaji`, `berat`, dan `tinggi` sedikit *skewed*\n",
        "* Berarti ada kemungkinan kita perlu melakukan sesuatu pada kolom2 tersebut nantinya\n",
        "* Selain kolom2 itu distribusinya terlihat cukup simetrik"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CyWdEfDKWcnd"
      },
      "source": [
        "### Count plot (categorical)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-29T00:32:14.834179Z",
          "start_time": "2020-08-29T00:32:13.916291Z"
        },
        "scrolled": true,
        "id": "SACYYuBKWcnd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "outputId": "334c0b8e-2e0f-4ce3-f1bc-341f9340ae70"
      },
      "source": [
        "for i in range(0, len(cats)):\n",
        "    plt.subplot(2, 3, i+1)\n",
        "    sns.countplot(df[cats[i]], color='blue', orient='v')\n",
        "    plt.xticks(rotation=20)\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iqVbOyFWcnd"
      },
      "source": [
        "Terakhir kita lihat count plot untuk setiap data. Seperti pengamatan kita sebelumnya, distribusi `pendidikan` dan `pekerjaan` didominasi 1-2 value.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "na6hG7iJWcne"
      },
      "source": [
        "## Multivariate Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xR7Sg6NgWcne"
      },
      "source": [
        "Setelah menilik setiap kolom secara individual, sekarang kita akan melihat hubungan antar kolom. Mengetahui hubungan antar kolom dapat membantu kita memilih fitur-fitur yang paling penting dan mengesampingkan *feature* yang redundan."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qa7EPitBWcne"
      },
      "source": [
        "### Correlation heatmap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-08-29T00:32:15.304922Z",
          "start_time": "2020-08-29T00:32:14.836179Z"
        },
        "id": "Q1KXwJlPWcne",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "0ee1718e-058e-4655-e3ae-3038a90ae02e"
      },
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "sns.heatmap(df.corr(), cmap='Blues', annot=True, fmt='.2f')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61b5120910>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5IV0XEBRWcne"
      },
      "source": [
        "Dari *correlation heatmap* di atas dapat dilihat bahwa:\n",
        "* Target kita `botak_prob` memiliki korelasi positif kuat dengan `is_keturunan`\n",
        "* Ia juga memiliki korelasi positif lemah dengan `umur`, `is_menikah`, dan `is_merokok`\n",
        "* `tinggi` juga memiliki korelasi positif kuat dengan `berat`. Ada kemungkinan kedua *feature* ini redundan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGfqMhp0Wcne"
      },
      "source": [
        "## EDA Conclusion\n",
        "Beberapa hal yang kita temukan dari EDA dataset ini adalah:\n",
        "* Data terlihat valid dan tidak ada kecacatan yang major\n",
        "* Namun masih ada baris dengan target variabel bernilai negatif, yang mana tidak mungkin, jadi perlu didrop nantinya. \n",
        "* Juga masih ada data-data yang kosong/hilang, harus diurus ketika preprocessing\n",
        "* Ada beberapa distribusi yang sedikit *skewed*, hal ini harus diingat apabila kita ingin melakukan sesuatu atau menggunakan model yang memerlukan asumsi distribusi normal\n",
        "* Beberapa *feature* memiliki korelasi yang jelas dengan *target*, mereka akan dipakai\n",
        "* Beberapa *feature* terlihat sama sekali tidak berkorelasi, mereka sebaiknya diabaikan\n",
        "* Dari Heatmap: ada feature yang korelasinya tinggi satu sama lain (berat/tinggi) mungkin cuma akan dipakai satu"
      ]
    }
  ]
}