Bot_Class_Prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/sanchitmisra/Bot-Class-Prediction/blob/main/Bot_Class_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xeisDgKiAST"
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      "source": [
        "#**Problem Statement**\n",
        "\n",
        "Build a Model that can detect the Non Human Traffic present on a website!!!\n",
        "\n",
        "##**Features Description**\n",
        "\n",
        "**ctry_name**: Indicated from which country user is from\n",
        "\n",
        "**intgrtdmngmtname**: team handling the particular domain for the user\n",
        "\n",
        "**intgrtdoperatingteam_name**: Operating team name IP is managed from\n",
        "\n",
        "**city**: Indicates from which city ip is coming from\n",
        "\n",
        "**st**: Indicates from which state ip is coming from\n",
        "\n",
        "**seclvldomn**: (.com)(.net) known as secondary domain for example in google.com(.com is secondary domain and google is primary domain)\n",
        "\n",
        "**device_type**: From which device ip is operating from\n",
        "\n",
        "**operating_sys**: From which operating system ip is coming from\n",
        "\n",
        "**ip_addr**: Indicates the user information from where it's operating in the from of unique IP address\n",
        "\n",
        "**user_agent**: From which browser version user is operating to any website\n",
        "\n",
        "**VISIT**: How many times ip is visited\n",
        "\n",
        "**ENGDVISIT**: How ipaddr or user is getting engaged\n",
        "\n",
        "**VIEWS**: How many time user has visited the webpage\n",
        "\n",
        "**pageurl**: Website Url ipaddress visiting\n",
        "\n",
        "**wk**: indicated the week\n",
        "\n",
        "**mth**: indicate the month\n",
        "\n",
        "**yr**: indicate the year\n",
        "\n",
        "**pagevwts**: Giving full info about when and at what time user has visited any website\n",
        "\n",
        "**target**: 1 or 0 whether it's an bot or not\n",
        "\n",
        "* **BOT** : A bot is a computer program that carries out tasks for other programs or users, especially on the internet."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x00spCll2Bkd"
      },
      "source": [
        "#Importing the libraries:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "XRITRM-XTdos"
      },
      "outputs": [],
      "source": [
        "# Import neccessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import math\n",
        "from datetime import *\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder , MinMaxScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
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              "        ctry_name intgrtd_mngmt_name intgrtd_operating_team_name       city  \\\n",
              "0  United Kingdom                UKI                      Europe     BARLOW   \n",
              "1   Liechtenstein               DACH                      Europe      VADUZ   \n",
              "2  United Kingdom                UKI                      Europe    THORNEY   \n",
              "3           China      Greater China         Greater China Group    BEIJING   \n",
              "4           India   India-South Asia                Asia Pacific  NEW DELHI   \n",
              "\n",
              "             st sec_lvl_domn device_type         operating_sys  \\\n",
              "0    DERBYSHIRE     HSBC.COM         NaN    MICROSOFT_WINDOWS7   \n",
              "1         VADUZ       LLB.LI         NaN  MICROSOFT_WINDOWS8.1   \n",
              "2  PETERBOROUGH          NaN         NaN    MICROSOFT_WINDOWS7   \n",
              "3   BEIJING SHI          NaN         NaN   MICROSOFT_WINDOWS10   \n",
              "4         DELHI      TCS.COM         NaN    MICROSOFT_WINDOWS7   \n",
              "\n",
              "                                             ip_addr  \\\n",
              "0  d96016a12f445fb7fadb4ff2396934c8345684f9f60299...   \n",
              "1  80e5ef49efbe02e7a7f34a5fb482cd748aa27f3409acc8...   \n",
              "2  9d847da112b5b428419bee77e4387f8d08e7139045285d...   \n",
              "3  16d56bf0f01a14b3f5c97389648b9a01f9f05e0890d0b1...   \n",
              "4  7deb0d8f51dd6d5e10fa7fbc1a7eec42a9a6837058e1e0...   \n",
              "\n",
              "                                          user_agent  VISIT  ENGD_VISIT  \\\n",
              "0  MOZILLA/5.0 (WINDOWS NT 6.1; WOW64) APPLEWEBKI...      1           0   \n",
              "1  MOZILLA/5.0 (WINDOWS NT 6.3; WIN64; X64) APPLE...      1           0   \n",
              "2  MOZILLA/5.0 (WINDOWS NT 6.1; WIN64; X64) APPLE...      1           0   \n",
              "3  MOZILLA/5.0 (WINDOWS NT 10.0; WIN64; X64) APPL...      1           1   \n",
              "4  MOZILLA/5.0 (WINDOWS NT 6.1; WOW64) APPLEWEBKI...      1           1   \n",
              "\n",
              "   VIEWS                                           page_url  wk  mth    yr  \\\n",
              "0      1  www.ibm.com/support/knowledgecenter/en/ss88xh_...  11    3  2019   \n",
              "1      1  www.ibm.com/support/knowledgecenter/ssphqg_7.2...  11    3  2019   \n",
              "2      1  www-01.ibm.com/support/docview.wss?uid=swg2197...  11    3  2019   \n",
              "3      1  www.ibm.com/support/knowledgecenter/zh/sstfxa_...  11    3  2019   \n",
              "4      1  www.ibm.com/support/knowledgecenter/en/ssplfc_...  11    3  2019   \n",
              "\n",
              "                   page_vw_ts  target  \n",
              "0  2019-06-04 02:28:14.011700       1  \n",
              "1  2019-06-04 07:17:42.032000       1  \n",
              "2  2019-06-04 09:33:01.080800       1  \n",
              "3  2019-06-04 06:42:19.017100       1  \n",
              "4  2019-06-04 06:34:28.068900       1  "
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              "      <td>United Kingdom</td>\n",
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              "      <td>Europe</td>\n",
              "      <td>THORNEY</td>\n",
              "      <td>PETERBOROUGH</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Greater China</td>\n",
              "      <td>Greater China Group</td>\n",
              "      <td>BEIJING</td>\n",
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              "      <th>4</th>\n",
              "      <td>India</td>\n",
              "      <td>India-South Asia</td>\n",
              "      <td>Asia Pacific</td>\n",
              "      <td>NEW DELHI</td>\n",
              "      <td>DELHI</td>\n",
              "      <td>TCS.COM</td>\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "#Import the dataset\n",
        "train_df= pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Almabetter/Project/train.csv\")\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Almabetter/Project/test.csv')\n",
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ManvpRaVi9QI",
        "outputId": "74e96008-820c-431b-90c9-a770bb5cf724"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of Train dataset is: (186814, 19) \n",
            " --------------------------------------------------\n",
            "The shape of Test dataset is: (46704, 18)\n"
          ]
        }
      ],
      "source": [
        "print(f'The shape of Train dataset is: {train_df.shape}','\\n','-'*50)\n",
        "print(f'The shape of Test dataset is: {test_df.shape}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2CrgIJZ2XBB"
      },
      "source": [
        "#Data Inspection & Cleaning:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmM68dSVi_xU",
        "outputId": "48285315-50d2-42cf-8b2d-8e7f3d6fd133"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 186814 entries, 0 to 186813\n",
            "Data columns (total 19 columns):\n",
            " #   Column                       Non-Null Count   Dtype \n",
            "---  ------                       --------------   ----- \n",
            " 0   ctry_name                    186814 non-null  object\n",
            " 1   intgrtd_mngmt_name           186814 non-null  object\n",
            " 2   intgrtd_operating_team_name  186814 non-null  object\n",
            " 3   city                         186814 non-null  object\n",
            " 4   st                           186814 non-null  object\n",
            " 5   sec_lvl_domn                 132723 non-null  object\n",
            " 6   device_type                  2361 non-null    object\n",
            " 7   operating_sys                186814 non-null  object\n",
            " 8   ip_addr                      186814 non-null  object\n",
            " 9   user_agent                   186814 non-null  object\n",
            " 10  VISIT                        186814 non-null  int64 \n",
            " 11  ENGD_VISIT                   186814 non-null  int64 \n",
            " 12  VIEWS                        186814 non-null  int64 \n",
            " 13  page_url                     186814 non-null  object\n",
            " 14  wk                           186814 non-null  int64 \n",
            " 15  mth                          186814 non-null  int64 \n",
            " 16  yr                           186814 non-null  int64 \n",
            " 17  page_vw_ts                   186345 non-null  object\n",
            " 18  target                       186814 non-null  int64 \n",
            "dtypes: int64(7), object(12)\n",
            "memory usage: 27.1+ MB\n"
          ]
        }
      ],
      "source": [
        "#Check info of train dataset\n",
        "train_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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        },
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        "outputId": "f790a8ab-8b18-4ba9-e168-b0675119a9e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "ctry_name                           0\n",
              "intgrtd_mngmt_name                  0\n",
              "intgrtd_operating_team_name         0\n",
              "city                                0\n",
              "st                                  0\n",
              "sec_lvl_domn                    54091\n",
              "device_type                    184453\n",
              "operating_sys                       0\n",
              "ip_addr                             0\n",
              "user_agent                          0\n",
              "VISIT                               0\n",
              "ENGD_VISIT                          0\n",
              "VIEWS                               0\n",
              "page_url                            0\n",
              "wk                                  0\n",
              "mth                                 0\n",
              "yr                                  0\n",
              "page_vw_ts                        469\n",
              "target                              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#Check Null values of train dataset\n",
        "train_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-j4W7ZYdqD1",
        "outputId": "212a353e-56a8-449d-f10e-348f17b7f5b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ctry_name                          0\n",
              "intgrtd_mngmt_name                 0\n",
              "intgrtd_operating_team_name        0\n",
              "city                               0\n",
              "st                                 0\n",
              "sec_lvl_domn                   13329\n",
              "device_type                    46080\n",
              "operating_sys                      0\n",
              "ip_addr                            0\n",
              "user_agent                         0\n",
              "VISIT                              0\n",
              "ENGD_VISIT                         0\n",
              "VIEWS                              0\n",
              "page_url                           0\n",
              "wk                                 0\n",
              "mth                                0\n",
              "yr                                 0\n",
              "page_vw_ts                       127\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "#Check Null Values of test dataset\n",
        "test_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Zozhxcc72lv5"
      },
      "outputs": [],
      "source": [
        "# Remove the Null and duplicate values\n",
        "#train_df.dropna(inplace = True)\n",
        "train_df.drop_duplicates(inplace= True)\n",
        "#Reset index of Dataframe.\n",
        "train_df.reset_index(drop= 'index', inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "nuu9kxNOfQBZ"
      },
      "outputs": [],
      "source": [
        "# Remove the Null and duplicate values\n",
        "test_df.drop_duplicates(inplace= True)\n",
        "#Reset index of Dataframe.\n",
        "test_df.reset_index(drop= 'index', inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "qbqRYfKh3TIq"
      },
      "outputs": [],
      "source": [
        "#Change the datetime format\n",
        "train_df['Date'] = pd.to_datetime(train_df.page_vw_ts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "qWFT6I8wd0JH"
      },
      "outputs": [],
      "source": [
        "#Change the datetime format\n",
        "test_df['Date'] = pd.to_datetime(test_df.page_vw_ts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "56wftNOn30TJ"
      },
      "outputs": [],
      "source": [
        "#New columns month , day and  year added to dataset\n",
        "for df in [train_df]:\n",
        "  train_df['month'] = train_df['Date'].dt.month\n",
        "  train_df['year'] = train_df['Date'].dt.year\n",
        "  train_df['day'] = train_df['Date'].dt.day\n",
        "  train_df['hour'] = train_df['Date'].dt.hour\n",
        "  train_df['minute'] = train_df['Date'].dt.minute\n",
        "  train_df['second'] = train_df['Date'].dt.second"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "fOqe1uBFd_Ff"
      },
      "outputs": [],
      "source": [
        "#New columns month , day and  year added to dataset\n",
        "for df in [test_df]:\n",
        "    test_df['month'] = test_df['Date'].dt.month\n",
        "    test_df['year'] = test_df['Date'].dt.year\n",
        "    test_df['day'] = test_df['Date'].dt.day\n",
        "    test_df['hour'] = test_df['Date'].dt.hour\n",
        "    test_df['minute'] = test_df['Date'].dt.minute\n",
        "    test_df['second'] = test_df['Date'].dt.second"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PneETf_7jjL9",
        "outputId": "f67ed387-431b-4938-d9d9-b7846ea7754e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mThe number of duplicate values in the data set is\u001b[0m :  0\n"
          ]
        }
      ],
      "source": [
        "#Check Duplicate Values\n",
        "value=len(train_df[train_df.duplicated()])\n",
        "print(\"\\033[1mThe number of duplicate values in the data set is\\033[0m : \",value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtuQGCZZeQc9",
        "outputId": "9390a92f-53dd-4708-94c5-91da5ca36598"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mThe number of duplicate values in the data set is\u001b[0m :  0\n"
          ]
        }
      ],
      "source": [
        "#Check Duplicate Values\n",
        "value=len(test_df[test_df.duplicated()])\n",
        "print(\"\\033[1mThe number of duplicate values in the data set is\\033[0m : \",value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miGcAbC_kXiI",
        "outputId": "bb58915a-34e5-4b4b-c3a9-806b184998cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ctry_name': 124,\n",
              " 'intgrtd_mngmt_name': 20,\n",
              " 'intgrtd_operating_team_name': 7,\n",
              " 'city': 1900,\n",
              " 'st': 654,\n",
              " 'sec_lvl_domn': 2811,\n",
              " 'device_type': 3,\n",
              " 'operating_sys': 41,\n",
              " 'ip_addr': 7207,\n",
              " 'user_agent': 688,\n",
              " 'VISIT': 65,\n",
              " 'ENGD_VISIT': 29,\n",
              " 'VIEWS': 94,\n",
              " 'page_url': 5457,\n",
              " 'wk': 1,\n",
              " 'mth': 1,\n",
              " 'yr': 1,\n",
              " 'page_vw_ts': 7185,\n",
              " 'target': 2,\n",
              " 'Date': 7185,\n",
              " 'month': 1,\n",
              " 'year': 1,\n",
              " 'day': 2,\n",
              " 'hour': 24,\n",
              " 'minute': 60,\n",
              " 'second': 60}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#Checking the number of unique values in each columns.\n",
        "uniq = dict(train_df.nunique())\n",
        "uniq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_hH5CIazkk8j",
        "outputId": "bb72a6fc-1a39-467e-cf13-0211dab746ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The column \u001b[1m\u001b[4mintgrtd_mngmt_name\u001b[0m has unique values :\n",
            "United States           2053\n",
            "India-South Asia         605\n",
            "Japan                    565\n",
            "DACH                     451\n",
            "CEE                      388\n",
            "Middle East & Africa     331\n",
            "UKI                      303\n",
            "ASEAN                    284\n",
            "Canada                   266\n",
            "SPGI                     250\n",
            "SSA                      240\n",
            "France                   236\n",
            "Greater China            213\n",
            "Brazil                   181\n",
            "Nordic                   172\n",
            "Italy                    153\n",
            "Australia/NZ             142\n",
            "BeNeLux                  141\n",
            "Mexico                   117\n",
            "Korea                    116\n",
            "Name: intgrtd_mngmt_name, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mintgrtd_operating_team_name\u001b[0m has unique values :\n",
            "North America           2319\n",
            "Europe                  2094\n",
            "Asia Pacific            1147\n",
            "Japan                    565\n",
            "Latin America            538\n",
            "Middle East & Africa     331\n",
            "Greater China Group      213\n",
            "Name: intgrtd_operating_team_name, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mdevice_type\u001b[0m has unique values :\n",
            "MOBILEPHONE    132\n",
            "TABLET          21\n",
            "OTHER            1\n",
            "Name: device_type, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4moperating_sys\u001b[0m has unique values :\n",
            "MICROSOFT_WINDOWS10     3932\n",
            "MICROSOFT_WINDOWS7      2196\n",
            "MICROSOFT_WINDOWS8.1     269\n",
            "MACINTOSH_OS X 10.14     264\n",
            "LINUX                    173\n",
            "MACINTOSH_OS X 10.13     119\n",
            "MACINTOSH_OS X 10.12      37\n",
            "IOS_12.1.4                31\n",
            "ANDROID_8.0.0             31\n",
            "MACINTOSH_OS X 10.11      18\n",
            "MICROSOFT_WINDOWS8        16\n",
            "ANDROID_9                 14\n",
            "ANDROID_8.1.0              9\n",
            "ANDROID_6.0.1              9\n",
            "IOS_12.1                   9\n",
            "ANDROID_7.0                9\n",
            "ANDROID_7.1.2              7\n",
            "MACINTOSH_OS X 10.10       7\n",
            "MICROSOFT_WINXP            6\n",
            "ANDROID_5.1.1              5\n",
            "CHROMEOS                   4\n",
            "MACINTOSH_OS X 10.9        4\n",
            "IOS_12.1.2                 4\n",
            "IOS_12.1.1                 3\n",
            "IOS_12.0                   3\n",
            "ANDROID_4.4.2              3\n",
            "MICROSOFT_WINVISTA         3\n",
            "ANDROID_5.1                3\n",
            "ANDROID_7.1.1              3\n",
            "IOS_11.3                   3\n",
            "MICROSOFT_WINNT            2\n",
            "MACINTOSH_OS X 10.8        2\n",
            "IOS_12.1.3                 1\n",
            "IOS_12.2                   1\n",
            "IOS_11.4                   1\n",
            "IOS_12.0.1                 1\n",
            "ANDROID_5.0.2              1\n",
            "IOS_9.3.5                  1\n",
            "IOS_11.2.1                 1\n",
            "IOS_10.2                   1\n",
            "MACINTOSH_OS X 10.6        1\n",
            "Name: operating_sys, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mVISIT\u001b[0m has unique values :\n",
            "1      6226\n",
            "2       578\n",
            "3       150\n",
            "4        71\n",
            "5        36\n",
            "       ... \n",
            "34        1\n",
            "111       1\n",
            "788       1\n",
            "141       1\n",
            "65        1\n",
            "Name: VISIT, Length: 65, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mENGD_VISIT\u001b[0m has unique values :\n",
            "0      3925\n",
            "1      2849\n",
            "2       253\n",
            "3        86\n",
            "4        30\n",
            "5        13\n",
            "6        11\n",
            "7         5\n",
            "9         4\n",
            "12        4\n",
            "8         4\n",
            "25        2\n",
            "33        2\n",
            "23        2\n",
            "10        2\n",
            "19        2\n",
            "39        1\n",
            "17        1\n",
            "287       1\n",
            "70        1\n",
            "35        1\n",
            "22        1\n",
            "11        1\n",
            "318       1\n",
            "16        1\n",
            "49        1\n",
            "68        1\n",
            "13        1\n",
            "281       1\n",
            "Name: ENGD_VISIT, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mVIEWS\u001b[0m has unique values :\n",
            "1      4788\n",
            "2      1100\n",
            "3       412\n",
            "4       231\n",
            "5       103\n",
            "       ... \n",
            "59        1\n",
            "463       1\n",
            "94        1\n",
            "111       1\n",
            "65        1\n",
            "Name: VIEWS, Length: 94, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mwk\u001b[0m has unique values :\n",
            "11    7207\n",
            "Name: wk, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mmth\u001b[0m has unique values :\n",
            "3    7207\n",
            "Name: mth, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4myr\u001b[0m has unique values :\n",
            "2019    7207\n",
            "Name: yr, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mtarget\u001b[0m has unique values :\n",
            "1    6379\n",
            "0     828\n",
            "Name: target, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mmonth\u001b[0m has unique values :\n",
            "6.0    7185\n",
            "Name: month, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4myear\u001b[0m has unique values :\n",
            "2019.0    7185\n",
            "Name: year, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mday\u001b[0m has unique values :\n",
            "4.0    7184\n",
            "5.0       1\n",
            "Name: day, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mhour\u001b[0m has unique values :\n",
            "7.0     559\n",
            "6.0     546\n",
            "8.0     501\n",
            "9.0     447\n",
            "13.0    421\n",
            "5.0     361\n",
            "15.0    353\n",
            "12.0    334\n",
            "3.0     333\n",
            "14.0    318\n",
            "2.0     309\n",
            "11.0    306\n",
            "4.0     290\n",
            "10.0    276\n",
            "16.0    269\n",
            "0.0     256\n",
            "1.0     242\n",
            "17.0    197\n",
            "18.0    193\n",
            "20.0    175\n",
            "19.0    172\n",
            "21.0    131\n",
            "22.0     99\n",
            "23.0     97\n",
            "Name: hour, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mminute\u001b[0m has unique values :\n",
            "39.0    154\n",
            "29.0    151\n",
            "28.0    144\n",
            "55.0    144\n",
            "6.0     138\n",
            "21.0    137\n",
            "52.0    133\n",
            "7.0     132\n",
            "0.0     131\n",
            "56.0    130\n",
            "26.0    129\n",
            "33.0    129\n",
            "27.0    129\n",
            "22.0    128\n",
            "25.0    128\n",
            "35.0    128\n",
            "9.0     127\n",
            "58.0    127\n",
            "24.0    126\n",
            "59.0    126\n",
            "3.0     126\n",
            "8.0     123\n",
            "44.0    123\n",
            "20.0    122\n",
            "36.0    122\n",
            "50.0    122\n",
            "48.0    121\n",
            "1.0     121\n",
            "43.0    120\n",
            "42.0    119\n",
            "30.0    119\n",
            "5.0     118\n",
            "41.0    118\n",
            "47.0    118\n",
            "49.0    117\n",
            "31.0    116\n",
            "32.0    115\n",
            "40.0    115\n",
            "12.0    115\n",
            "38.0    115\n",
            "10.0    114\n",
            "34.0    114\n",
            "18.0    114\n",
            "13.0    113\n",
            "2.0     112\n",
            "51.0    112\n",
            "53.0    110\n",
            "19.0    109\n",
            "57.0    108\n",
            "16.0    108\n",
            "15.0    107\n",
            "54.0    105\n",
            "14.0    105\n",
            "46.0    103\n",
            "23.0    103\n",
            "4.0     100\n",
            "37.0     99\n",
            "17.0     99\n",
            "45.0     98\n",
            "11.0     96\n",
            "Name: minute, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4msecond\u001b[0m has unique values :\n",
            "17.0    147\n",
            "14.0    146\n",
            "21.0    142\n",
            "0.0     138\n",
            "40.0    136\n",
            "2.0     135\n",
            "59.0    133\n",
            "1.0     133\n",
            "36.0    130\n",
            "18.0    129\n",
            "54.0    129\n",
            "4.0     129\n",
            "19.0    128\n",
            "33.0    127\n",
            "53.0    127\n",
            "13.0    126\n",
            "46.0    126\n",
            "22.0    126\n",
            "5.0     125\n",
            "11.0    125\n",
            "30.0    124\n",
            "56.0    123\n",
            "9.0     123\n",
            "26.0    123\n",
            "48.0    122\n",
            "38.0    121\n",
            "45.0    121\n",
            "15.0    121\n",
            "47.0    120\n",
            "51.0    120\n",
            "10.0    120\n",
            "49.0    120\n",
            "31.0    120\n",
            "6.0     119\n",
            "43.0    118\n",
            "34.0    118\n",
            "35.0    116\n",
            "52.0    116\n",
            "55.0    115\n",
            "20.0    114\n",
            "8.0     114\n",
            "12.0    114\n",
            "25.0    113\n",
            "42.0    112\n",
            "27.0    112\n",
            "24.0    112\n",
            "16.0    111\n",
            "58.0    111\n",
            "39.0    111\n",
            "50.0    111\n",
            "41.0    110\n",
            "28.0    108\n",
            "32.0    108\n",
            "44.0    108\n",
            "29.0    107\n",
            "3.0     104\n",
            "7.0     104\n",
            "57.0     98\n",
            "23.0     94\n",
            "37.0     92\n",
            "Name: second, dtype: int64\n",
            " --------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "#Getting the number of unique values of some categorical variables with their counts.\n",
        "for var in uniq :\n",
        "  if (uniq[var]) <= 100:\n",
        "    print(f'The column \\033[1m\\033[4m{var}\\033[0m has unique values :\\n{train_df[var].value_counts()}\\n', '-'*50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpGTMZMe36-j"
      },
      "source": [
        "#Descriptive Statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lC9UBZ37XITr",
        "outputId": "a6a28667-f963-4f9d-acaf-10af3440680c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             VISIT   ENGD_VISIT        VIEWS      wk     mth      yr  \\\n",
              "count  7207.000000  7207.000000  7207.000000  7207.0  7207.0  7207.0   \n",
              "mean      2.258360     0.757736     3.627029    11.0     3.0  2019.0   \n",
              "std      15.889235     6.310398    17.466996     0.0     0.0     0.0   \n",
              "min       1.000000     0.000000     1.000000    11.0     3.0  2019.0   \n",
              "25%       1.000000     0.000000     1.000000    11.0     3.0  2019.0   \n",
              "50%       1.000000     0.000000     1.000000    11.0     3.0  2019.0   \n",
              "75%       1.000000     1.000000     2.000000    11.0     3.0  2019.0   \n",
              "max     788.000000   318.000000   806.000000    11.0     3.0  2019.0   \n",
              "\n",
              "            target   month    year          day         hour       minute  \\\n",
              "count  7207.000000  7185.0  7185.0  7185.000000  7185.000000  7185.000000   \n",
              "mean      0.885112     6.0  2019.0     4.000139     9.805567    29.608490   \n",
              "std       0.318909     0.0     0.0     0.011797     5.847822    17.306694   \n",
              "min       0.000000     6.0  2019.0     4.000000     0.000000     0.000000   \n",
              "25%       1.000000     6.0  2019.0     4.000000     6.000000    15.000000   \n",
              "50%       1.000000     6.0  2019.0     4.000000     9.000000    29.000000   \n",
              "75%       1.000000     6.0  2019.0     4.000000    14.000000    44.000000   \n",
              "max       1.000000     6.0  2019.0     5.000000    23.000000    59.000000   \n",
              "\n",
              "            second  \n",
              "count  7185.000000  \n",
              "mean     29.171051  \n",
              "std      17.430337  \n",
              "min       0.000000  \n",
              "25%      14.000000  \n",
              "50%      29.000000  \n",
              "75%      44.000000  \n",
              "max      59.000000  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VISIT</th>\n",
              "      <th>ENGD_VISIT</th>\n",
              "      <th>VIEWS</th>\n",
              "      <th>wk</th>\n",
              "      <th>mth</th>\n",
              "      <th>yr</th>\n",
              "      <th>target</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>second</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7207.000000</td>\n",
              "      <td>7207.000000</td>\n",
              "      <td>7207.000000</td>\n",
              "      <td>7207.0</td>\n",
              "      <td>7207.0</td>\n",
              "      <td>7207.0</td>\n",
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              "      <td>7185.0</td>\n",
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              "      <td>7185.000000</td>\n",
              "      <td>7185.000000</td>\n",
              "      <td>7185.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.258360</td>\n",
              "      <td>0.757736</td>\n",
              "      <td>3.627029</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.885112</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>4.000139</td>\n",
              "      <td>9.805567</td>\n",
              "      <td>29.608490</td>\n",
              "      <td>29.171051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15.889235</td>\n",
              "      <td>6.310398</td>\n",
              "      <td>17.466996</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.318909</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.011797</td>\n",
              "      <td>5.847822</td>\n",
              "      <td>17.306694</td>\n",
              "      <td>17.430337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>788.000000</td>\n",
              "      <td>318.000000</td>\n",
              "      <td>806.000000</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>59.000000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#Get some statical information of numerical as well as categorical columns\n",
        "train_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7Id9cT34Xmk"
      },
      "source": [
        "#EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "il6LNR3J4gZM",
        "outputId": "54b54c51-0449-4ef9-9595-115a46b921c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['VISIT', 'ENGD_VISIT', 'VIEWS', 'wk', 'mth', 'yr', 'target', 'month',\n",
              "       'year', 'day', 'hour', 'minute', 'second'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "#Assign numerical features.\n",
        "numerical_features = train_df.select_dtypes(include=['int64','float64'])\n",
        "numerical_features.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vrCu3Pj34lPs",
        "outputId": "596ed19c-6bfa-418b-9be4-251ca424b298"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ctry_name', 'intgrtd_mngmt_name', 'intgrtd_operating_team_name',\n",
              "       'city', 'st', 'sec_lvl_domn', 'device_type', 'operating_sys', 'ip_addr',\n",
              "       'user_agent', 'page_url', 'page_vw_ts'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "#Assign categorical features.\n",
        "categorical_features = train_df.select_dtypes(include=['object'])\n",
        "categorical_features.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "I_a7otQn40n3"
      },
      "outputs": [],
      "source": [
        "#We define a function to implement Univariate Analysis.\n",
        "def UVA_KDE(data, var_group):\n",
        "  size = len(var_group)\n",
        "  plt.figure(figsize = (7*size,3), dpi = 100)\n",
        "  for j,i in enumerate(var_group):\n",
        "    mini = data[i].min()\n",
        "    maxi = data[i].max()\n",
        "    ran = data[i].max()-data[i].min()\n",
        "    mean = data[i].mean()\n",
        "    median = data[i].median()\n",
        "    st_dev = data[i].std()\n",
        "    skew = data[i].skew()\n",
        "    kurt = data[i].kurtosis()\n",
        "    points = mean-st_dev, mean+st_dev\n",
        "    plt.subplot(1,size,j+1)\n",
        "    sns.kdeplot(data[i], shade=True)\n",
        "    sns.lineplot(points, [0,0], color = 'black', label = \"std_dev\")\n",
        "    sns.scatterplot([mini,maxi], [0,0], color = 'orange', label = \"min/max\")\n",
        "    sns.scatterplot([mean], [0], color = 'red', label = \"mean\")\n",
        "    sns.scatterplot([median], [0], color = 'blue', label = \"median\")\n",
        "    plt.xlabel('{}'.format(i), fontsize = 20)\n",
        "    plt.ylabel('density')\n",
        "    plt.title('kurtosis = {};\\nskew = {}; range = {}\\nmean = {}; median = {}'.format(round(kurt,2),round(skew,2),\n",
        "                                                                                     round(ran,2),\n",
        "                                                                                     round(mean,2),\n",
        "                                                                                     round(median,2)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "edDFzp-D476R",
        "outputId": "031f8054-e4dc-44bd-96ea-a3e90b321f33"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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utxyamW2hJE0lmR08g2TM57eBHYDDI+KFAoZmZgXklkMzsy3X4yTvyj6TZALKFODbTgzNtmxuOTQzMzOzrKqr6puZmZnZFszJoZmZmZllOTk0M9tCSfqppKhSNkfSHQUKycxaASeHZla0JJVLuk3SW5LWS5pTQ91LJT0qaZGkkPTTel6rvaRrJL0vaZWkienbX6wVquv3LWlPSb+S9Iqk1WndPvW8Vj9JT0paLmmJpLslbd8Et2FWEE4OzayYnZpuFcD7tdS9iuRVf6838Fp3kLzL+R7guyTvO35c0iENPF9rtSfJq/mKXV2/74OB80leDzmjvheRtCPJqxx3I3nTzGiS1wo+JWnr+p7PrDXwUjZmVsx+BJwREZ9I+juwTw11d4mIOZK2I3lVXJ1J+ixwMnBRRIxOy+4C3iJ5R/HABkXfChXoVYTNoa7f96NA14j4WNKFwGfqeZ0fAZ2BAyJiLoCkScBTJK9VvKXekZsVmFsOzVqxzJgwSXtI+qOkCkkfSLpSid6SHpFUKWmhpO/nOUd7SZdLek/SGknzJP1CUvsq9YZLelbS4rTedEln5znfHEl/l3SIpElpV9y/JJ3WnM8in4h4PyI+qWPdOY241IkkLYXZv+gjYjVwG3CwpN6ZcknbSeorqVNtJ02/2xslnZQ+71WSJkjqn+4/K/3eVksan6+7U9JBaZdmhaSVkp6X9Pk89Q6R9Gp6rlmSzqompo3GHEraVtJoSW+m3aaVkp6QtG+V44ak9/O1tEt3fnqtZyTtVtuzaGp1/b4jYklEfNyIS/0v8PdMYpie82ngn8DXGnFes4Jxy6FZcfgTSZfXD0m6rC4DlpC8A/dZ4GJgKDBa0quZRYwltSFpGTmEJLGZAfQHvgfsAXwl5xpnA2+n9dcBxwO/ldQmIm6qEs9uwJ9JkqM7gW8Bd0h6LSLerulGJHUD2tbhnldGxMo61GsJ+wH/jIjKKuWT0p+fAealv48E/h9wKMnbR2ozCPgSkHnGlwB/l/QL4Bzgt0A34AfA7cBhmQMlHQY8AbwGXA5sAIYDz0oaFBGT0nr9gXEkLWg/Jfl//+XAojrE92mSPycPArNJ3qRyFvC8pL0iomp3/g/TOEYDXdK47wEOqukiktql9etiSURsqGPdZiPpU0APYHKe3ZOAY1s2IrOm4eTQrDhMioizACTdAswBrgUuiYhr0vL7SMbdfYtkDBQk4/GOAAZHxEuZk0l6C7hZ0sCIeCUtHpz7nl3gRklPkoyzq5oc7gl8ISJeTM/3AElyNBy4sJZ7eR3YuQ73fDlJItMa9AIW5CnPlO3QiHPvCfTNtHRJWgr8nuQfAHtkWrUktQUukdQn7S4VcDPwHHBMpG80kPR7kiT/KqA8vcYVgIBBOV2fDwFv1iG+N9M4ssmYpLuBd0het3dllfodgM9ExNqc+/mNpH0i4q0arvP59F7qYheS/wYKrVf6s7o/G9tKar8ZddXbFsLJoVlx+EPml4hYL2kysCNJy12mfJmkmSQtPRknkbQWvpOOvcp4Nv15KPBKenw2MZTUBWgHPA8cJalLRFTkHD89kximx36Q59rVGQp0rEO9f9WhTkvpCOT7C351zn4AIuKn1C+pfaZKF+jE9OdDVbo7M+WfJkmMPgPsTpIEdk9yxf+eExiWthwLOAr4a5WuzxmSxlJL61ZuYpMmqF2B5cBMYP88h4zJJIapzJ+TT5OM0azONKCus78X1rFec8t877X92XByaEXFyaFZcZhb5XMFsDoiPsxT3j3n8+5AP6ofkN8j80s6Tu1yktmbVcfLdUnPXV08AEtJuj9rFBEv11anFVoFtM9T3iFnf0Pl+27hv93UVcszz3j39OedNZy7C0ncHYF38+yfSS3JYZpgfpeki3sXNh4S8FGeQ6rez9L0Z41/NiJiKfB0TXVaocz33lx/NswKwsmhWXFYX8cySFqKMtqQdAteUE3deQCSdiVpbXonrTsPWEuSOHyPTSev1eXa+Ssk67/VZczh8ohYXod6LWEB8Kk85ZluxdqW0alJdc+ytmec+U4uAqZWU3c5+ROX+vgRSdfx7cCPSca6bgB+Tf5JjQ36s6Fk2Zdt6xjTBxFR3XVaUqY7uVeefb1Ixka61dCKjpNDs83bLGBfkq7LqKHe8SRJxJdyux4lHdoMMb1K8Y05nAocKqm0yqSUg3L2t7RZ6c/KdHZsXpI+IGm92j3P7j3rcJ0Tgeci4ttVztsVqNpy3RgDKbIxhxHxn/T5Hphn92cpzJ8Ls0Zzcmi2eXuApPXvDKqstyapI9AmIlbw39Ye5ezvQjLBpKm16jGH6djM7YC5ObOl/0wy0eZMklm4pEsBDQcmRkTVLuCW8BpJgnihpHurtrJK2j4iPkjHqI4FviJpp5wJKf1IxiLWZj1VWv0knUTSkvpeU9xIqtWPOUxb2ImIWTnFDwHflNQ78+dA0uEkqwH8quWjNGs8J4dmm7e7SdZauzltBXyZpEu3b1p+FMkyHONIupH/ls52LSFJKBeTv8uswZpyzKGkASTLwECyvE4XSZeln6dFxN9y6g4jabHMjKf8Qk7duyPi3+nvmyxFExETJT0IXC2pB0lS9E2gD8mM3dyYfpo5PiLGN8mN5hERGyR9h2Qpm7cljQH+Q5K0HQpUkrQIk8ZzNPCipN+S/L//PJJZzQNqudTfgZ+k53+FZCmkoTRx8t7UYw7r+n2n/wg6Ly3PrA85UtIyYFlE3Jhz2mfSn31yyn5GMvHrOUm/Iflv5yKS4Rxjmup+zFqSk0OzzViaQHyFZNzgacBXgZUkf7H/hmShXiJipqQTSWa+jiZpmfkdyUSW2wsQel3tz6ZLqWQ+3wn8Laf828DgnM+HphvAS8C/qdlp6bmHkUyueAM4LrOmZI4SIGiB1q2IGC/pYJKxgCPTay8kmdn8+5x6b0g6CriOZFmb+SQJYy9qTw5/RvIGkFOBrwNTSNba/HmT3kzTq+v33Y1N/wxlFpP/N3AjNYiIeZIGkzzbn5P8I+sx4Pseb2jFSjUPQzIzs/pQ8uq0f0fESYWOxcysIZwcmpk1EUmlJK2tn4mIGYWOx8ysIZwcmpmZmVlWvjWqzMzMzGwL5eTQzMzMzLKcHJqZmZlZlpeyaQJK3ni/A/BxoWMxMzOzLcI2wPu1vP2qQZwcNo0dSNYNMzMzM2spO5Isft+knBw2jY8B5s2bR2lpaaFjMTMzs81YZWUlvXv3hmbqsXRy2IRKS0udHJqZmVlR84QUMzMzM8tycmhmZmZmWU4OzczMzCzLYw7NzMy2AOvXr+eTTz4pdBhWB+3ataNt27YFu76TQzMzs81YRLBw4UKWLVtW6FCsHrp27UrPnj1JllJuWU4OzczMNmOZxLBHjx506tSpIMmG1V1EsHLlShYvXgxAr169WjwGJ4dmZmabqfXr12cTw+7duxc6HKujjh07ArB48WJ69OjR4l3MnpBiZma2mcqMMezUqVOBI7H6ynxnhRgn6uTQzMxsM+eu5OJTyO/MyaGZmZmZZTk5NDMzM7MsJ4dmZma22ZkzZw6SmDp1aoOOHz9+PJK2yCWAnByamZlZ0Tj99NP5yle+UugwNmtODs3MzMwsy8mhmZnZFiQiWLFiRYtvEVGvOP/85z/Tv39/OnbsSPfu3TniiCO46KKLuPPOO3nkkUeQhCTGjx8PwKRJk9hvv/3o0KEDBx54IK+//nq9rvf444+zxx570LFjRw499FDmzJmzSZ2XXnqJQYMG0bFjR3r37s3555/PihUrAPjRj37EQQcdtMkx++67L1dccUW9Yik0L4JtZma2BVm5ciUlJSUtft3ly5fTuXPnOtVdsGABp5xyCr/4xS/46le/yscff8yLL77Iaaedxty5c6msrGTMmDEAbLvttixfvpzjjjuOI488kj/+8Y/Mnj2b7373u3WObd68eZxwwgmce+65nHnmmUyePJnvf//7G9WZNWsWRx99NFdddRW33347H3zwASNHjmTkyJGMGTOGoUOHcvXVVzNr1ix23XVXAN5++23eeOMNHnrooTrH0ho4OTQzM7NWZcGCBaxbt44TTjiBnXfeGYD+/fsDydtD1qxZQ8+ePbP177jjDjZs2MBtt91Ghw4d2HvvvZk/fz5nn312na73u9/9jl133ZVrr70WgD333JM333yTa665Jlvn6quvZujQoYwaNQqA3Xffneuvv57Bgwfzu9/9jr333pt9992Xe++9lx//+McA3HPPPRx00EHstttujX8oLcjJoZmZ2RakU6dOLF++vCDXrat9992Xww8/nP79+3PUUUdRXl7OiSeeSLdu3fLWnzFjBgMGDKBDhw7ZsoMPPrjO15sxY8YmXcJVj582bRpvvPEG99xzT7YsItiwYQOzZ8+mX79+DB06lNtvv50f//jHRAT33XcfF1xwQZ3jaC2cHJqZmW1BJNW5e7dQ2rZty1NPPcUrr7zCuHHjuOGGG7j00kuZOHFiwWJavnw5Z511Fueff/4m+3baaScATjnlFC6++GKmTJnCqlWrmDdvHl//+tdbOtRGc3JoZmZmrY4kPv/5z/P5z3+en/zkJ+y888785S9/Yeutt2b9+vUb1e3Xrx933303q1evzrYe/uMf/6jztfr168ejjz66UVnV4/fff3+mT59eYxfxjjvuyODBg7nnnntYtWoVRx55JD169KhzHK2FZyubmZlZqzJx4kR+9rOfMXnyZObOncvDDz/MBx98QL9+/ejTpw9vvPEGM2fO5MMPP+STTz7h1FNPRRJnnHEG06dP5/HHH2f06NF1vt6IESN49913ueiii5g5cyb33nsvd9xxx0Z1Lr74Yl555RVGjhzJ1KlTeffdd3nkkUcYOXLkRvWGDh3K/fffz4MPPsjQoUOb4nG0OCeHZmZm1qqUlpbywgsvcOyxx7LHHntw2WWXce2113LMMcdwxhlnsOeee3LggQey/fbb8/LLL1NSUsLf/vY33nzzTfbbbz8uvfTSjSaT1GannXbioYce4q9//Sv77rsvN998Mz/72c82qjNgwACef/55/vnPfzJo0CD2228/fvKTn7DDDjtsVO/EE0/ko48+YuXKlUW7WLfqu+6QbUpSKVBRUVFBaWlpocMxMzMDYPXq1cyePZtddtllo8ka1vrV9N1VVlbSpUsXgC4RUdnU13bLoZmZmZllFV1yKOlcSXMkrZY0UdJna6l/kqR30vpvSjq2hro3SwpJo5o+cjMzMyuEESNGUFJSkncbMWJEocNrdYpqtrKkrwPXASOAicAoYKykPSNicZ76A4H7gEuAvwOnAn+VtH9EvFWl7leBzwHvN+9dmJmZWUu64ooruPDCC/Pu83CwTRVVcghcANwaEWMAJI0Avgh8C/h5nvrfBZ6MiF+mn38s6UhgJEmCSXqeTwE3AEcBjzVf+GZmZtbSevToUZRLyhRK0XQrS9oaOAB4OlMWERvSz9Utg35wbv3U2Nz6ktoAdwO/jIi36xhLe0mlmQ3Yps43YmZmZtaKFU1yCGwHtAUWVSlfBPTctDqk5bXVvxhYB1xfj1guASpytvn1ONbMzMys1Sqm5LDJSTqApOv59Kjfmj5XA11yth2bITwzMzOzFldMyeGHwHqgrEp5GbCwmmMW1lJ/ENADmCtpnaR1wM7AtZLmVBdIRKyJiMrMBnxcrzsxMzMza6WKJjmMiLXAa8DhmbJ0vODhwIRqDpuQWz91ZE79u4EBwGdytveBX5JMTjEzMzPbohRNcpi6DjhD0jcl9QN+B3QGMrOX75J0dU793wBHS/q+pL6SfgocCNwIEBEfRcRbuRvwCbAwIma24H2ZmZlZI4wfPx5JLFu2rNChFL2iSg4j4k/AhcAVwFSSlr6jIyIz6WQnoFdO/VdI1jY8E5gGnAh8peoah2ZmZlbcBg4cyIIFCzKvlauXyy+/nG984xvNEFVxKrZ1DomIG0lb/vLsG5Kn7EHgwXqcv09DYzMzM9ssrVkKaxbB2grYuiu07wHtuxU6qo1svfXW9OxZ3eIlNXvkkUf44Q9/2MQRFa+iajk0MzOzFrZiHrx8Mvy9H4z7HPy9b/J5xbxmveyQIUM477zzGDVqFN26daOsrIxbb72VFStWMHz4cLbZZht22203nt8fJlMAACAASURBVHjiCWDTbuU77riDrl27MnbsWPr160dJSQlHH300CxYs2Og68+bN4+233+boo48GQBK///3vOe644+jUqRP9+vVjwoQJvPfeewwZMoTOnTszcOBAZs2alT3HrFmz+PKXv0xZWRklJSX8z//8D08//d9llt955x06derEvffemy174IEH6NixI9OnT2+2Z9hQTg7NzMwsvzVLYeJ3YOG4jcsXjkvK1yxt1svfeeedbLfddkyaNInzzjuPs88+m5NOOomBAwcyZcoUysvLGTZsGCtXrsx7/MqVKxk9ejR33303L7zwAnPnzt3kNXqPPvooQ4YM2eg1eldeeSWnnXYaU6dOpW/fvpx66qmcddZZXHLJJUyePJmIYOTIkdn6y5cv59hjj+WZZ57h9ddf5+ijj+b4449n7ty5APTt25fRo0dzzjnnMHfuXObPn8+IESO45ppr2GuvvZrhyTVSRHhr5AaUAlFRURFmZmatxapVq2L69OmxatWqhp2gYkbEPVS/Vcxo2oBzDB48OA455JDs53Xr1kXnzp1j2LBh2bIFCxYEEBMmTIjnnnsugFi6dGlERIwZMyaAeO+997L1b7rppigrK9voOkceeWTceOON2c9AXHbZZdnPEyZMCCBuu+22bNl9990XHTp0qDH+vffeO2644YaNyr74xS/GoEGD4vDDD4/y8vLYsGFDtcfX9N1VVFQEEEBpNENeU3RjDs3MzKyFrK1o3P5GGjBgQPb3tm3b0r17d/r3758tKytLljJevHjxRi1/GZ06dWLXXXfNfu7VqxeLFy/Ofq6srOT555/ntttuq/a6mWtUve7q1auprKyktLSU5cuX89Of/pTHHnuMBQsWsG7dOlatWpVtOcy4/fbb2WOPPWjTpg1vv/02kur1PFqKk0MzMzPLb+taZv7Wtr+R2rVrt9FnSRuVZZKrDRs21Pn4yHkh2hNPPMFee+1F7969qz0uc42arnvhhRfy1FNPMXr0aHbbbTc6duzIiSeeyNq1azc677Rp01ixYgVt2rRhwYIF9OrVi9bIyaGZmZnl174MepZvOuYQkvL2VV9CVlweeeQRvvzlLzf6PC+//DKnn346X/3qV4FkDOKcOXM2qrNkyRJOP/10Lr30UhYsWMDQoUOZMmUKHTt2bPT1m5onpJiZmVl+7bvBQX9IEsFcPcuT8la2nE19rFu3jieeeIIvfelLjT7X7rvvzsMPP8zUqVOZNm0ap5566iatmSNGjKB3795cdtllXHfddaxfv36TyTGthVsOzczMrHqde8Pn789Z57BL0mJYxIkhwPPPP09JSQn7779/o8913XXX8a1vfYuBAwey3XbbcfHFF1NZWZndf9ddd/H444/z+uuvs9VWW7HVVlvxxz/+kUMOOYTjjjuOY445ptExNCXl9r1bw0gqBSoqKiryDog1MzMrhNWrVzN79mx22WUXOnToUOhwWpXzzz+fdevW8dvf/rbQoeRV03dXWVmZeRNMl4iozHuCRnDLoZmZmW1x9tlnHw4++OBCh9EqOTk0MzOzLc6ZZ55Z6BBaLU9IMTMzM7MsJ4dmZmZmluXk0MzMzMyynByamZmZWZaTQzMzMzPLcnJoZmZmZllODs3MzMwsy8mhmZmZmWU5OTQzMzOzLCeHZmZmVrOlS+Gdd2DiRJg5M/nczIYMGcJ5553HqFGj6NatG2VlZdx6662sWLGC4cOHs80227DbbrvxxBNPZI956623OOaYYygpKaGsrIxhw4bx4YcfZvc/+eSTHHLIIXTt2pXu3btz3HHHMWvWrOz+OXPmIImHH36YQw89lE6dOrHvvvsyYcKEZr/f1sTJoZmZmVVv3jw4+WTo1w8+9zno2zf5PG9es1/6zjvvZLvttmPSpEmcd955nH322Zx00kkMHDiQKVOmUF5ezrBhw1i5ciXLli3jsMMOY7/99mPy5Mk8+eSTLFq0iK997WvZ861YsYILLriAyZMn88wzz9CmTRu++tWvsmHDho2ue+mll3LhhRcydepU9thjD0455RTWrVvX7PfbWigiCh1D0ZNUClRUVFRQWlpa6HDMzMwAWL16NbNnz2aXXXahQ4cO9T/B0qVJIjhu3Kb7ysvh/vuhW7fGB5rHkCFDWL9+PS+++CIA69evp0uXLpxwwgncddddACxcuJBevXoxYcIEnn76aV588UXGjh2bPcf8+fPp3bs3M2fOZI899tjkGh9++CHbb789b775Jvvssw9z5sxhl1124Q9/+APf/va3AZg+fTp77703M2bMoG/fvs1yr/nU9N1VVlbSpUsXgC4RUdnU13bLoZmZmeW3aFH+xBCS8kWLmvXyAwYMyP7etm1bunfvTv/+/bNlZWVlACxevJhp06bx3HPPUVJSkt0yyVym6/jdd9/llFNO4dOf/jSlpaX06dMHgLlz51Z73V69emWvsaXYqtABmJmZWStVUdG4/Y3Url27jT5L2qhMEgAbNmxg+fLlHH/88VxzzTWbnCeT4B1//PHsvPPO3Hrrreywww5s2LCBffbZh7Vr11Z73dxrbCmcHJqZmVl+Sddlw/e3oP3335+HHnqIPn36sNVWm6Y3H330ETNnzuTWW29l0KBBALz00kstHWZRcLeymZmZ5VdWlowtzKe8PNnfSpx77rksWbKEU045hVdffZVZs2YxduxYhg8fzvr16+nWrRvdu3fnlltu4b333uPZZ5/lggsuKHTYrZKTQzMzM8uvWzf4wx82TRDLy5PyZpqM0hA77LADL7/8MuvXr6e8vJz+/fszatQounbtSps2bWjTpg33338/r732Gvvssw/f+973+OUvf1nosFslz1ZuAp6tbGZmrVGjZytnLF2aTD6pqEi6ksvKWlViuDkq5Gxljzk0MzOzmnXr5mRwC+JuZTMzMzPLKrrkUNK5kuZIWi1poqTP1lL/JEnvpPXflHRszr52kq5Jy1dIel/SXZJ2aP47MTMzM2t9iio5lPR14DrgcmB/YBowVlKPauoPBO4DbgP2A/4K/FXSPmmVTul5rkx/ngDsCTzajLdhZmZm1moVVXIIXADcGhFjImI6MAJYCXyrmvrfBZ6MiF9GxIyI+DEwBRgJEBEVEXFkRDwQETMj4h/pvgMk7VRdEJLaSyrNbMA2TXiPZmZmZgVTNMmhpK2BA4CnM2URsSH9fHA1hx2cWz81tob6AF2AAJbVUOcSoCJnm19T7GZmZmbFomiSQ2A7oC1Q9UWOi4Ce1RzTsz71JXUArgHuq2Vq+NUkSWRm27HGyM3MzMyKhJeySUlqBzwACDi7proRsQZYk3Ns8wZnZmZm1kKKKTn8EFgPVH1XTxmwsJpjFtalfk5iuDNwWHMsKGlmZmZWDIqmWzki1gKvAYdnyiS1ST9PqOawCbn1U0fm1s9JDHcHjoiIj5owbDMzM2vFhgwZwqhRo7Kf+/Tpw69//esCRlR4xdRyCMkyNndKmgxMAkYBnYExAJLuAv4TEZek9X8DPC/p+8BjwMnAgcCZaf12wJ9JlrE5DmgrKTMecUmakJqZmdkW4tVXX6Vz586FDqOgiio5jIg/SdoeuIJkUslU4OiIyEw62QnYkFP/FUmnAlcBPwPeBb4SEW+lVT4FfCn9fWqVyx0KjG+O+zAzM7PWafvtty90CAVXNN3KGRFxY0TsHBHtI+KgiJiYs29IRJxepf6DEbFnWn+fiHg8Z9+ciFA12/iWuyszM7PWa+lSeOcdmDgRZs5MPje3IUOGcN555zFq1Ci6detGWVkZt956KytWrGD48OFss8027LbbbjzxxBPZY9566y2OOeYYSkpKKCsrY9iwYXz44YfZ/StWrOC0006jpKSEXr16ce21125y3ardytdddx39+/enc+fO9O7dm3POOYfly5dn999xxx107dqVsWPH0q9fP0pKSjj66KNZsGBBMz2Z5ld0yaGZmZm1nHnz4OSToV8/+NznoG/f5PO8ec1/7TvvvJPtttuOSZMmcd5553H22Wdz0kknMXDgQKZMmUJ5eTnDhg1j5cqVLFu2jMMOO4z99tuPyZMn8+STT7Jo0SK+9rWvZc930UUX8fzzz/PII48wbtw4xo8fz5QpU2qMoU2bNlx//fW8/fbb3HnnnTz77LP84Ac/2KjOypUrGT16NHfffTcvvPACc+fO5cILL2yWZ9IiIsJbIzegFIiKioowMzNrLVatWhXTp0+PVatWNej4JUsiyssjYNOtvDzZ31wGDx4chxxySPbzunXronPnzjFs2LBs2YIFCwKICRMmxJVXXhnl5eUbnWPevHkBxMyZM+Pjjz+OrbfeOh544IHs/o8++ig6duwY3/3ud7NlO++8c/zqV7+qNq4HH3wwunfvnv08ZsyYAOK9997Llt10001RVlbWsBtP1fTdVVRUBMkLO0qjGfKaohpzaGZmZi1n0SIYNy7/vnHjkv3dujXf9QcMGJD9vW3btnTv3p3+/ftny8rKktXqFi9ezLRp03juuecoKSnZ5DyzZs1i1apVrF27loMOOihbvu2227LnnnvWGMPTTz/N1VdfzTvvvENlZSXr1q1j9erVrFy5kk6dOgHQqVMndt111+wxvXr1YvHixQ276VbA3cpmZmaWV0VF4/Y3Vrt27Tb6LGmjssxLKDZs2MDy5cs5/vjjmTp16kbbu+++yxe+8IUGXX/OnDkcd9xxDBgwgIceeojXXnuNm266CYC1a/+7oEm+OCPpWSxKbjk0MzOzvLp0adz+lrT//vvz0EMP0adPH7baatP0Ztddd6Vdu3ZMnDiRnXbaCYClS5fyz3/+k8GDB+c952uvvcaGDRu49tpradMmaU974IEHmu8mWgm3HJqZmVleZWVQXp5/X3l5sr+1OPfcc1myZAmnnHIKr776KrNmzWLs2LEMHz6c9evXU1JSwre//W0uuuginn32Wd566y1OP/30bNKXz2677cYnn3zCDTfcwL/+9S/uvvtubr755ha8q8JwcmhmZmZ5desGf/jDpglieXlS3pzjDetrhx124OWXX2b9+vWUl5fTv39/Ro0aRdeuXbMJ4C9/+UsGDRrE8ccfzxFHHMEhhxzCAQccUO059913X6677jquueYa9tlnH+655x6uvvrqlrqlglEx94m3FpJKgYqKigpKS0sLHY6ZmRkAq1evZvbs2eyyyy506NChwedZujSZfFJRkXQll5W1rsRwc1TTd1dZWUmXpE+/S0RUNvW1PebQzMzMatStm5PBLYm7lc3MzMwsy8mhmZmZmWU1KDmUdGhTB2JmZmZmhdfQlsMnJc2SdJmk3k0akZmZmTUpTz4tPoX8zhqaHH4KuBE4EfiXpLGSviZp66YLzczMzBoj8+aOlStXFjgSq6/Md1b17SstodFL2UjaHxgOnJIW3QvcFhHTGhlb0fBSNmZm1lotWLCAZcuW0aNHDzp16pR95Zy1ThHBypUrWbx4MV27dqVXr16b1Gn1S9lExBRJC4GPgB8C3wLOkTQBGBERbzf2GmZmZtYwPXv2BGDx4sUFjsTqo2vXrtnvrqU1ODmU1A74MkkyeCQwGRgJ3AdsD1wFPAjs1fgwzczMrCEk0atXL3r06MEnn3xS6HCsDtq1a0fbtm0Ldv0GJYeSbiDpRhZwN/CDiHgrp8oKSRcC7zc+RDMzM2ustm3bFjThsOLR0JbDvYDzgIcjYk01dT4EvOSNmZmZWRFp6Gzly4EHqyaGkraS9AWAiFgXEc83NkAzMzMzazkNTQ6fA7bNU94l3WdmZmZmRaihyaGAfGvgdAdWNDwcMzMzMyukeo05lPRw+msAd0jK7VZuCwwAXmmi2MzMzMyshdV3QkpF+lPAx8CqnH1rgX8AtzZBXGZmZmZWAPVKDiNiOICkOcDoiHAXspmZmdlmpEFL2UTE5U0diJmZmZkVXp2TQ0lTgMMjYqmk18k/IQWAiNi/KYIzMzMzs5ZVn5bDR4DMBJS/NkMsZmZmZlZgiqi2AdDqSFIpUFFRUUFpaWmhwzEzM7PNWGVlJV26dAHoEhGVTX3+hr5buTcQETE//fxZ4FRgekTc0oTxmZltlpbOr2TRkvZULAu6dhM9uq2h247+x6WZFV5DF8G+l/S9yZJ6Ak8DnwX+T9JPmii2vCSdK2mOpNWSJqaJaU31T5L0Tlr/TUnHVtkvSVdIWiBplaSnJe3enPdgZlu2ebPWcPLwzvTbtz2fG9yBvgPac/LwzsybVd2r6s3MWk5Dk8N9gEnp718D3oyIgcBQ4PQmiCsvSV8HriN5t/P+wDRgrKQe1dQfCNwH3AbsRzJW8q+S9smp9gPgfGAEcBDJG17GSurQXPdhZluupfMr+c6IrRj3dNuNysc93ZbvjNiKpfObvIfIzKxeGpoctuO/k1OOAB5Nf38H6NXYoGpwAXBrRIyJiOkkCd1K4FvV1P8u8GRE/DIiZkTEj4EpwEhIWg2BUcBVEfFIRLwBnAbsAHyluiAktZdUmtmAbZrqBs1s87ZoSftNEsOMcU+3ZdGS9i0ckZnZxhqaHL4NjJA0CDgSeDIt3wH4qCkCq0rS1sABJF3YAETEhvTzwdUcdnBu/dTYnPq7AD3Z+JwVwMQazglwCcnbYjLb/Lreh5lt2SqW1TwJsKLCkwTNrLAamhxeDJwFjAfui4hpafmX+G93c1PbjuT9zYuqlC8iSfDy6VlL/Z45ZXU9J8DVQJecbcca6pqZZXXpqpr3d6l5v5lZc2voG1LGS9oOKI2IpTm7biHp5t2sRcQa/tutTtI7bWZWu7Jt11B+xKZjDgHKj1hP2bZrAHctm1nhNLTlkIhYXyUxJCLmRMTixoeV14fAeqCsSnkZsLCaYxbWUn9hTlldz2lm1mDddizlDzevo/yI9RuVlx+xnj/cvM7L2ZhZwTUoOZRUJuluSe9LWidpfe7W1EECRMRa4DXg8Jw42qSfJ1Rz2ITc+qkjc+rPJkkCc89ZSjJrubpzmpk1Su9d23P/mBXMmLaGf7ywmhnT1nD/mBX03tUthmZWeA3qVgbuAHYCrgQWUMN7lpvYdcCdkiaTjG0cBXQGxgBIugv4T0Rcktb/DfC8pO8DjwEnAwcCZ0KyirekXwOXSXqXJFm8EngfvyLQzJpRtx1L6bbRaGUnhmbWOjQ0OTwEGBQRU5symNpExJ8kbQ9cQTJhZCpwdERkJpTsBGzIqf+KpFOBq4CfAe8CX4mIt3JO+wuSBPMWoCvwUnrO1c19P2ZmZmatTYPerSxpOjA0Il5v+pCKj9+tbGZmZi2lud+t3NAJKaOAn0vq03ShmJmZmVmhNbRb+U9AJ2CWpJXAJ7k7I2LbxgZmZmZmZi2vocnhqCaNwszMzMxahYYugn1nUwdiZmZmZoXX4EWwJe0q6SpJ90nqkZYdI2nvpgvPzMzMzFpSQxfBHgy8SbJY9AlASbprX+DypgnNzMzMzFpaQ1sOfw5cFhFHAmtzyp8FPtfoqMzMzMysIBqaHPYH/pKnfDGwXcPDMTMzM7NCamhyuAzolad8P+A/DQ/HzMzMzAqpocnh/cA1knqSvFe5jaTPA6OBu5oqODMzMzNrWQ1NDn8EvAPMI5mMMh14EXiF5D3GZmZmZlaEGrrO4VrgDElXkIw/LAFej4h3mzI4MzMzM2tZdU4OJV1XS5XPSQIgIi5oTFBmZmZmVhj1aTncr8rn/dPjZ6af9wDWA681QVxmZmZmVgB1Tg4j4tDM75IuAD4GvhkRS9OybsAYkrGHZmZmZlaEGjoh5fvAJZnEECD9/bJ0n5mZmZkVoYYmh6XA9nnKtwe2aXg4ZmZmZlZIDU0O/wKMkXSCpB3T7X+B24CHmy48MzMzM2tJDVrKBhhBsuD1vUC7tGwdSXJ4URPEZWZmZmYF0NB1DlcC50i6CNg1LZ4VESuaLDIzMzMza3ENbTkEIE0G32iiWMzMzMyswBo65tDMzMzMNkNODs3MzMwsy8mhmZmZmWU5OTQzMzOzLCeHZmZmZpbl5NDMzMzMspwcmpmZmVmWk0MzMzMzy3JyaGZmZmZZRZMcStpW0j2SKiUtk3SbpJJajukg6SZJH0laLukhSWU5+/eVdJ+keZJWSZoh6bvNfzdmZmZmrVPRJIfAPcDewJHAccAXgFtqOeZXwPHAScBgYAfg4Zz9BwCLgW+k5/4/4GpJI5s0cjMzM7MioYgodAy1ktQPmA78T0RMTsuOBh4HdoyI9/Mc0wX4ADg1Iv6clvUFZgAHR8Q/qrnWTUC/iDisHvGVAhUVFRWUlpbW7+bMzMzM6qGyspIuXboAdImIyqY+f7G0HB4MLMskhqmngQ3AQdUccwDQLq0HQES8A8xNz1edLsCSmoKR1F5SaWYDtqn9FszMzMxav2JJDnuSdP9mRcQ6kiSuZw3HrI2IZVXKF1V3jKSBwNepvbv6EqAiZ5tfS30zMzOzolDQ5FDSzyVFLVvfFoplH+AR4PKIGFdL9atJWhgz247NHJ6ZmZlZi9iqwNe/Frijljr/AhYCPXILJW0FbJvuy2chsLWkrlVaD8uqHiNpL+AZ4JaIuKq2oCNiDbAm5/jaDjEzMzMrCgVNDiPiA5JJIzWSNAHoKumAiHgtLT6MpOVzYjWHvQZ8AhwOPJSeZ09gJ2BCzrn3Bp4F7oyISxt4K2ZmZmabhaIYcxgRM4AngVslfVbS54EbgfszM5UlfUrSO5I+mx5TAdwGXCfpUEkHAGOACZmZymlX8nPAuLRez3TbvsVv0szMzKwVKHS3cn0MJUkInyGZpfwQcH7O/nbAnkCnnLLv5dRtD4wFzsnZfyKwPck6h9/IKf830KdJozczMzMrAkWxzmFr53UOzczMrKV4nUMzMzMzazFODs3MzMwsy8mhmZmZmWU5OTQzMzOzLCeHZmZmZpbl5NDMzMzMspwcmpmZmVmWk0MzMzMzy3JyaGZmZmZZTg7NzMzMLMvJoZmZmZllOTk0MzMzsywnh2ZmZmaW5eTQzMzMzLKcHJqZmZlZlpNDMzMzM8tycmhmZmZmWU4OzczMzCzLyaGZmZmZZTk5NDMzM7MsJ4dmZmZmluXk0MzMzMyynByamZmZWZaTQzMzMzPLcnJoZmZmZllODs3MzMwsy8mhmZmZmWU5OTQzMzOzLCeHZmZmZpbl5NDMzMzMsoomOZS0raR7JFVKWibpNkkltRzTQdJNkj6StFzSQ5LKqqnbXdJ8SSGpa/PchZmZmVnrVjTJIXAPsDdwJHAc8AXgllqO+RVwPHASMBjYAXi4mrq3AW80SaRmZmZmRaookkNJ/YCjge9ExMSIeAk4DzhZ0g7VHNMF+DZwQUQ8GxGvAcOBgZI+V6Xu2UBXYHRz3oeZmZlZa1cUySFwMLAsIibnlD0NbAAOquaYA4B2aT0AIuIdYG56PgAk7QX8BDgtPV+tJLWXVJrZgG3qcS9mZmZmrVaxJIc9gcW5BRGxDliS7qvumLURsaxK+aLMMZLaA/cBF0XE3HrEcwlQkbPNr8exZmZmZq1WQZNDST9PJ4DUtPVtxhCuBmZExB8bcFyXnG3Hpg7MzMzMrBC2KvD1rwXuqKXOv4CFQI/cQklbAdum+/JZCGwtqWuV1sOynGMOA/pLOjFz2vTnh5L+LyL+X74TR8QaYE1OLLXcgpmZmVlxKGhyGBEfAB/UVk/S/2/v7mMlq+s7jr8/LA/ytIuNUSLWolU2GtuiiBZD4QqSWGoIqdrSkpomBqQttWm1gFhN1aahtlEQaU0IBitGiyHQgqlYSC/lYbEFaYsKajbI0/JQRHcBWUD49o9z7tlh9t69c+fO3LkP71dyMnN+52F+5zuTcz975jdnNwEHJDms/WEJNMFuN+Cbc2x2K/AMcCxwWbufjcDLgU3tOu8E9u7Z5nDg88CvAZsXdDCSJEmrwKSvHA6kqu5I8nXgwiSn0fzQ5LPAV6pqC0CSg4BrgfdU1X9W1dYkFwGfSvIosA04H9hUVTe3+31eAEzyovbpHbOMVZQkSVr1VkQ4bJ1MEwivpflV8WXA+3uW7wFsBPbpafvTnnX3Aq4G/nApOitJkrQSpaom3YcVr72dzdatW7eyfv36SXdHkiStYtu2bWPDhg0AG6pq26j3v1JuZSNJkqQlYDiUJElSx3AoSZKkjuFQkiRJHcOhJEmSOoZDSZIkdQyHkiRJ6hgOJUmS1DEcSpIkqWM4lCRJUsdwKEmSpI7hUJIkSR3DoSRJkjqGQ0mSJHUMh5IkSeoYDiVJktQxHEqSJKljOJQkSVLHcChJkqSO4VCSJEkdw6EkSZI6hkNJkiR1DIeSJEnq7D7pDqwm27Ztm3QXJEnSKjfuvJGqGusLrAVJDgLum3Q/JEnSmvKyqrp/1Ds1HI5AkgAvbWfvA14GPDa5Hq1q+2ONl4J1XhrWefys8dKwzkujt84AW2oMQc6vlUegfWPuT7K+bXqsqvyOeQyaHA5Y47GyzkvDOo+fNV4a1nlpLFWd/UGKJEmSOoZDSZIkdQyHo/UU8LH2UeNhjZeGdV4a1nn8rPHSsM5LY0nq7A9SJEmS1PHKoSRJkjqGQ0mSJHUMh5IkSeoYDiVJktQxHM4hyVFJrkyyJUklObFveZJ8PMkDSZ5Mck2SVy9g/2e1+z139L1fOcZV5yQHJbkkyY/a7W5P8sbxHcnyNY4aJ1mX5BNJ7mq32ZzkI+m5Q+taM0CdfzPJN9rPZCU5dMD9vjvJnUm2t5/j48dzBCvDOOqc5JQk1yf5cTtdk+RN4zuK5W1cn+We7U9qt7titD1fWcZ4zjggyQXtOf2pJN9f6HnDcDi3fYH/Af5ojuVnAO8HTgPeDDwBXJ3kBfPtOMnhwPuA/x1NV1e0kdc5yQuBG4FngF8HXgt8APjx6Lq9oozjs3wm8AfA6cBr2vkzgD8eUZ9XovnqvC9wA02tBpLkLcCXgYuA1wNXAFcked3iurqijbzOwBRNnd8KHAHcC3wjyUHDd3NFG0eNAUhyMPB3p7uG0gAACAdJREFUwPVD9m01Gcc5Y0/g34CDgXcBG4FTgIX9/8tV5TTPBBRwYs98gAeAD/a0bQC2AyfNs6/9gO8DbwOmgXMnfXzLZRpVnYFzgOsnfTzLcRphja8CLupruwy4ZNLHuBym/jr3LTu4XX7oAPv5J+Cqvrabgc9N+hiXwzSqOs+y7TpgG/CeSR/jpKdR1rit643Ae4GLgSsmfXzLZRrhOeM0YDOwx2L645XD4bwCOBC4ZqahqrYC36T5V+euXAB8raqumWc9DV/nE4Bbknw1ycNJbktyyni7umINW+ObgGOTHAKQ5FeAI4F/HV9X16Qj6HlvWlcz/3lGi7MPsAfw6KQ7ssp8FHi4qi6adEdWsROATcAFSR5K8u0kZydZt5Cd7D6evq16B7aPD/W1P9SzbCdJTgLeABw+pn6tNkPVGXglzVeenwL+mqben0nydFV9YeS9XNmGrfE5wHrgziTP0lwR+HBVfWn0XVzTDmTh740W72+ALewczDWkJEfSXDFc0PhELdgrgWOALwHHA68C/p7mHzsfG3QnhsMlkuTngfOA46pq+6T7s8rtBtxSVWe387e1Y7ROAwyHo/FbwMnA7wLfoTnhn5tkiwFcK1mSs4CTgCnP1aORZH/gi8ApVfXIpPuzyu0GPAycWlXPAre2Y2f/HMPh2D3YPr6EZrwWPfP/Pcc2hwEvBr7V84POdcBRSU4H9mrfSO0wTJ1p1/1uX9sdwDtH17VVY9ga/y1wTlV9pZ2/PckvAB/CAD5KD9K8F71ewo73TSOU5IPAWcDbqsofDI7OL9KMm7uy5+/fbgBJfgZsrKrNk+naqvMA8ExfnrgDODDJnlX19CA7cczhcO6iOTkfO9OQZD3NLz03zbHNtcAv0VxhmZluobn0e6jBcFbD1BmaAc8b+9oOAe4edQdXgWFrvA/wXF/bs3hOGbVN9Lw3rePY9XujISQ5A/gI8PaqumXS/Vll7mTnv3//Avx7+/zeyXVt1bkReFWS3nPxIcADgwZD8MrhnJLsR/Nd/YxXtPcYerSq7klzf8K/SPIDmj+wn6AZo3JFzz6uBS6vqs9W1WPAt/te4wngR1X1vPa1ZNR1bps+DdyU5GzgUuBNwKnttOaMqcZXAh9Ocg/N18qvB/4M+PzYD2iZGqDOPwe8HHhpu3xjexXlwap6sN3HPwL3V9WH2nXOA65L8gHgazRfd76RNfpZhvHUOcmZwMdphkn8MMnMmM7Hq+rxsR/UMjPqGrdfz/f//fsJgH//Rn7O+AeaW4ydl+R84NXA2cBnFtS5Sf98e7lONPe9qlmmi9vloTmZPEhz249rgEP69vFD4C938RrTrPFb2YyrzsA7gNvbbe6gGesy8eNdLTUG9gfOpbka+yTNrRP+Cthz0se7jOv8+3Ms763r9Mz6PW3vBr4HPEXzB/b4SR/raqtz+/ne5TZraRrXZ7nvNS5mjd/KZoznjCNobnm1vT03nw2sW0jf0u5IkiRJcnyQJEmSdjAcSpIkqWM4lCRJUsdwKEmSpI7hUJIkSR3DoSRJkjqGQ0mSJHUMh5IkSeoYDiVpmUkynaSSTE+6L5LWHsOhJEmSOoZDSZIkdQyHkiRJ6hgOJUmS1DEcSpIkqWM4lKQhJLmq/UXxzXMsn2qXV5JHk+x0vk1yYM86py3w9X8vyTPttjcmOWDYY5GkXoZDSRrOde3jYUn2m2X50T3PXwj88jzrTA/6wkn+BPgCsDtwNXBcVf1k0O0laVcMh5I0nOn2cXfgyFmWT80z39v2UFXdOciLJvkYcC4Q4FLghKr66SDbStIgDIeSNJxvAY+1z6d6FyTZC/jVdvbK2dZpzVw5vG6WZc+TxvnAR9umC4HfqaqnB++yJM3PcChJQ6iqZ4Eb2tmpvsVvBl4AbAU+3bYd1TvuMMmLgde0s7sMh0l2B74InN42fbKqTq2q54Y+AEmag+FQkoY317jDmSuCNwA3AU+y87jDgcYbJtkbuBw4uW06s6rOXESfJWmXDIeSNLzp9rF/3OHUzPKqegq4ua+99/n/VdV359j//sDXgXcAzwGnVtUnF9VjSZqH4VCShncr8Hj7fAogyZ7AEW3bdN/jVM+2g4w3fANwVPv8c1V14dA9laQBGQ4laUhV9TPgxnZ2qn08HNibZrzhbW3bTAA8KsluSV4EvLZv2Wy+A3yvff6+JL89in5L0q4YDiVpcfrHHU618ze0P1qB5mvl7ewYd3g0za1oYNf3N3wEOAb4AbAOuCTJu0bWc0maheFQkhZnun2cGXd4dF87s4w7nFnnEZqrg3Oqqi00AXFz+xpfTnLi4rstSbMzHErS4twCPNE+Pw54S/t8um+9mfkpdlxd/I+qqvleoKruowmId9EExEuTnDBshyVpVwyHkrQIVfUMze1qAN4L7MvzxxvOmPn6+RjgdX1tg7zOPcBbgbuBPYCvJvmNIbstSXMyHErS4s2EvA3tY+94wxk3A0/R3J5mkPGGO6mqu2kC4r3AnsBlSd4+TIclaS6GQ0lavOl55qmq7ewYdwjwKHD7Ql+oqu6iCYj3A3sBlyc5bqH7kaS5GA4lafH+C/hpz/z0HOv1tg803nA2VbWZJiBuoflv+v45yTHD7EuS+mXIc5MkSZJWIa8cSpIkqWM4lCRJUsdwKEmSpI7hUJIkSR3DoSRJkjqGQ0mSJHUMh5IkSeoYDiVJktQxHEqSJKljOJQkSVLHcChJkqSO4VCSJEkdw6EkSZI6hkNJkiR1/h/5GnKQNNXW1AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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RxgB6y7rUFIi8juuavgrXYoWIRAPTgcWqWtVSVNWlGuct7RF4/p8CW01FZDBuDNysEOveFKpa9AKX3hHgV4dwzQ9x3aZX4iZ5VM1+vS4wUwPul+o0yhhAEUnEtZCWBaQJblgC1NxCWZX3IuAY4ObAVtY2di8YUycLAI05vCQA20XkdVwwVYhrzTgGtyzHQVR1j9dK9CXwsYic6A3Wv85LWy0iT+C+eFNxs0J7AqMDLlMV3A3mwMVvvwDOwnWVfUMdGmsMYB1lLBaR14AHvNbRjbjxX31xM2ADPYdbzDlwgsYjuIDoPa/rsAIX5O7CjTPbT0TO4YfPKRIYJSJVgcg7qvqtl68vrvX0WVWd1sC39h1u9ussEemBG3N5PgePBayPt3FLmvxFRAZ4ZfyEH7pRA9c4rM/9cpBGHAM4FnhZRF7G/WxjccMhTgD+parLqzKKyI9wE1fm4ZbDOR73h8AHeAFvgEO6F4xpdVTVHvawx2HywK1d9hCuGzMfFwCuBK4JyjcfWBOU1h83Zmkd0NlLOwI3oSQTKMd1i/0HOL+asnfhAoKuAWkneGlftPRnE1TXGODP3vsqxQU5Z1STb777NXlQek/cmol5QIH3mQyoJt9s7/1X95gWkG+El/ZACHWfjRsHV92xobhxgAW4rs5/4daqCy6v2mvgulY1KK0zbqHpfNwM82dwXfUKXBSUN+T7pQl/tv2AV3EBdQluHORS4GpAqrnnq2asl+LWYvwdENXY94I97NHaHqJa5yL1xhhjmpCIXIsL3Pur6q6Wrk9dROSnuHX9TtSmn71rjGkCthOIMca0vInAw60x+JODt5ELx+0uko+bXGSMaYNsDKAxxrQwVb2wpetQi797QeBC3ESiKbgu4Nv1wF1HjDFtiHUBG2OMqZGIXIKbQDQAN3ZyI/Coqv6jRStmjDkkFgAaY4wxxrQzNgbQGGOMMaadsQDQGGOMMaadsQDQGGNMSEREReTugNfTvLS+LVYpY0yDWABojGmzRCRORK4TkXkikikiBSKyQkSu8ZYrCc4/QEReF5F9IlIsIl+KyMR6lnmaiHwqInleecu87cMC81wkIi+IyPdegDT/EN+qaSIiEuYFsu+ISIaIFInIGhG5U0RiqsmfKiLPiEi2iJSIyHIRCXkWt4hEi8iDIrLTO3/xIe7XbEyDWABojGnLjgD+jtue66+4PX4347boejowo4j0wi1lciJuF5DbgHhgnrclWJ1EZDpu27AK3JZ3t+C2u+sVlPUa4FwgA9jXgPfVVjyP22pta0tX5BDE4XY36QI8BtyI2xnmHmCut48wsH+f4S9xW+w9jrvfCoBXvdnSoZiN2y7uRdw+zT7gfRE5sTHejDGhslnAxpg2S0Q6A6mqujYo/Wncnq4DVXWjl/ZP4CpghKpu8NLicPvb7lbVo+ooqy9um7wnVPVXdeTtBexQVb+IrAH2qOqE+r/D1kVEFLhHVe9u6bo0FhGJAo5W1a+D0n+PCwJPV9WPvbRbcDu2nKqqn3ppYcAi3B8BfVS1vJayjgUWA7eo6iwvLQZYA2Sr6vjGfn/G1MRaAI1p5UTkbq8bcZDXrZgnIrtF5D5xeonIHBHJF5EsEflNNdeIFpF7RGSjiJR5XV0PiUh0UL7pXvdmtpdvnYhcU831tojIuyJyoogsEZFSEdkkIpc35WcRTFX3BAd/nre856EBaScBK6qCP+/8YuAdYKyIDKyjuBlAOPB7ABGJD2wdCqpXhqr6Q3kPIjJERHqHkK8574NoEflf7/oFXvdoz2qud9AYQBE5V0Te87o4y0QkXUTukqAueRGZ73W1DhORz8R1ye8Qkd+G8rk1FlUtDw7+PDXdQ7urgj/vfD9u7+FuwMl1FHcBrsXvXwHnlwJPAeO8PxyMaRYWABrTdvwb93/2d7hWhDtx3VUfATuAW3GL9M4K7NL0WijewXVX/Qe3jdfbwK+9awa6Bteddz9u8d8M4BERua6a+gwAXvfK/w2uq3O2iAyv642ISEcR6RzCIy6kT+Zg3bznPQFp0UB1O1cUe8+1tgACp+FaC88Wke24rr+9XgB2KL9L1wPP1SN/c9wHT3rXnOeVUwG8F2L9pgGFuC75XwHLgHuBP1WTtyPwAbAKdw99BzwoImfVVYiIJIV4D8WHWO9gTXEPjQH+q6r5QelLvOcj61VDYw6FqtrDHvZoxQ/gbkCBxwPSwnHBmR+4NSA9GfdlNDsg7VJcq8OJQde92rvu+IC02GrK/wBID0rb4p17UkBaF6AUmBXCe6o6v67H3Q34vKKAtcAmICIg/R1ckJoQlP9rr6zf1HHdPCDHe4/34saBveid+0At560B5tdyXGs73tz3ATDae/3PoHxV7/XugLRpXlrfOu6hx4AiIDogbb537mVBP7tM4PUQPo+q8+t6zK7rWjVc/yPvZ54ckPaw9xn2Ccr7slfW3+u45hrgk2rSh3nnX92QutrDHg152F7AxrQdT1b9Q1V9IrIU6InrPqpKzxWRDbjJEVUuxLUyfSduzFyVqm6sibggCA3Y21VEkoBI4HPgDBFJUtW8gPPXqeqCgLJ3V1N2TabiJg/UZVMIeYL9A/eF+mNVrQxIfxQ4B/i3iNyBC0iuBY72jtdVn3i8ljdVfdBLe0NEOgG/EpH7VbWgvpVV1Wq7kWvR1PfB2d7rh4PK/T+gzokOQfdQAq7VbAEu0ByCa+2rUgi8EHBuuYgsIbR76De4FsS67AwhzwFE5HZci++1qpobcOhJ3FCAV0Xk18Au4GfAed7xuu6hWKCsmvTSEM83ptFYAGhM27Et6HUeUKqqe6pJTwl4PRA3jml3DdftWvUPETkBN/B9HG52ZKAk79o11QdcC1udX8qq+lVdeRpC3CD9K4G7VPX9oDLnisj1uK7I5V7yRuAO3MD+wjouXwJ0wLX2BHoZOBPXvffFIb2B0DT1fdAH16KYHnR8AyHwhgD8ETgFSAw6nBT0eruqBs9E3AeMqqscVV0WSn3qS9ySPn8EnlLVR4PK/FbcbN/HgKp7OAvXXf4ood1D0dWkxwQcN6ZZWABoTNvhCzEN3LIoVcKA1bilJ6qTASAi/YFPcOOwbvLSy3EtQr/m4DHDoZRdfQaRLrjuy7oUqmpdX6pV15wGPAg8pqp/rC6Pqv5DRJ7BBRjlwErgl97h/9ZRxE5cELUrKD3bew6lNaoxNOl9cChEJBnXYpyPmyyTjmvdGov72TTmPdQJ12Vcl5Kgluvarnk6bjzme7iWvoOo6usi8g6uqzwc98fEBO9wXfdQJtCjmvQ077nerZXGNJQFgMYc/tJxX1afVNPaEugcXOvET1R1fyuT1HOh5BB9g2tpqss9uLFvtRKRc3Hdc28C1U1Y2U9Vi3DrAVadexqu5aWuVslluACwBwd2TXf3nmtqWWstQr0PtuICtf4c2Oo3OIQyJuBaHaeo6v7WUBHpV+/a1u1N6p51C/AsbqxirUTkONzM36XAz4KGDxxA3VIv3wSce5r3z4/rKGYlMFFEEvXAiSDHBRw3pllYAGjM4e9VXCvelQQsPwEgIrFAmBcUVbXGBC58m4RbT6+xNdoYQG+m6yu47tepGuLyK96544EpwKOBrUQikobrrkxX1Qov+d/Az3Ethnd4+cJwn08OLkBszUK9D+biZoHfwIHB9I0hlFHdPRSFG2vZ2BptDKCIDMW1+m0BJgeOYwzh3IG41sJ3VfW/Aemdgc7ANnXLDYGbNX8zbj3KqnUAo3H30GJVPeRWWGNCZQGgMYe/53ED1R/zWvO+wnVdDfHSz8C1eszDdYv+R0Qex016uBLXxZlWzXUbrLHGAIpIH9zsXsV9uV4oBy7N962qfhuQ91UvfxYwHPfF/S1uV49ADwC/APrhggKAObgu8tu8L/dVwE9xO4tcrar7B/d7QWnVEixdgA4icqf3+oug1jEFPtemXyg6pPtAVVeKyMvAtd4fAF8Dp+KW/anL17gxfM+KyMN4s3wJoUu3vhprDKA3UeVDXDD5Z+DHQfdQuqoGthivA17DjcXsh1s6KYeDu4xnAn/ATa6Z79V5sYi8BjwgIl1xY1B/AfTlh6EIxjQLCwCNOcyp243ip7hxfJfjZiwW41rX/oY3bklVN4jIBbgB8LNwQdKjuK7Np6u5dGvQjx8mFvyzmuP34AI8cOPSMnFfzJ1wa+Y9DPxPKLN3VVW9z/GPwEW4bsUNwKWq+mJQ9lNwX/6B7guo0xfgFpP20jLrKv9QhXofeK7A/dyn4oLcT4EfU8c4QVXdKyKTgb/gPqd9uFm+n+CCrNYohR+28qturcJnCRgygAv8pwOpuDUCXwX+oKrZ1Zxbnctx98JluKDzW1yrY3NMIDJmP9sKzhhjWoiInA28C4xW1dUtXR9jTPthO4EYY0zLmQi8YsGfMaa5WQugMcYYY0w7Yy2AxhhjjDHtjAWAxhhjjDHtjAWAxhhjjDHtjC0D04TELSbVHaj3BvHGGGOMMQ2QAOysY8cfCwCbWHdge0tXwhhjjDHtSk/cWqc1sgCwaRUAZGRkkJiY2NJ1McYYY8xhLD8/n169ekEIPY8WADaDxMRECwCNMcYY02rYJBBjjDHGmHbGAkBjjDHGmHbGAkBjjDHGmHbGxgAaY4wx7ZjP56OioqKlq2FCEBkZSXh4eKNcywJAY4wxph1SVbKyssjNzW3pqph6SE5Oplu3brilhhvOAkBjjDGmHaoK/rp27UpcXNwhBxSmaakqxcXFZGdnA5CWlnZI17MA0BhjjGlnfD7f/uAvJSWlpatjQhQbGwtAdnY2Xbt2PaTuYAsAjTGmifj8yt7CMorKfZRX+gkTSIiJJCk2ktioxhnHY0xDVI35i4uLa+GamPqq+plVVFRYAGiMMS2toLSCxZty+HZ7Lt/uyOP7XYXsyi+l0l/9dpwp8VH07xzP0LQEjunXiWP7dqJrYkwz19q0d9bt2/Y01s/MAkBjjGmgLXuKeH9NJh+v28WqjDx8qiTHRtK3cwfG9k6mc0I0KR2iiY0MIzI8DL9CcXklhWWVZOWXsjO3hA/X7uLZhVsBGN49kTOHd+PsUWn07xLfwu/OGHM4swDQGGPqYW9hGXNW7uT1ZdtZl5lPdEQYo3omMe2EvozskUTXhOh6/4W+r7icdTvzWbo1h0fmp/OXj/7L2N7JXHRML84Z3Z24KPtVbYxpXC2+ELSIXCciW0SkVEQWi8ixdeS/UES+8/KvFpGzg45PEZF5IrJXRFREjgw63tdLr+5xYUC+6o7/vHHfvTGmLVBVvt64h2tfXMZx93/C/e+vp0N0ODeeOpDHLj2Km04fzGlDU0lNjGlQ90zHuChOGNCZX506iMcuPYobThmIz6/87o3VHH//Jzzw/np25JY0wTszpv3ZsmULIsLKlSsbdP78+fMRkTa/fE6L/lkpIhcBfwVmAIuBG4EPRWSwqmZXk3888DJwG/AucAnwtoiMVdU1XrYOwJfAq8AT1RSbAQTPnb4KuAWYG5Q+Hfgg4HXb/mkbY+qlsKySN5ZtZ/bXW9i8p4ieHWO55LjenDCgM4kxkU1SZlREGOP6pzCufwrZ+aV8tH4XLyzeypMLNnPO6DRmTOjPkG6JTVK2MW3ZtGnTyM3N5e23327pqrQJLd2vcBPwhKo+AyAiM4AfA1cAf6om/6+AD1T1z97ru0TkdGAmLohEVZ/3rtW3ugJV1QdkBaaJyHnAq6paGJQ9V1WzCJGIRAPRAUkJoZ5rjGk9MnKKeearLby6NIPi8kqO7deJS4/vw9BuCc06aL5rYgxTj+vD+WN78tmGbN5fncnbK3dy+tBUZp4ygNG9kputLsaYw0uLdQGLSBRwFPBxVZqq+r3X42o4bVxgfs+HteQPpR5HAUcCT1Vz+J8iskdElojIFVL3b/7bgLyAx/aG1ssY07xUlaVbcpjx/FJO/vNnvLY0g1OGdOXhn4/hV6cOYlhaYovNmIyJDOesEWn870VHMuPkI1i7M49z//kVlz+1mKVbclqkTubwo6oUFRU1+0O1+pnyNXn99dcZOXIksbGxpKSkcNppp3HLLbfw7LPPMmfOHEQEEWH+/PkALFmyhDFjxhATE8PRRx/NihUr6lXe+++/z6BBg4iNjWXixIls2bLloDxffvklJ3dWiy0AACAASURBVJ10ErGxsfTq1YsbbriBoqIiAG6//XaOO+64g84ZPXo09957b73q0phasgWwMxAO7ApK3wUMqeGcbjXk73YI9fglsF5Vvw5K/z3wKVAMTAIeAeKBh2u51gO4Lu0qCVgQaEyrVunzM3dNFk8u2MSq7Xl0T45l2vh+/GhQZ6IjWtdafRFhYZw8qCsnDejCos17mbNyBxc8tpDj+nXi+lMGcsKAFFvWwzRYcXEx8fHNP/u8sLCQDh06hJQ3MzOTiy++mIceeojzzjuPgoICFixYwOWXX862bdvIz8/nmWeeAaBTp04UFhYyefJkTj/9dF544QU2b97Mr371q5DrlpGRwZQpU7juuuu46qqrWLp0Kb/5zW8OyJOens6ZZ57JH//4R55++ml2797NzJkzmTlzJs888wxTp07lgQceID09nf79+wOwdu1avv32W954442Q69LYWroLuEWJSCxuHOF9wcdUNTBthYh0wI0TrDEAVNUyoCzg+o1XWWNMo8orqeDf32xj9ldb2JlXyojuidxyxmCO7JVMWCv/vxsWJozv35njj0hh2dZ9vL1yB5c+tZjRPZO4ZsIATh+WSnhY634PxjREZmYmlZWVTJkyhT59+gAwcuRIwO2SUVZWRrduP7QJzZ49G7/fz1NPPUVMTAzDhw9n+/btXHPNNSGV9+ijj9K/f3/+8pe/ADB48GBWr17Ngw8+uD/PAw88wNSpU7nxxhsBGDhwIA8//DAnn3wyjz76KMOHD2f06NG89NJL3HXXXQC8+OKLHHfccQwYMODQP5QGaskAcA/gA1KD0lMJGqMXIKue+etyARAHPBdC3sW4MYfRXqBnjGmDMnKKefqrzfz7mwzKK/2M75/CDacOpE9KaC0QrUmYCMf07cTRfTqyekcec1buZMYLy+jTKY7/96MjmDKmBx2i2/Xf+aYe4uLiKCwMHgrfPOWGavTo0Zx66qmMHDmSM844g0mTJnHBBRfQsWPHavOvX7+eUaNGERPzwyLr48aFPmps/fr1B3XfBp+/atUqvv32W1588cX9aaqK3+9n8+bNDB06lKlTp/L0009z1113oaq8/PLL3HTTTSHXoym02G8GVS0XkWXAqcDbACIS5r3+Rw2nLfSO/19A2uleekP8EnhHVXeHkPdIYJ8Ff8a0Tcu27uOJBZuYtzaLDlERTBrWjUnDU+kYF9XSVTtkIsKonsmM6pnMxuwC3ludyR/mrOGhud9x4dG9mHp8b1tY2tRJRELuim0p4eHhfPTRR3z99dfMmzePv//979xxxx0sXry4xepUWFjI1VdfzQ033HDQsd69ewNw8cUXc+utt7J8+XJKSkrIyMjgoosuau6qHqCl/zT8K/CsiCwFluCWgekAVM0Kfg7Yoaq3efn/BnwuIr8B3gN+DhyNW8YF75xOQG+gu5c02OuKzQqc0SsiA4AfAQesI+gdOwfXsrgIKMUFmbcDsxrlXRtjmoXPr3y0LovHv9jEim25dE+KabXj+xrLgK4J/OrUBHYXlPHx+l28tiyDp7/azFF9OvKzo3ty5vA0kuKaZgkbY5qDiHDCCSdwwgkn8Pvf/54+ffrw1ltvERUVhc/nOyDv0KFDef755yktLd3fCrho0aKQyxo6dCjvvPPOAWnB548dO5Z169bV2p3bs2dPTj75ZF588UVKSko4/fTT6dq1a8j1aAotuhC0qv4buBm4F1iJa2U7U1WrJnr0JmDNPm+ixiW4gG8Vrgv3pwFrAAL8BFiBCxABXvFezwgq/grcBI151VStArgO17K4Ergat2TNPQ15n8aY5lVa4ePFxVs5ZdZ8ZrywnLIKPzdPGsyfLxzN6cNSD9vgL1CXhGguPrY3/7h4LDecMpBKn5/fvbGao/74EdOfWcIrS7axK7+0patpTL0sXryY+++/n6VLl7Jt2zbefPNNdu/ezdChQ+nbty/ffvstGzZsYM+ePVRUVHDJJZcgIlx55ZWsW7eO999/n1mzQm/LmTFjBt9//z233HILGzZs4KWXXmL27NkH5Ln11lv5+uuvmTlzJitXruT7779nzpw5zJw584B8U6dO5ZVXXuG1115j6tSpjfFxHBKp7/RrEzoRSQTy8vLySEy0hVuNaWoFpRW8sGgbTy7YxL7ico7t14nJo7pb96dnX3E5SzbnsGjTXv67qwC/wtC0BMYd0ZnjjujEmF7JdE2MqftCps0rLS1l8+bN9OvX74Dxca3d+vXr+fWvf83y5cvJz8+nT58+XH/99cycOZPdu3czdepUFi5cSGFhIZ999hkTJkxg0aJFzJgxg/Xr1zNs2DDuuusuzj//fFasWMGRRx5ZZ5nvvvsuv/71r8nIyODYY49l+vTpXHHFFezbt4/kZLcW5zfffMMdd9zBwoULUVX69+/PRRddxO23377/Orm5uXTr1o3w8HB27drV4BnXtf3s8vPzSUpKAkhS1fzarmMBYBOyALD9Ka3wkV9SQVmln4SYCBJiIm02ZjPIK6ngma8289SXmymt8PGjgV2YPKo73ZLazhdbcysoreDb7Xms2p7LhqwCsgvc8ObO8VEM757EEV06cETnDnRPjiU1MYauCdEkxkYSE1l762mlz09RmY/C8krySyrco9T9u6C0gsKySgrKKikt91FW6aes0r9/HbjwsDBio8KIi4ogOS6STnFRpCbF0KtjHD07xtZZtgldWw0ATeMFgC09BtCYNktVWZeZz7y1u1iZkcuqjFxySyoOyCNAr05xDO+eyKieyUwc0oXBqc27m8ThrKC0gqe/3MKTCzZRVunn1KFdmTyqO506tP2JHU0tISaSEwZ05oQBnQHYU1jG5t1FbNpTREZOMfPW7iIrvxSf/8BGgqjwMKIjw4gMDyMiTPCr4leoqPRTWumjwldzo0JkuBAXFUFsZDjREWFERYQRES4I7v+Dz6+U+/yUVfgoKKukoLRy/7lhAn1SOjCseyKjeiRxdN+OjOiR1C66841pChYAGlNPZZU+XlmSwUuLt7FhVwHx0REM7BrPqUNT6ZoQTYfoCKIiwij2Wjoyc0vYmlPMZxuyefCD70hLiuHcI3tw0TG96Ne5dc+4a61KK3w8t3ALj8xPp6isktOHpnLO6O4kHwYzeltK5/hoOsdHc0y/TvvTfH4lr6SCfcXl5BZXUFxeSVGZjwqfn0q/4vMrYeIG5UeGC1HhYURGhBEXGU5MZDgdosOJi4ogLso9R0XUb9i5z6/kFJWzu6CUzPxSMnJK2LS7kE/W76K0wk90RBjH9uvEjwZ2YeKQrgzoal39pn5mzJjBCy+8UO2xSy+9lMcee6yZa9R8rAu4CVkX8OHF71fmrNrBnz/cQFZeKcf0dV88o3olERFW9xdbhc/P+sx8vtmyj0Wb9lJYVslx/ToxY0J/JgzqYq2CIfD5lbdX7GDWvA1kF5QxYVAXpoztaS1+7YzPr2zdW8T6zALW7Mxl3c4Cyn1++nXuwJkjuvHjkWkM795yW/e1hLySCnbmlrAzt4S9heXsKy6noLTSC9T9xESGEx8dQacOURzRpQM9EyLI2bWj3XcBZ2dnk59ffU9pYmJii8/UrY6NAWwDLAA8fGTnl3LTq6v4cuMejunbkYuO7k2PjrENvl55pZ9vtuTw4dosvs8uZHC3BG48dSBnjujWrr606mPRpr3c85+1rM8s4Lh+nbjomF6kJTX8Z2AOH+WVftbsyGPp1hyWbt1HQWklfVPiOPfIHpw3pgd9D6OW9qKyStZl5rPee3y/q5D03YXsKz5w+IlrdQ0nIiyMsDD3GZWU+ygqd8uk9EgI539OS6Vfv350SuxAfHSE/e5pIywAbAMsADw8fLYhm5v+vRKAGSf3Z1TP5Ea7tqqyPquAOSt28O2OPEZ0T+TWs4Zw0sAujVZGW5eRU8z/vLeOD9buYmDXeC49vg+DUhNaulqmlar0+1m7I5+v0/fwzZZ9lFT4GNM7mfPH9mTyqLQ2NUzA71fSdxeybOs+lm3dx8qMXNJ3F+JXiAgTenSMpUdyLN2TY+mWGEOXBNeNnxgbUWOvRFmlj6y8UvblFzEyqYyOqT3xhbnu+ZQO0XTqEGUT11o5CwDbAAsA275Xlmzj9rdWc2SvZK7+UX8SY5tuAd11mfm8+k0GG3YVcPKgLtz546EMbMeBTmmFj0fnp/PY5+nER0fw82N7M75/Sqvfp9e0HmWVPpZt3ceC7/fw7fZcwsOEU4d05adjejJxSJdWN4HE51fWZ+azaNNelmzOYcnmHHJLKrwJMHEc0Tme/l3iOaJLB3okxxIRfghL+frKiS3dS68+fdDwKPJLKigq8xERLnRPjiExJtJaBFspCwDbAAsA2y5V5ZH56fz5ww2cPiyVaeP6EtYMfxWrKt9s2cdLS7ayp6Ccy8b14denDWpXOzeoKh+vz+bud9aSXVDK2SPT+OmRPWwJEHNIcovL+Tp9L19t3MOmPUUkREdw5ohuTB7dnfH9U4g8lGCqgfx+ZX1WPos25bAwfQ+LN+dQUFpJVHgYA1PjGdwtgSHdEhnQJZ7YqEa+//cHgH2JjnZBRIXfz97CcorLK0mMiaRXp1jCQxjfbJqXBYBtgAWAbdcj8zfy0AcbOH9sT84f26PZ/xKu8Pn5YE0Wb63YQUxkGL89cwgXHd2rWYLQlrR1bxF3v7OWzzbsZnTPJH4xvq+N8zONbvu+Yr7auJfFm/eSmVdKQkwEpwzpymlDUzlhQOcmm1RUXulnzc48lm7J2d/Cl19aSWS4MCg1gaFpiQxPS6R/1/imD0irCQAdpajcx+6CMiLDw+ibEke0/fHVqlgA2AZYANg2vbl8Oze9uoopY3tw4VG9WrQu+4rLeXnJNhZ8v4dRPZK496cjOLJX441BbC1KK3w89nk6j3yWTmJsBJcf35ej+3a0LijTpFSVLXuLWbo1h+Vb97FlbzEAw9ISObZfJ47slczInkn06RRX7+7WwrJKNmYXsiErn3U781m5PZd1O/Op8CnREWEM7BrPkLREhqa5Fr76LpFzyGoMAJ0Kn5+s/FL8qvRL6UBctK0a11pYANgGWADY9nz5/R5+8cwSThrYmatOOqLVBCAbsgqY/fVmtuwt5sKjevLbM4fQJSG6pavVKD5Zv4u731lLZp7r7j1vjHX3mpaRU1TOmh15rNmRx8bdhWTmub2SI8KE3p3i6J4cS9eEaJLjooiODCMqPAyfXymr9FFYVsmewnKyC9x6hTlF5YBbDD4tOYZ+neMZ0KUDA7om0LdzXEhLRzWpOgJAAL8qWfmlVPj89O8S32b/X86fP5+JEycesHVbW2YBYBtgAWDbsjO3hLP+toC+KXHcfMbglv8FHcTvVz75bhevLd2OX5UbTh3ItBP6trqB7KHavKeI+95dx6ffZTOyh+vu7ZFs3b2m9SgsrWTL3iJ25pWQmVvqFsQuqaCorJJKn9u1JCJMiAwPIyYyjMSYSBJjI+mSEE1qYgxpSTH07BjbOv+PhhAAggsCd+aVoAr9u3QgqjW+lzqUl5eTk5NDampqvf+ov+eee/j+++9rXCy6JdhWcMY0okqfnxteXkFkuDBz4sBWF/wBhIUJpw/rxrgjOvPasgwe/OA7nl+0ldvOGsrZI9vO+oEFpRX849ONPPXlZpLjIrnx1IEc269Tm6m/aT/iYyIY0SOJET2SWroqLSZMhLTEGHbmlbJ5TzEDusYfuExM2T4o2wXleRCVDNFdIbpjy1W4GlFRUXTr1q1B586ZM4ff/e53jVyj1qH1fcsZ0wIe/uR7lm/bx3UTBxAf07r/LoqPiWD6Cf146PzRdE2I5rqXlnPuP77iq417WrpqtSqv9DP7q8386KHPmP31Fn46pgezLhzNcUekWPBnTCsWHhZGamIMFT4/mXklPxwoyoCvfg7vDoV5x8O7Q9zroowmrc+ECRO4/vrrufHGG+nYsSOpqak88cQTFBUVMX36dBISEhgwYABz584FXBewiJCbmwvA7NmzSU5O5sMPP2To0KHEx8dz5plnkpmZeUA5GRkZrF27ljPPPBNwWx4+/vjjTJ48mbi4OIYOHcrChQvZuHEjEyZMoEOHDowfP5709PT910hPT+fcc88lNTWV+Ph4jjnmGD7++OP9x7/77jvi4uJ46aWX9qe9+uqrxMbGsm7duib7DMECQGNYtjWHv3+6kQuO6sWQbm2nq75Hx1huOWMId5w9lOIKH1OfXMxFjy/k6417aE1DOyp9fl5ftp1T/zKfe99dx+heyfzlwtGcP7Zn6+waM8YcJCo8jJQOUeQUlZNbXO5a/hb/P8iad2DGrHkuvWxfk9bn2WefpXPnzixZsoTrr7+ea665hgsvvJDx48ezfPlyJk2axGWXXUZxcXG15xcXFzNr1iyef/55vvjiC7Zt28bNN998QJ533nmHCRMmHDCE67777uPyyy9n5cqVDBkyhEsuuYSrr76a2267jaVLl6KqzJw5c3/+wsJCzj77bD755BNWrFjBmWeeyTnnnMO2bdsAGDJkCLNmzeLaa69l27ZtbN++nRkzZvDggw8ybNiwJvjkfmBjAJuQjQFs/Sp8fn788AL8CvecM7zNLrOiqizduo+3Vuxg854ixvRO5sqTjmDSsNRDWyz2EJRW+JizcgePfJbO1pxijunbkQuP6kWvTnEtUh9jTIAQxwAeSNlVUEZpuY9h8ZnIe0Nrzjp5PSQOaZSqBpswYQI+n48FCxYA4PP5SEpKYsqUKTz33HMAZGVlkZaWxsKFCyktLT1gEsjs2bOZPn06GzdupH///gA88sgj3HvvvWRlZe0vZ9KkSZx77rlcd911gGsBvPPOO7nvvvsAWLRoEePGjeOpp57iiiuuAOCVV15h+vTplJQEtJQGGTFiBDNmzDggUJw8eTL5+flERUURHh7OBx98UGPPiI0BNKYRPLlgMxuzC/mf80a22eAP3C+mY/p24ug+HVmZkct/Vu3k2heXk5YUw9TjejNlbE+6N9MEi525Jby6NIPnF24lp6ico/t25OqT+9PvMNqP1Zj2SegSH832fcX4ynJrDyDK85q0JqNGjdr/7/DwcFJSUhg5cuT+tNTUVACys7OrbYCJi4vbH/wBpKWlkZ2dvf91fn4+n3/+OU899VSN5VaVEVxuaWkp+fn5JCYmUlhYyN133817771HZmYmlZWVlJSU7G8BrPL0008zaNAgwsLCWLt2bbMMi7EA0LRbGTnF/O3j/3LmiDT6phwewYmIMKZ3R8b07sjmPUV8uDaLv3+6kb/M+y/jB6Tw45HdOX1YaqMvIZNXUsGn3+3i7RU7+eK/u4mODOPEAV04e2Q3W8jZmMNImAidOkTjq0ysPYCIatqJM5GRB+6OJCIHpFUFUH6/P+TzA3tE586dy7Bhw+jVq1eN51WVUVu5N998Mx999BGzZs1iwIABxMbGcsEFF1BeXn7AdVetWkVRURFhYWFkZmaSlpZWy7tvHBYAmnbr3v+sIz4mgguP6tnSVWkS/Tp3YMbJ/bl8XB8Wb8rhq/Q93Pn2au54azUjeyQxbkAKx/dLYUSPpHoHhCXlPtZl5rFoUw5fp+9h8aYcKv3KoNR4/t9JRzDuiJTG37rKGNMqxEeHU+TvRFS3SUjwGECAbpMgOrX5K9aI5syZw7nnnnvI1/nqq6+YNm0a5513HuDGBG7ZsuWAPDk5OUybNo077riDzMxMpk6dyvLly4mNbdo/nls8ABSR64BbgG7AKuB6VV1SS/4LgfuAvsD3wK2q+n7A8SnADOAooBMwRlVXBl1jPnBy0KUfV9UZAXl6A48CE4FC4FngNlWtbNAbNa3Ksq05fLR+FzMnDmizi5uGKi4qgolDujJxSFfySytYvnUfq3fk8eo3GTz++SYAOsdH0b9LPD2SY0lNiiE+OoIOXgBX7vNTXO4ju6CMrLxS0ncXsm1vMQrERoYzuFsClx7fh6P7dCQl/vBYnNoYUxtBozpSOvZxYpZffWAQ2G0SHPdkq1sKpj4qKyuZO3fuQZNCGmLgwIG8+eabnHPOOYgId91110GtkjNmzKBXr17ceeedlJWVMWbMGG6++Wb++c9/HnL5tWnRAFBELgL+igvYFgM3Ah+KyGBVza4m/3jgZeA24F3gEuBtERmrqmu8bB2AL4FXgSdqKf4J4PcBr/dPFRKRcOA9IAsYD6QBzwEVwO31f6emNVFV/jT3O/qmxDGuf0pLV6dZJcZEMmFwVyYM7oqqkl1Qxpa9RWzbW0xWfilrM/P5Kn0PpRV+Ssp9AESGC1ERYSTHRZEcG8nQtETOGNaN3ilx9E3pcOCaYMaYdiE6PJzski50GD2blKPykIo81+0bndqmgz+Azz//nPj4eMaOHXvI1/rrX//KFVdcwfjx4+ncuTO33nor+fk/zM147rnneP/991mxYgURERFERETwwgsvcOKJJzJ58mTOOuusQ65DTVp0FrCILAa+UdWZ3uswIAP4u6r+qZr8/wY6qOrkgLRFwMrA1jsvvS+wmZpbAFeq6o011OssXIDZXVV3eWkzgAeBLqpaXsN50UBgE0gCsN1mAbcun23IZvoz3/DbMwYzpnfb/kVljDEN0qBZwAeq9PvJyCmhW1I0XRIado3W6IYbbqCyspJHHnmkpatSrcaaBdxi6wCKSBSum3b/ioiq6vdej6vhtHGB+T0f1pK/NlNFZI+IrBGRB0QkcG2KccDqquAvoJxEYHgt17wNyAt4bG9AvUwT8vuVB+d+x9BuCRzZq+3vCWmMMS0lIiyMhJgIdheU4/cfPkvKjRgxgmuuuaalq9HkWrILuDMQDuwKSt8F1LR4ULca8td3j5eXgK3ATmAUrmVvMDCljnKqjtXkAVyXdpUELAhsVeat28V3WQX84ZxhtvuEMcYcouS4SApKK9hbVN7oqwu0lKuuuqqlq9AsWnwSSEtQ1X8FvFwtIpnAJyLSX1XTazovhOuWAWVVry3AaF1Ulcc+T2doWkKb2vHDGGNaq4iwMOJjItldUEZKh6g2vZ5qe9OSW8HtAXxA8FzxVNzki+pk1TN/qBZ7zwPqKKfqmGmDlm7dx8qMXCaP6t7SVTHGmMNGcmwklX4/uSUVLV0VUw8tFgB6EymWAadWpXmTQE4FFtZw2sLA/J7Ta8kfqiO956qdoBcCI0Wka1A5+UDT7s5smsxj89Pp1THWxv4ZY0wjigwPIy4qgr2FZa1qH3JTu5buAv4r8KyILAWW4JaB6QA8AyAizwE7VPU2L//fgM9F5De4ZVp+DhwN7O+wF5FOQG+gqplnsNcVm6WqWSLSH7d8zPvAXtwYwP8FvlDVb71z5uECvedF5Le4cX9/BP7pdfOaNub7XQV88l02M07uT5h1zRtjTKNKjIkgK7+UknIfcdEtHVqYULRkFzCq+m/gZuBeYCWuJe7MgNm3vXFr8FXl/xoXvF2FWzT6AuCnAWsAAvwEWIELEAFe8V5XLRNTDpyGC/K+A/4CvAGcE1COD5iM66JeCLyAWwcwcN1A04Y8uWAzKR2iOKGdrftnjDHNIS4qnMjwMPYWVbtKmmmFWjxMV9V/AP+o4diEatJeA16r5Xqzgdm1HM/g4F1Aqsu3FTi7rnym9csrqWDOyh385MgeRIS36N88xhhzmBISYiLYV1xBms9vv2vbAPsJmcPeW8u3U+FXJgzu0tJVMcaYw1ZCTCQAOcXWCtgWWABoDmuqyouLt3FUn450jItq6eoYY8xhK1yEDlHh7Csqt8kgbYAFgOawtmzrPr7PLuS0ocGr+hhjjGlsCTGRlFX6KanwtXRVTB0sADSHtRcWbaVbYgzDu9vCz8YY09iiCnKJ27yRuJXLiNu8kaTSAiLDw9hX3HRrAk6YMIHrr7+eG2+8kY4dO5KamsoTTzxBUVER06dPJyEhgQEDBjB37tz956xZs4azzjqL+Ph4UlNTueyyy9izZ8/+4x988AEnnngiycnJpKSkMHnyZNLTf9gXYsuWLYgIb775JhMnTiQuLo7Ro0ezcOGhrkLXciwANIet3OJy3l+dxSlDutrSL8YY08his3cSeelUwoYNJWzc8YQNG0rkpVPpX7qP/JJy/E3YDfzss8/SuXNnlixZwvXXX88111zDhRdeyPjx41m+fDmTJk3isssuo7i4mNzcXE455RTGjBnD0qVL+eCDD9i1axc/+9nP9l+vqKiIm266iaVLl/LJJ58QFhbGeeedh9/vP6DcO+64g5tvvpmVK1cyaNAgLr74YiorK5vsfTYlsX76piMiiUBeXl4eiYnWAtXcXli0ld/PWcM/LxlLso3/M8aYH/jKiS3dS68+fYmOjqn36VEFuUReOhWZN++gYzppErufmE1Ml84kxkY2Rm0PMGHCBHw+HwsWLADA5/ORlJTElClTeO655wDIysoiLS2NhQsX8vHHH7NgwQI+/PDD/dfYvn07vXr1YsOGDQwaNOigMvbs2UOXLl1YvXo1I0aMYMuWLfTr148nn3ySX/7ylwCsW7eO4cOHs379eoYMGdLo77MmpaWlbN68mX79+hETc+DPLj8/n6SkJIAkVc2v7TrWAmgOW2+t2MGonskW/BljTCOL2LOn2uAPQObNI7lgH/uacDbwqFGj9v87PDyclJQURo4cuT8tNdWN+87OzmbVqlV89tlnxMfH739UBWxV3bzff/89F198MUcccQSJiYn07dsXgG3bttVYblpa2v4y2qIWXwfQmKawbW8xy7buY+bEAXVnNsYYUz95ebUeDsvPpyC5Ep/fT3hY47c1RUYe2LIoIgekeTuA4ff7KSws5JxzzuHBBx886DpVQdw555xDnz59eOKJJ+jevTt+v58RI0ZQXn5gEFtTGW2RBYDmsDRn5Q5iIsM4qk/Hlq6KMcYcflw3Y40kOQm/KgWllS3eCzN27FjeeOMN+vbtS0TEwWHP3r172bBhA0888QQnnXQSAF9++WVzV7PZWRewOeyoKm+u2MExPgVHvQAAIABJREFUfToRExne0tUxxpjDTmXnzuikSdUe00mT8HXuQnREOHklTTcbOFTXXXcdOTk5XHzxxXzzzTekp6fz4YcfMn36dHw+Hx07dvz/7d15fB11vf/x1ydp0jRplq5Z6Aq1C21lLxZUkKXiVRC9IooLgsIFQakoKl5QQC6gaEURF7jIqqKoF1SE9lfZS+kGhRYopfuatknapE2a/fP7YyblNM16cpJJct7Px2MezZn5npnPTE9PPv2uDBs2jLvvvps1a9bw9NNPc/XVV0cddrdTAij9zoqt5awvqeT97xkedSgiIv1SbXYejffcc0gS6LNm0XjPPdRm55E1MJW91fU0NkY72LSoqIgFCxbQ0NDArFmzmD59OrNnzyYvL4+UlBRSUlJ45JFHWLZsGdOmTeMb3/gGt99+e6Qx9wSNAu5GGgUcjRv/8QaPvbqVOz97LKkpmv5FROQQXRwF3CR97x4GlJQEfQJzc6kfPpza7DwA6hoa2by7irHDssjthtHAySpRo4DVB1D6lcZG58kVxZwwbqiSPxGRblabnXcg4WsuLTWF9AEpVOyvUwLYC6kJWPqV17bsobiimhPHD406FBGRpJeVPoCK6rpunRRa4qMEUPqVp1YWkzsojckFanIXEYla1sBUGhqdypq+uVpGf6YEUPoNd+dfK7Zz3NghpKj5V0QkcumpKaSlpvSK0cByMCWA0m+8ub2Czbv3M2Ocmn9FRHoHIzM9GA2sQae9ixJA6TeeWllM1sBUphap+VdEpLfITE+lrqGR6vq+uWJGfxV5AmhmV5jZBjOrNrNFZjajnfLnmdmqsPwKM/uPZsc/aWbzzKzUzNzMjm52fKiZ3Wlmb5vZfjPbZGa/MLPcZuW8he0zibtzSbR/rdjOsWOGMCA18o+1iIiEMtJSSTFjb7WagXuTSH9Tmtn5wBzgRuBY4DVgrpmNbKX8ScAfgXuBY4DHgMfMbFpMsSzgReA7rVy2KNy+BUwDvgScFZ6zuYuAwpjtsY7fnfSktbv2sXZXpZp/RUR6GcPISEtl734NBOlNop4H8GrgHne/D8DMLgM+ClwM3NZC+auAp9y9aYru683sTOBK4DIAd38oPNe4li7o7iuB/4zZtdbM/ht42MwGuHvsJ3SPuxfHeW/Sg55+ayfpqSlMH9X2+pQiItLzMtNTKd1XS31Do1ppeonI/hbMLB04DpjftM/dG8PXM1t528zY8qG5bZTvqFygolnyB3CXmZWY2WIzu9jM2hxaamYDzSynaQOyuxiXdND8t3YwtSiHgQO09q+ISG+TmZ6K4+zrQ9PBnHrqqcyePfvA63HjxnHHHXdEGFFiRVkDOBxIBXY0278DmNzKewpaKV8QbxBmNhy4Hri72aHvA08DVcAs4FfAYOAXbZzuWuAH8cYi8SnfX8fSDbu58KSxUYciIiItGJASrAqyt7qevMz0qMOJy5IlS8jKyoo6jISJugk4UmEt3RPAm8ANscfc/YcxL181syzgGtpOAG8l6NPYJBvYkpBgpVXPr95FgzvHjBkSdSgiItKKzLQBB6aDaadBrVcaMWJE1CEkVJQN8SVAA5DfbH8+0Fq/u+JOlm+VmWUDTwF7gU+4e3vDkxYBo8xsYGsF3L3G3SuatvDc0s2eWbWTscMyGT641b8aERHpBpV7jU3rU1m5PJXN61Op3Nt6YpeZnkp9YyP76xq6dM1TTz2Vr33ta8yePZshQ4aQn5/PPffcQ2VlJRdddBHZ2dlMmDCBJ5988sB7Vq5cyUc+8hEGDx5Mfn4+X/jCFygpKXn3Pior+eIXv8jgwYMpLCzkpz/96SHXbd4EPGfOHKZPn05WVhajR4/mq1/9Kvv27Ttw/P777ycvL4+5c+cyZcoUBg8ezFlnncX27du7dP+JElkC6O61wDLg9KZ9ZpYSvl7YytsWxpYPndlG+RaFNX/zgFrgHHev7sDbjgZ2u3tNZ64l3auh0Xn67Z0cM7rlxchFRKR7lO5M5YufT2HqkcbMmcaRRxpf/HwKpTtb7os9MC2FFLOE9AN84IEHGD58OIsXL+ZrX/sal19+Oeeddx4nnXQSr7zyCrNmzeILX/gCVVVV7Nmzh9NOO41jjjmGpUuX8tRTT7Fjxw4+/elPHzjfNddcw3PPPcfjjz/OvHnzePbZZ3nllVfajCElJYVf/OIXvPHGGzzwwAM8/fTTfPvb3z6oTFVVFT/5yU946KGHeP7559m0aRPf+ta3unz/iRB1E/Ac4AEzWwosBmYTTOPSNCr4QWCru18blv858JyZfZOg6fYzwPHApU0nNLOhwBiCqV4AJoVVzcXuXhyT/GUCnweaBmwA7HL3BjM7m6Bm8WWgmiDJ/B7wk8Q/AumK5Zt3s6eqTs2/IiI9qHKvceklMG/ewTV+8+YZl17iPPiwkZV98MofTdPB7KuuZ2QXh0geddRRXHfddQBce+213HbbbQwfPpxLLrkEgO9///v8+te/5vXXX2f+/Pkcc8wx3HLLLQfe/7vf/Y7Ro0ezevVqioqKuPfee3n44Yc5/fSgjumBBx5g1KhRbcbQfIDIzTffzGWXXcavfvWrA/vr6ur4zW9+wxFHHAHAlVdeyU033dS1m0+QSBNAd/+TmY0AbiIYyLEcOMvdmwZ6jAEaY8q/ZGYXADcDtwDvAOeGU7s0OYcwgQw9Ev55I0E/v2OBE8N9a5qFNB7YANQBVwA/AywsdzVwT5y3Kt3k32/tJCdjABNGDI46FBGRpFFaknJI8tdk3jyjtCSFrOxDm3oHpaVSVlVLY6N3ac329773vQd+Tk1NZdiwYUyfPv3Avvz8oLfYzp07ee2113jmmWcYPPjQ3xNr165l//791NbWcuKJJx7YP3ToUCZNmtRmDPPnz+fWW29l1apVVFRUUF9fT3V1NVVVVWRmZgKQmZl5IPkDKCwsZOfOnfHddIJFXQOIu/8S+GUrx05tYd+jwKNtnO9+4P42jj9LkNS1FdNTBP0DpZd7bvUupo/K69IXiYiIdE5FeXzHB6Wn4pVOVW09gzPS4r5+WtrB7zWzg/Y1DTJpbGxk3759nH322fzoRz865DyFhYWsWdO8Lqh9GzZs4GMf+xiXX345//M//8PQoUN58cUX+fKXv0xtbe2BBLClOHvLmsiRJ4Ai8SrZV8Mb2yq4/JQj2i8sIiIJk9POnPutHU9PNVJTjL01XUsAO+PYY4/lr3/9K+PGjWPAgEPTniOOOIK0tDQWLVrEmDFjANi9ezerV6/mlFNOafGcy5Yto7GxkZ/+9KekpATDKf785z933010A03HLX3WgjXBCC6t/iEi0rOGDW9k1qyWa7JmzXKGDW9s8RgYg8J+gD3liiuuoKysjM9+9rMsWbKEtWvXMnfuXC666CIaGhoYPHgwX/7yl7nmmmt4+umnWblyJV/60pcOJHYtmTBhAnV1ddx5552sW7eOhx56iN/85jc9dk+JoARQ+qznV5cwdlgmQ/ropKIiIn1VVrZz9z0ckgTOmhXsbz4AJNag9FT21zVQ39BakphYRUVFLFiwgIaGBmbNmsX06dOZPXs2eXl5B5K822+/nQ984AOcffbZnHHGGbz//e/nuOOOa/WcRx11FHPmzOFHP/oR06ZN4/e//z233nprj9xPolhvaYvuj8LRxeXl5eXk5OS0W146zt2Zccu/OXH8UD53olYAERHplIZaBlWXMnrsOAYOzIj7NJV7gwEfFeVBs++w4Y1tJn8A9Y2NbCqrYuzQTHL1H/hOq66uZv369YwfP56MjIP/7ioqKsjNzQXIDecjbpX6AEqf9PaOvezaW8N7R2n+PxGRqGRle4ujfdsyICWF9NQU9tbUKwGMkJqApU96fvUuBg5IYVJ+FyeTEhGRHpeRlkplTddWBJGuUQIofdLzq0uYUphN+gB9hEVE+pqMtFRq6huo66F+gHKouH57mtmHEh2ISEdV1zWweH0Z04rU/Csi0hcNSgvSj8oELAsn8Ym3+uQpM1trZteZ2eiERiTSjmUbd1Pb0KjpX0RE4hZOnh/RQNDUsB9gItYFTjaJGrwbbwJ4GMHqHZ8C1pnZXDP7tJmpN6d0u5fWlpA7KI1RQwZFHYqISN+UkkojUF29P7IQ1A8wPlVVVcChq4x0VlyjgN29hGCd3J+Z2bHARcCvgF+Z2R+Ae939tS5FJtKKF9eUcGRhDimm5d9EROJiKdSlDqJk1y4AMjIGQQ9/p6Z6A9XVNeytTCUtVf252+PuVFVVsXPnTvLy8khNTe3S+bo8DYy7v2JmxUAp8F3gYuCrZrYQuMzd3+jqNUSaVFTXsWJLORefPD7qUERE+rTGgblU18COnTsjGRHa6FBaWUt9eTqZ6V1LZpJJXl4eBQUFXT5P3AmgmaUBHydI+M4ElgJXAn8ERgA3A48CR3Y5SpHQkvVlNDpMO0z9/0REusSMxow8ajwHGhuAnu8P+Ovn3+aEcUP5xpkTevzafVFaWlqXa/6axJUAmtmdwGcJepE+BHzb3VfGFKk0s28B27oeosi7FqwpZcTggYzMHhh1KCIi/YOlQERNsENzBjPv7TKuPTv+1UgkPvH+jR8JfA0ocvfZzZK/JiWApouRhFqwtoQji3Iw9f8TEenzjizMYX1JJTsrqqMOJenEmwDeCDzq7jWxO81sgJl9EMDd6939ua4GKNKkdF8NbxfvZWqR1lUWEekPJhcGqzkt2bA74kiST7wJ4DPA0Bb254bHRBJu4bpSAKYWqf+fiEh/MCQzncLcDBavL406lKQTbwJotNxbdBhQGX84Iq1buLaUorwMhmZpukkRkf5iYn42i9aXRR1G0unUIBAz+1v4owP3m1lsE3Aq8F7gpQTFJnKQhWtLmVKg5l8Rkf5kckE2z6/eRfn+OnIHdW1yY+m4ztYAloebAXtjXpcDxcDdwOc7c0Izu8LMNphZtZktMrMZ7ZQ/z8xWheVXmNl/NDv+STObZ2alZuZmdnQL58gws7vCMvvM7K9mlt+szBgze8LMqsxsp5ndbmZdnjdR4rNrbw3rSiqZUqgEUESkP5lckIMDr2xUP8Ce1KmExt0vAjCzDcBP3L1Lzb1mdj4wB7gMWATMBuaa2SR339lC+ZMI5hm8FvgncAHwmJkdGzMSOQt4EfgzcE8rl/4Z8FHgPILk9ZfA34CTw+ukAk8QJLUnAYXAg0Ad8L2u3LPEZ1HYP0QJoIhI/5KfM5AhmWks3lDGhyaPjDqcpBFXH0B3v7GryV/oauAed7/P3d8kSASrCCaXbslVwFPufru7v+Xu1wOvEExA3RTbQ+5+EzC/pROYWS7wZeBqd3/a3ZcRLGV3kpm9Lyw2i2Cqm8+7+3J3fxK4HrhC6x1H4+V1pRTmqv+fiEh/Y2ZMKshm0ToNBOlJHU4AzewVMxsS/vxq+LrFrYPnSweOIyZRc/fG8PXMVt42k0MTu7ltlG/JcUBas+uuAjbFnGcmsMLddzS7Tg4wtbUTm9lAM8tp2oDsTsQlbXh5XZlq/0RE+qnJBTm8vqWc6rqGqENJGp1pAn4caBr08VgCrj2cYODIjmb7dwCTW3lPQSvlO7MoXgFQ6+572jhPa9eBtq91LfCDTsQiHVCyr4Y1O/fx4aldX/tQRER6n8kF2dQ3Oss37+F9hw+LOpyk0OEE0N1vbOlnOcitBH0am2QDWyKKpd9YHE4PMKVAFaoiIv3R6CGZZKWnsmR9mRLAHhJXH0AzG21mo2JezzCzO8zs0k6cpgRoAPKb7c8nGHzRkuJOlm/tHOlmltfGeVq7DrRxLXevcfeKpo1gpLR00cvrSinIyWDYYK3/KyLSH6WkGBPzs1myQfMB9pR4J4L+A+E6v2ZWQNCfbgbwP2b2/Y6cwN1rgWXA6U37zCwlfL2wlbctjC0fOrON8i1ZRjCaN/a6k4AxMedZCEw3s9jhSGcCFcCbnbiWJMDL60qZUqjaPxGR/mxifjbLNu6mobGldSYk0eJNAKcBi8OfP00wYOIk4HPAlzpxnjnAJWZ2oZlNAX5NMI3LfQBm9qCZ3RpT/ufAWWb2TTObbGY3AMcTTONC+J6h4dx/R4a7JpnZ0WGiiruXA/cCc8zsQ2Z2XHi9he7+cvieeQSJ3kNmdpSZfRi4Gbir+frH0r3KKmtZvWOfBoCIiPRzkwqyqaxt4O1iNZ71hHgTwDTeHRByBvD38OdVBHPmdYi7/wn4FnATsBw4GjgrZvTtmNjzuftLBHP/XQq8BnwKODdmDkCAc4BXCebxA3gkfH1ZTJlvEMwj+FfgeYJm3U/GXKcB+BhBE/VC4GGCeQA7VLspidPUHDBZK4CIiPRrR4wYzIAUY+lGNQP3hHhXtngDuMzMniBoGr0+3F8EdGoiH3f/JTE1eM2OndrCvkeBR9s43/3A/e1csxq4ItxaK7MR+I/WjkvPWLy+jBGDBzIiW/3/RET6s/QBKYwfkcXSDbv54sxxUYfT78VbA/gd4L+AZ4E/uvtr4f5zeLdpWKTLFq8vY5JG/4qIJIWJI7NZrIEgPSLelUCeJZjHb7i7x67acTcHN7WKxG1fTT1vbCtnsgaAiIgkhUkF2RSXV7N1z/6oQ+n34q0BxN0b3H13s30bWlrDVyQeyzbuptFhivr/iYgkhYn5wX/4l6oWsNvFOw9gvpk9ZGbbzKzezBpit0QHKclpyfoy8galUZibEXUoIiLSA3IHpVGUl8HSDbvbLyxdEu8gkPsJRuj+ENgOaNIeSbhF60uZWJCNmUUdioiI9JD3jNSE0D0h3gTw/cAH3H15IoMRaVJd18DyzXu4YMaYqEMREZEeNKkgm+dX76Kiuo6cjLSow+m34u0DuBlQtYx0m9c276GuwZmsCaBFRJLKpPxsHHh1056oQ+nX4k0AZwO3mdm4xIUi8q4lG8rISk9lzJDMqEMREZEeVJibQU7GAJapGbhbxdsE/CcgE1hrZlUEa+se4O5DuxqYJLdF68uYmJ9NSooqmkVEkomZ8Z78bJZoIEi3ijcBnJ3QKERi1Dc0smzjbs45qijqUEREJAIT87N57NWt1Dc0MiA17hnrpA1xJYDu/kCiAxFp8tb2vVTVNmj9XxGRJDUpP5v9dQ28tX0v00flRh1OvxR3Wm1mR5jZzWb2RzMbGe77iJlNTVx4kowWbygjLdU4fERW1KGIiEgExg/PYkCqsXSj+gF2l3gngj4FWAGcCHwSGBweOgq4MTGhSbJasr6UCSMHk6ZqfxGRpJQ+IIUjhg/WhNDdKN7fsLcB17n7mUBtzP6ngfd1OSpJWu7O4g27mZSv5l8RkWT2nvzBLN1YhrvWmugO8SaA04H/a2H/TmB4/OFIslu7q5KyylomF2RHHYqIiERoUn42Oypq2Lpnf9Sh9EvxJoB7gMIW9h8DbI0/HEl2SzaUkWLvLgguIiLJqen3wLKNagbuDvEmgI8APzKzAoJ1gFPM7GTgJ8CDiQpOks+S9WWMG57FoPTUqEMREZEI5QxKoygvQ/0Au0m8CeD3gFUES8INBt4EXgBeAm5OTGiSjBatL2OSav9ERASYODKbJVoRpFvElQC6e627XwIcDnwM+Dwwyd2/4O4NiQxQkse2PfvZume/5v8TEREAJhZk83bxXiqq69ovLJ3S4YmgzWxOO0XeZxYs2+XuV3cmCDO7ArgGKABeA77m7ovbKH8e8ENgHPAO8B13/1fMcSOYjuYSIA9YAFzu7u+Ex08Fnmnl9DPcfUm4zvH6Fo7PdPeXO3F70kFN/8vTABAREQGYnJ+NA69u2sMpE0dEHU6/0pmVQI5p9vrY8P1vh68nAg3Ass4EYGbnA3OAy4BFBMvMzTWzSe6+s4XyJwF/BK4F/glcADxmZse6+8qw2LeBrwMXEiRxPwzPeaS7VxM0VTcfxPJD4HRgabP9ZwBvxLwu7cz9ScctXl/GYXmDyBmUFnUoIiLSCxTkZpA7KI2lG8qUACZYhxNAd/9Q089mdjWwF7jQ3XeH+4YA9xH0BeyMq4F73P2+8DyXAR8FLiaYb7C5q4Cn3P328PX1ZnYmcCVwWVj7Nxu42d0fD8/5RWAHcC7wiLvXAsUx95MGfBy40w+dcKjU3YuRbrdofZlq/0RE5AAz4z0jB7NEA0ESLt5BIN8Erm1K/gDCn68Lj3WImaUDxwHzY87TGL6e2crbZsaWD82NKT+eoCk59pzlBLWLrZ3zHGAYQQLb3N/NbKeZvWhm57RzPwPNLKdpA5TNdFBZZS1rdu5jkhJAERGJMakgm+WbdlPX0Bh1KP1KvAlgDtBSXewIOpf0DAdSCWrnYu0gSOJaUtBO+YKYfR0955eBue6+JWbfPoJk9jyCGskXCZqa20oCrwXKY7YtbZSVGE39/6YUagCIiIi8a2J+NtX1jby5rSLqUPqVeBPA/wPuM7NPmtmocPtP4F7gb4kLr/uZ2SjgwwSxH+DuJe4+x90XufsSd/8u8DDBYJXW3ArkxmyjuinsfmfJ+jJGDB7I8MEDow5FRER6kfHDs0hPTdF0MAkWbwJ4GfAk8AdgY7j9AXgK+GonzlNCMHAkv9n+fGL66DVT3E754ph9HTnnRQQDO/7egXgXARNaO+juNe5e0bQR9JOUDli0vkzNvyIicoi01BSOGJnFUq0IklDxzgNY5e5fJeg3d0y4DXX3r7p7ZSfOU0swavj0pn1mlhK+XtjK2xbGlg+dGVN+PUGiF3vOHODE5ucMB4xcBDzo7h2ZZOhoYHsHykkn7Kup581tFRoAIiIiLZqYH0wIfeg4TYlXZ6aBOUSY7L3exRjmAA+Y2VJgMcEI3izCARlm9iCw1d2vDcv/HHjOzL4JPAF8BjgeuDSMyc3sDuA6M3uHd6eB2QY81uzapxEMGvnf5kGZ2YVALfBquOuTBCOTv9LF+5VmXtm4mwZ3Jqv/n4iItGBSfjaPL9/GhtIqxg/PijqcfqFLCWAiuPufzGwEcBPBII3lwFnu3jSIYwzQGFP+JTO7gGDJuVsIJoI+N2YOQIAfEySRdxNMBP1ieM7qZpf/MvCSu69qJbzrgbFAPcHSd+e7+1/ivllp0eL1ZeQOSqMoNyPqUEREpBeamJ+NEfQXVwKYGKbq1O4TNj2Xl5eXk5Oj2q3WfOrXL5GSYnzjjIlRhyIiIr3UtX97nePHDeUn5x0VdSi9VkVFBbm5uQC54ViEVsU7CEQkIarrGnhtyx6maP1fERFpw8T8bBav10jgRFECKJF6ZdNu6hqcKYUaACIiIq2bXJDDprIqdlY0780l8VACKJFavL6M7IwBjB6aGXUoIiLSi00OKwoWaz7AhFACKJF6eV0Zk/KzSTGLOhQREenFhmSmU5CTwRI1AyeEEkCJTE19A69u2s1k9f8TEZEOmFSQzSIlgAmhBFAi8/qWcmrqG9X/T0REOmRSQTZvF++lfH9H1m6QtigBlMgsWldKZnoq44ZpTicREWnf5IJsnGABAekaJYASmQP9/1LU/09ERNpXkJPBkMw0NQMngBJAiURtfSPLNu7W8m8iItJhZsbkghxeXlcadSh9nhJAicTrW/awv66BqUVKAEVEpOOmFOawYks5lTX1UYfSpykBlEgsXFtKlvr/iYhIJx1ZmEODO0vVD7BLlABKJBasLWVyQQ6p6v8nIiKdUJSXQe6gNBapGbhLlABKj6uua+CVjbs5Us2/IiLSSWbGlMJsFioB7BIlgNLjXtm0m9qGRvX/ExGRuKgfYNcpAZQet3Btqdb/FRGRuB1ZmEN9o7NM/QDjpgRQetxLa0uZUpij9X9FRCQuh+UNCvoBrlczcLyUAEqPqqyp57XNe9T8KyIicTvQD3CtEsB4KQGUHrV0427qG52phblRhyIiIn3YlMIcXlc/wLgpAZQetWBNCUMy0yjKy4g6FBER6cOmFeVS3+gs1rJwcekVCaCZXWFmG8ys2swWmdmMdsqfZ2arwvIrzOw/mh03M7vJzLab2X4zm29m72lWZoOZebPtu83KvNfMXgivs9nMvp24u05Oz63exbTDcjH1/xMRkS4ozM1g2OB0XlxTEnUofVLkCaCZnQ/MAW4EjgVeA+aa2chWyp8E/BG4FzgGeAx4zMymxRT7NvB14DLgRKAyPGfzaqfvA4Ux250x18kB5gEbgeOAa4AbzOzSrtxvMtu1t4a3i/cy/TA1/4qISNeYGVMLc5QAxinyBBC4GrjH3e9z9zcJkrYq4OJWyl8FPOXut7v7W+5+PfAKcCUEtX/AbOBmd3/c3V8HvggUAec2O9dedy+O2Spjjn0OSAcudvc33P0R4BdhvBKHBeE/UiWAIiKSCNMOy+Xt4r2U7KuJOpQ+J9IE0MzSCWrX5jftc/fG8PXMVt42M7Z8aG5M+fFAQbNzlgOLWjjnd82s1MxeNbNrzGxAs+s87+61za4zycyGtHI/A80sp2kDslu5h6T0/Du7GDssk7zM9KhDERGRfmBaWKHwkkYDd1rUNYDDgVRgR7P9OwiSuJYUtFO+IGZfW+f8BfAZ4EPAb4HvAT/uwHVir9HctUB5zLallXJJx9154Z0S1f6JiEjCDMlMZ/SQQSx4R83AnTWg/SL9k7vPiXn5upnVAr81s2vdPd665FsJ+jM2yUZJIACrd+xj194aJYAiIpJQU4tyeWHNLtxdAww7IeoawBKgAchvtj8fKG7lPcXtlC+O2dfRc0LQRDwAGNfOdWKvcRB3r3H3iqYN2NvG9ZLKC+/sIi3VmFygCaBFRCRxph2Wy7Y91Wwqq4o6lD4l0gQw7F+3DDi9aZ+ZpYSvF7bytoWx5UNnxpRfT5CgxZ4zh2A0cGvnBDgaaAR2xlzng2aW1uw6b7u7Fh/spBfeKWFyQQ7pA6L+P4eIiPQnUwqzSbHg94x0XG/4bTwHuMTMLjSzKcCvgSzgPgAze9DMbo0p/3PgLDP7pplNNrMbgOOBXwK4uwN3ANeZ2TlmNh14ENhGMGUMZjbTzGab2VFmdriZfQ74GfBwTHL3B6AWuNcIJ6slAAAcWklEQVTMpobT1VzFwU280gHVdQ0sWleq5l8REUm4zPQBTMrP5tm3d7ZfWA6IvA+gu//JzEYANxEMrlgOnOXuTQMuxhDUzDWVf8nMLgBuBm4B3gHOdfeVMaf9MUESeTeQB7wYnrM6PF5DMADkBmAgQa3hz4hJ7ty93MxmAXcR1FKWADe5+92Ju/vksHBdKdX1jRw9Oi/qUEREpB967+g8/r58GzX1DQwckBp1OH2CBRVm0h3Cpufy8vJycnKSt+/b9x9fyZMri/n5+Uerg66IiCTchtJKrv3bCn7/lRM5ecLwqMOJTEVFBbm5uQC54ViEVvWGJmDpx9ydf7+1k6NH5yn5ExGRbjF2aCZDMtN4ZpWagTtKCaB0q7W79rF1z36OUfOviIh0EzPjqFF5PKN+gB2mBFC61dOrdjJwQApTizQAREREus/Ro/NYu6uSLbs1HUxHKAGUbvXvt3YytUjTv4iISPeaPiqXFINn394VdSh9gn4rS7epqK5j2cbdHKXmXxER6WaZ6QOYVJCtZuAOUgIo3eaF1SXUNzrHjB4SdSgiIpIEjh6Vx4I1JVTXNUQdSq+nBFC6zdw3tjN+eCYjsgdGHYqIiCSB48YNpbquUauCdIASQOkWNfUN/PutnRw/dmjUoYiISJI4LG8Qh+UNYu4bxVGH0uspAZRu8dKaUiprGzhhnBJAERHpOcePG8L8t3ZQ39DYfuEkpgRQusVTK4spystg1JBBUYciIiJJ5PixQ9lTVceSDbujDqVXUwIoCVff0Mi8N4s5fuxQrf4hIiI96vARWQzLSmfem2oGbosSQEm4JRt2s7uqjhnj1fwrIiI9K8WMY8cO4amVxbh71OH0WkoAJeGeWrmdYYPTOXx4VtShiIhIEjph3FC2l1fzxraKqEPptZQASkI1NDpPrizmBDX/iohIRKYUZpM9cABPrNgedSi9lhJASaiX15Wyc28NJ08YFnUoIiKSpAakpDBj/FAeX76VxkY1A7dECaAk1P+9upXC3AyOGDE46lBERCSJnTxhONv2VPPKJo0GbokSQEmY6roGnlyxnZOOGKbmXxERidSkgmyGDU7n8eXbog6lV1ICKAkz/60dVNY2cPKE4VGHIiIiSS7FjJmHD+Ofr2+jTpNCH0IJoCTMY69uZcLIwRTmavJnERGJ3vsnDGd3VR0vam3gQ/SKBNDMrjCzDWZWbWaLzGxGO+XPM7NVYfkVZvYfzY6bmd1kZtvNbL+ZzTez98QcH2dm95rZ+vD4WjO70czSm5XxFrb3Jf4J9H27K2t59u1dnHyEav9ERKR3GDM0k1FDBvHY8q1Rh9LrRJ4Amtn5wBzgRuBY4DVgrpmNbKX8ScAfgXuBY4DHgMfMbFpMsW8DXwcuA04EKsNzZoTHJxPc+38BU4FvhGVvaeGSZwCFMduyeO+1P3t8+VYcmHmERv+KiEjvYGacPGE4c1cWU76/LupwepXIE0DgauAed7/P3d8kSMSqgItbKX8V8JS73+7ub7n79cArwJUQ1P4Bs4Gb3f1xd38d+CJQBJwL4O5PuftF7j7P3de5+9+BnwCfbOF6pe5eHLPpE9SMu/Pwok0cP3YIuYPSog5HRETkgFMmjqCu0XnsVdUCxoo0AQybXI8D5jftc/fG8PXMVt42M7Z8aG5M+fFAQbNzlgOL2jgnQC5Q1sL+v5vZTjN70czOaeP9mNlAM8tp2oDstsr3F0s27GbNzn2cMSU/6lBEREQOMiQznePGDOH3izZqabgYUdcADgdSgR3N9u8gSOJaUtBO+YKYfR06p5lNAL4G/DZm9z7gm8B5wEeBFwmamttKAq8FymO2LW2U7Td+//JGCnMzOLIoJ+pQREREDnHa5JGs3rFPcwLGiDoBjJyZHQY8BTzq7vc07Xf3Enef4+6L3H2Ju38XeBi4po3T3UpQk9i0jerG0HuF0n01/Gvldk6bPJIUzf0nIiK90PRRueTnDOQPizZHHUqvEXUCWAI0AM3bDvOB4lbeU9xO+eKYfW2e08yKgGeAl4BLOxDvImBCawfdvcbdK5o2YG8Hztmn/WVZUMn5wYkjIo5ERESkZSlmnDppJP98fRvlVerKDxEngO5eSzCq9vSmfWaWEr5e2MrbFsaWD50ZU349QaIXe84cgtHAC2P2HQY8G17/orDvYXuOBrSydKi+oZEHF27kxPHDyMnQ4A8REem9Tp04goZG509LN0UdSq8wIOoACKaAecDMlgKLCUbwZgH3AZjZg8BWd782LP9z4Dkz+ybwBPAZ4HjCGjx3dzO7A7jOzN4hSAh/CGwjmDImNvnbCHwLGNG0dJm7F4dlLgRqgVfD636SYGTyV7rjIfRFT6zYztY9+7nytFYrRUVERHqFvMx0Tp4wnP99YT0XnjSOgQNSow4pUpEngO7+JzMbAdxEMEhjOXCWuzcN4hgDNMaUf8nMLgBuJpi37x3gXHdfGXPaHxMkkXcDeQQDOM5y9+rw+JkETbkTOHSgRmxHtuuBsUA9sAo4393/0rU77h/cnV89s5ajR+cyblhW1OGIiIi06+z3FvHc6l08/uo2Pn3C6KjDiZRpSHT3CZuey8vLy8nJ6V8jZJ9etYOL71/K9z92JFMK+9e9iYhI//XTeW9TWlnLv68+hZSU/jV4saKigtzcXIDccCxCq6IeBCJ91K+eWcvE/MFMLkiKqQ5FRKSfOPuoItaXVDLvzeazxSUXJYDSaS+vK2Xpxt2cfVQRpqlfRESkD5mYn82UgmzuemZNUk8MrQRQOqWx0bnlibc4YkQWx44ZEnU4IiIinfafx41ixdZynliRvBN7KAGUTvnniu28vrWcC04cq4mfRUSkT5palMsxo/P40ZOrqK3vyCxw/Y8SQOmwmvoGfvTkKo4fO4QjNfBDRET6sM/OGMPWPft5+OWNUYcSCSWA0mEPvrSR7eX7+cyMMVGHIiIi0iWjh2ZyysSR/Pzf71C+P/lWB1ECKB2yuayKn81fzRlT8jksb1DU4YiIiHTZecePora+kduefCvqUHqcEkBpl7vz3b+9TmZ6Kucn+cSZIiLSfwzJTOezM0bzx8WbefGdkqjD6VFKAKVdjyzZzII1pXzl/YeTmR754jEiIiIJc/qUfKYW5fCdv75OZU191OH0GCWA0qbNZVXc/M83+dCkERw1Oi/qcERERBIqxYxLPnA4JftquOVfydMUrARQWlVVW88lDy5lcMYAPnfi2KjDERER6Rb5ORl87sSx/H7RJv6ybEvU4fQIJYDSInfnW4++xobSSr555iSyBqrpV0RE+q8zpozk1Ikj+N7fVvDa5j1Rh9PtlABKi+58eg3/WlHM5adMYPTQzKjDERER6VZmxsXvH8/YYZlc+tBSisurow6pWykBlEP89rm1zPl/qznvuFHMGD806nBERER6RFpqCrPPmEhDo/OZuxf26yRQCaAc5LfPreXWJ1fxiWMO4xPHHBZ1OCIiIj1qaFY61330SPbV1PfrJFAJoABQ19DIDx5feSD5O++4UZjW+hURkSSUn5NxIAn85K8WsGJLedQhJZwSQGHn3mouuOdlHl60iYtOHqfkT0REkl5+TgY3nD2VQemp/OevX+LRpZujDimhzN2jjqHfMrMcoLy8vJycnJyowzlEY6Pz56WbueXJt0g1Y/YZE5mYnx11WCIiIr1GbX0jDyzcwNOrdnLmlHxu+PjUXrskakVFBbm5uQC57l7RVlklgN2otyaA7s5La0v56by3eWXTHk6ZOIILZowhZ1Ba1KGJiIj0Si+vK+WhlzdSVVvP5adM4EsnjSM3s3f93uxzCaCZXQFcAxQArwFfc/fFbZQ/D/ghMA54B/iOu/8r5rgBNwKXAHnAAuByd38npsxQ4E7gbKAR+CtwlbvviynzXuAu4ARgF3Cnu/+4E/fVqxLAypp65r1ZzO9e3MCKreUcPjyLz71vLEcWRh9bcykGo7NqGDJgNyl1FTSm5bK7Po/NlQNpjP4jK5IwKQaDLZO6fensrYChuc6I9D3klq6lMTub3Vl5bHZ97qX/6kvf91W19fz1la3Mf3MHA1KNz84Yw/knjO41rWd9KgE0s/OBB4HLgEXAbOA8YJK772yh/EnA88C1wD+BC4DvAMe6+8qwzHfC4xcC6wmSxenAke5eHZZ5EigE/gtIA+4Dlrj7BeHxHGA1MB+4NXz/74DZ7n53B+8t8gRw2579LFhTwvOrd/H/3tpBdV0j04pyOPuoIqYfltsr+/qlGEwbUkHmK5dhO+Yd2N9YMIv9x/yGlbtzet2Xgkg8UgxyG3O56opU5s1799/irNPr+d+rVjD6sx+g8eST2X/Xb1iZos+99D999ft+T1Utc98oZv5bO9lXU8+kgmzOmlrAyROGc/ToPNIHRDPEoq8lgIsIEq8rw9cpwGaC2rbbWij/JyDL3T8Ws+9lYLm7XxbW/m0DfuruPwmP5wI7gC+5+yNmNgV4EzjB3ZeGZc4C/gWMcvdtZnY58D9AgbvXhmVuA85198kdvLceSQCr6xoo2VfD9vJqNpZWsbG0kje3V/DG1gqKK6oxYPzwLE4YN5STJwxjRHZGt8WSCGMH11C48qKDvgyaNBbMonjqfWzcNzCCyPoOd6d6f1XUYUg7slMy+e7XM3j66UP/I3baqfU8cMJdDLn9v2k87TR2/OK3bGpMjyBKiVfGoMxe+Z/s3qSvf9/XNTTy2pY9vLSmlNe37qGypoGMASlMKsxmalEu7xk5mDFDMxk9NJOR2QPJHZTWrZ+JPpMAmlk6UAV8yt0fi9n/AJDn7h9v4T2bgDnufkfMvhsJErOjzOxwYC1wjLsvjynzHEGSeJWZXUyQIA6JOT4AqAbOc/f/M7MHgRx3PzemzIeAp4Gh7r67hdgGArGf1Gxgy+bNm7slAXR3PnzH82zb0/ocReOGZTIpP5usjL6zlNtVM2DYCye3erz0Awv4easdBASgtno/v770jKjDEElql989n/SM3jlYoLfoT9/3jY3O5rL9vL2zgn3VDa2W+8HZR3Le8aO7JYaKigpGjx4NHUgAo84KhgOpBLVzsXYArdWyFbRSviDmOB0oc1DzsrvXm1lZszLrWzhH07FDEkCCZucfNN8Z/mVEYjPwQmRXj8/P2y3R+peFiEhvof+EtS8Zv++/cgd8pfsvkw306gSwv7kVmNNs31CgLM7zZQNbgFHA3i7E1Zcl+zNI9vsHPQPdf3LfP+gZJPv9Q+eeQTZBV7g2RZ0AlgANQH6z/flAcSvvKW6nfHHMvu3NyiyPKTMy9gRhE/DQZudp6Tqx1ziIu9cANc12t5mBtyWmn8De9qpy+6tkfwbJfv+gZ6D7T+77Bz2DZL9/6PQz6NAzinQlkHBwxTLg9KZ94SCQ04GFrbxtYWz50Jkx5dcTJGix58wBTowpsxDIM7PjYs5xGsHzWBRT5oNmFjvJz5nA2y31/xMRERHpK3rDUnBzgEvM7MJwdO6vgSyCaVkwswfN7NaY8j8HzjKzb5rZZDO7ATge+CWAB6Na7gCuM7NzzGw6wTQz24DHwjJvAU8B95jZDDM7OXz/I+7eVG36B6AWuNfMpobT1VzFoU28IiIiIn1K1E3AuPufzGwEcBPB4IrlwFnu3jTgYgzBRM1N5V8yswuAm4FbCCaCPrdpDsDQjwmSyLsJJoJ+MTxn7HDZzxEkff/m3Ymgvx5znXIzm0UwEfQygubqmzo6B2CC1BBMaN28WTmZJPszSPb7Bz0D3X9y3z/oGST7/UM3PIPI5wEUERERkZ7VG5qARURERKQHKQEUERERSTJKAEVERESSjBJAERERkSSjBLCXMrP/NrOXzKzKzPa0UmaMmT0RltlpZreHE1r3C2Z2hZltMLNqM1tkZjOijqm7mNkHzewfZrbNzNzMzm123MzsJjPbbmb7zWy+mb0nqngTzcyuNbMlZrY3/Cw/ZmaTmpXJMLO7zKzUzPaZ2V/NrPlk7X2SmV1uZq+bWUW4LTSzj8Qc77f33hIz+2747yB2zfd+/QzM7IbwnmO3VTHH+/X9A5jZYWb2cHiP+81shZkdH3O8v38PbmjhM+Bmdld4PKGfASWAvVc68CjBvIiHMLNU4Imw3EnAhcCXCKbT6fPCeRfnEAx7PxZ4DZhrZiPbfGPflUVwj1e0cvzbBNMUXUYwqXklwfPI6Jnwut0pBFMuvY9gwvU0YJ6ZZcWU+RlwNnBeWL4I+FsPx9ldtgDfBY4jmNf0aeBxM5saHu/P934QMzsB+C/g9WaHkuEZvAEUxmzvjznWr+/fzIYAC4A64CPAkcA3gdiFF/r79+AJHPz3f2a4/9Hwz8R+BtxdWy/eCJK6PS3s/wjhMnox+y4DyoH0qONOwH0vAn4Z8zoF2Ap8N+rYeuDenWBuy6bXRrCs4bdi9uUC1cBnoo63m57BiPA5fDDmfmuBT8WUmRyWeV/U8XbTMygDvpxM9w4MBlYDZwDPAncky98/cAOwvJVjyXD/twEvtHE8Gb8H7wDWhPee8M+AagD7rpnACn93wmyAuUAOMLXlt/QNZpZOUBMyv2mfuzeGr2dGFVeExhNMkh77PMoJkuT++jxywz/Lwj+PI6gVjH0Gq4BN9LNnYGapZvYZglrhhSTRvRPUAj/h7vOb7U+WZ/CesBvIOjP7vZmNCfcnw/2fAyw1s0fDbiCvmtklMceT6nsw/D34eeB3HmR7Cf8MKAHsuwqAHc327Yg51pcNB1Jp+f76+r3Fo+mek+J5WLAe+B3AAn93hZ8CoNbdm/eH7TfPwMymm9k+gpn+fwN8wt3fJAnuHSBMeo8Frm3hcDI8g0UELT5nAZcTJDwvmFk2yXH/hxPc9zvAhwm6P/3CzC4MjyfV9yBwLsFKZveHrxP+Geg3Awb6AjO7DfhOO8WmhFm9SLK6C5jGwf2fksHbwNEEtZ+fAh4ws1OiDalnmNlognXez/SDl+xMGu7+ZMzL181sEbAR+DSwP5qoelQKsNTdvxe+ftXMphF0bXogurAi82XgSXff1l0XUA1gz/opMKWdbV0Hz1UMNB/9kx9zrC8rIezf2Gx/Pn3/3uLRdM/9/nmY2S+BjwEfcvctMYeKgXQzy2v2ln7zDNy91t3XuPsyd7+WYFDQVSTBvRM0b40EXjGzejOrJ+jk/vXw5x30/2dwkLCmZzUwgeT4DGwH3my27y2gqRk8mb4HxxL0g/3fmN0J/wwoAexB7r7L3Ve1s9V28HQLgenNRsWeCVRw6D+iPiV8BsuA05v2hc2CpxPcd7JZT/APPPZ55BCMgusXzyOc3uGXwCeA09x9fbMiywhGB8Y+g0kEvxz6xTNoQQowkOS4938D0wlqQJu2pcDvY37u78/gIGY2GDiCIDFKhs/AAmBSs30TCWpBIQm+B2NcBOwkmOmjScI/A2oC7qXCzr9DCf5yU83s6PDQGnffB8wjSPQeMrNvE/QBuBm4y91roog5weYQNIEtBRYDswk6xd8XaVTdJPyynxCza3z4d17m7pvC+dCuM7N3CL4IfwhsAx7r+Wi7xV3ABcDHgb1m1tSnpdzd97t7uZndC8wxszKC/+jcCSx095ejCTlxzOxW4EmCDt3ZBM/iVODD/f3eAdx9L7Aydp+ZVQKlTf1A+/szMLOfAP8gSHiKCKbAagD+mAyfAYIpTl4ys+8BfwZmAJeGG+7uSfA92FTZcRHwgLvXN+3vls9A1MOctbU6/Pt+guHdzbdTY8qMBf4FVAG7gJ8AA6KOPYHP4EqCL8Magg7SJ0YdUzfe66mt/H3fHx43gjkeiwmmPZgPTIw67gTef0v37sCXYspkECSKZQTzf/0NKIg69gTd/73AhvCzvjP8+z0zGe69jWfyLOE0MMnwDIBHCJKZGoJ5IR8BjkiW+w/v8WPAivA77i3gkmbH+/X3YHiPs8LvvkPuK9GfAQtPKiIiIiJJQn0ARURERJKMEkARERGRJKMEUERERCTJKAEUERERSTJKAEVERESSjBJAERERkSSjBFBEREQkySgBFBEREUkySgBFRLrIzE41Mw+3U6OOR0SkPUoARURERJKMEkARkSSi2koRARgQdQAiIn2duz9LsFC9iEifoBpAERERkSSjBFBEREQkySgBFJF+y8xuaOrvFr7OCfetMLN9ZrbTzP5lZic1e99IM7vZzN4ws0ozKzWzx83smFau02a/OjO7Pzy2IXydZ2Y3xZx/j5k9b2afa+NexsVc40vt3PeGsNz9zd8PPBNT9JmYc7Z5bjP7kJk9YGbrzKzKzCrC53i7mRW1FY+I9D7qAygiScHMRgPzgYkxu7OAjwCzzOyz7v6omb0X+BdwWEy5TOAc4MNm9hF3j02iOhvHJOApYFyzQx8APmBmM939ynjPn2hmlgHcB3ymhcPTwu3y8Pn9o0eDE5G4qQZQRJLFo8Ao4FbgFOAE4BtABZAK3Gtm44F/AoOA/wbeD5wI/ACoBQYC95tZepwxZAL/AIYBNwOnAscDlwBbwjJXmNmH4zx/W7YC04GLY/ZdHO6L3R5rOmhmBvyFd5O/fwBfAE4GZgJXAZsIEum/mNnx3RC3iHQD1QCKSLI4GjjF3RfF7FtqZu8QJH3ZwCKC0bwz3H1tTLnFZlYC3AWMAT4K/F8cMYwA0oGZ7v5GzP5lZvYssALIAL4KzI3j/K1y9zpgpZkNj9m93t1XtvG2rxDcax1wjrs/1ez4y2b2EPACMBW4gyBpFpFeTjWAIpIs7miW/AHg7k8AG8OXI4DrmyV/Te4DqsOfP9CFOK5vlvw1xbGGd2vfIk+iwtq/74Qvf9FC8geAu+8Grglfnmxm7+mJ+ESka5QAikiyeKSNY6+Hfzrwp5YKuPt+4J3w5eFxxuDAH9o4viz8c6iZ5cV5jUQ5Ejgi/Pkv7ZR9Pubnmd0TjogkkpqARSRZrG7j2J7wz5KwRqu9ctlxxlDi7qVtHC+L+Tk75npRiO3PtzCoEOyQgm6IRUQSTDWAIpIU3L2qjcON4Z9tlYktlxpnGB09f1eukSgj43xfZkKjEJFuoRpAERFpSWwCejawoYPv25n4UEQk0ZQAioj0DbG1g+213mQl4HqxTdV72hktLCJ9jJqARUT6hr0xPw9prZCZDSWYZ7A13sHrvRrz88kdfI+I9BFKAEVE+oBwcErToJC2Jlz+DMFchq2pjvl5YBvlXuHdyakvDVcEEZF+QgmgiEjf0TTdysfN7IjmB8Nl5n7Yzjm2x/x8yDmauHsjcEv48nDgQTNrNWEM11nuNUvYiUjb1AdQRKTv+BXBmsSDgGfN7AaCptrBwOkES7PtAhoIJrU+hLtvMrMtBMvifSv8+e3wPQA73L2pufk3wJnAJ4DzgGPN7LfAYqAcyAEmEyxpdw5B7eIvE3e7ItJdlACKiPQR7j7XzH4BfJ0ggfvfZkU2ESRiT7ZzqlsIksnxwOPNjl0E3B9ez83sfODnwGUENYY/buO8GgEs0keoCVhEpA9x96uACwiagyuA/QQ1eLcBx7r7Wx04x6+B/wTmESRt9W2UrXP3rwJHAXcSrFdcTlBjWA4sB+4FPgVMifvGRKRHmXtHB4SJiIiISH+gGkARERGRJKMEUERERCTJKAEUERERSTJKAEVERESSjBJAERERkSSjBFBEREQkySgBFBEREUkySgBFREREkowSQBEREZEkowRQREREJMkoARQRERFJMkoARURERJKMEkARERGRJKMEUERERCTJ/H+LoFFXYjYQ6AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Check distribution of all numerical features individually\n",
        "for num in numerical_features:\n",
        "  UVA_KDE(train_df,[num]);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qo376RWe6Fm3"
      },
      "source": [
        "##Check Outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 937
        },
        "id": "Y12KeWAZ6DTU",
        "outputId": "b28c60b7-7632-42fe-ad13-f9dfa33d4806"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 13 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Plotting the boxplot for all independent numerical variables.\n",
        "n = 1\n",
        "plt.figure(figsize=(25,25))\n",
        "for j in numerical_features:\n",
        "  plt.subplot(6,4,n)\n",
        "  n=n+1\n",
        "  sns.boxplot(x=train_df[j])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2Osqhrh7ALO"
      },
      "source": [
        "##Multicollinearity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "zmuTKWfzYsv9",
        "outputId": "868bb22f-a468-4a24-db38-074dcd5aad9f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Check multicollinearity among all independent variables\n",
        "plt.figure(figsize=(15,8))\n",
        "correlation = train_df.corr()\n",
        "sns.heatmap(abs(correlation), annot=True, cmap='coolwarm');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zH8FqYH7J5t"
      },
      "source": [
        "#Feature Engineering & Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "PqUIbc94TITb"
      },
      "outputs": [],
      "source": [
        "#Fill the missing values sec_lvl_domn column\n",
        "train_df['sec_lvl_domn'].fillna(train_df['sec_lvl_domn'].mode()[0], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "TVP451GgTM4t"
      },
      "outputs": [],
      "source": [
        "#Fill the missing values sec_lvl_domn column\n",
        "test_df['sec_lvl_domn'].fillna(test_df['sec_lvl_domn'].mode()[0], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "yQoOt2H9z9O4"
      },
      "outputs": [],
      "source": [
        "#Create new column by combining ENGD_VISIT & VIEWS\n",
        "train_df['mean_view_visit_engd'] = train_df.ENGD_VISIT + train_df.VIEWS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ULAfWkCGevAy"
      },
      "outputs": [],
      "source": [
        "#Create new column by combining ENGD_VISIT & VIEWS\n",
        "test_df['mean_view_visit_engd'] = test_df.ENGD_VISIT + test_df.VIEWS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "bGOJTUHfu3wc"
      },
      "outputs": [],
      "source": [
        "cols = ['intgrtd_mngmt_name','intgrtd_operating_team_name','st','operating_sys','user_agent','sec_lvl_domn','page_url']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBX1HJGxy9t_",
        "outputId": "52537cd6-0ff1-4e8b-ca50-76b8baa53e9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The column \u001b[1m\u001b[4mintgrtd_mngmt_name\u001b[0m has unique values :\n",
            "United States           2053\n",
            "India-South Asia         605\n",
            "Japan                    565\n",
            "DACH                     451\n",
            "CEE                      388\n",
            "Middle East & Africa     331\n",
            "UKI                      303\n",
            "ASEAN                    284\n",
            "Canada                   266\n",
            "SPGI                     250\n",
            "SSA                      240\n",
            "France                   236\n",
            "Greater China            213\n",
            "Brazil                   181\n",
            "Nordic                   172\n",
            "Italy                    153\n",
            "Australia/NZ             142\n",
            "BeNeLux                  141\n",
            "Mexico                   117\n",
            "Korea                    116\n",
            "Name: intgrtd_mngmt_name, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mintgrtd_operating_team_name\u001b[0m has unique values :\n",
            "North America           2319\n",
            "Europe                  2094\n",
            "Asia Pacific            1147\n",
            "Japan                    565\n",
            "Latin America            538\n",
            "Middle East & Africa     331\n",
            "Greater China Group      213\n",
            "Name: intgrtd_operating_team_name, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mst\u001b[0m has unique values :\n",
            "TOKYO                   400\n",
            "CALIFORNIA              292\n",
            "NONE                    196\n",
            "TEXAS                   167\n",
            "NEW YORK                163\n",
            "                       ... \n",
            "NAGASAKI                  1\n",
            "RAVENNA                   1\n",
            "PENZENSKAYA OBLAST'       1\n",
            "COAHUILA DE ZARAGOZA      1\n",
            "ZULIA                     1\n",
            "Name: st, Length: 654, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4moperating_sys\u001b[0m has unique values :\n",
            "MICROSOFT_WINDOWS10     3932\n",
            "MICROSOFT_WINDOWS7      2196\n",
            "MICROSOFT_WINDOWS8.1     269\n",
            "MACINTOSH_OS X 10.14     264\n",
            "LINUX                    173\n",
            "MACINTOSH_OS X 10.13     119\n",
            "MACINTOSH_OS X 10.12      37\n",
            "IOS_12.1.4                31\n",
            "ANDROID_8.0.0             31\n",
            "MACINTOSH_OS X 10.11      18\n",
            "MICROSOFT_WINDOWS8        16\n",
            "ANDROID_9                 14\n",
            "ANDROID_8.1.0              9\n",
            "ANDROID_6.0.1              9\n",
            "IOS_12.1                   9\n",
            "ANDROID_7.0                9\n",
            "ANDROID_7.1.2              7\n",
            "MACINTOSH_OS X 10.10       7\n",
            "MICROSOFT_WINXP            6\n",
            "ANDROID_5.1.1              5\n",
            "CHROMEOS                   4\n",
            "MACINTOSH_OS X 10.9        4\n",
            "IOS_12.1.2                 4\n",
            "IOS_12.1.1                 3\n",
            "IOS_12.0                   3\n",
            "ANDROID_4.4.2              3\n",
            "MICROSOFT_WINVISTA         3\n",
            "ANDROID_5.1                3\n",
            "ANDROID_7.1.1              3\n",
            "IOS_11.3                   3\n",
            "MICROSOFT_WINNT            2\n",
            "MACINTOSH_OS X 10.8        2\n",
            "IOS_12.1.3                 1\n",
            "IOS_12.2                   1\n",
            "IOS_11.4                   1\n",
            "IOS_12.0.1                 1\n",
            "ANDROID_5.0.2              1\n",
            "IOS_9.3.5                  1\n",
            "IOS_11.2.1                 1\n",
            "IOS_10.2                   1\n",
            "MACINTOSH_OS X 10.6        1\n",
            "Name: operating_sys, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4muser_agent\u001b[0m has unique values :\n",
            "MOZILLA/5.0 (WINDOWS NT 10.0; WIN64; X64) APPLEWEBKIT/537.36 (KHTML  LIKE GECKO) CHROME/72.0.3626.121 SAFARI/537.36                   1857\n",
            "MOZILLA/5.0 (WINDOWS NT 6.1; WIN64; X64) APPLEWEBKIT/537.36 (KHTML  LIKE GECKO) CHROME/72.0.3626.121 SAFARI/537.36                     732\n",
            "MOZILLA/5.0 (WINDOWS NT 10.0; WOW64; TRIDENT/7.0; RV:11.0) LIKE GECKO                                                                  439\n",
            "MOZILLA/5.0 (WINDOWS NT 6.1; WOW64; TRIDENT/7.0; RV:11.0) LIKE GECKO                                                                   418\n",
            "MOZILLA/5.0 (WINDOWS NT 10.0; WIN64; X64; RV:65.0) GECKO/20100101 FIREFOX/65.0                                                         286\n",
            "                                                                                                                                      ... \n",
            "MOZILLA/5.0 (WINDOWS NT 10.0; WOW64; TRIDENT/7.0; BWB30012014; RV:11.0) LIKE GECKO                                                       1\n",
            "MOZILLA/5.0 (LINUX; ANDROID 8.1.0; JSN-L21) APPLEWEBKIT/537.36 (KHTML  LIKE GECKO) CHROME/72.0.3626.121 MOBILE SAFARI/537.36             1\n",
            "MOZILLA/5.0 (WINDOWS NT 6.1; WIN64; X64) APPLEWEBKIT/537.36 (KHTML  LIKE GECKO) CHROME/65.0.3325.181 SAFARI/537.36 FIREPHP/4CHROME       1\n",
            "MOZILLA/5.0 (LINUX; ANDROID 4.4.2; LG-D625) APPLEWEBKIT/537.36 (KHTML  LIKE GECKO) CHROME/72.0.3626.121 MOBILE SAFARI/537.36             1\n",
            "MOZILLA/5.0 (COMPATIBLE; MSIE 9.0; WINDOWS NT 6.1; WOW64; TRIDENT/7.0)                                                                   1\n",
            "Name: user_agent, Length: 688, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4msec_lvl_domn\u001b[0m has unique values :\n",
            "COMCAST.NET          2149\n",
            "RR.COM                 63\n",
            "VSNL.NET.IN            61\n",
            "UCOM.NE.JP             58\n",
            "VERIZON.NET            50\n",
            "                     ... \n",
            "LIQUIDTELECOM.NET       1\n",
            "PIMCO.COM               1\n",
            "MAGICSOFTWARE.COM       1\n",
            "NAVY.MIL                1\n",
            "CLEARWAVE.COM           1\n",
            "Name: sec_lvl_domn, Length: 2811, dtype: int64\n",
            " --------------------------------------------------\n",
            "The column \u001b[1m\u001b[4mpage_url\u001b[0m has unique values :\n",
            "www.ibm.com/developerworks/community/forums/html/topic                                                             59\n",
            "www.ibm.com/search                                                                                                 54\n",
            "myibm.ibm.com/dashboard                                                                                            51\n",
            "www.ibm.com/support/home                                                                                           40\n",
            "www.ibm.com/us-en                                                                                                  38\n",
            "                                                                                                                   ..\n",
            "www.ibm.com/support/knowledgecenter/en/ss3jsw_6.0.0/installing/installing/integrator/si_about_installation.html     1\n",
            "www.ibm.com/support/knowledgecenter/en/ss9h2y_7.7.0/com.ibm.dp.doc/action_addingjsonwebaction_decrypt.html          1\n",
            "www.ibm.com/support/knowledgecenter/en/ssgks6                                                                       1\n",
            "www.ibm.com/security/data-breach/threat-intelligence                                                                1\n",
            "www.ustream.tv/channel/live-iss-stream/pop-out                                                                      1\n",
            "Name: page_url, Length: 5457, dtype: int64\n",
            " --------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "#Check the maximum counts of categorical variables.\n",
        "for var in cols :\n",
        "  print(f'The column \\033[1m\\033[4m{var}\\033[0m has unique values :\\n{train_df[var].value_counts()}\\n', '-'*50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "05PuNOof1oiA"
      },
      "outputs": [],
      "source": [
        "#Filter the useful data from given categorical columns\n",
        "train_df['page_url']= train_df['page_url'].apply(lambda x: 1 if x in train_df['page_url'].value_counts()[:5].keys()  else 0)\n",
        "train_df['intgrtd_mngmt_name']= train_df['intgrtd_mngmt_name'].apply(lambda x: 1 if x in train_df['intgrtd_mngmt_name'].value_counts()[:5].keys()  else 0)\n",
        "train_df['intgrtd_operating_team_name']= train_df['intgrtd_operating_team_name'].apply(lambda x: 1 if x in train_df['intgrtd_operating_team_name'].value_counts()[:5].keys()  else 0)\n",
        "train_df['st']= train_df['st'].apply(lambda x: 1 if x in train_df['st'].value_counts()[:5].keys()  else 0)\n",
        "train_df['operating_sys']= train_df['operating_sys'].apply(lambda x: 1 if x in train_df['operating_sys'].value_counts()[:5].keys()  else 0)\n",
        "train_df['user_agent']= train_df['user_agent'].apply(lambda x: 1 if x in train_df['user_agent'].value_counts()[:5].keys()  else 0)\n",
        "train_df['sec_lvl_domn']= train_df['sec_lvl_domn'].apply(lambda x: 1 if x in train_df['sec_lvl_domn'].value_counts()[:5].keys()  else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "lHfCv53aa7iL"
      },
      "outputs": [],
      "source": [
        "#Filter the useful data from given categorical columns\n",
        "test_df['page_url']= test_df['page_url'].apply(lambda x: 1 if x in test_df['page_url'].value_counts()[:5].keys()  else 0)\n",
        "test_df['intgrtd_mngmt_name']= test_df['intgrtd_mngmt_name'].apply(lambda x: 1 if x in test_df['intgrtd_mngmt_name'].value_counts()[:5].keys()  else 0)\n",
        "test_df['intgrtd_operating_team_name']= test_df['intgrtd_operating_team_name'].apply(lambda x: 1 if x in test_df['intgrtd_operating_team_name'].value_counts()[:5].keys()  else 0)\n",
        "test_df['st']= test_df['st'].apply(lambda x: 1 if x in test_df['st'].value_counts()[:5].keys()  else 0)\n",
        "test_df['operating_sys']= test_df['operating_sys'].apply(lambda x: 1 if x in test_df['operating_sys'].value_counts()[:5].keys()  else 0)\n",
        "test_df['user_agent']= test_df['user_agent'].apply(lambda x: 1 if x in test_df['user_agent'].value_counts()[:5].keys()  else 0)\n",
        "test_df['sec_lvl_domn']= test_df['sec_lvl_domn'].apply(lambda x: 1 if x in test_df['sec_lvl_domn'].value_counts()[:5].keys()  else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "2abvD0-24CZR"
      },
      "outputs": [],
      "source": [
        "#Remove the non-required columns.\n",
        "train_df = train_df.drop(train_df.select_dtypes(include=['object']), axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "NvtEaTMZe_-M"
      },
      "outputs": [],
      "source": [
        "#Remove the non-required columns.\n",
        "test_df = test_df.drop(test_df.select_dtypes(include=['object']), axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "EPwu9atj6Hf5"
      },
      "outputs": [],
      "source": [
        "#Drop original date flown column\n",
        "train_df.drop(['wk','mth','yr','Date','month','year','day','VISIT','ENGD_VISIT','VIEWS'], axis=1 , inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "lvETrbukfKQ8"
      },
      "outputs": [],
      "source": [
        "#Drop original date flown column\n",
        "test_df.drop(['wk','mth','yr','Date','month','year','day','VISIT','ENGD_VISIT','VIEWS'], axis=1 , inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "k4SFl8yg9ZzC"
      },
      "outputs": [],
      "source": [
        "#Fill NAN values using statistical method.\n",
        "train_df['hour'].fillna(train_df['hour'].mean(), inplace=True)\n",
        "train_df['minute'].fillna(train_df['minute'].mean(), inplace=True)\n",
        "train_df['second'].fillna(train_df['hour'].mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "iAcWUpGr-_ug"
      },
      "outputs": [],
      "source": [
        "#Fill NAN values using statistical method.\n",
        "test_df['hour'].fillna(test_df['hour'].mean(), inplace=True)\n",
        "test_df['minute'].fillna(test_df['minute'].mean(), inplace=True)\n",
        "test_df['second'].fillna(test_df['hour'].mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "xzKWcFD1REYR",
        "outputId": "1d782a37-db2d-4b17-f424-0ed6295d2b4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      intgrtd_mngmt_name  intgrtd_operating_team_name  st  sec_lvl_domn  \\\n",
              "0                      0                            1   0             0   \n",
              "1                      1                            1   0             0   \n",
              "2                      0                            1   0             1   \n",
              "3                      0                            0   0             1   \n",
              "4                      1                            1   0             0   \n",
              "...                  ...                          ...  ..           ...   \n",
              "7202                   1                            1   0             0   \n",
              "7203                   1                            1   0             1   \n",
              "7204                   1                            1   1             0   \n",
              "7205                   1                            1   1             1   \n",
              "7206                   1                            1   0             0   \n",
              "\n",
              "      operating_sys  user_agent  page_url  target  hour  minute  second  \\\n",
              "0                 1           0         0       1   2.0    28.0    14.0   \n",
              "1                 1           0         0       1   7.0    17.0    42.0   \n",
              "2                 1           0         0       1   9.0    33.0     1.0   \n",
              "3                 1           1         0       1   6.0    42.0    19.0   \n",
              "4                 1           0         0       1   6.0    34.0    28.0   \n",
              "...             ...         ...       ...     ...   ...     ...     ...   \n",
              "7202              1           1         0       0  15.0    34.0    10.0   \n",
              "7203              1           1         0       0  23.0    55.0    15.0   \n",
              "7204              1           1         0       0  20.0    50.0    54.0   \n",
              "7205              1           0         0       1  10.0    29.0    43.0   \n",
              "7206              1           0         0       0   6.0     0.0    59.0   \n",
              "\n",
              "      mean_view_visit_engd  \n",
              "0                        1  \n",
              "1                        1  \n",
              "2                        1  \n",
              "3                        2  \n",
              "4                        2  \n",
              "...                    ...  \n",
              "7202                     1  \n",
              "7203                    88  \n",
              "7204                    32  \n",
              "7205                   287  \n",
              "7206                    28  \n",
              "\n",
              "[7207 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47c063ad-186e-4dcd-812c-dd7e9d0dc353\">\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>intgrtd_mngmt_name</th>\n",
              "      <th>intgrtd_operating_team_name</th>\n",
              "      <th>st</th>\n",
              "      <th>sec_lvl_domn</th>\n",
              "      <th>operating_sys</th>\n",
              "      <th>user_agent</th>\n",
              "      <th>page_url</th>\n",
              "      <th>target</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>second</th>\n",
              "      <th>mean_view_visit_engd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7202</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7203</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7204</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7205</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7206</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7207 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47c063ad-186e-4dcd-812c-dd7e9d0dc353')\"\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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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      padding: 0 0 0 0;\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-47c063ad-186e-4dcd-812c-dd7e9d0dc353 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-47c063ad-186e-4dcd-812c-dd7e9d0dc353');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "#Check dataframe last time before training the model\n",
        "train_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wrO0mEjT-mqt"
      },
      "source": [
        "#Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "XQfCvWHcfWWC"
      },
      "outputs": [],
      "source": [
        "#Divide the independent and dependent variables\n",
        "X_train = train_df.drop(['target'],axis=1)\n",
        "Y_train = train_df['target']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "koKCwpPLDruQ"
      },
      "outputs": [],
      "source": [
        "#Oversampling the target variable, as we have seen class imbalance in it\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "oversample = RandomOverSampler(sampling_strategy= 'minority')\n",
        "x_over,y_over = oversample.fit_resample(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "u7IQniWpZCfR",
        "outputId": "af310641-f56c-4614-b7b1-a44fd7cfc4d4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       intgrtd_mngmt_name  intgrtd_operating_team_name            st  \\\n",
              "count        12758.000000                 12758.000000  12758.000000   \n",
              "mean             0.567879                     0.922637      0.166170   \n",
              "std              0.495390                     0.267177      0.372248   \n",
              "min              0.000000                     0.000000      0.000000   \n",
              "25%              0.000000                     1.000000      0.000000   \n",
              "50%              1.000000                     1.000000      0.000000   \n",
              "75%              1.000000                     1.000000      0.000000   \n",
              "max              1.000000                     1.000000      1.000000   \n",
              "\n",
              "       sec_lvl_domn  operating_sys    user_agent      page_url          hour  \\\n",
              "count  12758.000000   12758.000000  12758.000000  12758.000000  12758.000000   \n",
              "mean       0.333281       0.935570      0.516774      0.032215      0.456840   \n",
              "std        0.471405       0.245527      0.499738      0.176578      0.260468   \n",
              "min        0.000000       0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000       1.000000      0.000000      0.000000      0.260870   \n",
              "50%        0.000000       1.000000      1.000000      0.000000      0.434783   \n",
              "75%        1.000000       1.000000      1.000000      0.000000      0.652174   \n",
              "max        1.000000       1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "             minute        second  mean_view_visit_engd  \n",
              "count  12758.000000  12758.000000          12758.000000  \n",
              "mean       0.496954      0.492454              0.007463  \n",
              "std        0.290877      0.295090              0.035773  \n",
              "min        0.000000      0.000000              0.000000  \n",
              "25%        0.254237      0.237288              0.000000  \n",
              "50%        0.491525      0.491525              0.000890  \n",
              "75%        0.728814      0.745763              0.002671  \n",
              "max        1.000000      1.000000              1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf6b62c5-30bb-4fb3-997f-a696fc17f392\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>intgrtd_mngmt_name</th>\n",
              "      <th>intgrtd_operating_team_name</th>\n",
              "      <th>st</th>\n",
              "      <th>sec_lvl_domn</th>\n",
              "      <th>operating_sys</th>\n",
              "      <th>user_agent</th>\n",
              "      <th>page_url</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>second</th>\n",
              "      <th>mean_view_visit_engd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "      <td>12758.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.567879</td>\n",
              "      <td>0.922637</td>\n",
              "      <td>0.166170</td>\n",
              "      <td>0.333281</td>\n",
              "      <td>0.935570</td>\n",
              "      <td>0.516774</td>\n",
              "      <td>0.032215</td>\n",
              "      <td>0.456840</td>\n",
              "      <td>0.496954</td>\n",
              "      <td>0.492454</td>\n",
              "      <td>0.007463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.495390</td>\n",
              "      <td>0.267177</td>\n",
              "      <td>0.372248</td>\n",
              "      <td>0.471405</td>\n",
              "      <td>0.245527</td>\n",
              "      <td>0.499738</td>\n",
              "      <td>0.176578</td>\n",
              "      <td>0.260468</td>\n",
              "      <td>0.290877</td>\n",
              "      <td>0.295090</td>\n",
              "      <td>0.035773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.260870</td>\n",
              "      <td>0.254237</td>\n",
              "      <td>0.237288</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.434783</td>\n",
              "      <td>0.491525</td>\n",
              "      <td>0.491525</td>\n",
              "      <td>0.000890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.652174</td>\n",
              "      <td>0.728814</td>\n",
              "      <td>0.745763</td>\n",
              "      <td>0.002671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf6b62c5-30bb-4fb3-997f-a696fc17f392')\"\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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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
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              "      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-cf6b62c5-30bb-4fb3-997f-a696fc17f392 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-cf6b62c5-30bb-4fb3-997f-a696fc17f392');\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": {}
        }
      ],
      "source": [
        "#Scaling the train dataset\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "x_train = scaler.fit_transform(x_over)\n",
        "x_train = pd.DataFrame(x_train, columns=x_over.columns)\n",
        "display(x_train.describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "r98r_yQYqX0Z"
      },
      "outputs": [],
      "source": [
        "#Imports Liberaries of classification models\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.svm import SVC\n",
        "import time\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import plot_confusion_matrix, classification_report\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import time\n",
        "# Importing  metrics for evaluation of our model performance\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "BbVcvM3OUwTn",
        "outputId": "84fb7da1-dd3a-4681-967b-9abfa0040757"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       intgrtd_mngmt_name  intgrtd_operating_team_name   st  sec_lvl_domn  \\\n",
              "0                     0.0                          1.0  0.0           0.0   \n",
              "1                     1.0                          1.0  0.0           0.0   \n",
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              "3                     0.0                          0.0  0.0           1.0   \n",
              "4                     1.0                          1.0  0.0           0.0   \n",
              "...                   ...                          ...  ...           ...   \n",
              "12753                 0.0                          1.0  1.0           1.0   \n",
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              "12756                 1.0                          1.0  0.0           0.0   \n",
              "12757                 1.0                          1.0  1.0           1.0   \n",
              "\n",
              "       operating_sys  user_agent  page_url      hour    minute    second  \\\n",
              "0                1.0         0.0       0.0  0.086957  0.474576  0.237288   \n",
              "1                1.0         0.0       0.0  0.304348  0.288136  0.711864   \n",
              "2                1.0         0.0       0.0  0.391304  0.559322  0.016949   \n",
              "3                1.0         1.0       0.0  0.260870  0.711864  0.322034   \n",
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              "...              ...         ...       ...       ...       ...       ...   \n",
              "12753            1.0         1.0       1.0  0.739130  0.050847  0.711864   \n",
              "12754            1.0         1.0       1.0  0.869565  0.576271  0.949153   \n",
              "12755            1.0         1.0       0.0  0.173913  0.762712  0.711864   \n",
              "12756            1.0         0.0       0.0  0.913043  0.525424  0.406780   \n",
              "12757            1.0         1.0       0.0  0.086957  0.983051  0.644068   \n",
              "\n",
              "       mean_view_visit_engd  \n",
              "0                  0.000000  \n",
              "1                  0.000000  \n",
              "2                  0.000000  \n",
              "3                  0.000890  \n",
              "4                  0.000890  \n",
              "...                     ...  \n",
              "12753              0.000000  \n",
              "12754              0.010686  \n",
              "12755              0.000000  \n",
              "12756              0.011576  \n",
              "12757              0.000890  \n",
              "\n",
              "[12758 rows x 11 columns]"
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ea2a7ad5-239f-48c8-989b-f185947b201f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "WkkEUdJnqZoF"
      },
      "outputs": [],
      "source": [
        "#Declare the models\n",
        "lr_model=LogisticRegression(C=10)\n",
        "dt_model=DecisionTreeClassifier()\n",
        "rf_model=RandomForestClassifier(max_depth= 60, min_samples_leaf = 1, min_samples_split= 5)\n",
        "gbc_model=GradientBoostingClassifier()\n",
        "xgb_model=XGBClassifier()\n",
        "svc_model=SVC()\n",
        "Gnb_model=GaussianNB()\n",
        "\n",
        "#Create a list of models\n",
        "models=[lr_model,svc_model,Gnb_model,dt_model,rf_model,gbc_model,xgb_model]\n",
        "\n",
        "#Creating dictionay for storing the confustion matix\n",
        "dct_train={}\n",
        "dct_test={}                            #Create emplty dictionaries & list\n",
        "lst_imp=[]\n",
        "# Fuction for calculation the eveluation matrix\n",
        "def score_model(x_train,y_over):\n",
        "    df_columns=[]\n",
        "    df=pd.DataFrame(columns=df_columns)\n",
        "    i=0\n",
        "    #Read model one by one\n",
        "    for model in models:\n",
        "        start = time.time()\n",
        "        model.fit(x_train,y_over)           #fit the model on train data\n",
        "        end= time.time()\n",
        "        y_pred_train=model.predict(x_train)\n",
        "        \n",
        "        #Compute metrics\n",
        "        train_accuracy=accuracy_score(y_over,y_pred_train)\n",
        "        p_score_train=precision_score(y_over,y_pred_train)\n",
        "        r_score_train=recall_score(y_over,y_pred_train)\n",
        "        train_auc = roc_auc_score(y_over,y_pred_train)\n",
        "        fp, tp, th = roc_curve(y_over, y_pred_train)\n",
        "\n",
        "        #Insert in dataframe\n",
        "        df.loc[i,\"Model_Name\"]=model.__class__.__name__\n",
        "        df.loc[i,\"Train_Accuracy\"]=round(train_accuracy*100,2)\n",
        "        df.loc[i,\"Precision_Train\"]=round(p_score_train*100,2)\n",
        "        df.loc[i,\"Recall_Train\"]=round(r_score_train*100,2)\n",
        "        df.loc[i,\"ROC_AUC_Train\"]=round(train_auc*100,2)\n",
        "        df.loc[i,'AUC'] = auc(fp, tp)\n",
        "        df.loc[i,'Model_training_time ']= end-start\n",
        "        #Inserted in dictionary\n",
        "        dct_train[model.__class__.__name__]=confusion_matrix(y_over,y_pred_train)\n",
        "\n",
        "        i+=1\n",
        "    # Return the data frame and dictionary\n",
        "    return df,dct_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "1Vp0MZs8qgdQ"
      },
      "outputs": [],
      "source": [
        "# Get performance matrix dataframe for different classifiers.\n",
        "final_result_df,dct_train=score_model(x_train,y_over)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "1oFXZsxMqjGu",
        "outputId": "6964ba2a-9925-43a5-f501-5d349b2d7dc7"
      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Model_Name  Train_Accuracy  Precision_Train  Recall_Train  \\\n",
              "0          LogisticRegression           64.04            61.33         75.98   \n",
              "1                         SVC           63.51            61.66         71.44   \n",
              "2                  GaussianNB           60.19            56.04         94.59   \n",
              "3      DecisionTreeClassifier           99.99           100.00         99.98   \n",
              "4      RandomForestClassifier           99.99           100.00         99.98   \n",
              "5  GradientBoostingClassifier           70.82            66.75         82.98   \n",
              "6               XGBClassifier           70.03            65.64         84.07   \n",
              "\n",
              "   ROC_AUC_Train       AUC  Model_training_time   \n",
              "0          64.04  0.640383              0.078738  \n",
              "1          63.51  0.635131              7.422208  \n",
              "2          60.19  0.601897              0.003326  \n",
              "3          99.99  0.999922              0.029159  \n",
              "4          99.99  0.999922              0.838980  \n",
              "5          70.82  0.708183              0.966823  \n",
              "6          70.03  0.700345              0.554581  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model_Name</th>\n",
              "      <th>Train_Accuracy</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>64.04</td>\n",
              "      <td>61.33</td>\n",
              "      <td>75.98</td>\n",
              "      <td>64.04</td>\n",
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              "      <td>0.078738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SVC</td>\n",
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              "      <td>61.66</td>\n",
              "      <td>71.44</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>60.19</td>\n",
              "      <td>56.04</td>\n",
              "      <td>94.59</td>\n",
              "      <td>60.19</td>\n",
              "      <td>0.601897</td>\n",
              "      <td>0.003326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "      <td>99.99</td>\n",
              "      <td>100.00</td>\n",
              "      <td>99.98</td>\n",
              "      <td>99.99</td>\n",
              "      <td>0.999922</td>\n",
              "      <td>0.029159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>99.99</td>\n",
              "      <td>100.00</td>\n",
              "      <td>99.98</td>\n",
              "      <td>99.99</td>\n",
              "      <td>0.999922</td>\n",
              "      <td>0.838980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GradientBoostingClassifier</td>\n",
              "      <td>70.82</td>\n",
              "      <td>66.75</td>\n",
              "      <td>82.98</td>\n",
              "      <td>70.82</td>\n",
              "      <td>0.708183</td>\n",
              "      <td>0.966823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBClassifier</td>\n",
              "      <td>70.03</td>\n",
              "      <td>65.64</td>\n",
              "      <td>84.07</td>\n",
              "      <td>70.03</td>\n",
              "      <td>0.700345</td>\n",
              "      <td>0.554581</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92ba9f10-52be-4d3a-a52e-2b7ecdbc6c65')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      \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",
              "      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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-92ba9f10-52be-4d3a-a52e-2b7ecdbc6c65 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-92ba9f10-52be-4d3a-a52e-2b7ecdbc6c65');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "#  Get performance on different models\n",
        "final_result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "Rq_oKwMlfWDF"
      },
      "outputs": [],
      "source": [
        "#Imports Liberaries of classification models\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.model_selection import cross_val_score,GridSearchCV, KFold\n",
        "# Importing  metrics for evaluation of our model performance\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.metrics import roc_auc_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "NolygSlVo1w4"
      },
      "outputs": [],
      "source": [
        "#Set parameters and initiate model\n",
        "gbc = GradientBoostingClassifier()\n",
        "parameters = {\n",
        "    \"n_estimators\":[50,250,500],\n",
        "    \"max_depth\":[11,13,15],\n",
        "    \"learning_rate\":[0.1,0.25,0.3]\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_SfF9gBpQQq",
        "outputId": "67a8725c-0d8f-4b7a-bf2e-5bcee413f8ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid={'learning_rate': [0.1, 0.25, 0.3],\n",
              "                         'max_depth': [11, 13, 15],\n",
              "                         'n_estimators': [50, 250, 500]})"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "#Fit the model on train dataset using cross-validation\n",
        "cv = GridSearchCV(gbc,parameters,cv=10,n_jobs=-1)\n",
        "cv.fit(x_train,y_over.values.ravel())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "C4nUVKttuRDS"
      },
      "outputs": [],
      "source": [
        "# Get best estimator\n",
        "gb_optimal_model = cv.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiB4xtOIpY76",
        "outputId": "b0fc487c-4fd3-474a-9f10-3ca3e2d9093e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(learning_rate=0.3, max_depth=13, n_estimators=500)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "#Fit optimized model on train dataset\n",
        "gb_optimal_model.fit(x_train,y_over)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "GdvrvDcr1Qyn"
      },
      "outputs": [],
      "source": [
        "#Predict and check train dataset\n",
        "x_train_pred = gb_optimal_model.predict(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pQKNLxo1wG7",
        "outputId": "68864b8f-374c-4a9e-950e-19ff4f03036f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROCAUC score: 0.9999216178084339\n",
            "Accuracy score: 0.9999216178084339\n"
          ]
        }
      ],
      "source": [
        "#Check AUC score for train dataset\n",
        "print('ROCAUC score:', roc_auc_score(y_over, x_train_pred))\n",
        "print('Accuracy score:', accuracy_score(y_over, x_train_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "CrD3JkAXaTK8",
        "outputId": "363cbae9-c3db-4342-b5b7-51f1feae6d83"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       intgrtd_mngmt_name  intgrtd_operating_team_name           st  \\\n",
              "count         6885.000000                  6885.000000  6885.000000   \n",
              "mean             0.564125                     0.924764     0.168192   \n",
              "std              0.495907                     0.263791     0.374064   \n",
              "min              0.000000                     0.000000     0.000000   \n",
              "25%              0.000000                     1.000000     0.000000   \n",
              "50%              1.000000                     1.000000     0.000000   \n",
              "75%              1.000000                     1.000000     0.000000   \n",
              "max              1.000000                     1.000000     1.000000   \n",
              "\n",
              "       sec_lvl_domn  operating_sys   user_agent     page_url         hour  \\\n",
              "count   6885.000000    6885.000000  6885.000000  6885.000000  6885.000000   \n",
              "mean       0.328976       0.949455     0.520552     0.033696     0.422695   \n",
              "std        0.469875       0.219082     0.499614     0.180460     0.253041   \n",
              "min        0.000000       0.000000     0.000000     0.000000     0.000000   \n",
              "25%        0.000000       1.000000     0.000000     0.000000     0.217391   \n",
              "50%        0.000000       1.000000     1.000000     0.000000     0.391304   \n",
              "75%        1.000000       1.000000     1.000000     0.000000     0.608696   \n",
              "max        1.000000       1.000000     1.000000     1.000000     1.000000   \n",
              "\n",
              "            minute       second  mean_view_visit_engd  \n",
              "count  6885.000000  6885.000000           6885.000000  \n",
              "mean      0.501461     0.493669              0.002182  \n",
              "std       0.293326     0.295603              0.016823  \n",
              "min       0.000000     0.000000              0.000000  \n",
              "25%       0.254237     0.237288              0.000000  \n",
              "50%       0.491525     0.491525              0.000890  \n",
              "75%       0.745763     0.745763              0.001781  \n",
              "max       1.000000     1.000000              1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1eaf56a1-9077-4f1a-953d-9977b838921a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>intgrtd_mngmt_name</th>\n",
              "      <th>intgrtd_operating_team_name</th>\n",
              "      <th>st</th>\n",
              "      <th>sec_lvl_domn</th>\n",
              "      <th>operating_sys</th>\n",
              "      <th>user_agent</th>\n",
              "      <th>page_url</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>second</th>\n",
              "      <th>mean_view_visit_engd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "      <td>6885.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.564125</td>\n",
              "      <td>0.924764</td>\n",
              "      <td>0.168192</td>\n",
              "      <td>0.328976</td>\n",
              "      <td>0.949455</td>\n",
              "      <td>0.520552</td>\n",
              "      <td>0.033696</td>\n",
              "      <td>0.422695</td>\n",
              "      <td>0.501461</td>\n",
              "      <td>0.493669</td>\n",
              "      <td>0.002182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.495907</td>\n",
              "      <td>0.263791</td>\n",
              "      <td>0.374064</td>\n",
              "      <td>0.469875</td>\n",
              "      <td>0.219082</td>\n",
              "      <td>0.499614</td>\n",
              "      <td>0.180460</td>\n",
              "      <td>0.253041</td>\n",
              "      <td>0.293326</td>\n",
              "      <td>0.295603</td>\n",
              "      <td>0.016823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.217391</td>\n",
              "      <td>0.254237</td>\n",
              "      <td>0.237288</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.391304</td>\n",
              "      <td>0.491525</td>\n",
              "      <td>0.491525</td>\n",
              "      <td>0.000890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.608696</td>\n",
              "      <td>0.745763</td>\n",
              "      <td>0.745763</td>\n",
              "      <td>0.001781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1eaf56a1-9077-4f1a-953d-9977b838921a 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-1eaf56a1-9077-4f1a-953d-9977b838921a');\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": {}
        }
      ],
      "source": [
        "#Scaling the test dataset\n",
        "test_df = scaler.transform(test_df)\n",
        "test_df = pd.DataFrame(test_df, columns=x_over.columns)\n",
        "display(test_df.describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "oZXL15YvgXvl"
      },
      "outputs": [],
      "source": [
        "#Predict on test dataset and save csv file\n",
        "submission = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Almabetter/Project/sample_submission.csv')\n",
        "final_predictions = gb_optimal_model.predict(test_df)\n",
        "submission['target'] = final_predictions\n",
        "submission.to_csv('finalsubmission.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Qie1bzeNmHe"
      },
      "source": [
        "#Conclusion:\n",
        "\n",
        "\n",
        "\n",
        "*   Engaged visit and views variable are majorly helped us to identify the bots.\n",
        "*   We were able to recognise bots by counting how often each system was accessed.\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "mount_file_id": "1Px9JKyfmvzkI-didw2H3lM97fhYJ_MkK",
      "authorship_tag": "ABX9TyP62eHcUrtEiZsSKVgYyJVE",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}