Data_Exploratory_and_Ploting.ipynb

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        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Machine-Learning/blob/master/Data_Exploratory_and_Ploting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uDd2FjLK9XE"
      },
      "source": [
        "# **Table Content**\n",
        "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
        "    <ul>\n",
        "        <li><a href=\"https:\">Introduction</a></li>\n",
        "        <li><a href=\"#downloading_data\">Import libaray</a></li>\n",
        "        <li><a href=\"#pre-processing\">Data Loading</a></li>\n",
        "         <li><a href=\"#pre-processing\">Preprocessing</a></li>\n",
        "        <li><a href=\"#evaluation\">Data Exploring\n",
        "        <ul>\n",
        "        <li><a href=\"#evaluation\">Dtale</a></li>\n",
        "        <li><a href=\"#evaluation\">Lux</a></li>\n",
        "        <li><a href=\"#evaluation\">Pandas Profiling</a></li>\n",
        "         <li><a href=\"#evaluation\">Logistic Regression</a></li>\n",
        "         <li><a href=\"#evaluation\">KNN</a></li>\n",
        "         <li><a href=\"#evaluation\">Naive Bayes</a></li>\n",
        "         <li><a href=\"#evaluation\">Deep Neural Multilayer Perceptron (MLP)</a></li>\n",
        "         <li><a href=\"#evaluation\">Gradient Boosting</a></li>\n",
        "         <li><a href=\"#evaluation\">ExtraTreesClassifier</a></li>\n",
        "         <li><a href=\"#evaluation\">LGBM Classifer</a></li>\n",
        "         <li><a href=\"#evaluation\">CatBoost</a></li>\n",
        "    </ul>\n",
        "</div>\n",
        "<br>\n",
        "<hr>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dEH-aOwYdNx"
      },
      "source": [
        "# **Introduction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiE4HQuXA1jb"
      },
      "source": [
        "# **Import libaray**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYcSX5ofA8Yz"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from prettytable import PrettyTable\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69-GFSEUYpQA"
      },
      "source": [
        "# **Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucwJyax-cZmZ"
      },
      "source": [
        "**Load data from google drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYNNsGgxmxyQ",
        "outputId": "e673c640-53e5-4a9f-bf1e-d38a65132e1b"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkoAdyxInLR4"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/Datasets (1)/Student field Recommendation /Placement_Data_Full_Class.csv\")"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Builten Iris dataset in scikit-learn**"
      ],
      "metadata": {
        "id": "Uk_xHWnyyfR6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "iris = datasets.load_iris()"
      ],
      "metadata": {
        "id": "_3SclwDryw0G"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris = datasets.load_iris()\n",
        "type(iris)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z_DnSRayy67-",
        "outputId": "03fe24a2-2d0e-4d18-a1f5-afb4ff13798c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils._bunch.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.keys())"
      ],
      "metadata": {
        "id": "A-9qgg94zKJe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a984358e-dbee-4441-8df1-88e75929cfaa"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(iris.data), type(iris.target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlTclGdnzRtW",
        "outputId": "086e3461-5f1e-4cbf-990e-194af6869a08"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(numpy.ndarray, numpy.ndarray)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXwMgLcZzWrF",
        "outputId": "ca03ffe3-5e7f-4939-997d-300e4f92c52a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tR5rFe5Xzawd",
        "outputId": "bedbb094-d783-4af7-9c15-7bb239ea3907"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wX_F6ZgVOzFU"
      },
      "source": [
        "# **1- Exploratory data analysis (EDA)(Begineer Level)**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "id": "Ibss7s_B1L_y"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(X, columns=iris.feature_names)\n"
      ],
      "metadata": {
        "id": "QOgRERXv1O4I"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
        "                     columns= iris['feature_names'] + ['target'])"
      ],
      "metadata": {
        "id": "ixpX6WXa4Wul"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3p7EUqDIz7BX",
        "outputId": "645510f3-fa69-4066-9f04-e82a9706da95"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                  5.1               3.5                1.4               0.2   \n",
              "1                  4.9               3.0                1.4               0.2   \n",
              "2                  4.7               3.2                1.3               0.2   \n",
              "3                  4.6               3.1                1.5               0.2   \n",
              "4                  5.0               3.6                1.4               0.2   \n",
              "..                 ...               ...                ...               ...   \n",
              "145                6.7               3.0                5.2               2.3   \n",
              "146                6.3               2.5                5.0               1.9   \n",
              "147                6.5               3.0                5.2               2.0   \n",
              "148                6.2               3.4                5.4               2.3   \n",
              "149                5.9               3.0                5.1               1.8   \n",
              "\n",
              "     target  \n",
              "0       0.0  \n",
              "1       0.0  \n",
              "2       0.0  \n",
              "3       0.0  \n",
              "4       0.0  \n",
              "..      ...  \n",
              "145     2.0  \n",
              "146     2.0  \n",
              "147     2.0  \n",
              "148     2.0  \n",
              "149     2.0  \n",
              "\n",
              "[150 rows x 5 columns]"
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              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
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              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
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              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.0</td>\n",
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              "      <th>146</th>\n",
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              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>147</th>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
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              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2.0</td>\n",
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              "      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-ed8d7257-1996-4f5f-b045-65f2268017e5 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-ed8d7257-1996-4f5f-b045-65f2268017e5');\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": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.1-Numerical EDA**"
      ],
      "metadata": {
        "id": "epXmom091y2H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This will show the first five rows from the DtaFrame df:\n",
        "\n"
      ],
      "metadata": {
        "id": "uNAMmIjz0TuM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**df.head()**"
      ],
      "metadata": {
        "id": "uDXgXqHHu0C1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.head())"
      ],
      "metadata": {
        "id": "S6iIMIi_1SCo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52f8a6c4-8aff-4a31-ede6-bf88268dd256"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
            "0                5.1               3.5                1.4               0.2\n",
            "1                4.9               3.0                1.4               0.2\n",
            "2                4.7               3.2                1.3               0.2\n",
            "3                4.6               3.1                1.5               0.2\n",
            "4                5.0               3.6                1.4               0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OE_f4-bToHWA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "14f989b1-ff72-4d7c-d8a6-3d92a35d0905"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
              "4                5.0               3.6                1.4               0.2"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-be2c725e-34f4-4d1a-af97-25afceff7540\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be2c725e-34f4-4d1a-af97-25afceff7540')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-12a5c148-419b-4835-9cab-b1cb7af8d391')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-12a5c148-419b-4835-9cab-b1cb7af8d391 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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-be2c725e-34f4-4d1a-af97-25afceff7540 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-be2c725e-34f4-4d1a-af97-25afceff7540');\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": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**df.info()**"
      ],
      "metadata": {
        "id": "TKl9vj1mvJyg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "QM0dZDVKbxAG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "describe() method to compute some summary statistics for each feature."
      ],
      "metadata": {
        "id": "lfH0TLdX0fE6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**df.describe**"
      ],
      "metadata": {
        "id": "bStTd0Mgvie0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='all')\n"
      ],
      "metadata": {
        "id": "Rl34SBBSb1_N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**D-tale**\n"
      ],
      "metadata": {
        "id": "Y3Jh5dDExG9O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dtale"
      ],
      "metadata": {
        "id": "NNU4JmMkxLhc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dtale\n",
        "import pandas as pd\n",
        "d = dtale.show(df)\n",
        "#d.open_browser()"
      ],
      "metadata": {
        "id": "mwtEVu42xUnl"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtale.show(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubYyfPuIxf9U",
        "outputId": "c41d1b5a-eda1-4a28-b3c4-8111b04bc78b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "http://bcac0927976e:40000/dtale/main/5"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pandas Profiling**"
      ],
      "metadata": {
        "id": "2N2uF7wZy9xr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas-profiling"
      ],
      "metadata": {
        "id": "ondpaTbtzBoi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import pandas_profiling\n",
        "import numpy as np\n",
        "#descriptive statistics\n",
        "pandas_profiling.ProfileReport(df)"
      ],
      "metadata": {
        "id": "Y8Lqj1vlzMEC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Sweetviz**"
      ],
      "metadata": {
        "id": "hrkzQ2N9zgfJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSQxSAA-zzbg",
        "outputId": "47a709e5-cd13-420e-acfc-afcce9f88065"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sweetviz"
      ],
      "metadata": {
        "id": "FBcNXeJYzkXp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sweetviz as sv\n",
        "import pandas as pd\n",
        "# Create a SweetViz instance\n",
        "report = sv.analyze(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fcf60eee289e43c4a28d7cc542da43da",
            "999f838f4b5f453fb781288f82940d93",
            "a0156754745941cd9762368f459db66c",
            "1c9ce4ea1e6349b8a2413c44050ba0ce",
            "23c26ff816b44b02b5087eae40dad1ab",
            "6d0981b56a1d45669a25ad6d165c912d",
            "3953bc7273e84f3988531c5efec3f192",
            "de0f73dd6c5346c488f4f7da41a940bd",
            "8ad3e5a4a466427e9725ca4b5199c32d",
            "b1c2f4009753447d8d8819de4576f1f9",
            "f84804cf072a416f88098dabdae6af60"
          ]
        },
        "id": "ESkhhKn1zqsg",
        "outputId": "13b4bb9a-9bfc-41e9-8126-797f915fc293"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                             |          | [  0%]   00:00 -> (? left)"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fcf60eee289e43c4a28d7cc542da43da"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "report.show_html('FinalReport.html')"
      ],
      "metadata": {
        "id": "84jIanJT0zhz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install mitoinstaller\n",
        "!python -m mitoinstaller install"
      ],
      "metadata": {
        "id": "eHyL_dALB4fP",
        "outputId": "584bf522-d14e-4757-b93b-4e473a1cfa3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mitoinstaller\n",
            "  Downloading mitoinstaller-0.0.230.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting analytics-python (from mitoinstaller)\n",
            "  Downloading analytics_python-1.4.post1-py2.py3-none-any.whl (23 kB)\n",
            "Collecting colorama (from mitoinstaller)\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from mitoinstaller) (2.3.0)\n",
            "Requirement already satisfied: requests<3.0,>=2.7 in /usr/local/lib/python3.10/dist-packages (from analytics-python->mitoinstaller) (2.27.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from analytics-python->mitoinstaller) (1.16.0)\n",
            "Collecting monotonic>=1.5 (from analytics-python->mitoinstaller)\n",
            "  Downloading monotonic-1.6-py2.py3-none-any.whl (8.2 kB)\n",
            "Collecting backoff==1.10.0 (from analytics-python->mitoinstaller)\n",
            "  Downloading backoff-1.10.0-py2.py3-none-any.whl (31 kB)\n",
            "Requirement already satisfied: python-dateutil>2.1 in /usr/local/lib/python3.10/dist-packages (from analytics-python->mitoinstaller) (2.8.2)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->analytics-python->mitoinstaller) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->analytics-python->mitoinstaller) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->analytics-python->mitoinstaller) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->analytics-python->mitoinstaller) (3.4)\n",
            "Building wheels for collected packages: mitoinstaller\n",
            "  Building wheel for mitoinstaller (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mitoinstaller: filename=mitoinstaller-0.0.230-py3-none-any.whl size=25103 sha256=ad5f7fd457b65545cdced26bf07a172f70ab5164703603f4eda2d0ce817e58fa\n",
            "  Stored in directory: /root/.cache/pip/wheels/9b/7f/9a/7d2f70c6cecab998cad3ed6b3cb30f119a7ade2c65a4a69893\n",
            "Successfully built mitoinstaller\n",
            "Installing collected packages: monotonic, colorama, backoff, analytics-python, mitoinstaller\n",
            "Successfully installed analytics-python-1.4.post1 backoff-1.10.0 colorama-0.4.6 mitoinstaller-0.0.230 monotonic-1.6\n",
            "Starting Mito install. This make take a few moments.\n",
            "\n",
            "In the meantime, check out a 2 minute intro to Mito: https://www.youtube.com/watch?v=LFfWfqzdKyE\n",
            "\n",
            "Upgrade mitoinstaller\n",
            "Setting up environment\n",
            "Check dependencies\n",
            "Remove mitosheet3 if present\n",
            "Install mitosheet\n",
            "This may take a few moments...\n",
            "Activate extension\n",
            "Creating a Mitosheet starter notebook\n",
            "Start Jupyter\n",
            "|DEBUG|Paths used for configuration of jupyter_notebook_config: \n",
            "    \t/etc/jupyter/jupyter_notebook_config.json\n",
            "|DEBUG|Paths used for configuration of jupyter_notebook_config: \n",
            "    \t/usr/local/etc/jupyter/jupyter_notebook_config.d/jupyter_server_ydoc.json\n",
            "    \t/usr/local/etc/jupyter/jupyter_notebook_config.d/jupyterlab.json\n",
            "    \t/usr/local/etc/jupyter/jupyter_notebook_config.d/panel-client-jupyter.json\n",
            "    \t/usr/local/etc/jupyter/jupyter_notebook_config.json\n",
            "|DEBUG|Paths used for configuration of jupyter_notebook_config: \n",
            "    \t/usr/etc/jupyter/jupyter_notebook_config.json\n",
            "|DEBUG|Paths used for configuration of jupyter_notebook_config: \n",
            "    \t/root/.local/etc/jupyter/jupyter_notebook_config.json\n",
            "|DEBUG|Paths used for configuration of jupyter_notebook_config: \n",
            "    \t/root/.jupyter/jupyter_notebook_config.json\n",
            "|INFO|google.colab serverextension initialized.\n",
            "|WARNING|Loading JupyterLab as a classic notebook (v6) extension.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.061 LabApp]\u001b[m 'file_to_run' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'file_to_run' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'allow_root' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'allow_root' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'port_retries' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'login_handler_class' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'allow_origin' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'allow_remote_access' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'disable_check_xsrf' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[33m[W 2023-07-25 07:51:44.062 LabApp]\u001b[m 'disable_check_xsrf' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n",
            "\u001b[32m[I 2023-07-25 07:51:44.071 LabApp]\u001b[m JupyterLab extension loaded from /usr/local/lib/python3.10/dist-packages/jupyterlab\n",
            "\u001b[32m[I 2023-07-25 07:51:44.071 LabApp]\u001b[m JupyterLab application directory is /usr/local/share/jupyter/lab\n",
            "|WARNING|Error loading server extension panel.io.jupyter_server_extension\n",
            "    Traceback (most recent call last):\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/notebook/notebookapp.py\", line 2027, in init_server_extensions\n",
            "        mod = importlib.import_module(modulename)\n",
            "      File \"/usr/lib/python3.10/importlib/__init__.py\", line 126, in import_module\n",
            "        return _bootstrap._gcd_import(name[level:], package, level)\n",
            "      File \"<frozen importlib._bootstrap>\", line 1050, in _gcd_import\n",
            "      File \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\n",
            "      File \"<frozen importlib._bootstrap>\", line 992, in _find_and_load_unlocked\n",
            "      File \"<frozen importlib._bootstrap>\", line 241, in _call_with_frames_removed\n",
            "      File \"<frozen importlib._bootstrap>\", line 1050, in _gcd_import\n",
            "      File \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\n",
            "      File \"<frozen importlib._bootstrap>\", line 992, in _find_and_load_unlocked\n",
            "      File \"<frozen importlib._bootstrap>\", line 241, in _call_with_frames_removed\n",
            "      File \"<frozen importlib._bootstrap>\", line 1050, in _gcd_import\n",
            "      File \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\n",
            "      File \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\n",
            "      File \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\n",
            "      File \"<frozen importlib._bootstrap_external>\", line 883, in exec_module\n",
            "      File \"<frozen importlib._bootstrap>\", line 241, in _call_with_frames_removed\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/panel/__init__.py\", line 48, in <module>\n",
            "        from . import layout  # noqa\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/panel/layout/__init__.py\", line 31, in <module>\n",
            "        from .accordion import Accordion  # noqa\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/panel/layout/accordion.py\", line 9, in <module>\n",
            "        from .base import NamedListPanel\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/panel/layout/base.py\", line 17, in <module>\n",
            "        from ..io.model import hold\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/panel/io/__init__.py\", line 9, in <module>\n",
            "        from .document import init_doc, unlocked, with_lock  # noqa\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/panel/io/document.py\", line 20, in <module>\n",
            "        from .model import monkeypatch_events\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/panel/io/model.py\", line 20, in <module>\n",
            "        from bokeh.models import Box, ColumnDataSource, Model\n",
            "    ImportError: cannot import name 'Box' from 'bokeh.models' (/usr/local/lib/python3.10/dist-packages/bokeh/models/__init__.py)\n",
            "|INFO|Serving notebooks from local directory: /content\n",
            "|INFO|Jupyter Notebook 6.4.8 is running at:\n",
            "|INFO|http://localhost:8888/?token=c8d4a9ca7ed5b8a6dfed9827d8783f4c74d3adc8b00fd691\n",
            "|INFO| or http://127.0.0.1:8888/?token=c8d4a9ca7ed5b8a6dfed9827d8783f4c74d3adc8b00fd691\n",
            "|INFO|Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n",
            "|WARNING|No web browser found: could not locate runnable browser.\n",
            "|CRITICAL|\n",
            "    \n",
            "    To access the notebook, open this file in a browser:\n",
            "        file:///root/.local/share/jupyter/runtime/nbserver-7877-open.html\n",
            "    Or copy and paste one of these URLs:\n",
            "        http://localhost:8888/?token=c8d4a9ca7ed5b8a6dfed9827d8783f4c74d3adc8b00fd691\n",
            "     or http://127.0.0.1:8888/?token=c8d4a9ca7ed5b8a6dfed9827d8783f4c74d3adc8b00fd691\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mitosheet\n",
        "mitosheet.sheet(data)"
      ],
      "metadata": {
        "id": "3T3omVQ5CANr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.2-Visual EDA**"
      ],
      "metadata": {
        "id": "pFqwAMLmbgnf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Histograms**"
      ],
      "metadata": {
        "id": "Xw3xafqZ0y4a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df.hist(figsize=(6,8))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "Q4MEoemH0xcL",
        "outputId": "a9fa6784-8770-4a74-d1de-126a1ad33d4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure()\n",
        "sns.countplot(X='sepal length (cm)', y='Setosa', data=df, palette='RdBu')\n",
        "plt.xticks([0,1], ['Virginica', 'Setosa'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Dgl_6g4iceID"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OugaKicqPKus"
      },
      "source": [
        "## **1.2-Hiding Function**[5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wd5sPyyBPSBG"
      },
      "source": [
        "Sometimes when you do analysis and presenting the result to the other, you only want to show the most important aspect. I know when I present my Data Frame to the non-technical person, the question is often about the Index in their default number such as “what is this number?”. For that reason, we could try to hide the index with the following code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "9X1lWJMdPVz1",
        "outputId": "c1555652-dade-49bf-ed51-6836dd069fd7"
      },
      "source": [
        "#Using hide_index() from the style function\n",
        "\n",
        "df.head(10).style.hide_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-ae401fca442f>:3: FutureWarning: this method is deprecated in favour of `Styler.hide(axis=\"index\")`\n",
            "  df.head(10).style.hide_index()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd6d6eed7b0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_3c235\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_3c235_level0_col0\" class=\"col_heading level0 col0\" >sepal length (cm)</th>\n",
              "      <th id=\"T_3c235_level0_col1\" class=\"col_heading level0 col1\" >sepal width (cm)</th>\n",
              "      <th id=\"T_3c235_level0_col2\" class=\"col_heading level0 col2\" >petal length (cm)</th>\n",
              "      <th id=\"T_3c235_level0_col3\" class=\"col_heading level0 col3\" >petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row0_col0\" class=\"data row0 col0\" >5.100000</td>\n",
              "      <td id=\"T_3c235_row0_col1\" class=\"data row0 col1\" >3.500000</td>\n",
              "      <td id=\"T_3c235_row0_col2\" class=\"data row0 col2\" >1.400000</td>\n",
              "      <td id=\"T_3c235_row0_col3\" class=\"data row0 col3\" >0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row1_col0\" class=\"data row1 col0\" >4.900000</td>\n",
              "      <td id=\"T_3c235_row1_col1\" class=\"data row1 col1\" >3.000000</td>\n",
              "      <td id=\"T_3c235_row1_col2\" class=\"data row1 col2\" >1.400000</td>\n",
              "      <td id=\"T_3c235_row1_col3\" class=\"data row1 col3\" >0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row2_col0\" class=\"data row2 col0\" >4.700000</td>\n",
              "      <td id=\"T_3c235_row2_col1\" class=\"data row2 col1\" >3.200000</td>\n",
              "      <td id=\"T_3c235_row2_col2\" class=\"data row2 col2\" >1.300000</td>\n",
              "      <td id=\"T_3c235_row2_col3\" class=\"data row2 col3\" >0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row3_col0\" class=\"data row3 col0\" >4.600000</td>\n",
              "      <td id=\"T_3c235_row3_col1\" class=\"data row3 col1\" >3.100000</td>\n",
              "      <td id=\"T_3c235_row3_col2\" class=\"data row3 col2\" >1.500000</td>\n",
              "      <td id=\"T_3c235_row3_col3\" class=\"data row3 col3\" >0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row4_col0\" class=\"data row4 col0\" >5.000000</td>\n",
              "      <td id=\"T_3c235_row4_col1\" class=\"data row4 col1\" >3.600000</td>\n",
              "      <td id=\"T_3c235_row4_col2\" class=\"data row4 col2\" >1.400000</td>\n",
              "      <td id=\"T_3c235_row4_col3\" class=\"data row4 col3\" >0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row5_col0\" class=\"data row5 col0\" >5.400000</td>\n",
              "      <td id=\"T_3c235_row5_col1\" class=\"data row5 col1\" >3.900000</td>\n",
              "      <td id=\"T_3c235_row5_col2\" class=\"data row5 col2\" >1.700000</td>\n",
              "      <td id=\"T_3c235_row5_col3\" class=\"data row5 col3\" >0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row6_col0\" class=\"data row6 col0\" >4.600000</td>\n",
              "      <td id=\"T_3c235_row6_col1\" class=\"data row6 col1\" >3.400000</td>\n",
              "      <td id=\"T_3c235_row6_col2\" class=\"data row6 col2\" >1.400000</td>\n",
              "      <td id=\"T_3c235_row6_col3\" class=\"data row6 col3\" >0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row7_col0\" class=\"data row7 col0\" >5.000000</td>\n",
              "      <td id=\"T_3c235_row7_col1\" class=\"data row7 col1\" >3.400000</td>\n",
              "      <td id=\"T_3c235_row7_col2\" class=\"data row7 col2\" >1.500000</td>\n",
              "      <td id=\"T_3c235_row7_col3\" class=\"data row7 col3\" >0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row8_col0\" class=\"data row8 col0\" >4.400000</td>\n",
              "      <td id=\"T_3c235_row8_col1\" class=\"data row8 col1\" >2.900000</td>\n",
              "      <td id=\"T_3c235_row8_col2\" class=\"data row8 col2\" >1.400000</td>\n",
              "      <td id=\"T_3c235_row8_col3\" class=\"data row8 col3\" >0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3c235_row9_col0\" class=\"data row9 col0\" >4.900000</td>\n",
              "      <td id=\"T_3c235_row9_col1\" class=\"data row9 col1\" >3.100000</td>\n",
              "      <td id=\"T_3c235_row9_col2\" class=\"data row9 col2\" >1.500000</td>\n",
              "      <td id=\"T_3c235_row9_col3\" class=\"data row9 col3\" >0.100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xg-xui0jPqWE"
      },
      "source": [
        "Just like that, we hide our index. It is a simple thing but in the working environment I know sometimes it would become a problem. Just one extra column that becomes a question.\n",
        "In addition, we could try to hide unnecessary columns with the chaining method. Let’s say I don't want to show the ‘method’ and ‘year’ columns then we could write it with the following code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfTfZk4TPuQT"
      },
      "source": [
        "#Using hide_columns to hide the unnecesary columns\n",
        "df.head(10).style.hide_index().hide_columns(['setosa', 'versicolor', 'virginica'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ID9eKkpcQl0N"
      },
      "source": [
        "## **1.3-Highlight Function**[5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZaCbhBcQsO9"
      },
      "source": [
        "There is a time when we want to present our data frame and only highlight the important number, for example the highest number. In this case, we could use the built-in method to highlight it with the following code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wb0DKYZJQ0SD"
      },
      "source": [
        "#Highlight the maximum number for each column\n",
        "data.head(10).style.highlight_max(color = 'yellow')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FmnZc6FHQ-nl"
      },
      "source": [
        "In the data frame above, we highlight the maximum number in each column with the color yellow. If you want to highlight the minimum number instead, we could do it with the following code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_1Ju9UlRAO8"
      },
      "source": [
        "data.head(10).style.highlight_min(color = 'lightblue')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GE4-6pMrRM6a"
      },
      "source": [
        "and if you want to chain it, we could also do that."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnzAqwXVRMNL"
      },
      "source": [
        "#Highlight the minimum number with lightblue color and the maximum number with yellow color\n",
        "data.head(10).style.highlight_max(color='yellow').highlight_min(color = 'lightblue')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLwN4DgERZST"
      },
      "source": [
        "Instead of each column, you could actually highlight the minimum or maximum number for each row. I show it in the following code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5kVoBcayRcns"
      },
      "source": [
        "#Adding Axis = 1 to change the direction from column to row\n",
        "data.head(10).style.highlight_max(color = 'yellow', axis =1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCzIjmcvRjys"
      },
      "source": [
        "As we can see, it is useless right now to change the axis as it did not highlight any important information. It would be more useful in the case when each column is not that different from each other.\n",
        "As an addition, we could highlight the null value with the following code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdiB7_2_RoqS"
      },
      "source": [
        "#Higlight the null value\n",
        "data.head(10).style.highlight_null(null_color = 'red')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hx2Z7EvIEmuB"
      },
      "source": [
        "## **1.4-Gradient Function**[5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8Td6uexE2rQ"
      },
      "source": [
        "While presenting your data, we could also use all the information as the main way to present the data. I often present the data with a background color to highlight which number is in the lower area and where is the one in the higher area. Let’s use the example by the following code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "dS9h-JzJE4QA",
        "outputId": "0c7523fe-7ac6-4a6d-cd78-64f5d9deec92"
      },
      "source": [
        "#Gradient background color for the numerical columns\n",
        "data.head(10).style.background_gradient(cmap = 'Blues')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        }</style><table id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >sl_no</th>        <th class=\"col_heading level0 col1\" >gender</th>        <th class=\"col_heading level0 col2\" >ssc_p</th>        <th class=\"col_heading level0 col3\" >ssc_b</th>        <th class=\"col_heading level0 col4\" >hsc_p</th>        <th class=\"col_heading level0 col5\" >hsc_b</th>        <th class=\"col_heading level0 col6\" >hsc_s</th>        <th class=\"col_heading level0 col7\" >degree_p</th>        <th class=\"col_heading level0 col8\" >degree_t</th>        <th class=\"col_heading level0 col9\" >workex</th>        <th class=\"col_heading level0 col10\" >etest_p</th>        <th class=\"col_heading level0 col11\" >specialisation</th>        <th class=\"col_heading level0 col12\" >mba_p</th>        <th class=\"col_heading level0 col13\" >status</th>        <th class=\"col_heading level0 col14\" >salary</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >67.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >Others</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >91.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >Others</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >Commerce</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >58.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col8\" class=\"data row0 col8\" >Sci&Tech</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col9\" class=\"data row0 col9\" >No</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col10\" class=\"data row0 col10\" >55.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col11\" class=\"data row0 col11\" >Mkt&HR</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col12\" class=\"data row0 col12\" >58.800000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col13\" class=\"data row0 col13\" >Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row0_col14\" class=\"data row0 col14\" >270000.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >79.330000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >78.330000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >Others</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >Science</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >77.480000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col8\" class=\"data row1 col8\" >Sci&Tech</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col9\" class=\"data row1 col9\" >Yes</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col10\" class=\"data row1 col10\" >86.500000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col11\" class=\"data row1 col11\" >Mkt&Fin</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col12\" class=\"data row1 col12\" >66.280000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col13\" class=\"data row1 col13\" >Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row1_col14\" class=\"data row1 col14\" >200000.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >65.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >68.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >Arts</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >64.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col8\" class=\"data row2 col8\" >Comm&Mgmt</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col9\" class=\"data row2 col9\" >No</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col10\" class=\"data row2 col10\" >75.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col11\" class=\"data row2 col11\" >Mkt&Fin</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col12\" class=\"data row2 col12\" >57.800000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col13\" class=\"data row2 col13\" >Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row2_col14\" class=\"data row2 col14\" >250000.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >56.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >52.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >Science</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >52.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col8\" class=\"data row3 col8\" >Sci&Tech</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col9\" class=\"data row3 col9\" >No</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col10\" class=\"data row3 col10\" >66.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col11\" class=\"data row3 col11\" >Mkt&HR</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col12\" class=\"data row3 col12\" >59.430000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col13\" class=\"data row3 col13\" >Not Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row3_col14\" class=\"data row3 col14\" >nan</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >85.800000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >73.600000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >Commerce</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >73.300000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col8\" class=\"data row4 col8\" >Comm&Mgmt</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col9\" class=\"data row4 col9\" >No</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col10\" class=\"data row4 col10\" >96.800000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col11\" class=\"data row4 col11\" >Mkt&Fin</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col12\" class=\"data row4 col12\" >55.500000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col13\" class=\"data row4 col13\" >Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row4_col14\" class=\"data row4 col14\" >425000.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >55.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >Others</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >49.800000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >Others</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >Science</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >67.250000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col8\" class=\"data row5 col8\" >Sci&Tech</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col9\" class=\"data row5 col9\" >Yes</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col10\" class=\"data row5 col10\" >55.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col11\" class=\"data row5 col11\" >Mkt&Fin</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col12\" class=\"data row5 col12\" >51.580000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col13\" class=\"data row5 col13\" >Not Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row5_col14\" class=\"data row5 col14\" >nan</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >7</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >F</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >46.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >Others</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >49.200000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >Others</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >Commerce</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >79.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col8\" class=\"data row6 col8\" >Comm&Mgmt</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col9\" class=\"data row6 col9\" >No</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col10\" class=\"data row6 col10\" >74.280000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col11\" class=\"data row6 col11\" >Mkt&Fin</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col12\" class=\"data row6 col12\" >53.290000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col13\" class=\"data row6 col13\" >Not Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row6_col14\" class=\"data row6 col14\" >nan</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >8</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >82.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >64.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >Science</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >66.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col8\" class=\"data row7 col8\" >Sci&Tech</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col9\" class=\"data row7 col9\" >Yes</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col10\" class=\"data row7 col10\" >67.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col11\" class=\"data row7 col11\" >Mkt&Fin</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col12\" class=\"data row7 col12\" >62.140000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col13\" class=\"data row7 col13\" >Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row7_col14\" class=\"data row7 col14\" >252000.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col0\" class=\"data row8 col0\" >9</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col1\" class=\"data row8 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col2\" class=\"data row8 col2\" >73.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col3\" class=\"data row8 col3\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col4\" class=\"data row8 col4\" >79.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col5\" class=\"data row8 col5\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col6\" class=\"data row8 col6\" >Commerce</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col7\" class=\"data row8 col7\" >72.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col8\" class=\"data row8 col8\" >Comm&Mgmt</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col9\" class=\"data row8 col9\" >No</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col10\" class=\"data row8 col10\" >91.340000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col11\" class=\"data row8 col11\" >Mkt&Fin</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col12\" class=\"data row8 col12\" >61.290000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col13\" class=\"data row8 col13\" >Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row8_col14\" class=\"data row8 col14\" >231000.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col0\" class=\"data row9 col0\" >10</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col1\" class=\"data row9 col1\" >M</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col2\" class=\"data row9 col2\" >58.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col3\" class=\"data row9 col3\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col4\" class=\"data row9 col4\" >70.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col5\" class=\"data row9 col5\" >Central</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col6\" class=\"data row9 col6\" >Commerce</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col7\" class=\"data row9 col7\" >61.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col8\" class=\"data row9 col8\" >Comm&Mgmt</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col9\" class=\"data row9 col9\" >No</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col10\" class=\"data row9 col10\" >54.000000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col11\" class=\"data row9 col11\" >Mkt&Fin</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col12\" class=\"data row9 col12\" >52.210000</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col13\" class=\"data row9 col13\" >Not Placed</td>\n",
              "                        <td id=\"T_24497d2a_49f4_11ec_8d3a_0242ac1c0002row9_col14\" class=\"data row9 col14\" >nan</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f1baef58f50>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xPY5g9xvFIRm"
      },
      "source": [
        "With the background_gradient function, we could color the data frame as a gradient. The color would depend on the cmap parameter where the parameter is accepting colormaps from the matplotlib library."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNq_-5ZpFLYQ"
      },
      "source": [
        "We could also use a bar chart as our gradient background color. Let me show it in the example below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26cjWZbcFM0g"
      },
      "source": [
        "#Sort the values by the year column then creating a bar chart as the background\n",
        "data.head(10).sort_values(by = 'ssc_p').style.bar(color= 'lightblue')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQbNM_hTFn3w"
      },
      "source": [
        "As we could see above, we now highlight the number from the lowest to the highest number in a different way than the background_gradient function is. We could also see the index is not in order because of the sort function; it is better to hide the index as I told you in the passage above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uVpNfRuKFs2f"
      },
      "source": [
        "## **1.5-Custom Function**[5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5nq64w1FzEX"
      },
      "source": [
        "If you prefer to have a more specific requirement to style your data frame, you could actually do it. We could pass our style functions into one of the following methods:\n",
        "- Styler.applymap: element-wise\n",
        "- Styler.apply: column-/row-/table-wise\n",
        "Both of those methods take a function (and some other keyword arguments) and apply our function to the DataFrame in a certain way. Let’s say that I have a threshold that any number below 20 should be colored red. We could do that by using the following code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCRGUN2RF62-"
      },
      "source": [
        "#Create the function to color the numerical value into red color\n",
        "def color_below_20_red(value):\n",
        "    if type(value) == type(''):\n",
        "        return 'color:black'\n",
        "    else:\n",
        "        color = 'red' if value <= 20 else 'black'\n",
        "        return 'color: {}'.format(color)\n",
        "#We apply the function to all the element in the data frame by using the applymap function\n",
        "data.head(10).style.applymap(color_below_20_red)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7r4GCZ_r578"
      },
      "source": [
        "# **Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JL1FYrIa5kv8"
      },
      "source": [
        "**Check Missing Value**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6sqt75_5o9y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "1a52b0b2-b083-4f83-c227-d9649e734469"
      },
      "source": [
        "total = data.isnull().sum().sort_values(ascending=False)\n",
        "percent = (data.isnull().sum()/data.isnull().count()).sort_values(ascending=False)\n",
        "missing = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing.head(4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ba34a52e-7fc5-484e-a1f7-c25844a360e7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>salary</th>\n",
              "      <td>67</td>\n",
              "      <td>0.311628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sl_no</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ssc_p</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba34a52e-7fc5-484e-a1f7-c25844a360e7')\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ba34a52e-7fc5-484e-a1f7-c25844a360e7 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-ba34a52e-7fc5-484e-a1f7-c25844a360e7');\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",
              "  "
            ],
            "text/plain": [
              "        Total   Percent\n",
              "salary     67  0.311628\n",
              "sl_no       0  0.000000\n",
              "gender      0  0.000000\n",
              "ssc_p       0  0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_DhzYPtFlrL",
        "outputId": "b0114f7f-ec80-42d2-a644-048cf6f796be"
      },
      "source": [
        "missing_v = [col for col in data.columns if (data[col].isnull().sum()/data.shape[0]) * 100 > 0.001]\n",
        "print(len(missing_v))\n",
        "for cols in missing_v:\n",
        "    del data[cols]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21xYr0iDF-wc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0a1feeb-2125-4a05-bca7-f992b36f8953"
      },
      "source": [
        "np.sum(data.isnull().any(axis=1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnvVuWKg6vj1"
      },
      "source": [
        "**Hot Encoding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Fa0DkFL6yEq",
        "outputId": "a19c16ae-4ca2-45bb-e9a0-1fb32c73e9a7"
      },
      "source": [
        "data.select_dtypes(include=['object']).columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['gender', 'ssc_b', 'hsc_b', 'hsc_s', 'degree_t', 'workex',\n",
              "       'specialisation', 'status'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7g24nkqr7Qx8"
      },
      "source": [
        "data['gender'] = data['gender'].fillna(data['gender'].mode()[0])\n",
        "data['ssc_b'] = data['ssc_b'].fillna(data['ssc_b'].mode()[0])\n",
        "data['hsc_b'] = data['hsc_b'].fillna(data['hsc_b'].mode()[0])\n",
        "data['hsc_s'] = data['hsc_s'].fillna(data['hsc_s'].mode()[0])\n",
        "data['degree_t'] = data['degree_t'].fillna(data['degree_t'].mode()[0])\n",
        "data['workex'] = data['workex'].fillna(data['workex'].mode()[0])\n",
        "data['specialisation'] = data['specialisation'].fillna(data['specialisation'].mode()[0])\n",
        "data['status'] = data['status'].fillna(data['status'].mode()[0])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLaZar8T-YkY"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "lencoders = {}\n",
        "for col in data.select_dtypes(include=['object']).columns:\n",
        "    lencoders[col] = LabelEncoder()\n",
        "    data[col] = lencoders[col].fit_transform(data[col])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5GR2wIi-k--"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1X3_s7X8_hEc"
      },
      "source": [
        "**Feature Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQgnWr7Q_jey"
      },
      "source": [
        "# Standardizing data\n",
        "from sklearn import preprocessing\n",
        "r_scaler = preprocessing.MinMaxScaler()\n",
        "r_scaler.fit(data)\n",
        "data = pd.DataFrame(r_scaler.transform(data), index=data.index, columns=data.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2m6tWQEIAQfb"
      },
      "source": [
        "**Data spliting**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9t6Ik9SAUPX"
      },
      "source": [
        "X=data.drop('specialisation',axis=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f52X64tDAYXY"
      },
      "source": [
        "y=data[['specialisation']]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-S8fYFyApQB"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=100)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2-Data Visualization**"
      ],
      "metadata": {
        "id": "gdnWbi_14bjp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Visualization is very important in data analysis and machine learning, it allows us to have a better understanding about the pattern of some variables in our data, conclude some correlation between multiple variables, and eventually we can take the right decision based on that [9]"
      ],
      "metadata": {
        "id": "YZ_whQZH4t0w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I will use the Matplotlib package, which is a 2D graphical library in Python language, it supports plotting graphics and images of the data in an attractive way[9]"
      ],
      "metadata": {
        "id": "hGOB8pIb44Zf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.1- Histograms**\n"
      ],
      "metadata": {
        "id": "VpNCaqPP3PSB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df.hist(figsize=(6,8))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SJv8eMD03dOd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Box Plots**\n"
      ],
      "metadata": {
        "id": "PN585MTA46fP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=\"target\", y=\"sepal length (cm)\", data=df1)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Mv9PvVX95DTu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Correlation Matrix**"
      ],
      "metadata": {
        "id": "LdLhBGgC6Jym"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = df1.corr()\n",
        "sns.heatmap(corr_matrix, annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "qXp1Tu8Y6R1B",
        "outputId": "cbf17420-1cde-4da4-f976-c82a8bfc3080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Density Plots**\n"
      ],
      "metadata": {
        "id": "OGKDcQFE7FeK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(df1['sepal length (cm)'], fill=True)\n",
        "plt.savefig('Density_Plot.png', dpi=600)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "md6CpPek7M7Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Violin Plots**\n"
      ],
      "metadata": {
        "id": "qJufocwV9Lyq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.violinplot(x=\"target\", y=\"sepal length (cm)\", data=df1)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ihWXani79TiO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.2- Matplotlib**"
      ],
      "metadata": {
        "id": "PH_Mp32l4lGH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Simplest Way to Create Visualizations**"
      ],
      "metadata": {
        "id": "yAJXNS294_We"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "URphCBaF6Vs7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new=data.filter(['gender', 'ssc_p','ssc_b'])\n"
      ],
      "metadata": {
        "id": "TfUqWB6K5mtj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new"
      ],
      "metadata": {
        "id": "w25cuSRa6j8K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "One can simply plot out the values for each of these columns per row simply with data.plot.bar();, where data should be replaced with the name of the DataFrame. Note that adding the semicolon (;) after the statement removes the cell from outputting other prints (that is, something like) [9]"
      ],
      "metadata": {
        "id": "r-DESqQI9ec1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Alternatively, try adding a parameter stacked=True — this is a very easy way to create a stacked bar chart straight from the data source [9]"
      ],
      "metadata": {
        "id": "qDUYRTxQ9zpI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot.bar(stacked=True);"
      ],
      "metadata": {
        "id": "bElCshba5ESI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Or, try using barh, which draws horizontal bars: data.plot.barh(stacked=True). All these variants can be easily created with only one line of code because they establish a direct flow with the data.[9]"
      ],
      "metadata": {
        "id": "R_nZbKuu-BE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot.barh(stacked=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "6zsqXNj789XR",
        "outputId": "7ec42cd0-3c44-42ed-f872-2169157aa6c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another method of displaying this type of data would be an area chart, using"
      ],
      "metadata": {
        "id": "SNMYejYT-e6g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.plot.area();\n"
      ],
      "metadata": {
        "id": "z2XNtjgD-kgp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Parameters within the code can be adjusted as they would normally would with a matplotlib or seaborn model. In the case of data.plot.area(stacked=False);, the parameter alpha (transparency) is set to 0.5 by default, but can be manually adjusted [9]"
      ],
      "metadata": {
        "id": "v7olFiLs-ytO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.plot.area(stacked=False);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YgdTBWA2-5hs",
        "outputId": "e6e7b39b-02c7-454c-d85f-00b8dcb57800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One of the major benefits of using pandas directly is that many of pandas’ helpful DataFrame manipulations can be directly used. For example, consider the result of data.diff(), which simply takes the difference between one row and the row before it (hence the presence of NaN in the first row). This is helpful in many time series applications."
      ],
      "metadata": {
        "id": "njzqqel1_FMc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.diff()"
      ],
      "metadata": {
        "id": "Y_K2nyFt_JRq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For example, consider the following code, which plots out the differenced data, as well as demonstrating the usage of various parameters in pandas plotting, in this case, color:[9]"
      ],
      "metadata": {
        "id": "dCdchXof_aba"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.diff().plot.box(vert=False,\n",
        "                     color={'medians':'lightblue',\n",
        "                            'boxes':'blue',\n",
        "                            'caps':'darkblue'});"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bJc2hEeg_f9h",
        "outputId": "272ea43a-1537-49ba-e851-15f8932a146c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X6srMDbX30D3ACW3ad0t1DeM1wO2Z+bPM/DlwO3BBj+paCvS3ad9Dysxv0fggOJRFwOez4R7g+RExmzaN1ZQJh1H6TeBHtcc/rtqOBp7MzF8Nam+H4zJzT3X/P4HjRlh/CeUb8+rqsPKjEXFEl+uaERGbI+KegVNdTKDxiohzaHwa/EGtuV3jNdT7pek61Xg8RWN8RrNtJ+uqW0bjE+iAZq9pN+t6c/X6rI6IF4xx207WRXX67WRgfa25U+M1kqHqbstYTW+ptAkmIu4AfqPJouWZ+eVu1zNguLrqDzIzI2LI7xZXnwpOB9bVmj9AY5I8nMb3nd8P/G0X6zopM38SEacA6yNiG40JcNzaPF7/AlyWmc9UzeMer0NRRFwK9AGvqDUXr2lm/qB5D233FaA/M/dFxJ/SOOo6v0v7Ho0lwOrMPFBr6+V4dcwhFQ6Z+aoWu/gJ8ILa4xOqtidoHLJNrz79DbS3XFdEPBYRszNzTzWZPT5MV38I3JqZ+2t9D3yK3hcR/wy8r5t1ZeZPqp8PR8RdwIuBL9Hj8YqIXwNuo/HB4J5a3+MeryaGer80W+fHETEdeB6N99Notu1kXUTEq2gE7isyc99A+xCvaTsmuxHryswnag8/S+Ma08C2vzdo27vaUNOo6qpZAryr3tDB8RrJUHW3Zaw8rXSw7wIvjMY3bQ6n8UZYk42rPBtonO8HuAxo15HImqq/0fRbnOusJsiB8/wXAk2/2dCJuiJi5sBpmYg4BngZsL3X41W9drfSOB+7etCydo5X0/fLMPVeBKyvxmcNsCQa32Y6GXgh8J0WahlTXRHxYuAzwBsy8/Fae9PXtIt1za49fAOwo7q/Dnh1Vd9M4NUcfATd0bqq2k6lcYF3U62tk+M1kjXAW6tvLb0EeKr68NOeserEVfaJeAPeSOPc2z7gMWBd1X488NXaeq8FHqSR/Mtr7afQ+OXdBXwROKJNdR0N3Ak8BNwBzKra+4DP1tabQ+MTwXMGbb8e2EZjklsJHNWtuoBzq31vrX4umwjjBVwK7Afurd3O7MR4NXu/0DhN9Ybq/ozq+e+qxuOU2rbLq+12Ar/f5vf7SHXdUf0eDIzPmpFe0y7V9XfAA9X+NwCn1rZ9ezWOu4A/6mZd1eMPAdcM2q5j40Xjg+Ce6r38YxrXht4BvKNaHsA/VTVvo/YtzHaMlf8+Q5JU8LSSJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKnwfzoZpX0jh4YqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another application of pandas’ handy data manipulation functions is with .rolling().mean(), which takes the average rolling mean, a common statistical method to reduce the noisiness of data that averages moving windows of data.[9]"
      ],
      "metadata": {
        "id": "PPcgWJ8-_scZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_new['gender'].plot()\n",
        "X_new['gender'].rolling(10).mean().plot()"
      ],
      "metadata": {
        "id": "BdBOZjuw_y1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are various other types of plots that can be created directly from the data:\n",
        "kde or density for density plots"
      ],
      "metadata": {
        "id": "PO07y0kNA8pB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.plot.kde(); #distribution plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "EScmY5x1ALIo",
        "outputId": "edb0cd5d-149c-42e0-d181-89af8de14940"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.plot.pie(subplots=True, figsize=(8, 4));\n",
        "\n"
      ],
      "metadata": {
        "id": "OFBw5HDsBGqK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As another example of the convenience of subplots in direct-pandas plotting, consider plotting line data (the default when using .plot()):[9]"
      ],
      "metadata": {
        "id": "ikf5gg62B0td"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.plot(subplots=True,figsize=(20,10));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "VDIsSYb-BqK9",
        "outputId": "cf2bd28a-8f7e-42d9-8802-2a61f7c5f9e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Consider the result when adding a parameter layout=(2, 2) in the line of code that plots the visualization (after subplots=True): pandas automatically formats the subplots in a format according to the layout. The dimensions of each subplot are determined by the figsize argument, which specifies the size of the “master-plot” that encompasses all subplots [9]"
      ],
      "metadata": {
        "id": "j8iJJKTRB9Hp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.plot(subplots=True,figsize=(20,10), layout=(2, 2));"
      ],
      "metadata": {
        "id": "AkuXPOYxCnWt",
        "outputId": "6f09c3ca-f9fe-4cad-c53b-0994f25550df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Seaborn**"
      ],
      "metadata": {
        "id": "91KNE5EjZY_p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Developers created a new library based on matplotlib called seaborn. Seaborn is as powerful as matplotlib while also providing an abstraction to simplify plots and bring some unique features[11]"
      ],
      "metadata": {
        "id": "77KgVGw6ZwOa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures.\n",
        "Seaborn’s design allows you to explore and understand your data quickly. Seaborn works by capturing entire dataframes or arrays containing all your data and performing all the internal functions necessary for semantic mapping and statistical aggregation to convert data into informative plots.\n",
        "It abstracts complexity while allowing you to design your plots to your requirements [11]"
      ],
      "metadata": {
        "id": "pSlRJ7t6Z3bB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install seaborn notebook\n"
      ],
      "metadata": {
        "id": "j2GDuYbdXDCa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib"
      ],
      "metadata": {
        "id": "tY3RMGzqXJwj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new"
      ],
      "metadata": {
        "id": "O-zm76cGXLiq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Scatter Plot**\n"
      ],
      "metadata": {
        "id": "DMD9qiEOaCww"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A scatter plot is a diagram that displays points based on two dimensions of the dataset. Creating a scatter plot in the seaborn library is so simple and requires just one line of code:[11]"
      ],
      "metadata": {
        "id": "ecvUdsfraL6R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data=df)\n"
      ],
      "metadata": {
        "id": "oXt5w4M8XisC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "616dc7f3-70e8-4a4e-b28e-2eb830b634ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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i946r0YsPLMH6PnmH1Am3Y+t6GgFTc5GQSoX0rk727duXUN1VVFRESkpKlfYpq2GCM2+GPTtihQtRhIZNasQr5FDZvn07PXv25JNPPqFjx46V3r62RRudM2cOr7zyCmvWrKmR/X/11Vfcc889fPHFF3ExVaqTYcOGMX78+DLD1cOhXa8VQRAEGjVqxO7du48J9euxNl6o+2NO9xWRc+NYkCSyps4m556bEraz9z8L6eIbCOx3n9+9ezdpapCciRck9NSwtOuMfdIMPMaRya/hFCE4ZwbBdQmeZaJI1txF5MvW+LoE1PU5LkFRlApF3qy5J/MRQLA7EKc8BP2HQGZ9SMuM/L//kFopVBwNzJ8/n59//pmtW7eyePFinnnmGf7973/X2PEGDRrEmDFjajSMdl5eHkOGDIm62ZqY1FYEQTiiia0sFguBlV9F/t26HcFfkxuU+79dhlUNRv8WRRH1r41J3T9Dv/2CEjpyXlmWUDCxUAGg64Q3/lopI/xjiaNqKQRAsDkiLqWjr6oRQ02TWP7++2+eeOIJCgoKaNy4MVdddRXXX399jR7zyiuvrNH9Z2RkcO2119boMUxMDgW7KGALBdB2bUNQLIgNsvEqFkLaEVg2KHn5yzJGWdpTTYvx2hMEASMcKnPXxhFaBqnIsY1gwHy/JOGoEyxKUxcmvWnTpuzcufNId6PKTJ8+nenTpx/pbpiYHDO4BQP103fIeXN+dMlXsNpIu/kehNYdCB5GVXs4HMbRox+eJQsI/7UR9/DReN55LWFba+fuhGVL1E5S0zSU1u0StgWQmzRHt1bN0Ls60Kw2lOatCG/dnLDe0q4z3vI8EY9RjqqlEBMTE5OjGUmSEP7eiGfRizF2ZEYwQP5Dd+Lwew5rfwzDQM+oj7Vrbwy/D3XPDmxde8e1EyxW3JffiP+gJF9Bqx3nvy6I37EokTrhdrxS9bqQVwafKJNy7e2RaJ0H4TjrXEJ2c2k9GUe1xsLExMTkaMKuq3gWvZC4Utfxf/4+lhEXEwqVvcRQnRQhkDbhdmw/fY/3gzdJOf9SbN164/l4CXpxIdaTeuAadTHFNhe6rsdokn0GpJx7EZYOJ+F5cz5aXg6WEzrhunA8PldajbibVhRN0/BlNSbrsZfxLHyB0G+/IKVn4hx5CULbThTVXRvMGscULExMTEzqCKKuof2zJ2m9tmMr8hHwOigwBJTukXgVhmFg2B24up2GoOuEFSv5up7UZqFIF5DanIhjykOIuoYqWyjQDQz9yL+5Q7pO2JGC/YpbsKshdFHCLykxSQ5N4jEFCxMTE5M6giYpKK3aEvxpVcJ6pcNJqEfItiwcDhNGBAEIBAEx4upfAa2Dpml4EECQ4UgYoJaBYRj4NCPSN4PyIzybmDYWJiYmJnUFPwKuMVdDAuFBsDuw9R5wWJdBTEwSYQoWJiYmJnUEXdcJpGeRftfDSJkHAhUpzVuRed8zFCkVC9hkYlKTmILFUcJNN93E5ZdfXm37y87OZunSpUnrt2/fTnZ2NuvXry9zP6NGjWLatGmVPn4oFOLUU09l9erVld62Msfo0aMHv/zyS40dw8Skugno4GvVntT7nyNzzgKy/vMazrsfpSg1E7WWLSPUJmyKjFMwsMvVnzPKJBZTsDBJyE8//cTpp59e4fYrVqwgOzs7Lq15VXnllVdo2rTpIef8KAuLxcI111zDvffeW2PHMDGpCVRVpVCQKHCkkG9zUYxwRD0oajMWUSAj6MV4/Tn8D9yBNv8J0orzsZtvvxrjqDy1B8dir8ux2Y8U9evXx2o9MmpVwzCYN29epRKiVZURI0awevVq/vjjjxo/lomJyeFFlmXsO7ew78ax+D55h/Cm3/B//Qk5N1+M/NvPWKSj8hV4xDnqzuoPO4q5dMkmthVEYtJvKwhy6ZJN/LCjuMaO+cEHHzBw4EBatWpFhw4duOCCC/D5fNH61157jX79+tGyZUv69u3L/Pnzo3UlSwrvvvsuw4cPp2XLlgwYMICVK1dG22iaxqRJk+jZsyetWrWiT58+/Pe//61w/wzDoFOnTnzwwQfRskGDBnHyySdH//7hhx9o0aIFfr8fiF8K+emnnzjzzDNp2bIlgwcPjlkC2b59ezQ/SPv27cnOzuamm26KOf6sWbPo0KEDJ510ErNnzy6zv+vWrWPr1q0MHDgwpnzXrl1cffXVdOjQgdatWzN48GDWrl0LwOzZsxk0aBALFy6ke/fuHH/88UyZMgVN03jqqac46aSTOPHEE5kzZ07MPtPS0ujWrRvvvvtuRU6liYlJHcIZDlI4Z0ZCz5TCJ+/HWU5IcZOqcVS5m67aVsST3++hMKhxzxfbGd+1Pi/8+A8FAY0nv9/DdT3hlCbuaj3m3r17mThxInfddReDBw/G4/GwatWqqJZkyZIlPPLII8yaNYuOHTuyfv16Jk+ejMPh4Pzzz4/uZ+bMmUyfPp02bdrw3HPPcemll7Jy5UoyMjLQ92cDfPbZZ0lPT2fNmjXcdttt1K9fn5EjR5bbR0EQ6NmzJytXrmTYsGEUFBSwadMmbDYbmzZtonXr1qxcuZLOnTtjt8eH0PV6vVxyySX07duX//znP2zbto3/+7//i9Y3btyY559/niuvvJJvvvkGt9uNzWaL1r/55ptcddVVvP/++/z444/cfPPNdO/enb59+ybs76pVq2jZsiUulyumD6NGjaJRo0bMmzePevXq8b///S8mpfLWrVv54osvePXVV9myZQtXX30127Zto2XLlixevJgff/yRW265hT59+tClS5fodieffDI//PBDuefRxKSuUJIc61hfHhG8xWi5+xLWGQE/Rn4OZDY8zL06+jlqBAvDMHhixU4Kg5EbKdevMnfVHrzhyIunMKgxd9Ueume7qtVw559//kFVVYYMGUKTJk0AaNfuQPz72bNnM23aNIYMGQJAs2bN2LhxIwsWLIgRLC677DKGDh0KwP3338+XX37JwoULufbaa1EUhVtvvTXatlmzZvz444+8//77FRIsAHr16sWCBQuAyIu7Q4cO1K9fnxUrVkQFi549eybc9u2330bXdR555BFsNhtt27Zl9+7dTJkyBYg8xNLS0gDIysoiNTU1Zvt27dpxyy23ANCyZUvmz5/P8uXLkwoWO3bsoEGDBnF9yM3N5dNPP40KHC1atIhpo+s6jz76KC6XizZt2tC7d282b97MK6+8giiKtG7dmrlz57JixYoYwaJBgwbs2LGjIqfRxKRW4xDAGvQT/v1/IMtY2nTAp1gJmKvBiTGXyWuEo0awEASB+85qwd2f/k2uPxLApESoAMi0y9wzoGm1WwO3b9+e0047jYEDB9KvXz/69evH0KFDSUtLw+fzsWXLFiZNmsTkyZOj22iahtsdqznp2rVr9N+yLNO5c2f+/PPPaNn8+fNZuHAhO3fuJBAIEA6H6dChQ4X72bNnT6ZNm0Zubi4rV66kd+/e1KtXj5UrVzJ69GjWrFmTNKPnn3/+Sbt27WK0EKX7Wx6lBS2I2G/k5OQkbR8IBOLsO3799Vc6duxIeno64SQZFJs2bRqj5cjKykIURUTxwIpfvXr14o5ts9miS0AmJnUVt2AQ/uANct559UChIJBy2Q04eg/Edyy+Q51uxPRM9PzcuCrBakPIqJdgI5ND5aiysWiebmN81/o4ldhhORWR8V3r0yyt+o0RJUli4cKFLFiwgDZt2jBv3jz69u3Ltm3b8Hq9ADz88MN8+umn0d/nn3/O+++/X+FjvPvuu8ycOZMLL7yQ119/nU8//ZTzzz+/UoFw2rVrR1paGitXruT777+nV69e9OrVi++//56ff/4ZVVXp1q1bpcdfEWQ5Vn4VBCFmCeNgMjIy4rxLSgs1lTmOoihxZQcfu6CggMzMzHL3b2JSW5EkCWHrZrylhQoAw6DoxTlY8nOOSRdLj2wh7YapkQigB5F6zW14ZcsR6NXRz1ElWGzND/DCj//EaCogorl44cd/ogad1Y0gCHTv3p1bb72VTz75BEVR+Pjjj6lXrx4NGzZk69attGjRIubXrFmzmH2UGCFCxJVs3bp1HH/88QCsXr2arl27cumll9KxY0datGjB1q1bK93HHj168Mknn7Bx40ZOOeUU2rdvTygUYsGCBZx44ok4HImz9R1//PH89ttvBAKBhP0Foi/w6ljT7dixI5s3b47x5mnXrh2//vor+fn5h7z/g/n9998rpf0xMalt2Awd7+KXktb73l+IVY7P0nm0o2oagWatyHrsZez9z0Zp3gpbz/5kPfIi6ondCJXxgWNSdY4awcIwDO785MAyCBCjucj1q0z7fFu1u56uXbuWJ554gl9++YWdO3fy0UcfkZeXFxUKJk2axJNPPskLL7zA5s2b+e2331i0aBHPPvtszH7mz5/Pxx9/zKZNm7jzzjspLCzkwgsvBCK2BOvWreOrr75i8+bNPPTQQ1UK6tSrVy/effdd2rdvj9PpRBRFevTowdtvv02vXr2SbjdixAgEQWDy5Mls3LiRzz//nGeeeSamTZMmTRAEgWXLlpGbmxvV1lSF3r174/V6Y1xAzz33XOrVq8cll1zC6tWr2bp1Kx9++CFr1qyp8nFK+OGHH+jXr98h78fE5EghaipafvLlRQwDuxomPeAlPeAlRdCjBp51Bbskkq6FSPMVk6aHscplr+TbZIk0PYzdUwQ2O7bLbsRx92zkq26lIK0eflOmqDGOGsFCEARu6J1NqjVys2TaZSb2aEimPXLxpVolru3RsNrVgW63m1WrVjFu3Dj69OnDQw89xLRp0xgwYAAAF110EY888giLFi3ijDPOYNSoUbzxxhtxGos777yTuXPnMmjQIFavXs28efPIyMgAYOzYsQwePJgJEybwr3/9i/z8fC655JJK97Vnz55omkbv3r2jZb169ULTtDIFC6fTyfz58/n9998566yzePDBB7nrrrti2jRq1IhJkyZx//3307lz57j6ypCRkcHZZ5/N22+/HS2zWCy8/vrrZGVlMW7cOAYOHMjcuXMP+eG4Zs0aiouLo4azJiZ1EVWxYO1wcsI6a6euOM86l4LpN5Fz/Whyrh9N8fSbcefsRkmwRFAbSRcM9NeeJWfiBeTeOIa8G8ciLn2LVCHxh2KaCMK7r5F3w0Xk3jiGnIkXElr0X2RBwK/pZmyjGkYwjtAZ3rdvX0IjvKKiIlJSUqq0T0VR+O7vPOau2sPMgc1olmZlW0GQqZ9vY2KPhtXualodbN++nZ49e/LJJ5/QsWPHSm+vKEpSY8a6zIYNGxg9ejQrVqzA6XRGy6t7vNdccw3t27fnhhtuqNL2h3K9VgRBEGjUqBG7d+8+Jh6Gx9p4oXrGLAgC6QEv+24aB2qp+0OWybrncXKm3wIHx2xQLNSb8wp5SryLeU1S2fG6BIPAUw8QXLsyvm7UJRhDzyegHliCtUsi+hsv4Fv6dlx7e99BKBdfj/cwX1pHy3WtKAr16pVv8Fo3xNVKcEoTN/PPax011GyWZmX+ea1rpVBhkpz27dtz5513sm3btho7RigU4oQTTuDKK6+ssWOYmBwODMOg2O4i8/5nUFq2iZa7zhuHf+VX8UIFQDiE78PF2MpZUjjSKH5vQqECwPveQuyhQEyZLeTH9+l7Cdv7v12G5aD2JtVP7b6iqsjByx3HojX00cAFF1xQo/u3WCwxEUJNTGoLVlnCEg5hCCIBWUFV1XK3Ces6xZkNcU55GDkcAASwWCm8b3LSbUK//YJDDVFbvzEFQUDbtzdpvREKYvi84Eo7UObxgJ7EiNwwMIoKzKBYNcxRKVjUJZo2bcrOnTuPdDdMTExqAZIokhoO4HvvTYpXL0d0OHH860LkTl0pMMr/QNI0jWIE2L+8YZdEpHoNCf+1MfHxshqgyTKotdOS0TAMpNS0MtsI1lhXdKEc13TB7iyz3uTQqZ1iqomJickxhiAIpIZ85Ey6FO/7i9D27CT810YK58zA8/SDpFD5l39AM3CeNzZpvfO8cQS02r3mr6ekITVonLDO2rUXIWusjUjY5sDS7sSE7ZXmrdAcpmBR05iChYmJiUktwCYIeF57HsPriasL/rgCIWdvpZd1DcMgnNmQlPE3gVjKg0qUSLn8RsJZDWu9MaFHspAx7VGkrNgw/0rLtqRcc1tcRFEvIqk3/R9y0+NiyqWG2aTd8QAe0VTU1zTmGTYxqQCCINT6B7BJ3caihShe+WXS+uC3n2G58EqCwcoF+vMaYOs9kKzup6FtjQSek49rjV+xEtBr/zWtaRqFdjdp9z8Duf+g7duL1KgpeloG+Yhx96VhGBTIVlKmPY5QkIu2ZydS/UYYGVkUiEqZUX9NqodKCRYTJ05k3774THFnnnkmV1xxRbV1ysSktiAZBkbQjxEKIlptCBYbmmkMbFJDCJKMkcxQ86Dw9JUhoBsEJAti60iEWV3XoQ4IFSVomkYBEkJWY8T6TSIRfg3Y/584dF2nEBEhvT5iZkN0fX/sClOoOCxUSrC4//77Y6S9bdu2MWvWrDKDK5mY1FUkTUXduS3WwlySkbOboYl1K2qhSe0nqFixnz4E39IlCevtfc+ioBL5gRJR17/WDcOoVNqAyrY3qR4qZWORkpJCWlpa9Ld27VoaNGhA+/bta6p/JiZHBMkw0HbviHdb01S0vbsQk3wpmZhUlYCq4TxvLFK9BnF1jrPPI5ySbi7HmdQJqmxjoaoq3377LUOHDi3ToCgcDsdEShQEAbvdHv23yaHx3XffMWLECDZs2EBqamrCNtnZ2bzwwgucffbZh7l38cyePZulS5fy2WefVXrb66+/nhNOOIGJEyfWQM8iTJgwgc6dOzPxyisw1MQRPo1gAEHXo8ZwNXkdl+z7WLlXjrXxQuyYC2Ur6fc9Q3jt9wS+W4bgdOMYdj56g2w8iBwNZ+VYn+NjgSoLFj/88ANer5f+/fuX2e7tt99m8eLF0b9btGjBgw8+mDQsqN/vj0t1XRkOZdsjycKFC7n77rvZtGlTlbZXFKXMsUuSdNjPTf369Zk/fz5DhgyJlomimDCdeXmsX7+eL7/8kkceeaRGxzFp0iTOOeccxp3/bxLnegWEyBhkDBQ1TJahIqWmIzpdNdavhg2PrYA+x9p4IXbM9sZNcQ0cCpKEaIlEET4aYgdrhQXoRfmENv1OljsFMSML0XJ0pS431DBaXg56USGCoiCmpiOlZWBoGlmigV5cFJnX1HTk9Mwj3d0aocqCxZdffslJJ50UTZSVjBEjRjBs2LDo3yUS2759+xJGkwuFQlXOBVGX82aUnIuq9v9gzdDBaJp2RM7NwcctMaKqbF+ef/55hg4disvlqtFxtG7dmubNm7P43Xe5eEBfOFj1LIjIDbPRcvehe4sJ7d7FnnmPYu89AOel11NQzR7cgiDQsGFD9uzZc0yowY+18cKxMWZRFEkN+ih87B5CG38FQLBYcY26GHngvyiuQPCvuoATA2PV1xQveBrD7wMisTPS73yQ8IZfKPjvY1F3YrlJc9ImzaQ4LavO2IHIslxzuUL27dvHunXrGDhwYLltFUXB4XBEfyXLIBAxrDn4VxcZNWoUd911F3fddRcnnHACHTt25KGHHooZTzAYZMaMGXTt2pXWrVszbNgwVqxYAcCKFSu45ZZbKCoqIjs7m+zsbGbPng3A4sWLGTx4MG3atOGkk05i4sSJ5OSUkR65AuzcuZOrr76adu3a0aFDBy677DK2b98erb/pppu4/PLLeeaZZzj55JPp0KEDd955Z8wLfe/evYwbN45WrVrRs2dP3n77bXr06MHzzz8PQI8ePQAYP3482dnZ0b9LWLx4MT169OCEE05gwoQJeDzxvvslaJrGhx9+yKBBg2LKg8Eg9957L926daNFixaceuqpvP7660DknGZnZ/PVV19x5pln0qpVK/7973+Tk5PDF198Qb9+/Wjbti0TJ07E7/fH7HfQoEG88977iCnpcX2R0tLRiwrQPUUHhA5dx798GZ7nZ+MQEl/Xh/KD6t9nbf4da+M92sZss7qRhIN+hgP/7n1RoQIi4biLX3se7fuvkWX5iPf7UH+SJCFuXE/R87OjQgWAHgygbvyV/DkzY2KUqDu2knvXBNwh/xHve0V/FaVKGosvv/yS1NRUunTpUpXNa5QVXxYT8MefAJtdoPfpNadMfPPNN7nwwgv54IMPWLduHbfddhvZ2dmMGTMGgLvvvpuNGzfy1FNP0aBBA5YuXcrYsWNZtmwZ3bp1Y/r06TzyyCN88803ANGMnqqqMnnyZFq1akVOTg7Tp0/n5ptv5pVXXqlSP8PhMGPGjKFr164sWbIEWZaZM2cOY8aMYdmyZVj2qyVXrFhB/fr1efPNN/n777+ZMGECHTp0iI7nxhtvJC8vjzfffBNFUZg+fXqMwPPRRx9x4okn8uijj3L66afHpDffunUrn3zyCS+99BKFhYVcc801PPnkk9xxxx0J+/zbb79RVFRE586dY8pvvPFGfvzxR2bOnEn79u3Ztm0beXl5MW1mz57Nvffei91u5+qrr+aaa67BYrEwd+5cvF4v48eP58UXX4yx2zjppJN44oknCDtcWBQFLT8XNBVBVhBcKWjb/07Yz8Cqb3BdPBGv5fBmizQxqU2EQ/D26/HJA8/tl3ip0PPGi6R1P5VCoW6HVXKoQYoXPB1X7hw0nOK3X0u4jeHzEvrpe5SeA+qstj0RlZ5JXdf56quv6NevX8zLorYQ8Bt4ixO5VNVskNHGjRszffp0BEGgdevW/P777zz//POMGTOGnTt3smjRIn744YfoOuo111zDl19+yaJFi5gyZQputxtBEKhfv37Mfi+88MLov5s3b87MmTMZMmQIXq83Jp14RXnvvffQdZ1HHnkkuiz16KOP0q5dO1auXEm/fv0ASE1N5d5770WSJFq3bs3AgQNZvnw5Y8aMYdOmTXz77bd89NFH0Zf9ww8/zGmnnRY9TmZmZnQ/B49J13Uee+wxXK7Ig2bkyJEsX748aZ937NiBJElkZWVFyzZv3sz777/P66+/Tt++faPn52Buu+02unfvDsDo0aO5//77WbFiRbTt0KFDWbFiRYxg0aBBA0KhEHv27aNp06bITjcRf3kBtLKTQRneYjAFCwAsioItFEAwDEKKhYBeua8ek8OPVZGxhSIBuIKKJSYdeU2hF+YjhMNgqduChWgYqLu2x5XLDRoT3vZX0u3Cf27AfuoZOEKRc+1XrIQqkHSuNlPpmfzf//5HTk4Op59+ek30p87SpUuXGIvfrl278uyzz6JpGr/99huaptGnT5+YbUKhEOnp8er20qxbt47Zs2ezYcMGCgsLo37oO3fupE2bNmVum4gNGzawZcuWuG2DwSBbtmyJChZt2rSJERwbNGjAb7/9BkRe6rIs06lTp2h9ixYtSEtLq1AfmjZtGhUqIGLkmZubm7R9IBDAYrHEnN9ff/0VSZLKjaFS2hW6Xr162O32GAGkXr16/PzzzzHb2PYnMfL7IyrKSECsyLElsWwBVTDzECAIAmloBD5ZQuHSJRjBALZT+pB+/uUU2RyoWt2OpXA0IooiqXqYwAeLKVj2AYYaxt7rdDJGXkyhxV6jNgCCwwly3TS6L42BgJRZHy33n5hyLT8HuWFj1B1b47ZRmrfCPewCPPP/g//7rxAsVhxnnkP6oOEUINVZQbzSgkXnzp154403aqIvRy1erxdJkvj444/jtDxlaR18Ph8XXXQR/fv358knnyQzM5OdO3dy0UUXEapioByv18uJJ57If/7zn7i6Ei0DJPauqa6LXJZjLztBEMoM3JORkYHf7ycUCkX7ZSsng2GyYx08rkTHLigoAGLPRwmGKCHYnRh+b1ydpV1nwjZHsmCAxwyphkbh/bfFZNT0f7WUwKpvyJo9j3yLo84+MI9WUvQwBf93I+quA0sYvs/eI7DyKzIfeZE8qRo8N5JouJ3DL8RvsUEd/0r3KlZc519G4dMPxpT7ln2Aa9j5FDzzcOwGskLaFTez7+5ro7YXhteDZ9GLBFZ+RerU2RRQ+1YFKoKZhKya+Omnn2L+Xrt2LS1atECSJDp27IimaeTm5tKiRYuYX8kygcViifsq2LRpE/n5+UyZMoUePXrQunXrQzbc7NSpE3///TdZWVlxfUlJSanQPlq1aoWqqqxfvz5a9vfff0dfyCUoilItXzodOkTCEG/ceOBF1a5dO3RdZ+XKlYe8/4P5448/aNSoUUKPJx2QGjRCsMUud1jadiD15v/De4zfUpIkoW/5M2GabsPvw/PmS9jEo8MD4GhBlmW0X3+OESpK0D1F+D58E6t86C84qV7D2OBfgoB9wDCsg4YTrONCBUTs4cRup+I8ZzSU0mxqhflYTuyO+8IrYoQr5xn/wrd8WcKkc+q2v9D+/K1WmhtUhLq9qFWL2LlzJ/fccw9jx45l/fr1vPjii0ybNg2IvIjPO+88brzxRqZNm0bHjh3Jzc1l+fLltGvXjjPOOIMmTZrg9Xr59ttv6dChA3a7nezsbCwWC/PmzWPcuHH88ccfPP7444fUz/POO4+nn36ayy67jMmTJ9OoUSN27NjBxx9/zIQJE2jcOHF64tK0bt2aPn36cNttt3H//fdHjTdtNlvMckWTJk1Yvnw53bt3x2KxVHip5GAyMzPp1KkTq1ev5uSTTwYiyyn//ve/mTRpUtR4c8eOHeTk5DB8+PAqHaeEVatWRZeEEqEhIDbMRtB1FGcKWU+8iupwJUyIdKQQRRGLxYJhGJVOWnUoKLJM4JtP48oFixVrx5NB17BqGv4aDvVksVgQRRFVVRO6tR8rCIIQNcgOhUIJr08Fg8A3nyTdR+D7r0j51wUEK2hcqVhgxOhmceWSVSD13mcQPYUIgQBGegZBi42CclbG6tJcFulgH34RWYNHoufsRbTZIT0Lv81ByqhLsPU/Gy3nHwSrFSkljZw7rorbh5iajuWETug7t2Lt2BVtv9Y12fzVRo46wcJmF0ikiImU1xyjRo0iEAgwbNgwJEli/PjxjB07Nlr/6KOPMmfOHGbMmMGePXvIyMigS5cunHHGGQB0796dcePGMWHCBPLz87nllluYNGkSjz32GA888AAvvvgiHTt2ZOrUqVx22WVV7qfdbmfJkiXce++9XHHFFXi9Xho2bMhpp52G211xr5k5c+Zw6623MnLkSOrVq8eUKVPYuHEjVqs12mbatGlMnz6d1157jYYNG7Jq1aoq93v06NEsXryYq646cCPef//9PPDAA9x5553k5+fTuHFjbrjhhiofAyL2HJ988gkLFiwos52OAKKEarWTb3eXmRDpcCIIAimGBtv/JrB8GYLLTXr/swm50vAehu4ZxNuZuM69CFunrvh/XIHh98OOv0ltchxFQvWvIVtEEVfIT2D5J2jbt2DreDJKxy4UK7Za/1KqblIEA2HfboJfLwVRJLX/YLSMevExIwQRwZY0HByCzY4hVFwT5w8UJSxX/eAXJISUTBq1bcTu3bsxykiEZpFEXMGD5rJDF4ottXsu/bqBX7IgNGxGqqGhb92EtuILilIzsPY9k3DDJvgMSEFDsB5Y0hUsVtKuuAnB4SSwbg1K6xMQtm3C9+1nCLJCav+zUdOz8NSBmB+CcYREoH379iV0rykqKqqwSv5gjlSArFGjRtG+fXtmzJhx2I9dW4KC7dq1i+7du7Nw4cI4I9XqwO/307dvX55//nlOOumkat9/CS+99BJLly6NxsMoj0O5XiuCIAg0arT/IVyBWzVdMCh84A7Cm36LKXddMB5x0HC8NfxQEgSB1IJ95N56eeS4545BEKD47Vdj2imtTiB1ygPkG2Lc9pUZb8w+RRHHjs3kzbgFSi3BiSmpZN73LAV2V61MwnUoY05GqmDg++9sAt9/E1Nu73c2tnETKCp1HYiiiHv3NvLuvjbhvlKunkyw5+llvsztthTCCcy+FEu8oHHweJNtazf8FFw9rE7NZWnSBZ3CWbcS3hIbTdl98bXQ92zCkozy9ccUz4/Yu2VMnoX34yUE168l4+Z78H75EcGff4jZ1jFoOJYLxh+xgGKKotRcgCwTk+XLl/Ppp5+ybds2Vq9ezbXXXkvTpk3p2bNnjRzPbrczZ86cuDgV1Y2iKMycObNGj1FTWGSZ4BcfxAkVAJ5FL2ApLqzxXAWGYaCmZeIcfiGCxYqtU5c4oQIgvPl3Ap+9j0WuPqWpUw2R/8CUmBcRgF5USMHj03EadSO64aEiSRL6xvVxQgWA/+ulsG0zYikbAF3X0RtmYz9jWFx7S/vOKN1PK1dDUBK74uBfIoGhotv69uYmnMvCOTNw1PK5tMoygY/eihMqAIpffgqrt4iwqmI97QyUNh1QWrdD27uL4Pq1KMe1RivKjxMqIGJQK+7eHjN/tZGjbinE5PCgqioPPPAAW7duxeVy0a1bN5588skazePRu3fvGtfQXHTRRTW275rGHg5S8HHilNsA/s8/wHL++Bq3ufAYAq7ho8kafB6+Dxcnbef7+C3SBg4jJBy6gZogCBj7dsdEPCxNeNNvyAEfWI9+d2CbruF7N7nGzffeImw3TKX0mSo2BNwXXIlj0Dn4l72HEQxiHzAUsptTiMgRWeZLopEI/bmBlIAfrMmXb4409nCQvKXJ78XA8mVYhl1IQThMyuR7kTxF5D/6f5Fte52O78ulSbf1vf8G1gl34K/FGhtTsKgGSidZO1bo379/uQnoTMrHIQpYQ36MwgIEuwPN4cIjylVS8wpEIvklw/AU1ajGQpZEXGoIobgQQ9MQGmaX2R/d76s2E05BEJIKFSUY5QQ3O1oQDB29jHNh+L2R7LwHUYyAkNkQ68XXIwDFoVDkOqyFBoN1YS6NgD953f570TAMChFJcbmj169gtSZ0Z49u6/Mg1HKNjSlYmJgcIdIEA9+rz1L85UfRh7fc9DjSpzxIoc1VaVfdkKxg7dqLwIovE9Zb+wzCW0PaHoskYt+1lfyH7kIvKgBAbtSUlEuuxffFhwm3sXXtRUiWQTv0F5eu60jZzUEQEr4IxbQMsNdc9tnaREi2YOvVH0+SaI+2UwcQkuSEcSMMwyAQCNR0Fw+JyFzWbs1TWJKxntyT4I8rEtZbe/anuNS9GJIt2E7pg/ejxYT+WI/1pB6ou3ck3rbPGYREGbQjb1uXjNq9UGNicpRilSWCHy3G/8WHMS9CdfsW8v7vBtxa5QOg+XUD90VXIZTyzClBOa41YrNWNRZB0RXwkXfPjVGhAkDdvR2CQZSW8RFiBYsV95ir8VejNjdoteEcMiphXcoVt+BV4s9LXcVuS0EW4392WwohVcU+cBhiSlrcdlJmPSw9+xOuxV4VJYiuxEbRKVfW/rn0GfuNNJX4wGJK63bQuFmMVjKgajiGX4jgdOFf9S2O3qcnHL9UryGWLr1qhcF+WZheIXWcY23MtW28Vb1e0/QweTeMwQgm/jrMuPdpiho0AaiUx4Aiirj9xRS/+iyB1csRbQ4cZ4/AdvZ5FNRQnA2LxYL04SI8b8yLqxOcLrLufIjAzz/gXfo2ut+LrdupuMdcTbEjhfBBKvlD9ZBIEQ30n1fjeXMe2j97UFoej/uS6wg3aoav9mn0gaqNWRZTEib6GjG6GapehCRJpIb8eBe/hP/bT0EQsfcfjHPEGAoVW7ULmDXiFWIB44dlpeayDe5LJhJq1IwEeSZrHYoo4vYWUvzKMwR+WonocOIYMgrbGcMT3ouSJJEa9OJZ+ALhbX+RPv4mPPujnyJJOAYOxTF8NAWS5Yh5xFTUK8QULOo4x9qYa9t4D75eZVnGEQ4iGgaGIOCz2BL2Ny3gIff65Iaiqdfdib/Lqei6XumXjiAI2EWwqGEMQSAgW2s0sqFdUVDn3ktgdeJEcmJqOhmPvYyuqggChCQZv544RHx1uF4qioI9HEA0QBVF/KJco7kuDpWKjlkURezoKJqKT0rnrdfjE16VCBYl2GUJ6/63dmB/UjFBELCJAlY1Uh6SFPxG1UL2W2UJezgEGKiijE8Qy33pVWaO69pcHowgCNgFsOgqFouVQkMgWMbzKzI3IlY1iCGI6BYL0n5j64BiJaCq++9vAcv++QtKFgKVTGteVSoqWJg2FiYm1YRLMBB++4niV55G3b0DKbMern9fhr37qRTpB5kpWqwIdkdSg0Mpu3mVv0oMw8Cnga8kUmINq701QUA5vn1SwULObkZI1/GWeH9Ug01FWYTDYcJI+61ZiXNZrIsokoiruIDief+h6OdVWGa9UqHt/KqGv+S8q9r+r2IfntefJ/e7z8EwsPXoS/q4CZWy6ylJNOdf8ip5n76LEQxg6diF1MtvwJuaRaiavqjr+lwahoHPAL8g0yirPqHdu8tt79c0/CX3bkiF6PypB7QaC56leOVXANh7n076mKsotDprjdBl2liYmFQDFlnGWPUNBQ/dFTW60nL3UfjMQ4TeeQ3HQfkx/BY7zuGjE+5LapgNWQ3qTPjeUCiErc8gBEvidW/XmKsPvNxMKo0gCLh9xeTcejnBn/dHr63itZGiBsm94yr833waeUnrOoGVX5EzeTypoeReDAeTamgUzJyE9/1F0eW80Pq15Ewej7Mot9bHWairpIYD5N52Jf7vPgddA13Dv3wZubddSWq49hjdmrNfx/nuu+/Izs6msLCwWvZ30003cfnll5fZZtSoUdE8KMlYtGgR7dq1q1IfHnroIW677bYqbVtR7rvvPu6+++5q258j5Kd4wdMJ67wfvok1FHvTB1UVy5nDcQ47PyYxkdK6HRn3PE6xWLeUicVWOxkz/hOTZEpwOEm7YSrhBk1qfZTE2oxdFPC8/l9KGyIo3lzO7Qfn9oORI+szYnQzRoxulshW8MA2ikJw+TL0wvy4OsPrwf/pO1grELBMFEX07X+jbt0cX6lpFL80F7thznd1Y5Fl/F98hF4c/6zXiwvxf7UUSw3GEaoMdevpdRSzaNEi7rnnHn77LT5q4uFkxowZlf5S7tGjB1dccQVXXnnlIR//n3/+4YUXXuDzzz8/5H2VxTXXXEOvXr248sorad68eYW3E0rcGff7oEfxepLHUdB19Jy9CI2ax2xTqAvYz7uYrGHnR/zarTZUu5MCQapTL2JRFJEkiXD2caTd9yyCtwhD08Cdik+xElJrh3q2rqKoITw/x+bZ8T5wc/Tf9t6nI19zB4FgELWMj1aLGiawKj4ap5iRhe2kHiDJWHSN8sKnWSwWggn2U0Jw3RpcunZAhX+YsSgKkqGjCSKhCthjKYqCbBjookAwVDn7rZJtDVEkGA7XqJZRUcP4kyw3AgRXfYN94DBCNZzgryKYgoVJDDWZ96IivPbaa3Tr1o0mTZrU6HEyMjLo168fL7/8MlOnTi23vQCIuo5eXADBINhsSO7U6C0slPOlINgcCR86fk3HL1kgNStSYAB16GsvRTAQdm3B//EStHAQccBQpNbtKSxJMGYKFYeMgYDodKMlSK8NIKZmVCgupi6JsS6MskzaFTcj2h34V36FWlyIZfMG0lq0PTB/B+ESDJT8fQScyeNIiA7XEUnHZ5FEXAEf/k/eQv3rD+SWbckYMASPzUlIi7+nZEkkJRQk8PVHhDf8jNS4KelnjsDvdBMoIzkaRDw4UtQgoVXLCf3wLWJaJmlDRhJKzaixfDyGKCK6ksdiEV1uDFGscRuminDULoW89dZbvPzyy7z11ls1fqxRo0Zx1113cdddd3HCCSfQsWNHHnrooZgbMxgMMmPGDLp27Urr1q0ZNmwYK1ZEgqesWLGCW265haKiIrKzs8nOzmb27NlAJKrn4MGDadOmDSeddBITJ04kJyenwn2bMWMGF198cfTv559/nuzsbL788kAQpVNPPZXXXnsNiF8K8fl83HDDDRx//PGcfPLJPPPMM3Fj37FjB/fcc0+076X56quv6NevH8cffzxjxoxh7969Zfb3vffeY9CgQTFluq7z1FNPceqpp9KkSRO6d+/OnDlzANi+fTvZ2dm89957jBgxglatWjFkyBA2b97Mzz//zODBgzn++OMZO3Ysubm5MfsdNGgQ7777bnmnEAAxFETd/hd6fi66z4Oel4O67S+kUABJklDtTpTjWifeNi0DUtMrdJy6RIqg45//H/KmXod/xRcEVn9HwYN3UnT/7aRhChTVhV+x4vzXBUnr7YOGEwqVH/ckqIPjnAujf6dfNYnA6u/Ie2w6/u+/JrB6OXn33U7xI3eRSvyL2CUYhJe8Qs7NF2M7sVvS4ziGjsJ/mONMyJKEfddW9t1wEZ7FLxFY+z2exS+x74Yx2HdtRS613FgSA0TU7BTm+gm27Y0+4lro2JucG8dg+WMdljJsRARBIDXkJ+/2Kyl6+iECP67E9/kH5Ey6DOPbT6mpRNpBQcRxTnJPMse5FxGoJZlPj1rBwuv1UlBQgNebPDRqdfLmm28iSRIffPABM2bM4Lnnnou+rAHuvvtufvzxR5566imWLVvGsGHDGDt2LH/99RfdunVj+vTpuN1ufvrpJ3766SeuueYaIJKTY/LkyXz22We88MILbN++nZtvvjlZN+Lo2bMnq1evjloLf//992RkZLBy5UoAdu/ezZYtW+jVq1fC7WfOnMn333/Piy++yGuvvcbKlSv53//+F61//vnnadSoEbfeemu07yX4/X6eeeYZnnjiCZYsWcLOnTvLTPCVn5/Pxo0bOfHEE2PK77//fubOncuNN97It99+y9y5c+NcnmbPns2NN97I0qVLkSSJ6667jlmzZjFjxgzefvtt/v77bx5++OGYbU4++WR2797N9u3xbnulkQwDde/OeIM5w0Dd+hcuLYRXkEi7dWZcUCLBaiP9rocplstY/K6DiKKIsGs7geXL4urCf/1BaOVXKNWYYOxYJqyqKL1Ox5rgZe4eew2hlLQKqeB1XUdv3BzH2edFbGFkhUCCyJChP34l/ON3MXl/BEFALsjF99Fi0DT8K74kZew1cdta2nbANugcQoc5AJdLDVEwe1q8B5SqUjB7Gi71gOBVkvTsrUU7eedLnXe+hne+hrAzEwyDgjkzcKrJBTW7KFD88lz0/Ny4uuKX5mILVtwItjJomgbNW2MfGJ8szj5wGDRrVWuWUM07v5po3Lgx06dPRxAEWrduze+//87zzz/PmDFj2LlzJ4sWLeKHH36gYcOGQGSN/8svv2TRokVMmTIFt9uNIAjUr18/Zr8XXnjgC6N58+bMnDmTIUOG4PV6cZahjiyhR48eeDwe1q9fz4knnsj333/PhAkTWLo0kuRm5cqVNGzYkBYtWsRt6/V6WbhwIU888UQ0Ffrjjz9Ot24HHnDp6elIkoTL5Yrrezgc5oEHHuC4444D4NJLL+Xxxx9P2tedO3diGAYNGhwwAPR4PLzwwgvMmjWL888/H0VRaNKkCaecckrMttdcc000d8kVV1zBtddey6JFi+jevTsAo0eP5o033ojZpuQ4O3bsoGnTpkn7ha4ldXMzwiFETzG6K41CRwoZj7yIunED4Y3rkZu1QunUheJSAYlEsXw//9pMSa4RqyThLyPhme/jt3D16Ee4lniD1ObzXhEPiiJBwnXDVFw5ewmu+gbB7sTaqx9BuwtvJTTfxYaAa9QluM69iKLnH03azv/RWzi69Ca8/9vTYrEQKBWa3fvpuzgHDSfrnjkE169F93mxnzoAvX5jCqopaVll5kzwFCV80QPo+bkIniJwV0BrKEoINgf63l0IjZsnFNgs4SDF33+ddBehn1Yhn3pGuRlhq0IxIq7RV+Ic+m8CKyNaZ1uv0wmnpFFUS7QVcJQJFm+88QbFxcUA0QyOwWCQl19+GQCn08nIkSNr5NhdunSJSfDUtWtXnn32WTRN47fffkPTtOjLuYRQKER6etkX+7p165g9ezYbNmygsLAweqPt3LmTNm3iQyUfTGpqKu3bt2flypUoioLFYmHMmDHMnj0br9fL999/n1RbsWXLFkKhEF26dImWpaen06pVq3KPC5FU5yVCBURe5GUt45TkKLDZbNGyP//8k2AwyGmnnVbmsUp7oGRlZcWV1atXL24ppOQ4fn85XxjlfA0aekRo0DSNfEFGan8y8ondCWgaHlVF0HXcgoHsLUbbuwsxLQMjPROPZKk1fuflYRUFHGoIbdd2MHSU446nKJTczM8IhxCOyEr7AURRxK2HEQry0PNzkeo3QnOlUFxDEUgri1MAS9CPtmsbgX07SU3PwiNbUEvZAzhEAVsogLpjC4LNDg2aIIy8GFXT8YVCVXp/ewwB3WrDKCOnuREOIxgGJUZEgiBgHJQV1/vZe3g//xBL2w6IrhSMBtkUGolztVQUSZIiiewKctEL8pAaZqM6XHjKmTOjHAGk5B4t89hpGWTd/TBazl5EWSLVUCk+6B51CSBratLMqwBGKFDtyf5sooA9HETbtQ0EEbKbIQ4fTVg3KKhho9GqcFQJFh6Ph4KCgpgyv99f/oujhvF6vUiSxMcff4wkxX7BlaV18Pl8XHTRRfTv358nn3ySzMxMdu7cyUUXXVShNdUSevXqxYoVK7BYLPTs2ZP09HRat27NDz/8wMqVK7n66qurPLayODiFunCwJ8VBZGRkAFBQUEBmZiYQK2SUhVxK7V5yU8sHqeIP/vopuVZKjpUUSQJRTPwwkSRwp8YUaZoW8zBKQ6Po8RmE1q89sFn9RmRMe4xCh7vWCxd2AaRfVpHz1IOgRqzm5WYtcI8Yl1CVDmA77QyCiq3Gg3MlQxRF0oI+8mbcjLZnZ7Tc0rYTabfOpCCJceLhIlU08C94lqIvP4qWia4U0qc+gjerMWFdJ0UwCL33OvveWxh9WQt2B+m33YfaLLE9T0VDa4cQsZ0+mOC6NQn3Y+tzBkHFGp2/UCiEu99Z+D7/ILahrhH6bR32/mcRFCVIYCRZUSRJItVbSN70m9FyDthiWTt3J+36u8kvy9vBnYJgsyfMKCrYHQgJ8qYcjB7wkzPjlgP9qdcgco86U9E0jTTRwPvC4xjNW2NpdyKh39Yl3I/15B74q/G6dwrAyi/ImffEAc2prJA64Takzj0I1S6ZAjjKbCxcLhdpaWmkpaVht9uByFdzSVlFlg6qSmnbAoC1a9fSokULJEmiY8eOaJpGbm4uLVq0iPmVLB9YLPFfr5s2bSI/P58pU6bQo0cPWrduXSnDzRJ69erF6tWrWb58eVQ70atXL9555x3++uuvpBqL4447DkVRWLv2wAuxoKCAv/6KzZqoKEq1vByPO+443G43f/75Z7SsRYsW2Gw2li9P7mZVVX7//XcURSlX86MLIlJm/YR1cnZzfHJyQzWHJOJ9aW6MUAGg/bObvOk3xaz91kYEQcBWlEfhE7OiQgWAuu1vBIslocGqmJaB4+zzajSMeHm4dZX8WZNihAqA0B//w/P8bBxHUGtsUWTC33yKv5RQAaB7isibdj0uNYgsyxi/rsX77usxGgDD7yNv1iScwcSuzSX2Awf/DhY2VFVF7tgFuelxcfsQM7Iiboul5k/XdWjUFEuHk+LaCw4nrvPHl+tJUR5uNUTePTfGCBUAwV9W433tWexS8mU1n2Ij5YrEtmcp42/CW8Y9WsLBQom2by9599yIWw1hlWWCHy0msOJLPJ+8Tcq/L02YYMx22hmE3RWzeakIoiii7NtF0X8fi12OVcMU/udebIV51a4dqQ6OKo3F+eefH83L8PLLL+P3+7FarTFeETXFzp07ueeeexg7dizr16/nxRdfjAaRatWqFeeddx433ngj06ZNo2PHjuTm5rJ8+XLatWvHGWecQZMmTfB6vXz77bd06NABu91OdnY2FouFefPmMW7cOP74448ybRSSUWJnsWzZMu68804AevfuzVVXXUWDBg2SLm04nU4uvPBCZs2aRXp6OllZWTz44INxa8JNmzZl1apVnHPOOVit1qjmobKIokifPn344YcfOPvss4GIxmLixInce++9KIpCr1692Lt3Lxs3bmT06MSRKyvKDz/8wCmnnBIVQpNhAILTjWyxouXti6j5FStSRhaGP0CoDKHKGvRT/F3imBzavj0IBbkIGbU3yqZVEvG+uzBhXf7TD5J150ME//gV7ydvQziMrc8ZOAaPpFCxHdHwy2JRAequxEa5gdXLcV16PV6lYtqw6sYeClLw9qsJ64xgkPCvP+M8sRtFi+KTugGgaQRXfIFl0LmV0lweTJGokDbtcYLfLMX36Xugadj7nYX9rHMplK1x81ckSKTePJ3wD9/g+3BxJKHcKX1xnjuaQosjErukigiCgJGzFy13X8J6/9efkHX+5RHX7ASENA35pJ5k3Ps03tefJ7xzK0p2c5yjryTcIDvmHlUskZwqAiDoGoKuo+7bg+LJ4eCzqeXuw8j9B3tGPfI+ingY6nk5FC9ZQNbUR/B++i7BX39GSknDOWIsYscukeWgasImGHgXv5y03vvu69guuwF/LXPrPqoEiyPJqFGjCAQCDBs2DEmSGD9+PGPHjo3WP/roo8yZM4cZM2awZ88eMjIy6NKlC2eccQYA3bt3Z9y4cUyYMIH8/HxuueUWJk2axGOPPcYDDzzAiy++SMeOHZk6dSqXXXZZpfqWlpbGCSecQE5ODq1bR74we/Toga7r9OzZs8xtp06ditfr5dJLL8XlcnH11VdH7VhKuPXWW7n99ts59dRTCQaD7Ny5M8neymf06NFMnjyZu+++OyrA3HTTTUiSxCOPPMLevXupX78+48aNq/IxSnj33XeZNGlShdrqAIoFsUF2NECWBuj+ssPoGsFAmeuxWu4+hMyGh12wEEURh6Ehh4IIokhIseJLkIhK0jTCe3Yk3IfhKSb/qQdwz5yL+9SBCEBQsZKnqkdUqBAEAS1BdMkohoER9EMNChaKouAIBRDCIZBlAhYbgf0Pf0HXYtLLH4y2ayvKiV1R9+1J2kbd/vchh83WdZ18RCwDh5PS5yyg7PkzDIMCBOReA3F1Pw3BgJBiIU9NbtxcUURRRM/5J3kDTYvcS46IYGFVZOxBf2SpRlHwWmz4wipiw6bYb7oHh66iiTLFCZKilV4SEgSB9GAR++4eFydUlKDn7EVOTY8JgBdcv5bQnxtw9DuLlAsuR2pyHIHmx0ftxKoLUVVR/9mVtF73FuI0NKxqxP5FVawVSgRX0xy12U3feuutqOdETRlsljBq1Cjat2/PjBkzavQ4iaht2T4PFcMwGDZsGFdeeSXnnntuXH11jfeLL75gxowZLFu2LM4WozKUd72Wlx496/FXyHemJqyD6sn2eTCKKOIq2EfRMw8T+nMDCALWLj1JufwmihzuGGt2qywjvP0K3vdeT7gve/+zkS65vtq+mKprvOkBDznJssfKCllPvk5+kq/fQ8UhGMgb11M07z8RrZTFin3gUBwjL6YACbehUjzt+mhOmbi+3/UQtGqH98E7CP3xa8I2qdfeQaB73zjPg/LSqdcGks1xenEeObdcmngbu4OMx1+hQFRIEyH42bt431uI4fMipqThumA8Uo9+FFXhkknTw+TdNC5p5NysR+eju1IouP3KpJ4nGbPmUtwoeeLAql7XdllCe2Uu/i8+iqtTWrYh7ZrbKFrwTNRWxtKuMylX30pxSgZqDQj3Fc1uelTZWJRm5MiRXHzxxTUuVJhUL4Ig8OCDD9aIq1ZpfD4fjz766CEJFRUhYLHjPCfxko3lhBPRXFUToquKIAi4/cXk3H5VRKgAMAyCP64kd8pVuMPxOU0cZ49IuJ6MKOEaeTGBQzDYqylUuwtr5+4J65xDRuK3lr38VVVkWUb643/kP3QX2n6NgxEK4vt4CUWPTMWNhk+24r54YsLtpcz6iMcdjxcRV5I2ojsV5aRTavweOdxoKWkox3dIWOc6bxx+ix2HCP7XnsOz8AUMXyRGkV5UQNHzs1E/fw+bXHn3Zr/Fjuu8sQnrlNbt0VLS8Ck23GOuSthGbtQEoUF2jWgJApqOa8TYmHxCAIgiqZfdQM49N8YY4IZ++4Xc268kJYkNzuHiqBUsTOouHTt2ZNSoUTV6jGHDhsW40dYUAVXFcua5uM6/DMG634BMFLH1Pp2USTMoPsy3oE0U8Sx+OcYQswS9qJDQ91/FefMU2xxkzpqL3LhZtExq0JiMGU/gdaTUSvsQDwLu6+/G3mdQxKMHECxWXOeOwTp8dHRZorpxhoMUz/tPwrrQ7/9DLMiLeA0d357UiVNiQmxb2neOfPnud3EMNWhC+m33RSK37kdp1ZbM+56mKIkxYon9wMG/spKT1RaKkUi9bRa2Hv1gv0GiYLXhvvAK5NOHEFRVrKEAvlLxNErjeesV7Ek0g2URVFXkAUNxX3gFgnX/8pggYOvRj9Tb76UYibCqIpzUi5QrbkZwHHACsHY+hfR75lBUQ0kDDcPA43CTMf0JpAaNo+WOgf8iuG5NVLiK2SYYwPfhG1irIGRVF0ftUsixwrE25to23operzZZxhbyg9+LYLUTtNrw6/E2DQdTngrVosgo4TCGJBEw4l1qDybFUCm6c0Kc5X0J1s7dsdx0D779L15ZlrFqKqIkIWoqos8T6UdKKhoiuqYRkqtvTqp76cchiViD/ohNhc1BwGovV6iwyTKSFkaTZIKaXql+pAV95F53YdL6lKsnE+x5OqqqYpFlHKEA+L0odgd+WcFH7Pq4Iss4w0HweRBkGdXmwFvHktQdTFlzLAgCdgGsoUBkzuxO/BYbwf1zlrp3O3l3x2pyBKsNe+8BSFn1sQ0YQp5cNdsZqyxh3z8fgtWO5nChh8NokkRg/3VgkaX9c+ZDsFgIWe34jLLd6Msbc0WQZRmnGkTwexEQwOGk6KG7CG1MvFQmN22Bc+pjeITq/XCp6FKIabxpYnIYCKgqAVEBZ1qk4BATBUmSREo4SHDZRwRWfYPgcuMaPhqatigzAp8uSIhp6UkFCymjHrogIgg6qYaG+sv3+D+J5FOxnzkcoVM3wED79Wd8H78V8SQYMBTnyT0pEuVa98LzaXrEHbjkC78MocIqCjh8xfjeW4j/7z+Rmx5H2jkX4XenVdyVUpYjvyTLFFJGVvQchVSVkCgjuNJo1KgRObt3YxyUgC6sqhQIEpTY4dSxJHWVxTAMfAZJ50x0xIYMcPQ7C0e/s/B++TGBn1ahFeSSPnw0PoebYCXdX4OqRlBUUNwZuEN+Qm+/Qmj9T0hZ9Uk9dwzhrIZ4VY2QqByYDx2qI8poeaiqSiES2CMfMXZJQkxPHn9HSs/AkCQ4RBfgqmIKFiYmdYySJEi5t1+JXsr7IfjTKhxnDMd14Xg8SYQLvyTjPO9iCh66M2G9Y9i/KVBVUg2NogenEN70W7Qu9NsvZN75IMXvLSS0/kDcltDv/0Nu0py0/3ucfGpHCO/KIksStm1/kjNjUiSEOxDe9Bv+r5aSftu9aG1PJFwBW5KgxYa975kJje0Eqw3puNZHRPiqaOCs2o7uSkWq1xBt3x6snbpi7diFnJmTorE+wpt+w/f5R2RMnY3W/PioAWOi8ScauyRJuApyyJlyVTTSqGXUVRSHbYh+EaVU5tYjee6CuoH7nIsIJElf7xgxDg8Ch0PoSYQpWJiY1DFsoojn9edjhIoSfMveI2vIeQgpmQlVrpqmIbTpgGPQOfg+K5XZVRBIufR6gqkZkWytP62KESoApIbZ6AV5MUJFCeqOrQRXfInS92zCddCo0KWGyH98RlSoiGIYFDxxLxlzXqFAKP9x6dd00kdfhbplM+G//oiWC1YrGdMewyNby3Q/rilKAmcdzIjRzRK0rr14ZAvpdz9C3tSJuIb9m7w5M+NDiOsaBXNmkP7QCxFtD4nHn2jsDl2lcO59MeHLw85M3vkaIG//L/n2hwtd19HqN4p8RCx8IabOOfxCjKYtj6j2sFYKFoZh1MpoYiYmpTlSN65VDZGbIKtoCYHvvkAZnjzse5Eh4LzgcrKGX0Dof2sQFCuWjifjV2z4DXBqKv5P49PJ207uiW/lV0mP61/2Hq7epxOugNZCkiQEQaiSZ4MoioiiiKZpMcJTsvKKIHiK0AvyEtYZfi8U5kNa+WvLAAWCRMqdDyLk/ENo469ImfWRW5+AR7FWSOtxNFJ6bg4FTdMocqeT/sg8xNx9CY0XAfSCvIonHiuFFPAT3vxH+Q2TIMuR5cDD8WzwGAKOM84hq8+ZhP73Ixg6lk5dCdmcFB9he+pKCxZ5eXksWLCAn3/+mWAwSMOGDbn22msrnJiqPKxWK36/H4fDUS37MzGpCXRdp7i4uEbDxCfCXZIEqawHRwVeql5DwGtzIfeKBGjzqAftM9E6fnmaVcOAsvI5ABZRxKkGUf9cj+EtRmnTgbDTnXTppjSyJOEOB9G2/oWWuw+lVVv01Ex8koxLDaJv3Yq2bw9Ki+MxMrIo4sjkAzEMg0JEhKxGSA2aYBhG5IV6DAoViiTiCgfR/t6Elp+H5fj2aCmVe9kfjKZpFAgyaeW4ihuHcRnALRjIxYWE/tyAlJqOdFxrPLKVcA0LGD4DfBY7co/+AHgPvo+PEJUSLDweD1OnTqVDhw7ceeedpKSksHv37mp9uFqtVrxeL4WFhZXWWlgslkMKcVsXOdbGXJvG63Q6azwORmnSRSh+6gHC9Rth79EHfxLtgbX3AAor6KWRSGMQkhXsZwyPC84U/Hk1ruEXEPx5VcJ92QcMJaBYIJxYC2EVBaybfiXn4btj3F1tvfqTdsUtFJTxDJYlCde+XeTec1NEg1ByzH5nknrOGHKmXY/uObDerbQ4nvQ7HyJfkCskXBguN2JKWsKImILdAVV4GRqGUaZGpvS6f15OAElwg1D37B6SoYgijl1byZ15S8zSgvXEbthunVmpfSWykfBluHHe9QTee2+Iay+mpEEVYsRoVjtKi+MJ//1n+Y33ky4YFD91P8G130fLBIuV9Dsfwt+0FaHDoL2obTFNKvVUfPfdd8nMzOTaa6+NlpUk0apOqiKo1ESEwtrOsTbmY228pbHIMoGliwn+uIKQ3RHJ0bH+J/Tiwph29r5noaVlHNL5CYfDuE7qgXJca8JbNkXL1V3bkDLrY2nbkdAf62O2kRpmY+0zCF8SoQLAGfKz78EpcTYGgZVfYTnhRJS+ZyeNFugOB8m958a46Ij2Hv3IuefGGKECIPz3nxS/MAf7VZPwVUD77lGspN5wN/n3To7T+KROuB2fEp8741A5WuwekuFSg+RMv5mDJYLgujV43nkV6/CLCJRxvZQm2bkaeV6CBIKCQOr1d+FVrGV6ASXCJ8mkTpxCzpRr4vqdCGvJfVlKqIBIULS8WZPIenIhoRqK8FqbqZRgsWbNGjp37syjjz7Khg0byMjI4Mwzz4zmu0hEOByO8XEXBCGa9Kk67ShK9nUs2WYca2M+1sYLB8ZqDwXIe38REMlwmf/cbDImzyKwejnBdWsQXG6c545BaNGGIkQO9RQVCjKpdz1CeO3KiJGnYWAfNByhZRvck+9FXbca/8dLMDQV++lDsfTsS5FkQUjydaYoCoFvlyY1XPS88yppvQdQvD/QUOk5FkURbevGOKFCsDuhjLwbgR++wXXJRPwVyAmiaTqhFieQ9fgreN96BXXrJqTsZrhGXkIgLZOwrh/W6666j6VYhIQCi2IR0ILVPy5Jkgj/8mPSl7Pno7fIOPs8gqKSsL6iGFYbWbPn43nrJbSd25Cbt8J53jj87nRU7cCcJRp/orHruo43swH15ryC9/1FhDf8glXSGPnvJiAr6Adtb/UHo/dlHKpKeN0alFP6RW1LjpVnV6UEi3/++YfPPvuMoUOHMmLECDZv3sy8efOQZZn+/fsn3Obtt99m8eLF0b9btGjBgw8+WKEgG1WhYcOGNbLf2syxNuZjbbyGpqGgY3g90TJ1+9/k3HMjtq69sPcegNKyDY7epwNwqAuTan4uRnERCGDpdybOvmcCBlJK2oFGjbJx9jodMBDdqZEPhnL2m/fP7qR1ekEeVkXBWa8BED/HxT9+G7eN6HCiFxYkP6CuI+sajRo1KqdnpWmI9aapGAE/gtWGaLOXO66qkpeTOEqkLMvUz6pMnw+VmrETKsxNnlTMCPixiGKF5ybZuZJkGfsJHbFOmoERDCDY7IhWWyXmLNnYG2C56lYMvxdBscTFzyhB3VOQ1IAUIgnMSr/ravrZpRXkR7SYho7oSkHKyKrR4yWjUoKFruu0atWKiy6KJPdp0aIF27Zt47PPPksqWIwYMYJhw4ZF/y6R2Pbt21et60KCINCwYUP27NlzzKjJj7UxH2vjBXAKBuL/1mAEA8jNWqJu++tApa4TWP0dgdXfkTHtMfbu3XtI1uiKKOLYt4vC/8xC3bE1Uta8VUStnNGAsDeJYOD1l7tvSZKwn9QDlr6dsN7Suh1+VcW7Z0/cHAuCQMpx8SpvrTAPuWF20mMKLjdhxULO7uQCTZn4AkBB1batAJLgTliuqiq7q9rnWoIoirjadExaLzdqQtCAfRUcZ4XPla96s4tGCEBhYpsXNwZy0xao2/9OWK+078zevXsxDKNGn13y/vgbhU/MjNqHyI2bknrdnfgbNiVUTYGyZFmu/sib6enpNGnSJKasSZMmrFqV2JgLIirQg3MPlFATJ9gwjGPmpVPCsTbmY2W8siwj/vYT+Y/PQG7cjJQLx5P36P/Ft2vUBKFpi0N25XP5itl314SYqJHhrZvJvXMCWXNeqXKoZIi8AORWbZHqNUDbFx/10335DRSLMsZ+waj0HBuGgZ6eiXJ8B8J//lp6p4R3bMF6YreYREzRfV54BT7FhlHLDNuiJNGKWywyoVD8i7QuGXVqmgYNs5GbHoe6fUtcfer4m/AqVoyKhoIvYwXhSD4LfLKFlMtvJG/6TXF1cuOmCNnHRWLH7P+gTvTsqo7gZe6Al5w7rorJoqzu2k7u1Ouo9/grhOzuw3qeKiVYtG3bll27YnPD79q1q8aWNUxMjmUc4SBFL80FIoaTwQ0/k379XRQtehHtn92RZGY9++G+9HoKRPmQAi/ZZAnvG68nDEVthIL4lr6D9dyxBA/hJV0oKmTMeori/z5OYM1yMAykhtmkXnUrgXqNYx7AB1OMSNrt9+J77Xl833wCqoqYnomQlknqjVPxLH4J37IPIBxCTE3HPfoKhG6nEaqtQgUHEoZBRIgs0eCGw/pRYdRZLMqkT3sMz8tP4f/uC9A1pKwGpFx+A9ZOXSks9pS/k/2UPlcHl6s1oaSoIKqqEmrakvQpD1L038ciGW1Faf99eV2F7stDNeK1KAr+z96OESqiaBqet17CdvH1+A8xjUBlqJRgMXToUKZOncqSJUvo3bs3mzZt4vPPP+eqqxKnkzWJRRRFbIKBpKpokkwAodblVjCpPYiairb3gCDvXfo2oY0bSDn/MkR3KlJmPUJZDclXteiXfnlYZRlLOIghCARkS/RlJofDeP/4X9Ltwr/9gu1fFxAsJ05FWei6Tr5kwX7NbbhCN2BoGrrVile2lrksKkkSdl1D1Q2sl0zEfv5loKoYFgs+i53icBjb+ZeTee6YyFPaYsVrsRGuoMdBMgRBwC4KSGoYXZLxV/P9WvI1KggC9bMakffPPyjhIEHp0OI81ASKomANBxEMg5BirZCAqes6+YKM7bIbyRpzNYaqYlis+K12XC43VEKwSPblfiSFihL8BqhtTyT1vqcRgkEEWSZosZGvG2XelyX3YvAQA0/Imkrw1/houCWEfl+PWw2DcPjC7VdKsGjdujW33norr732Gm+99Rb169fnkksuoU+fPjXVv6MGuwDWvL14F76Ab9tfyE2PI+WC8QQzG+A/+rX6JlVBkhAczhjjsPBff5D/5H0ApN18D76MBhVScUqSRKoaxP/hW3i++xwUC44hI3F1O5VCJHRJRspqEGvDUXr7eg3RJbnS7nsHE5tkan9hGS+pFMGAP/+Hd8kraPl5WDt1xXneWIrtrkjwof2q9ICqR5K8Wfcvux6iUGETBeyFuXgWvYjv7z+RGzbBfcHlqA0a461AMK/KIEkS4V3b4Z3XKF7zHfItj1Xr/g8FQRBIRUP94Wt8H76JHgxg7306GWefR6FsLXf5zTAM/JqBX7LAfrdLoRZlJ64uwuFwJEmYdX9gxzKCoUmSRGo4gP/9Nyhe8SXyTbMP6di6JCHVbwy//pywXs5qELl3D2NCskpH9+natStdu3atib4ctVgkCfnXH8mdPS1apu3bQ3Dt96TdNA3LiT0IVbOPvEndx2+x4Rx2AZ43XoyrE6xW5BM6VdiuIlUNRpKW5edGy4qeeRhL249JuXUWxYaMe+TFBNeuTLi949yLKDzMkSOdAgQXz8dXyuDT9/kH+L75lKwHn6MwNatGNH6KLGP5cz05998ejWmh/bOb4LrVpFx9K9ZT+hOspuMKgkCKr5i9t14eDfwlhYIJ21osMoTigz4lW4uvjrX7FEOjeM70mPwwniUL8C37gMyHXyDvEN1Fa5KKJh473KSGA+ROHh91k5YqEC+jLIKqRtq/zsf/ZXziOwDnvy+h+DAnJKuVuUKONhzhIHlPP5iwrvDZR8iYs4BQBRIcmRxbBMMqaWedg7btL/zffxUtFxzOSiW0ssoyvncXxggVJYT+WI+x5U+EVu1RG2TjvmQixa88fWC/okTKlTcTSq932I3kLH4PRYm8SMIhip59GMfk+/ZncKxenOEg+XPvTxgavejFJ8jq0uuQ4y+UYBeh+OW5MdFEkxEKVc724lDX7kVRhO1/J0w6pxcV4H37VWwXjCdwiFqsmqKiiccOJzZZwrv41aSxV6qCYRgEUjJInTiFwmceOhDITRRxXzgerUmLw77kbr7NDgfFhQl9naV6DZHrN0LwecGZmnBTURSjiW1qW9hWk/IpmT9N06rktVFoiNS/cSrOMVehbt2MmJKG0CAbj2JNGqXyYKzhAEXffpa03r/sfSzHd8Sradj6nEVWr9NRt/yJIIhIzVvhVywEavC5JMsyoijGBNKTZZnQ+rVJtwn98SvuUAgUa/V3yFuUMHMsAOEQeu4/UO+Am2vpe1TX9XLv19LtLeEAxauXx9Qr3lzO7ZcZaZuSBu7U/YkZq2V0FcaiKPi/+DBpfWD5MtLOG0vA/CiqMJZwkILvPo8pi5lvhwvSI5mJK2OY6jfA2qU3WU8tQtv6F4amIR/XmoDFiucImPGZV8RhQJDEmL/lxk1JveQ69MI8wju2om//m/Q2HShSbNGXjyiKpOhh9K2bCP+xHkujpijtTjwsiW1MDh1JknCrIfS/NxL+cwPW7ObIbTtSrNgqLBBE95WSSoHXh9Du5APuapXahwBS8ltdkBWM/W+tgAEByYJ4fCdgfwbXGrrcrAI4wgFCq1ehFxVg73wK1DsQQEiQy9YKCGLNvGkFsWwjt5L66D26bTPqlk3YT+6BVpBP8LdfsNRvhNLhZDyKLXq/CoJAiqHBnh2E1q1BSc9CPrkHiGKM5sn7wM3Rf7vOvwxt6AWEQyFksfK5L6qKXQCHGsJTVi4cWaa8pHMmCTjonJaeb8eAoXDpDQSDwUobpgZ1g6BoQWzVHqjZe7c8TMHiMGA4UxDTMtAL8pAy65N29a3kPXpPzFeR4HSROespilIz0XWdtHCAvKnXRdyXStpYrWRM/w+eeo1RTeGi1iKKIqlBH3l3X4uWuy9aLtjsZMx8kuLMhlXSXlRVnRmwWHGcMZzi155NWG8/ewTFB31d17Tq1CaCsn4tOY9PP7DksPAFLG07Yr3rYVRVxdXhpKTbW0/qQUix1ohBmu5wIdVvFHHpPQjB7oSMLARBIE0NkjftevSiAjKnPEDeg3ei7t5xoLGskPF/j+HLPo6wppNmaBTefzvhvw6k5dYGDcfe58yk6+O23gPIP8xJ9xyCAd98Qs7br5I24TZ8XyTum+OMYQQs1gobylY26Vp12IjUNgKKDcfAoXgWv5yw3jLmRvxhC7IYq4mrzJhrg6ehWH4Tk0PFI1tIu2U6SBLuEWMofPGJOFWr4fWQf99kXFoIBwZFTz0QI1QAGMFIYhuXVjuye5okxmFoFM6ZESNUQCSMcf7MSbjVxMZ5NUUorGI9fTBy0+Pi6my9+2M0bHLYH0b2oJ+C0kLFfkJ/rMfz/qJI0jWLHfclE+O2FV0ppFx5M74aMvnwyhbSbr4HDtaYiCJpN03DK1lwCFD07MNoe3fhPONfeN5fFCtUAKhh8mfdiiscxCZL+Ja8HCNUAPi+/gTXgCFImfGxgJz/uoBQFTJ0HgqCIGAtyqf45afQiwvR9u3F3ntAXDu5URNsZ40gWAnvmxKbh7df38abr/wV/Xcy28XS7Uv/DtHW8YgSUlVsZ41AbtQkrs7e72yCKEfFmE2NxWFA1TT82S2o98SrUFxI+L+J3cm0fXsRi4uw2OwUJYgkCGB4ijH+2YPQsGlNdtnkEFCCfgp/TxwTQi8qgLwcyDy8+U4KRZnUaY+h/foz/i8/QrBYcQz9N0aT4yiqZvfJ8lAUheB3nyY0jgTwfPAmGWeeQwESzj5nkdmxK74PFqHn5WDp0hNb74EUKlaMGvKkUlUVb/1s6v3nVfyfvUd44wbkpi2wDx6J3+kmrOu41ABFP0UiDltP7IbnwzcT7ssIBtC2/429WUtyP4+3VzBCQfKemEXW/z1O4NefCaz4AtGdgn3YBWj1G+EpNTeVDRJVlaBSFouCb+mS6N+FL80lbfyN2Hv0xffd5xgBP/b+ZyN36kqhqBxSULaaJtH4j3RALYgEikud8R/U//1I4KulCHYHjqGjMBo1x3eULC2ZgsVhIqTr5FkcpFvKvqr1oD/OJiOuTXEhQqNmx0RY67qIUc7nhe71IGQJ1Tp/FlnCEQqCtxhBVtDsDrySgigIOMMBKCpGEGWkzt2xntQD3YDiUsaGrnAQwVsMooBud+FVyg5adSiIooien5e03vB7EXQDRPAaIKTXw3rpjciGTliU8IXDlU5hLggCDgEsQT9GwIdgdxK02vElcaEN6zp5sg3r8DFYtEhAu/xwGHQDuyQi+kIHBCNdL/MFqxcVIOk6RhI3Um3fHvyrv0U/aySWHv0wRJFiTY/TIlU2SFRVgkqJhoFaem50jYLnH0VMz8Te/TQElxvp5B7kq0a1CRWirpGuhfFbbASqUVhMNP4jLVRAZKmiAAn55N5YTuoZM99Hywv5aBlH3cHpRrBYEz9kRAkxPRNDlBBTUtGLChPuQm4WcR86VlLw1jnsrrjAVqWRGzWpVqHCKRgIq78lb/5/oseUm7Yg486HCP25gfznZqN7Ig9ZuXFT0ibNoDi9PrquYxMFLJs3kP/kfegFkReKVK9BJPhWw6aEaiB2RTgcxt2lJ953X0tYb2nbEVWSo273hmEQKBFyqtAfQRBIQ8Pz3KMU/fBtRCAQJRz9zyZtzFUU6Mnvo2CJp4oe+X+aCIG3XkI9oVPUbkr3eZAy68UtfZWgtGyLKskozVsR3ro5YRvbid3IDwYjwsRhjhdSmrBuYOvZj8BBnip6fi7eT9/F3vdMgkb1xkTQ9uwkd+aVOM+9iJSzzqWojPk4mlBVFRWO6HzXFKaNxWHGp9hwnX9pwjrn0FEEFBteiw33xdclbGPvM4hwSXQ3k1qJz2LFPfaahHX2gcMIWqqezOtgJElC2rqJwqceiBFkDJ8XdcufFDz6f1GhAvYnJrrrWtwhfyRkdVEe+fdOjgoVEFmSy512Pa5A+bEVqoKu69C4GXLzVvGVgkDalbfgK8czozI40Sl+YhaBVd+U0jJo+L74EP+rz+EoR0NYgk2WCLy3EO+Hb+JZuoSUf18KgHfpEtznX5ZwG2vX3qiuFHySgvuKmxO2Udp0QG6YWNi021KQxfif3VYztheqqqJ06oaUWT9BRy24zr+cQA0YzBqhIJ435qF+8xmWJEkrK0Ki81XWuUp2fp2OjEqVJztGyf4lwR01WK3J+astCMYR0qfv27cvxm/9UBEEgUaNGrF79+5av0TgFsD4ZRWe155Hy/0HMS0D16hLkHudTuF+4dUpgLRxPUUvz0XbsxPRlYLznNFYBgylYH+bujTm6qAujdclGAi//ULxK0+j/bMb0Z2Kc8RYlH5nRee4IpQ3Zjc63lmT4r6EUy4YT+CXHwglsfVwX3o90oAhBJ55iMCKLxO2cQ4fjXHeuEoZ6FUUQRBIM1R8S17B//mHGKEgSqu2pFx5C7Y27dmbV1Btc5we9JJz3ejElaJE1tyF5Mvlx8NI00LkXn8RJZZ07hFjkLObU/zWy9i6nYrSsi3Fb85H3bUNweHEOex8bGedS/7+L3CbANbd2yj+72OEt25GsNpwnHkO9nMvwt4wO+Ecy2JK0iBXql4znhElIac9rz2Hf/ky0DQsHbuQMv4mPCkZVXZ3L/HyENVwjMeN4s2NulwKLjcZs+dTICpV8gpJdL7KOlfJz+9xvP36lkqUJz5GZeevtnvCKIpS/WnTTaqHYgPkLqeS2qkbgq5hiCI+xYa31Jq21wC53UmkzHgSUVNBkvFZbJH15VqOVZawhUOAQVCxHVBjx7SRsYUDgEBIseCvpdH7KkIkSZaKpOvogohPkvFoGkqn7qTM6oS4f479ig1fNdstSIZBOEF+D6lhY8LvJ877AaD+vg7LaWeg/v1n0jbhTRuwajVjZ2EYBvlI2P49noxzx4KhY9gdGOEQenExLouCEPQjGAZhScFvVD09dmltTAly46Y4z/gXUmY9REkkRQRBU9FECb8oJ3YHDvgp/dQvfvtVlOatcJ0zGik9C7lZC1JnPhlps3++80vNd8CAcHYLnHc/iqSF97exUqDp2Ks0sppB0zTyJQu2S24g86KrwdBRZQuFgnhI3kP+QBGyLKP89CX+Zx6Olpd+jxqe4sj5syq1OvFYTXG0jNkULI4QqqpSKEggSSUFCdsUCRLI+9vUcqGiJNGVb8mrFHy9FAyw9zuTjOGjowmLRFEkVVcJfLiIgmXvY4RD2HsPIGPkOAotjirFdziSOARQtm/Gs+AZwtv/Rm7UBNdFV6G3aIsnHCZczhwfKroAUoPGaHt2xpRreTnIDZvEuTeWIB3XGl1WkBo2iXeT3I/ctMX+5EU1F/E1oGkERJk00UD95lO8Hy4m9eIJGHt3Ubz0bfSiAqwdTyZtzDV43emEqvBiE92xUW1TLhyPVL8Rng/ewNb9NBAlPO+8irpzG3LjZrjGXo3WrFVcsjHBZo/MZalrNLx1MwX7X5KZD79AQZotmmwr0XxrmhbJ2xBtkzxV/JEkkjxMwy/uf0UYgHHotgC6riM3jHe1LEGw2SOf5yZ1GtPGwqTaSFGD5E25Bu/7i9CLCtGLC/F+8Ca5d1xFihr5LknRwxTccyOexS+hF+RheD34PnuP3FvHkxryH+ERVA6LJCH+/D15/3cDoT83YAT8hP/+M2Kz8O0n2MSav718shX36Cviyz//ANfQUYk3kiRsfc/Eo+m4ktgGIAg4howiUAPLIAeTIhh4nryfohcexzX4PHyff0DhS3PR9u7C8PsIrP6OnEmX4sjbiyRV3vZCc7hRWrYFwH7aQDAg/4lZSPUaIigW8mZPI7z5j8j8/fUH+TNugVVfYz3oWAGLDXu/sxIeQ27UBCMto/KDr0VU1p6jKvYfuq4jNG6GmORcOYeMwl+NNkg1yelnNeaModmcMTQbEGvc/qUuYQoWJtWCIssEV3yJlvtPXJ2el0Pw28+w2axoG35B3bk1vo2nCN8Hi7CVFUK4luEIByl6YU7CuuJXn8N+GAJhqaoKHbriGnUJlDJ41D3FKG074h43ISaEsOByk/F/j+O1OdF1nVD9RqROuB3BcsDGQLA7SL/9PvyuxPlrqhNBEBDz9hH86XsEmx25cRMCPybIsKppFD83G4deeY2WR5RIu/0+lJZtcJ4+hKIlrwDgPGMYxW/OT7hN8ctzcYRj9c9+Tcd50dXYup8aUy43aU76tMcorsWZPitCZQNSVTWAlUexUu++p5EaNI4pt/c9C+vQUQTryLKoJAss+3Anyz7cyduvb6mzwaxqgrrzFK9BLBZLNAlSZVXxJduqqnpIfv+yLEeTVVXEqLWk/ZFEFEWU/RbcSiiI/6DkOqUJfLeMlIFD8X7zafI2K78m5V+jCYh15LL0FCXPSqmGMfJzD0sgrGID7INHkXXGv9Bz/kGwWiE1nULZgtx/CFmnDYyUKxZIy8AjH0hg5tPB2r0PmSedgp67LxJDJS0Lr2IlfBiWpRRFIbhfkFCatiD0x69J24b+3IA7HIRShpYVuf90XadAtuKa8jBC7t4DdhJGJIBVIoxgEKOoENKyYsrzDQHX1bfjutSHnpeD6E5Fc7opEOWaSeFehSBXtR1N07A0b0XqrKcQiwvRfR7EjCxCVnvUML2qVDYoVrLza7WJFSgv/7u89P5lWY5eo3V5/ipCHXmC1ww2AewBL4HP3kbP+QfHKachtTyBIkkp9yFhEUVcIT+B7z5F2/Y3tg4noXTqStF+W4KKIkkSKWqQ8JqVhH9bh+W4Vri698FjsSe0vo60D6Gu/5nQT6sobNyUtN6n47M6CNaAG1gyUtAR9u4i8PVSBFnBOmQUAWtyFaZgtWOIIoI9uZmaYLNj1FBiqZpAKEewEw7Bba6y+HUDv2SBBqXWrzUdFQiIFqhfujz2+gxqeiQNeL3GSdvUFIZhIDgi7tOGGo4IRckQpUjCLsAqCjiDfgJffYi2ewf2zt2R252Y9P7TdZ0iBNJLr9+Xs1QlyImXXTwGYHEgNGp+wKC0hiJQHi3GfIkoEiQMdzpCSkbkPFbDKaxsUKyk59eXuH3p8ookhSvZvyAI1M864N11NMxfWRyzgoVNBGXdanLmzIyW+b74EKlBYzJmPkm+qCS1QldEEfvOv9k34+YDBlpffIiYkkrmfc9QYHdX6OtFkiRSivPJvfMaDK/nQMUrz5B5zxw8jZqilgqeIggCqUEfuXdNQM/LOdD+tedIv+0+jOM7EDoMwkWqYOB9+iGCP66IlgV+XEHK6CsIJkl17Rh+AV7NwDl4FP7liTUbjv1xPGq7kWoJusMZMZzcuyuuTkxNx3DX/FJCXSccDuPqdhrF8/5DeMsmUi+9DgQhYbhv+2kDCSpWLAZYN29g3wN3RF/ovi8+REzPJPO+Z8hXbEnvXcOdeiCwVXERUmb9hMt3UmY9dGfZL47a7vJcVzjazqPFIkMo/trxeav/uVZb3VOPWRsLeyhIwROz4sq1vbvwvPostjK+nJ1qkPz774iz+taLCil47B6cRsW+9pxamIJHpsYKFQDhEPkP3IFLjb1iHAIUPT87VqgA0HXyH7kb52FY3JNlGf3Xn2KECohEzyMcxnZKn7htrN1ORTy+A6qqojdojOOM4XFtLCd0QjmlT7XGNqlpPJKF9Mn3IhysqZEV0m+7D08FYiOYQMBqJ+XyG8Ew8H31CanjJsS1keo1wDXmagK6Ebn/Hr47Tkug5+dS9MxDOMqICumRLaTffh8oFjzvvEra1bfG2JcACBYraZPvxSOb3gkmlScU0hPangT81a8FrK2J2o5JjYUsy4R/WpE0CZJ/+TIyL7oKfwJjLEEQMHL2Jl1bD2/+AzngA6uz3H5Ifi/q9r8T1unFhZCfCxkNomWW0IHER3GoKurfGxHbdKrRTJU2NYz3/YUJ6/KfeYiMW2fiPPci/MveB8PANnAYRoNsCve77hUbAu4Lx2Mf9C/8y97HCAawnT4EoclxFCJRnaGCaxpN0yjOqE/mnAWEfviG8O/rkVu1xdZ7AMVWR9SOwaRs/AY4eg8kq3M3/Ms+QHA4qD97Hr5vPkHL2Ye1Rz+kEzpRICkIgLZ1M8menMFfVuMOBfAqiZflVE3D26AJ9Z54lcCKLwj+uYF6Dz5P4OdVhP/8Dfn4dth6nU6xxV7nXJ8PlcOR5OxoItn4a6H38GHnmBQsBEFAP1hLUBpNi3wNJdHnGP4kC3Al9aoKFfhYNcoJPmSEghFBZr8AZGhqUmEIwPB5atwnXjB0DF+yBUgVz/uLsE26Fy69EYDicDhO0Ck2BITMhlgvvg4B8IRCkTZHSCUqiiJOQ0P2eTB8XoSUVEJWB94KLCup+4MJKX0HI/UfimoY5IVCh81G4WjBZ4DPkYrt/PG40tLYl5ODNPIyRMCvaRGjN11HkiR0Xxn3LkSynpZh3hLWdPJkK5YzzkEUBIoNAwb8C3Hg8GN6/g5HkrOjibKifx7rHJOChaqqODt1TVqvtGyDplgSfjwbhoGU3SzpOrCYmg6O8rUVQCQhmdMVvxQCkYRk9RrGrD/qVjtSw+y4YEjRfrfpgKeGH4ghxYqtZ188SxYkrLedegZBQUANlu1qaRgGgcCRfwKJokiaGqTgwTtjgknZep1O+hU3RcMxl0c4HKbuLOLUXoLBIIIoopUIEwehaRpKqxOSbi/Vb4RRhhFxaUIh0y/QxKQmOCZtLAzDQEvLwNqlV3ylKJJy1SR8Zbg8BhUbzmHnJ6xLueLmpGrYg/HJ1sjacgJc540hoFgPam8h9ZrJCXVt9r5nErZXUKA5BIKqiv2sEXHRDAGkrAZYup1aY+m2awKXrpI/69a4CJWBlV/ie/2/2CuYoMrk8BGyu7D3H5ywLvXqyXgV07alohyOoFgmxx7HdBKyNMEgtHwZ3vdeRy/Mx9rhZNyXXIcvLbNc180UEfRfVuN540W0f/agtDwe9yXXEW7UDF8lzqhDMFC2/0XxK08T3rIZuWFjXBdeAe1PpjjBfmwi2HL2UDx/LqGN65EysnCOGIfco2+lklsdCpIkkRry4XljHoHvPo+koB4wFMc5oymQLDVm41ETScjSi3LJmZQk+qQkkTV3EfnSkTPiq0uJ16qDio43VQR19bd433oFLW8fluPb477kOgL1GhGoY6fpSM5xZZNkVUdStKP9mk7mqeFyWyksyqnWMR9urxAzCVkFKDAElP5DSOs9AAEISxKFVCzRTpEOSpfepHTqgmiAKop4kiUvKgOfISA1b4NzysNIho4uCPhkS9Kv/oAOoazGOCbNxG1oWG028jTwHqKQJooidnQUTQVRxBBlBDUECAQUC4FS0fA0TSNftmEbO5GMC68EQUCXLejhAClaiKCsENBrvxuZKIpoOfGuhlE0DSPgB+fhESwkScKhq0iaBoKAz2IjXIe0P4eTQh3kHqeT0rV39P4rEqQaNVw2MakIiV7ogiDgcLoorOZ3fW21czmmBQuIrI0XCvsD4ejR/1R42zASCETsMapo3xBNTCRULFmVrut4AEGQaZRRD3X37jLbl4ciiriK8yl+8QkCaoiU8y/D887rBH/5AWQFx4AhZIy8hIJSgcNKkhSFZAupAQ/eeXMIfP8NCGDvPYD0i66i0Fq7k4rpuo5Ur4zImLISSYp0GLALYNn5N8XzniD810ZEVwrOf12AY+Cww3L8uoiqqhSVvv+qIUmWiYnJoXPMCxbHOoIg4PYWsu/WyxEUhcwpD5I761aM0H7jy3AI3yfvEPxlNekznySf2GiEqeEAObddEUl3vB//t58R/GU1mY+8SJ5Quy8xzZ2K0rJtwiygzkHDCVjsNe4hIMsylr//IG/GzdEy3VNE8evPY/1jPdZbZ9To8U1MTGofyZY5rDaZYCD+4/NIB8UqTe1+6pvUOHYRihc8A2oYxxn/wvvxWweEilJoe3ai/rEeuUPX6DKNVZbxf7g4RqgoQS8qIPDNp1jOGE7oMGTIrCrFgkT6lPspfHwGoV9/jhSKIo7+g7GNvJiCw6BxcahBCp97JGFdcO3KyHKNwzSOM6l+zNgVtZeS4FcHM2L0cUntXGoLR4VgIYoiVllGDwUjwa/qUPTGRCiKgmwY6KJAMBROUC4SLOUqpygKkh6xzwipatS2oaS9IYoEw+GENg8WNYznl9WRfx9/AgXz/pO0X8Hly5A7dadETFDUEL7V3yVvv+obbP0HE6JqsTUEQcCqKAiGTtigUssqgiBgkWVEw0AVhJhrwmqxIOp6tDxfUHDePIOUgA8j4ENwuglabBQcptwrYiiY1IUYIPjbOuQe/ev8dX0sE713D7pHjzRm7AqTmqDOCxZuwUDcuwP/x2+R6/dh63sWznadKBLLTyRW25AliZRQJLFS+LdfkLKPI/3M4QRcqdhDAQJffUr4fz8i1W9M+tkjCKVmoO7dhbLiS0I/fY+YWZ+0s88jnJ6FEg4SWrWc0A/fIqZlkDp4FOG0TLwHPc90BESXGy0YwAgEEJ1utAQaCAAxLQOjlKurIYgITlfS8QjuFAxRAq3y8+ASDOSc3fg+egvD58Xe5wzk9idVaFuHANaiPHxL30LN3YelSy9c3U8jJFuwFBfgX7IE9Z/dKJ264erVnyLFFon/YXVEfgCHMaEbkhxJsJUkJbiUklZrXkQmlaMkyWBoxdeEflyBmFGPtMHnEUzJqJT3mIlJXaJOCxYuwSD05jx8n7wTLQus+Q65SXPS/m8O+XUoTIckSbgL9pFz54SIJwLA2u/xr/yCrDsfYt/U62ICafmXLyNr5n/YO+169KLCA+Xffka9B54lZ/rNMTlFfJ9/iHvsNdj7D8Ff6oHmt1hxDP03xS8/hW/55zgHDKXo9ecT9tF+5jkUlNKUBEQJ57ljCG34JWF75/DRCV1my8MlGKhvL6DwwzcPHGvNd8iNmqDc90yZ29oEEFZ9Rc7zjx7Y9seV2Df/jq1T15ikc4EfV+J5cx6ZDzxHoSPliAmiIYsNW89+BFZ8EV8py1jatK/VRrCHi9qacCkZkaSBXnLvuBq9MD9a7vvsPVKuuAl7z4H49wuMPm8YSXBzsHKvJsZW2fNY1857VanpcZaeY4tFJhSKPG8isRb1Qz5WsuRnR2Ke6qxgIQgCct4/FJYSKkpQd2wl8Ok7WIecT7COuOs5tTAFj91zQKjYj2vwSAqefiguOqfzrHMpnPefGKECwHH6YIpf/298ojIithRZvfrjtziiZeGwiqvfWQTXfk9o/Vrc/7oAa8cucVlK3RddTagkvfF+NE2Dlm2x9x+M/6uPY/tx1rnoTY6r9MtaEASUwtwYoaIEdfcOit9/HevwMQSS2G04wkH2/fexuHJn3zPJmTkprtzweih66gEck2ZRdqDomsOvG6Rfeh3hv/6IXRIRJdJvvx8xox7k5h6h3tUekq8515615dLYBSh+cU6MUFFC0QtzyOrSG//+YHoBv3bYxlbZ81jXzntVqelxlp7jM4Zms+zD+OXPQzlWSfKz6txnVamzgoXVasX/2ftJ632fvkvaoHMIClLCekEQIjlDynjxVaRNdSH5fajbt8SVK81aUvTK03HllrYdKF78Uly5rfMp5D54Z9LjhH5cidL3bDRNwzAMDMOgSJBx3fR/sG8PwR9X4L7oSlJEEf+qbxHsDqy9+hO0u+KWUQCKDAHX2Gtw/usCAiu/BFHE1ut0wu5Uio1424qSXCbJVPsWiwX/lx8nrBPsDvwrviL17FEEEsyrLMuEfvk+LtS6lFkPdc+upN4dod/WkRLygxLrWiqKFYtpcqgYhkGBZCFt5lz07X8R+mUNUv0GWLv0xmd14LKYWTZrC5W5JizhIMVrViSuNAxC69cid+9raqNMEpLMUNZqE2t98rNKCRZvvPEGixcvjilr3Lgxjz/+eHX2qUIYhoERSm4xZIRDJEr2IUsiLjWEse8fDE8RUnZzQjY73lLPCkmScKshyN2HXlSA1KgJYbsTT4IXZXVhJEu1nmxtPZkNgCAkXatHFFGym2H1e1C3/43coDGixUJoy1awOxAaZiOcMwavrhMOh1HOHQuALxQqM+moxxAQUjNR/jUagIIEhqIWUcCphtD37MBQNaQmzfDLVgIHjUMQBIxg7Lxa2nbEfd5YjHAII+BHUcM4rBK+g+fM0AgHE1wTkpw0G2YJhqaDEnlxuHUVoTAPPS8HqX4jNFcKxYg1aueg6zoFiIjN26C07oCkhTC8HiybfiWYs4vUtCyKJQWtjtkNHQ0IgoALHdlThPbPbsT0TIy0DDySpWyhwDDKTqyXwPvKxKSEpIaySXJA1qbkZ5XWWDRt2pSpU6dG/xbFI2PHEA6HcfUfjP/rTxLW23sPIKRYoVTESFmScOXsJn/mpEha8pK2/c8mbdwECnQhYmxVlEfe9JvQ8w+on23dTyX96tvIryHhwrC7EDOy4pYwtPychInH1D07UVq2IfzXxpjy8OY/sLTvnNDuIX3iFAI/fIv3k3dIm3Abvh++xfv5B9GHn2B3kj7lAdQmLTAMo1JJmspqbxNA+XUtuU/ed8CVVZRwj7sGZ5+zYjQhoVAId98z8X32HgDWE7vhPHsEeXNmYvgOpKp3nH0eqf++NBKBURJx5+0l797byLg+Xluj5exFbtoiad/l7OboNnskIVnQR/7MW1B374jWW9p2Iu3WmRQIUo0bUeq6jj0UiAQc++6AzYWYmk7GtEcpTq9f59Kx1+U1ekEQSDNUimb/H6Hf10XLpYbZZEx7lAKrM6kGQ1MsWI5vT+jPDQnrLZ264lXVGs9IXBVi56zqz/hkc+/zmh5OlaGu3UOVvmJEUSQtLS36S0k5MlKSrusITY7DckKnuDrB5cY58mL8auwD2B0Okjf1uhihAsD/1VJCX36MRZZxqyHypl0XI1QABFZ/h//tBdikxEsrh4pXsZI64fY4fZbnw8WkXXkLHCTAeT5aTOql1/8/e+cdHkd1dvHfnbpV3UXuBfcGpgRCCSRACCX0UBICJCHElFBCDSW0wAck9JIEQujFNja9gykGjLvkIvdu9a7t074/RlppVWzJlmzZ1nkeJXh2dnbu3Ll33nnfc8+BZucT+uQt0n8zxb3jmkAbMwm7robwx2+hjRiLE4sS/uzdlDcqJxqm8q5r8MdTeR47AyEE3nAN1Q/9LVUfw7aoe+FJlMKNKcGpbdvQdwDa+MkABM+4gMqH7kgJKgAiH83Amv8tiqIQNBJU3HYFdlU5iZXL8B55XOpJ2DaxxT8QOOWclicoyaRNuYGIohGsNyRrGlQAJFYuIfSff+DbBfO/rijEP5qRElQA2DVVVNx2BUFzz3vLbahdN//bThKpW8AnIPTswylBBbi6LlV3/4Wg3fYDMixk0i69DpSW72/eY36B4Q92+vl2Fpr2WV3tjgcBbfV9LLpnBce7G3vaGOpwxqK4uJhLL70UVVUZOXIk559/Pjk5OW3ubxhGyvp7IQRerzf53zuDOqGQft09JH74ksj7b+LEo3h+9BN8vzyXWt2HaPJmJ8syZv7CFmn2BoTfepXMo3+BU7ipBSGyAZFP3yH7l+cS7wJTKsuyMIaNJufB/1L32jMY61ah9O6L/5zfw5AR5PzzeUKvP0ti1TLk7F4Ezr4IZ+BQ+jz2KrWv/of48sXImdn4z7wAO3cAvR55kdC0/xHPm4+clkH6JddQcc91APh+dhJ1M19p/URMk/jcr9GOPqlTdBN0VSXyXksiZgPC057He/UdRJrcC7XIpF/9N+zVy923PbP18whNe56Mg4/AXFuAE3Xzg7XT/kfmn65HHz2B8GfvYlVXoo87AO/PTsb2B1DH7U9o+gtYFWVoo8YTOPcPRNIysSwLqa4Gs7Al+QncVSmBeJSI1rUS395EjMpWiKsATiSMtX418ojxe9xS6rbQfA5o+Hdrc4OqiTbEmQRWvGuiPi0RpXbuN61+ZhZtQaqtRqS3Pv85jkM4qw+9Hn6R0NT/EV+6EDk9E/+Zv0Uauz+1jkAIt60er7zL2tbR62iZDsee1B+AYJpKg+3Bzp5bd8vUdOX91byPNU3h9POG1H/WdFVIx3+r4TrujvHRFjoUWIwYMYLLLruMfv36UVVVxfTp07n99tv55z//mQwWmmPmzJkpvIyhQ4dy//33t8shrb3wnHo+gZ+cgGM7CE3DiYTINONIPj9yRlZyv+qizW0eww7VoikKsfKSNvdxTBNFUeglgWMYCI8HOatX5w6QvrnoN96HHYsgNB25wZ68Ty76dXe7D1BVRU7LSH4l65q/uW/0ioKcnpncrl15G064DmQZJ5FIllnkjCyssuI2T8HavGGbwWJHYFZXUtksA5DyW7U1+BUZv2ng2DaSP5Bsg9O3H1VP3tf2d8tL0DSNcNMykW1T9dT9KIOG4fvJz5H8QTyHHIHap5/7eZ9+eCcdDIaB8PmRvD4a7txo4Ya2G+I4yGaC3MHD2tnybR3Kwaoow4nHEKqKlJmNVJ9hMgo3JYOk1mAXb6XPET/b6XPYlagsbz2YVxSF3jm5rX7Wt+82PFxahb+D+7cPiXWrts2TqKsht5WsaSr6oF19G044dYw2P2OfX23j+9tvm1VbjR2qQ0gSIpiGvMPZEPe3mvbZrI8Lk/999gXDyMrxtdi/LbTV97Ajfbw70Tn3V9t9vO3f2pExtL1jdiU6FFgccMAByf8ePHhwMtD4/vvv+elPf9rqd04//XROPrnRSKnhIVxWVtamg+eOQJZlsmyDyn89QGzOV2BbqIOHk3bpdcT6DMQQAv+IsW1/v08/4qaBNGBI6zuoGtk33kvo3TcIfzgDJx5Dysoh+Js/ISYd0jXEzkQEQq08ZBImhKMIIejbty+lNbVu7T9hQqQ1QzKTIDbKoGGYm9ZhFm5GHToCY21LfwwAddwBO90/siSRFqklOutD1KEjWixfBbdOnXn5TVQ9cR+x+bPBcVCHjyL90uuJZPfFkiS8Y/eHD2e0fp6DhxOLJ1CGj2rZ4k3rqH3paeTs3kiTD6O8NaO2RA1UN2anMjKz226QomJpHop20vDNj41YupC6l57CqihD6B58Pz8N76nnUe1IpDmiVa5NA+RhIykpKdmjMhayaP0hZ5pmi+vZcE8XFxd3C1GwDE13y4pt5ZyzenXsnmhljO5Mm1VJwl9VSu2/HnSze0KgTz6UtN9fTa03uMMrTjrSZztyHKDb9PGuwM7e153VHzsLRVHalRTYKeal3++nX79+FBe3/farqio+ny/51zSz0bDcsTP+Akac0psudUWG6ldFGBvXUnHr5XiqSnEcB2ngMOScPq2eZ/C3lxFRdJzs3iiDWr6Vpp3zO8IfzSD01qvJcopdWU7NY/fgzPsGVZY7tT3t/WvvdQxLCsELLwcg9MnbBE/7davXQUrLQB23P0b9yo4d/UszYpTfeAmhmS/jOfgIhK63+K3MP/6FivtuJDbvm+RbobF2JeU3X4ovVI1lWSijxiM1ycKk9NnFVxIWEqLfIOSGjETzfS6YQljV23XOpjeAvv8hrR7Hf+KZxHRvcl+PHkQWqX8ePbjN4yuKDIvmUP3InVgVZW7fxWOE33mduifvI4BNRPMQ/PWlrZ6D3Lc/ou+AlKXCu+qvtfa2p83bm0R35p7eFX9RzYP/pLNaPXd94kGYvu23vz1/O9JmgECkjvIb/9hIEHUc4gu+p+KmPxI0Yjt1Ph3ps44eR5YFEoEW95PPm9nh+2xHxuLu+NuZ+7qz+qMz2tAeCKcjezdDLBZjypQpnH322Zx44okd+m5ZWVmneR/IsoxvxWKqH7il1c/18QeiX/03okIiw4hR88TfSSxdBIAUSCP42yk4kw8n7NSTU+0Etf96kPhCVxNBBILk3P4wZTf8odXjS4E0Mv/5PNXSrpUFEUKQm5tLUVFRuzrdJxzkpQuofe5RfEcehzJwKLWvPZsU8FH3G0PGNX+j1pe2U9kKTVWRP3qT0OvPuv8eM5Hgmb+l5oUnklodnoMOx3PQj6n+14OtHsNz2NGof/gLcSGRHq2j5tG7SaxaBrjBT9rvr8YaN5lIQ5+ZcWqfvI94/nz32vgDBH99KeKQo9qdTRJCkIFF+MWniM7+HGwLoen4TzwL7ZRfUWM3HkeR0lqI0Zx+3iBMu22GdoZjUnXdxW1yeHIefZkqXxoB4eDM+ZK6155JCqPpEw8m7fKbqFb03ZKtaK29sP02Q8cY7R29p3cF0gUkPphG+L2pLgFZkvAe/jP8F15BdScsQ97RNntlCevFx4l+2frKuLTfX03iyJ/v0DzbWasQ2jqOJMm8+cr6Fttdg60NrWxv+z7bkbG4q7Gz93V3WRWiqmq7MhYdehK++OKLHHTQQeTk5FBVVcXUqVORJIkjjjhih0+0M6CqKom2hGiAeMFiArZFWEC16sF39R2kxWOu1oXXT0TzkKh/kNq2TZVQ8V32V4LxKE4ijvAHMVcva/P4dqgW4jG8wXQky8RSNHBsZMvEklVi3UD9U1MUhGXA5MPIHHsAREJIPj85kw/DjoRBUTE9XqqFjL2N81VVFc00QAhiQmo11aqaBrHFPyT/nSjIp/rZhwn+8lzkXn1BllGGjaTmqfvb/J3EskX4LIOIUKj2BPDdcC9psSgyDqbmIazqGE37TNbwXXU7wVi0Rb9KkoQuBJJtYioq8TZUOx3HoQoJ30V/Juf8S3BiUYTHT1T3UGN2Aos9Fm0zqAAwN65FHn8QIctCO/xYsg4+AqIRVJ+PqKxR5YCzB5VAGrCnG1fVOOA5+Vyyjj8VYhGE7iWue6mybLYp8NLFUM0E0SWL2vw8vuA71COOY0de3zqrz1o7jhACRWo9C9mD1rGnjaEOBRaVlZU8+uij1NXVkZaWxujRo/n73/++25acNsC2bfeB1QbkjCzsJvuGEKB53T+AZg9Sx3EIOxBWPaB6EEKQ1oQs2Rz+436JbCaw/vcoou8AfPsfTOiTt0ls2Yg6fBSZJ/+KiC+N+G54KMiyTLoRI/bpe8TmzUYE0/D/8jzsAUOobniT99cTRB3Aaf0cJUki3TZI/DCb2KwPEbKC78QzkUaOp7aZvoMty0hZqeRPq3gr1f/5JwD6uAPw3fR/yDm92zxvKT0LW7iVuoY+Ex4/ubm5VBQV4bTVZ8361S8c1NKthN96BauiDH3igWQddyq1uq9NPYiIZRORdfDXl286I6gAhKq5y4bbuA+kjKxkNiJhWiQkFRHIIDc3l/Ju9Aa/LyJmWcRkDfz1K8J2wFivs2FLMlJmFlZFaaufy1m9cIQE9Czt7MGuRYcCi6uvvrqLTmPnkEgkyDzyOEJTn2uVwe0/9Xyiqt4igGgvHMdB9M5FCqa30MDwHHQ46n6jKb32IvT9D0Eeuz9lf52S/NxYU0Dk03fJuuMRrIHDO5Wwuj0kTZBuuCTlvOML5+A74Qz8Z11IuJ1lgnTbpPpvV6Usx4wvW4Q24UDS/nwbNU3oOjHLJv2X5xL7blarx/KdcQFhwyTt+NMIf/Bmq/v4T/8NUUXb4T4D16vB+fJDKl5uNC4z1hQQ/uBNsv/v39QEs3ZpWSGuefAcfCSxH75q8ZkIBBF9+u/24KGtlKsQHaNjdZfU7d6MqKTgP+OCNkvAvpPOorobZEu7Ap0l4NVZ6LnfU7H7e6STEPMGyPrLna79dBN4DjkS9fCfJdPmO4o6RSfzbw8jvKnLdgKnnEPNc4+6//2LM6l5/vGWX7Ytqh+5E7+xa8WNvJKg7qWnWwRD4ApMaa1sbw2qqhD/+uNWNR4SSxbgbFiN3ESoy3EcjJxcgudf0mJ//ynn4AwejmVZxILppP/phhaiYN6jfo488aCdDsI8iRh1r/y7xXYnFqX26QfwtSWj3kWIOhD8/dUtVECF10fW3x4mpHa+PkpH0ZYQT0fjnT1N0GdPhGVZiJHj8R13auoHQpD2u6uIp2fv9kC1q9BZAl5dcT499/sebELWHDHHIf2wY+j11FgSyxe7PgvjD8BKzyYkJLB37iFlWRahrN5kP/Ii1oY1rqT2iDHgODjxeH1A47RwJ22AXVmOFK6DQMZOnUdHoJkJ6ua0fDtuQGzuN2gnnLld6W5PIk7o8/fb/Dz68Vtol4+jacvDDvh+ego5hx9LYulCsCy0CQeS8PqTVupRGzwHH0HOpIMxli3CiUXRJhyI4Q9Ss5PLd2VZxli6tE0NgsTKpaQl4lDvLtlRtGYQpGrbrnk6jkO1rJJ2+8NQVoSxZgVy71yUoSOpU/VuLdWtaa0bH22vzT3oWtQ6Av85vyfnlHNILJmP0HS08QcQVT1Eu3FM0ZYgWFsGW9u6zxoEvFLFu3ruy92JvSawAJA8XqoUHWn/w9AkgYhHcVbk4wnXoY4ci+FP2ym9CdOyqZJUpP3GIY2cQNSySCtuIrrV3d4Otnc67ThfvwDdMgm1wb1oPE7LY0UciGhelEN+AkDYNFvsFrMhpugoBx7R5j47jO22b8d/qLX0ZnsmMtu2qUFC9B6AnDsYx3FcAmw3DioAEgmzVZZ9z+S9+xF2IOwNoPz4WABCnTmGugg+v0pNbXmLjEpbBlvbus8aBLyargTpuS93L/aqwKIBimOjriqg7IFbUuSgPYf+hIxL/kL1TpbVbdtO1ualXn0Qmu4uQ5MVhO5pVTZcyszG2cXeAAlVxXPwEcR++LrVzz2HHEn1NpaipQmIT3+e6uKteA87mroZL7e6n/f4U1u1VG9Ae0oanc09sSwLdfT4Nj9X9xuDqXm2OwG3VjvdkbppqzVYGzQNotaO1WB3V123O9eTu/O5dTV2JX+rBz3YFvbKwMKfiFH2fze3sA+PzfkKdfRE1KNP7DQNjYiqk/bHv1DzxL2EP5pB+m8vo/qZh1J3kiQyrryVkKLt0jfTqA2ZF15OfOnCpB5CA7w/OxkjLaPNGqyiKDjLFhD5aAYIQfC084h8N6uFy6o2ZiJi2KgdVvjrSsQ1L4FzfkfojedStgvNNXyrEXKbq2Aa0FA7bYrWUrXbQ2vH2dFjdeUxu/Pvtgfd+dx60IN9BXtdYKGqKrHZH7cIKhoQfusVMn78U/eh0gmIWzbyAYeS/cB/Cb/5AlZdDTl3P07ogzexCjejDN0P/+kXEAmk7/IauuM41HgC5Dz0AtFP3yY+71ukQBq+038NQ0ZQu42ykMdMEH7zxYYDUfno3WRdeQuJlcuIzvsGoaj4fnEm8oTJ1CB3vzIQbikmcNxpZI2fTPjNl7CrytHGT8Z38tnUab49ShZ7V6E17kjD9o6klzvrOD3oQWvYEY7Trj6fhu374v2+1wUWkiRhl6Rqp0uBNPy/OANt1HiwLBQcVEVJWSkihMArQE/EcBIJ8HqJqI3CWdtCxIZoVm88l96AZJnEVA31kuvRLQNLUam2bBy76x+8qqrgT8QgGkVoKgnNS8S2qZRU9JPPxffzM3AkmXAbwlZNITk2VhPreLuqgvK7rkUbtz/eQ45E6tUX56AjqI7F6M4F3ZADUr8heP58K5JlYSoalaYJto1XlvDEo25/ezxENS/xfTyd3JXCSDtynB70oDXsKMepq9Bzv6dirwssTNPEt//BhD909RHkXn3IvPJW6qY/T9205wGX75D2u6tQx+zfKAltGdT992Hq5rq+FVIwneCv/4h20JGE2vHcdByHqOUAEiSVHaVOE1faHgLCgXnfUPXSU666oxB4DjqczEuupVrWiBsmcSSwHdojmGMqGtqYicS+/zJle2LZYhLLFrtywd2w/NEabNvG5YRJYJr10t024ecfp3z2Z650t9dP4OwLSTvqBGq7b5zUgx70oAfdHnuNjkUDLMtCGjYqqcSZ8burqHrsHuL5C5L72FUVVP/zdpSNq1EUhaBtUnX3tS7JsT6lb9fVUPOvByF/LqraHqvb3QdFURDLFlHz5H2NktGOQ2zebKruuJqg3XE+SdR2CJz7+xa6IABSMB3tkKM6jaeyqxHApvaftxP9urFk5kTD1L34FObXH6ErOx5vez1pKFLLv7YEpjRNaXV/r2f3qtnuq2it/3r6ogc96Bj2uowFQK2kknXPk4Snv4AdqsMqL2l1v7rnHyfwt0ehvBhzc0tDHIC6l/9FxvgDO42T0RXwJWLUvPR0q5+ZhZugpBApd3CHOAWO4xAOZJB971PUPv0Axsa1gOvomPbH66hR9W6/RLItKHU1JAryWv0sNO0Fsg7/GXHJDSY7Wsttizx4zoXDWq3BGoa9w2TD3VXX7c715J09t84i6/agB/sy9srAosGUKvjbyzHefKHN/czNG1AkQWLtyraPVVmOMBKN/hPdEJJptBk8ARirC5AHDOswWTFhO1i9BxC49Z9IiRhCkkhoHqqRsHdjUKEoCroRRzgOhqoR60C5SZKkVhVEG+BEwxCLgs8NLDqrltuWDoQi7fjb8O6q63bnenJ3Prce9GBfwV4ZWID7xh0xLfTcgW3uI6Wl49gg985tcx+h66B071KIIysIj7dN1U+5T78dNkCzLItaJNB87gY7+T+7BVZVBZ78uYTffhU7VIc++TCyfnkutboXsx3GUI7jIGXmtL2DJLt93oMe9KAHPdgh7LWBBbhEzuD+B7uBgdmSD+A//TdEFAX/kP0QXr/7ttoMvuNPI6Z5dsoMq6sR0734TzyTUCsCVsLjRRnePXUmOoogNlVP3U/0uy+S2yIfvkn0yw/JefA5qjz+7XojOI6DyOmDlJmN3WTVSwO8Pz6GuOZt1b2yo2JZx/y8H7LSsKRXSmYndqVYU1uCUbpHIR5reU/vKeJaTY+jaQqJhNtfQoDjNMo67+2iWLBvi4L1oHtirw4sAEKql6w7HqHq79fjRBv1Yr1HHY921PFEDBNb0ci6+3Gq7rgaO9Q4EPUDfoT31POp6sZBBUDcNMk48WyMTeuJz/82uV34A2T97RHqVM8ey4dogBACqaosJahogBONUPfik3gvvYFIO1Z01CkaWXc+RuXf/pwSXGijJuC/6Aqq21ga3NH6u6wIPnt/a4vtu7Jm37Zg1JA9Wlyr6XGOPan/br/OuxM9omA96G7Y6wMLw7KI5A4m65GXcYo2Y4dqUYbsh+HxJ6W9TcsmlNWHzIeexyktwq6uRBk4FNMfpKpeREpRFLc+b5qdIqwky3JKFqH5vzuKKkcQ+NMNBMN1mJvXI6VnIvr0I6Ro3drcqjWoqooQAsMwkhkIVVWJt2I3DqAOG4kdCaM5FpF2LHSyLIvaQAaZ9z+DU16KXVGK0n8wVloG1Ug75QiZSh7c/rl0ZyLkvojuJrzUgx7sidjrAwsAw7aplhTEgGEIIdzAoNmzw7QsqoWC6DsI0bCCwgFVkgiYMYxFc7FKCvGMnog0YDC1krrjAYbqISZpaCKOY8aRVI2o8KDJJiTacOFpB0KOAF8a0pgDcBzHfUC2g3fQXeAV4IlHScz5HDsSwTf5UOzMnEaFUCXVVtx76E/wn3AGiTXLsetqESWFpPfuR62QtxscWA393bs/Ut+BblDnwM6KfTVNPbeHmNlDNuxe6G7CSz3owZ6IfSKwaEDyYdvOfVRJwl+0ifI7r6ZpEVPu25+sOx+jStY6/narevhqYx0Pz1rL3SeNYXJugFWVcf4ycx4X/WgQvxyTvVPBBbBHSlX7BIg5syh/9uHkttBrz6BPPoyMy26iOpEg89CfEHr9WcANKjyTD6X8rmugvr2ht19DHT6ajJv/j6p2SrQk3UX3IqTW3HedVE13q/VrmgKJlsFd8/PpLKO5HvSgBy72qcCiowiYCSru+QvNZx2reCt1zz6M9083tqum3xRC0fhydTkOcNv7BfxibB8+LijFchxmr6vgtAl9sVuZnPd26OHalKCiAfGF36P/8CXq4cdjBNIJnPZrQm+9gv8XZ1B+Z2NQ0QBj7Qqib7+GfsZvie8i1dPuhub8g93xu02xu2r9iUT7NEJ6tCt60IPORU9g0QYkScLavKHNJZyx+d8SSMSIKB1cmhir4/afj+Suj1excEsNHyx39SdG9Q7wfyePwYnue29Juq4TfeedNj8Pv/06aQcfRZ2k0Puci/H85OcYi39oEVQ0IPLJ22Sdcg5x0bm3d0fr792BP2GZTjK4CKapNCwV1j3SHi2u1fQ4mqZw+nlDgNRVIaJtj729Ct3hPutBD5qiJ7BoA0II7LqatndwHBwjAdsJLHyScI3NomGE109c9xC3E5w+sR8LtzQe/+RxfVEdA6sbuoR2NQRgV7Zc+tkAu64GUc99kNMyiBgWchsBH4ATj7lBRyeLpXa0/t4d+BOzPi5M/vfp5w1KinSZbVTb9hRxrabHaeu7OyM+tiehO9xnPehBU+x1XiGdBcuyUIeMaPNzKTMbx+Nr83MhBJnCwXzuYcov+xUV115E+eXnYH7zOWtrbO74cEXK/g/NWkNeWRzR0QzIXgDTstAP/Umbn+sTDsRoIlJmGAbqxIPb3F8dNhJL7t6iZj3oQQ96sLeiJ7DYBgx/EM/BR7b6WdrFfyaiedr8rk84hP79INFvv0gam2HbOJMO5R9frMFyHEb1DvDuHw9j8sB0HODhWWtRvW0HK3srTNNEHjUBuU+/lh8qCoFfX0q0SdXDcRzok4s6fHTL/YUg7Q/XEJF7knG7Ak1Nu3qmk0a0ZUbXY2jWg30BwtmZRfs7gbKysk51xxRCkJubS1FR0U7pEDRHhuQQ/+Qdwu+9gRMOofQbSPCiKzGHjd4mcTMzEaX88nNabJcyszH/+hhPrIpz1ZFDUYwwlubnidkbuOTQwfidaLtXKXRVm3cHXOv6BKHXnyX69SdgmmhjJpF2yTWE07NJ2E5KewEysIm+9TKRT9/BicdRh44g7Q/XEOszkNgefj0asCN9vCtXZyhSWpL42FRptCmfoyO/25n3dHuvQ1esCml6XZqiaTmqAXvTOG4P9rX2wt7TZlVV6dWr13b363mt2w6qbYF2wplk/ewksG1sWSGiaJjbUeN0Qq1PSnZVBcq9f+bmh14hnghhOw5SPMTVRwwGo/1BRbeFrCHhYFtu0CgpKg4Cx9z2UhfbtqmSVDy/uYzsc/8Ato2patQgtbp81nEcqhB4zryYrFPOA8fGUhTqJHXPv4Y7id1Vc2+Tz7Gbav3tvQ492hU96EHnoiewaAcSpklCKI1kwHZIfAufv83P7KoKvJEaYvWOqY7jYMVCnXGquxVC1ckvS5Dj1+jvVRFAYVympDbOpN4eHGPbs7XjOEQth2jDtW6H4VnMNIlJ9bexwx4vXd6DHvSgB3s6egKLLoLp9aONnkBixZIWn+kTD8LQvTsr8titIKsaBVUmt71fgCpLPHbmBCRJcOX0fOKmzf2/HMf4LA2ztdx0D7aJSNhAFkF3+UwT9Ig47R60ZYCm6TKJeE9g24Me9AQWXYQQEpnX3kX1g7dgrF6e3K6NmUTwyluoRrA3RRaOZTIs28+ADC+bq6NcPi0fBFi2Q/8ML/vl+LCNlu6xPdg+YlGrWwlP7etoywCt+X/3oAf7KnoCiy6C4zhUSQqBG+9DiYSwa6qQ0rMwfQGqEXs0gac5ZFnGtm00I8xjZ07gD68vpiKcAAcyfSqPnzkBNRHaI6XGe7Bt9IgztY62hMn29evSg30DPYFFF8JxHOoQ4A0ifGluMNEJRlfdCbIsU4cXjyZQ7Tjl4QR1sUYOSihuUhaK088jt6mU2YM9Fz3iTK2juxFZe9CDXYmehee7CHtThqIBsixT43i4dOpinpq9gaK4whXT80lYNn3TPOSmeTAshyunL6EwJiN6RKt2K/Z1bYXW2r+r297aOcgiSCTceUvve9CD3Y2ejEUPdhiOEFRHDSIJi1XlYWKmha5I5AR0bjx2JH2DOn+ZuYSqqIFPk5Ew6KG27T50N5OwXY3uYDbWVh+cfcGwXXoePehBV6InsOjBDsM2TQb5VB4+YwJXz1jCw1+s4Z6TxwHgUwR+J8YjZ4wnathkiNg+ry+xo/B45R4eQzdCWwZomi63aobW00892NewU4HFW2+9xauvvsqJJ57IRRdd1EmntHdCUlQ0XxAjXINlWciyjOJPx4iEsLcjHtWtYVsMSA+Q5lFYVxHm/s9W4VNlHjx1LFasDk2Y6EL0BBU7AZ9fpaa2vEU5redhtXvQlgGa2YYvXk8/9WBfww5zLNasWcOnn37K4MGDO/N89kpIisqmsODcF+ZR7XjQdZ0ax8P5L8xjfchBUrTdfYo7BEmSMFQ/V81YQlXEQBJQWB1ldVmIv8xchqUFXPGvnqCiBz3oQQ/2GexQYBGLxXj88ce59NJL8fvbVpjsgQvFG+Cuj1dSFTGYMjWPucUJpkzLozJicOdHK1G3odLZnSEpCstLQmyqiqLJEk+dPYnHzpqIIgnWVoRZUxFBVvd+wuauJAXuSgLm3kz2PObn/eqXg0pttmtvbn8PetCV2KFSyLPPPssBBxzAxIkTmTFjxjb3NQwjxWxMCIHX603+d2eh4VideczOgh2t49EzJnBZfTBx+wcFAGR4VZ44cyJWpG6Hznt3t9kyDCb29vDX40YyKNPLAJ8NCB45YwJloQSjMlUsI95p57e729sW2iIFdsZ5Nm/ztgiY2/s9VRNtcDUEVrzld3fmt3YUXdnHqe2XmPnahpTPm7erK9rfVh94vDIJs3vd112F7jqOuxL7Wps7HFh8++23rF+/nvvuu69d+8+cOZPp06cn/z106FDuv//+djmk7Qj69u3b7n1N2yaSsEjzNL5V18aMlH93Fno5DjcfP5rr32qU+L7h2JEM6x0kHPcS3Ilz6EibuwLHB4NosoSquAmwzEwLw3bwa13DDd7d7W2OyvKWRXRFUeidk9tpv9HQ5tZ+q3N+r2XWrOt+a/vo6j5uT5/t6vb7/N3rvu5qdLdxvCuwr7S5QzN/eXk5zz//PLfeeiua1j5ewOmnn87JJ5+c/HdDxFZWVrZdh9COQAhB3759KS4ubpdmhKwoFMcVZq+r4PRxvZCtOGHh5aX5m/nDjwYi4p1nCibLMtWOzj0fFaRsf+KrtYzsHeCZb9cz5fDByIkwtubn2R82c8FBA7drod7RNu9qdLaLRXdtryyCLbaZppm0d98ZNG9za7/Vmb/XFLvytxqwq/q4PX22q9rfXe/rrsK+1l7Ye9qsKErn26avW7eOmpoabrzxxuQ227YpKCjgo48+4tVXX0WSUmkbqqqitlFn74oL7DjOdo8rSRJRycvl0+cTM2wMy+aMSf257PVFlIcTxE2bKw4fhNNJjqOyN8jVLy+gJmaS4VW57eej+PsnK7n1hNHc9M5S1pSF2VAZ4eEzJnD/JyuZva6S79dX8uIFB+LUVXVKm/cmdLv2tpHd7Og5NjW3aopI2Ghs8zYyqTt6Tdr6XSHapmB19fVvaG9b57bTBmzt6bMuuNbbQre7r7sY+1p7Yd9ps3A60MpoNEpZWVnKtqeffpp+/fpx6qmnMmhQ+8VmysrKUrgXOwshBLm5uRQVFbWv43Q/rywqZuqirSmb/ZrM07+aRJacwOqkjIokqxQlZG57v4BHzphApmxQbam8vGALx4zoxQ1vLcVqds43HzeSw/r7tmk13uE27+Horu1t7eG3Iw8+RUprUzwpblR12YO2rd8958JhJBItx0BXuqo27+O2zq2pTPaOoD191mVBTTN01/u6q7CvtRf2njarqtr5GQuv19sieNB1nWAw2KGgolsgHua3B+ZSHorzxery5Oanf7U/WXK804IKANsyyNXghV9PxozUEo9bBGSTy48YipWIcdeJo7nl/cYyyZTDh/DjAX4ULBrmNU3TcBwnGYypqtqppaS9CUIIZFlOuT4N10tRlJSAVlXVnQ5wW3vIdJV2wa705kgkzFYf3nuDLkN7+qzHB6X9kCQJ0UyvpjPGVldA0zQSicaIsbue556MfdYrRJZlIqYgvzB18vhkRQmW3Pm6ErZlEK+rSg48y7JI1FVhCYUPC0pS9v1idTmWorGyxgHdD5qXlTUOtXjBE0QoGhvCEo4e6PTz3NMhhMDSA2yJKY36IJ4A60IC25PGxoiEUOu3635W1QKad7edbw96sKdDkiQSqp8yU0NS6sveniDrwxJC0XfvyTWH7mdFtQOaDwCh6qwLCfC0zqfpwY5hpwOLO+64Y49T3ZQkiZjs47JpeZSHE/g1mR8PzQLg5flbeHNJWfLG61J4Ajw4ay2z11UC8NMROchCcNR+vZi6sJCrZyxhWWmUj1ZXc/WMJTz/w0bKIhbLqywun5bHnR+voiwU7/rz3EPQEFTc/F4Bl03LY33IQfGn8+S3G4maDrd/UMDl0/JYWWWh+tN5du5Wrp6xhE/WVOOont19+vs0GjQjZBGksjyGLIIoUto2eR492P0QQpBQ/Vw1Yyl/mppHSVxG8qVz9yeruWJaHksrDOgmAoCO5uONvBKumbmE6UtK0QLpzC+KccX0fB6ctbYnuOhE7JNeIbZto0owPMdP1LB4+leTyPbIvLDAy5uLCzlgQDrCNrve3Nw2OXhQJrPXVXLzcSM5ckg6p4zvy6qyCHZ9HW5tRYSY4WY5PiooYX1FmFWlIRzAtBz24HJd18Bxr4tlO1wzcynDsv1srIxw/Oi+mLaD7cCN7yxjv14BVpW65NyE5epv9GD3oW3NiCG7/mR60G4IIXAcsB2HuGlz+fR8+qV7WF8RAdwl/d1pbJm2O2G+NH8L8zbXsKKkzt1uOTh0pzPds9Eh8mZnYreTNwE8QaKmQ4CYy6nQ/YQtQYAEzq7y79A8RGwVvzBwjBhC0Qih4Vdlpi3eysvzt3Dp4UOpiRq8vnBL8mvjc4Pcc+Jo+mcF93hCUHvRnj4WQmBpAa57Zzlry8PJ7dcdsx9HDM/mr+8tZ3lxXXL7Hw4bzCmjsyDRhtHDLkRbZMFAUG/VK6Srf7crSZrN0Z0IpLsaezqxT5Ik4oqfK99cQlFtIwHlzl+MZv8+OhipWdXd2l7dz8sLi5i2uDC56ZBBmdxy3H4Qq9vGF3cOe3ofN6C95M19O88YqyPgRBuJmvEwaU60fUGF6kENZqIobtJHUlT0tKzGGmN7kYgRdCLJ1R+OmSDNiSLMGPvluByKdeVhBmWllmYGpHtRBFRFElBfx5QkCcmXvmvKON0UjuMgCYe+wdTa7oAMDxIO/dNTSx7Ds/0ISUINpPalZ0f6cicRjdVi2ql/llOHz9+159Ha75p2bbd4cDcQSLvjufXARUMGOLvZfdo/w4tU7/DaXSBsk+E5qWJwg7O84PT4GXUm9u3AAloIULXLMEv18N2WMOe9MJ8aR0fzeNkQEpz+7A9sjkgdfiA1/01bKOSVJrjzoxUcO6o3Bw/O5MHPVgHucliA9ZURNtUanP7M9+SXxVF0LzHFzyVv5PH28vJ9N7jwBHjsmw18u97lrfg1GQGUhAz+/f0mPl1ZltwOsGhrDV+tr+W8F1P78rQd7Mse7DwafDy25+XRg+4BxxPk7k9Ws7TIfeNvGFuXT6vnXHSTMSRUnXlFMf7vs9VA43lOW1zItPzSfXfO7ALs84HFjkDVvfz72w3URA2mTMvnwzW1XD1zCVHD4rGv1iLpO3mDKiqfrCzFAX4+pjc/bKjEAcblpvHfXx/I7w8bzLkHDuTleZsIJyxue7+A91ZVc+WbSyiujfHqwi04stpCrGxvhxACy5FYvNV9m7366OG8cfHBjO0bZEi2j/mbXLGxP/54MNN/dwiT+qdx6sR+PPt96335+Nfrdr4ve9BhyIrgs/e38tn7W5n52gZmvraJma9tarVc04PdC1mWiZkOBfVchbtOHM3LFxxE/3QPcdNmzsYqbNFNqHyKxicrSgE4dHAmM37/I351QH8Avllb2W0CoL0B+zbHYgchyzK1eJgyLZ+aaGMbhuf4+cepY5HjoZ0/B0+Qd5aX8suxvQGHd5eXc9R+OQRUgYLF1+trOXx4Dnd/vIKFm6sbv6a6LqO9VaNTtTi6A9rTx7IsExJe8gprOXSAD2HEsbQA36yv5LCh2cxZX8lRQ4III4atB5i9vprJAzOYMnVx1/XlTmBvqc1uD015HkLIzHh1fYt9dlYUq7tiT+9jWZaptD1sqoowoZeGZBnEFT9fr6vkZ8PTIR5J2X+3trfpvBqrA93PJ6ur+NmIbFQjjG13TelmT+/jBvRwLNqApGjQ5C1UlmWkDi4zsiyLTNXmokNSRcFuPHYEuhXrnBsnVseZY7Ldmz8W4owxWWQpFqoRxolHOHpIAI8V4ZbjRwNuQHHHiWO46JBB5PqkJBm120fhip6y1FNSVFe7YwdhWRYBJ8qP+3sh4faFnAjxs6FpeIwQxwwJQCLqcjHi7r8zFZMLm/XlTZ3Zlz3YLhp4HpZThyz3cPM7G47qTdGUEIqOo3aOfotlWWRJMSbmqGDEsW0b3Qxz/LBgi6Bit6PpvAoQD3PiiPQuDSr2RexTgYWkaKyrc3hpQTHofmRFocr28OCX63A6EFxIisqaaosnvlmXsv36t5ZSbWtJEuDOwmgyKI14BCsRTd78jpkghM7l0xbjUSXuOWkcHy8vYXTfNArK4+BN47m5W9kUFt02uBCqhx8Ko3yxrhY0L5KisikseG7uVtgJ8S/LsrCbMNEdx8GMu9fOSsRSttuWyZpqiyeb9eV1by/r1L7sQQ92GzQf766oJL88gVB1hKKTX57gnYKKTuMVWJaVQnpvPta6E4xmwU7D3NCDzsM+E1jIskxM8nDtzKVMzyvk+XmFVNo6U6bl8fXaCp6YvQHJ076HmeYLcsM7y7Bsh+E5fp45d3/SvSo1MZO/vleAaOdxdgbCE+C+z1ZTXBvjpuNG8VZ+Id9vqOT295fj0VT+8/0m3l5azFUzlmAovm7Ht9A0jc1hh3s/XcVjX6/j07U1bAgJrpqxhLeXFvP28jIUvesVMZv35X8a+jJqcMv7u6YvOwMNAlPN/3oIj/s2VM3DgsII/52zkdveL2BRSZzFZXFue7+A537YxLzCMKrWzdQxe7DHY9/iWGg+3lpezv9+SF0vn+3XeOqsidu1KW+ArKhsjkg8+tVa7jtlDLoVo9rWuOX9Au47eay7hLU9q0t2Ag1rx298dzl3njgaWQimTM2jOpp6TaccPoQTRmZ2C52GFtADPPntJj5ZWZqyeVi2j3+cOg4lkcpv6Io6pVTfl4/txr7cFtrb5q4y69rVEEKQnpZDqK6louzepF3RFF1df3c8Qe76ZBULN9ekbJ88IJ3bfz4SaRfziPYWvkFHsLe0eZ/hWMiyjCw3pquFEChqGxKyiQhnTezNT0emXph/n7N/u4MKAMs0GOiz+edp45DjIQzDII0YT541cacfRJqeqrOgajpCtKw527aNx4rw9DkHkCkSpBHjX+fsn7LPiWP7cPKYnHYFFYqqpWQ1ZFlBluUda8R2IMsykixDPMRVPxnK6D6NZSivKvPYmRNTggpZTl2yJoRAafaWJUkScpN+F0K0603MNg0GNevLdBHvlL7swY7B51exnLoe7YpOgojVcfcvxtCnibZLn6DO3SeO2eVBRQ/2DezRgUXD6ozihJJ88BiqnxXVdqvmN7IsUxl1mLuxKmX7q/M3Yykd84qwTAMzXJMclJZlYYSqd+5BpPv5bmu0kV+gellYamDrgTaDi0yfhmWZmLKHl+amvrF+u66CmoSz3QBBqDoFVTaG6ndFtmSVooRCLZ5O5xjIikKFpVNp6Si6hw1VMdaUhZKfRw2LT1aUJgmdsiwTFl62RBs1JUwtwNIKE1G/T0P2Zk2tg1A1hBDYeoD5JYl2GYw170vTNHe+L3vQg24Coegs3lpDaZMsUGldnEVba7qNj0cP9i7ssYFFcsnn1Dwun55PUUyiuDbGNTOXcd1bS1lSnkgZNJIkEZF9TJmWRyhuku3X+MWYPgDMyC/itUXFu1UgxdH9PPPDFu7+eCX/mbMZPZjBrPW13Pr+cm77YMW2nUw1H/+bt5X3l7suqSeP60NGPedjytQ84oq/bY6FopFfluD6t5dyzcxlmFqAooTMFdPzmTItn1o8nZa5kGWZSkvjsml5PDRrLYVRiavezMe0HQZn+ThiWDYAj329jm83hZB1LxHJy2XT87lyej6bwoLSuhg3vLOMG99ZxtzCCKieehOkJVw7cwkrqywkXxp3fbyK2z8o4NM91GCsLVOuvYkz0RovRBZBIuEeC+vOgqxqrKqxuPX9AhxgUv80JvVPwwFue7+AVVVmSqavBz3oDOyxlHfHcdAUiYBHpaY6yhXT8wnqCuXhBKosyPZrSNg0cH1t20aXHA4cmM7SojqXU6FCbprOKwu28LNRvZBsg93FDRa2zcAM9+36/eUlLN5aw9Yal1XdJ+iBbViiSXaCE8b05v3lxfzh0MGcPDqHXx80kClT8zhkUCYKdpusZwmHLJ+GIgs2VkW45PXF1MZMEpZNTkBHkwWO0TmpUsdx8CgyPk1mdVmIUNyV101YNlcfPYL+6R58mszcjVWuEZwVQ5EV0nSFinCCP7+ZT5ZPozQUR5YEvQI6kmMjC8jwqWytiXHjO8voE/RQVBtDALnpHiTH6XpDuU5G26Zcg1rZe89EW208+4Jhu+Fs9k44lsmQTD/9Mrz0CWrcfvxIAO76ZBUltXGGZPlwzPB2jtKDHnQMezR5syEFfkW94iSALARPnj2RAV4by2zl+J4ApiOhm2E31a35sCUVxYzgWGYyHd5Qemj6721dquafb2//VqF5eX9lJf/5bmNy0zEjcrj6qCEQaywXNBy7gRBUXFwMkoyp+pCsBCSiyLJMXPGjYCMS4VbPreH/GwiMV0zPw3YgoLsP88fPmoBmuN9t3pbW2teeNsuyTERyM0fRhMU9J49DlgXpmkyOmsCSPZi4/dMQDJlagKtnLGVTdSNX5NEzJjAsKLDNRLL0cdN7BawoabxO95w0hok5Go6551nLd5SM2R2MxDqKttp49gXDiBtVHRo/nTL+dhO6mtgnSZJb5hQkORWOJ4jlgLYb9Bv2ZCLjjt5nXdXmHZ2HdxTtJW/usRmLBggBzfV0ZGkbAjuxEKoQWA0XPhFBkSRs1Yut+FCNEJbiwZZUNEVgRUPYQkZoXkQ8hN1asKJ6QdWREyEs00SoGrLux4qHcTqiQ+yArqSWHTRZSklWSIqK0APYiSiYcRzHwdKDCNtEidclJwnLstCcEAgJ25sOZgISEXeS0QJowsFEQtgmmG6WRyDon+Hhb78YQ1GN+8YvhMDU/AgaJyVJkjC1ABI2xELJB7sN21WqdE3CXHviiGFx87tL8aoyj54xwbWAj4fQJAlL82MjUI2w28dN+lQAiiRonsXRmt0IiiRA7FkT146ireDB7J5SAp0L3Ycjq8jxkPuyoOpIug8nFsbeVS7F3Ri2bSfJ0A2jQcTqUIXA3sMe7LsTsqLi6H7XMDIRQ5ZlN2BzbIiHtn+AToYQAksPuPN0g+CXJ4iFQEmEdqs2xx7LsRBCkFD9XDtzGVtrYqiyINOnYjkOV0zPp2gb5jfNH3y26uV/8wr53WsLiasBXllUzIWvLGB1eYSY5OWHrRHO+O+c1k2pVC+frq3hV8/PpSShonn9rKqy+eUzc1he4QYZ7YGjepi1vpbHv3aFmnoHXPLpxytK+feczaAHXAGpiMTp/53DnMIIisfHuoowF768gFcXFWM3V9ITEpWWxnnPz+O9FRXIHj8xxc99n65iS53Jb19ewJfrqilOKFwxPZ++6R5uPm4UN729hO/WV7C8NEJCC3L928u58+OV2HogmQm5bFo+D321HjwBbD3AHR+t5Pq3l7s3eitEU0jNMJWHE+iKhEeRqI4aTJmaR6WlIckKtubn3s/W8Nd3C0hoQW54ZznrKyPIklvicoCrZixhU8QV/7L1AHd+vJL8wjoEkON3r/lf31vOikqr3X3Qgz0Quo8ZS8s5/4X5VNo6qtdPXmmcU5+Zw7o6x1Xa7UGrwf6eli3YnZAVlcK4zFnPzeWrDXUoXj8h4eX3ry3iv/O27JSg347AfeEL8Je3lnH3J6tcgUdPkAdnreWKN5eQULfBq9sF2GMzFpIkETNtKiLu2/bjZ06kX4aPKVMXs7U6ytaaGL17b3+5oSRJGELm81VlOEBNzOTjFaXURA1uf385x43qzfTFWwGYumgr1/5kCDTJWsiazpt5hcQMm8un53HWpP68umAztgMvz9/MfSePaVfWQkgKy4vdqPPYkb247qf78faSIp7+dgPrKsIgJBxFZ+qiDRiWw72frOKcyf35aHkJNTGTj1aUct6BA5CapMEcReP9JSVEDItnvt9IaSjBDxurOH50b75ZW05tzGRFaQhNkYmbNseP7s2q0joqIwYfF5QQNSz+N2cDa8vDyJKgPGLS36eztLCOotoYRbUxDMshFDPJL6pFABurYoxIUzFbabMQgrq4RXU04XqanDWJoEfh8mn5lIXilNTFyclRqYrbLNxSTZZPo6QuRlkogSwJbj9hDCN6Bbj1vaWsq4iwsTLCQH8Ay4HCmjgCuOfksUzql8ZN7y5jaVEdq8tCjMzKokckeu+DEAJklfeWFRNOWEyZmsepE3J5dcEWAF5dsIVbjtvPzdb1oAc7AVvWmJm/mbhp89CstWyq6scXq8qojBh8saqcCw8ZhCZJuyxLICsqqyujrK+IsK4C7vx4JboiM2eDu+JxeXGIA3qrYO+ee3+P5ljIskyV7SGcMBngs+nTqxcby+tYXR5mbLarW9/e41Q77gqTLJ/GtT8dwe0fLE8xpTpiWBbXHzO8UWO+Hg1v4Ve+uYSi2sa889i+Qf5+0uiOmVjpAb7dWMPhQ9JdToXmZX5hhAMHpDWuN/cEeeCLtUlbcIB0r8rTZ08ijVZ0F/QA/523hXeWFCc3eVSJF39zEDPzt/Lagq2cNrEf4/qm0SdNp3dA4/NVZTzz3YbG6yMJHj59PEMDLqcB1cO3m8M88Pnq5D4C+PvJYxmfo+Js47rLikKpoYIDvTUDHIeI5GNzdZQR6RKOmUBWVLZEJa58M59+aV7+8rMR1EQNInGDn43qQ20swdKiOib11sGIuUGm4mdDZZRRGTJYCWw9wLzNNRzSPwCJbuZX0A405UwoioJZbyjXnTkTHUVbvJBAUKemtrxd46ZhOfJl0/OpCDce7KCBGdx6/IjGFHE3xp7MOdgR7LHt1QM8OnsDX6wqT24K6ApP/2oSmSK2zeXpXdFmoerklSa4/YOClKLwzceO4NABfjA6vw66TwhkWZZFhhSjv8fGrp94PVaECdlKu4OKhuNk6nDsqF5srIqwvLi2hYjWpYcPRTFbCk3Zto1XGJwzuX/K9j/+eAiaFe/YTRQPccxgfyNRMxHlsH6eFBEb2Yhw6RFDU7524pg+ZKhO8sYWQiD70pAUFcmI8LtDh6Tsf+iQLAIq/PrAQchC8FZ+Id+sK+etvELSNMHpE3LRlcZbY1TvAPtl+5L1amHGOXq/bDJ9jWWhgZledxnbdq67ZZr0Ugx61buvWpaFz44wKkMkvQYs02Bgus6E3HQ2VkV48LNVvLOkkCOG55Du01ATIQ7soyUHjm3beMwwYzIlnHreiRQPcfgAb4eDCllRkH1pyXKOLMso/vSdWnLb/BhN+6ctNDXlysrxJAWj9pagAhrb2PTPcurw+dvvbWNZFgHF5tQJuSnb/3TEUNRWxmt3QfIekBvb6uh+hNojr90ASVE7fSzuDGQzyqU/Tp17fzoihyydztO88QRSNJgc1dumWZxjxJk8MJ1+GY2fZ/s1jhye3SVBRUewRwcWALZlYVuNmQXbtrE6QpgEUD3MWlfDzPwizty/P6osMTOvMGWXK6blUePoLQSjhKqxvMLksa/Wpmy/6d1lFMdl5A4KTCXiqTeEkWgMThq0O66clpeyz2sLtzB7cx1C9SQJPTe/t4IKUyGu+PnTG4tT9s/bWkNZ1GbK1MVJEuvXa8qZu6mKkrDFn9/MJ242pvSWF9fx9HcboZ4/YesB/vrecqoijdd9U1WUBz5bDe3w1rAtE9tqtHS3LAurafZK9/Pi/K0s3FINwObqKPM2VXPZ1DyKatyHhZlIDWCa97vjOBjxjq0GkRWFkoTKze+twNIDKIpCRPJx7VvLCAnvDk1osiwTkd1j1AkviqJg6QFu/WAlW6OtcHZ60DGoOguKIjw3Z2PK5qvezKfC0nbbQ2hbaBijt3ywgqKEywWrCMd5YvYm8koTPcEFblBRGJO55YMV2PVjMSS8XPvWMiKSb5f3qyzLhISXKVMXp2x/Z2kxn6+tBq0TtHL0AI99s4ElFQk3uNB8vL+ykndXVLauseQJct+nq9jaZLVcRTjBrR8UdMhUsyuwxwcWOwtJkkD18PCXawnqCucdOIB/f+sSKH80JIv/nDs5aTD24OdrcJp1sOYNcNdHK7Adt/zxxkUHk5vmIWbY3PXRynYbm7ULup8HPl9DTcwk3avy8m8P4vB6Ual/fL4GSfcieQI88tV6lhTVsrwkzL+/30hxbQyPKvHP0ydw6oRczjtwIDMWb2VzdRRNlnjq7Emcd2B/zpzUn09WlLKmnlPx8Onj+cNhgwF4d2kxW0MGutfPV2srWbzV5VTcfeIYrjtmPwC+WlvBspLITpkaKYpCRczhjUUur+XXBw7gybMnodbrbLw8bxNSO9Q0dwSqL40/v5nPkqJabnhnORWWzmXT8lhZGuL6t5chezs+WIXHDcJWloa4bFoeZaZGYUWCSw8cgogLZAI9hmE7CCEEmsfPvZ+uAtzyx2sXHkS2XyOcsLj309WdZiLXmSZvksfPE99sIG9rbVL47Z+fr+bTlaXc9kEBMaF3y4BoV0L1BblqRj55W2u5+b0CykwtORZvfm95586r7YHm49Gv11EZMQjoCi/85kB+OjIHgEe/Wocje3aKLKnqXj5YWc7nq8q59b0CFpXGeaeggme+38h/52xkdVUCTWskIquazuKiELPXuSXxG48dwR2/GI0AFm6u4dsN1aj67hMG3KM5Fk2xMzUsSVHZEBK8vaSIq44aypbaOFMXudyDYdle6mIW//hiDbceP6KFMZYrUa3xxNfrueW4/VCMCDHZx92frOLW40fitSKdliZrYALf/fEqrv/ZCDKkOMKbxv2fruTcyf0Z4LVxbIu44ufPM5ZQEU5w98njmL5oC786YABxw2R8bpAX527hrMn9+dc36znvwP7099rYssbUvBJOGNeXl+Zu4uRxfVxOhaTw3ooKMn0ahw+sr9vpfl5cUMiBAzNdTgWCbzeHqYokOHl09k5zGoSqM78oxrqKMGdP7I1sGWyJSry+cCs3HT+KRG1l16z3VzTWhxyumbkUy248vl+TefpXk8iSE1imuY0jtIQsy9Q4HqZMy6M25n736V9OYsEHJS32bU2jYo+tR+8gOtpeSVEpScj8b85mrv/pMFQzSkh4uO/T1dz285GdptPQmSZvkiTVq8UuZUt1arnm6p8M55ihaWB03zLOzqI9fSwUjdU1Nte/vZQmQ5FgPachYzuchs6Gm6kNcs+nq7jqJ8PJkuKYsocnv9vIyeP6NvLPtvH9bbW5IRPcmlncqRP68ruDB7Rc0qr5mLm8nNw0D4f29yFwyCtNsLSolvMP6Avxzhc+ay/HYq8NLGRZxhZysm4vSRJCUbESrafHJUXFVnSIhdz0dH1ZwY7WuZGo7seO1rV6U8iKAqoXO9ao8yA8AYiHO/3mF0IgeYMQdyfM3NxciirrkMxYUhBMkiQsPchpz/6Arkjc+vPRbKgIc8b4XpjREGg+hG0iFA3MeFKbw9F8SLaFI6tIVgLbTCCpOkgKtm25RElZASGwJBXJsZKcCkfRQUiINiZEqb4k1MCFad4/LaDqOEJC1BuoyYqKrXjIzQqm9LEj5EbuhxDImo4Z3359sbX7QdW9COCz9XX8c9Ya/JrM8WP68LMROQwLODiSjEBgxKPJ/Rv+e1tQFIXChMolry0CundgIRQNybGS960kKzgIHGvXyGzvSHsbxqsddYmasiyD7seJdd5a/s52j5UkCcebxi//Mye57dhRvbj2qMEYka7VRBCyisBJliO3OxabQFbc+8FuMtegqNhtzKsp35VlHEnGMY2ksJ+kam2OV9Xj5f2V1Tw5ez1BXeFno3pz0tje9PdYGIaB6vFixHZdACaEcDMliSYvi3ogOVdu77tt3ddC1ZBsC9u2UQMZ/Op/86iLu30zoleAR88YhxGqae2wOKoXgZPkVAhFx5ZkRBcR1vcJ8mZbaFjlsbLa1TBoeEOYvTnSpimVbRpJ0qRtGtjROqxIrStAZVnJ/24NlmliNQk6bNvGitR2SUTtOE7LY8dDKSqjtubj0S9dzkfctLnt/eW8PH8zRWETWVEhEcExE9ixVMEvkYi4KpXxUP3qD53FZQnClkC2DWRFZWtMpiguo9hGClFTmPFtBBUqmyMyJXEFWVFRFNfgbHmV2apZHABGPBlUgEvobBqxy7JMnfCypMJEqHoym/PF+tB2PV8alAi/3tTkfvAE+WpTmDpH5YW5m/BrMnefPI4+AZ3KiElc8fPR2jq2RKhfMx7g/dU1sJ1aphCChOLl4Vlrtrlfd4BQ3bfEKsdT70CrUGFqbAjTrfUgGsZrAxrG6+4UCNoebNXHY1+m8rK+WVvBlrr6MdpFkBSNjWFBmakmHYybzpXbgqy498PaWpDq59W44ueHwth2OQYNirsLStxVZQCWFuDjtXWg+1vdv9aUeXXhFoK6wt0njyU3zcPGqhgJ2QOeIO+v2v7460w4joMVrWsx9+6UCJuqk1dqEBZe0HzMyCtKBhUAa8pCLNxS2+Y8KYxoClHTMeNdFlR0BHtdYNHwwJkyNY8b31nGikoLUwvw5xlL+b/PVvP52prkjb03Quh+/vP9ZmatcZdE/eaggeSmeYibNpdPyycqedtVCxSKxuLSOLe9X8Bl0/IIS97kEtArp+dTarTPVl1WFIpiMlfNyHfN4uIyNY7OlGn53PzOcpZXGW7mpANIGspNzeOv7y1ncWkcvGlc9/Yy/jlrDW8XVLhqqG3A0gNcNWMpD3y+mg9WVqEHM7n3szWMzU3jsqmLqQgnuP/U8fx79jr+9e16BmR6ufX9ZTz+1Vpm5hdiIfP0d5t58pt13PnJKoS37Tq75A3ytw9Xsry4DknAJYcNQW5DQGx3QlJU1tQ4XP/2UqZMzaPK8VBhakyZlsc1M5eyMUyXPvD2JQjdz3PztvLZqjIAzj9wAP0zvMRNmyun5xMWnWf81xSyorI5Irh65hIun5ZPqakml9nf+M4yVlfbbZKJZVmm2nY5R395aylramwSaoAr3lzCPR+vZPbGELT18BOiXlQvjzs+XMH3W0KUh+Lc9N5yHvtqLa8tbmkAKbxpbvkwanL/qeN5/Ku1PD17HQUldRiOxD++XM+T36zjwVlrd2lw0ZkQisaikji3fVDAW0uL+WpDDc98vwGAY0b0YlL/9KRZ3Mawg6ruOeNvjxXIagu2bePVBIOzvCwtquOmd5fh1xTq4ia6IjG+XxqyY7G3GmJLVpxzJvfnyzXlTDl8KEcPS+O0CX258s0l/GhwJpqw2vUmJzkWw7L9BHSFyojB715dRNy0XSfSTC/pHmW7S0sBHNsmy6fSO6CztSbGZdPyUCWJiGGR7lUZlOFFcmId6g/bttFU2C/Hz/zN1dz+wQrSPAq1MRNFEkwekI7kmG0aygnbYlL/NDZXR/n3dxt4feEWamImQ5YV89tDBvHvb9fTJ6hzyOBMVpaG+GFDJRNy01lWVMeHy0v4bl0FNfV8icOGZOG0JvPe0P5ElD/+eAjXzlzKvSePZVwvDSPe/d7+hWPTL81Lpk+jIpzgktcX4ThuxqtPUKdPUO8xq+okSGaMs/d3BZZ+d+ggjtsvk3MOHMhlUxdzQP80PJKNZXRBttO26BXwkOPXKKmLM2VqHkJAzHDHaP90D8JpPeto2zZ+XWJAhpeVpSH+8tZSfJpCKG7iUSXG9AkiO0ar49hxHBThMLpPgNnrKrnv09U89c16amImshAcMjgTYZspWgxmIsYFBw3kfz9sondQ58CBGawtD/Pm4q18tqIkOf4OH5aNsIw9zmQQ3Dl2eP0c+8mKUu44cQz90z2M75fOEcOymdAvjbs+LMDBYWC6jrkbZMN3FHslxwLA1gPc+G4BK0tDaLJEmkfh3pPH0s9jYZkGsizvUKmi+fd29DidgbbqdrKiIDxBzHg0KSBl60GwzQ4RehrSpL9/zQ0qsv0aPlXm0TPGoxqNxmZtErDq38wb3limTMujLOSmDX2qzLPn7U+6iCfFnzrcXk+Q2z5cSX5hY437ibMmMtjvtO7p0hSeAE99u4mPV5QmN916/CgOHhBEUVWcWB2WrPHqohLeWLSVPx0xjNK6GDPqlyHnpnk4ZVwfThqdlTR9a+s+kBQVxRvEiIVwjARebzqGIcBx3L96tCZ+tUOcg2YKgJIktWok1xwNS+oueX1xMh2b6VP5zzn74zF3jVlVZ3BKumKMdrbJmyzL4E3DiscQZozc3FwKq0JgW11CumtAg5jcpW8sprJ+uXhAV3j23P0JOK0I7DVBA4HxmplLWV/ppttlIXjmvP3pXa9Ls014gvz9szXM3ViV3PSPU8czMkNqnd+heZFVHWJ1WIqH/80v5K38ouTH1x6zH0cPDrjeHbsBze+rhnHXdLssy8mxl5ubS0lJCbZtN3LxJIkqW+cPry0ioCvcc/I4SupiTOwbQDXCWJpbJpI6IrTYhdhnTMhagxACyyGZpbjrpLEEdYW4aWMLAbqfiCPhFbHtP4Caov57PlFPlFQ9xCQNTYq36+19V8EyTUS4OvnQsm0bEWubI9ImhEQoZtZnKXzceNwowgkTw3LQJYm47JYb1FaY9w28FkmAZkZJWE6KNoZh20QSFmn6jpUFhBCYDsmVFg2oCCcYFGjPcldBeTh1Miupi4HjwwrXB02Kh7JQAkm4wjMrS9w6/kU/GsxR++VQUFzjElZVjajkQZMNSLQiomYaGKFGt85otKZVB8LOMAyTZIWo7EUXDsRDyTq4Im1/cnIQRA2LhNXYTwnTJmbaeLth+aZVNBvbopPGaGebvFmW1ThGGwLwWOvk8M6EEIK4aaeORcsmalgElG33sRAC03aINMmm2I5DbdSkl7bt8qoQAhtS1IwBysNxRmS25FgAkIhiGzH3mqgS5aHU/iutjeGI3bNEW1JUIpIXXTEhHkGWZaKyD68iiJgOumIjW3EikhdVllGEzabKCLIWBMdKrh7UZEFdTRzTcqgIJ7j6zbzkKjRNiKRy7O4PKTqGvY5j0WBOdtWMpZSHEtx90lhem7+ZW95bhiMEETy8sKCI37y4gMJY20ZlLaD7+d+8Qi54aQGFcRnVG+CbTSHOfm5utxS1aT5BdXwJrpLkVPRP93D9sSP567tLeW3BZjbXxokpfv7y1jKunrnUdfhrwttoKnP+0Ky11OBNLrdM8yj0CmgYlsPl0/MpjsvJFSPtRQNR8/q3l7OhMoIiCQZnujXav324wuVctEUKBfAEuefTNSzYXA3A8Bx3Ynvm+418sKoKR/GAJ8Dj32zki9Vl3HzcaH7YUMGs1WVceMhgJAF/eHUBmqywocZgZbXNOf+by8erq9okB+9sf7QHkqxQaWlc9PJCHp+9EckTJKb4uXx6Pje/V4C9LYM4WaHcVLl8Wn4yO5XhVZMeHA2Ezm4N3c/LC92xvTUqoXoDfLclzFnPzWVhcbxtovBuwq5+A21qXRBOuKXIbL9G3LS5bFo+ZabqrvpqBU0NBEvq4nhUif7pHhzgL28tZW2N0yZXqmEp5U3vuRlkWQiGZLnj9f8+W82crZE2x2tDZvIfX65LajY0jNeXF2zhzaVlLbSFuhqSorIxLDj/hXlMyy9F8QWpcTw8NGsNqytiXPDSAj5aVUmpofHblxcwM28rJWGTC16cx6NfrWF9VZyo4ufpbzdQUBbhqhn5WI5Dn6COIgkqIwaXTcsnLO2YKF93wF6XsRBCYDtuFN4nqDMgw8va8jB1cZO/fbCccX3TmL2uAoB5m6o4ZVQW0I6shSQzb3N1kmB19IhefFzgLhn8YlUZE3OHdEhGvLvDQZCwbGwHfjQ4i+poguqowcLN1SRMm7qYycYq96FeFjbo75GgPmshJJmSujgltXE0WaIinMCyHdK9KnefNJZeAZ3rZuZTGoqTsHZscnWAuGWjSIJHz5jAoEwvd360kvmbq4ka207ZO0DcdN+67vzFaA4elMHTszfw7rJiogkLR4BAEKvfZ79efr5d75JhfzaqFw98thLbgfs+XcHxY/rwSUEJtgNfrqngF6N7Y7N7NAhsIbOkqJaIYfHZqjLq4iYbKiOU1MWpiRnUxW2yZLn10pMA03aSE9wTZ03Est3gL5KwMC0bZztvtLsdkswPG6tIWDZ/fnMJPx3Zi4/qx+jnq8s4oP9QMPeeMdpRONT3se2Q5VN56uxJyJLgiun5VEcNDNthW259luNgWHbSQDDbr3LDO8tZVRoiblk423ic2I6b/ZKF4MHTxjEuN4O7Pypg9rqK+jEntmkUGKsf0zceO4Kjh2fzv7mbmbpoa0r2ZFfBklS+X1+KaTu8PH8LW2piLNxczS8n9GNh/TOiMmIwZ0MVMcOmLmGxaHM1EcNi1upyQnGLTVURhmb7iRs2tg2Ds3w8cvp4QnGLKdPykv20p2Kv5FgIIYgpfmpiJv08NpWmwpSpeSnLeC48ZBBnjMtpt5hTQ8R+1cylKRKqR++XwzU/GdLo77EL0dUaB5KisSUq6B3QUB2DhUUx7vhoRfJzRRI8euZEBvnsFiUlSdFYV+dw7cylDM3xcfmRw5ElgW3b7JcuE0OlMmKQ67HaXY5q3scJ1U952KBf/TEcT5B1lTGGp8vb1MpveIPaUBVjWND1F0H3U1AeY0y23nhPeIKsLo8yIlMBWWFleYxeQY1Mj8qt7y9naVHjEsexfYPce9LoTq+FdriPVS9frK/h0a/WJTd5VFddtbdiYFlt18FlRaU4oZDuUfCYYYQQhISXmGmTLSdSZNi7CjsldFe/jPjqmUvZVNU4Ro8Yls31xwxrYSDYXbArtUok2RX08ygSASeK4zjJubKPaqbYIzSHLMuEJS+RhEUv1cC2LCw9wJaaOIP9bFMHoyHLWFSXYJAf+vbKprCyjlXlUUZmqtsXBPMEWVMRZb8MJSnSV1AeY0yOp0s5KW1C8/Hq4pKkQjBAhlflv+dP5v2lRTz3wyZ+d+gQDNvmpbmbuOCQQaiSxHNzNiT396oy/z1/f2qiFn0CKqrhjrkq24Ms0SH+2a5Cl+hYfPLJJ1x33XVceOGFXHjhhdxyyy0sWrRoh0+ys1ETTeDo/qQpVa4PbMVDpkdiQr/UWtzJ4/oiW+1/e7FtG4+w+Fm9jGsDzt6/X8pxJEVFDWQgSRKyoqIFM/GkZbnmVrKMFsjosH9IZ0EIgeJPT01ZegJtLs20zQSDfDaaEQYjzuSBGQT1xnPvn+FlcIan1cDAsQyG5/jom6azpizMo1+u4T/frmN4tg/biKObYfp5zJTvOpoP9CZSvaqO1MSEKOXcbPe8BjQ5hojVMTpDpAQVQkk9hqSoKP50FCPCyHThBhUA8TCTsuXUQDNWx9gsyT1eLMSELIm+qolixThtQr+U8zl1Qq6r7dHJDwZJkqiKJBBNSnbCG2xzybRkxTlmRC8USeBRJR46fSIXHjKI3n4VyzJxdH+bOh+WadBXM5JETcuyCDhRsuV4+4IKzecevwGqjuRNc3+vSb8KRUf2pbdZlmkPhKIh+xuPISsKii8NXbI5blTqxHf2/v1QHSM5LsF9SGrBzJSxKMmKu60+/SxJkvudbSzB1IKZyfJBcnxtRw9im+0Swm1X0zGqBxCa172P6zVbhDcNGq4t4Ght92tT2JZJthxPEjUb5sq+moEla6maEqrHvdfqYVkWfjtKL8VVoHVUDxK44nFmokWfNIXjOCiJEEP8djIAEfEQ47Kk9qmMxuoYkyk1ju14mEk5CpKVQGulX1VN71BftgVZllEDGcml1kIIJF8ainA4baJrfJfpU/nXOQdw7uQB+GSbE8f1BeDD5cUcM6IXkoCPl5dw5PBsFKnx2ozo5Sddkxnks1ASoeSYy5RipBHDVLyp+iAN40sPpJZcNS/ogZZjW/UgdtNS3A4FFllZWZx//vn83//9H/fddx/jx4/ngQceYPPmzV11fu2H5mXaoq08+s0G8ARwZJX8kjgLt9by3NytfNfEZhzgj28s6pixlOrhqw21vDg3ta3XvrWUwpiMrKhIisrWqMSfpuZh6QGKEwqXvL6YtRURopKPsOTjD2/kUWqouzy4aDA+umrGUlZV20iqBp4gD85az2fratoMLkzDfViaWoCrZ+SnZH02Vka46+NVLdaRN2QEbvuggK017kSwoTLC0qI6rnt7GZYecMVmmkbjmo8ZS8t4+vtN7kSqesgrjXPTNrgBtm23iOiNRONbk1B08ssT3PiuewxF1SiOy/zxjTwikg+7GQM+kWj5xtX0eIlEAsOBFZUW9366MmW/+z9bxfIKw72unQQ3S+bjktcWsjEEsqrheILc+fEq5mwJI5oFFw2M/z9NzUORBfecNI7/ztmAX1P5al01+NJ5Zs4Wpi8p3UZwYaYQcS3LanGdWoXu5/W8Ev47d2uy/+YXxfh+Uw3vrqjgidkbXedGVWdphcH17yzbJudjWxCKxvJKk2vfcu8lRdUoTahMyy/mm411/HdOqkLmawu2sL4O/jQ1j7jiR9M0avHwh9cWUWFqyIqS5Jj84bVF1AkvmqbVr57Ia5WL1aCX03AMRdWwtABXz1zKyip7h4KLhjF67VvLKKgyXdVbT4Anv91ElSHz1/dWsLgkge0JcsdHq/h6XRVLy2LgTef5+Vt5dXFJq2JTzWFbVspqBtu2sWSdFxcU8tLCIvcYqodvN4e56+PVKePbaviu5uWjVVU89NU6TFlHqDorqiyunen2SVvBhdksS220MubaQvN9TcumKOaO51h9v4aEl/s+W01xXOYPr7lzfNO+LOoAr65B1OuPb+S5ooCahq0HeOSrdZQlJC59YzGZPpU7TxzL/Z+tZHhOgLKow6VvLCY3zcPNx4/ixreXkOPXufWE0dz0zlLMJuWN/MJaHv16A6asp7yQWJaFpfq47/O1zFpf5wYXup+pS0rZWmfx6OyNvLey3phM8/HeikrWVsf579ytvJFfWt9/XmZtqOP/vtg9Oh8dCiwOOuggJk+eTG5uLv369eO8887D4/GwevXqrjq/dkFRFCri8O9v1/PFqnIe/WYD84tjPDl7PX3SvLy1xF2idNGPBvHahQcR1BWqIgavLtiC006xLEX38vS3GwC3/PH2JYcmRW0e+2odaD40f5Ab3lnGpqooCVtw/duuF8AdHxSwtiLCZdPy2Fod5cZ3lqHsgKHVzkDSfTzz/SbWlIe54Z2lLCpN8OCsdcxeV8HjX68jZMstnFsbIGs636yrZF2Fy6l44qyJ3H7CKADmbqpiXWUMtYlBjqJqrCiNsGiLa1R2z0ljePj08chCsLI0xNxNNShNyK6qqlIcsXlx3mY+Kijl6e83MWdrhNs/WMGyojpeX1SErHfMeMyV3/Xxtw9XsLy4jpvfK2Bljc0V0/PZUh3l75+uatck3ByK7uehL9ckTefe/eNhjM8NYjvw0JdrUTwdP2abbdB9PPLVOjZWRrh6xhKWVpjc/ckqFmyu4f8+W40p66liZ6rOu0uLKa6NccHBg1i4pZplRbU8NGs1kYTJv77dwPvLS3hp/ha2hq0UU6OdgaZpbKo1eG3hVt5dWsyzc7cwe3OIqYu20jfNw7Pfb+TTVWU89s1G5hXFuOW95RSUhHhp/lZkvWPEOyEEitfPbR8UsKo05Nb4axyumpnPz0b24qlv3BLQEcOyeeeSQxmc6eXan43kpnfc8siVby5hTR1MmZbP1poYN727HNkbRPEFuend5UmtlTV1cMX0fDZXR7nhnWVo/mbBsyfAHR+uSO6/utbhureXs7oszC3vLUf1ND5c22tgJmk+/jd3C6tKQ9z0znIWFMd5+KsNDMj08cHyYpYU1bKqPMzM/BIWbqnmn1+sZnNVlGfnbOStJcW8sWgrG2qMDgspaZrG6so4M/KLmL64kBcWFPHlhjoe+Hw18zdX8/6KMlRP4/hTFIUaU+Lpbzfw1ZoKHvpqPQuL49z07jJWlYX43w+bO9yvO4KG+XZzdZQrpuezpg4um5bHHw4bws3vLmNrTYwpzfry+neWofnaN/cK3c99n61mS/3xV1TZ/PX9FRw0OIsX5m6iNmZy5VHDmZlfyPqKCJurI7y5eCsV4QRn7t+f2WvLKQslOH1SP+ZurKSkLo5XlXn4jIlcedRwAD5bVUpF1E6Ze1Xdy2erK5izoZJHvlzLp2tqeHVxMRVhg9XlIb5YVcYz323k7YJy3i4oZ2VpmIqwwbtLi3l1wRZeXVzMp2ureeTLtXy3vpLPV1eidnD+3FnsMMfCtm2+//57nnzySR544AEGDBjQoe93OsdC8/L1xhD/+CI1yDlvcn8OG5ZD3pZqfjkmG9mKU+14eG3BFv542MB2cyNkWabK8fDGwi1ccuhApESEuOLnidnrueqoocjxEJIsU5pQuWx6HoMzfVx6+DBufW9ZCsEoUG+ik9kJJjodqc0mrZrfdx+0TXHzsSM4dIB/m7wENB8zl5Vz8OAMBvkcHCGxoDiOadv8qJ+v5Vpy1cM3m0Jk+TTGZ7veBOvqHJaX1HHiyKyW3BbVyydrq3nym/Upmw8amMGtx49AxOo6XItuMDK64e1lSXt4gP7pHh49YwL6DmgzNGQFnmro94S71vyxbzYw5fAhnar30NBn1721nLUVjXVkAdx14hgm9dZaLqHUA7y0oJCTxvUhy6vy909XM3dTVcouUw4fwgkjM1tdGrvD0Hy8t8J1Y2yKG44dQcJ0eOTLVEnzAwak8befj2rBSWlPHyf5O2+lmsUdNCiDq48ewSvzN/GnwwYhJcIYqp8Z+cUcM7IXl09bnCQBAqR73LGYhnvvNqyYaJqV86oyT509kV7N+CkN98EV0/MpqWvsA1kSPHLGBIY00VLZns9IQ5uLi4uxND+3f7CS/KLG5a2aLPHs+ZN5/Ku1LNpSzZ0njuXtJYUpehAAvz90ML8cs4MmgJqPN5eW8eK81IzsUcOz+cvRQ1vOk/UZjQc+T51vx+cGuefEbXONOotT0iA5f9m0vJQ5dnxukBuOHcXlUxe36Munz55EttI+vlBbvDpdEbzwm4N5bs4Gfn/oIDRZ4oZ3lrGuIsLtJ4zhu/UVnDCmD32COm8s3MKJ4/qS6VV5Y9EWjhvVB4FNrl/l87VV9E/3MDpLxmkukuIJ8Ng3G/m8Xp0V3HH/+NmT+LighHeXFqfs/o/TxjN/UzWvL9ySsv3Ykb248sjBncYB7DITsk2bNnHLLbdgGAYej4c///nPTJ48uc39DcNICSCEEHi9XsrKyjqVmCKEwJ+RzV/fXcacDW7ZQ5YEH1x6GEY0jCUkRP3DT5ZlbMWD0w7SjyzL2LKKk4glvyfhYMUjrsCJ5sOON4pFabqH2Vsi3PPxSo4Z2YtDBmdyf72tM8ADp45jYrbSatp9R9rct29fiouL2/egFQLFn8GJ//ouuS766P1yuOGYoa452XbgqF5kpwkvQtFwEIg2mPaOoiMcB6z6mqqiYgs52Q/NofiC3PrBKhZuqQbcCfXdPx5KIlSN4zgdbi+A6vHxal4pLzWZMN+46ODtigFtC5IkufXO+n4XQiB0PyQiHQ4qZN2HnYgm2yNUD5JtpArseNI4+d/fJb9zyri+TDlsAJZtI1kJVxNB9SAJcCwTW9aQcMA2UH1BfvmfOUndgon907n/pNEYkY6LOrWGBu6HYxqo/jSue6eAZfUPRa8q89YlP8IyTe78eDU/bKxiQr80+qd7uebo4SRC1a6oUP34gvbf06rHy7SlFTw3pzGQeeW3B5GtGBhCTY7thjEqYzJ7U4R7PmksYT165gRGponk/KRpGsurLK6duTS5zx0njObQ/h4S8Zb3uFLvbHzBSwtQJMGFPxqMV5E4aVQGtuPqlAgrAbavzcDCcupS2gygBjI46V/fYzmO60psmVz6o0HIqsIv/zMHx4GXfnswF788P/lAHds3yD9PHYsRjyE5dvLBKWTVHevtMJFT/elcNXMpq0rduSCgK8z4/SHEaqta3V/xBrjvi3V8s9ZdZScJ+OBPP8aoH69tYUfGcVvQdJ05hTHu+LCRVP7P08YzqbdOXmmcv7zVpC9/MZpD+7Xel23BJaz6+NX/5ia3/fqggfxm/95YjkCY9Tob3jRO/vf3yJLgqp/sR3koyrn752LaNpIALBOPP0gsGsWxTNerSdGRHbtNgz89mMk5z8+jul77Y2i2j6fOnogkBBe+sojiWnfM9E3z8MKvD8BxHP40NZ8N9eJlGV6VNy46mHhd6/23I1AUpWsEsvr168eDDz5IJBJhzpw5PPnkk9x5551tZixmzpzJ9OnTk/8eOnQo999/f7tOriOIJky+W1/JDxsauRSW7fDA52u4/tgR9PG3tk56++IqGyvDfL6yjDMm9SPDp1FSG+OVBZu54OBB5CSFmBpTa+vLwzzx9VL6Z3g5fWI/7vywIOV4D36+mn+fO5mBudk70sxW0bdv33btVx6K8/ePV6SIrXy9tpxfTuzHwYN641F335rpcMLkmzXlyaACIGHZPPLVWq4+egRZ/sa0fXvba1o2BSV1TG3C3Ab424cF/OP0ieQGd9YzpnlKtWPlrfJwnJfnbuLcA10/l+pogrfyCzlmRC8G93ZLKmWhOLe9tyzlex8WlHD+wYP4cnUFx4/pQ1pQYWZeIYcOyUKWFHL9Hr5bV8GoPkH+9fW6FDGk/K01zN1Sy9H79cKn7xzPJ25YLNhSjQDG9snm67UVyaACIGpYfL6qnEyvytyNVUzsl8ZvDh5ESV2cT1eWcfyYPtTGTD5fWcIZk/qT4WtfH5u2zcqSEK8uSH27vuPDAh4+YxJ90zw0H9vrysM8/s3SlG33fbqap885gAE5bpp4c1WE+z9LJaM/+vVaxp07mSG5WS3Oo7g2xh3T81Akwd9+MYYlhbVMHJZNxNGYmV/I8aN749f8eK3WK86KotA7JzelzRXhOPd/uhLLcfjTEcMwLJsj+mdTa8CL364lYdrccOxI/jdnQ8pb+vLiOtZXxSmsiTOid4BRfXKwbJu8rbVEDZODB/fGu43xHYobfFxQmgwq3G0m//l+I3/88VAyfalls6hhMXdDJd/WL90Hd0npfZ+t4ubjRpHd6nybivaO421hQ2WYx75KHR/PzdnI304cywOfp/b3o1+13ZdtobQuxh0zl6Rsm754Kz8d2ZuxfQMochrloTh3fODO85bt8NCs1ciS4OAh2UwekIGmNF73dG/7yo/VkQTP/7AxGVQArK+IsL4yxuIt1cmgAtz7cGlxiC3V0WRQAVAdNXhx3mYu+tHglLG1K7DTy03vvvtu+vTpwx//+MdWP98VGQtFUSg1VC5+ZSEOMKlfGocMzkymZc+Z3J8LDuiLHW9/irBBvvZ3ry2iIpzgNwcN4NcHDeTqGUtYWRriqOHZXHf0sBTHTS2YyfkvzKMyYvDKhQdz9ZuuhHVAV/jjj4fyr9nriBgWuWkenjv/AIxQ9U61uyORv6T7eGZuIW/X802mHDGEr9ZUJM2xXrrgIDI6oTyzI1BVlS1RmUtedyf1gwZmMKFfGv/7wX3Lu+DggZw7sRd2Itbu9gohcLxpnP3cXOKmTf90D+cfNICHvliL5TgcNCiDO34+MsUVc5dC9/PQVxv4ck05w3P8PH7WRF5fsIUX520my6fy3PmTUbB5+Ov1fL6qHAH8+SfDeW9pMT8Z0YtwwuSNhVu49PChWI7Ds99tINuvce8p4yisibG+IoxlO7wy3334XnjIQAqKQ8myyL/P2Z9BPnuHy5GKorApInP5tDwm9U/n4kMHc+X0PAAOGpTJyF6uj8sVRw3nqul5DMjwcuvPR/PnN/OImzZXH7MfBw7I4OoZ+VSEE/zu0MGcNiYbYW67j4UQCF86Zz33AzHDpm+ah98ePJB/zlqDZTtM6p/O308cldKvWiCT816cR1XEIN2jcPmRw3joyzXEDJuBGV7+fc4khBD84bVFbK2J4VVlrjl6OI9/vY66uEm2X+OV3x5Eosmbn+xL48Z3C1haVMvtJ4zmqzXlfLWmnHtOHstXa8r5dEUpQ7J8/P2Uccgxh8/e3NKiLc0zFiVVtTw/v5Dpiws578ABBD0q//l2PZcePpSYYfPC3I2cd+BAfKrEf+szNadP6kdxbQy/pnDAgAwe/HxVfflmEuGEydUzlmA7Do+eMZFhQVJckBugqioraxyunuE+QI8clk3/DA+vL3QD8suPHMZJI9OT9uaKolBualz0ygJsxy09HD4sm3/Xc9DOmNSP3x2U2+Z821kZCy2Yya9fnE9FOEFQV7jyqGE88uVanvjV/tz23rJ292VbkLwB7vx4DfM2VSEJV0L8tYVb2VodRVckpl18CJJj8vjsRmuAK48axkcFJawuCyNLgtcuPAi/HcW27Xa3WdE8fLqulofr3W/PmtSPyoiBLAkmDUjnH/Xlp5PG9cFxoDpqctzo3smX2J+O6EWWT2V6vf3AX47Zj58NDWImdl7at8syFs1h29uenFRVbZNM1FlL8wzDIMuj88sJuWyoCHPnL0YhOSZBj8LrC7dy9qR+OIlwh37PcRywTY4d2Ys3Fm3l5flbmLqokITlirycOakfwoxjNzlmIhLixmNHcO+nq1EluPm4Udz5YQF3nTSOXgGVp8/Znyun53HzcSMwox07n+2d63aPZcT49UED+H59Jb8/dBA/Hhjg56N68df3VzA+N42A4mDGd8+a6UQiQW9fgBPG9KY8lOC240eAbeLTZN5eUsxpE/piJ1K9SbbXXsdxIBHnT4cPYfriQh47YwIeYdD31HHc9dEKrjpqOMQ7rw86CslMcOakfnyzroK15WFOe+aHpJT2T0f2QtgmmDH+cNgQFm2p4Zqj9+OAPh6OHp7FE9+s5+zJA3lvaRGfryrliqOGE9AVKsIJrpyWR8KyyfCqPHDqBL5ZW85JY/vwi1FZ2BP6cu+na8jwqvQNKCR2ou5qmiZ9gh5G9PaTt7WGikiCo4bnEDUszj6gPyNy/AR0meqowcnj+1JcG6VPms4BA9KZs6GKR2atQZMlEpaNX5M5er8cJCuWHE9t9bHjOBCPcsWRw3h5/haeOHMCPmHQ/7Tx3PZBAdcePRwSkZTvxqMhbvzZCP7vs9U8dfZEMlWL4WdN4uoZS7j5uJFYMbdsctOxI/nre8t57MwJ9PU6jP3VJC6fns9Nx44gEU3lDDixMNceM5yr3lzCkCwfOfv3Y/a6Ct5ZUsSpE/oxa1UZGyojXPzyAh49aUKb1zGljYko5xzQn9nrKhnTO8DwXgFeX7CZqYu2cOeJY5m1upSgLvOTEb34YlUZhw/PZmzfIOP6pvGfb9czsneAPkGdkro4l7y+CNtxsB0Ynu1nQLqOlWid95BIJBiaGeDI4dlIwLVHDwXHRpMlvl1fybEjsjGbcCYMwyBd1zhtQi6rykIup8I2SfeovDB3E+dN7t+u+bZd89Y2kIiGuOnYkdzzyUqePGsiOZrFyLMn8eK8jdxw7ChufW8Zj585kT5eu0lfjmzRl20iHuGqnwzj8ml53H7CaEZnKRw2ZDx/nrGUMyfmukvVrQS/+9Eg5m+u5sojh3FQroefjsjmhneWc9iQLHQsTNNMEnnb02YzEeOo4Vl8WFDCAQPSOXdSXxCCf323iWHZfib1S2dwtpffHzwAcPjf/EKy/RqHDskiqMlcceRghOOgyoJFW2s4clhmSv/tCnQoY/Hqq6+y//77k5OTQywWY/bs2bz99tvccsstTJw4sUM/3BUCWZ70bKLxOCJWfxE1D4rmwY7U7jChTuh+/vPDFt5bVpLc9s/TXOMcuxVXIqFoaL4AZrgGW8jovgCSgFi4DhwHzZ9GIhLC3oaYTLvPrYMkKFmWkbxpGPXmZEII8ARxutj4qN3QA65vQoOQkepF1nScqNt/O0T6Ur0oup68B5r2T1dkZxRFSWbiGpjeTf/dNEsnKRprax2ubpJqPWFMby778SCc+oe+oiiogQxi4RBOfZ8JbxrCsaiMOVz48gKG9/Jz1dEjuGr6Yhq4jGP7BnnglDE4Qrj3aQNR0xMEnHaRuWRZThomQf1Km3pDM6ifJD1BLp++hKKaGLeeMBqPIrNfjhefHcVWdJAUbNsCBIoZxVJ93Px+AcvqxcUkAS/+5sBktqzdfax6UPTGsS3V96sRqW3dDEvR0Jv0u6woqL40EpG6JGdIUlQ0XxAjXItlmciyjOJPdx9ErYx1WVZQ/e4xQLCm1uaamUs5ZHAmp4zP5bb3lwNw/4njyfXpZPtUwE4aP6gaJOrN/Pr27ZsUfpN8aZjxGLJtUIuX3768gKBH4f5TJ7CipJZjhmcBrvcGtgWxEI4niCxB3IKLX1mUJC32TfPwr7MnIsXb4UPiqdcaabg3NB9CUZN+FU3nUEVRMGUdJLnR46RZn7SFhj5u+gzY3lhpjobPhaqheQM4sRCJRAJN9yDpPox4FFX3YsfCJBJxdF0H3Z/sy4bvb2+8Oo7j3gOREI6ZQJIkJG8aphFPjilZlpF9aRixCI4Rd+dVbxDHNJNE2g4Tz1ubmz0BBAJH4NadGrLluh9EQ7mtydjW/QhJhk70oOkSE7KamhqefPJJqqqq8Pl8DB48eIeCiq5Cpk8jVlPReBETsZ1K/wghsJBS6o4ACzdXs19Wb6DlZOOYCeK1DTwPi1htqn5G83/vSliWhRVqTAE6jgPRziHxdQqa2wIbUaz2iOdsC0YUs8kxUvunk6F5KU0IsjQFHEGpIeFTZTy6hbAMyk2VDI8nOfBtSWHhlrKUQ6wuC2M5ArnepMyyLHr7NIpqXMKZ4zg4kRpQPawojeAAY/qksb48TFMF4OLaGKGETcCJQtMAqp3qkw06Dbomkk62phbActznogR4nRgVMZOqSALLcbjzwwJk4ZIic9N9GKZNwAmBZeF4gtQJD8J0KK5tJM/ZDqwqDXFgrif1PLcHI4bZhARsm4ltj61m/W6ZJlaz/W3TSDmGZVkt9mkKy2pyDN3Hovq+3H9ABsuLG8fVjR8sZUiWj0fOmEBN1CJDx82WGQrVjodAEwMv27ax60uktqKztiyMaTtURQwufW0h6V6VgwdmEGzWryJWB4pKcVikcC+qowkqowa9FXmbqqtAy2AzEUGYrpmgYTkEhEt4djxBKuM2mSSw4k0sy5v1ybZQHopTbmpkKq6IYUXcRpUFfsXAkWTKEpCpKq2vVNP9lMUdslQZx4gTJ0KloZDl9VASNcmywzhGHNu2qTRkMr1pFEVNsuwIGAmEqlFmKGR6fJTGTLI0FRyHUkMmoMtoio1kxqmwNNJ1kXIP2LaNHa5OOR3LsrDqms2rO0mObnVujoVaNyNr66UwHt5t5mUd0rGYMmUKTz75JK+++irPPvsst912W7cJKroCth7kpvcKWFUWRhaCif3TAXhlwRam5ZfucvObHnRjaF4+WFnFxa8sZFOdzafrarj4lQV8WFBCUdhmVY3Nxa8u4F/fbXYF3DQvM5Y1Lu+b2C8NWRKsLQ9z3TvL2xQZAne1zbzCCPd+sooTxvRhTN8gD81y664jewcI6AqVEYPLp+cTFt5UrYt2QJZlavHyp6l5XPPWMgzVj6kFuO+z1ZSEDC6bmsf9n6+mwtKThlbZfo3BWT6CHhUbiaveXMJl0/IICS/CG+TxrzdQFjK4bFoeFeEEfk1mZG/3Dfmuj1eysDgObZhYdXc4mpc3l7h9efGhg4nETV5b4HIqJvRLQ5EEQ3P8LNhSw8WvLuCZH7Yg+9IoM1UueX0xt3+wktK6Zg9QRSO/PMHt9aTA/Xr5CXoUqqPuNaxrJu7XYIx19YwlWLbDgAwvvQIaMcPm8qTBWMfI2Q0OxVfNWMrl0/OTfXnPJ6v5w+uLKIx3wMSx6fXSA9zxQQGXvr6YqOTlwVlr+d2rC1lXESUidKYtKeOiVxYyrzACzc0ddT//m7eVi19ZyLJyA9WXxtcbQyzcUsMrC7a63yuKofqCzC+K8c26SmYuKeHiVxby9cYQqi+NZeUmH68o4+NV5Vz8ykJWVib4bkuEi19ZwJuLCymsM9kQlrj41YU8/NWGxkxOD9qNvc6ErDMhcMjwqshC8M/TxzMyx8sL87byxqKt9Al6EE7n6BX0YC+AkFlT7r45FNbGWVHsvv09/8NGCkrqmLexEtuBjVUR12rdcTkKAGdO6sfvfzSANRVRrpm5lEyvigTJpazNIeGQ6dOQBOSm6fRP97jErv7p/P7QIQQ9CpdNXYxfk1FlgdNBozdHSNRGDSIJk5qowZ9nLEUCPJpMzLCoi5tsqopSGzPxqDIeVeaOX4yhT9DDE1+7ehU1UYO6uMll0/LoHdAprI3xy4n9CGgKMcNytVy8Cn//ZDXzNlWT6VO3aULVnSEcp34lCgzM8CYzRz8f04fTJ/bDchx+2FDJluootgPvLStha3WMFaUhooZFSSiOYTnJLFXDMTO8KrIkmNg/jTt+Poq6eoMqn6agSgLHbNKvjkNAV9FkicGZHv5x2jhM2+GK6fkkLBuPKiM6+PoqhCBq2FRHDUJN+nJFfQa3tC5OH71jgYWbBRYU1kRxhENJXYIt1TFM2+FvHyxn/wEZzK8nGK8uC3Nw/2YPdUliQ6Wbqfvre8s5dEgW32+o5N5TxiVXBN798UoOq99++wlj+HK1S6z856w1fLc+izkbKrn+2JEsrl+BVlQbZ3V9m15fuIV1FWEWbK7Gsh02V0dxkPbYe3N3Ya80IevUJnmClEdNequWy4vQ/RSHLfp62bag1C7ArjQv6g7o9u31BHj0mw18ubqcvx4/mm/XVzCricBNC6My1UNxFPr6ZYiHkRSNMkMm26skSxZttVkoGqUJmV4+BcmMU2YopHs1hGOhWVFqHB2vIqEaOybYJSkqmyMSf34zPylDrEiC/5zrrpe/bFoeWT6Nm493FVhl4TDILzBl9y3ZclwJ7VATQvB9p4xlVO8AkYTVuAKp2fjq9n3cFpr0pbAtimOCTJ+KKhxkM0aZoZDl0/iooIR/1a+eAJJOskNygi3aLCkqZYaSvB9kWaYGD7ostSruJskK1bZOQJOQE6GkGaNpOwR3ULelwWq9eV/edeJoJvXWd8jRWZZlLE8aV0xdTG3M4K6TxvHwrNUpSyV/dUB/fn1A3xZp/ga7gL99uJK8wsZSwXGjenHFUcO575OVzGkiHPbjIZnceNwoHv1qDV+sKk9u379fGneeNJZnvlvP+8tKuP7YkSwprOXD5Y3CU8Nz/Pzj1LHInUB83GPv62boEhOyfRKxOvqqRiPZMh5moNfa7UFFD7ofJMvgyGE52A58t76CQwZlpnx+0MAMZMdqnFiMmHsvxcMuSdMXoI8HiNUhSRKKP6NNszHHTNBPNyFWh20m6KMm0I06lEQIyzRJJ5YSVLikwPRU8yJPINX0renxLZN+6Trp3sY30nSvSrZPoZdfJcevUVQb456PVvDIrDXkpnnAMpDjIdJEnDRddrfVQ5Ml9svxo5uR1GXNsTpyNbNtMrPqbTS2Uj0ogQzX1E+WkRQt+d+7CpKs1JsKutfFNbfKROAk+9IxYgzwmPisCCLmkkP7qAaKEeFHg1M1FEbkBPApgspIAhQ37d9A0LUllT6qkQwyLcsinVibirG2ZZIlx5HrV380GIxtM6hofg9oPpccXJ8psyyLdF2mb1pjSUKTJcb0CSLbO7aKzLZt/JrCuNwgNTGTTVURRvZ2NWBOGZ/L/359IMeMyEGyEkiKmjRxBDeLJ9kmPxqSOrYOHZKF5Jj8eFjq9T10aBaSbXDE0FTdoIMHZyLbJkcOz8YBZq+t4JDBqcfcv386imN3eiDQYDSXYjDmCe6QxUB3RU9g0Q60MLrqxExLD/YOCEVjWYXBXR+t4JgRvTh8WDYPfu4qrsr1k/SL8zbz0eqqFMM3wzBc7wVH59cvzmdNtYmqezBUP5dNz+frDXXUxVqfwJveh6ZpusS6+kmwwbkSGqXB//LWUj5ZXY3QvG525esNPPXdphY15AaL62tmLKUinEDgygnHDItNNQluqPfUAFfka11FmMun5RNX/AghMBQvd328itVloWT7E5bNpW8spo6WgVKb40n1Mmt9LX9+cwm2J41vN4eZMjWP1eURDC3A+hCc98I8avHskuCiQUL6nOfnURiX0XQPMcXPxa8uJK80gdlkOjUMI2XecBwoNRQum+ZqfTTcEyvL6igOm1z00nwWFseQNA+2HuDWD1bw5tIyTCmVd9K0X1uDaZopD8IGx8xW4Qnw0Jfr+df3Lu8HzceMZeX89f1G478G07s1ZeHkeScs281gdMTEsQkczcez36/nowJ3qfSGygifrCjhlPG57NfLzy3vLcO0odbR2RqTOef5eVRarlmcawZZx3++25hyHfMLa/l+Ux0PzVqbsn1JUR2LiqPc/fHKlO0rS8MsK4/x13cLOGxIFr8Y24d7Pl6Rss+beYXMWFbWLtfY9qJhbP35zSXMWl9XPxaDPDhrLc/O3brXBBc9gUUPetAJkDQvL8zdhAOcM3kAb+UVYjswuk+QN353CCeM7QPAtEWFyFoqIc1RvTz85ToqwgmunbmUb7dEuOatZWysjPDo12uJJMw2iZztgax5+KCgjFVlYZ6cvZ63Cip4+Kv1fLG6nI9XlLKxOpGiNSMrGqvKI6yvrDedO3siT549ibF9g9RGDVaWhJCF4KHTx/PMuQegKxJFtTEWb61F1T1URk0Wba0B4M5fjObFCyYnCaVfr63AkbdP0pQkCUfVeeKbdZSHE8RMh4e/XMOmqigPfLaS2esquWbmEiojBv/4Ym2nTv5tnpPHz98/XUV11ODK6fnMKYwlvULu+3QV6jYM6GxF5638IqKGRZ+gzpu/P4Qphw/h1wcPYvrirRTVxrjjoxV8uIaSWAAASyVJREFUvr6O2z9cSX5hLS/N20zYkrokaFI1jYKyKF+treDDghKe+nYTU5eU8sLcTSwtqmPWmkp0r5+KSGNf3nViY19WhBN8s64SR+44x8KRFD5YVoxfkzliWDafrSxFVyQuPnQwT89eR2FNjHs/ca/Bn6fnUx01uPfT1Qjdj6J7eLWeGPvTETm8/6fDmNQvjRPH5SbF4A4dnMkHUw7j0CFZnDI+l5fnbUoKJ77/p8P46YgcTp7g7m85DudMHsC7S4uwbIfhOX5evegQTp/YD4C3lxQhKdpOjb+mkDQPM5cUs64iwiNfruWDVVU88MVaZq+rdA0Ew1aHTeS6I3o4Fnsw9rU2d+f2NmQFXpy3lYsO7o8DvDBvK6dMyCVdl1CFw+uLizl9Yi5eK5LyFimEwNIC3PReASubLG1WZcGjZ0xk0sAMKkpLd67NeoB/z9nMB8tLUjZfd8x+HDk40KK0J1Sd+UUx+qR5GORz35A3RyRM2yYUt/CoEvulSUg4FCdkCopDHDU4CEYUSdFYH3Kojhrs31tHsk2qHA+z11W0bkDX7Do29LGQleRKh0n90zlz//787YPlGE3IqPvl+Hmwk+rg20NbplRtGZW1gB7gtcXFnDkp1zWrU3RmravliP1yeOCzVSmmYgKXkzI2S8HpBM2bVqF5+WR1NU/OXp+y+YxJ/bjwwNwk76ehLyf11pFtk2rHwzft6Mu2oCgKdcLH5ytLOHVcL+oM+GRlGYcNyUKWRAuzuEGZXh45fTyq4WZN4oqfqYsL+fXkXEQ8jK0HmLq4mDP378er8zfz24P6I+IhHD3AG3lFnLX/AF6au4mLDm7Y7mfq4mLOOmAAL87dxG8P7o8EPD9/K6dOyCWgSmiSw/T8Ek4c22enfIUakDJ36f4WBmMAfz1uJIf2b8XQsRuhy0zIOgs9gcXOY19rc3dvrxAC2ePDirmTraz7XN0JI4Zt28geHxjxVicpWZaJKX7Oeq7R7Oi8yQP47YF9yMlI75Q2e9OzOP3ZHwgnmhhX/XIsiXBN619QXDOzBgEpWVGx3PUqCEg+8GRFxRJySnAiKe6yU+oN6pqa+TXdx2piSqcoCrai0zersb2a189zC4qYtqiQ3x82hLqYydRFjfLYUy8+GF+zQK0jaG4kKIRA0v1gREH1phgMSh4/qmNSGJP53asLk8e48qhhnDginURs+5orsseHk4g1ljNUDwo2ii+NE56a3WgOOCKHG44eihHpWsl5PZjBlGn5rK9INa5qqufRnr5sCklRsYXiXsP6/R3Fg11PxExKmFdWY8ej9Z/r7vgRDm8XVPGvbxuDnRd/cyDZcjxZWnL7zJMcZw3jzo5HkTQvZqyR8Cl7XNfmhv0b+rJhLKJ6sOPudsXrx3Fw72PVi8DGqR+vQvMhWa2P3fag6dwF4EnL5JQm5oD790/n3pNGYYS7ka5QK+ghb/agB7sYjuMkpdodx8GMhbHijY6nVqz1B6AkSSQUX4oTI8DURVtZVZEgYXaCQqgnwH2frEoGFeAaV723vDSV0NkUZqLRyZZ6nwkzDmYi5S3aMo0WGQ+7Yd+GfSyrRVCxOSLx6doa0LxJnsnz84uoDLvfE4pGQUWcGYuL+PHQbAZn+XgzL9VQ7vq3lpFQfB3W6oCG6+7nye83JzkFth7gufmFxNUgj3+7CVsPuMf2BHk9r4RaR+fmd1NNr/7z3QY2h50koXNbsGKpDrjCjGPIOte/vSRFzOjL1eXM21KHaK7j0JnQfLy+qDAZVIBrXPXoV2tTeDfb68umkBSVwpjMB6uqQPMlhdaem1+YckwhRPIYlmW5D3czwcZaK8WxFuDGd5YRlRr1WGzbTgYV0DjubNtOCSoArFgYy7JaWCg0jEUr1rjdjIax4xFM1ccT320m5sjuZ7qfV/NKqHI6h8vj6AHu+mhFijng4q01fLqqIpXQuQejJ7DoQQ92M4THz98/Xc3GygiqLLj3ZHdZpuU4XPvWUqqixk7VeBXdxzvLypi1xl1ud+VRw/jFGJfz8eTs9Wyo3bV1XVmWCaFz1Yx8nvxmPR+tqiIkvEyZls+beYU8890GZI8fR/Nx/VvL8OsyVx+zH3d+WJCsg992wmhUWbCxKsLdn6xG2gERI9mXxp9nLuXD5SXc+f/tnXmAFNW1/7+1dvd0z8LMMM4gsokDgoAkMcnTF8XERJ8Ygws8f2OeEkWf4qAkJO7GJS4P43MJqDFPDPDMREcEUVSMGpPnkkjiwi6yyjbDLNAz03vX8vujp2qqe6q6q3qZXup+/qGpvlV1zr3d07fu+d5z3toB1jMM9731Jc6ZcBxuXrsZb33RgVvXbQfjrsTDf96NM8bV4rbXtuJIXxgujsHDF07G8VUuhAUJN768CWyZteq2AEA73Hjmo/3YdKgHFIA7ftCI0/p3E9395hfwyZyaajqbcLwD27pCWN5f6O+iKQ245l9GAwDe2HYE7+48Bs5hMOE0gGEYBCgnFqzahGc+2odXt3cjyLgx/6WNWLOpDcs+PhhbDdKBoiiwrnLctHozIqKEUcNcWHzhZDi5mH7njte3g3ZZ71+rUE4PHnx7F9ZvP4IbV28BXBV4dsMhvPDpIVzfuhGSozyz76KzDK0b2/DBntiK0M/OPhHfa6wFADz2l91oC4BoLDKBhEIyx24+l6q/DMPAT7lw05ot+OW5EzDaIyNCOXDruu2YferxOGficeg92pm2z8qT+J1vfIHzTj4OZ472QKZYPPP3A+AZGj85bcTQ14pxePC7vx/A6wmaj0oXh99dNh0eKQCBYvH3g36s2dyG//rhJHx6sBd//OQAbpoxHidUOvsFjzvw+EWnpBUHp1gHtnRHcce6bXGrBWeOr8XMyfW4/dWtEDV9fmKtG7f/YAJue3Ur/uvCSah3SAiAx0/XbMGNZ43DhGGMbk2RZCjZLX+2Ziuu/9cxmFTDQWZ43P/2LnzthEqc3zhsoM5LtnF68NSH++HmGVz5jRGALOFPO734+KtjuPMHJ6k1QizRnxnzlc3tcYfLHSyenjMNVVQsDKSbm4XjsbVLwG8/2ofHZk2GU46gLUTj9nXb8OisU1A5BNWXGZZDW5hB86pNcSsKAHDN6aNxwYSatHQlyt+u9vZ2CLwbt762HZdMGxHTVDAslrz/FardvG7ujkKCaCxsgN18LmV/lUJGQtAHSYitUNCuCkhCGA211Rn7TFEUKGd5LF9Ef9hCdrhBAWCEkJr3QLEFQNwfcYZh4trQNB0TnWbwh55xleO+P+1SS7kDwItzT8O44ZpkUZwTNBcrRCezPBjeBZamIAR6IVM0WJcHYqDXsh0cxyEajYLmnPjgoB+L39mpvnfz907CmWMq8FlbAHf1p9QGgBv+dSxmTqgGzfEQAn0QRSFWlKqsAmI4YDipUO6lwLJs3NZghmHAlQ9D2N8HWUk45SwHJMHUpELvegopx8zhie2FjfT/mPEuUDQLOhqIO5eiKDAMk7QwmALjKsdD7+7Bh3u71WN/vPIbaj6NZN9jiuPB8GWQ+gsPMiwLxlWe1hinC8NyOBikcV3rRvXYRVMbcNVpIyCF0vvRT9RY0K4KSNHwgFDT4QFkyXDSkovvXzoQjQWBUESIoohI3zFV0yDLMsRAT1qZDfWQZRlSsDdOC0GF/aDFCDoEHhHODZqm1dWTHk08mWEYHJOdCDLuWGKq/t0RXaJDTVxkFZZl0RuRsKMzvvDVX3Z2ojeoeeCIhiAFemI/QNEwRL8X4b5jsbi8EEWk/7UlHB7s7KNAcU6IDIe3v4hX5/95ZwcEmsOb2+NXU97d2YkwaER8XnX3hyRJEHxe45UKzoldfZSqL6BZDofCLEQ+pungeR7RaBSR3qPgtDm3Q32mJhU0y6E9wkHov56iaQgyZegSHQizA+MaZNyQGT4miuX7t/yGfQOTCgCIBEFHA/BRLrUeibLj6XCYS1kbhGEY9EVlbDsSL0J8Z0cnRCa1XkSORiD4vQO6JEFIb4wzQGT4QStpG746hohMp6XlSUT5bsft/gj74iYVPM9DkiTwfCx/h95YZkvzkQvIxIJAsCk0y+JIhMH1rRtx0+otiHBu+CkX5q/ahOtaN8IrO8FxHI5JTlzfuhHzX9qIAF2GCOvGTas347rWz3EkwoFmrE0uaJpGH+XE9S9tQk8wikoXp2YpffrDfXhl82HIbI5EbA4PnvxwP256eRN6JRb3vbUTnx70ggJw9knDQQGYNXUkHvzTDny0NxYH/17jcDAUhS+O+HDbuu2QneZi/TLnxEcH/bjx5U144v/2gXVXYp+PwvWtG3Hruu0QHR6EQiEsXboUjz/+OEIha9sMaYbFoSCN61s34mevbIXg8KCPcuHR93bhgDeE61o/x42rNyPMuhGgy3DDqo34quMYlixZglBYf8LKMAx8lAvzX9qI61o3okd2QnKU4xdrt+H61s+xP0AZTi5omkaALsP8lzbiWCCKcgeLb/dns1z296/w+o6jkA0yyRYMvBvL/nEIr22JhXLOOrEGDpbGoZ4Qbly9RZ3A5RrlcxEKh9ER5XBd6+cx/YlmLK9v3YhjktNycbmhgBQhIxDsikxBlGIr4Qe9Qcx/KVaw6lggCjfPQJJlyBQV+1eW0eWP4LrWz8ExNI70heFgaUiyDKsVmmRZhpOhMLrKha8APD17Kip4Gk9/tB9/2tGBUxoqwEBErp5Rhf6n4b/u6sakhgp8dtCLX5zTiBOqnPj+hDpsO9KLb4wahg1fHcMdP2jEGaMrMXPycfjFK1tx6vGVgGTOMgqA0J9z4887u3DAG8Kebj9ESVaPZwRFQZIBSZax72gAN7y0GYGoiOMrnbHy9jJwuCeE+S9tgijL6PZHTJXRjl0T6A0JuK51IypcHA55g2BoCqIkG14j9oQNjK0ugyD6Y5oKB41nPz6AdVuPYMqIilhK+8w9zxmUJOC0E4bhtc3tmHf6aMyaXIfDfVE0r9qEk+s8oJH9FN+pECUZsgwc6gnhes1YKt/RQoRoLIoYu/lsN3+B3PusbA9sXrVRTTzl5hk8PWcaqunYvn2GjaWyvq51I4LR2I8qz9B4cvZUNDjE2HZTiyiC0pAoo1wOxWL3Dg8CIjCqxp15QrBkOD14/P/24b2dXbj2jLH4xqhh2HmkD/86phKUGIUfPMpYCgFBRhkV20pLsXzsOCNbE9dxTvz1q4FU0wAwrqYMS2dNQDjgh8/nw/LlywEAc+fOhccTC5k4nU5EIqmFoMq23QUvb4LYXyyu3MHi2f83PVZdtnUj7vn+WHjoWPbWCjqClcuXJ70XwzDohQvXt36Onv508gxF4bGLT8FYD2Vc1wWacRXkAUGtww2/SKEcYVU/VMjfY4p1xMaaFoFIAAzLoQ8OlLEAQr6U5+te04TPPM+rq1bK52Lu3LnwlJdDkIHdXgG3vhn7HLk4Br+dMw01bASiCd1LtjCrsSArFgTCEMKwHNiyckQCPshCJBYjdZVDjIRzp/5PAiVLqHI54WAZRPt1A2U8Aw/PQI7G/gDKkgSPg0EZz6gTCwdLo9LJAVJ6DweyLIMO++CmqNgKgsMNimZQKQUQCDuBskrwDAXR3wuJZsE5y7Im4KMkEWOqYynAf/fhXqzf1o4LT6kHZAGyEEY5I0AMiShnGIj9OURkIYJyRoQYtnZ/Wh64l8Jx5U6Eg0EsXbo07rgywQCA5uZmU/F8SpZQXeYAz9AI9q+kuB0sXBwNmmLh5Gh4aAFr//d/TN9LkiSUOWh4nJw6sWAZCuMqWAj9yaSMJj6yLIMK9cFN0xCVfB1hPyoYZsiFhumifgb6c76IQhTljAQx1D+p1kwAzEwAlfbt7e3gOM6wvRL+0KIdp4uuuFZ9XcYz8DgYyFHrlYuHAjKxIBCGCGUr240tH+P+mSfj5BoXegUGNzz/KS7/+kh878TKIZ1cKELN+S/FSmI7WBqiJKPTF8GNq7fgiYtPgVMMIki7cNPLm9Htj4ClKTA0FXsafmkjnpo9DWVIL/OlkkgMDjf+99M2/OMrL564dCr+8PFX+OuuLtz2/QkYUenBpwe8ePwvW/HUnGkYxmS45ZB34c0vj+H3/fkb3DyD/ceCWPr+XvDsiThrdDnE6EDiJi1W70uzHPb5KPx0zWb1Xv6IiL/tO4ojX6tD84Ib4evrTXvFQhFqXt+/kuRgaQhSrE8P9YTxyze2oTckICCzmPUf10JGrIT76j/+r+G9lPTyP1+7NRb+oChwLIVQVMK+jmNYs/J3AFJPfBILpRXLpEIh2dhrJwBmJoBm2zudTjQ3NwOIX7Fwl1egrTeEHoEBz8TCj93+CG58eTOeuHiKYbXbfELEmwTCUMG78OT7exGMivjF2i14Y2cPbli1Cd3+CJ77eD8YzjkkwjAFmWaw52hAjdf+z2XT8eTsaeAZGm09IRzpi4BmOLT1hdHeG9NUPD1nGn532alw8wy6/RHsPRqATKUvHmMYBmGZxtrNbQhEBXiDAlZ9fggHvUE89PYOrN3cjl+9tQN9YQErPt4PmbOWtCkRmuXx552xRGEzJx2HNfO+he/2Jyh654tO0FzqAmlmkRgOf9nVpSb1enHuaVh09ngAwG3rd0NyVqg/7gDg8cSyfNI0bSoMItMMdnb64A1GUeFk8VzTdCy5ZCom1HnQ1htGly+CMo5BQ3UlXBVV+MWfDkDsF8WWl5fr3othGBwLCThwLKapeOziU7C86WuocnEFG88vFSKRiDomyufCU16OXX4Gzev24YE/78P/XHYqnp4zDQ6WRntvGG19YVBEvEkg2Bcp2Id7zmvEbeu2Y/sRH5b+3x4AgMfB4rdzpgGh3iGNOUvRCCbXOHD/zJMxapgL1XQIspPGkkunIhQVcUKZjEg4iNFuHo/MmgyPg0WDQwQg4KnZ03C4J4STh7GQhPS3xIqiiDIqhN9cMhU3vrwJj7+3E/fNnIRfvr4NB71BLP84lt7522OqseA7YyClk7RJgxzsxX/98GS8ub0TMyfWIthzFDd9ZwxOrPFg5qThkINZrM0RDuCKrzdguIfHuROHQwr04MzRHji+34ipIyrgEPzIRGUmRSOYVufEPf82ESfVulGJECrKaFz2tZFw8wweunAyGsodqGEjkFkKT146FeVybEVMqYuSiCAIGM5R+M0lUxARJZxUSSMc6sOyi05ET8/AFlKfzwePx2NaC1IKKCENn29AZ6H0AzB4lclqe11kGROrWDz4w0kYUeFUx3LppVPRFxYw2g2IWdQqZgsi3ixi7OYzRVEYVluHoz196p5vmmEB3gkpmJ6oKlMYhgEcZZCCvoEiVk4PENEPD7D9e9KvfP4T9di8fxmDiydVIxoanBxnKMaYYnnQsqjaSzMcQCGuTgjF8qDiCpKxkEBnrfIm5/LgiQ8P4K3tR3DTjPHY2enDG1sHsjeumfctcBGfpSV1hmEg8y7I/fUgaJoG7XRDDgdAsTyE8EDYiXOUQYgEdftYOU8KxZacY8nGPKAMxjgR1uGCGAmp16a4WMVXURTVHx+WZcGybFo/0hTHg5Y048dysWJaFAUakiruo1kWLM0gFAqm/FGjGRYyKIjR8KC4vxazWpA4e4v075YkSZb6wmp7LcrnQhkniuUTxpKDLFOQxaGd1JEEWYSSg2I5/HP/Mbyw8QjgcINmWHSLPB79675YpsIhhmEYBJgy3PenXRB4T6zqo8ODB97djV64BiWvYRgGfXDiZ/0xd4Xn/r4P27tjfzzygSxE4n4gJTEaN6lQ2sQXJBOyNqmgOCf+uq8Hb20/gh9NaQAFxE0qAMRSLFsoNsYwDLyyEw+/txeSIzY2Ec6Ne/+0CyGmDFJC4rFoOGA4qYiwbtzz1i6EWbc6xg+/t8d0giIhHD9hkTUVbiORmIC3vr4+7QctOZowfkIUshiNjatmx4AkCIhEwqZCLZIoQBaN7ZkzZw6ampoAYCDZVgJKkicl0ZMCx3Fob29XJ1bFQLbsVPqtqakJDkd8wjBtUiwlLBKJRMDzPMRICIx20iJE4yYV2nMLATKxIBQFDMPAJ/NYtGYT/vjpIbR81o5ukcf81o14b2cXnvnbftBO/QJHObOprAILV2/Ghq+OYdErWxHhPLj99S/wt71HseDlTWDdlXHtKacHd7/5Bbr9EXgcLJ7591Nx8nEeSDJw22vbwLmGJvlOIcEwDMI0j8Xv7MRwD4/Lvn4CnvjLLgDAaaOGYfGFp8QSFHmDePS9PaAMilglwrorMf+ljXh/dzfufWsHwpwbN67egg1fHcPP124FU1Zh6jq0qwK/eHUb/rH/GG5avRlhzo373voS7+/uxvzWjWASxrjUUASFzc3NmDt3rnq8trYWLS0tsSROBom9FNFiYptQKITHH3886bmFRigUQkdHB5qamnDllVeqx+fOnav2j9MZn/xL6Tu9fmtpaRk0uVOTYiX0idFxq22GEjKxIBQFoiiijJFx+TdGAQBe/OwQ5v7hUwSiIuo8Dsz91ihQ0aH9UglBH+74wQSwNIW9RwO4eNnH2NbeB5oCbjunEZFAfHhGDvnwq/NPxrgaN56eMw0nuEQ8dMHJmNJQgUdmTYbQH06xE6IowiGFcde5EzDxuHK4OQq/umAy/mVMNS7/xgmYVF+GJZdORWOdB4vOHgfZZA6JSMCPW89pBAXgs4O9uGTZBhzyBuFgadz+g0bTNR/ksB+3ff8kNfviJcs24NODsUqkt5xzEqKBwi0YlQ20gsKamhr1advvH/Db5/OpT8vaVQqttoBlWUhSLLlUouagkJ60E1H88fl8aG1tRUtLC9zugcmtkQgWGOg7j8ejrlRo+62vrw8cxw3qF5/PZ3hcuwKk19eJbfIF0VgUMXbzmaIouIfV4u7Xt+GDPQMFjtKJv2cLluexsVPA7eu2qcduOeckfOcEN8TI4IkOwzCgXeWQgn1qQSbaVQ45EtJNPJQ4xhzvgBCNDBScYjnIkmh5uxnDcZDFgfOUtMBSnrYFUpwDYDgg7EdlzXD0BkKgKQpioBc0w4JylEEK9lnyk+VdWL+7B0v6RbIA8NhFp6CxkoZgoQopy/HY1SvjptUDIawbvjMW559UFafTSJdi+R6b0QwA0G3T1NSElpaWpOdmow5HttHzWeuLGbslScLRo0d1/TfqFzP9Bej3tbZNtvuUaCwIJQfNsDjcE8KnB7xxx1/45KC6jW4ooSgKUdqB5/95IO74S58dQoTidMMaoigi6vOqk6BYQaLepNkMVTgnPu+MQnLEQiY0y2Ofn0KoP/Zv2m6Wx96+2Hk0TYNhWRyTHOgWHZbrfmQLORqG3J/VsIxnIQf7IPhjxcdEIQrB32NpUkHTNEIUizWb2uKOr/zHfkQZh+mQE0VRiDIOrPjH/rjjr2xuRwjskP8YGukWhgIzfVaMoTyrfSoIgqFOQg+n04nq6upsmFo0kBWLIsZOPivFka5q+UwNf5w6shJ/+qIDAHDlaSfg4lNqhzTBFF1WgVte266GPy6ZNgJrNrVBkGSMqynDby6ZAtHfk9E9lDE+3HkU7+/34ZE/78Kk+nIs/uEk7O4OYNGaLaj18Fh66VQ4TSTKoVkOu3uBRa8MnBeMiri+dSNkGbFU3kw4fysXWfpM02WVuP6lTTjYH/744Sn1ePnzw5ABfG1kJe49b0Ks2muq67gqcM9bO/DJgVj445JTR+C1Le0ICxKOr3Lht3OmQsrSGJvxWfsEPdRP+Xopp4GBxF5OpxOyLKulwTmex4r+NjfdtBCiKCAqAe3dXqx98flB5+Zr22qyPtVuGc0k9TrP8xAEAV6vN+46NTU1CIfDoCgKfX19alIso+Pa+wLQTwFu0TYrkJTehJJCFEW4nBSmn1CFnR0+LL10KlysjDoPj5c3tuGsk2rBiNGcFa7SQ45GcNHUBuw44sPiCydj0nAHZpxUi5te3oyZk+shZvFLTckSjit3gqaAbe19mPfC5+jyRSDKMqpcHFgK5n6IZRkVLg48Gyskdu2LnyMclRCIiqh183BxDKjCSuKXFpIQxXkT6/C//zyAJZdOxUgPg6+NrMKdr2/DRVNHQDaZe0OOhjFrygh8drAH951/Mr7WUIZzJ9ahedUmnDexDlIB5hDIFYpmAIBuYq9IJAJJkuLCBAr7jvrx4AdH0N4bwm/OH617biGi1UkoKDYr75shGo2ioaEhVhNHc51oNDqoT5MdT7yvmTb5gEwsCEUDFfbhznMnIhCOwiH4IQZFzJ5ah0umHQ8u6ocoDl0xHgCQoyF8a0QZVs/7FqhIANGgH6PKOKy6+lugomEgmsXVEzGKCcM4LL5wMn6xdiuO9MV+GE+sdeO/LjgZTMSc8FMSBdSxwJOzp+K6F2PlrQFgWBkXS88tpZeeu+CIBPDDk2sw85R6sJEAosEQTqlxYM28b4OKBiFHTU4shDCmDe8/LxJENOhHg4PDC1eeBlqKqvlUck1Wki3lCIfDgXB/GXatfYmrZ+29safrOk/q8EGuSVx9USiUPi12SCikiLGbz4q/7e3tcX+0aJrOa678xPtn0x7tGFMMh129En66Zov6/oQ6Dxb/8GTQYfM7ShiWxVGRxzUvfI6wELOzxs3jt3OmmQqn5JJsf6azNTZDNcaKz4mFrvQKVAGxvAgsy6KhoSFrf08TkzMla6PYv2TJEkPbKqqGYWePhNvXxypzrvqPqeAhQBQES0W8AGtFv5K1TyVEBeLDIpnaoKxYdHV1GV7HqN+tjEeuJ0NmQyFkYlHE2M1nu/kLaCZTnV3Y3Stj0ZotEGUZNW4exwIRSDIwqb4cD86caGpywTAMjkoOXN+6Ef6IiAoni7AgISxIOK7cYVqrkSvsPMZanxPj/srkQi/Wv3TpUixYsCBrwknl3mY1HMbai58gSDnQFaFwx/rdqHHz6PJHQFPAr2dNQWNF8hLsifYA5ndhpGqfSi8CZLZikWgDwzAl8bkmu0IIhBJCBIM9XX6IsowJdR78vmk6Fl94CmgKONIXRlQyp8iXKAYdfWEE+zUVz152Kn47ZxpcHIOeUBR9YaEolf2ljlGsXyGfP1Z6xbMAwF1ejrveO4Q71u/GAxdMwvLLp2NSfTkkGdjZ6YNE5694lpHNVgvBEfQhGgsCoQighRC+N64SVWUTMX1EORDsxYRhHP571hSMqHTAJQYgmlhlkIUIGofxeGTWKRhZ6UKZFEAZS+Gp2VMhyUAdJ0AUSkBjUaSk0lJQFKWGGJT3Etuk86StpznI9JqULGHppVPx1dEgGocxkAK9eHDmRPzjQC++21gHv7cLRtMhqxqIQtFMJBu/9vZ2cBxniwmLpVDImjVrsGHDBhw6dAg8z6OxsRE//vGPMWLECMs3JqGQzLGbz3bxl6IoMK4KyGE/JEmM+XysDxDCYCgKoiZBFs1yoGTJsuAy8Twlf4U0xALYROwyxlq0PouimDT2v2jRIoTD2SkMpo3LZ0vDkagtEAQBFMNC7E9IRlEUWN6B4TXVaGtrA8dxanuXy4VgMKi209NtGPmZiWZCe99MJx+ZFB6zylDpKrTkJBSybds2nHvuuXjggQdw5513QhRF3H///QWTn5xAKHYoioLAe3Dza9vQKXBgWA5dvjAeeW8PdhwTIcpy3A+uJETT2sWReJ4kCnmfVBBSo+y+yAZm6kuwLIuWlhbT99WGGJTtp6Imy6ksyxAiA9fS1hMJBoPq61xPKhPvW4zhj0KrD6LFUijkjjvuiPv/DTfcgHnz5mHPnj2YNGlSVg0jEOwI7fTg1+/sxtb2Psx/aROeuGQKVv7fF/hgTzc+2NONNfO+DVoU8rpzg5A7lMJVyQSFwECOiGwlRlLua3TNodbdOByOQfYAxuJKPfuTtc8VycaPZVmwLFtUk5d0yUhjEQjE9nBrxS8EAiF95LAfPzv7RDSv8scSWL3wufrerec0goqGyKSihDGbkCndxEhGWgoFp9MJh8OBpqYmeDwe9b2+vr6s/UBry6azLKuWX+/tHciE2t3drd6vvLxcPW7kZ6rkXQrKdydXOgyj8WMYBvX19WpW0nTJhRYmF6S93VSSJDz88MPw+/341a9+ZdguGo3GxeYoioLL5UJnZ2dcFrJMoSgK9fX1aG9vt1Vs1k4+28VfhmEQYt24ZNnH6rFZUxpw7bePhxAs7WqadhljLXo+a7UHSh4EPZR2ydpoSaXhaG5uTqlvULZPpovWhlTFtoABXQmQvC8UjPoule9A5r4Z2SAIQlY+12bGLxv2G8GybG5Tei9btgwHDhzAfffdl7TdmjVrsGrVKvX/Y8eOxeLFi00Zlw719fU5uW4hYzefS93fLl8Yj731Rdyxt77owMWnHo/G4+rB0KW/HbTUx1gPt9utltV2OByora3N+j3a29t1jysiTeVHMBksy2Y0PkY2GCGKIkaOHJn2/RQOHz6csk2mvqVCe+2+vj51vN1ud9zKjBGpfMi1/WZJa8Vi2bJl+Oc//4l7770XdXV1SduSFYvcYdZnp6MC0cjg9zmeQiicuhBToWAHfymHG7/54Cu8vaMTAHDlN0dh/fYjONIXhotj8Icrvg4+mt/smLnEzt/jgwcPDkqqlG20T9KJWgol0ZbD4UhZ3CqTHX0cx0EQBAiCAJZl0dERKyTIsixWrlwJYLA2Ihs7CFmWHSiQxnFYsWJFzu6ViN7nWrv6YHa8FR+qq6tzMjZm7p/1FQtZlvHcc89hw4YNuOeee1JOKoDYAHIcZ3i9bCMnqObtQCqfoxEZa/64f9Dxi/7fqKLsq1L2l4qGcPnXT8CHe47iphkn4l9HlWPW1BG47sXPcNb4WjCyWBq1PFJgx+9xIrnwX0+LkJhoy6hNtopbKemt29raIAiCbtGyXBTTCofDQ3YvI5J9rs2Mt+JDc3NzwRUe02Jpu+myZcvw/vvv46abboLL5YLX64XX6y0YZwiEYkcSBdSwEbx01TfxL8eXQQwHMHJYGf7nslNx+fR6IFzaGgs7oogZEwWFkiRBkiTwPJ+T+yoiTeV+Q3XfoYTnedUfowJpQ73jRbHJ7Hjr+aA9txAz5VoKhcyZM0f3+Pz58zFjxgxLNyYJsjLHrM8sXWH4BC9IhR0a0GInfymKgizLeftc5yP5DmDP73GqpEqJ4kUz46EdPwC6YynLckqRppI8y2xBMjP2acfYKEGWUdIqo8RWRu2N+labvTSTAm5mE21ZSYKWmEQrlQ+J9ufyu2s2QZalUEhra2vaBhEIBPPk+0dVSb6TzUyBBH2cTicWLlwIr9erm38hEolYKsIFxI8fAN2x1MsVoZf3IVXiKG3WTqufF23YJRwOq6+VZFmJ10y8l9Frpb1RfouysrKs5OhIZo9RP5jJVaLtb6s5Rgrhu0tqhRAIBEIeUTQHWjG7Nm6eK6FurrUUhYCZ/BZD7afZXCWJ7ZV2qdoXAmRiYQM4PhYG0DsuFF422Iyxm7/ZoliS79iFVAXJEsdDO35K4ilJktQtjdkcy1RFv7JVCE3xRZl0aRNqMQyjm1xrKD6z2rFRQhLJbMjlTg2tPcq99WwY0pBmugmyMoVoLDLHbj7bzV9gaH3OdQElM7F4O49xV1dXXP8YFQZTSBwPrWYiVeKpxHOtxuUz+awYjbHRNbW+GL02Y4dVLUgytLaa6WuGYQb5nI49RuM0VMXPcqKxIBAIhHTJJBZvB6LRaEbL25lMxMxoKYodbUihEPxMx55iGSdbr1i4nBWI6owPxwPBUP53DxjZ53CyCIcGlgaVZUKt3YXuWzrY+Wl2KHxOXE7NRnErLdqnKqOJBRnjAZ+1y+1mRH4cx6nJk5TEU1VVVfD7/VixYkVWx1Lvs6K1L9n1U/mbeM2FCxfGhUKUhFrDhw9HZ2enetwouVauQyGBQEAtMJYswVcuP9e5/u4qkBULE0QjMNyWWAgY2zcmpd2F7huh8MiFSCwXsXi7YEbkp+3f7u5utLS0qD8oLS0tWLBggZoqOpuCv1SiyHSub3RNQRDU44kJtfKV8Eo7NsoEKJkNuc41UWgCT1uvWBR6vgNj+8ZgzR/36RwfsLvQfUsH8jRbfHksrMZ+yRgP9jlZLN5MjgOKonKakySTPBZGY2x0Tat5LHJNOnkshmrlMZ95LEiQk0AYIrQZ9Ao9q2Ehx3KN+rGY+tcKylhYGY/W1la0tLQgHA7nfCzTsS/da2qPK3kvEl8n7pTJ5WfCyJ58fm8K4btr61BItigmPQPPs0CkQn0dicT2yFMUIMux10ZaDTPt84WZMcj3ONlRvGiUoMhMLN4Io34k/Zu72HqxYsfPRCFAJhZZoJj0DJGIpNp6zszj8c7rhwa1MdJqmGmfL8yMQT7GKZXGACjtP/7ZisWnynFAURTp3wKIrRcCdv/OFQK2nlgUeiIlI/scTlo9nrgrRLHb6NwCrFdT0hjlIlCe3AHyJGUGo37s6OjQzSFA+te+kO9c/rH1xMJo+bsQJhVAEvsCsX8pikJd7YAgSGu30bksXZFtM22FGbGWtk221ODaYk1Wn7aSieusCr0yEYZpl+2zJbarqND/PGuLTDkcjpxnPiwElP4txKfxxM9goZLNJFp2xhYTC6uxdTP5I7R6g2zZlCzWb2RTX28EDFUOJPx+WdUNnH3uCDCschFanYBQlP6sXtue5zkgUtXffkB7oe0vq7YZ+RvwZ/8HQqs70aJnp5miQ9o2ixYtSqoxAMz9AcskVpzsXKsFizIpcKRdtjcqMmWEkVbD7XaraZ05jsOKFSsAALW1tXjqqacAAAsWLCjI0tLZphBEe0YkfgZzSSpdj9JGr5+IJiM72GJiYTW2biZ/hFZvcM7M47NiU7JYv5FNFzeNzYpugGEpA/3EmJTtjbUXqfNtGGHk7+z/GJfyXKtodSdasqG90FZsLJQiSMWIkVYjHA7r5jJITHVth4kFIUYhFh6zG7aYWOQaUZDVyUV5BQdgYLdEoYRVFLTaC55nDScOCjxPW2qfL8zoZdLVnegVdwLiCx9ptS5GgrF0ftw4jkN7e3taIjSjIlY1NTUIh8Nxgkezxa0yKXBkJlmWmZCFw+FQx6Cvr0/3OBHt5Z9k493e3g6O4wpiLIjYM/uQiUUWeO+tw+prbQKqQptUAPFL+1r7jLQXkYig60+haTXM6GXS1Z2Ew+GUBYfMiAi1YRGjP1SJMV49IVriNZWy2no6D71z9XxIJmxLZUOyglbK+UB8P+pdSynWlIpIJKLb136/PytCThJnzw6pRJRDEWrQhkXMhD+0BAIBtX1DQ0NO7Sw1SACJQEhBtjLlmUmgo/yRW7p0qfrjluqaeu1DoRCOHj2aFbutovgQCoXi/MlWPxpdR7uClAlWx4BQuGSSvItlWbS0tKiJxgjmKfoVC63Ir+dYGAxVCVDxIkKtANFIpBgvNDRqb90mBbNiSiPRopGIMhcY9ZEZH6z2V679NSPENR7vmO8UF1KX2Ovq6tTX2qJDdXV1WRFpJuJ0OrFw4UJ4vV7dayYLrwiCgKampjhR49y5c1FTU4Pm5mZQFIW+vr6USZWylYTJ4XCkTJZlJhRiJM7L1RgQ0iOZiLKqqgosyxbEWBjZWVVVpbYhGh1rFP3EwkwCp3+/cpwmtk4b1NkYEBqefe6IOM2E0l57vKbWqeoNErNQmhFm6sX7jc4d7MMATheTlVwc8fYY9dEo3faJ2gu9/jLSnqTjb9jCxhAzQly98f7O94chcjT2Ry8S8eOPL8SW2OfPn68rFtQWSkpHMGakhwBiExilkJRyTSVMotUYaPUTPp8Pra2tqs0K5eXlceW5zSRVMpOESU+HIkkS/H6/ep3u7m7Vdi3aZFlm/oCbKVZldQxInD37JBNR1tfXo62tLV+mxaG1s6amRn1w0H52+/r6yOfAAkU/sTCDVidgRhsQr5kYY3Bcv5iXEDJ3D70n/2Tnan1QoCgK5RUN8Pm7By0PW9V3aO2xar+R9iIT7YmRv2VuD3qynH1bb7wpOoIVK58e1Larq0v9w+NyuXTjt2biuomY0VJor2lFP6G1mef5uFUBq7kPjNpr7THSoWhj6+n0UTJ7Eq9j9fokqVJuSRyPQsVIv0M+B9awxcSCQMgWyioAMPgPjCKiBKB73Ixg0wjttlWjH8k5c+agsrLSks1mch8kJsXSa291qVj7lJjJ05/RdbTHgYExMKqIadV+q9U3jdqYDf8UO9rxKGR/7VJRN9eU7MTCasKnfBKvAyg8+7KNkb9m9C9aHQ3vYBAJiwCyXxRNlnhcecX1AJRQSExLYRSvz6QwVigUQkdHh64ewuPxgGXZQfFo5QlQGxOura1VVyd4ns+axsBMUiye59HU1ITq6mo1FFJVVQW/3z/In6FeSk6W1CzdRGZmx9tMGzM7YQhDQybJtQgDFP3EQhvrdzg43Vi/FqPYvbb+hpnjyTQMRvoJo/ZaHYAZXUKuyaSGiplzk/mbSv+SmJgsVVE0M/VWUh2PCpz6WhuvBwaehPUKYyUe18brtU+zyfQQNE3rxqOVJ0CPx6Omr/b7/YbXUZ7IMyn6lag3AAa2knZ3d6OlpUX9A9zS0oIFCxYM0oYMVWZIo9wjDMPo5iFR9B8AdG1WMDPeynhor5+of9Ee93g8BZXXwc6Q5FrZoegnFsqTKUVRqBw2UDfDilYBGKi/Yfp4kh9YI/2EGQohJ0YmNVSsnmukZ8kWqeqtmDkuQz91e6aFsfTO9Xq9A/c1uSyrbItLdh2rS/1mclcAMNR5KPfNF0aaj1T6D8A45bTZ8TbKE2I1fwiBUKwU/cSCQMg12RIamkHZIgrEtmcqT8LJbKuurk55nVzEtY0mK8q9HQ4HKIrKSt/lOmmVtmiZVpRrRodhVAiNUNwM5fe+1CATiyHEKL+FXp6FUqOYdSRGAsFM8ilof7y0bcrKyiylAI9EIrp6C6vXScRM7gpZllVdhV4biqKyJtK0WhxKa782FDJ8+HDdPCSJRcv0iqUZ6TC0hdB4nkcgEEBTU1Pc9bX5Q/Q+E3o6GkJ+ydZn146QicUQYrXwWCmRmG+kFMgkn4LZ4mRW8jpkMyZsJneFJEloaWlBc3OzqXwYQ4nWfkEQ4nKPpFu0zGjMEguhabfVKng8HjV/SOI4MQxTUHkdCIRMKdmJRSYCxKGmmGzNBkZF28wIKrUCXd7BGCYpK8V+KwRSiTqzmaHQTNGyTCYvRsXMzIhvjc7VbjkmEOwKJedJYdXZ2ZnVuC9FUWhoGBBvFiIsXaG7YqEnJjVDMfisoOc7YM3/QvbXTF4Ds4XHtG2s+JypDiExX4XecSMBo6JRaGhoyOh7zfM8BEGAIAigKApLliwxbGtW7GiUW8Lo+kYCTy0LFizQPVer1Tj++ON1c2Yk5reIRqMF+7nOBYX8Pc4VpeIzx3EYPnx4ynYlu2JRKBSztoBgHjOJmox+6HOdLMosRvkqtEmxUmkvMl2x0E5cUuWWMDt50vaLNpzB83xGNUdSaWRkWdbVamj712y4i0AoJsjEIseUoraAYF/MaC+yRSodSqb3GgqNDIFgR8jEYghRtAX5SnyVT+ymIykWzCTCyvUPpBktRbGWLzcqLqft30JOcU0gpAPRWOSYbOsqtBSqz7nCbv4CufdZkiRdzYRCqjTeepqMXNignVzkYrKTK42MGd8YhrHV55p8j4vX55xpLLZt24ZXX30Ve/fuxbFjx/Dzn/8c3/zmN9MykkCwK1Z+sBLTPWdTpJnJSoCZAmZmMNIYaIWQAHIaYrBazEzb7yTfAYEQj+WJRTgcxpgxY/Dd734XjzzySC5sIqSJXgIuIDtFuQjZxWxxsnQLm5m5d2L5cqNEWEMRClG2bmqLsGmTVhVCuut0+l0veVli/5JQCKHUsDyxmD59OqZPn54LW0oSqwXJMkEvARdgnyRchU4qLQEQX9wrsU2yIlbKuUaTACMthYJyX2BoBIhae5QCZkBsJ4gyyfD7/Wr7bOWuyMTOdPrdTPIysiuEUGrkXLwZjUbjZuQURcHlcqmvs4VyrUL7kobCfYOOieHs2GnF50Lrl3Qo1DE2i1EOCCvFvVIVsTIqwW22qJhCrvvYqC8CgUDSImFDXWbc7JhZtUnbv8X+ubaK3fwF7OdzzicWa9aswapVq9T/jx07FosXLzYlAEmH+vr6nFy3kFF8PtqlvwzCsizqahuG0qScUqxj3N7enrKNVlNgFZZlDfsm1b1ZloXb7cbChQvhdrvjSofnAiN7UgnbkvmYC8yOWSqb+vr6sHDhQgAw7N9i/Vyni938Bezjc0a7QubMmZNSvGm0YtHZ2ZmycqMVKIpCfX092tvbi1p1a4VEnxmq3DAUIsqDV06KjWIfY47j4pbVjWLuwEAoRNtGGwqxGq/Xu3c+Y/1GfbFw4UI162ZfX59uUqxCsDObfVfsn2ur2M1foHR8Zlm2MDJvchwHjuN038tFB8uyXNQDlw6qz0lW2UqpT4p1jI0SMulpGvTi8kZFrMzoIYYysZUZkiWnGjlyJNra2nKSFCtbduai74r1c50udvMXsI/PJEFWCUGSUBEIBAIh31gOhSj76gHg5ptvxhVXXIFTTjkFHo8HtbW1pq9jlwRZucRuPpeSv1byWLAsC5Zlc5LHohDyLiT6U1tbi7a2trgwRCHYmmm/G1FKn2sz2M1foHR8zlmCrN27d+Pee+9V/79y5UoAwFlnnYUbbrjB6uUIBFtitjgZwzCor69HW1ubpXPN3DvfP9QKWn+0DxuFlniq0OwhEAoVyxOLyZMno7W1NRe2EAgEAoFAKHJIHW8CgUAgEAhZg0wsCAQCgUAgZA0ysSAQCAQCgZA1yMSCQCAQCARC1iATCwKBQCAQCFmDTCwIBAKBQCBkDTKxIJQUPM9DkiRIkgSe5/NtDoFAINgOktKbUFJoy1w3NzerCY0IBAKBMDSQv7oEAoFAIBCyBplYEIoebfjD5/Opx30+HwmJEAgEwhBDQiElTK6KJhUa2vCHluXLlwMgIRECgUAYSsjEooSxi97A6XSiubkZQGyVQplQzJ07Fx6Pp6QnVQQCgVBokIkFoejRVp30eDzqcY/HU1BVPAkEAsEOlOYjrM1RNAd6egOiOSAQCARCLiErFsifFkF7X5fLhWAwmJYNHMfF2a+nOVDCA0B8WCRT35XzCyXcoA2LZNKnBAKBQEgPsmKBAS3C0qVL1R/Zob5vMBhM24ZM7M/Ud+X8oey3ZChhEZqmM+pTAoFAIKQHWbEoQZSndj0ho/I+eXonEAgEQi6w7YpFvnIfaO/LsiyamprQ1NSE3t7eQTaksoPjOLS3tw+yX3k6Ly8vV48rQkZFzJiJDkOv7wpFw0FyWhAIBEJ+oWRZlvNx487OTkSj0axdj6IoNDQ0oK2tDWZckiRJN/eBQq62Z2rv29TUhJaWlqTtk9mRyodFixYhHA4DGLxKkYn/+eo7M2OcL9tyhdXPdbFjN38B+/lsN3+B0vGZ4zgMHz48ZTsSCklgzpw5YNlYt/A8n5eQgdYGrQAxUYyYSjcQDofVH9FSCH0oKzSiKFoWZhbCuBIIBIIdsO2KhXY3hFaLMH/+fDz11FMAcvN0q70vy7Lo6OhQX69cuVLXBm2SK+1rp9MJQRDg9XotaykUO9LRYej13VBoOLSrEYl9obfLZSjHNVeUypOOWezmL2A/n+3mL1A6PpMVixRokyrV1NSgqakJAOD3+9U2Pp8v65kbtfcVBEENhSxatEi1ged59TXDMOrrRB0GEJuQ6GkplHulskMvoZTZc5VzrJxrlcSJmFFfaMcpH+NKIBAIhBi2nVhoCYfDulqHoaw1obVBq70w0mEk5qUoVbQ5OVL1ReI4FcK4EggEgt0omYmFUfzdzGuKonSvqY3LOxwO3Zi+1SRXie2VSYGRDUYk6jBSJYUystPo3MTrAFATYSW+VsIyWn+1ibOSnauXXEtrq5l+MdJPWB3XZP6TlQ0CgUAwR8lMLBILbll5vWjRIt0iVrW1tYZaB+VJN9l99Z6GjQqD8Tyv2qBd8h8+fLj6WqvD0Nq2YMEC9TpKUqh07Ew8V/saAJYuXTrotXYLq56fyc5NvI5eH2lDRHV1dSn7Qnsth8NheVytjCWBQCAQBkP+WmJg90Si5mAof0y0GSMV7UVLSwtEUVRfV1RU6NpWzGKgVCjhjJaWlrh+MeoLLdo+zde4EggEgt0o6hULI2GfVvBo9NpI/CfLctxTsV57lmUhCELSNso1gfhl9cSkTVZ3UijL+H19fXHXqampQTgcHnR97XHl3EQ7jdoor5XEUk1NTZAkSRVCJtoPYNB4JJ5rdB3FBoqiDPtUkiT1tcPhUNsk9oWeMFPb3sxnxWgsSViEQCAQklPU202Nkk2Zea1Hc3MzKIrCkiVLkra/7rrr8Nvf/jZpG20cv6amRr1msnvrbZnUi/vrJYAyssOM/7nuOzPXsdKn1dXVcfcysidxVSdd25JdN9eUyjY1s9jNX8B+PtvNX6B0fDa73ZSsCSdgZtDNCApZllWX7a1+kLRL+NowjZJFs1DJ5Atjpk9bW1vVH36r9yrmLzOBQCAUE0W9YmGUbGr48OHo7OxM+lor/lOSO+kt5+slsFq0aBHa2toAxASFqZJcadtzHIcVK1bE3Rcwv8RulNhKG84wOh4IBMCy7CDfjdqEQiGwLIuqqirwPI+Ojg5UVVXB7/djxYoVg+yXZRltbW2orq5W+y7xXKPr1NTUpOzTuXPnoqqqSl0J0kuEpR1Lo50mZj4rep8PK+OULUrlSccsdvMXsJ/PdvMXKB2fbbFiYUbwaPRaK/5TkjtFIhHDayq7M5qamtDV1aUej0Qium0ULUFTUxM6OzvVNm63W71veXm54a6KVD4nJraKRqMpjytP/Im+G7VRXmv7gqZpNSFX4mSsu7sbLS0t8Pl8anuGYeLO1b6ura1FU1MTPB6Pem5inybaWl9fj2g0aijM1I5lJp8Vvc+HlXEiEAgEu1LUKxZaeJ6HIAgQBMFyHgszOSeMYvpGWgoz2ouGhoa0+0CxTVld0MtXYTXfRrI2gH7+Ce3WUD0/GxoaQFGU7rnhcDhpn1ZXVw+yo7a2dtCs38jnVH2X6Gch5rEolScds9jNX8B+PtvNX6B0fM5pSu/169fjtddeg9frxejRo3HVVVdh/Pjx6Vwqa0SjUXXgtMW3zLw2+tHQpofW7kjQ0traqr42kwEzsX26QsBIJAKGYVBfX6+GEBJt1vqlPW7ke6o22if2VE/vip96uS6U10ZfMO25WjuMJmFGPhth5KfVzweBQCAQBmN5xeKjjz7C0qVLcc011+Ckk07C66+/jr///e94/PHHUVlZafo6+S5CZhWj4lba+Lv2KddIn5HNeH0hzIIzKUiWqk8Tzy0Ef4cau/lsN38B+/lsN3+B0vE5ZxqLdevW4Xvf+x7OPvtsjBw5Etdccw14nsd7772XlqHFQqqYfuIODqNkTqUWr9frF7M+WtVJEAgEAqHwsRQKEQQBe/bswaxZs9RjNE1jypQp+PLLL3XPiUajcSsTFEXB5XKpr7OFcq1sXtPqvXPVPtV18uFzKjK1Se/8QvY3V9jNZ7v5C9jPZ7v5C9jPZ0uhkKNHj+K6667D/fffj8bGRvX4888/j23btuHBBx8cdE5raytWrVql/n/s2LFYvHhxhmbnl76+PjVrpNvtjitbrtfG4XCoOSiM2pcCis/p+GimTwkEAoFQ+OQ8pfdFF12ECy64QP2/MmPr7OxU02JnA4qiUF9fj/b29iGNYfl8vrg02nooOgKz7c2SL59TkamPRucXqr+5xG4+281fwH4+281foHR8Zlk2+7tCKioqQNM0vF5v3HGv14uqqirdcziOA8dxuu/looNlWS7qgUsHu/lsN38B+/lsN38B+/lsN38B+/hsSbzJsizGjRuHLVu2qMckScKWLVviQiMEAoFAIBDsieVQyAUXXIAnn3wS48aNw/jx4/HGG28gHA5jxowZOTCPQCAQCARCMWF5YnH66aejt7cXra2t8Hq9GDNmDG6//XbDUAiBQCAQCAT7kJZ487zzzsN5552XbVsIBAKBQCAUOUVdhIxAIBAIBEJhQSYWBAKBQCAQsgaZWBAIBAKBQMgaZGJBIBAIBAIha+Q886bhjdnc3DpX1y1k7Oaz3fwF7Oez3fwF7Oez3fwFit9ns/ZbLptOIBAIBAKBYETJhEKCwSBuueUWBIPBfJsyZNjNZ7v5C9jPZ7v5C9jPZ7v5C9jP55KZWMiyjL1799oiD7uC3Xy2m7+A/Xy2m7+A/Xy2m7+A/XwumYkFgUAgEAiE/EMmFgQCgUAgELJGyUwsOI7DpZdealiivRSxm8928xewn8928xewn8928xewn89kVwiBQCAQCISsUTIrFgQCgUAgEPIPmVgQCAQCgUDIGmRiQSAQCAQCIWuQiQWBQCAQCISsUdyJyzWsX78er732GrxeL0aPHo2rrroK48ePz7dZGbNmzRps2LABhw4dAs/zaGxsxI9//GOMGDFCbROJRLBy5Up89NFHiEajmDZtGubNm4eqqqr8GZ4lXnnlFbS0tOD888/H3LlzAZSmv0ePHsXzzz+Pzz//HOFwGPX19Zg/fz5OPPFEALEEO62trXj33Xfh9/sxceJEzJs3Dw0NDXm23DqSJKG1tRXvv/8+vF4vqqurcdZZZ+GSSy4BRVEAit/fbdu24dVXX8XevXtx7Ngx/PznP8c3v/lN9X0z/vl8Pjz33HP45JNPQFEUvvWtb+EnP/kJnE5nPlxKSTKfBUHACy+8gM8++wwdHR0oKyvDlClT0NTUhOrqavUaxeRzqjHW8rvf/Q7vvPMOrrzySsycOVM9Xkz+WqEkViw++ugjrFy5EpdeeikWL16M0aNH44EHHkBPT0++TcuYbdu24dxzz8UDDzyAO++8E6Io4v7770coFFLbrFixAp988gl+9rOf4d5778WxY8fw3//933m0Ojvs2rULb7/9NkaPHh13vNT89fl8uOuuu8CyLG6//XY89thjuOKKK+B2u9U2a9euxZtvvolrrrkGDz74IBwOBx544AFEIpE8Wp4er7zyCt5++21cffXVeOyxx3D55Zfj1VdfxZtvvqm2KXZ/w+EwxowZg6uvvlr3fTP+/eY3v8GBAwdw55134tZbb8X27dvxzDPPDJULlknmcyQSwd69e3HJJZdg8eLFWLRoEQ4fPoyHH344rl0x+ZxqjBU2bNiAnTt3YtiwYYPeKyZ/LSGXALfddpv87LPPqv8XRVG+9tpr5TVr1uTPqBzR09Mjz549W966dassy7Ls9/vlyy67TP7b3/6mtjl48KA8e/ZseceOHfkyM2OCwaB84403yhs3bpTvvvtu+fe//70sy6Xp7/PPPy/fddddhu9LkiRfc8018tq1a9Vjfr9fbmpqkj/44IOhMDGrPPTQQ/JTTz0Vd+zXv/61/MQTT8iyXHr+zp49W/7444/V/5vx78CBA/Ls2bPlXbt2qW0+++wzec6cOXJ3d/fQGZ8miT7rsXPnTnn27NlyZ2enLMvF7bORv93d3fJ//ud/yvv375fnz58vr1u3Tn2vmP1NRdGvWAiCgD179mDKlCnqMZqmMWXKFHz55Zd5tCw3BAIBAIDH4wEA7NmzB6Ioxvl//PHHo7a2tqj9f/bZZzF9+nRMnTo17ngp+vvPf/4T48aNw6OPPop58+bh5ptvxjvvvKO+39HRAa/XG9cXZWVlGD9+fFH63NjYiC1btuDw4cMAgH379mHHjh2YPn06gNLzNxEz/n355Zdwu91qKAwApkyZAoqisGvXriG3ORcEAgFQFIWysjIApeezJElYsmQJLrzwQpxwwgmD3i81f7UUvcait7cXkiQNiq9XVVWpf7hKBUmSsHz5ckyYMAGjRo0CAHi9XrAsG7dsDgCVlZXwer15sDJzPvzwQ+zduxcPPfTQoPdK0d+Ojg68/fbbmDlzJi666CLs3r0bv//978GyLGbMmKH6VVlZGXdesfo8a9YsBINB/PSnPwVN05AkCZdddhm+853vAEDJ+ZuIGf+8Xi8qKiri3mcYBh6PpyT6IBKJ4A9/+APOOOMMdWJRaj6vXbsWDMPg3/7t33TfLzV/tRT9xMJOLFu2DAcOHMB9992Xb1NyRldXF5YvX44777wTPM/n25whQZIknHjiiWhqagIAjB07Fvv378fbb7+NGTNm5Ne4HPC3v/0NH3zwAW688UaccMIJ2LdvH5YvX45hw4aVpL+EeARBwGOPPQYAmDdvXp6tyQ179uzBG2+8gcWLF6uCZDtR9BOLiooK0DQ9aIbn9XqLepdAIsuWLcOnn36Ke++9FzU1NerxqqoqCIIAv98f9xTf09NTlP7v2bMHPT09uOWWW9RjkiRh+/btWL9+Pe64446S8hcAhg0bhpEjR8YdGzlyJD7++GMAUP3q6emJE4D19PRgzJgxQ2Vm1nj++efxox/9CGeccQYAYNSoUejs7MQrr7yCGTNmlJy/iZjxr6qqCr29vXHniaIIn89XtJ9zYGBS0dXVhV/+8pfqagVQWj5v374dvb29mD9/vnpMkiSsXLkSb7zxBp588smS8jeRop9YsCyLcePGYcuWLepWH0mSsGXLFpx33nl5ti5zZFnGc889hw0bNuCee+5BXV1d3Pvjxo0DwzDYvHkzvv3tbwMADh8+jK6uLjQ2NubD5IyYMmUKHnnkkbhjTz/9NEaMGIEf/ehHqK2tLSl/AWDChAmDwnaHDx/G8OHDAQB1dXWoqqrC5s2b1R+eQCCAXbt24Qc/+MFQm5sx4XAYNB0v76JpGnJ/2aJS8zcRM/41NjbC7/djz549GDduHABgy5YtkGW5aLfRK5OK9vZ23H333SgvL497v5R8PvPMM+N0YADwwAMP4Mwzz8TZZ58NoLT8TaToJxYAcMEFF+DJJ5/EuHHjMH78eLzxxhsIh8Mlsay6bNkyfPDBB7j55pvhcrnUlZmysjLwPI+ysjJ897vfxcqVK+HxeFBWVobnnnsOjY2NRflD63K5VP2IgsPhQHl5uXq8lPwFgJkzZ+Kuu+7C6tWrcfrpp2PXrl149913ce211wIAKIrC+eefj9WrV6OhoQF1dXV44YUXMGzYMJx22ml5tt46X//617F69WrU1tZi5MiR2LdvH9atW6f+wS0Ff0OhENrb29X/d3R0YN++ffB4PKitrU3p38iRI3HqqafimWeewTXXXANBEPDcc8/h9NNPj8v7UEgk87mqqgqPPvoo9u7di1tuuQWSJKl/yzweD1iWLTqfU41x4sSJZVlUVVWpOYiKzV8rlEx10/Xr1+PVV1+F1+vFmDFj8JOf/AQnnXRSvs3KmDlz5ugenz9/vjpxUhJGffjhhxAEoSQSRmm55557MGbMmEEJskrJ308++QQtLS1ob29HXV0dZs6ciXPOOUd9X+5PqPTOO+8gEAhg4sSJuPrqq+MSpRULwWAQL774IjZs2ICenh5UV1fjjDPOwKWXXgqWjT3rFLu/W7duxb333jvo+FlnnYUbbrjBlH8+nw/Lli2LS5501VVXFWzypGQ+z549G83Nzbrn3X333Zg8eTKA4vI51RgncsMNN+D8888flCCrWPy1QslMLAgEAoFAIOSfos9jQSAQCAQCoXAgEwsCgUAgEAhZg0wsCAQCgUAgZA0ysSAQCAQCgZA1yMSCQCAQCARC1iATCwKBQCAQCFmDTCwIBAKBQCBkDTKxIBAIBAKBkDXIxIJAIBAIBELWIBMLAoFAIBAIWYNMLAgEAoFAIGQNMrEgEAgEAoGQNf4/QXVpAQR6+g8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df1, hue=\"target\")\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        },
        "id": "pcYdKVXZ4DEY",
        "outputId": "22c65b55-6551-4afd-fc85-af83b809b7e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1072x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Line Plot**"
      ],
      "metadata": {
        "id": "CwxbPk4Obp8x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This plot draws a line that represents the revolution of continuous or categorical data. It is a popular and known type of chart, and it’s super easy to produce. Similarly to before, we use the function lineplot with the dataset and the columns represent the x and y axes. Seaborn will do the rest.[11]"
      ],
      "metadata": {
        "id": "_gsN15qJbunx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(data=X_new)\n"
      ],
      "metadata": {
        "id": "941JVqb9X1Fa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df, hue=\"target\")\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dF7ilBCA3_Nk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Bar Plot**"
      ],
      "metadata": {
        "id": "afi5M1Nnb6hY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is probably the best-known type of chart, and as you may have predicted, we can plot this type of plot with seaborn in the same way as we did for line and scatter plots by using the function barplot.[11]"
      ],
      "metadata": {
        "id": "4EARvlY2cAHB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=X_new)\n"
      ],
      "metadata": {
        "id": "1fkNx_N_YEYK",
        "outputId": "aceae71a-45f4-4564-f59d-6696fe4fd2c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f16460ce090>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=X_new)"
      ],
      "metadata": {
        "id": "WKStiTgvYTi6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Making Beautiful Plots With Style**s\n"
      ],
      "metadata": {
        "id": "1WxcMGzucNfX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seaborn gives you the ability to change your graph’s interface, and it provides five different styles out of the box: darkgrid, whitegrid, dark, white, and ticks.[11]"
      ],
      "metadata": {
        "id": "kcZ8EN7UcSQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "sns.lineplot(data = X_new)"
      ],
      "metadata": {
        "id": "DaXYuNgIYiTC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.hist(X_new)"
      ],
      "metadata": {
        "id": "CgPsI9pWYxty"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjkTmt_o3QvP"
      },
      "source": [
        "#**Automatic Data Ploting**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7sC_bFu6NNVK"
      },
      "source": [
        "## **Dtale**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLwIuW0x2htu"
      },
      "source": [
        "!pip install dtale"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tnfxwn-V3EKO",
        "outputId": "b547eb8b-926d-4926-ceeb-cf7f0e70c4ae"
      },
      "source": [
        "import dtale\n",
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Datasets/Recommended System/Placement_Data_Full_Class.csv\")\n",
        "d = dtale.show(df, ignore_duplicate=True)\n",
        "d"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "http://4ce9203885b8:40000/dtale/main/1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGdUk6uW15rP"
      },
      "source": [
        "## **Chartify** [4]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giDmQBKx2GOj"
      },
      "source": [
        "Chartify is a visualization library that aims to make it as easy as possible for data scientists to create charts. It comes with user-friendly syntax and consistent data formatting compared to other tools. It takes less time to create beautiful and quick charts. This was developed by Spotify labs.\n",
        "Here, I am showing only the bar chart. For more details and charts, please check this documentation and notebook"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a02kU8q42KII"
      },
      "source": [
        "!pip install chartify\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u699hd9f2RNg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "272bbc6e-3b30-44c5-dcd0-2255614e0800"
      },
      "source": [
        "# importing necessary library\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import chartify\n",
        "#loading example dataset from chartify\n",
        "data = chartify.examples.example_data()\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>country</th>\n",
              "      <th>fruit</th>\n",
              "      <th>unit_price</th>\n",
              "      <th>quantity</th>\n",
              "      <th>total_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-10-21</td>\n",
              "      <td>US</td>\n",
              "      <td>Banana</td>\n",
              "      <td>0.303711</td>\n",
              "      <td>4</td>\n",
              "      <td>1.214846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-05-30</td>\n",
              "      <td>JP</td>\n",
              "      <td>Banana</td>\n",
              "      <td>0.254109</td>\n",
              "      <td>4</td>\n",
              "      <td>1.016436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017-05-21</td>\n",
              "      <td>CA</td>\n",
              "      <td>Banana</td>\n",
              "      <td>0.268635</td>\n",
              "      <td>4</td>\n",
              "      <td>1.074539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-09-18</td>\n",
              "      <td>BR</td>\n",
              "      <td>Grape</td>\n",
              "      <td>2.215277</td>\n",
              "      <td>2</td>\n",
              "      <td>4.430554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-12-08</td>\n",
              "      <td>US</td>\n",
              "      <td>Banana</td>\n",
              "      <td>0.308337</td>\n",
              "      <td>5</td>\n",
              "      <td>1.541687</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date country   fruit  unit_price  quantity  total_price\n",
              "0 2017-10-21      US  Banana    0.303711         4     1.214846\n",
              "1 2017-05-30      JP  Banana    0.254109         4     1.016436\n",
              "2 2017-05-21      CA  Banana    0.268635         4     1.074539\n",
              "3 2017-09-18      BR   Grape    2.215277         2     4.430554\n",
              "4 2017-12-08      US  Banana    0.308337         5     1.541687"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9Hs5RKw2gz0"
      },
      "source": [
        "# Calculating total quanity for each fruits\n",
        "quantity_by_fruit = (data.groupby(‘fruit’)[‘quantity’].sum().reset_index())\n",
        "ch = chartify.Chart(blank_labels=True, x_axis_type=’categorical’)\n",
        "ch.set_title(“Vertical bar plot”)\n",
        "ch.set_subtitle(“Automatically sorts by value counts.”)\n",
        "ch.plot.bar(\n",
        " data_frame=quantity_by_fruit,\n",
        " categorical_columns=’fruit’,\n",
        " numeric_column=’quantity’)\n",
        "ch.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tUJeO3hsNfvX"
      },
      "source": [
        "## **Lux**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lux will take any data frame and automatically suggest visualizations that the user can click on and create in an instant. Getting the syntax correct for visualizations can be time consuming and Lux reduces this time to only a few seconds[10]"
      ],
      "metadata": {
        "id": "SYIlv1qpHsmP"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vggj6kLN3Y0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "75e15c29-3182-4b88-8b76-e6083c07ec22"
      },
      "source": [
        "!pip install lux-api\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lux-api\n",
            "  Downloading lux-api-0.4.0.tar.gz (123 kB)\n",
            "\u001b[?25l\r\u001b[K     |██▋                             | 10 kB 28.7 MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 20 kB 20.7 MB/s eta 0:00:01\r\u001b[K     |████████                        | 30 kB 10.6 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 40 kB 8.5 MB/s eta 0:00:01\r\u001b[K     |█████████████▎                  | 51 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 61 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |██████████████████▋             | 71 kB 5.5 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▎          | 81 kB 6.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 92 kB 6.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 102 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 112 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 122 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 123 kB 5.2 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: altair>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from lux-api) (4.2.0)\n",
            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.7/dist-packages (from lux-api) (1.21.5)\n",
            "Requirement already satisfied: scipy>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from lux-api) (1.4.1)\n",
            "Collecting pandas<1.3.0,>=1.2.0\n",
            "  Downloading pandas-1.2.5-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (9.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.9 MB 19.0 MB/s \n",
            "\u001b[?25hCollecting iso3166\n",
            "  Downloading iso3166-2.0.2-py3-none-any.whl (8.5 kB)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from lux-api) (3.2.2)\n",
            "Collecting lux-widget>=0.1.4\n",
            "  Downloading lux-widget-0.1.10.tar.gz (2.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.5 MB 36.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from lux-api) (0.24.2)\n",
            "Collecting autopep8>=1.5\n",
            "  Downloading autopep8-1.6.0-py2.py3-none-any.whl (45 kB)\n",
            "\u001b[K     |████████████████████████████████| 45 kB 2.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: entrypoints in /usr/local/lib/python3.7/dist-packages (from altair>=4.0.0->lux-api) (0.4)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.7/dist-packages (from altair>=4.0.0->lux-api) (0.11.2)\n",
            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.7/dist-packages (from altair>=4.0.0->lux-api) (4.3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from altair>=4.0.0->lux-api) (2.11.3)\n",
            "Collecting pycodestyle>=2.8.0\n",
            "  Downloading pycodestyle-2.8.0-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[K     |████████████████████████████████| 42 kB 712 kB/s \n",
            "\u001b[?25hRequirement already satisfied: toml in /usr/local/lib/python3.7/dist-packages (from autopep8>=1.5->lux-api) (0.10.2)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0->altair>=4.0.0->lux-api) (4.11.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0->altair>=4.0.0->lux-api) (21.4.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0->altair>=4.0.0->lux-api) (3.10.0.2)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0->altair>=4.0.0->lux-api) (5.4.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0->altair>=4.0.0->lux-api) (0.18.1)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources>=1.4.0->jsonschema>=3.0->altair>=4.0.0->lux-api) (3.7.0)\n",
            "Requirement already satisfied: notebook>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from lux-widget>=0.1.4->lux-api) (5.3.1)\n",
            "Requirement already satisfied: ipywidgets>=7.5.0 in /usr/local/lib/python3.7/dist-packages (from lux-widget>=0.1.4->lux-api) (7.6.5)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (4.10.1)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (0.2.0)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (5.1.1)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (5.5.0)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (1.0.2)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (5.1.3)\n",
            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (3.5.2)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (5.3.5)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (5.1.1)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (1.0.18)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (4.8.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (2.6.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (0.7.5)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (0.8.1)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (57.4.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->lux-api) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->lux-api) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->lux-api) (3.0.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->lux-api) (1.3.2)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (4.9.1)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (1.8.0)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.13.1)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (5.6.1)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (22.3.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas<1.3.0,>=1.2.0->lux-api) (2021.3)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (0.2.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->lux-api) (0.14.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->lux-api) (3.1.0)\n",
            "Requirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.7.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->altair>=4.0.0->lux-api) (2.0.1)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (4.1.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (1.5.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.7.1)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.8.4)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.5.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (21.3)\n",
            "Building wheels for collected packages: lux-api, lux-widget\n",
            "  Building wheel for lux-api (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lux-api: filename=lux_api-0.4.0-py3-none-any.whl size=181967 sha256=945dd6ee597b67e6a2eb1ffcfdbc2f6a936bcbac9c67784ee55e0578a81d7940\n",
            "  Stored in directory: /root/.cache/pip/wheels/1d/2a/80/9e7f8b9c5642f147e99d9354bd41e21c14e912ef3224c30f43\n",
            "  Building wheel for lux-widget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lux-widget: filename=lux_widget-0.1.10-py3-none-any.whl size=2539952 sha256=08d68568b4abc4de9cc75cfbda1ee3a5b2923b6a83f223844c1dee456b0c4582\n",
            "  Stored in directory: /root/.cache/pip/wheels/6d/7c/ab/6b22af94201ab6ed98247aa8a2925eb5b62435ad8382b67f6e\n",
            "Successfully built lux-api lux-widget\n",
            "Installing collected packages: pycodestyle, pandas, lux-widget, iso3166, autopep8, lux-api\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.3.5\n",
            "    Uninstalling pandas-1.3.5:\n",
            "      Successfully uninstalled pandas-1.3.5\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\u001b[0m\n",
            "Successfully installed autopep8-1.6.0 iso3166-2.0.2 lux-api-0.4.0 lux-widget-0.1.10 pandas-1.2.5 pycodestyle-2.8.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pandas"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubsSefJVPWRH"
      },
      "source": [
        "!pip install git+https://github.com/lux-org/lux.git\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsygDch0OLcP"
      },
      "source": [
        "import pandas as pd\n",
        "import lux"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQg6omxvORTS"
      },
      "source": [
        "df = pd.read_csv(\"https://github.com/lux-org/lux-datasets/blob/master/data/hpi_full.csv?raw=True\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zfs3x64nOaZk"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lux also offer an intent feature, where you can specify which attributes (columns) they are interested in. Lux will offer charts specific to those columns."
      ],
      "metadata": {
        "id": "E-C-HEtrId5f"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prFSWvptO0AJ"
      },
      "source": [
        "df.intent = [\"Inequality\",\"AvrgLifeExpectancy\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3xW_3WiO1-Q"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyQlJ0oF1m0A"
      },
      "source": [
        "## **Pandas Profiling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5kn2WaDHWvt"
      },
      "source": [
        "This should be no surprise to those of you who’ve been following me. The benefits of this tool are obvious. The animation below is the results of calling the simple method df.profile_report(), take a look at the resulting report yourself[6]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31h4REkQHeAm"
      },
      "source": [
        "Using this tool is simple a matter of installing and importing the Pandas Profiling package.\n",
        "I wont spend too much time on this one since I’ve already written a small article on the subject, so if you want to know more about this gem of a package, then please read my article about Pandas Profiling here:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAhKmZjJ10Qx"
      },
      "source": [
        "!pip install pandas-profiling"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUTS3kzn18Zo"
      },
      "source": [
        "import pandas as pd\n",
        "import pandas_profiling\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-IQ-nXh2CRx"
      },
      "source": [
        "pandas_profiling.ProfileReport(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iapf0tK6MV8I"
      },
      "source": [
        "## **Sweetviz**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGZL6SQfLu4p"
      },
      "source": [
        "Sweetviz is an open-source python library that generates beautiful, high-density visualizations to start your EDA. Let us explore Sweetviz and its functionalities in detail. The version which we will be exploring is V 2.0.6 which is released on 06/02/2021.[7]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JShsU_Cx3j8Q"
      },
      "source": [
        "!pip install sweetviz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teKO1mOX3tc-"
      },
      "source": [
        "import sweetviz\n",
        "import pandas as pd\n",
        "import sweetviz as sv\n",
        "my_report  = sweetviz.analyze([data,'Train'], target_feat='specialisation')\n",
        "my_report.show_html('FinalReport.html')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRtP575MNHno"
      },
      "source": [
        "**Create Analysis Report**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-sLcKD9KNKJ3"
      },
      "source": [
        "Let’s start by creating an analysis report which will be helpful in performing Data analysis and creates an interactive HTML report of data with visualization.[7]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQSlHh2pNhKY"
      },
      "source": [
        "import sweetviz as sv\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clxKoowPNQnu"
      },
      "source": [
        "analyze_report = sv.analyze(X_train)\n",
        "analyze_report.show_html()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EciviMWOOVBx"
      },
      "source": [
        "This is the main page of the report, Here on the right side you can see associations as highlighted which shows the corelation of the features. Similarly we can highlight different feature columns in order to analyze them, let me show you one example of analyzing a column.[6]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfKbCUozOZ8G"
      },
      "source": [
        "So, here in this image you can clearly see how you can select a particular column to analyze it in depth with visualization.\n",
        "Next we will see how we can create comparison reports. Sweetviz allows you to create Inter comapison and intra comparison(based on a particular column)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MyosQksTObjN"
      },
      "source": [
        "**Train and Test Data Compaison[7]**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_UGNcVdOg5t"
      },
      "source": [
        "We can create a comparison report where we can analyze the similarities and differences between the test and the training data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "YekbdXokO8lE",
        "outputId": "ebf1a2c1-6d1c-4744-cc01-1b6a61e7d68d"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sl_no</th>\n",
              "      <th>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>ssc_b</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_b</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>0.564843</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.308096</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.018692</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.925788</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.602965</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.568293</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.160795</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>0.981308</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.818594</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.741351</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.673171</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.854167</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.872564</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>0.985981</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.352711</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.378913</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.536585</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.090330</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
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              "      <td>0.538240</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.494234</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.693778</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>213</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.477759</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.195122</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.338081</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.435168</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.345964</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.073171</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.337706</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        sl_no  gender     ssc_p  ...  specialisation     mba_p  status\n",
              "0    0.000000     1.0  0.538240  ...             1.0  0.284483     1.0\n",
              "1    0.004673     1.0  0.792414  ...             0.0  0.564843     1.0\n",
              "2    0.009346     1.0  0.497011  ...             0.0  0.247001     1.0\n",
              "3    0.014019     1.0  0.311482  ...             1.0  0.308096     0.0\n",
              "4    0.018692     1.0  0.925788  ...             0.0  0.160795     1.0\n",
              "..        ...     ...       ...  ...             ...       ...     ...\n",
              "210  0.981308     1.0  0.818594  ...             0.0  0.872564     1.0\n",
              "211  0.985981     1.0  0.352711  ...             0.0  0.090330     1.0\n",
              "212  0.990654     1.0  0.538240  ...             0.0  0.693778     1.0\n",
              "213  0.995327     0.0  0.682540  ...             1.0  0.338081     1.0\n",
              "214  1.000000     1.0  0.435168  ...             1.0  0.337706     0.0\n",
              "\n",
              "[215 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373,
          "referenced_widgets": [
            "ae131c2bfc6f456ab5f238b8395bcf19",
            "dcdfcef8528b47018b0d80ebc65c4974",
            "524a1483e24444749f2ea1ce3142628a",
            "bd224a67b3404b87b89f22378802f624",
            "4d7fc101198e4393a262f64b964e2c0f",
            "f0523f186f56458bb3f275bf6a5acefd",
            "aed47707f32f44c2b530db00d7caf2b6",
            "3d0c3cb46bb1483985820f4a79a63a0a",
            "571318fe094440efa0bfd55532d02b09",
            "0d6a77469ed847c9b7d76b674e58cd4b",
            "1b171bf41132490cbb677bdfb97cef50"
          ]
        },
        "id": "p_edCB4NOqK9",
        "outputId": "6513ef81-5e2f-46c3-bff9-5a37bb0a4d98"
      },
      "source": [
        "compare = sv.compare([X_train, \"Training Data\"], [X_test, \"Test Data\"], \"target\")\n",
        "compare.show_html()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae131c2bfc6f456ab5f238b8395bcf19",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "                                             |          | [  0%]   00:00 -> (? left)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-63-a2f79e5cc002>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcompare\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Training Data\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Test Data\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"target\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcompare\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sweetviz/sv_public.py\u001b[0m in \u001b[0;36mcompare\u001b[0;34m(source, compare, target_feat, feat_cfg, pairwise_analysis)\u001b[0m\n\u001b[1;32m     21\u001b[0m             pairwise_analysis: str = 'auto'):\n\u001b[1;32m     22\u001b[0m     report = sweetviz.DataframeReport(source, target_feat, compare,\n\u001b[0;32m---> 23\u001b[0;31m                                       pairwise_analysis, feat_cfg)\n\u001b[0m\u001b[1;32m     24\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sweetviz/dataframe_report.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, source, target_feature_name, compare, pairwise_analysis, fc)\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargets_found\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_bar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                 raise KeyError(f\"Feature '{target_feature_name}' was \"\n\u001b[0m\u001b[1;32m    174\u001b[0m                                \u001b[0;34mf\"specified as TARGET, but is NOT FOUND in \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m                                f\"the dataframe (watch case-sensitivity?).\")\n",
            "\u001b[0;31mKeyError\u001b[0m: \"Feature 'target' was specified as TARGET, but is NOT FOUND in the dataframe (watch case-sensitivity?).\""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILX_1atPNwpw"
      },
      "source": [
        "## **Autoviz**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9i0NMm8q4v7_"
      },
      "source": [
        "**Autoviz**\n",
        "\n",
        "It stands for Automatically Visualize. Visualization is possible with any size of the dataset with a few lines of code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y12-00F84Lj3"
      },
      "source": [
        "!pip install autoviz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JW_PXBYQ4UN-"
      },
      "source": [
        "from autoviz.AutoViz_Class import AutoViz_Class\n",
        "AV = AutoViz_Class()\n",
        "df = AV.AutoViz('/content/drive/MyDrive/Datasets/Recommended System/Placement_Data_Full_Class.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AjOni5tVIGkO"
      },
      "source": [
        "## **Cufflinks & Plotly**[6]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzobnDdHIWL1"
      },
      "source": [
        "Most of us who’re quote-unquote “experienced” Data Scientist or Data Analysts, will be quite familiar with the integration between matplotlib and pandas. Namely the fact that you can make quick plots of a simple pd.DataFrame or pd.Series by simply calling the .plot() method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVbMQmSTJmLl"
      },
      "source": [
        "! pip install cufflinks --upgrade"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpbtfKdIDD_P"
      },
      "source": [
        "!pip install -U ipykernel\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXKwwZ1bDKiW"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "ytfD7ZpPIPLv",
        "outputId": "f533cdd7-bc8a-4f26-bef7-95d0f0d2c39f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import cufflinks as cp\n",
        "cp.go_offline()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeU73wXaI0-t"
      },
      "source": [
        "X.satter_matrix()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "272Si6RTJQx8"
      },
      "source": [
        "Now this is all fine and dandy, but how about an interactive, pan-able, zoom-able, scale-able plot instead? Cufflinks to the rescue! (courtesy of Plotly)\n",
        "To install Cufflinks in your environment simply run ! pip install cufflinks --upgrade in your favorite terminal and you’re ready to go. Take a look at the following:"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Lens**"
      ],
      "metadata": {
        "id": "P3S3caSe11Ni"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lens is a library for exploring data in Pandas DataFrames. It computes single-column summary statistics and estimates the correlation between columns [11]"
      ],
      "metadata": {
        "id": "KvAwzKFJSgMI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lens\n"
      ],
      "metadata": {
        "id": "k9-n7oHF12hT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "JFq9y6rv2v4v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d1476f0-4d28-4c05-9375-0ca5883c4403"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"data\")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "BlE_sNFF2QGV",
        "outputId": "cdb45486-13df-48df-8ba2-b89816086ee9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "HTTPError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-b07daf975c84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/utils.py\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m(name, cache, data_home, **kws)\u001b[0m\n\u001b[1;32m    447\u001b[0m                                   os.path.basename(full_path))\n\u001b[1;32m    448\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m             \u001b[0murlretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m         \u001b[0mfull_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m    245\u001b[0m     \u001b[0murl_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msplittype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    529\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[0;34m(self, request, response)\u001b[0m\n\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m             response = self.parent.error(\n\u001b[0;32m--> 641\u001b[0;31m                 'http', request, response, code, msg, hdrs)\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36merror\u001b[0;34m(self, proto, *args)\u001b[0m\n\u001b[1;32m    567\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_err\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'default'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'http_error_default'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m \u001b[0;31m# XXX probably also want an abstract factory that knows when it makes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    501\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    504\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mhttp_error_default\u001b[0;34m(self, req, fp, code, msg, hdrs)\u001b[0m\n\u001b[1;32m    647\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mHTTPDefaultErrorHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    648\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    651\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mHTTPRedirectHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: HTTP Error 404: Not Found"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyyaml==5.4.1"
      ],
      "metadata": {
        "id": "dJzV0_RWTI-v",
        "outputId": "ea52e31c-659f-4128-f9a6-1e37ed7f53bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyyaml==5.4.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[?25l\r\u001b[K     |▌                               | 10 kB 11.6 MB/s eta 0:00:01\r\u001b[K     |█                               | 20 kB 12.5 MB/s eta 0:00:01\r\u001b[K     |█▌                              | 30 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |██                              | 40 kB 9.9 MB/s eta 0:00:01\r\u001b[K     |██▋                             | 51 kB 6.3 MB/s eta 0:00:01\r\u001b[K     |███                             | 61 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |███▋                            | 71 kB 4.0 MB/s eta 0:00:01\r\u001b[K     |████▏                           | 81 kB 4.5 MB/s eta 0:00:01\r\u001b[K     |████▋                           | 92 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 102 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████▋                          | 112 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████▏                         | 122 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████▊                         | 133 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████▏                        | 143 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████▊                        | 153 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 163 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 174 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████▎                      | 184 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 194 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████▎                     | 204 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████▉                     | 215 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████▎                    | 225 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████▉                    | 235 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 245 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 256 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 266 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 276 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 286 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 296 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████▍                | 307 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 317 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 327 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 337 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▌              | 348 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 358 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 368 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 378 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 389 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 399 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████▋           | 409 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 419 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▋          | 430 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 440 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 450 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 460 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▊        | 471 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 481 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 491 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▏      | 501 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▊      | 512 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▎     | 522 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 532 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▎    | 542 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▉    | 552 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▎   | 563 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 573 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 583 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▉  | 593 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 604 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▉ | 614 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▍| 624 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 634 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 636 kB 5.4 MB/s \n",
            "\u001b[?25hInstalling collected packages: pyyaml\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
            "      Successfully uninstalled PyYAML-6.0\n",
            "Successfully installed pyyaml-5.4.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "yaml"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import lens\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "80RcNroq2Cqv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf48fc00-273d-4612-e1ba-524dd23d9308"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning:\n",
            "\n",
            "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/distributed/config.py:20: YAMLLoadWarning:\n",
            "\n",
            "calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new"
      ],
      "metadata": {
        "id": "aqNX4wPrTyPO",
        "outputId": "84960795-adbb-4075-d567-be998c7149d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-1129f4f25788>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_new\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'X_new' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new._get_value(index,'name')\n",
        "\n"
      ],
      "metadata": {
        "id": "figDr0e03wXa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "explorer = lens.explore(X_new)\n"
      ],
      "metadata": {
        "id": "4k23jqBK2JGf",
        "outputId": "15957f6b-3d26-4c0f-edb4-969f037e5728",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-c302fbfcb854>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexplorer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/lens/explorer.py\u001b[0m in \u001b[0;36mexplore\u001b[0;34m(summary)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mexplore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;34m\"\"\"Create an Explorer instance from a Lens Summary\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mExplorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/lens/explorer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, summary, plot_renderer)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msummary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_renderer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_render\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSummary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only explore a lens Summary\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msummary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_renderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Can only explore a lens Summary"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **TensorFlow Data Validation**"
      ],
      "metadata": {
        "id": "6aWX9HnOz2uw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "What it does: Makes data exploration for machine learning more intuitive\n",
        "How easy it is to use: Easy\n",
        "Who should use it: For those who need to a quick look at a dataset stats.\n",
        "TensorFlow Data Validation is a dataset that makes data exploration when creating machine learning models easier. Although it is not as powerful as Bamboolib, it’s worth knowing about it. It’s the solution that TensorFlow found to make exploring the data less stressful and save users from typing a few lines of code. You can check for missing data, outliers, data anomalies, redundant features, features with little or no unique predictive information, labels treated as features, and more. [8]"
      ],
      "metadata": {
        "id": "2-nHultN3Ngg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "3lLD1ZQL3K5g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-data-validation"
      ],
      "metadata": {
        "id": "0hqVzT3R0M6Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Tensorflow Data Validation\n",
        "import tensorflow_data_validation as tfdv\n",
        "# Import Pandas\n",
        "import pandas as pd\n",
        "# Import Titanic dataset\n"
      ],
      "metadata": {
        "id": "LAi4XqO_2lHI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/Datasets/Student field Recommendation /Placement_Data_Full_Class.csv\")"
      ],
      "metadata": {
        "id": "wVODAR3C2vWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stats = tfdv.generate_statistics_from_dataframe(data)\n",
        "\n",
        "tfdv.visualize_statistics(stats)"
      ],
      "metadata": {
        "id": "fa_Xi4xg20Sw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CKqdX86iIBc"
      },
      "source": [
        "# **References**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Znwp0bXbL-4d"
      },
      "source": [
        "[1- Top Python Libraries to Automate Exploratory Data Analysis in 2021](https://www.analyticsvidhya.com/blog/2021/04/top-python-libraries-to-automate-exploratory-data-analysis-in-2021/)\n",
        "\n",
        "[2-Exploratory Data Analysis With One-Line of Code](https://medium.com/geekculture/exploratory-data-analysis-with-one-line-of-code-f4a8e0a55f56)\n",
        "\n",
        "[3-Handy Data Visualization Functions in matplotlib & Seaborn to Speed Up Your EDA](https://betterprogramming.pub/handy-data-visualization-functions-in-matplotlib-seaborn-to-speed-up-your-eda-241ba0a9c47d)\n",
        "\n",
        "[4-Five Cool Python Libraries for Data Science](https://pub.towardsai.net/five-cool-python-libraries-for-data-science-7f1fce402b90)\n",
        "\n",
        "[5-My top 4 functions to style the Pandas Dataframe](https://towardsdatascience.com/my-top-4-functions-to-style-the-pandas-dataframe-932cdc79be39)\n",
        "\n",
        "[6-7 things to quickly improve your Data Analysis in Python](https://towardsdatascience.com/7-things-to-quickly-improve-your-data-analysis-in-python-3d434243da7)\n",
        "\n",
        "[7-Automating Exploratory Data Analysis- Part 1](https://medium.com/swlh/automating-exploratory-data-analysis-part-1-f5f2b7d548e5)]\n",
        "\n",
        "[8-4 Amazing Python Libraries That You Should Try Right Now](https://towardsdatascience.com/4-amazing-python-libraries-that-you-should-try-right-now-872df6f1c93)\n",
        "\n",
        "[9-The Simplest Way to Create Visualizations in Python Isn’t With matplotlib.](https://medium.com/analytics-vidhya/the-simplest-way-to-create-complex-visualizations-in-python-isnt-with-matplotlib-a5802f2dba92)\n",
        "\n",
        "[10-3 Python Tools You Need to Try](https://medium.com/trymito/3-python-tools-you-need-to-try-72c3380974db)\n",
        "\n",
        "[11-Beautiful Plots With Python and Seaborn](https://betterprogramming.pub/beautiful-plots-with-python-and-seaborn-9d05c9ead6ed)\n",
        "\n",
        "\n",
        "[Bamboolib: One of the Most Useful Python Libraries You Have Ever Seen](https://towardsdatascience.com/bamboolib-one-of-the-most-useful-python-libraries-you-have-ever-seen-6ce331685bb7)\n",
        "\n",
        "[The Quickest Guide to Data Visualization in Python using Matplotlib](https://medium.com/mlearning-ai/the-quickest-guide-to-data-visualization-in-python-using-matplotlib-a6a079b5de07)\n",
        "\n",
        "[Automating Exploratory Data Analysis- Part 4](https://medium.com/swlh/automating-exploratory-data-analysis-part-4-43920f03b2cb)\n",
        "\n",
        "[How to Automatically Generate Python Code for Spreadsheet Operations?](https://python.plainenglish.io/how-to-automatically-generate-python-code-for-spreadsheet-operations-ca3f59474971)\n",
        "\n",
        "[3 Seaborn Functions That Cover (Almost) All Your Visualization Tasks](https://towardsdatascience.com/3-seaborn-functions-that-cover-almost-all-your-visualization-tasks-793f76510ac3)\n",
        "\n",
        "[33 DATA VISUALIZATION TECHNIQUES ALL PROFESSIONALS SHOULD KNOW](https://dipesious.medium.com/33-data-visualization-techniques-all-professionals-should-know-ab999abe601a)\n",
        "\n",
        "[5 Python Libraries That Will Help Automate Your Life](https://medium.com/geekculture/5-python-libraries-that-will-help-automate-your-life-34aa91d93b9b)\n",
        "\n"
      ]
    }
  ]
}