Kaggle_Notebooks_Analysis.ipynb

{
  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "name": "Kaggle Notebooks Analysis",
      "provenance": []
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#import linear algebra and data manipulation libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "#import date manipulation\n",
        "import datetime as dt\n",
        "from datetime import date, timedelta\n",
        "\n",
        "#import standard visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#import machine learning\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import xgboost\n",
        "\n",
        "from sklearn.model_selection import train_test_split #split\n",
        "from sklearn.metrics import r2_score, mean_squared_error #metrics\n",
        "\n",
        "# import helpers\n",
        "import re"
      ],
      "metadata": {
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        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import datasets related to kernels"
      ],
      "metadata": {
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        "id": "Zz2bQSp3BlA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kernels = pd.read_csv('../input/meta-kaggle/Kernels.csv')\n",
        "kernel_lang = pd.read_csv('../input/meta-kaggle/KernelLanguages.csv')\n",
        "kernel_tags = pd.read_csv('../input/meta-kaggle/KernelTags.csv')\n",
        "kernel_votes = pd.read_csv('../input/meta-kaggle/KernelVotes.csv')\n",
        "kernel_versions = pd.read_csv('../input/meta-kaggle/KernelVersions.csv')\n",
        "kernel_competitions = pd.read_csv('../input/meta-kaggle/KernelVersionCompetitionSources.csv')\n",
        "kernel_users = pd.read_csv('../input/meta-kaggle/Users.csv')\n",
        "datasets = pd.read_csv('../input/meta-kaggle/Datasets.csv')\n",
        "kernel_datasets = pd.read_csv('../input/meta-kaggle/KernelVersionDatasetSources.csv')\n",
        "dataset_versions = pd.read_csv('../input/meta-kaggle/DatasetVersions.csv')\n",
        "tags = pd.read_csv('../input/meta-kaggle/Tags.csv')\n",
        "competitions = pd.read_csv('../input/meta-kaggle/Competitions.csv')\n",
        "messages = pd.read_csv('../input/meta-kaggle/ForumMessages.csv')"
      ],
      "metadata": {
        "_uuid": "3089e52dcfaebd92aa52aab28bda5f21312df187",
        "execution": {
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        "trusted": true,
        "id": "Sg6GgwsGBlA_"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explore statistics for kernel votes"
      ],
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "id": "ygoKtBYeBlBB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Explore basic statistics for number of kernels"
      ],
      "metadata": {
        "_uuid": "7a78e043d4ffcf2448c58a6fbacba8973a511afd",
        "id": "86elWK6FBlBB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* How many kernels are there? \n",
        "* How many of them are public? \n",
        "* How many kernels have upvotes? \n",
        "* How many kernels have any medal?"
      ],
      "metadata": {
        "_uuid": "b5333a093d7dc4efb6abfe1e9726d69b90772f65",
        "id": "N2J5B4ijBlBC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_kernels = len(kernels['Id'].unique())\n",
        "print('Total number of kernels: {total_kernels}'.format(total_kernels = total_kernels))\n",
        "\n",
        "total_public = len(kernels[pd.notnull(kernels['MadePublicDate'])])\n",
        "print('Total number of public kernels: {total_public_kernels}'.format(total_public_kernels = total_public))\n",
        "\n",
        "total_upvoted = len(kernels[ kernels['TotalVotes'] > 0])\n",
        "print('Total number of kernels with upvotes: {total_upvoted}'.format(total_upvoted = total_upvoted))\n",
        "\n",
        "total_awarded = len(kernels[np.isfinite(kernels['Medal'])])\n",
        "print('Total number of kernels with any medal: {total_awarded}'.format(total_awarded = total_awarded))\n",
        "\n",
        "total_no_upvotes = total_public - total_upvoted\n",
        "print('Total number of public kernels with no upvotes: {total_no_upvotes}'.format(total_no_upvotes = total_no_upvotes))\n",
        "\n",
        "total_private = total_kernels - total_public\n",
        "print('Total number of private kernels: {total_private}'.format(total_private = total_private))\n",
        "\n",
        "total_upvoted_not_awarded = total_upvoted - total_awarded\n",
        "print('Total number of upvoted, but not awarded: {total_upvoted_not_awarded}'.format(total_upvoted_not_awarded = total_upvoted_not_awarded))"
      ],
      "metadata": {
        "_uuid": "771d0846839f3eeb6b80ed52d312e732997e7632",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:40:15.425011Z",
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        "outputId": "2e6a5cac-312d-4c3a-b871-3e02ffca051a"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Total number of kernels: 624042\nTotal number of public kernels: 599674\nTotal number of kernels with upvotes: 238289\nTotal number of kernels with any medal: 54900\nTotal number of public kernels with no upvotes: 361385\nTotal number of private kernels: 24368\nTotal number of upvoted, but not awarded: 183389\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot statistics for number of kernels:"
      ],
      "metadata": {
        "_uuid": "5ff182e83d9195dd2502974e4c3b0154701aaa42",
        "id": "t5ofYf6vBlBG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#plot statistics with matplotlib\n",
        "\n",
        "labels = 'Private', 'Public with no upvotes', 'Upvoted but not awarded', 'Awarded'\n",
        "sizes = [total_private, total_no_upvotes, total_upvoted_not_awarded, total_awarded]\n",
        "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
        "\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=180)\n",
        "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "fig1.set_figheight(7)\n",
        "fig1.set_figwidth(7)\n",
        "\n",
        "plt.title('Statistics for upvoted and awarded kernels')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
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        "execution": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. How does number of views and number of comments affect number of votes?"
      ],
      "metadata": {
        "_uuid": "fa901411d8c75fa39e4b3f4d85796d09f295c4f2",
        "id": "CvSTerT5BlBH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Is there a correlation between number of views and number of comments with number of votes?\n",
        "* Plot dependencies between number of views, comments and number of votes."
      ],
      "metadata": {
        "_uuid": "27a3ed557ce33c12d874cf7b783385d9ae6e777e",
        "id": "LhDypn7FBlBI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find out correlation between number of views, number of comments and number of votes:"
      ],
      "metadata": {
        "_uuid": "4d7deaab8d842624424a089a93f12c5e2ad7331b",
        "id": "E_xO1R-lBlBI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#columns to find out correlation\n",
        "cols = ['TotalViews', 'TotalComments', 'TotalVotes']\n",
        "\n",
        "#Find out correlation between columns and plot\n",
        "corrs = np.corrcoef(kernels[cols].values.T)\n",
        "sns.set(font_scale=1)\n",
        "sns.set(rc={'figure.figsize':(7,7)})\n",
        "hm=sns.heatmap(corrs, cbar = True, annot=True, square = True, fmt = '.2f',\n",
        "              yticklabels = cols, xticklabels = cols).set_title('Correlations heatmap')\n",
        "\n",
        "fig = hm.get_figure()\n",
        "fig.savefig('correlations.png')"
      ],
      "metadata": {
        "_uuid": "1df1e113867a44e5a104ce56d7fcf78194848223",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:41:45.673125Z",
          "iopub.execute_input": "2022-02-02T17:41:45.674030Z",
          "iopub.status.idle": "2022-02-02T17:41:46.058235Z",
          "shell.execute_reply.started": "2022-02-02T17:41:45.673985Z",
          "shell.execute_reply": "2022-02-02T17:41:46.057319Z"
        },
        "trusted": true,
        "id": "9cMnSun3BlBJ",
        "outputId": "08378ff6-1eb6-410e-a5b9-1fbfb31b8684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that votes, comments and views are __highly correlated__. So my first assumption would be that __we should get as many views of the kernels as possible to gain votes__.\n",
        "<br> Let's try to visualize dependency between views and votes:"
      ],
      "metadata": {
        "_uuid": "6ef82acd74b05a563c9d63cdd8184b15551bebda",
        "id": "Ch5Ld0mABlBJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get average number of votes per number of views\n",
        "votes_by_views = kernels.groupby('TotalViews').mean().reset_index()[['TotalViews', 'TotalVotes']]\n",
        "votes_by_views = votes_by_views.sort_values(by = 'TotalViews', ascending = True)\n",
        "\n",
        "# fit a linear regression model on views and votes data\n",
        "model = LinearRegression()\n",
        "model.fit(votes_by_views['TotalViews'].values.reshape(-1, 1), votes_by_views['TotalVotes'].values.reshape(-1, 1))\n",
        "\n",
        "inputs = np.linspace(1, 1200000, num=50)\n",
        "predictions = model.predict(inputs.reshape(-1,1))"
      ],
      "metadata": {
        "_uuid": "48c9777fee19e1afd16ca845e82a4f196d4c9812",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:42:22.008493Z",
          "iopub.execute_input": "2022-02-02T17:42:22.009444Z",
          "iopub.status.idle": "2022-02-02T17:42:22.138862Z",
          "shell.execute_reply.started": "2022-02-02T17:42:22.009398Z",
          "shell.execute_reply": "2022-02-02T17:42:22.138018Z"
        },
        "trusted": true,
        "id": "abu5yvuRBlBK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib along with predictions from the linear regression model\n",
        "\n",
        "plt.scatter(votes_by_views['TotalViews'], votes_by_views['TotalVotes'])\n",
        "plt.plot(inputs, predictions, color='green')\n",
        "\n",
        "plt.title(\"Number of votes per number of views\")\n",
        "plt.xlabel(\"Views\")\n",
        "plt.ylabel(\"Votes\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "b70a8577d3c85856b5753464f0c9a030e5b5915d",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:42:26.685319Z",
          "iopub.execute_input": "2022-02-02T17:42:26.685612Z",
          "iopub.status.idle": "2022-02-02T17:42:26.975294Z",
          "shell.execute_reply.started": "2022-02-02T17:42:26.685577Z",
          "shell.execute_reply": "2022-02-02T17:42:26.974149Z"
        },
        "trusted": true,
        "id": "6_KVQBWgBlBK",
        "outputId": "9a20071d-ce57-463b-bd3a-957071a36d17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualization of dependency between comments and votes:"
      ],
      "metadata": {
        "_uuid": "32b44dd24c0bf1777b6b278ae3d6b5ae78e0a4d0",
        "id": "MIlKa_cgBlBK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get average number of votes per number of comments\n",
        "votes_by_comments = kernels.groupby('TotalComments').mean().reset_index()[['TotalComments', 'TotalVotes']]\n",
        "votes_by_comments = votes_by_comments.sort_values(by = 'TotalComments', ascending = True)\n",
        "\n",
        "# fit a linear regression model on comments and votes data\n",
        "model = LinearRegression()\n",
        "model.fit(votes_by_comments['TotalComments'].values.reshape(-1, 1), votes_by_comments['TotalVotes'].values.reshape(-1, 1))\n",
        "\n",
        "inputs = np.linspace(1, 1000, num=50)\n",
        "predictions = model.predict(inputs.reshape(-1,1))"
      ],
      "metadata": {
        "_uuid": "e6430132294740e0a5e406e170d3c752a73247d0",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:42:51.770901Z",
          "iopub.execute_input": "2022-02-02T17:42:51.771252Z",
          "iopub.status.idle": "2022-02-02T17:42:51.871026Z",
          "shell.execute_reply.started": "2022-02-02T17:42:51.771220Z",
          "shell.execute_reply": "2022-02-02T17:42:51.870156Z"
        },
        "trusted": true,
        "id": "yx1iXSowBlBL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib along with predictions from the linear regression model\n",
        "\n",
        "plt.scatter(votes_by_comments['TotalComments'], votes_by_comments['TotalVotes'])\n",
        "plt.plot(inputs, predictions, color='green')\n",
        "\n",
        "plt.title(\"Number of votes per number of views\")\n",
        "plt.xlabel(\"Comments\")\n",
        "plt.ylabel(\"Votes\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "f972109bd27d3d1e103d76ac6d498cc0fa3411c9",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:43:07.105383Z",
          "iopub.execute_input": "2022-02-02T17:43:07.105670Z",
          "iopub.status.idle": "2022-02-02T17:43:07.349540Z",
          "shell.execute_reply.started": "2022-02-02T17:43:07.105638Z",
          "shell.execute_reply": "2022-02-02T17:43:07.348779Z"
        },
        "trusted": true,
        "id": "vhDjPW6GBlBL",
        "outputId": "7fe0f230-6ad7-475e-a016-44611d79e92c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Both plots for the views and the comments show us that there is linear dependency between number of votes and number of views/comments."
      ],
      "metadata": {
        "_uuid": "adce84dc52fa37e2ace9e250fe2d8e7d7bd5a02b",
        "id": "lEysgDM0BlBM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. How does the status of the author affect number of votes per kernel?"
      ],
      "metadata": {
        "_uuid": "6ac1cde50735933442e4513dd3fc4d87039f088e",
        "id": "wRA9os-gBlBM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I suppose that more active users and the users with higher visibility gain votes for kernels:\n",
        "* So how does performance tier of the user affect the number of upvotes?"
      ],
      "metadata": {
        "_uuid": "1a0dd93b93b79f7168abb48375058815b2ca0c7e",
        "id": "3Bos_eceBlBN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#merge kernels with users\n",
        "kernel_users_renamed = kernel_users.rename(columns={'Id': 'AuthorUserId'})\n",
        "kernels_user_tiers = kernels.merge(kernel_users_renamed, on = 'AuthorUserId')\n",
        "\n",
        "#get average number of votes per number of comments\n",
        "votes_by_usertier= kernels_user_tiers.groupby('PerformanceTier').mean().reset_index()[['PerformanceTier', 'TotalVotes']]\n",
        "votes_by_usertier = votes_by_usertier.sort_values(by = 'PerformanceTier', ascending = True)"
      ],
      "metadata": {
        "_uuid": "523931392e110a5f38c32dc2c17742a94c72d082",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:44:09.006493Z",
          "iopub.execute_input": "2022-02-02T17:44:09.006778Z",
          "iopub.status.idle": "2022-02-02T17:44:14.144315Z",
          "shell.execute_reply.started": "2022-02-02T17:44:09.006749Z",
          "shell.execute_reply": "2022-02-02T17:44:14.143618Z"
        },
        "trusted": true,
        "id": "Qxy_RayHBlBN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib along with predictions from the linear regression model\n",
        "\n",
        "plt.plot(votes_by_usertier['PerformanceTier'], votes_by_usertier['TotalVotes'])\n",
        "\n",
        "plt.title(\"Average number of votes per kernel depending on user performance tier\")\n",
        "plt.xlabel(\"Performance Tier\")\n",
        "plt.ylabel(\"Average number of votes per kernel\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "8811461b6fffe4a82aeb47a32e5e1f8c4c160c7d",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:44:50.856303Z",
          "iopub.execute_input": "2022-02-02T17:44:50.856598Z",
          "iopub.status.idle": "2022-02-02T17:44:51.100350Z",
          "shell.execute_reply.started": "2022-02-02T17:44:50.856568Z",
          "shell.execute_reply": "2022-02-02T17:44:51.099534Z"
        },
        "trusted": true,
        "id": "dwqFrH2oBlBN",
        "outputId": "615590cc-c214-4cb4-eca5-54aa3e1bdc03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So we can really see that __more proficient users gain more votes for kernels__."
      ],
      "metadata": {
        "_uuid": "df73b89872757532ac9ae21cb5a05765169c461b",
        "id": "Y4qf72WzBlBN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. How does dataset related to kernel affects the number of votes?"
      ],
      "metadata": {
        "_uuid": "fb833a17484fc874099451eff9feb953dff8817e",
        "id": "OhkvnxvLBlBO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I suppose that there might be some 'hot' datasets on Kaggle and kernels related to those datasets might optain more votes. So we should find out:\n",
        "* How does the number of votes for a dataset affect the number of votes for related kernel?\n",
        "* How does the number of downloads of a dataset affect the number of votes for related kernel?"
      ],
      "metadata": {
        "_uuid": "484358b57b9d059f8146f7a7cd9a84026ab720e4",
        "id": "Cl2YdP_zBlBO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#prepare data for the analysis\n",
        "\n",
        "kernels_truncated = kernels[['Id' , 'CurrentKernelVersionId', 'TotalVotes']]\n",
        "kernels_truncated = kernels_truncated.rename(columns={'Id': 'KernelId', 'TotalVotes':'TotalKernelVotes',\n",
        "                                                      'CurrentKernelVersionId':'KernelVersionId'})\n",
        "\n",
        "kernel_datasets_truncated = kernel_datasets[['KernelVersionId', 'SourceDatasetVersionId']]\n",
        "\n",
        "dataset_versions_truncated = dataset_versions[['DatasetId', 'DatasourceVersionId']]\n",
        "dataset_versions_truncated = dataset_versions_truncated.rename(columns={'DatasourceVersionId': 'SourceDatasetVersionId'})\n",
        "\n",
        "datasets_truncated = datasets[['Id', 'TotalVotes', 'TotalDownloads']]\n",
        "datasets_truncated = datasets_truncated.rename(columns={'Id': 'DatasetId', 'TotalVotes':'TotalDatasetVotes',\n",
        "                                                      'TotalDownloads':'TotalDatasetDownloads'})\n",
        "\n",
        "df_merge = kernels_truncated.merge(kernel_datasets_truncated, on = 'KernelVersionId')\n",
        "df_merge = df_merge.merge(dataset_versions_truncated, on = 'SourceDatasetVersionId')\n",
        "df_merge = df_merge.merge(datasets_truncated, on = 'DatasetId')"
      ],
      "metadata": {
        "_uuid": "5e046d99e404b4d58046e0a5a64f981a4e5b6438",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:45:57.940283Z",
          "iopub.execute_input": "2022-02-02T17:45:57.941219Z",
          "iopub.status.idle": "2022-02-02T17:46:01.285856Z",
          "shell.execute_reply.started": "2022-02-02T17:45:57.941168Z",
          "shell.execute_reply": "2022-02-02T17:46:01.284892Z"
        },
        "trusted": true,
        "id": "_CerkTNqBlBO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "At first let's look at correlations between TotalKernelVotes, TotalDatasetVotes and\tTotalDatasetDownloads."
      ],
      "metadata": {
        "_uuid": "e60cf007db3f20c0158b10339f8c6b1753918e7d",
        "id": "WqLtlWAIBlBP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#columns to find out correlation\n",
        "cols = ['TotalKernelVotes', 'TotalDatasetVotes', 'TotalDatasetDownloads']\n",
        "\n",
        "#Find out correlation between columns and plot\n",
        "corrs = np.corrcoef(df_merge[cols].values.T)\n",
        "sns.set(font_scale=1)\n",
        "sns.set(rc={'figure.figsize':(7,7)})\n",
        "hm=sns.heatmap(corrs, cbar = True, annot=True, square = True, fmt = '.2f',\n",
        "              yticklabels = cols, xticklabels = cols).set_title('Correlations heatmap')\n",
        "\n",
        "plt.yticks(rotation=0)\n",
        "fig = hm.get_figure()\n",
        "fig.savefig('correlations.png')"
      ],
      "metadata": {
        "_uuid": "1d99e3cb9289f178524f4bfaa28c32de6ca60111",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:46:10.934890Z",
          "iopub.execute_input": "2022-02-02T17:46:10.935184Z",
          "iopub.status.idle": "2022-02-02T17:46:11.304795Z",
          "shell.execute_reply.started": "2022-02-02T17:46:10.935155Z",
          "shell.execute_reply": "2022-02-02T17:46:11.303894Z"
        },
        "trusted": true,
        "id": "l4Hjt8OrBlBP",
        "outputId": "a088a5e5-24c2-40ef-d537-53fb80f3c4a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see there is no correlation between number of kernel votes and number of votes or downloads for a dataset.\n",
        "<br> Let's also make scatter plots:"
      ],
      "metadata": {
        "_uuid": "d7f9106a092882f4b8090bc0ac10902fafa84bfb",
        "id": "Z1zzB03tBlBP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get average number of votes per number of dataset votes\n",
        "votes_by_dvotes = df_merge.groupby('TotalDatasetVotes').mean().reset_index()[['TotalDatasetVotes', 'TotalKernelVotes']]\n",
        "votes_by_dvotes = votes_by_dvotes.sort_values(by = 'TotalDatasetVotes', ascending = True)"
      ],
      "metadata": {
        "_uuid": "c8cc8eef7277258f9c40e241709a9198f6070872",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:46:30.827420Z",
          "iopub.execute_input": "2022-02-02T17:46:30.828327Z",
          "iopub.status.idle": "2022-02-02T17:46:30.845799Z",
          "shell.execute_reply.started": "2022-02-02T17:46:30.828280Z",
          "shell.execute_reply": "2022-02-02T17:46:30.845118Z"
        },
        "trusted": true,
        "id": "7QglNkxEBlBP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib\n",
        "\n",
        "plt.scatter(votes_by_dvotes['TotalDatasetVotes'], votes_by_dvotes['TotalKernelVotes'])\n",
        "\n",
        "plt.title(\"Number of votes per number of votes for related dataset\")\n",
        "plt.xlabel(\"Dataset Votes\")\n",
        "plt.ylabel(\"Kernel Votes\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "a752f13ecfd1c02a417111614c8ec7df27345614",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:46:55.417881Z",
          "iopub.execute_input": "2022-02-02T17:46:55.418168Z",
          "iopub.status.idle": "2022-02-02T17:46:55.750497Z",
          "shell.execute_reply.started": "2022-02-02T17:46:55.418136Z",
          "shell.execute_reply": "2022-02-02T17:46:55.749582Z"
        },
        "trusted": true,
        "id": "pIdKhiAkBlBQ",
        "outputId": "37789347-d924-47ad-bc42-6a79d82554fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we can't see a clear dependency of kernel votes on dataset's number of votes. So I suppose that we can create a really helpful and popular kernel for an unpopular dataset and vice versa."
      ],
      "metadata": {
        "_uuid": "6a26bbd760003120448abc8ce830b8927e863297",
        "id": "cKVWD4EeBlBQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get average number of votes per number of dataset downloads\n",
        "votes_by_downloads = df_merge.groupby('TotalDatasetDownloads').mean().reset_index()[['TotalDatasetDownloads', 'TotalKernelVotes']]\n",
        "votes_by_downloads = votes_by_downloads.sort_values(by = 'TotalDatasetDownloads', ascending = True)"
      ],
      "metadata": {
        "_uuid": "daa973f85aec3123b5a98a5b24576173eb221993",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:47:42.002091Z",
          "iopub.execute_input": "2022-02-02T17:47:42.002391Z",
          "iopub.status.idle": "2022-02-02T17:47:42.022848Z",
          "shell.execute_reply.started": "2022-02-02T17:47:42.002359Z",
          "shell.execute_reply": "2022-02-02T17:47:42.021975Z"
        },
        "trusted": true,
        "id": "A2704hPcBlBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib\n",
        "\n",
        "plt.scatter(votes_by_downloads['TotalDatasetDownloads'], votes_by_downloads['TotalKernelVotes'])\n",
        "\n",
        "plt.title(\"Number of votes per number of downloads for related dataset\")\n",
        "plt.xlabel(\"Dataset Downloads\")\n",
        "plt.ylabel(\"Kernel Votes\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "143140f633b3d828077d5b20ae7a546c2acfaf2b",
        "execution": {
          "iopub.status.busy": "2022-02-02T17:47:45.721873Z",
          "iopub.execute_input": "2022-02-02T17:47:45.722551Z",
          "iopub.status.idle": "2022-02-02T17:47:45.946662Z",
          "shell.execute_reply.started": "2022-02-02T17:47:45.722507Z",
          "shell.execute_reply": "2022-02-02T17:47:45.945887Z"
        },
        "trusted": true,
        "id": "vg-4uqksBlBR",
        "outputId": "c4a41419-641d-4248-bff0-7d6c79d4822b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The picture looks similar to the previous one. There is also no clear dependency."
      ],
      "metadata": {
        "_uuid": "47c157bc276a5d4e24dcc01609cb1d4691de0e6c",
        "id": "jUbI9BzUBlBR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. How does kernel language affect the number of votes?"
      ],
      "metadata": {
        "_uuid": "ff6f052ac33d8ad9cbd09aff31be32dd73655f75",
        "id": "f22XjyXPBlBS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "What kernel types gain more votes?"
      ],
      "metadata": {
        "_uuid": "0b97458fd2fe4fc387cd939d9880f62ac7d41697",
        "id": "TBW-FUJNBlBS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kernel_lang"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:05:27.008604Z",
          "iopub.execute_input": "2022-02-02T18:05:27.008897Z",
          "iopub.status.idle": "2022-02-02T18:05:27.019808Z",
          "shell.execute_reply.started": "2022-02-02T18:05:27.008858Z",
          "shell.execute_reply": "2022-02-02T18:05:27.018993Z"
        },
        "trusted": true,
        "id": "kQKH0Wn1BlBS",
        "outputId": "1c100f04-2a3a-4cb2-d9a8-c57ba06549ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 35,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   Id                         Name DisplayName  IsNotebook\n0   1                            R           R       False\n1   2                       Python      Python       False\n2   5                    RMarkdown           R       False\n3   8             IPython Notebook      Python        True\n4   9        IPython Notebook HTML      Python       False\n5  12              R Notebook HTML           R       False\n6  13                   R Notebook           R        True\n7  14  Python Script - Interactive      Python        True\n8  15       R Script - Interactive           R        True\n9  16      RMarkdown - Interactive           R        True",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Name</th>\n      <th>DisplayName</th>\n      <th>IsNotebook</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>R</td>\n      <td>R</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Python</td>\n      <td>Python</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>RMarkdown</td>\n      <td>R</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8</td>\n      <td>IPython Notebook</td>\n      <td>Python</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9</td>\n      <td>IPython Notebook HTML</td>\n      <td>Python</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>12</td>\n      <td>R Notebook HTML</td>\n      <td>R</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>13</td>\n      <td>R Notebook</td>\n      <td>R</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>14</td>\n      <td>Python Script - Interactive</td>\n      <td>Python</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>15</td>\n      <td>R Script - Interactive</td>\n      <td>R</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>16</td>\n      <td>RMarkdown - Interactive</td>\n      <td>R</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prepare the data for the analysis\n",
        "\n",
        "kernels_trc = kernels[['Id' , 'TotalVotes', 'CurrentKernelVersionId']]\n",
        "kernels_trc = kernels_trc.rename(columns = {'Id': 'ScriptId', 'CurrentKernelVersionId' : 'VersionId'})\n",
        "\n",
        "kernel_versions_trc = kernel_versions[['Id', 'ScriptId', 'ScriptLanguageId']]\n",
        "kernel_versions_trc = kernel_versions_trc.rename(columns = {'Id' : 'VersionId'})\n",
        "\n",
        "kernel_lang_ren = kernel_lang.rename(columns = {'Id' : 'ScriptLanguageId'})\n",
        "\n",
        "kernels_trc_lang = kernels_trc.merge(kernel_versions_trc, on = ['VersionId', 'ScriptId'])\n",
        "kernels_trc_lang = kernels_trc_lang.merge(kernel_lang_ren, on = 'ScriptLanguageId')"
      ],
      "metadata": {
        "_uuid": "513ecc9432c89e029879ee574937efb510e9f66b",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:06:14.344814Z",
          "iopub.execute_input": "2022-02-02T18:06:14.345456Z",
          "iopub.status.idle": "2022-02-02T18:06:20.751232Z",
          "shell.execute_reply.started": "2022-02-02T18:06:14.345423Z",
          "shell.execute_reply": "2022-02-02T18:06:20.750411Z"
        },
        "trusted": true,
        "id": "aJIG36hwBlBS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#get average number of votes per language name\n",
        "votes_by_lang = kernels_trc_lang.groupby('Name').mean().reset_index()[['Name', 'TotalVotes']]\n",
        "votes_by_lang = votes_by_lang.sort_values(by = 'Name', ascending = True)\n",
        "votes_by_lang = votes_by_lang.rename(columns = {'TotalVotes' : 'Average Votes'})"
      ],
      "metadata": {
        "_uuid": "dcacb6045270bed15a9d05ba614a266c6efb8561",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:06:43.121318Z",
          "iopub.execute_input": "2022-02-02T18:06:43.121617Z",
          "iopub.status.idle": "2022-02-02T18:06:43.218544Z",
          "shell.execute_reply.started": "2022-02-02T18:06:43.121581Z",
          "shell.execute_reply": "2022-02-02T18:06:43.217866Z"
        },
        "trusted": true,
        "id": "3ZcqajrWBlBT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot bar chart with matplotlib\n",
        "\n",
        "plt.figure(figsize=(13,7))\n",
        "plt.bar(votes_by_lang['Name'], votes_by_lang['Average Votes'])\n",
        "plt.xticks(votes_by_lang['Name'])\n",
        "plt.xticks(rotation=60)\n",
        "\n",
        "plt.xlabel('Language Name')\n",
        "plt.ylabel('Average Number of Votes')\n",
        "\n",
        "plt.title('Average number of votes per kernel language')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "a8bcd72cff3e91dffc1a79c554f724174b07c143",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:06:47.626456Z",
          "iopub.execute_input": "2022-02-02T18:06:47.626716Z",
          "iopub.status.idle": "2022-02-02T18:06:47.911030Z",
          "shell.execute_reply.started": "2022-02-02T18:06:47.626688Z",
          "shell.execute_reply": "2022-02-02T18:06:47.910424Z"
        },
        "trusted": true,
        "id": "fatRisJaBlBT",
        "outputId": "b53be34e-7574-41a6-d997-65040ff51daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 936x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It seems that notebooks are more appreciated with Kaggle users. "
      ],
      "metadata": {
        "_uuid": "0376222e170378b166ddf837114e4a5594a64060",
        "id": "zqHLwCsXBlBT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. How do kernel tags affect the number of votes?"
      ],
      "metadata": {
        "_uuid": "ecd471c52726cede2bd0369580e2a85bd6eb8d16",
        "id": "TPIYdF_ABlBX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will look at the kernels' tags:\n",
        "* Are there any kernel tags, which are more specific to more popular kernels?"
      ],
      "metadata": {
        "_uuid": "a7fefbdf6042c3ca7c41b720da612f0e82b3d987",
        "id": "kTa2oOnNBlBX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#prepare data\n",
        "\n",
        "kernels_trc_tag = kernels[['Id' , 'TotalVotes']]\n",
        "kernels_trc_tag = kernels_trc_tag.rename(columns = {'Id' : 'KernelId'})\n",
        "\n",
        "tags_trc = tags[['Id', 'Name', 'DatasetCount', 'CompetitionCount', 'KernelCount']]\n",
        "tags_trc = tags_trc.rename(columns = {'Id' : 'TagId'})\n",
        "\n",
        "kernels_trc_tag = kernels_trc_tag.merge(kernel_tags[['KernelId', 'TagId']], on='KernelId')\n",
        "kernels_trc_tag = kernels_trc_tag.merge(tags_trc, on = 'TagId')"
      ],
      "metadata": {
        "_uuid": "eb26da2e6a4cfacc8623abae9cf91e4286e5d9f4",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:08:27.541921Z",
          "iopub.execute_input": "2022-02-02T18:08:27.542211Z",
          "iopub.status.idle": "2022-02-02T18:08:27.759659Z",
          "shell.execute_reply.started": "2022-02-02T18:08:27.542181Z",
          "shell.execute_reply": "2022-02-02T18:08:27.758957Z"
        },
        "trusted": true,
        "id": "JBiLj4egBlBX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have an assumption that Kaggle users may be attracted by most popular tags, so using them will help to gain more votes. That is why I will try to plot average number of votes against number of datasets/competitions/kernels, where kernel tag is used.\n",
        "\n",
        "1. Tag usage for datasets:"
      ],
      "metadata": {
        "_uuid": "58e93fa4b9f478712defb521db524d570ae602a9",
        "id": "u2jI0jAEBlBY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get average number of votes per number of tage usages in datasets\n",
        "votes_by_tag_dataset = kernels_trc_tag.groupby('DatasetCount').mean().reset_index()[['DatasetCount', 'TotalVotes']]\n",
        "votes_by_tag_dataset = votes_by_tag_dataset.sort_values(by = 'DatasetCount', ascending = True)"
      ],
      "metadata": {
        "_uuid": "f6133e6ac29d6c7afd361bc4741f8aaaad668911",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:08:30.809343Z",
          "iopub.execute_input": "2022-02-02T18:08:30.810163Z",
          "iopub.status.idle": "2022-02-02T18:08:30.874267Z",
          "shell.execute_reply.started": "2022-02-02T18:08:30.810099Z",
          "shell.execute_reply": "2022-02-02T18:08:30.873540Z"
        },
        "trusted": true,
        "id": "oDfUk-T4BlBY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib\n",
        "\n",
        "plt.scatter(votes_by_tag_dataset['DatasetCount'], votes_by_tag_dataset['TotalVotes'])\n",
        "plt.title(\"Number of votes per number of tag usages in datasets\")\n",
        "plt.xlabel(\"Tag usage for datasets\")\n",
        "plt.ylabel(\"Votes\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "83ec1f364f3f1f63ea435f02000ea342391eea51",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:08:33.967888Z",
          "iopub.execute_input": "2022-02-02T18:08:33.968735Z",
          "iopub.status.idle": "2022-02-02T18:08:34.169629Z",
          "shell.execute_reply.started": "2022-02-02T18:08:33.968690Z",
          "shell.execute_reply": "2022-02-02T18:08:34.168616Z"
        },
        "trusted": true,
        "id": "SDHkfT9jBlBY",
        "outputId": "1c704e5b-4f8c-44db-eb05-0a6be26db740"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Tag usage for competitions:"
      ],
      "metadata": {
        "_uuid": "138590aa78fcf38290a2619c943de8018d8e8075",
        "id": "oG7BqY8XBlBY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get average number of votes per number of tage usages in competitions\n",
        "votes_by_tag_competition = kernels_trc_tag.groupby('CompetitionCount').mean().reset_index()[['CompetitionCount', 'TotalVotes']]\n",
        "votes_by_tag_competition = votes_by_tag_competition.sort_values(by = 'CompetitionCount', ascending = True)"
      ],
      "metadata": {
        "_uuid": "6fb6d9987736da66485f9dbbb116f9744c05c1bd",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:09:17.469962Z",
          "iopub.execute_input": "2022-02-02T18:09:17.470238Z",
          "iopub.status.idle": "2022-02-02T18:09:17.511077Z",
          "shell.execute_reply.started": "2022-02-02T18:09:17.470210Z",
          "shell.execute_reply": "2022-02-02T18:09:17.510351Z"
        },
        "trusted": true,
        "id": "h9W_Y3mTBlBZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib\n",
        "\n",
        "plt.scatter(votes_by_tag_competition['CompetitionCount'], votes_by_tag_competition['TotalVotes'])\n",
        "plt.title(\"Number of votes per number of tag usages in competitions\")\n",
        "plt.xlabel(\"Tag usage for competitions\")\n",
        "plt.ylabel(\"Votes\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "4ae9aba4c0e4769df8cd77438e3b6939544b0eb9",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:09:19.924608Z",
          "iopub.execute_input": "2022-02-02T18:09:19.924905Z",
          "iopub.status.idle": "2022-02-02T18:09:20.186756Z",
          "shell.execute_reply.started": "2022-02-02T18:09:19.924874Z",
          "shell.execute_reply": "2022-02-02T18:09:20.185911Z"
        },
        "trusted": true,
        "id": "ZsxIeyLDBlBZ",
        "outputId": "10f1c454-3221-4b8f-9360-a74eecc7bcd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Tag usage for kernels:"
      ],
      "metadata": {
        "_uuid": "f86d4ce6f3f241fe462fb2e79d5282ed2a898341",
        "id": "9NEikIfiBlBZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get average number of votes per number of tage usages in kernels\n",
        "votes_by_tag_kernel = kernels_trc_tag.groupby('KernelCount').mean().reset_index()[['KernelCount', 'TotalVotes']]\n",
        "votes_by_tag_kernel = votes_by_tag_kernel.sort_values(by = 'KernelCount', ascending = True)"
      ],
      "metadata": {
        "_uuid": "bacd210701d761805a1272d3ed6b65e86e6ef562",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:09:23.706664Z",
          "iopub.execute_input": "2022-02-02T18:09:23.707121Z",
          "iopub.status.idle": "2022-02-02T18:09:23.748523Z",
          "shell.execute_reply.started": "2022-02-02T18:09:23.707050Z",
          "shell.execute_reply": "2022-02-02T18:09:23.747875Z"
        },
        "trusted": true,
        "id": "Cf2yOYCcBlBZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib\n",
        "\n",
        "plt.scatter(votes_by_tag_kernel['KernelCount'], votes_by_tag_kernel['TotalVotes'])\n",
        "plt.title(\"Number of votes per number of tag usages in kernels\")\n",
        "plt.xlabel(\"Tag usage for kernels\")\n",
        "plt.ylabel(\"Votes\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "e0889288f0575b855131cd01376f689552bc297a",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:09:25.836011Z",
          "iopub.execute_input": "2022-02-02T18:09:25.836411Z",
          "iopub.status.idle": "2022-02-02T18:09:26.053222Z",
          "shell.execute_reply.started": "2022-02-02T18:09:25.836380Z",
          "shell.execute_reply": "2022-02-02T18:09:26.052402Z"
        },
        "trusted": true,
        "id": "gHPAv4OBBlBa",
        "outputId": "91dfe427-423e-4781-a8b9-545e79634177"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looks like the popularity of the tag doesn't really affect the number of votes.\n",
        "<br> I would also like to look at average number of votes for the most popular tags:"
      ],
      "metadata": {
        "_uuid": "69f8123cc72acb704ddb38de703faaf7e8983ccd",
        "id": "7WiI77RWBlBa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#find out total usage of the tag and store in TotalCount column\n",
        "tags_total = tags_trc.copy()\n",
        "\n",
        "tags_total['TotalCount'] = tags_total['DatasetCount'] + tags_total['CompetitionCount'] + tags_total['KernelCount']\n",
        "\n",
        "#find out top-20 most popular tags\n",
        "tags_total = tags_total.sort_values('TotalCount', ascending = False)\n",
        "tags_total = tags_total[:20]\n",
        "\n",
        "#find average number of votes for each tag\n",
        "votes_by_tag = kernels_trc_tag.groupby('Name').mean().reset_index()[['Name', 'TotalVotes']]\n",
        "\n",
        "#filter votes by tag for only top-20 most popular tags\n",
        "votes_by_tag_popular = votes_by_tag[votes_by_tag['Name'].isin(tags_total['Name'].values.tolist())]"
      ],
      "metadata": {
        "_uuid": "d63ca1a663955fe58dfa661697ba21178ee35a89",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:09:47.482083Z",
          "iopub.execute_input": "2022-02-02T18:09:47.482371Z",
          "iopub.status.idle": "2022-02-02T18:09:47.567189Z",
          "shell.execute_reply.started": "2022-02-02T18:09:47.482343Z",
          "shell.execute_reply": "2022-02-02T18:09:47.566324Z"
        },
        "trusted": true,
        "id": "Gd_AbOfeBlBa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot bar chart with matplotlib\n",
        "\n",
        "plt.figure(figsize=(20,7))\n",
        "plt.barh(votes_by_tag_popular['Name'], votes_by_tag_popular['TotalVotes'])\n",
        "plt.yticks(votes_by_tag_popular['Name'])\n",
        "\n",
        "plt.ylabel('Tag Name')\n",
        "plt.xlabel('Average Number of Votes')\n",
        "\n",
        "plt.title('Average number of votes per tag')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "b8817db415fc4c9e7283fcc9b4261f7d7306f9c7",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:09:50.677748Z",
          "iopub.execute_input": "2022-02-02T18:09:50.678240Z",
          "iopub.status.idle": "2022-02-02T18:09:51.007713Z",
          "shell.execute_reply.started": "2022-02-02T18:09:50.678187Z",
          "shell.execute_reply": "2022-02-02T18:09:51.007055Z"
        },
        "trusted": true,
        "id": "4dxCdhRQBlBa",
        "outputId": "291aa0b6-0622-4132-e5a2-88d2c4fe60a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1440x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can really see that number of votes varies greatly even for the most popular tags.\n",
        "<br> It is also interesting to find out tags, which have the greatest average number of votes:"
      ],
      "metadata": {
        "_uuid": "f0944c5658e3dec04f42a2971b9498d0690a061a",
        "id": "A49mP1a4BlBb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#sort votes_by_tag by average number of votes and take top-20\n",
        "\n",
        "votes_by_tag_aver = votes_by_tag.sort_values('TotalVotes', ascending = False)[:20]"
      ],
      "metadata": {
        "_uuid": "770bacaf734dbe241a3d45450ccb7a3475026449",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:10:31.043445Z",
          "iopub.execute_input": "2022-02-02T18:10:31.043723Z",
          "iopub.status.idle": "2022-02-02T18:10:31.048429Z",
          "shell.execute_reply.started": "2022-02-02T18:10:31.043693Z",
          "shell.execute_reply": "2022-02-02T18:10:31.047858Z"
        },
        "trusted": true,
        "id": "F1PuIsjqBlBb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot bar chart with matplotlib\n",
        "\n",
        "plt.figure(figsize=(20,7))\n",
        "plt.barh(votes_by_tag_aver['Name'], votes_by_tag_aver['TotalVotes'])\n",
        "plt.yticks(votes_by_tag_aver['Name'])\n",
        "\n",
        "plt.ylabel('Tag Name')\n",
        "plt.xlabel('Average Number of Votes')\n",
        "\n",
        "plt.title('Average number of votes per tag')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "_uuid": "bba6cef7758d715ac4c5ce32439df59c7e3497e2",
        "execution": {
          "iopub.status.busy": "2022-02-02T18:10:34.383300Z",
          "iopub.execute_input": "2022-02-02T18:10:34.383891Z",
          "iopub.status.idle": "2022-02-02T18:10:34.688342Z",
          "shell.execute_reply.started": "2022-02-02T18:10:34.383855Z",
          "shell.execute_reply": "2022-02-02T18:10:34.687308Z"
        },
        "trusted": true,
        "id": "W6ESATSKBlBb",
        "outputId": "719ebb97-865e-4a1b-9efb-a4a4e0fad66e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1440x504 with 1 Axes>",
            "image/png": 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EBATQvn17vvnmG+uxHTt2EBgYiL+/P2+88QYJCQkAxMfH06lTJ4YPH07Hjh3p2rUrJ0+eBHKPNrzd6MPk5GR69+5N586d6dixI9OnT7ceCwsLY9CgQfTt2xdfX19SUlJynf/555/j7+9Pp06d6NmzJ2azOd/XnjhxIqdOnSIgIMB63NPTk/T09BzP5c/3np6ehIWF0aVLFz799FPS0tIYPXo0Xbt2xd/fn8mTJ2MymfL3wEVERERERERE5KHSxioPiJ2dHTExMZw+fZpevXpRr149AIYPH87SpUt54YUXWLFiBcOGDWPFihUAHDt2jDFjxjB9+nRWrVrF8OHDiY6Ozvc1ixcvzvz583FxcSE7O5t+/fqxY8cOWrRoAcChQ4eIjo6mdOnSuc5dtWoVW7ZsISIiAldXV65cuYKdXf5ryuPGjWPatGl3Fa+zszNRUVEAjB49mvr16zNlyhTMZjPDhg0jKiqK7t2757s/ERERERERERF5OFREfEC6desGQOXKlalevToHDhzAYDBQrVo1XnjhBQC6dOnChAkTSEtLA+D555+nQYMGAAQEBDB27FjrsfwwmUxMnz6d/fv3Y7FYuHjxIkePHrUWEVu0aHHLAiLA1q1b6dWrF66urgCUKlXq3m78LgQFBVlfb9myhUOHDvHVV18BkJGRQbly5Qo8Brk/Hh5uhR2C2Cj9dkRsh/JVxHYoX0Vsg3JVHhcqIj6C7O3tMZvN1veZmZm3bPfVV1+RkpLCihUrcHZ2ZuzYsTnauri4FNi1b3fun2sv3uq8YsWKWV9bLBbmzp1L+fLl7zpGKTzJyamFHYLYIA8PN/12RGyE8lXEdihfRWyDclVsiZ2dAXd319sff4ixPNb+nKb766+/cuTIEby8vPDy8uLo0aOcOnUKuDmFuHr16tbRf4mJiezduxeA2NhYqlatiqurK88//zzHjh0jKyuLrKysHGss/lVqaioeHh44Oztz4cIFNm/enO94W7duTUREhHXk45UrVwDyfW1XV9dcoyYrVKjA4cOHrfeTF29vbxYsWGBdB/Hy5cucPXs23/GLiIiIiIiIiMjDo5GID4jJZCIwMJAbN24wceJE3N3dAZg+fTrDhg3DaDRSunRpZsyYYT2natWqrFixgvHjx1OkSBHrxiheXl40btyYjh07UrZsWapVq0ZycnKua/bu3ZvBgwfj5+dHuXLlaNy4cb7jDQwM5MKFC/To0QMHBweKFSvGsmXL8n1tT09PKlWqhJ+fH5UrV2bOnDmMGjWKcePG4ebmho+PT57XDw4OZsaMGQQEBGAwGHB0dCQ4OFgjE0VEREREREREHkEGy5/zT+WeeXp6sm/fvruaPhwfH3/XG5NITv0mbyTpyo3CDuOJETszQMPw5Z5oCoeI7VC+itgO5auIbVCuii3RdGYRERERERERERG5L5rO/AAcO3bsrs9p2LChRiGKiIiIiIiIiIhNUBFRbNaiMe0KO4QnSkamsbBDEBEREREREZFCoiKi2KxLl9Iwm7Wkp4iIiIiIiIhIQdOaiCIiIiIiIiIiIpInFRFFREREREREREQkT5rOLDYrr23HpWBkZBpJTblR2GGIiIiIiIiIyEOmIqLYrH6TN5J0RQWthyl2ZgCphR2EiIiIiIiIiDx0ms4sIiIiIiIiIiIieVIR8TEQHx/P999/X+DXmT17NuvWrcuzzejRo9m7d2+BxyIiIiIiIiIiIg+PpjM/Bvbs2cP169dp1qzZXZ9rMpmwt7fPV9vBgwffsc2UKVPuOgYREREREREREXm0aSTiXdq/fz+9evWiU6dOdOrUyToC8NChQ/To0QN/f3969OjBoUOHADh37hwNGzZk5syZBAYG4uPjw08//cSYMWPw9/enW7duJCcnAxAdHc2bb77JgAED8PX15fXXX+fChQsAhIWFMW3aNGscf74/duwYkZGRrF69moCAABYsWADA9u3b6dmzJ507d6ZHjx4cOHAAuDlq0d/fn1GjRhEQEMCOHTty3N/o0aP5z3/+Y31//Phx2rRpg8ViYeTIkSxduhSATZs24e/vT0BAAH5+fsTHxwPQu3dvtm7dCsDFixd555138Pf3x9/fn9WrV1v79fb2Zvbs2fTo0QNvb29rvyIiIiIiIiIi8ujRSMS7cPXqVd59913CwsKoW7cuJpOJtLQ0srKyGDRoEFOnTqVx48bs3LmTQYMGsXHjRut5//jHPxg6dChffPEFffr0YcmSJUyePJnx48ezdOlShgwZAsCPP/7I6tWrqVy5Mp9++ilTpkxhzpw5t43J09OTnj17cv36dUaMGAFAYmIic+fOZdGiRbi6unLixAn69+/Ptm3bADh58iQTJ06kTp06ufoLCgpiypQpvPHGG8DNwmZQUBAGgyFHuzlz5lj7MJlM3LiRe4OTyZMn8+KLL/LZZ5+RlJRE586dqV69OlWrVgUgIyOD5cuXc+7cOfz9/QkKCsLFxeUuvxURERERERERESloKiLehQMHDlClShXq1q0LgL29PSVKlODYsWM4OjrSuHFjAJo0aYKjoyNnzpzBxcWFYsWK0apVKwBefvllnnrqKV566SXr+507d1qv8Y9//IPKlSsD0K1bN/z9/e86zu+++47ExEReffVV62dGo5GLFy8C8Pzzz9+ygAhQr1490tPTOXbsGFWqVCEuLo7ly5fnateoUSOmTp1Ku3btaNGihbUw+Fe7du1i5MiRAJQtW5aWLVsSHx9vbevr6wvAc889R/HixTl//jxVqlS56/uVh8vDw62wQxAbpN+NiO1QvorYDuWriG1QrsrjQkXEh8DJycn62s7OLsd7e3t7TCbTHfuwt7fHbDZb32dmZubZvnnz5kyfPj3X56dOnaJYsWJ5nhsYGMiqVato0KABVapU4dlnn83VJjg4mGPHjrF7924GDx7Mm2++Sffu3e94H3/l7OxsfZ3f5yCFLzk5tbBDEBvj4eGm342IjVC+itgO5auIbVCuii2xszPg7u56++MPMRab5+XlxalTp9i/fz9wc1OSa9euUalSJbKzs9m9ezdwcwSe0WikUqVKd32Nffv28euvvwIQFRVFo0aNgJujB3/++WfMZjNpaWnWqckArq6upKb+v/9Satq0Kd999x0nTpywfvbnGo35ERgYSFxcHCtWrKBz5863bHP69Gk8PT1544036NSpE4cPH87VpnHjxnz99dcAJCcns337duv9iIiIiIiIiIiI7dBIxLtQsmRJwsLC+Oijj7h+/Tp2dnaMGDGCJk2aMGfOHKZMmcL169cpVqwYs2fPzjHiML/q1q3LtGnTSEhIoEyZMsyYMQOAf/7zn6xbt44OHTrwzDPP8PLLL1vPadu2rXVjlY4dO/LWW28xY8YMRo8eTUZGBtnZ2dStW5datWrlK4ZnnnmGF154gT179vDxxx/fss3MmTNJSEjA3t6e4sWL33JX5jFjxjBu3DjrlOxhw4bx4osv3u0jERERERERERGRQmawWCyWwg5CboqOjmbbtm15bqQi/0+/yRtJupJ7QxcpOLEzAzQUX+6apnCI2A7lq4jtUL6K2AblqtgSTWcWERERERERERGR+6LpzI+Qzp0733YNQhERERERERERkcKiIqLYrEVj2hV2CE+cjExjYYcgIiIiIiIiIoVARUSxWZcupWE2a0lPEREREREREZGCpjURRUREREREREREJE8aiSg2K68dg6RgZGQaSU3RjtgiIiIiIiIiTxoVEcVm9Zu8kaQrKmg9TLEzA0gt7CBERERERERE5KHTdGYRERERERERERHJk00WEb29vTl+/PgD7XPTpk0cOnTogfaZH/m5l5SUFBYuXPiQIrozT09P0tPT7+nc2bNns27dugcckYiIiIiIiIiIFCSbLCLeC6PRmOfxey0ims1mLJaC3SE4JSWFL774okCv8bAMHjwYX1/fwg5DRERERERERETuwkNZE/HgwYOEhoZaR68NGjSIVq1aMXfuXI4cOcKnn37KjRs36N69O8OGDaNly5Z4e3vj6+vLzp07SU1N5Y033uC1117L1XdCQgLjxo3j8uXLODg4MGTIEFq0aAHcHDH37rvvsm3bNpo3b06HDh2YMGECN27cIDMzk+7du9OnTx++++47tmzZws6dO1mxYgVvvvkmgYGBLFiwgDVr1gBQs2ZNxowZg4uLC2FhYZw4cYK0tDR+//13Bg8ezKpVq1iwYAEAWVlZeHt78/XXX/PMM8/kiHfv3r1MmDABgPr16+coQE6bNo09e/aQnZ1NqVKlCAkJ4dlnn2XixImkpqYSEBBA0aJFiYyM5Msvv2Tt2rWYTCacnZ0ZP348L730Uq7ns2vXLj755BMyMzMxmUwMGDCAjh07AtC7d29q1KjBgQMHSEpKokOHDgwbNgwgX/2vX7/+tvd9/vx5Jk2ahNlsxmg0MnDgQPz8/Bg5ciQ1atTgtddeY9OmTcyePRs7OztMJhNjx46lYcOGd/nrEhERERERERGRglbgRcSUlBQ+/PBDFixYQNmyZUlKSqJr167ExcUxYMAA/vWvf7FkyRKOHDlC8+bNadmypfXcS5cuER0dzcWLFwkMDKRevXpUq1YtR//Dhg2je/fudOvWjZMnT/Lqq6+yfv16SpcuDYCzszNRUVEApKWlsXjxYpycnEhPT6dbt240b96c5s2b4+3tbS1uAWzfvp01a9YQGRmJi4sLI0aMYO7cuXzwwQcAHDp0iOjoaEqXLo3RaGT69OmcPXuW8uXLs27dOmrXrp2rgJiVlcWQIUMIDQ2lYcOGrFu3jmXLllmP9+/fnxEjRgCwYsUKQkNDmTVrFuPGjaNLly7ExMRY2wYGBtK3b18Adu7cyYcffsjXX3+d6/lXr16d8PBw7O3tuXjxIp07d6ZZs2aUKFECgD/++INly5aRnp5O27Zt6dq1KxUrVsxX///85z9ve9+TJk2iX79++Pn5YbFYSE3NvR3HnDlzmDhxInXq1MFkMnHjhjZJERERERERERF5FBV4EXH//v2cO3eO/v37Wz8zGAwkJCRQs2ZNZsyYQUBAAM888wzh4eE5zu3atSsAZcqUoVWrVuzZsydHETEtLY1ffvmFLl26APDCCy/w0ksvceDAAby9vQEICgqyts/IyGD8+PEcO3YMg8FAUlISR48epUqVKrni3rVrF76+vri6ugLQvXt3QkJCrMdbtGhhLVQ6ODjQo0cPIiMj+eCDDwgPD+e9997L1efp06cpWrSodbSdr68v48aNsx7fsWMH4eHhXL9+/Y7Tr3/66Sc+//xzrl27hsFg4Ndff71lu8uXLxMcHExCQgL29vZcu3aNM2fO4OXlBYCPjw92dna4ublRpUoVEhMTqVixYr76z+u+GzZsyLx580hMTKRp06bUrl071/mNGjVi6tSptGvXjhYtWlC1atU871keDR4eboUdgtgg/W5EbIfyVcR2KF9FbINyVR4XBV5EtFgseHp65hhx91fnzp3Dzs6OlJQUMjIyrEW7B6VYsWLW1x9//DEeHh589NFHODg40LdvXzIzM++pXxcXlxzvu3fvTlBQEN7e3qSkpNC4ceN89WMwGAD47bffmDp1KitXrqR8+fLs27fPOrX477Kyshg8eDBLly7l5Zdf5sKFC9Yp3H83fvx4vL29+fTTTzEYDLRv3z7HPTs7O1tf29vbYzKZ7qr/2913nz598Pb2ZufOnUyaNImmTZsyZMiQHOcGBwdz7Ngxdu/ezeDBg3nzzTfp3r17vp6bFJ7k5NyjSkXy4uHhpt+NiI1QvorYDuWriG1QrootsbMz4O5++7pcgW+sUqdOHRISEti9e7f1s0OHDmGxWLh27RrDhg3j448/xtfXl7Fjx+Y4d9WqVcDN0XTbt2/PtV6eq6srL730krXdqVOnOHr0qHWU3d+lpqby1FNP4eDgwPHjx9m7d2+Ovv465bZx48asX7+etLQ0LBYLK1eupEmTJre9z9KlS9OkSRPef/99XnnlFWtx8K8qV65MRkaG9bobNmwgJSUFuDmq0tHREQ8PD8xmM5GRkTliy8jIsI5OzMrKwmg08vTTTwPkGsH593t+9tlnMRgM/O9//yMhIeG2bf90N/3f7r7PnDlDhQoV6NmzJ6+//jqHDx/Ode7p06fx9PTkjTfeoFOnTrdsIyIiIiIiIiIiha/ARyKWKFGCuXPnMmPGDEJCQsjOzqZ8+fLMnz+f4OBgunTpQr169ahTpw59+vQhIiKCXr16AVCqVCk6d+5Mamoq//73v/H09MzVf2hoKOPGjWPx4sU4ODgwffp06zTjvxs4cCDDhw9n5cqVVKpUifr161uPderUiVGjRrFhwwbrxirHjh2jZ8+eANSoUYOBAwfmea9du3Zlw4YNOaZQ/5WTkxMff/xxjo1V/lw30dPTEx8fH3x9fSlVqhQtW7a0FhtLliyJv78//v7+lChRgsjISAYNGkTXrl0pWbIk7du3v21MQ4cOZcKECYSFhVGzZs1bPsO/c3V1zXf/t7vvJUuWEB8fj6OjI05OTowZMybXeTNnzrROsy5evDhTpky5Y2wiIiIiIiIiIvLwGSx/3R74EeLt7c38+fNtap28uXPnkpyczIcffljYoTxUhXXf/SZvJOmKNmN5mGJnBmgovtw1TeEQsR3KVxHboXwVsQ3KVbEld5rOXOAjEZ8UHTt2xN7enkWLFhV2KA/Vk3rfIiIiIiIiIiJPkke2iLhly5bCDuGurF27trBDKBRP6n2LiIiIiIiIiDxJHtkiosidLBrTrrBDeOJkZBoLOwQRERERERERKQQqIorNunQpDbP5kVzSU0RERERERETksWJX2AGIiIiIiIiIiIjIo00jEcVm5bVjkDw8GZlGUlO0S7aIiIiIiIjI40xFRLFZ/SZvJOmKileFLXZmAKmFHYSIiIiIiIiIFChNZxYREREREREREZE8qYgoIiIiIiIiIiIiebKpIqKnpyfp6emFHcY9CwsLY9q0aQ/tetHR0QwaNAiAzZs339e1f/nlF9atW5fjs4CAADIyMu4rRhERERERERERefTZVBHxdoxGY2GH8Mhr06YNI0aMuOfzf/nlFzZs2JDjs5iYGIoUKXK/oYmIiIiIiIiIyCPukd5YZePGjXz88cc4OzvTrl27HMc8PT1599132bZtG82bN8fe3p7r169bC2VhYWHW96mpqQQHB3PixAnKlStHuXLlcHd3Z8SIEWzatInZs2djZ2eHyWRi7NixNGzYMMe1kpOTef/990lPTyczM5OWLVsyfPhw63XOnDlDamoqZ8+epUKFCsyePZuiRYuSmprK6NGjOX78OB4eHjz11FOUKVPmlvfq6enJe++9x6ZNm7h69SqTJ09m586dfPfddxiNRmbPnk2VKlUAWLVqFeHh4ZhMJlxdXRk/fjyVK1cmKyuLyZMns3v3bkqVKsVLL71k7T86Oppt27YxZ84cAFauXMl///tfABwdHfn8888pWbIk//73v7ly5QqZmZnUqlWLCRMmkJ6ezpw5c0hLSyMgIID69eszZswYPD092bdvHy4uLhw6dIgpU6Zw/fp1ihUrxujRo6lVqxbnzp2jS5cu9OzZk+3bt3Pjxg2mTJlCvXr1uHTpEkOHDuXSpUsANG7cmODg4Pv92YiIiIiIiIiIyAP2yBYRL168yNixY4mIiKBy5cosXLgwVxtnZ2eioqKAm8W82/nss88oXrw4GzZs4OrVq3Tu3Jn27dsDMGfOHCZOnEidOnUwmUzcuJF7t9/ixYszf/58XFxcyM7Opl+/fuzYsYMWLVoA8NNPP7Fy5Urc3Nzo168fsbGxdO/enc8++wwXFxc2bNjA5cuX6dy5Mx06dLhtnMWLFycqKor169fz9ttv8/HHHzN06FAWLlzIvHnzCA0NZe/evaxfv55ly5bh5OTE9u3bCQ4OJjIykuXLl3Pu3DnWrl2L0Wjk1Vdf5bnnnst1nfj4eD7//HPCw8Px8PAgPT0dBwcH7O3tCQ0NpVSpUlgsFkaMGEFUVBS9evVi0KBBOYqQf5WVlcWgQYOYOnUqjRs3ZufOnQwaNIiNGzcCcPXqVby8vBgyZAhr1qwhNDSUyMhIYmNjqVChAosXLwbg2rVrt3028mjz8HAr7BDkEaffiIjtUL6K2A7lq4htUK7K4+KRLSIePHiQ6tWrU7lyZQB69OhBaGhojjZBQUH56is+Pp4xY8YAULJkSdq2bWs91qhRI6ZOnUq7du1o0aIFVatWzXW+yWRi+vTp7N+/H4vFwsWLFzl69Ki1iNisWTOKFy8OQK1atUhMTMx13dKlS/PPf/4zzzj/LDC+/PLLALRu3RqAGjVq8O233wKwZcsWjh49Srdu3QCwWCykpKRYrxcYGIijoyOOjo506tSJffv25brOtm3bCAgIwMPDAwAXFxfrfX755Zfs2LEDs9nMtWvX8jVd+cyZMzg6OtK4cWMAmjRpgqOjI2fOnMHFxYVixYpZ78XLy8u6NmPt2rVZvHgx06ZNo0GDBjRr1uyO15JHU3JyamGHII8wDw83/UZEbITyVcR2KF9FbINyVWyJnZ0Bd3fX2x5/ZIuI+VGsWDHra3t7e8xms/V9ZmZmvvoIDg7m2LFj7N69m8GDB/Pmm2/SvXv3HG2++uorUlJSWLFiBc7OzowdOzZH/87OzjniyO+1/+7Pfuzs7HBycrJ+bmdnZ1330WKx0KVLFwYPHnxP18hLbGwsP/74I8uWLcPV1ZX58+fz66+/3ne/t7uXOnXqsGrVKnbu3ElMTAwLFiwgIiLivq8nIiIiIiIiIiIP1iO7sYqXlxdHjhyxFrFWrFiRZ/vnn3+en3/+GbPZTFpaGtu2bbMea9CgATExMQCkpKSwefNm67HTp0/j6enJG2+8QadOnTh8+HCuvlNTU/Hw8MDZ2ZkLFy7kOD8vjRo1Ijo6GoArV66wadOmfJ2XF29vb2JiYjh//jxwc/TgTz/9ZL1eTEwMRqORjIwM4uLibtlHq1atiImJ4eLFiwDWtR5TU1MpVaoUrq6upKam5jj/z89upVKlSmRnZ7N7924Adu3ahdFopFKlSnney9mzZ3F1daVjx46MGjXK+v2JiIiIiIiIiMij5ZEdieju7s6kSZMYMGAARYoUybWxyt/985//ZN26dXTo0IFnnnnGOiUY4J133mHUqFH4+Pjg4eFBjRo1cHW9OTxz5syZJCQkYG9vT/HixZkyZUquvnv37s3gwYPx8/OjXLly1mm7d/L2228THBxsvW69evXu4gncWv369XnvvfcYOHAgJpOJ7OxsfHx8qFGjBt27d+fYsWP4+vpSqlQpatasad205K8aNmzIW2+9xZtvvonBYMDJyYn58+cTGBjI5s2b8fHxwd3dnX/84x/WUZWNGzfmyy+/pFOnTjRo0MA6TRtujjScM2dOjo1VZs+enWME4q3s2bOHxYsXY2dnh9lsZsKECdjZPbJ1bRERERERERGRJ5bBYrFYCjuIgpadnY3ZbMbZ2Zm0tDR69erFqFGjaNKkSWGHJveh3+SNJF3JvRGOPFyxMwO0xofkSevAiNgO5auI7VC+itgG5arYksd6TcT8SklJoX///phMJjIzM/Hz81MBUUREREREREREJJ+eiJGIIlJwMjKNpKZoRKjcnv76KmI7lK8itkP5KmIblKtiSzQSUR5bly6lYTarBi4iIiIiIiIiUtC0i4WIiIiIiIiIiIjkSUVEERERERERERERyZOmM4vNymuevjx6tHaiiIiIiIiIiO1SEVFsVr/JG0m6oqKUrYidGYCWExYRERERERGxTZrOLCIiIiIiIiIiInlSEfEJtGnTJjp06EBgYCC1atUiIyOjsEMSEREREREREZFHmKYzP4EiIyMZNGgQHTp0KOxQRERERERERETEBqiI+IQJCQnhxx9/5MyZM4SHh7Nnzx727duHi4sL3t7eBAQEsHPnTpKTk+nbty+vvfYaANOmTWPPnj1kZ2dTqlQpQkJCePbZZzl37hxdunShZ8+ebN++nRs3bjBlyhTq1asHwNatWwkLC8NoNGJnZ8dHH31EtWrVOHjwIKGhoaSnpwMwaNAgWrVqVViPRURERERERERE8qAi4hMmODiYX375hb59+9K6dWs8PT1zHM/IyGD58uWcO3cOf39/goKCcHFxoX///owYMQKAFStWEBoayqxZswC4evUqXl5eDBkyhDVr1hAaGkpkZCRnzpxhzJgxLFu2jIoVK5KVlUVWVhYpKSl8+OGHLFiwgLJly5KUlETXrl2Ji4ujePHiD/2ZiIiIiIiIiIhI3lRElBx8fX0BeO655yhevDjnz5+nSpUq7Nixg/DwcK5fv47RaMxxTrFixWjdujUAXl5eTJs2DYCdO3fSokULKlasCICTkxNOTk5s376dc+fO0b9/f2sfBoOBhIQEatas+RDuUgqLh4dbYYcghUTfvYjtUL6K2A7lq4htUK7K40JFRMnB2dnZ+tre3h6TycRvv/3G1KlTWblyJeXLl2ffvn0MGzbM2s7Jycn62s7OLleR8e8sFguenp4sW7bswd+APNKSk1MLOwQpBB4ebvruRWyE8lXEdihfRWyDclVsiZ2dAXd319sff4ixiI1KS0vD0dERDw8PzGYzkZGR+TqvadOm7Nixg19//RWArKws0tLSqFOnDgkJCezevdva9tChQ1gsloIIX0RERERERERE7pNGIsodeXp64uPjg6+vL6VKlaJly5bs3bv3judVrFiRSZMmMWTIEEwmE/b29nz00Ud4enoyd+5cZsyYQUhICNnZ2ZQvX5758+djMBgewh2JiIiIiIiIiMjdMFg0/EtsVL/JG0m6cqOww5B8ip0ZoGH8TyhN4RCxHcpXEduhfBWxDcpVsSWaziwiIiIiIiIiIiL3RUVEERERERERERERyZPWRBSbtWhMu8IOQe5CRmbeu3aLiIiIiIiIyKNLRUSxWZcupWE2a0lPEREREREREZGCpunMIiIiIiIiIiIikieNRBSbldeOQfLoycg0kpqi3bRFREREREREbJGKiGKz+k3eSNIVFaVsRezMAFILOwgRERERERERuSeaziwiIiIiIiIiIiJ5UhFRRERERERERERE8qQi4hOqd+/ebN269a7OGT16NHv37s2zTf/+/UlMTLyf0ERERERERERE5BGjNREl36ZMmXLHNgsXLnwIkYiIiIiIiIiIyMOkkYj34eDBg/Tu3ZvOnTvTuXNntm3bBsCECRP44osvrO2OHDlC+/btsVgspKWlMXr0aLp27Yq/vz+TJ0/GZDIB8Omnn+Lj40NAQACBgYGkpKTkuuauXbvo0aMHgYGB+Pv7s3btWuux3r17M23aNHr16kWbNm0IDQ21Hjt58iTdunWjY8eODBkyhMzMzFveU58+fdi0aZP1/datW+ndu7e1/z9HLy5fvpwOHToQEBCAv78/p06dAsDb25vjx48DkJCQwBtvvIG/vz9BQUHs2LHD2q+npyfz58+nS5cutGnThm+++Sb/D15ERERERERERB4qjUS8RykpKXz44YcsWLCAsmXLkpSURNeuXYmLi+O1115jwIAB9OvXD4PBwNKlS3nllVcwGAxMnTqV+vXrM2XKFMxmM8OGDSMqKop27dqxePFivv/+e4oUKUJaWhpFihTJdd3q1asTHh6Ovb09Fy9epHPnzjRr1owSJUoA8Mcff7Bs2TLS09Np27YtXbt2pWLFigwfPpzevXsTFBTEgQMH6NWr1y3vKygoiNWrV9O2bVsAoqOj6dKlS65206dPZ/369ZQtW5asrCxrIfSvhg0bRvfu3enWrRsnT57k1VdfZf369ZQuXRoAV1dXoqKi+PHHH3nvvfdo3779PX8fYhs8PNwKOwQpJPruRWyH8lXEdihfRWyDclUeFyoi3qP9+/dz7tw5+vfvb/3MYDCQkJBAzZo1KV++PDt27MDLy4stW7YwatQoALZs2cKhQ4f46quvAMjIyKBcuXK4ublRoUIFhg8fTrNmzWjVqhWurq65rnv58mWCg4NJSEjA3t6ea9eucebMGby8vADw8fHBzs4ONzc3qlSpQmJiImXKlOH48eMEBAQA4OXlRdWqVW95X+3atWPq1KlcuXIFgD179jBt2rRc7Ro1asTIkSNp3bo1rVq1onz58jmOp6Wl8csvv1gLkC+88AIvvfQSBw4cwNvbGwBfX19rPElJSWRmZuLs7Jy/L0BsUnJyamGHIIXAw8NN372IjVC+itgO5auIbVCuii2xszPg7p67FvUnFRHvkcViwdPTk2XLlt3yeO/evYmIiODUqVO0a9cONzc363lz587NVXQD+Prrr9m3bx+7d++mc+fOfPHFF1SrVi1Hm/Hjx+Pt7c2nn36KwWCgffv2OaYm/7UIZ29vf8sRgnkpWrQobdq0IS4uDoA2bdpQrFixXO0+/fRTDh8+zO7du3n99dcZP348LVu2vKtr/Rmrvb09AEajUUVEEREREREREZFHkNZEvEd16tQhISGB3bt3Wz87dOgQFosFgJYtW3LmzBm++uorXnnlFWsbb29vFixYYC3uXb58mbNnz5KWlsbly5dp0KABgwYNomrVqpw4cSLXdVNTU3n22WcxGAz873//IyEh4Y6xurq6UrVqVWJjY61x/rlu4a0EBQWxatUqVq1aRefOnXMdNxqNnD17llq1avHWW2/RtGlTfvnll1zXfOmll1i1ahUAp06d4ujRo9YRkyIiIiIiIiIiYjs0EvEelShRgrlz5zJjxgxCQkLIzs6mfPnyzJ8/H4PBgJ2dHYGBgezYsSPHaMLg4GBmzJhBQEAABoMBR0dHgoODcXR05P/+7//IyMjAYrFQvXp12rVrl+u6Q4cOZcKECYSFhVGzZk08PT3zFe/06dMZNWoUCxcupGrVqtSsWfO2bevVq0daWpr19d+ZzWZGjhxJamoqBoOBp59+mqFDh+ZqFxoayrhx41i8eDEODg5Mnz7duh6iiIiIiIiIiIjYDoPlz6Fz8sC9+eabdO/enQ4dOhR2KI+lfpM3knTlRmGHIfkUOzNAa4E8obQOjIjtUL6K2A7lq4htUK6KLbnTmoiazlwADh8+TNu2bXFzc9OOwyIiIiIiIiIiYvM0ElFEHoqMTCOpKRo5+iTSX19FbIfyVcR2KF9FbINyVWyJdmeWx9alS2mYzaqBi4iIiIiIiIgUNE1nFhERERERERERkTypiCgiIiIiIiIiIiJ50nRmsVl5zdMX26G1EkVEREREREQefSoiis3qN3kjSVdUfLJ1sTMD0DLDIiIiIiIiIo82TWcWERERERERERGRPKmImE+HDx9m6NChBdZ/dHQ0gwYNKrD+H4YLFy7Qu3fvwg5DREREREREREQeMBUR86lmzZrMnDmzsMPIk9FoLNTrlytXjiVLlhRqDCIiIiIiIiIi8uBpTcS/uXHjBiNGjODkyZM4ODhQqVIlZs+eTXx8PNOmTSM6OhqApUuX8t///hc3NzdatmzJsmXLiI+P59y5c3Tp0oWePXuyfft2bty4wZQpU6hXrx5Go5F///vfXLlyhczMTGrVqsWECRNwcnLKM6bevXtTrVo19u/fz7Vr1+jQoQPvv/9+jmMHDx6kRIkSzJ8/n9DQUL777jsAmjdvzrBhw7C3tyc1NZWQkBB++uknDAYD9erVY9y4cWRlZTFr1ix++OEHsrKy8PT0ZPz48bi4uLB8+XIWL16Mk5MTZrOZTz75hEqVKjFx4kR2796Nk5MTxYoVIzIy0nrv8fHxAHh6ejJkyBC+/fZbrl69yvDhw2nfvj0A33zzDbNmzaJIkSL4+Pgwa9Ys9u3bh4uLS0F9tSIiIiIiIiIico9URPyb77//nvT0dNatWwfAtWvXcrU5evQon3/+OTExMZQuXZrJkyfnOH716lW8vLwYMmQIa9asITQ0lMjISOzt7QkNDaVUqVJYLBZGjBhBVFQUvXr1umNcp06dIjIykszMTHr27EmdOnVo3bo1AGfPniU8PBwHBwfCw8P55ZdfrMXO/v37s3z5cl555RVCQkIoVqwYMTEx2NnZcfnyZQC++OIL3NzcWLlyJQAzZsxgwYIFDBkyhOnTp7N+/XrKli1LVlYWJpOJo0ePEh8fz7p167Czs7vlM/qTq6srUVFR/Pjjj7z33nu0b9+eixcvMm7cOJYvX07FihVZvHjxnb8YEREREREREREpNCoi/k21atU4deoUEyZMoEGDBrRq1SpXmz179tCyZUtKly4NQNeuXYmNjbUeL1asmLXA5+XlxbRp0wAwm818+eWX7NixA7PZzLVr1yhSpEi+4goMDMTBwQEHBwd8fX3ZvXu39Rr+/v44ONz8Knft2kVQUJB1dGPnzp3ZtGkTr7zyClu3biU6Oho7u5uz2P+Mf8uWLaSlpfHNN98AkJWVRbVq1QBo1KgRI0eOpHXr1rRq1Yry5ctTvnx5jEYjo0ePpmHDhtY4bsXX19f6HJKSksjMzOTgwYNUr16dihUrAtClSxemTp2ar+cgjycPD7fCDkEKmL5jEduhfBWxHcpXEdugXJXHhYqIf1O+fHni4uLYvXs3O3bsYNasWTkKhPnx1+nJdnZ21rUKY2Nj+fHHH1m2bBmurq7Mnz+fX3/99b5jLlas2H2db7FY+PDDD2ncuHGuY59++imHDx9m9+7dvP7664wfP56WLVuydu1a4uPj2blzJ6GhoaxateqWfTs7OwNgb28PFP66jfJoSk5OLewQpAB5eLjpOxaxEcpXEduhfBWxDcpVsSV2dgbc3V1vf/whxmITzp8/j729PW3btmXUqFFcvnyZq1ev5mjToEEDduzYYZ0OfLsC2t+lpqZSqlQpXF1dSU1NJS4uLt9xrVmzBqPRyPXr11m/fj2NGjW6ZbvGjRuzevVqsrOzyc7OZvXq1TRp0gSA1q1bs2jRIiwWC4A1fm9vbxYvXkxGRgYAaWlpnDp1CqPRyNmzZ6lVqxZvvfUWTZs25ZdffuHy5cvcuHHDut6im5sbZ8+ezfe91K5dmyNHjpCYmAjk//mJiIiIiIiIiEjh0EjEvzl27Jh1F2az2cxbb71FuXLlcowYrFatGv/617/o2bMnrq6uNGrUCDe3Ow9PDgwMZPPmzfj4+ODu7s4//vEPMjMz8xVX5cqV6dmzp3VjldtNIe7RoweJiYkEBQUB0KxZM7p37w7AqFGjCAkJwc/PD3t7exo0aMCYMWN46623+PTTT+natSsGgwGDwcC7775L+fLlGTlyJKmpqRgMBp5++mmGDh3K77//ztixYzEajZhMJlq0aIGXlxe///57vu6lTJkyjB8/nv79+1O0aFFatWqFo6MjRYsWzdf5IiIiIiIiIiLycBksfw5Lk7uSlpaGq+vNIZ5hYWEkJCQQGhpaINfq3bs3ffv2zXPtQVvz1+cXFRXFypUriYiIuKs++k3eSNKVGwURnjxEsTMDNLz/MacpHCK2Q/kqYjuUryK2QbkqtuRO05k1EvEezZw5k3379pGdnU358uWZOHFiYYdkU5YsWcKGDRswmUyUKFEi1w7XIiIiIiIiIiLy6NBIRLFZGon4eNBIxMef/voqYjuUryK2Q/kqYhuUq2JLNBJRHluLxrQr7BDkAcjI1I7dIiIiIiIiIo86FRHFZl26lIbZrIG0IiIiIiIiIiIFza6wAxAREREREREREZFHm0Yiis3Ka56+2JaMTCOpKVrfUkRERERERORRpSKi2CxtrPL4iJ0ZgJYaFhEREREREXl0aTqziIiIiIiIiIiI5ElFRBEREREREREREcmTiohPmOjoaAYNGgTA5s2bmTZt2n336enpSXp6ep5tzp07x/Lly+/7WiIiIiIiIiIi8vCpiPgEa9OmDSNGjHgo1/rtt99URBQRERERERERsVHaWMWGDB06lDNnzpCdnU2FChUICQmhRIkSREdHs23bNubMmQOQ431WVhaTJ09m9+7dlCpVipdeesna39/PW7BgAWvWrAGgZs2ajBkzBhcXl1xxbNy4kY8//hhnZ2fatWuXrxgnTpzIuXPnCAgI4Pnnn2fOnDlMmzaNPXv2kJ2dTalSpQgJCeHZZ58tqMcnIiIiIiIiIiL3SEVEGzJ69GhKly4NwKxZs1i4cCHDhg3L85zly5dz7tw51q5di9Fo5NVXX+W5557L1W779u2sWbOGyMhIXFxcGDFiBHPnzuWDDz7I0e7ixYuMHTuWiIgIKleuzMKFC/MV47hx45g2bRrR0dHWtv3797eOhFyxYgWhoaHMmjXr7h+MPBY8PNwKOwQpQPp+RWyH8lXEdihfRWyDclUeFyoi2pCYmBhiY2PJzs7m+vXrVKxY8Y7nxMfHExgYiKOjI46OjnTq1Il9+/blardr1y58fX1xdXUFoHv37oSEhORqd/DgQapXr07lypUB6NGjB6GhofcU444dOwgPD+f69esYjcY73os83pKTUws7BCkgHh5u+n5FbITyVcR2KF9FbINyVWyJnZ0Bd3fX2x5XEdFG7N27l4iICCIjIyldujSxsbF8/fXXANjb22M2m61tMzMzH7kY/+63335j6tSprFy5kvLly7Nv3747jqoUEREREREREZHCoY1VbERKSgqurq6ULFmSrKwsoqKirMeef/55jh07RlZWFllZWXzzzTfWY40aNSImJgaj0UhGRgZxcXG37L9x48asX7+etLQ0LBYLK1eupEmTJrnaeXl5ceTIEX799Vfg5jTk/MTo6upKWlqa9X1aWhqOjo54eHhgNpuJjIy852cjIiIiIiIiIiIFSyMRbUTz5s1Zs2YN7du3p1SpUtSrV4/Dhw8DNwt7jRs3pmPHjpQtW5Zq1aqRnJwM3JyWfOzYMXx9fSlVqhQ1a9bk0qVLufpv2bIlx44do2fPngDUqFGDgQMH5mrn7u7OpEmTGDBgAEWKFMmxsUpeMXp6elKpUiX8/PyoXLkyc+bMwcfHxxpXy5Yt2bt37wN/biIiIiIiIiIicv8MFovFUthBiNyLfpM3knTlRmGHIQ9A7MwArRPyGNM6MCK2Q/kqYjuUryK2QbkqtuROayJqOrOIiIiIiIiIiIjkSdOZxWYtGtPuzo3EJmRkanduERERERERkUeZiohisy5dSsNs1mx8EREREREREZGCpunMIiIiIiIiIiIikicVEUVERERERERERCRPms4sNiuvHYPE9mRkGklN0W7bIiIiIiIiIo8iFRHFZvWbvJGkKyo6PS5iZwaQWthBiIiIiIiIiMgtaTqziIiIiIiIiIiI5KnQi4ienp6kp6cDEBAQQEZGRqHGExERweLFiwusf29vb44fPw7A6NGj2bt3LwCbNm3i0KFDBXbd2/nr878XYWFhZGVlWd/Pnj2bdevWPYjQRERERERERETkEXFX05n/+OMPLly4gJeXV4EEExMTUyD93o1evXo9tGtNmTLF+nrTpk3UqFGDWrVq3VefJpMJe3v7+w0t3z799FP69u2Lk5MTAIMHD35o1xYRERERERERkYcjX0XE33//nffff5+jR49iMBjYv38/GzZs4LvvvstRCMuPjRs38vHHH+Ps7Ey7du1yHPP09GTfvn24uLjg7e2Nv78/u3fv5sKFCwwdOpRLly4RFxfHtWvXCAkJoX79+gBs376defPmkZWVhaOjI6NGjcLLy4v4+HhCQkKoXbs2+/fvx2AwMGvWLKpUqcLp06cZNWoUN27cwGw2ExQURL9+/QgLC+P69euMGDECk8lEaGgo3333HQDNmzdn2LBh2NvbM3LkSJycnPj11185f/48Xl5eTJs2DYPBQGxsLP/973/Jzs4GYMSIETRu3DjXs+jduzd9+/bFwcGBLVu2sHPnTlasWMGbb77JunXrCAoKokOHDtbnFhkZyZdffpmjj/j4eCZPnkyNGjU4cuQI7733Hs8//zwhISFcuXKF7Oxs3njjDbp06ZLn8z937hxdunQhPj7+lu+3bt1KWFgYRqMROzs7PvroI5YvXw5Az549sbOzY8mSJYSEhFCjRg1ee+010tPTmTx5MocPHwZujjTt37+/9d5r1KjBgQMHSEpKokOHDgwbNuyufksiIiIiIiIiIvJw5KuIOG7cOFq1akV4eDgNGzYEoGnTpkybNu2uLnbx4kXGjh1LREQElStXZuHChXm2z8rKYvny5Rw6dIjXX3+dDz74gJUrV7Ju3To+/vhjIiIiSExMZO7cuSxatAhXV1dOnDhB//792bZtGwAnT55k6tSpTJw4kXnz5jF37lxmzpxJeHg43t7e/Pvf/wbg2rVrua6/fPlyfvnlF6KjowHo378/y5cv55VXXgHgxIkTLF68GIPBQFBQEDt37qRp06Y0a9YMPz8/DAYDp0+fpk+fPuzYseO299m8eXO8vb2txTeA0qVLs3DhQmsRcdmyZfTu3fuW5588eZKJEydSp04djEYj3bt3Z8aMGVSpUoW0tDS6dOmCl5cXJUqUuKvn/6czZ84wZswYli1bRsWKFcnKyiIrK4sPP/yQ8PBwIiMjcXFxyXXe3LlzMZvNxMbGkp6eTo8ePahatSotW7YEbo5sXbZsGenp6bRt25auXbtSsWLFfMUkIiIiIiIiIiIPT76KiIcPH2bBggXY2dlhMBgAcHNzIzX17vZSPXjwINWrV6dy5coA9OjRg9DQ0Nu29/X1BeDll1/mxo0b1oJajRo1SExMBOC7774jMTGRV1991Xqe0Wjk4sWLAFSqVInq1asD4OXlxdatWwGoX78+M2bM4MaNGzRs2JBGjRrluv6uXbsICgqyTtXt3LkzmzZtshYR27Zti7OzMwDVq1cnMTGRpk2bcvbsWYYOHcqFCxdwcHDg4sWLJCcn4+Hhke9n1bx5c0JCQjh16hQAZ8+epXXr1rds+/zzz1OnTh0Afv31V06dOsX7779vPZ6dnc3p06exs7O7q+f/p507d9KiRQtrgc/Jycn6TPKya9cugoODMRgMuLq60rFjR3bt2mUtIvr4+GBnZ4ebmxtVqlQhMTFRRcQnnIeHW2GHIAVE362I7VC+itgO5auIbVCuyuMiX0VEd3d3EhISqFSpkvWzkydP8vTTTxdYYIC1QPfnGn9/vrezs8NoNFrbNW/enOnTp+c6/9SpUzmKXX89r3379nh5efG///2PhQsXEhUVla+C2q3i+zNGk8kEwPvvv8/IkSNp27YtZrOZ2rVrk5mZeVd9GwwGXnvtNcLDw4GbBb/brXVYrFgx62uLxUKpUqVuub7k5s2bb3s9BwcHLBaL9f3dxnsvbvf85MmVnHx3f5gQ2+Dh4abvVsRGKF9FbIfyVcQ2KFfFltjZGXB3d7398fx00rdvXwYMGEBUVBRGo5G4uDiGDBliXd8uv7y8vDhy5Ai//vorACtWrLir82+ladOmfPfdd5w4ccL6WX52OU5ISMDDw4POnTvzzjvvWNft+6vGjRuzevVqsrOzyc7OZvXq1TRp0uSOfaempvLcc88BEBUVlWP34ttxdXXNNbIzMDCQTZs2sW7dOrp163bHPuDmyMsiRYqwevVq62enTp0iLS0tz+dfpkwZsrOzSUhIACAuLs56rGnTpuzYscN6XlZWFmlpaQC4uLhYX/9d48aNiYqKwmKxkJaWxrp16/L1/ERERERERERE5NGSr5GIXbt2pWTJkixfvpynn36aVatWMXjwYNq2bXtXF3N3d2fSpEkMGDCAIkWK5NpY5V5UrFiRGTNmMHr0aDIyMsjOzqZu3bp33OV4/fr1xMbG4ujoiMFgIDg4OFebHj16kJiYSFBQEADNmjWje/fud4xp1KhRvP3225QoUYLmzZtTsmTJO57TqVMnRo0axYYNG3jzzTcJDAzE1dWV5s2bk5GRQenSpe/YB9wcUTh//nxCQkJYtGgRZrMZd3d3Pvnkkzyfv4ODA6NHj+bNN9+kdOnStGrVynqsYsWKTJo0iSFDhlh3f/7oo4/w9PSkb9++vP766xQpUoQlS5bkiOXtt99m0qRJ+Pv7W++xRYsW+boPERERERERERF5dBgsf53DKo8Uo9FIp06d+Oijj+5YFH0S9Zu8kaQrNwo7DHlAYmcGaJj/Y0pTOERsh/JVxHYoX0Vsg3JVbMmdpjPnayQiwN69ezly5AjXr1/P8fmAAQPuPTq5rc2bNzN58mTatm2rAqKIiIiIiIiIiBSqfBURJ02axPr166lXr16OzTD+3KlZHrw2bdrQpk2bwg5DREREREREREQkf0XE2NhYYmNjKVeuXEHHI5Jvi8bc/5qa8ujIyDTeuZGIiIiIiIiIFIp8FRGfeuopnJycCjoWkbty6VIaZrOW9BQRERERERERKWj5KiJOmTKFsWPH0rFjR8qUKZPjWP369QskMBEREREREREREXk05KuI+PPPP7Njxw5++OEHihQpYv3cYDCwbdu2gopNJE957Rgktikj00hqinbcFhEREREREXnU5KuIOGvWLObPn0+TJk0KOh6RfOs3eSNJV1RwepzEzgwgtbCDEBEREREREZFc7PLTqGjRotSrV6+gYxEREREREREREZFHUL6KiIMGDSIkJITk5GTMZnOOfyIiIiIiIiIiIvJ4y1cRMTg4mMjISFq0aMHLL7/Myy+/TPXq1Xn55ZcLOr4nzuLFi7l06ZL1fUREBIsXL77jeSNHjmTp0qUFGNn/ExYWxrRp0+7p3AsXLtC7d+8HHJGIiIiIiIiIiBSkfK2JuHnz5oKOQ/5///3vf2nSpAnu7u4A9OrVq5AjerDKlSvHkiVLCjsMERERERERERG5C/kqIj777LMFHcdD88033zBr1iyKFCmCj48Ps2bNYt++fbi4uHDw4EFCQ0NJT08Hbk7jbtWqFQCrV69m0aJFAFSoUIGJEyfi7u7Ovn37mDRpEmazGaPRyMCBA/Hz82PkyJE4ODhw8uRJrly5Qv369Rk3bhxOTk7Exsby3//+l+zsbABGjBhB48aNmTdvHklJSQwaNAhnZ2dmzpzJ+vXruX79OiNGjODYsWNMmDCBGzdukJmZSffu3enTp88d73no0KGcOXOG7OxsKlSoQEhICCVKlCA+Pp6QkBBq167N/v37MRgMzJo1iypVqpCcnMz7779Peno6mZmZtGzZkuHDh+fq28/Pj5CQEGrVqgXAV199xenTp5kwYQITJ05k9+7dODk5UaxYMSIjIzl37hxdunQhPj6eGzduMGLECE6ePImDgwOVKlVi9uzZD+BbFhERERERERGRBylfRUS4ORrxhx9+4MqVK1gsFuvn06dPL5DACsLFixcZN24cy5cvp2LFijmmCaekpPDhhx+yYMECypYtS1JSEl27diUuLo7z588TGhpKdHQ0ZcuW5ZNPPmHSpEl88sknLFy4kH79+uHn54fFYiE19f/tLXvw4EEiIyNxdnbmrbfe4uuvv+a1116jWbNm+Pn5YTAYOH36NH369GHHjh0MHDiQFStWMGfOHKpWrZor/meffZbFixfj5OREeno63bp1o3nz5lSpUiXP+x49ejSlS5cGbu60vXDhQoYNGwbAyZMnmTp1KhMnTmTevHnMnTuXmTNnUrx4cebPn4+LiwvZ2dn069ePHTt20KJFixx9v/rqq0RERFCrVi0sFgsRERHMmTOHo0ePEh8fz7p167Czs+PatWu54vr+++9JT09n3bp1ALdsI08eDw+3wg5BCoC+VxHboXwVsR3KVxHboFyVx0W+ioiffvopkZGR+Pr6smHDBnr06EFcXBy+vr4FHd8DdfDgQapXr07FihUB6NKlC1OnTgVg//79nDt3jv79+1vbGwwGEhISOHDgAC1btqRs2bIA9OzZk4CAAAAaNmzIvHnzSExMpGnTptSuXdt6vq+vLy4uLgAEBgayceNGXnvtNc6ePcvQoUO5cOECDg4OXLx4keTkZDw8PPKMPyMjg/Hjx3Ps2DEMBgNJSUkcPXr0jkXEmJgYYmNjyc7O5vr169b7B6hUqRLVq1cHwMvLi61btwJgMpmYPn06+/fvx2KxcPHiRY4ePZqriBgQEMBnn33G1atXOXToEO7u7lSrVo3U1FSMRiOjR4+mYcOGtG7dOldc1apV49SpU0yYMIEGDRpYR33Kky05OfXOjcSmeHi46XsVsRHKVxHboXwVsQ3KVbEldnYG3N1db388P51ERUXx5ZdfEhwcjKOjI8HBwcyfP59z5849sEALm8ViwdPTk5iYGOu/7du3U7NmzTzP69OnD/PmzaN06dJMmjSJWbNm3fFa77//Pq+88gpr165l1apV2Nvbk5mZecfzPv74Yzw8PFi1ahVr1qyhVq1adzxv7969RERE8MUXXxAbG8t7771HVlaW9biTk5P1tZ2dHUajEbg5LTklJYUVK1YQGxtL27Ztb3mtYsWK4e/vT3R0NOHh4bz66qsAuLm5sXbtWnx9fTl27BgdO3YkOTk5x7nly5cnLi6Opk2bsmvXLgICAvL1HERERERERERE5OHKVxExJSXFOr3W0dGR7OxsatWqxQ8//FCgwT1otWvX5siRIyQmJgKwatUq67E6deqQkJDA7t27rZ8dOnQIi8VCw4YN2b59u7UI9vXXX9OkSRMAzpw5Q4UKFejZsyevv/46hw8ftp6/YcMGrl+/jtFoJCYmhkaNGgGQmprKc889B9ws0P61qOfi4pJjSvRfpaam8tRTT+Hg4MDx48fZu3fvHe85JSUFV1dXSpYsSVZWFlFRUfl6VqmpqXh4eODs7MyFCxfy3FznlVde4T//+Q8//fQT7dq1A+Dy5cvcuHGD5s2bM2zYMNzc3Dh79myO886fP4+9vT1t27Zl1KhRXL58matXr+YrPhEREREREREReXjyNZ25QoUKnDhxghdffJEXX3yRiIgIihcvTokSJQo6vgeqTJkyjB8/nv79+1O0aFFatWqFo6MjRYsWxc7Ojrlz5zJjxgxCQkLIzs6mfPnyzJ8/n6pVqzJs2DD69u0L3BxBN3HiRACWLFlCfHw8jo6OODk5MWbMGOv1atasSd++fbl8+TINGjSge/fuAIwaNYq3336bEiVK0Lx5c0qWLGk95/XXXyc4OJgiRYowc+bMHPEPHDiQ4cOHs3LlSipVqkT9+vXveM/NmzdnzZo1tG/fnlKlSlGvXr0chc7b6d27N4MHD8bPz49y5crRuHHj27YtX748lStXplatWtaRjX/88Qdjx47FaDRiMplo0aIFXl5e/P7779bzjh07Zr1Hs9nMW2+9Rbly5e4Ym4iIiIiIiIiIPFwGy193SbmN7du3U6xYMerXr8/BgwcZNmwY169f58MPP7SOPLMVaWlpuLrenN8dFRXFypUriYiIeODXGTlyJDVq1OC111574H0/atLS0vDx8SEqKuqhFgH7Td5I0pUbD+16UvBiZwZovZDHkNaBEbEdylcR26F8FbENylWxJXdaEzFfIxFbtmxpfV27dm2+/fbb+4+skCxZsoQNGzZgMpkoUaIEkydPLuyQbFpERATz5s2jb9++GkUoIiIiIiIiIvKYynMk4l+nnt7OM88880ADEpEnV0amkdQUjS593OivryK2Q/kqYjuUryK2QbkqtuS+RiJ6e3tjMBj4e53RYDBY//PIkSMPIEyRu3fpUhpm8x1n44uIiIiIiIiIyH3Ks4j4888/5/osMzOT8PBwvvjiC2rVqlVggYmIiIiIiIiIiMijIc8ior29vfW1yWRixYoVzJ8/nwoVKjB37lzq1q1b4AGKiIiIiIiIiIhI4brjxioWi4WYmBg+/fRTSpcuTUhICE2aNHkYsYnkKa95+mK7tC6iiIiIiIiIyKMnzyLihg0bCAsLw9HRkdGjR9O6deuHFZfIHfWbvJGkKyo2PW5iZwagZYdFREREREREHi15FhHfe+89SpYsSbNmzVi/fj3r16/P1Wb69OkFFpyIiIiIiIiIiIgUPru8Dr7zzju89tprVKxYkQoVKtzynxQOT09P0tPTAejfvz+JiYkFdq389B8WFkZWVpb1/ezZs1m3bl2BxSQiIiIiIiIiIg+PwWKxWAo7CLl7np6e7Nu3DxcXl8IOBSiceDSd+fEUOzOA5GRNaH6ceHi46TsVsRHKVxHboXwVsQ3KVbEldnaGPPefyHMkotgGb29vjh8/DkDv3r2ZNm0avXr1ok2bNoSGhlrbnTx5km7duuHn58ewYcPo3r07W7duzdXH39//9fWnn36Kj48PAQEBBAYGkpKSwoQJEwDo2bMnAQEBpKSkMHLkSJYuXQpAVlYW06ZNw8/Pj06dOvHOO+8AsG/fPoKCgggICKBjx47ExcUV8JMSEREREREREZF7ccfdmcX2/PHHHyxbtoz09HTatm1L165dqVixIsOHD+eNN94gICCAw4cP071797vq9+rVqyxevJjvv/+eIkWKkJaWRpEiRfjwww8JDw8nMjLyliMRFyxYwNmzZ4mOjsbJyYnLly8DsHDhQvr164efnx8Wi4XUVP11RkRERERERETkUaQi4mPIx8cHOzs73NzcqFKlComJiZQpU4bjx4/j7+8PQM2aNfH09Lyrft3c3KhQoQLDhw+nWbNmtGrVClfX2w9z/dPWrVsZOXIkTk5OAJQuXRqAhg0bMm/ePBITE2natCm1a9e+yzuVx5WHh1thhyAPmL5TEduhfBWxHcpXEdugXJXHhYqIjyFnZ2fra3t7e0wmk/W9wWC45Tn29vaYzWbr+8zMzFu2+frrr9m3bx+7d++mc+fOfPHFF1SrVu2e4uzTpw/e3t7s3LmTSZMm0bRpU4YMGXJPfcnjRWuGPF60DoyI7VC+itgO5auIbVCuii2505qI+Soirly58pafOzk58dRTT+Hl5WUdZSaPJldXV1588UXi4uLw9/fn559/zrEGYoUKFTh8+DDVqlVj165dXLx4MVcfaWlpXL9+nQYNGtCgQQMOHDjAiRMnqFatGi4uLqSlpd1yOnPr1q35z3/+Q+3ata3TmUuXLs2ZM2eoVKkSFSpUoFixYqxevbogH4GIiIiIiIiIiNyjfBURY2Ji2L9/P2XKlOGpp57i/PnzXLx4kRo1avDbb78BMHfuXGrWrFmgwcr9mTZtGsHBwSxYsICqVatStWpV3NxuDqsePHiwdTOURo0a8cwzz+Q6Py0tjf/7v/8jIyMDi8VC9erVadeuHQB9+/bl9ddfp0iRIixZsiTHeW+99RYzZ84kMDAQR0dHnn/+eebMmcOSJUuIj4/H0dERJycnxowZU/APQURERERERERE7prBYrFY7tRowoQJVKpUiddff9362dKlSzl9+jRjx45l3rx5bN++neXLlxdosHJ/0tPTKVasGAaDgZMnT9K7d282bNhAiRIlCju0e9Jv8kaSrtwo7DDkAYudGaDh/o8ZTeEQsR3KVxHboXwVsQ3KVbElD2Q6c1xcHPHx8Tk+69WrF40aNWLcuHH861//YtGiRfcXqRS4/fv3M336dP6sG0+aNMlmC4giIiIiIiIiIvLw5KuI6O7uzpYtW2jbtq31s23btll32c3MzMTBQXu0POqaNWtGs2bNCjsMERERERERERGxMfmq/I0ZM4bBgwfz4osv8vTTT/PHH39w4sQJZs+eDcDBgwfp3bt3gQYq8neLxrQr7BCkAGRkGgs7BBERERERERH5m3ytiQhw5coVtm/fTlJSEmXLlqVly5aUKlWqoOMTua1Ll9Iwm/P18xWRQqR1YERsh/JVxHYoX0Vsg3JVbMkDWRMRoFSpUgQGBj6ImERERERERERERMSG5KuIaDQaCQ8P54cffuDKlSv8dfDismXLCiw4kbzkVR2Xx1dGppHUFO3KLSIiIiIiIvIw5auIOHXqVHbv3k337t355JNPeO+994iIiKBjx44FHZ/IbfWbvJGkKyomPWliZwagyQAiIiIiIiIiD5ddfhpt3LiRhQsX8sYbb2Bvb88bb7zBZ599Rnx8fEHHJyIiIiIiIiIiIoUsX0XEjIwMnn76aQCKFCnCjRs3qFKlCkeOHCnQ4ERERERERERERKTw5VlEjIuLA6BKlSocPnwYgBo1ahAWFsbcuXMpV65cwUd4HzZt2kSHDh0IDAzk9OnT99THL7/8wrp16x5wZA9HREQEixcvfqjXDAgIICMj46FeU0REREREREREClaeRcRx48YBEBwcjL29PQAjR47kyJEjbN26lUmTJhV8hPchMjKSQYMGsXr1aipXrnxPffzyyy9s2LDhns41Go33dN6D0qtXL/r06fNQrvXnvcbExFCkSJGHck0REREREREREXk48txY5c9dmGvVqmX9rGLFig99dNu9CAkJ4ccff+TMmTOEh4ezZMkSDh48SGhoKOnp6QAMGjSIVq1aYTQa+fe//82VK1fIzMykVq1aTJgwgfT0dObMmUNaWhoBAQHUr1+fPn360KVLF+t6kOfOnbO+//N1586drRvRtGnThsmTJ/P777+TmZlJx44dGTBgwC1jXrBgARs3bsRkMlGuXDkmTZqEh4cHYWFhnDlzhtTUVM6ePUuFChWYPXs2RYsWJTU1leDgYE6cOEG5cuUoV64c7u7ujBgxgrCwMK5fv86IESOIjo4mLi6O4sWLc+LECdzc3AgLC8PDwyPPa2dlZTFr1ix++OEHsrKy8PT0ZPz48bi4uDBy5Ejs7e05c+YM6enpxMTE4Onpyb59+3BxccHb25uAgAB27txJcnIyffv25bXXXgNg7969TJgwAYCGDRuyefNmPv/8c6pWrVrQPw0REREREREREblLeRYRzWYzu3fvthYTb6Vx48YPPKgHITg4mF9++YW+ffvSunVrUlJS+PDDD1mwYAFly5YlKSmJrl27EhcXh5ubG6GhoZQqVQqLxcKIESOIioqiV69eDBo0iG3btjFnzhzgZtEwL1evXqVmzZqMGDECgDfffJO3336b+vXrk5WVRZ8+fahZsyZNmzbNcV5MTAxnz57l66+/xs7OjvDwcD766CNmzpwJwE8//cTKlStxc3OjX79+xMbG0r17dz777DOKFy/Ohg0buHr1Kp07d6Z9+/a3jO3w4cOsWbOGp59+mjFjxrB06VKGDBmS57W/+OIL3NzcWLlyJQAzZsxgwYIFDBkyBLg5UnPp0qUUK1bsltfMyMhg+fLlnDt3Dn9/f4KCgnB0dOT999/n448/pl69enz77bcsWbIkn9+sCHh4uBV2CHKX9J2J2A7lq4jtUL6K2Ablqjwu8iwiZmVlMXr06NsWEQ0GA5s3by6QwB60/fv3c+7cOfr372/9zGAwkJCQQPXq1fnyyy/ZsWMHZrOZa9eu3fOUXGdnZzp06ADA9evX2bNnD5cvX7YeT09P59SpU7mKiFu2bOGnn34iKCgIAJPJhKurq/V4s2bNKF68OHBzZGhiYiIA8fHxjBkzBoCSJUvStm3b28ZWt25d6wY5tWvXZufOnXe89pYtW0hLS+Obb74Bbv4mqlWrZu3Tx8fntgVEAF9fXwCee+45ihcvzvnz58nOzqZIkSLUq1cPgH/+85/WexPJj+Tk1MIOQe6Ch4ebvjMRG6F8FbEdylcR26BcFVtiZ2fA3d31tsfzLCIWLVrUZoqEd2KxWPD09GTZsmW5jq1evZoff/yRZcuW4erqyvz58/n1119v2Y+Dg0OOompmZmaO40WLFsVgMAA3R3IaDAZWrlyJo6PjHeMbOHAgXbt2veVxZ2dn62t7e/tc182Pv/dhMpnueG2LxcKHH3542xGneRUQ87qmiIiIiIiIiIjYjjw3Vnmc1KlTh4SEBHbv3m397NChQ1gsFlJTUylVqhSurq6kpqZad6UGrJ/9qUyZMmRnZ5OQkACQo+3fubq68o9//IMFCxZYP/vjjz9ITk7O1dbb25vw8HCuXbsG3Bzxd/To0TveV4MGDYiJiQEgJSXlnoq+eV3b29ubxYsXW3dcTktL49SpU3d9jb+qXLkyN27c4McffwRu7qKdkpJyX32KiIiIiIiIiEjBydfGKo+DEiVKMHfuXGbMmEFISAjZ2dmUL1+e+fPnExgYyObNm/Hx8cHd3Z1//OMf1pF+jRs35ssvv6RTp040aNCAMWPGMHr0aN58801Kly5Nq1at8rxuaGgoU6dOxd/fHwAXFxemTJli3dDkT4GBgVy9etW68YjFYqFXr145pg7fyjvvvMOoUaPw8fHBw8ODGjVq5JgGnR95Xfutt97i008/pWvXrhgMBgwGA++++y5VqlS5q2v8lZOTEzNnzmT8+PHAzUKou7s7bm5aJ0JERERERERE5FFksDxOlcInUHZ2NmazGWdnZ9LS0ujVqxejRo2iSZMmhR1antLS0qzFzt27dzNq1Cg2b96MnV3+B8f2m7yRpCs3CipEeUTFzgzQmiI2RuvAiNgO5auI7VC+itgG5arYkvtaE1EefSkpKfTv3x+TyURmZiZ+fn6PfAERYOPGjSxevBiLxYKTkxOhoaF3VUAUEREREREREZGHRyMRRcSmZGQaSU3RCFRbor++itgO5auI7VC+itgG5arYEo1ElMfWpUtpmM2qgYuIiIiIiIiIFDTNHxUREREREREREZE8qYgoIiIiIiIiIiIiedJ0ZrFZec3Tl8eX1kQUERERERERefhURBSb1W/yRpKuqJj0pImdGYCWJRYRERERERF5uDSdWURERERERERERPKkIqLkS+/evdm6desdj82ePZt169Y9zNBERERERERERKSAaTqzPFCDBw8u7BBEREREREREROQBUxHxEXXw4EFCQ0NJT08HYNCgQbzwwgt06dKFnj17sn37dm7cuMGUKVOoV68ely5dYujQoVy6dAmAxo0bExwcDMCCBQvYuHEjJpOJcuXKMWnSJDw8PAgLC+P06dOkpaXx66+/8vLLL/PWW2/x0Ucf8fvvv/PPf/6TESNGWGPauXMnn332GdeuXaNDhw68//77ueIeOXIkNWrU4LXXXiMsLIwzZ86QmprK2bNnqVChArNnz6Zo0aKkpqYSHBzMiRMnKFeuHOXKlcPd3T3H9URERERERERE5NGgIuIjKCUlhQ8//JAFCxZQtmxZkpKS6Nq1K59//jlXr17Fy8uLIUOGsGbNGkJDQ4mMjCQ2NpYKFSqwePFiAK5duwZATEwMZ8+e5euvv8bOzo7w8HA++ugjZs6cCcDPP/9MVFQUxYoVIygoiJkzZ/LFF19gNBpp06YNPXr0oGLFigCcOnWKyMhIMjMz6dmzJ3Xq1KF169Z53stPP/3EypUrcXNzo1+/fsTGxtK9e3c+++wzihcvzoYNG7h69SqdO3emffv2BfZMRURERERERETk3qmI+Ajav38/586do3///tbPDAYDRqORYsWKWQt3Xl5eTJs2DYDatWuzePFipk2bRoMGDWjWrBkAW7Zs4aeffiIoKAgAk8mEq6urtd9mzZrh5uYGgKenJ9WqVcPJyQknJycqVapEYmKitYgYGBiIg4MDDg4O+Pr6snv37jsWEZs1a0bx4sUBqFWrFomJiQDEx8czZswYAEqWLEnbtm3v65nJk8XDw62wQ5C7pO9MxHYoX0Vsh/JVxDYoV+VxoSLiI8hiseDp6cmyZctyfH7u3DmcnJys7+3s7DAajQDUqVOHVatWsXPnTmJiYliwYAERERFYLBYGDhxI165db3ktZ2dn62t7e/tc700m033dy9/7y8zMvK/+RACSk1MLOwS5Cx4ebvrORGyE8lXEdihfRWyDclVsiZ2dAXd319sff4ixSD7VqVOHhIQEdu/ebf3s0KFDWCyW255z9uxZXF1d6dixI6NGjeLnn3/GbDbj7e1NeHi4dXpzVlYWR48evae41qxZg9Fo5Pr166xfv55GjRrdUz8ADRo0ICYmBrg5fXvz5s333JeIiIiIiIiIiBQsjUR8BJUoUYK5c+cyY8YMQkJCyM7Opnz58owdO/a25+zZs4fFixdjZ2eH2WxmwoQJ2NnZERgYyNWrV3nttdeAm6Mce/XqRbVq1e46rsqVK9OzZ0/rxip3msqcl3feeYdRo0bh4+ODh4cHNWrUyDHNWkREREREREREHh0GS17D20QKSHZ2NmazGWdnZ9LS0ujVqxejRo2iSZMm+e6j3+SNJF25UYBRyqModmaApgPYGE3hELEdylcR26F8FbENylWxJXeazqyRiFIoUlJS6N+/PyaTiczMTPz8/O6qgCgiIiIiIiIiIg+PiohSKNzd3YmOji7sMEREREREREREJB9URBSbtWhMu8IOQQpBRqaxsEMQEREREREReeKoiCg269KlNMxmLekpIiIiIiIiIlLQ7Ao7ABEREREREREREXm0aSSi2Ky8dgySx19GppHUFO3OLSIiIiIiIvIwqIgoNqvf5I0kXVER6UkVOzOA1MIOQkREREREROQJoenMIiIiIiIiIiIikicVEUVERERERERERCRPKiLKXfP29ub48eP3dG5ERASLFy9+sAGJiIiIiIiIiEiB0pqIhcxkMmFvb1/YYTw0vXr1KuwQRERERERERETkLmkk4n24ceMGgwYNwtfXl06dOjF48GAAoqOjGTRokLXdX99HR0fTp08f3nnnHfz8/Ni/fz+BgYE5+u3cuTN79uwBYNWqVXTr1o3OnTvz+uuvc/r0aQD8/Pw4dOiQ9ZyvvvqKsWPH5orx2LFjvPLKKwQFBeHr65tjFODIkSMZN24cr7/+Ou3atWP48OFYLBYAYmNj6datG4GBgQQGBrJr165cfR86dAg/P78cn3Xq1Il9+/Zx+vRpevToQadOnfDz82PRokUAhIWFMW3aNAD27dtHUFAQAQEBdOzYkbi4uDs/dBEREREREREReeg0EvE+fP/996Snp7Nu3ToArl27lq/zDh48SExMDBUqVADg+vXrHD16lGrVqnHs2DFSUlKoX78+e/fuZf369SxbtgwnJye2b99OcHAwkZGRvPrqq0RERFCrVi0sFgsRERHMmTMn17WeffZZFi9ejJOTE+np6XTr1o3mzZtTpUoVAE6cOMHixYsxGAwEBQWxc+dOmjZtSrNmzfDz88NgMHD69Gn69OnDjh07cvRdq1YtihUrxp49e2jQoAF79+7Fzs6OunXrMnnyZLy9vfn3v/9922ezcOFC+vXrh5+fHxaLhdRU7bUrd8fDw62wQ5B80nclYjuUryK2Q/kqYhuUq/K4UBHxPlSrVo1Tp04xYcIEGjRoQKtWrfJ1Xt26da0FRIDAwEBWrVrFqFGjWLVqFYGBgRgMBrZs2cLRo0fp1q0bABaLhZSUFAACAgL47LPPuHr1KocOHcLd3Z1q1arlulZGRgbjx4/n2LFjGAwGkpKSOHr0qLWI2LZtW5ydnQGoXr06iYmJNG3alLNnzzJ06FAuXLiAg4MDFy9eJDk5GQ8Pjxz99+7dm/DwcBo0aMCyZct49dVXAahfvz4zZszgxo0bNGzYkEaNGuWKrWHDhsybN896zdq1a+fr+Yn8KTlZhWdb4OHhpu9KxEYoX0Vsh/JVxDYoV8WW2NkZcHd3vf3xhxjLY6d8+fLExcXRtGlTdu3aRUBAAJmZmdjb22M2m63tMjMzc5zn4uKS431gYCBr164lMzOTuLg4goKCgJtFwy5duhATE0NMTAxr1qxh27ZtABQrVgx/f3+io6MJDw+3Fu/+7uOPP8bDw4NVq1axZs0aatWqlSOePwuIAPb29phMJgDef/99XnnlFdauXcuqVauwt7fPdR8APj4+HDx4kCNHjhAfH2+d3ty+fXuWLVtGhQoVWLhwIR988EGuc/v06cO8efMoXbo0kyZNYtasWbd91iIiIiIiIiIiUnhURLwP58+fx97enrZt2zJq1CguX77M1atXef755zl27BhZWVlkZWXxzTff5NnPM888wwsvvMDkyZN54YUXePbZZ4GbuyDHxMRw/vx54OYmLD/99JP1vFdeeYX//Oc//PTTT7Rr1+6WfaempvLUU0/h4ODA8ePH2bt3b77uLTU1leeeew6AqKgosrKybtnO0dGRLl26MHDgQPz9/SlatCgACQkJeHh40LlzZ9555x0OHz6c69wzZ85QoUIFevbsyeuvv37LNiIiIiIiIiIiUvg0nfk+HDt2jJkzZwJgNpt56623KFeuHOXKlaNx48Z07NiRsmXLUq1aNZKTk/PsKygoiOHDhzN9+nTrZ/Xr1+e9995j4MCBmEwmsrOz8fHxoUaNGsDNkZCVK1emVq1aODk53bLfgQMHMnz4cFauXEmlSpWoX79+vu5t1KhRvP3225QoUYLmzZtTsmTJ27bt1q0bn376aY6dl9evX09sbCyOjo4YDAaCg4NznbdkyRLi4+NxdHTEycmJMWPG5Cs2ERERERERERF5uAyWP7fjFZuTlpaGj48PUVFRlCtXrtDiiImJYe3atSxYsOChXrff5I0kXbnxUK8pj47YmQFaW8RGaB0YEduhfBWxHcpXEdugXBVbcqc1ETUS0UZFREQwb948+vbtW6gFxH79+pGYmMi8efMKLQYRERERERERESlYGokoIjYpI9NIaopGotoC/fVVxHYoX0Vsh/JVxDYoV8WWaCSiPLYuXUrDbFYNXERERERERESkoGl3ZhEREREREREREcmTiogiIiIiIiIiIiKSJ01nFpuV1zx9EdC6iSIiIiIiIiIPioqIYrP6Td5I0hUViOT2YmcGoCWMRURERERERO6fpjOLiIiIiIiIiIhInlREfALEx8fTuXPnwg4jT56enqSnpxd2GCIiIiIiIiIicgsqIoqIiIiIiIiIiEietCaijRs6dChnzpwhOzubChUqEBISQokSJZg1axbr1q2jePHiNGjQwNq+T58+vPbaa7Rt2xaArVu38uWXX7JkyRK+/PJL1q5di8lkwtnZmfHjx/PSSy8BN0cKDhkyhG+//ZarV68yfPhw2rdvD8D+/fuZPn26dSTh8OHDadasGadPnyYkJIQrV66QnZ3NG2+8QZcuXQDYuHEjH3/8Mc7OzrRr1+5hPjIREREREREREblLKiLauNGjR1O6dGkAZs2axcKFC6lbty5btmxh9erVFClShHfeecfaPigoiNWrV1uLiNHR0dbCXmBgIH379gVg586dfPjhh3z99dfWc11dXYmKiuLHH3/kvffeo3379ly9epV3332XsLAw6tati8lkIi0tDaPRyLBhw5gxYwZVqlQhLS2NLl264OXlRYkSJRg7diwRERFUrlyZhQsXPqzHJSIiIiIiIiIi90BFRBsXExNDbGws2dnZXL9+nYoVK5KdnY2vry8uLi4AdO3alblz5wLQrl07pk6dypUrVwDYs2cP06ZNA+Cnn37i888/59q1axgMBn799dcc1/L19QXAy8uLpKQkMjMzOXDgAFWqVKFu3boA2NvbU6JECU6ePMmpU6d4//33rednZ2dz+vRp7OzsqF69OpUrVwagR48ehIaGFtxDkieah4dbYYcg6HsQsSXKVxHboXwVsQ3KVXlcqIhow/bu3UtERASRkZGULl2a2NjYHCMHb6Vo0aK0adOGuLg4ANq0aUOxYsXIyspi8ODBLF26lJdffpkLFy7QokWLHOc6OzsDNwuFAEaj8bbXsVgslCpVipiYmFzHNm/efFf3KXI/kpNTCzuEJ56Hh5u+BxEboXwVsR3KVxHboFwVW2JnZ8Dd3fX2xx9iLPKApaSk4OrqSsmSJcnKyiIqKgqARo0asX79eq5fv47JZLJ+/qegoCBWrVrFqlWrrLs2Z2VlYTQaefrppwEIDw/PVwxeXl6cOnWK/fv3A2Aymbh27RqVKlWiSJEirF692tr21KlTpKWl4eXlxZEjR6wjHVesWHE/j0FERERERERERAqYRiLasObNm7NmzRrat29PqVKlqFevHocPH6Z169YcOHCAgIAA68YqFy5csJ5Xr1490tLSrK/h5nqHgwYNomvXrpQsWdK6acqdlCxZkrCwMD766COuX7+OnZ0dI0aMoEmTJsyfP5+QkBAWLVqE2WzG3d2dTz75BHd3dyZNmsSAAQMoUqSINlYREREREREREXnEGSwWi6WwgxC5F/0mbyTpyo3CDkMeYbEzAzR14BGgKRwitkP5KmI7lK8itkG5KrZE05lFRERERERERETkvqiIKCIiIiIiIiIiInnSmohisxaN0VqKkreMzNvvIC4iIiIiIiIi+aciotisS5fSMJu1pKeIiIiIiIiISEHTdGYRERERERERERHJk0Yiis3Ka8cgkbuRkWkkNUU7fYuIiIiIiIjcjoqIYrP6Td5I0hUVfuT+xc4MILWwgxARERERERF5hGk6s4iIiIiIiIiIiORJRUQRERERERERERHJk4qIcteio6MZNGhQYYchIiIiIiIiIiIPiYqIIiIiIiIiIiIikidtrPKE+Oabb5g1axZFihTBx8eHWbNmsW/fPk6ePEloaCjp6ekADBo0iFatWgGwevVqFi1aBECFChWYOHEi7u7uufpesGABa9asAaBmzZqMGTMGFxcXUlNTCQ4O5sSJE5QrV45y5crh7u7Oe++9R5s2bYiOjqZs2bIATJ48mTJlyjBgwICH8DRERERERERERORuqIj4BLh48SLjxo1j+fLlVKxYkcWLFwOQmprKhx9+yIIFCyhbtixJSUl07dqVuLg4zp8/T2hoqLXQ98knnzBp0iQ++eSTHH1v376dNWvWEBkZiYuLCyNGjGDu3Ll88MEHfPbZZxQvXpwNGzZw9epVOnfuTPv27XF2diYwMJCvv/6ad999l/T0dNauXUtcXNzDfzgi/z8PD7fCDuGxpucrYjuUryK2Q/kqYhuUq/K4UBHxCXDw4EGqV69OxYoVAejSpQtTp07l559/5ty5c/Tv39/a1mAwkJCQwIEDB2jZsqV1pGDPnj0JCAjI1feuXbvw9fXF1dUVgO7duxMSEgJAfHw8Y8aMAaBkyZK0bdvWet6rr77Kq6++yoABA1izZg1Nmza95ShHkYclOTm1sEN4bHl4uOn5itgI5auI7VC+itgG5arYEjs7A+7urrc9riLiE8xiseDp6cmyZctyHTtw4ECBXvvpp5+mRo0abN68mfDwcCZOnFig1xMRERERERERkXunjVWeALVr1+bIkSMkJiYCsGrVKgBefvl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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that tags with the greatest average number of votes don't really match with the most popular tags, so I guess my assumption that using popular tags in kernels will help to gain more votes is wrong."
      ],
      "metadata": {
        "_uuid": "0074ed0cd9c0001b5ef99f04cd612f40009c8009",
        "id": "P5YckRxeBlBb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7. Does \"advertising\" of your kernel on Kaggle forums help to gain votes?"
      ],
      "metadata": {
        "id": "-YaQ8LDQBlBc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mentioning a kernel in forum messages can potentially help to promote the kernel among kagglers and eventually get more votes. Let's analyze the correlation between the number of mentions of a kernel with its number of votes."
      ],
      "metadata": {
        "id": "e0F2jhWJBlBc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The process of obtaining the number of mentions in forum messages is illustrated on figure below:\n",
        "1. Analyze the ForumMessages table:\n",
        "    * Find all messages which contain urls.\n",
        "    * Extract kernel name and name of the author from the url address.\n",
        "2. Merge Kernels, KernelVersions and Users tables:\n",
        "    * Find name of the author for the kernel.\n",
        "    * Find the title of the kernel.\n",
        "    * Translate the title as in url address.\n",
        "3. Merge kernel titles and titles from url addresses where the names of the authors match. Count the number of rows for each kernel. "
      ],
      "metadata": {
        "id": "1PVpGujDBlBc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![engineering steps](https://github.com/Lexie88rus/kaggle_kernel_votes_analysis/blob/master/kernel_promotions.png?raw=true)"
      ],
      "metadata": {
        "id": "aWgLcIEZBlBc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`1.` Find all forum messages with urls to Kaggle and extract possible kernel titles from urls:"
      ],
      "metadata": {
        "id": "BlGYB3aHBlBc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create msg_ref dataframe by removing rows with empty messages from the original dataset\n",
        "msg_ref = messages[['Id', 'Message']].dropna(subset = ['Message'])\n",
        "\n",
        "# filter rows: find rows with messages which contain references to kaggle\n",
        "msg_ref = msg_ref[msg_ref.Message.str.match('.*https://www.kaggle.com/(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+.*')]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:11:58.196317Z",
          "iopub.execute_input": "2022-02-02T18:11:58.196606Z",
          "iopub.status.idle": "2022-02-02T18:12:00.806052Z",
          "shell.execute_reply.started": "2022-02-02T18:11:58.196573Z",
          "shell.execute_reply": "2022-02-02T18:12:00.805186Z"
        },
        "trusted": true,
        "id": "rktEKD8bBlBd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_referenced_object(row):\n",
        "    \n",
        "    # extract url from message\n",
        "    url = re.findall('https://www.kaggle.com/(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\\\n",
        "                         , row['Message'])[0]\n",
        "    \n",
        "    # remove tags\n",
        "    cleanr = re.compile('<.*?>')\n",
        "    clean_url= re.sub(cleanr, '', url)\n",
        "    \n",
        "    # get the last part of url, which corresponds to kernel title\n",
        "    ref = clean_url.split('/')[-1]\n",
        "    \n",
        "    return ref\n",
        "\n",
        "def get_author(row):\n",
        "    \n",
        "    # extract url from message\n",
        "    url = re.findall('https://www.kaggle.com/(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\\\n",
        "                         , row['Message'])[0]\n",
        "    \n",
        "    # remove tags\n",
        "    cleanr = re.compile('<.*?>')\n",
        "    clean_url= re.sub(cleanr, '', url)\n",
        "    \n",
        "    # get the last part of url, which corresponds to kernel title\n",
        "    author = clean_url.split('/')[-2]\n",
        "    \n",
        "    return author\n",
        "\n",
        "# create column 'Ref' with the last part of the url\n",
        "# which is the title of the kernel (in case of url to a kernel)\n",
        "msg_ref['Ref'] = msg_ref.apply(lambda row: get_referenced_object(row),axis=1)\n",
        "msg_ref['Ref_author'] = msg_ref.apply(lambda row: get_author(row),axis=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:12:03.469744Z",
          "iopub.execute_input": "2022-02-02T18:12:03.470529Z",
          "iopub.status.idle": "2022-02-02T18:12:10.558050Z",
          "shell.execute_reply.started": "2022-02-02T18:12:03.470472Z",
          "shell.execute_reply": "2022-02-02T18:12:10.557313Z"
        },
        "trusted": true,
        "id": "0ajWq4j6BlBd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "msg_ref.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:12:10.559488Z",
          "iopub.execute_input": "2022-02-02T18:12:10.559706Z",
          "iopub.status.idle": "2022-02-02T18:12:10.569983Z",
          "shell.execute_reply.started": "2022-02-02T18:12:10.559679Z",
          "shell.execute_reply": "2022-02-02T18:12:10.569121Z"
        },
        "trusted": true,
        "id": "E6ZT-2ToBlBd",
        "outputId": "c088f7a7-38ba-452c-be7e-c6a407957a32"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 52,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         Id                                            Message  \\\n644    6737  <p>The new competitions list page at https://w...   \n1456  14823  <p>I'll put in a vote for two different views,...   \n1458  15314  <p>The <a href=\"https://www.kaggle.com/c/job-r...   \n1558  15674  <p>I've created a <a href=\"https://www.kaggle....   \n1649  20390  <p>This is a working draft for best practices ...   \n\n                                                    Ref  \\\n644                                        Competitions   \n1456                                              14813   \n1458                                         prospector   \n1558  ideas-for-competition-changes-to-reduce-cheati...   \n1649                       ModelSubmissionBestPractices   \n\n                   Ref_author  \n644            www.kaggle.com  \n1456  visualization-1st-place  \n1458       job-recommendation  \n1558                     2898  \n1649                     wiki  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Message</th>\n      <th>Ref</th>\n      <th>Ref_author</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>644</th>\n      <td>6737</td>\n      <td>&lt;p&gt;The new competitions list page at https://w...</td>\n      <td>Competitions</td>\n      <td>www.kaggle.com</td>\n    </tr>\n    <tr>\n      <th>1456</th>\n      <td>14823</td>\n      <td>&lt;p&gt;I'll put in a vote for two different views,...</td>\n      <td>14813</td>\n      <td>visualization-1st-place</td>\n    </tr>\n    <tr>\n      <th>1458</th>\n      <td>15314</td>\n      <td>&lt;p&gt;The &lt;a href=\"https://www.kaggle.com/c/job-r...</td>\n      <td>prospector</td>\n      <td>job-recommendation</td>\n    </tr>\n    <tr>\n      <th>1558</th>\n      <td>15674</td>\n      <td>&lt;p&gt;I've created a &lt;a href=\"https://www.kaggle....</td>\n      <td>ideas-for-competition-changes-to-reduce-cheati...</td>\n      <td>2898</td>\n    </tr>\n    <tr>\n      <th>1649</th>\n      <td>20390</td>\n      <td>&lt;p&gt;This is a working draft for best practices ...</td>\n      <td>ModelSubmissionBestPractices</td>\n      <td>wiki</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`2.` Merge kernel votes with kernel titles and preprocess kernel title as in url:"
      ],
      "metadata": {
        "id": "uK_BBcfRBlBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# merge kernel votes and kernel titles (for current kernel version)\n",
        "kernel_title = kernels[['Id', 'AuthorUserId', 'CurrentKernelVersionId', 'TotalVotes']]\\\n",
        ".merge(kernel_versions[['Id', 'Title']].rename(columns={\"Id\": \"CurrentKernelVersionId\"}))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:12:15.518066Z",
          "iopub.execute_input": "2022-02-02T18:12:15.518348Z",
          "iopub.status.idle": "2022-02-02T18:12:21.479188Z",
          "shell.execute_reply.started": "2022-02-02T18:12:15.518316Z",
          "shell.execute_reply": "2022-02-02T18:12:21.478298Z"
        },
        "trusted": true,
        "id": "cla1dTUmBlBe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# merge kernel votes and kernel authors\n",
        "kernel_title = kernel_title.merge(kernel_users[['Id', 'UserName']].rename(columns={\"Id\":\"AuthorUserId\"}))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:12:21.480931Z",
          "iopub.execute_input": "2022-02-02T18:12:21.481246Z",
          "iopub.status.idle": "2022-02-02T18:12:24.866878Z",
          "shell.execute_reply.started": "2022-02-02T18:12:21.481205Z",
          "shell.execute_reply": "2022-02-02T18:12:24.865998Z"
        },
        "trusted": true,
        "id": "WW8W4C4NBlBe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_kernel_title(row):\n",
        "    '''\n",
        "    Function removes symbols from title, translates to lower case,\n",
        "    and replaces spaces with '-'\n",
        "    Example:\n",
        "        input: 'How to get upvotes for a kernel on Kaggle?'\n",
        "        returns: 'how-to-get-upvotes-for-a-kernel-on-kaggle'\n",
        "    '''\n",
        "    try:\n",
        "        title = row['Title']\n",
        "\n",
        "        # remove specific symbols and translate to lower case\n",
        "        title_cleaned = re.sub('[!@#$%?*,.;:()]', '', title).lower()\n",
        "\n",
        "        # replace spaces with '-'\n",
        "        title_cleaned = title_cleaned.replace(' ', '-')\n",
        "    except:\n",
        "        return ' '\n",
        "    return title_cleaned\n",
        "\n",
        "# create column title_cleaned with preprocessed title as in url\n",
        "kernel_title['title_cleaned'] = kernel_title.apply(lambda row: process_kernel_title(row),axis=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:12:24.868051Z",
          "iopub.execute_input": "2022-02-02T18:12:24.868270Z",
          "iopub.status.idle": "2022-02-02T18:12:34.155773Z",
          "shell.execute_reply.started": "2022-02-02T18:12:24.868242Z",
          "shell.execute_reply": "2022-02-02T18:12:34.154899Z"
        },
        "trusted": true,
        "id": "-P_XEAMHBlBe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kernel_title.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:12:34.157357Z",
          "iopub.execute_input": "2022-02-02T18:12:34.157605Z",
          "iopub.status.idle": "2022-02-02T18:12:34.170079Z",
          "shell.execute_reply.started": "2022-02-02T18:12:34.157574Z",
          "shell.execute_reply": "2022-02-02T18:12:34.169298Z"
        },
        "trusted": true,
        "id": "XZ2zREwhBlBe",
        "outputId": "46fd17b2-2a06-428e-df98-18819ad2adc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 56,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     Id  AuthorUserId  CurrentKernelVersionId  TotalVotes  \\\n0     1          2505                   205.0           0   \n1    86          2505               2307564.0           5   \n2   107          2505                   740.0           0   \n3   108          2505                   748.0           1   \n4  4345          2505                  8879.0           0   \n\n                           Title   UserName                  title_cleaned  \n0                          hello  jeffmoser                          hello  \n1                      Hi Python  jeffmoser                      hi-python  \n2  Yet another Python Sleep Test  jeffmoser  yet-another-python-sleep-test  \n3            More Python Testing  jeffmoser            more-python-testing  \n4                 Kaggle.io Test  jeffmoser                  kaggleio-test  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>AuthorUserId</th>\n      <th>CurrentKernelVersionId</th>\n      <th>TotalVotes</th>\n      <th>Title</th>\n      <th>UserName</th>\n      <th>title_cleaned</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2505</td>\n      <td>205.0</td>\n      <td>0</td>\n      <td>hello</td>\n      <td>jeffmoser</td>\n      <td>hello</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>86</td>\n      <td>2505</td>\n      <td>2307564.0</td>\n      <td>5</td>\n      <td>Hi Python</td>\n      <td>jeffmoser</td>\n      <td>hi-python</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>107</td>\n      <td>2505</td>\n      <td>740.0</td>\n      <td>0</td>\n      <td>Yet another Python Sleep Test</td>\n      <td>jeffmoser</td>\n      <td>yet-another-python-sleep-test</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>108</td>\n      <td>2505</td>\n      <td>748.0</td>\n      <td>1</td>\n      <td>More Python Testing</td>\n      <td>jeffmoser</td>\n      <td>more-python-testing</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4345</td>\n      <td>2505</td>\n      <td>8879.0</td>\n      <td>0</td>\n      <td>Kaggle.io Test</td>\n      <td>jeffmoser</td>\n      <td>kaggleio-test</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`3.` Merge kernels and mentions in urls:"
      ],
      "metadata": {
        "id": "8jFMeMw1BlBf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# merge kernels and messages (outer)\n",
        "promotions = pd.merge(msg_ref, kernel_title, how='outer', left_on='Ref', right_on='title_cleaned',\n",
        "         suffixes=('_messages', '_kernels'), copy=True)\n",
        "\n",
        "# remove rows with NaN\n",
        "promotions = promotions.dropna()\n",
        "\n",
        "# remove rows with title_cleaned in ['', 'competitions', 'rules']\n",
        "promotions = promotions[~promotions['title_cleaned'].isin(['', 'competitions', 'rules'])]\n",
        "\n",
        "# remove rows where authors don't match\n",
        "\n",
        "promotions = promotions[promotions['Ref_author'] == promotions['UserName']]\n",
        "promotions.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:13:03.723580Z",
          "iopub.execute_input": "2022-02-02T18:13:03.723864Z",
          "iopub.status.idle": "2022-02-02T18:13:05.389747Z",
          "shell.execute_reply.started": "2022-02-02T18:13:03.723820Z",
          "shell.execute_reply": "2022-02-02T18:13:05.388800Z"
        },
        "trusted": true,
        "id": "0KK5XeafBlBf",
        "outputId": "3ecf8d89-8bb6-4459-c7aa-2b20cccdd1c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 57,
          "output_type": "execute_result",
          "data": {
            "text/plain": "       Id_messages                                            Message  \\\n4285      210910.0  <p>Eike shared a lot of insights. What he ment...   \n5330      240040.0  <p><a href=\"https://www.kaggle.com/issatingzon...   \n5997      841998.0  <p><a href=\"/dave117\">@dave117</a>  check out ...   \n6298      842005.0  <p>Yes sure, please check out the notebook as ...   \n74039     463741.0  <p>Thank you for sharing Chris, this was a hel...   \n\n                                 Ref     Ref_author  Id_kernels  AuthorUserId  \\\n4285   exploratory-analysis-rossmann         thie1e     19422.0      227184.0   \n5330       exploratory-data-analysis    issatingzon    390413.0      699774.0   \n5997       exploratory-data-analysis  darshangandhi   9317243.0     3790539.0   \n6298       exploratory-data-analysis  darshangandhi   9317243.0     3790539.0   \n74039                        titanic       mauricef   2767015.0     1025384.0   \n\n       CurrentKernelVersionId  TotalVotes                          Title  \\\n4285                 124149.0       376.0  Exploratory Analysis Rossmann   \n5330                1615052.0        20.0      Exploratory Data Analysis   \n5997               33407275.0        21.0      Exploratory Data Analysis   \n6298               33407275.0        21.0      Exploratory Data Analysis   \n74039              10077506.0        13.0                        titanic   \n\n            UserName                  title_cleaned  \n4285          thie1e  exploratory-analysis-rossmann  \n5330     issatingzon      exploratory-data-analysis  \n5997   darshangandhi      exploratory-data-analysis  \n6298   darshangandhi      exploratory-data-analysis  \n74039       mauricef                        titanic  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id_messages</th>\n      <th>Message</th>\n      <th>Ref</th>\n      <th>Ref_author</th>\n      <th>Id_kernels</th>\n      <th>AuthorUserId</th>\n      <th>CurrentKernelVersionId</th>\n      <th>TotalVotes</th>\n      <th>Title</th>\n      <th>UserName</th>\n      <th>title_cleaned</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4285</th>\n      <td>210910.0</td>\n      <td>&lt;p&gt;Eike shared a lot of insights. What he ment...</td>\n      <td>exploratory-analysis-rossmann</td>\n      <td>thie1e</td>\n      <td>19422.0</td>\n      <td>227184.0</td>\n      <td>124149.0</td>\n      <td>376.0</td>\n      <td>Exploratory Analysis Rossmann</td>\n      <td>thie1e</td>\n      <td>exploratory-analysis-rossmann</td>\n    </tr>\n    <tr>\n      <th>5330</th>\n      <td>240040.0</td>\n      <td>&lt;p&gt;&lt;a href=\"https://www.kaggle.com/issatingzon...</td>\n      <td>exploratory-data-analysis</td>\n      <td>issatingzon</td>\n      <td>390413.0</td>\n      <td>699774.0</td>\n      <td>1615052.0</td>\n      <td>20.0</td>\n      <td>Exploratory Data Analysis</td>\n      <td>issatingzon</td>\n      <td>exploratory-data-analysis</td>\n    </tr>\n    <tr>\n      <th>5997</th>\n      <td>841998.0</td>\n      <td>&lt;p&gt;&lt;a href=\"/dave117\"&gt;@dave117&lt;/a&gt;  check out ...</td>\n      <td>exploratory-data-analysis</td>\n      <td>darshangandhi</td>\n      <td>9317243.0</td>\n      <td>3790539.0</td>\n      <td>33407275.0</td>\n      <td>21.0</td>\n      <td>Exploratory Data Analysis</td>\n      <td>darshangandhi</td>\n      <td>exploratory-data-analysis</td>\n    </tr>\n    <tr>\n      <th>6298</th>\n      <td>842005.0</td>\n      <td>&lt;p&gt;Yes sure, please check out the notebook as ...</td>\n      <td>exploratory-data-analysis</td>\n      <td>darshangandhi</td>\n      <td>9317243.0</td>\n      <td>3790539.0</td>\n      <td>33407275.0</td>\n      <td>21.0</td>\n      <td>Exploratory Data Analysis</td>\n      <td>darshangandhi</td>\n      <td>exploratory-data-analysis</td>\n    </tr>\n    <tr>\n      <th>74039</th>\n      <td>463741.0</td>\n      <td>&lt;p&gt;Thank you for sharing Chris, this was a hel...</td>\n      <td>titanic</td>\n      <td>mauricef</td>\n      <td>2767015.0</td>\n      <td>1025384.0</td>\n      <td>10077506.0</td>\n      <td>13.0</td>\n      <td>titanic</td>\n      <td>mauricef</td>\n      <td>titanic</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`4.` Group by kernel id to find number of metions in forum messages:"
      ],
      "metadata": {
        "id": "QZTPtF5yBlBf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_promotions = promotions[['Id_kernels', 'CurrentKernelVersionId', 'TotalVotes']]\\\n",
        ".groupby('Id_kernels').count().reset_index()[[\"Id_kernels\", \"CurrentKernelVersionId\"]]\\\n",
        ".rename(columns = {'CurrentKernelVersionId' : 'Promotions'})"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:13:08.465407Z",
          "iopub.execute_input": "2022-02-02T18:13:08.466472Z",
          "iopub.status.idle": "2022-02-02T18:13:08.479830Z",
          "shell.execute_reply.started": "2022-02-02T18:13:08.466414Z",
          "shell.execute_reply": "2022-02-02T18:13:08.478812Z"
        },
        "trusted": true,
        "id": "p23YLBhRBlBf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`5.` Merge number of promotions with total votes:"
      ],
      "metadata": {
        "id": "YBUaVqg1BlBg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_kernel_promotions = kernels[['Id', 'TotalVotes', 'Medal']].merge(n_promotions.rename(columns = {'Id_kernels' : 'Id'})\\\n",
        "                                                          , how = 'outer')\n",
        "n_kernel_promotions = n_kernel_promotions.fillna(0)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:13:11.816436Z",
          "iopub.execute_input": "2022-02-02T18:13:11.817059Z",
          "iopub.status.idle": "2022-02-02T18:13:12.085662Z",
          "shell.execute_reply.started": "2022-02-02T18:13:11.817023Z",
          "shell.execute_reply": "2022-02-02T18:13:12.084734Z"
        },
        "trusted": true,
        "id": "Vg3IVc-gBlBg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`6.` Analyze correlation between the number of votes and number of mentions in forum messages:"
      ],
      "metadata": {
        "id": "FfSlvDRiBlBg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First of all let's plot the number of votes against the number of promotions in forum messages:"
      ],
      "metadata": {
        "id": "YcuOwe45BlBg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fit a linear regression model on mentions and votes data\n",
        "model = LinearRegression()\n",
        "model.fit(n_kernel_promotions['Promotions'].values.reshape(-1, 1), n_kernel_promotions['TotalVotes'].values.reshape(-1, 1))\n",
        "\n",
        "inputs = np.linspace(1, 20, num=50)\n",
        "predictions = model.predict(inputs.reshape(-1,1)).reshape(50)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:13:15.189962Z",
          "iopub.execute_input": "2022-02-02T18:13:15.190238Z",
          "iopub.status.idle": "2022-02-02T18:13:15.216236Z",
          "shell.execute_reply.started": "2022-02-02T18:13:15.190211Z",
          "shell.execute_reply": "2022-02-02T18:13:15.215463Z"
        },
        "trusted": true,
        "id": "LMejI3egBlBg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with matplotlib\n",
        "\n",
        "plt.scatter(n_kernel_promotions['Promotions'], n_kernel_promotions['TotalVotes'])\n",
        "plt.plot(inputs, predictions, color='green')\n",
        "\n",
        "plt.title(\"Number of votes per number of mentions\")\n",
        "plt.xlabel(\"Number of mentions in Kaggle forum messages\")\n",
        "plt.ylabel(\"Votes\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:13:37.425552Z",
          "iopub.execute_input": "2022-02-02T18:13:37.425880Z",
          "iopub.status.idle": "2022-02-02T18:13:38.707247Z",
          "shell.execute_reply.started": "2022-02-02T18:13:37.425831Z",
          "shell.execute_reply": "2022-02-02T18:13:38.706268Z"
        },
        "trusted": true,
        "id": "OwCHFJvjBlBh",
        "outputId": "f7a3aa81-3784-4870-e36a-5b41dffe73f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check the correlation coefficient:"
      ],
      "metadata": {
        "id": "Fe3gFr8mBlBh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#columns to find out correlation\n",
        "cols = ['TotalVotes', 'Promotions']\n",
        "\n",
        "#Find out correlation between columns and plot\n",
        "corrs = np.corrcoef(n_kernel_promotions[cols].values.T)\n",
        "sns.set(font_scale=1)\n",
        "sns.set(rc={'figure.figsize':(7,7)})\n",
        "hm=sns.heatmap(corrs, cbar = True, annot=True, square = True, fmt = '.2f',\n",
        "              yticklabels = cols, xticklabels = cols).set_title('Correlations heatmap')\n",
        "\n",
        "fig = hm.get_figure()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:13:45.209158Z",
          "iopub.execute_input": "2022-02-02T18:13:45.209641Z",
          "iopub.status.idle": "2022-02-02T18:13:45.445748Z",
          "shell.execute_reply.started": "2022-02-02T18:13:45.209611Z",
          "shell.execute_reply": "2022-02-02T18:13:45.444871Z"
        },
        "trusted": true,
        "id": "K5S0XOgjBlBh",
        "outputId": "35ebf95c-52bb-46b7-d7ea-16152d1ec510"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find out the correlation between the medal rank and the number of mentions of the kernel:"
      ],
      "metadata": {
        "id": "-pkweZcZBlBh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#columns to find out correlation\n",
        "cols = ['Medal', 'Promotions']\n",
        "\n",
        "#Find out correlation between columns and plot\n",
        "corrs = np.corrcoef(n_kernel_promotions[cols].values.T)\n",
        "sns.set(font_scale=1)\n",
        "sns.set(rc={'figure.figsize':(7,7)})\n",
        "hm=sns.heatmap(corrs, cbar = True, annot=True, square = True, fmt = '.2f',\n",
        "              yticklabels = cols, xticklabels = cols).set_title('Correlations heatmap (medals)')\n",
        "\n",
        "fig = hm.get_figure()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:13:54.091373Z",
          "iopub.execute_input": "2022-02-02T18:13:54.091643Z",
          "iopub.status.idle": "2022-02-02T18:13:54.299476Z",
          "shell.execute_reply.started": "2022-02-02T18:13:54.091615Z",
          "shell.execute_reply": "2022-02-02T18:13:54.298866Z"
        },
        "trusted": true,
        "id": "UNC8G5hQBlBi",
        "outputId": "40d5bad0-79c9-435e-da3b-91436ee0fc5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x504 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, some numbers considering the number of mentions of the kernels on Kaggle forum:"
      ],
      "metadata": {
        "id": "ePALayO0BlBi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "votes_with_mentions = n_kernel_promotions[n_kernel_promotions['Promotions'] > 0]['TotalVotes'].mean()\n",
        "print('Average number of votes for advertised kernels: {}'.format(votes_with_mentions))\n",
        "\n",
        "kernels_with_promotions = n_kernel_promotions[n_kernel_promotions['Promotions'] > 0]['Promotions'].count()\n",
        "print('Total number of kernels mentioned in forum messages: {}'.format(kernels_with_promotions))\n",
        "\n",
        "votes_no_mentions = n_kernel_promotions[n_kernel_promotions['Promotions'] == 0]['TotalVotes'].mean()\n",
        "print('Average number of votes for non advertised kernels: {}'.format(votes_no_mentions))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:14:01.020105Z",
          "iopub.execute_input": "2022-02-02T18:14:01.021143Z",
          "iopub.status.idle": "2022-02-02T18:14:01.045757Z",
          "shell.execute_reply.started": "2022-02-02T18:14:01.021101Z",
          "shell.execute_reply": "2022-02-02T18:14:01.044811Z"
        },
        "trusted": true,
        "id": "sgNkIJ4uBlBi",
        "outputId": "5a079f7f-1415-4b03-ca81-949c1a2d5944"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Average number of votes for advertised kernels: 85.53714436248683\nTotal number of kernels mentioned in forum messages: 3796\nAverage number of votes for non advertised kernels: 3.2342377056845186\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparison of medal rank for kernels with mentions on Kaggle forums and kenels without mentions:"
      ],
      "metadata": {
        "id": "jPdFcMy4BlBi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "medals_with_mentions = n_kernel_promotions[n_kernel_promotions['Promotions'] > 0]['Medal'].mean()\n",
        "print('Average medal rank for advertised kernels: {}'.format(medals_with_mentions))\n",
        "\n",
        "medals_no_mentions = n_kernel_promotions[n_kernel_promotions['Promotions'] == 0]['Medal'].mean()\n",
        "print('Average medal rank for non advertised kernels: {}'.format(medals_no_mentions))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T18:14:06.831853Z",
          "iopub.execute_input": "2022-02-02T18:14:06.832142Z",
          "iopub.status.idle": "2022-02-02T18:14:06.855356Z",
          "shell.execute_reply.started": "2022-02-02T18:14:06.832112Z",
          "shell.execute_reply": "2022-02-02T18:14:06.854485Z"
        },
        "trusted": true,
        "id": "DaXeiCE2BlBj",
        "outputId": "ac2a7ed1-b5db-47fb-f964-aaac66bf2735"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Average medal rank for advertised kernels: 1.518177028451001\nAverage medal rank for non advertised kernels: 0.2263037568964572\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b> Conclusions: </b>\n",
        "\n",
        "* There is no strong correlation between the number of mentions in Kaggle messages and the number of votes.\n",
        "* The average number of votes for advertised kernels is higher than the number of votes for non-advertised kernels.\n",
        "* The average medal rank for advertised kernels is higher than the average medal rank for non-advertised kernels.\n",
        "\n",
        "\n",
        "<br>My experience tells me that promoting your kernel on Kaggle forum helps to gain votes, but the analysis doesn't show that. Probably, this happens because the number of kernels mentioned in forum messages (and identified by me) is tiny, so we don't have enough data for the analysis. </br>"
      ],
      "metadata": {
        "id": "s-B77XqjBlBj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8. Machine learning to predict number of votes for kernel"
      ],
      "metadata": {
        "_uuid": "8b33445bcffc2f2a48983f5f675a7f5359c92ffd",
        "id": "n2z9PcucBlBj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The last thing left to do is to try to create regression model, which predicts the number of votes for a kernel."
      ],
      "metadata": {
        "_uuid": "497facfe1f454fe578127d61eb7251e9d440de07",
        "id": "1wN2whqZBlBj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#prepare data\n",
        "\n",
        "def has_tag(row, tag):\n",
        "    ''' Function returns 1 of tag id found in row['Tags'] column '''\n",
        "    \n",
        "    for row_tag in row['Tags'].replace(' ','').replace('{','').replace('}','').split(','):\n",
        "        if row_tag == tag:\n",
        "            return 1\n",
        "    return 0\n",
        "\n",
        "def is_forked(row):\n",
        "    ''' Functions returns 0 if row['ForkParentKernelVersionId'] is nan, 1 - otherwise '''\n",
        "    if np.isnan(row['ForkParentKernelVersionId']):\n",
        "        return 0\n",
        "    else:\n",
        "        return 1\n",
        "\n",
        "def has_linked_topic(row):\n",
        "    ''' Functions returns 0 if row['ForumTopicId'] is nan, 1 - otherwise '''\n",
        "    if np.isnan(row['ForumTopicId']):\n",
        "        return 0\n",
        "    else:\n",
        "        return 1\n",
        "    \n",
        "def get_days_from_registration(row):\n",
        "    '''Function returns number of days since users registration date'''\n",
        "    \n",
        "    today = dt.datetime.now().date()\n",
        "    days = (today - dt.datetime.strptime(row['RegisterDate'], \"%m/%d/%Y\").date()).days\n",
        "    \n",
        "    return days\n",
        "\n",
        "def get_days_from_kernel_creation(row):\n",
        "    '''Function returns number of days since kernel creation date'''\n",
        "    \n",
        "    today = dt.datetime.now().date()\n",
        "    \n",
        "    days = (today - dt.datetime.strptime(row['CreationDate'], \"%m/%d/%Y %H:%M:%S\").date()).days\n",
        "    \n",
        "    return days\n",
        "\n",
        "def prepare_data(kernels, kernel_lang, kernel_tags, kernel_versions, kernel_users, kernel_datasets, datasets, dataset_versions, tags, competitions, kernel_competitions):\n",
        "    '''\n",
        "    Function to prepare meta Kaggle data to be used in regression models\n",
        "    \n",
        "    INPUTS:\n",
        "    1. kernels - Kernels.csv dataset, \n",
        "    2. kernel_lang - KernelLanguages.csv dataset,\n",
        "    3. kernel_tags - KernelTags.csv dataset,\n",
        "    4. kernel_versions - KernelVersions.csv dataset,\n",
        "    5. kernel_users - KernelUsers.csv dataset,\n",
        "    6. kernel_datasets - KernelVersionDatasetSources.csv dataset,\n",
        "    7. datasets - Datasets.csv dataset,\n",
        "    8. dataset_versions - DatasetVersions.csv dataset,\n",
        "    9. tags - Tags.csv dataset.\n",
        "    \n",
        "    RETURNS:\n",
        "    df - dataset, wchich contains preprocessed data\n",
        "    '''\n",
        "    #1. merge datasets\n",
        "    \n",
        "    #remove irrelevant columns from dataset and rename other columns to perform merge\n",
        "    df = kernels[['Id', 'CurrentKernelVersionId' , 'ForkParentKernelVersionId', 'ForumTopicId', 'TotalVotes', 'AuthorUserId', 'CreationDate']].rename(columns = \n",
        "                                                                                                                      {'Id': 'KernelId', \n",
        "                                                                                                           'TotalVotes': 'TotalKernelVotes', \n",
        "                                                                                                                      'CurrentKernelVersionId':'KernelVersionId'})\n",
        "    \n",
        "    # create new column for age of the kernel (number of days since the kernel creation)\n",
        "    # and drop old column CreationDate\n",
        "    df[['CreationDate']] = df[['CreationDate']].fillna(value=dt.datetime.now().strftime('%m/%d/%Y %H:%M:%S'))\n",
        "    df['KernelAge'] = df.apply(lambda row: get_days_from_kernel_creation(row),axis=1)\n",
        "    df = df.drop('CreationDate', axis = 1)\n",
        "    \n",
        "    #merge with users to add PerformanceTier of the author and number of days since author's registration on Kaggle\n",
        "    kernel_users['DaysSinceRegistration'] = kernel_users.apply(lambda row: get_days_from_registration(row),axis=1)\n",
        "    kernel_users_renamed = kernel_users[['Id', 'PerformanceTier', 'DaysSinceRegistration']].rename(columns={'Id': 'AuthorUserId',\n",
        "                                                                                  'PerformanceTier' : 'AuthorPerformanceTier'})\n",
        "    df = df.merge(kernel_users_renamed, on = 'AuthorUserId').drop('AuthorUserId', axis = 1)\n",
        "  \n",
        "    #merge with versions to add VersionNumber and KernelLanguageId\n",
        "    kernel_versions_renamed = kernel_versions[['Id', 'ScriptId', 'ScriptLanguageId', 'VersionNumber']].rename(columns = {'Id':'KernelVersionId', 'ScriptId':'KernelId'})\n",
        "    df = df.merge(kernel_versions_renamed, on = ['KernelVersionId', 'KernelId'])\n",
        "    \n",
        "    #merge with datasets to add TotalDatasetVotes and TotalDatasetDownloads\n",
        "    kernel_datasets_truncated = kernel_datasets[['KernelVersionId', 'SourceDatasetVersionId']]\n",
        "\n",
        "    dataset_versions_truncated = dataset_versions[['DatasetId', 'DatasourceVersionId']]\n",
        "    dataset_versions_truncated = dataset_versions_truncated.rename(columns={'DatasourceVersionId': 'SourceDatasetVersionId'})\n",
        "\n",
        "    datasets_truncated = datasets[['Id', 'TotalVotes', 'TotalDownloads']]\n",
        "    datasets_truncated = datasets_truncated.rename(columns={'Id': 'DatasetId', 'TotalVotes':'TotalDatasetVotes',\n",
        "                                                      'TotalDownloads':'TotalDatasetDownloads'})\n",
        "\n",
        "    df = df.merge(kernel_datasets_truncated, on = 'KernelVersionId')\n",
        "    df = df.merge(dataset_versions_truncated, on = 'SourceDatasetVersionId')\n",
        "    df = df.merge(datasets_truncated, on = 'DatasetId').drop(['SourceDatasetVersionId', 'DatasetId'], axis = 1)\n",
        "    \n",
        "    # merge with type of competition\n",
        "    # if kernel is not linked to a competition - NaN\n",
        "    competitions_type = competitions[['Id', 'HostSegmentTitle']].rename(columns={'Id': 'SourceCompetitionId'})\n",
        "    kernel_competition_type = kernel_competitions.merge(competitions_type, on='SourceCompetitionId')[['KernelVersionId', 'HostSegmentTitle']] # merge kernel versions with competitions\n",
        "    df = df.merge(kernel_competition_type, on = 'KernelVersionId', how='outer')\n",
        "    df = df.drop('KernelVersionId', axis = 1)\n",
        "    \n",
        "    #create dummy variables for competition types and drop HostSegmentTitle\n",
        "    df = pd.get_dummies(df, columns = ['HostSegmentTitle'], prefix = 'comp', dummy_na = True)\n",
        "       \n",
        "    #merge with tags\n",
        "    tags_trc = tags[['Id', 'Name']]\n",
        "    tags_trc = tags_trc.rename(columns = {'Id' : 'TagId', 'Name' : 'TagName'})\n",
        "    df = df.merge(kernel_tags, on = 'KernelId').drop(['Id'], axis = 1)\n",
        "    df = df.merge(tags_trc, on = 'TagId')\n",
        "    \n",
        "    #find most popular tags\n",
        "    top20_tags = tags[['Id', 'Name', 'DatasetCount', 'CompetitionCount', 'KernelCount']].rename(columns={'Id':'TagId'})\n",
        "    top20_tags['TotalCount'] = top20_tags['DatasetCount'] + top20_tags['CompetitionCount'] + top20_tags['KernelCount']\n",
        "    top20_tags = top20_tags.sort_values('TotalCount', ascending = False)\n",
        "    top20_tags = top20_tags[:20].drop(['DatasetCount', 'CompetitionCount' , 'KernelCount'], axis = 1)\n",
        "    \n",
        "    #filter-out rows only for most popular tags\n",
        "    df = df[df['TagId'].isin(top20_tags['TagId'].values.tolist())]\n",
        "    df = df.drop('TagId', axis = 1)\n",
        "    \n",
        "    #introduce column 'Tags' with set of most popular tags used for kernel\n",
        "    df_1 = df.groupby('KernelId')['TagName'].apply(lambda x: \"{%s}\" % ', '.join(x)).reset_index().rename(columns = {'TagName':'Tags'})\n",
        "    df = df.drop('TagName', axis = 1)\n",
        "    df = df_1.merge(df, on='KernelId')\n",
        "    \n",
        "    #create dummy columns for top-20 tags\n",
        "    for top20_tag in top20_tags['Name'].values.tolist():\n",
        "        df['tag_' + str(top20_tag)] = df.apply(lambda row: has_tag(row, top20_tag),axis=1)\n",
        "        \n",
        "    df = df.drop('Tags', axis = 1)\n",
        "    \n",
        "    #2. process column values\n",
        "    \n",
        "    #introduce column IsForked for kernels forked from other and drop original column\n",
        "    df['IsForked'] = df.apply(lambda row: is_forked(row),axis=1)\n",
        "    df = df.drop('ForkParentKernelVersionId', axis = 1)\n",
        "    \n",
        "    #introduce colum HasForumTopic for kernels linked to forum topic id and drop original column\n",
        "    df['HasForumTopic'] = df.apply(lambda row: has_linked_topic(row),axis=1)\n",
        "    df = df.drop(['ForumTopicId', 'KernelId'], axis = 1)\n",
        "    \n",
        "     \n",
        "    return df\n",
        "\n",
        "df = prepare_data(kernels, kernel_lang, kernel_tags, kernel_versions, kernel_users, kernel_datasets, datasets, dataset_versions, tags, competitions, kernel_competitions)\n",
        "\n",
        "df.head(5)"
      ],
      "metadata": {
        "_uuid": "61e623ec2b9b750d5289002fd285522ac5beb223",
        "execution": {
          "iopub.status.busy": "2022-02-02T19:09:36.778380Z",
          "iopub.execute_input": "2022-02-02T19:09:36.778649Z",
          "iopub.status.idle": "2022-02-02T19:15:05.900246Z",
          "shell.execute_reply.started": "2022-02-02T19:09:36.778621Z",
          "shell.execute_reply": "2022-02-02T19:15:05.899411Z"
        },
        "trusted": true,
        "id": "a0V3i3jcBlBj",
        "outputId": "31c53af5-9353-4b78-d3da-caa96e9bc1f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 78,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   TotalKernelVotes  KernelAge  AuthorPerformanceTier  DaysSinceRegistration  \\\n0              12.0     2284.0                    0.0                 2332.0   \n1               6.0     2221.0                    1.0                 2632.0   \n2               2.0     2218.0                    1.0                 2539.0   \n3            1242.0     2211.0                    5.0                 4265.0   \n4            1242.0     2211.0                    5.0                 4265.0   \n\n   ScriptLanguageId  VersionNumber  TotalDatasetVotes  TotalDatasetDownloads  \\\n0               5.0            8.0               94.0                 5766.0   \n1               9.0            4.0              680.0                48092.0   \n2               5.0           41.0              203.0                13425.0   \n3               9.0           15.0             2798.0               247096.0   \n4               9.0           15.0             2798.0               247096.0   \n\n   comp_Featured  comp_Getting Started  ...  tag_data visualization  \\\n0              0                     0  ...                       0   \n1              0                     0  ...                       0   \n2              0                     0  ...                       0   \n3              0                     0  ...                       0   \n4              0                     0  ...                       0   \n\n   tag_education  tag_exploratory data analysis  tag_investing  tag_music  \\\n0              0                              0              0          0   \n1              0                              0              0          0   \n2              1                              0              0          0   \n3              0                              0              0          0   \n4              0                              0              0          0   \n\n   tag_utility script  tag_earth and nature  tag_software  IsForked  \\\n0                   0                     0             0         0   \n1                   0                     0             0         0   \n2                   0                     0             0         0   \n3                   0                     0             0         0   \n4                   0                     0             0         0   \n\n   HasForumTopic  \n0              1  \n1              1  \n2              1  \n3              1  \n4              1  \n\n[5 rows x 38 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TotalKernelVotes</th>\n      <th>KernelAge</th>\n      <th>AuthorPerformanceTier</th>\n      <th>DaysSinceRegistration</th>\n      <th>ScriptLanguageId</th>\n      <th>VersionNumber</th>\n      <th>TotalDatasetVotes</th>\n      <th>TotalDatasetDownloads</th>\n      <th>comp_Featured</th>\n      <th>comp_Getting Started</th>\n      <th>...</th>\n      <th>tag_data visualization</th>\n      <th>tag_education</th>\n      <th>tag_exploratory data analysis</th>\n      <th>tag_investing</th>\n      <th>tag_music</th>\n      <th>tag_utility script</th>\n      <th>tag_earth and nature</th>\n      <th>tag_software</th>\n      <th>IsForked</th>\n      <th>HasForumTopic</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12.0</td>\n      <td>2284.0</td>\n      <td>0.0</td>\n      <td>2332.0</td>\n      <td>5.0</td>\n      <td>8.0</td>\n      <td>94.0</td>\n      <td>5766.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6.0</td>\n      <td>2221.0</td>\n      <td>1.0</td>\n      <td>2632.0</td>\n      <td>9.0</td>\n      <td>4.0</td>\n      <td>680.0</td>\n      <td>48092.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.0</td>\n      <td>2218.0</td>\n      <td>1.0</td>\n      <td>2539.0</td>\n      <td>5.0</td>\n      <td>41.0</td>\n      <td>203.0</td>\n      <td>13425.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1242.0</td>\n      <td>2211.0</td>\n      <td>5.0</td>\n      <td>4265.0</td>\n      <td>9.0</td>\n      <td>15.0</td>\n      <td>2798.0</td>\n      <td>247096.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1242.0</td>\n      <td>2211.0</td>\n      <td>5.0</td>\n      <td>4265.0</td>\n      <td>9.0</td>\n      <td>15.0</td>\n      <td>2798.0</td>\n      <td>247096.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#split into train and test\n",
        "TEST_SIZE = 0.3\n",
        "RAND_STATE = 42\n",
        "\n",
        "X = df.drop(columns = 'TotalKernelVotes')\n",
        "y = df[['TotalKernelVotes']]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = TEST_SIZE, random_state=RAND_STATE)"
      ],
      "metadata": {
        "_uuid": "d9a9094717f808a963efdcc68852c8a0eef9b9a6",
        "execution": {
          "iopub.status.busy": "2022-02-02T19:17:48.803114Z",
          "iopub.execute_input": "2022-02-02T19:17:48.803874Z",
          "iopub.status.idle": "2022-02-02T19:17:48.853864Z",
          "shell.execute_reply.started": "2022-02-02T19:17:48.803822Z",
          "shell.execute_reply": "2022-02-02T19:17:48.853160Z"
        },
        "trusted": true,
        "id": "VoiQfKMNBlBk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train XGBoost model\n",
        "xgb = xgboost.XGBRegressor(n_estimators=100, learning_rate=0.08, gamma=0, subsample=0.75,\n",
        "                           colsample_bytree=1, max_depth=7)\n",
        "xgb.fit(X_train,y_train)\n",
        "\n",
        "#calculate and print scores for the model for top 15 features\n",
        "y_train_preds = xgb.predict(X_train)\n",
        "y_test_preds = xgb.predict(X_test)\n",
        "\n",
        "print('MSE train: %.3f, test: %.3f' % (\n",
        "        mean_squared_error(y_train, y_train_preds),\n",
        "        mean_squared_error(y_test, y_test_preds)))\n",
        "print('R^2 train: %.3f, test: %.3f' % (\n",
        "        r2_score(y_train, y_train_preds),\n",
        "        r2_score(y_test, y_test_preds)))"
      ],
      "metadata": {
        "_uuid": "d1668555d035ba932073a4673f66f60580c98bc8",
        "execution": {
          "iopub.status.busy": "2022-02-02T19:17:52.108471Z",
          "iopub.execute_input": "2022-02-02T19:17:52.108951Z",
          "iopub.status.idle": "2022-02-02T19:18:00.250756Z",
          "shell.execute_reply.started": "2022-02-02T19:17:52.108909Z",
          "shell.execute_reply": "2022-02-02T19:18:00.249977Z"
        },
        "trusted": true,
        "id": "77eW_lKfBlBl",
        "outputId": "6cab3467-cbc8-4970-e8f8-a93bcbf005d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "MSE train: 382.704, test: 712.035\nR^2 train: 0.897, test: 0.789\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see the feature importances:"
      ],
      "metadata": {
        "id": "fwspFYKmBlBl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#plot bar chart with matplotlib\n",
        "plt.figure(figsize=(7,10))\n",
        "\n",
        "y_pos = np.arange(len(X.columns))\n",
        "\n",
        "plt.barh(y_pos, xgb.feature_importances_, align='center', alpha=0.5)\n",
        "plt.yticks(y_pos, X.columns)\n",
        "\n",
        "plt.ylabel('Features')\n",
        "plt.xlabel('Feature Importance')\n",
        "\n",
        "plt.title('Feature importances')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-02-02T19:18:00.252320Z",
          "iopub.execute_input": "2022-02-02T19:18:00.252838Z",
          "iopub.status.idle": "2022-02-02T19:18:00.675006Z",
          "shell.execute_reply.started": "2022-02-02T19:18:00.252801Z",
          "shell.execute_reply": "2022-02-02T19:18:00.674374Z"
        },
        "trusted": true,
        "id": "RO0qyVUcBlBl",
        "outputId": "0d620a2f-9d7b-44b2-9d6d-06493dd1dea8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x720 with 1 Axes>",
            "image/png": 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k4sSJd4zRr18/kpKSePHFF7GyssLe3p4vv/xS1k0JIYSoEDqlVNXZ4lk80P7+jb2Ktva747Jp51/MZareVCQfBSQXxiQfxswlHyU95pOZKSGK4NWyXpVrJyOEEKLspJgSD4y33377nl6vmpUFBouqU0wJIYS4O/KrqBBCCCFEOUgxJUQRMnIN5N3vIIQQQlR6UkwJUYS9hy+SlWMo+UAhhBBVmhRT4q5169aN3377rdjPe/Xqha+vL76+vkY7mZfWre0XymPYsGHs3LmzXGMIIYQQRZEF6KJC/b0NTlnk5clDNiGEEJWfFFOi3JYuXcqWLVuwtbVFp9OxevVqatSoUeTxe/bsYfHixeTl5eHk5ERwcDCPPfYY8fHxzJw5k2eeeYbjx4/zn//8x+i8uLg4Zs2axaJFi/jHP/7BnDlzOHnyJFlZWXh6ehIUFISlpSWnT58mKCiImzdv4urqekerGyGEEMKUpJgS5XLt2jVWrVrF999/j52dHXq9Hjs7O+3zMWPGaH35xo8fT9OmTZkwYQJr1qzhySefZP369YwfP57169cDcPr0aYKDg2nZsiUA//d//wfA5s2b+d///senn36Ki4sLkydPpk2bNsyaNQuDwcD48eOJiIhg0KBBTJgwgWHDhuHn58dPP/3EkCFD7ure7KvZltj8tyqpCs1Ky0LyUUByYUzyYawq5EOKKVEu1atXp0GDBkyYMIGOHTvSpUsXHB0Ldon9+2O+HTt20KRJE5588kkA+vfvz/Tp07U+eo899phWSN0SGRmJra0t//vf/7Sxd+zYwdGjR/niiy8AyMzMxMXFBb1ez2+//Yavry8Abm5ud/2Y8WZGVrHNf6sSc9nF2FQkHwUkF8YkH8bMJR+yA7qoUBYWFqxbt45Dhw4RFxeHv78/n376KU2aNLmr8ezt75wFaty4MQcOHODMmTO0aNECAKUUy5Yto379+kbH3irKhBBCiHtFvs0nykUpRWpqKh4eHowZMwZXV1dOnTpV5PFubm6cOHGCM2fOALBx40aaNm1qNJv1d82aNSMkJITx48fz448/AvnfFFy5cqW2SD01NZXExEQcHR1xdXUlOjoagKNHjxb7jUMhhBCivGRmSpRLeno6U6ZMITMzE6UUTZs25bnnnivyeCcnJ+bPn8/48ePJzc3FycmJBQsWlHidJk2a8PHHHzNq1CjeffddJk2axIIFC/D19UWn02Ftbc2kSZOoX78+8+fPJygoiE8++QRXV1eeffZZU96yEEIIYUSnlJLmY0IUYu13x2nf/FHspQGw2ax7MBXJRwHJhTHJhzFzyYesmRLiLnm1rIeVhe5+hyGEEKKSk1+5hShCNSsLLO93EEIIISo9mZkSoggZuQby8kzzFNzWWgozIYQwV1JMCVGEvYcvor+ZY5Kxnmv7uKy9EkIIMyV/uwshhBBClIMUU2YqJCSE7Ozs+x0GAIcOHcLb25t+/fqxbds2Pvnkk/sdkhBCCGEyUkyZqaVLl5KTY5pHVOUVFRVFv3792LRpE08//TSffvpphV0rNze3wsYWQgghCiNrpszQ9OnTARg8eDAWFha8+uqrrF69WiuuAgMDadeuHQAHDhzQjvf09CQmJoYVK1YU2c/u0KFDzJgxA4PBQG5uLqNGjcLb25srV64wdepUEhISAHjllVfo168fn376Kd9++y12dnZER0dTq1Yt0tPT8fX1pVq1asyePZu3336br7/+mtzcXDw9PRk1ahSvvvoq33zzDTExMSxatIjPP/+cr7/+mry8PGxtbZk2bRpPP/00kN9u5q233mLXrl14eXnx6quvMmfOHE6ePElWVhaenp4EBQVhaSlLwIUQQpieFFNmaOrUqYSFhREeHo6DgwNpaWl4e3uj0+k4e/YsI0aMYM+ePWRnZxMQEMDixYtxd3dn27ZthIaGFjv2J598wiuvvIK3tzdKKdLT8zdjmzlzJk899RQfffQRly9fxt/fn6ZNm/Lqq69y+vRpnnnmGYYOHcrFixfp378/UVFR2ph6vZ7Lly/z+++/89RTT7Fv3z5effVV4uLiaNu2LQD9+vVj5MiRAMTGxjJ16lTWrVunjWFra0tERAQAkydPpk2bNsyaNQuDwcD48eOJiIhg0KBBZcqjfTVblM40k7d2dtY4O93Zd/BBUhU6v5eF5KOA5MKY5MNYVciHFFNVQGJiIuPGjSMpKQkrKyuuXLlCcnIyKSkp2NnZ4e7uDkDPnj2pUaNGsWN5enqyfPlyEhIS6NChg9Z4eN++fUycOBGAhx9+mM6dOxMfH1/kDNft2rZty759+7h48SIvvvgin376KdnZ2cTGxvLaa68B8PPPP7NixQquXbuGTqfj/PnzRmP4+flpP+/YsYOjR4/yxRdfAJCZmYmLi0vpknWbmxlZ3DDRt/kyM3Me6F2AzWUXY1ORfBSQXBiTfBgzl3zIDuiCgIAAJk6cSI8ePTAYDLRo0YKsrKy7GmvEiBF069aN2NhYZsyYQYcOHXjnnXfKFV/btm2Ji4vj4sWLLFiwgP379/P111+jlKJ+/fpkZ2czduxY1qxZQ7NmzUhKSqJTp05GY9jbF8z6KKVYtmwZ9evXL1dcQgghRGnIAnQz5eDggF6vB/KbEderVw+AiIgI7Vt+jRo1IiMjg4MHDwKwfft2rl+/Xuy4586do0GDBgwePJjhw4dz7NgxANq1a6c9dktOTmb37t3aI7rbOTo6kpmZabRQvF27duzdu5dr165Rt25d2rdvT0hIiLauKzs7m9zcXB555BEAwsLCio2xW7durFy5kry8PABSU1NJTEws9hwhhBDibsnMlJkaOXIkw4cPx87OjqCgIEaPHk3NmjXx8vKiVq1aANjY2LBo0SKmTZsGgIeHB3Xq1KF69aKfb4eGhhIfH4+1tTU2NjZMmTIFgClTpvDee+/h4+MDwPjx43nqqafuOL9WrVr4+Pjg4+NDzZo1CQ8Pp27dujg4ONC6dWsgf6bq0qVLWjHm6OjImDFjGDBgALVq1eL5558v9t4nTZrEggUL8PX1RafTYW1tzaRJk2SmSgghRIXQKaVM0y9DPJD0ej2OjvnPgePi4ggKCiImJgYLC5m0vJh0XdrJ/MVc1j2YiuSjgOTCmOTDmLnkQ9ZMiWJt3bqVVatWoZTCxsaGhQsXSiH1l2pWFhgs5HcNIYQQxZNiqorz9/fH39+/0PdvrTm6pUWLFgQHB9+r0IQQQogHghRTolCRkZH3OwQhhBDigSDPc4QQQgghykGKqRJUpobBRbl+/fodzYOHDRvGzp0771kMISEhzJs3z2TjXbx4ka+++spk4wkhhBAVRYqpElSmhsGFyc3N5fr16xXaPPh++P333++6mJJmx0IIIe4lWTNVjL83DA4NDWX37t0maxoMsHLlSrZu3UpeXh4uLi7MmDEDZ2dn9u3bxwcffEBWVhZ5eXm8+eab9O3bF8ifdWrSpAlHjhyhZs2a6HQ6o+bB4eHhAPz444+sXLmSy5cv07t3b8aPH3/H9ZOTkwkICODGjRtkZWXRuXNnJkyYAOTPNp07d4709HQSExNp0KABH374IdWqVSM9PZ3Jkyfz22+/4ezsTN26dXnooYcKvcfGjRvzzjvvsG3bNq5evcqECRO0vaLGjRvHuXPnyMnJoUGDBsyePZuaNWsSHBzMxYsX8fX15bHHHmPJkiU0btyYQ4cO4eDgoI1767U0OxZCCHHfKFEsV1dXpdfrtdepqanKYDAopZQ6c+aM8vLyUkoplZWVpby8vNT+/fuVUkpt3bpVubq6qpMnTxY59qZNm9SUKVNUXl6eUkqpL7/8UgUEBCillLp69arKzc1VSimVnJysvLy81NWrV5VSSg0dOlS98cYbKicnRymlVGJiovLw8DAae+jQoWrs2LEqLy9PXb9+XXl4eKhz587dEUNmZqZ2f9nZ2WrYsGFq9+7dSimllixZonr27KmuXbumDAaDevnll9VXX32llFJqzpw5auLEiUoppVJSUlTnzp3V3Llzi8xhaGioUkqpAwcOqI4dO2qfpaSkaD8vXrxYLViwQCmlVFxcnPLz87tjnNv/LG5/7erqqlasWKF9NmnSJLVx40allFJ5eXnqnXfe0WIXQgghTElmpsrIlE2Dd+zYwc8//6w16c3Ly9M20ExNTWXSpElcuHABS0tLrl27xrlz53BzcwPAx8cHK6vi//h69eqFhYUF1atX54knniAhIYHHH3/c6Ji8vDzmz5/P4cOHUUpx5coVTpw4ofW+69ixo3YfzZs3JyEhAYD4+Hht93MnJyd69uxZbCx9+vQBwM3NjcuXL5OVlYWtrS1RUVFER0eTk5PDzZs374ivLEzd7DglRY/BIPtMgflsvGcqko8Ckgtjkg9j5pIP2bTTxEzZNFgpxahRoxgwYMAdn02bNo1u3bqxdOlSdDodzz//vNF1bm/sWxRbW1vtZ0tLyzv2jQL44osvuH79OuvXr8fW1pZ3333X6Dp/H+Nu7/XWOLces+Xm5nLs2DHWrl1LeHg4Tk5OREdHa/39CmNpaYn6a8P+wuKQZsdCCCHuB1mAXoLbGwaDaZsGd+vWjbCwMK5duwbkN/Q9ceKEdp1HH30UnU7HDz/8wIULF4ocp7DmwaWVnp6Os7Mztra2JCUlERMTU6rz2rZtq+1FlZaWxvbt28t87evXr+Po6EitWrXIzs4mIiJC+8zR0dEo7wANGjTQGitHR0cXO7Y0OxZCCHGvyMxUCW5vGBwaGmrSpsH9+vXj6tWrDB06FMifTRkyZAhNmjRh3LhxTJ8+nZCQEJ599lkaN25c5DiFNQ8urWHDhjF27Fi8vb1xcXHRFtOXZPTo0UyaNIlevXrh7OysPd4sCy8vLzZv3szzzz9P7dq1cXd314qlxo0b07BhQ7y9vWnUqBFLliwhKCiI9957j+rVq9OrV69ix5Zmx0IIIe4VaXRsQtI02LzImqkC5rLuwVQkHwUkF8YkH8bMJR+yZuoekqbBQgghRNUjxZQJSdNgIYQQouqRYuoekKbBQgghhPmSZ1BCCCGEEOUgxZQQQgghRDlIMVWJhISEaPtW3Uvx8fF8//332uukpCSGDRumvW7cuDE3btwA4LXXXtN2QY+MjOTcuXP3Nti/3B5Hce5njEIIIaoGKaYqkaVLl2oNlO+lH3/8kR9++EF77eLiQmhoaKHHfvLJJzRo0ACAjRs3cv78+XsRosZgMKCUMoqjOPcjRiGEEFWL7DNVSUyfPp2wsDBcXV2xsLAgNDSU3bt3s3r1aq3ACgwM1DbVPHDgANOnTwfA09OTmJgYVqxYgaura6Hjh4SEcPPmTQIDA41e9+vXj5EjR2IwGHj44Yfp27cvffr0oX///sTHxwP5M1OHDh3CwcGBbt268fHHH3Ps2DFmzpyJk5MTjo6OBAYGMnv2bGbPnk3z5s2B/FY1Z8+eZcaMGUaxHDp0iBkzZmAwGMjNzWXUqFF4e3uTnp7O7Nmz+fnnn9HpdLi7u/Pee+8REhLCqVOn0Ov1XLp0ia+++go/Pz8+/vhjXF1dGTZsGE2aNOHw4cNcu3aN3r17ExAQQERExB0xtm/f3vR/eEIIIao0+TZfJTF16lTCwsIIDw/HwcEByG8y7O3tjU6n4+zZs4wYMYI9e/aQnZ1NQEAAixcvxt3dnW3bthU5k1SSxo0bM3jwYKNC6+LFiyWe179/fzZt2sTIkSPp2rUrAP/6179Yu3YtzZs3RynF2rVrWbJkyR3nfvLJJ7zyyit4e3ujlCI9PX9Dt9mzZ2Nvb09UVBQWFhakpqZq5xw9epTIyEicnJwKjefMmTOEh4eTlZXF4MGDadmyZaExloVs2lnAXDbeMxXJRwHJhTHJhzFzyYds2vkAS0xMZNy4cSQlJWFlZcWVK1dITk4mJSUFOzs7rYVLz549qVGjxn2OFnx9ffnoo4+4evUqR48epU6dOjRp0uSO4zw9PVm+fDkJCQl06NCBFi1aALBz504iIyO1jU5vL5w6depUZCEF+a15rKyssLKyok+fPsTFxd1VASWEEEKUlayZqsQCAgL45z//yddff83GjRuxtLQkKyvrrsaytLTEYDBor+92nOLY29vj4+NDZGQkYWFh/Otf/yr0uBEjRrB8+XKcnJyYMWMG77//folj35qtE0IIISobKaYqEQcHB/R6vfY6PT2devXqARAREaF9069Ro0ZkZGRw8OBBALZv387169eLHfuxxx7jl19+wWAwoNfr2bVrl/aZo6Oj9qitrPH+/bx//vOf/O9//+Pnn3/mueeeK/S8c+fO0aBBAwYPHszw4cO15sZdu3bls88+49Yyvtsf85Vk8+bN5ObmcvPmTb799lvatm1bZIxCCCGEKUkxVYmMHDmS4cOH4+vry/Xr1wkKCmL06NH4+fmRmJhIrVq1ALCxsWHRokVMmzYNHx8f9u3bR506dahevXqRY/fs2ZOaNWvSu3dv3n77bZo1a6Z91qNHD44dO4avry8rV64sdbwvvvgiH330Eb6+vsTGxgJQv359GjVqRP/+/bGxsSn0vNDQUPr27Uu/fv1Ys2YN//nPfwAICgrixo0beHt788ILL7Bs2bJSx9KoUSMGDx6Mr68vXbp00R7xFRajEEIIYUrybb4HlF6vx9ExfzFcXFwcQUFBxMTE3PfGynq9nl69ehEREYGLi8s9ueawYcPuepF5cWQBegFzWURqKpKPApILY5IPY+aSD1mAbqa2bt3KqlWrUEphY2PDwoUL73shtXbtWpYvX87IkSPvWSElhBBC3G8yM2Vm/P39ycvLM3qvRYsWBAcH36eIHlwyM1XAXH67NBXJRwHJhTHJhzFzyYfMTFUxkZGR9zsEs5GRayAvr6CYsrW2wPI+xiOEEKJykmJKiCLsPXwR/c2C9j7PtX0ce2v5zoYQQghj8i+DuMP9arhcXr6+vmRmZt7vMIQQQlQxUkyJO9yvhsvlFRUVhZ2d3f0OQwghRBUjj/mEkVvNkwcPHoyFhQWvvvqqyZotR0ZGsmXLFqpXr87JkydxcXHh3XffZd68eSQkJPDMM8+wcOFCdDrdHdsd3P566dKlbNmyBVtbW3Q6HatXr6ZGjRpGDZnPnDnDrFmzSE5OBvL38PLz86vQ3AkhhKiapJgSRv7ecDktLc2kzZaPHTtGdHQ0devW5Y033mDcuHGEhoZib2+Pn58f+/bto3379kWef/XqVVatWsX333+PnZ0der3+jtmo3NxcRo8ezX/+8x969+4NQFpaWplzYV/NFqUrmLy1s7PG2cm+zOOYC2fnojeFrYokHwUkF8YkH8aqQj6kmBLFMnWz5VatWlG3bl0Ann76aR599FHtvCZNmnDhwoVii6nq1avToEEDJkyYQMeOHenSpYu2eekt586dIzc3VyukAGrXrl3me7+ZkcWN2xagZ2bmmMVXfO+GuXy92VQkHwUkF8YkH8bMJR8lbY0ga6ZEsUzZbBnA1tZW+9nS0vKO17f2yCqqMbOlpSXr1q1j6NCh/Pnnn/j7+3PixIm7jkcIIYQoLymmxB1ub7hsymbLZdGgQQOtAfLp06f59ddfgfx2NampqXh4eDBmzBhcXV05deqU0bkNGzbEysqKb7/9Vnvvbh7zCSGEEKUhj/nEHW41XLazs9OaLdesWRMvL69Cmy0DeHh4lNhsuSxee+01xo4dS0xMDE2bNqVp06ZAfjH19ttvk5mZiVKKpk2b8txzzxmda2VlxbJlywgODmbZsmXodDpGjhxJv379TBKbEEIIcTtpJyPuWmVttmwqF5Ouyw7ofzGXdQ+mIvkoILkwJvkwZi75kHYyosJUxmbLplTNygKDhfyuIYQQonhSTIm75u/vj7+/f6HvS7NlIYQQVYUUU8LkzKXZ8t8bHVdmVfkRpBBC3G9STAlRhL83Oq7MpAmzEELcP/K3rxBCCCFEOUgxZUZCQkK0faBMKSYmhnnz5pl83OJcv36dTz75xOi9yZMnc+DAgXsahxBCCFESKabMyNKlS7WGxKbUvXt3AgMDTT5uca5fv86nn35q9N6sWbO09jVCCCFEZSFrpszE9OnTARg8eDAWFha8+uqrrF69WiuuAgMDadeuHQAHDhzQjvf09CQmJoYVK1bg6upa6NiRkZHs2rWLJUuWEB8fz+zZs2nRogWHDx9Gp9Px/vvv88QTTzBixAiGDh1Kjx49ANi5cyeff/45oaGhXL58mZkzZ3Lp0iWysrLo27cvb775JgaDgeDgYOLi4rCxscHe3p7w8HCCg4NJT0/H19eXatWqER4ezrBhwxg5ciRdu3Zl4sSJ2NjYcP78ef7880/c3NyYN28eOp2OpKQkJkyYwJUrV6hfvz4AHTt2ZOjQoWXK6d8bHVdm96IJc1VoVloWko8Ckgtjkg9jVSEfUkyZialTpxIWFkZ4eDgODg6kpaXh7e2NTqfj7NmzjBgxgj179pCdnU1AQACLFy/G3d2dbdu2ERoaWqZrnT59mjlz5hAcHMzy5ctZtmwZixYtws/Pj02bNmnFVGRkJP379wfyi7nRo0fTpk0bsrOzGTFiBM8++yy1a9cmPj6eb775BgsLC65duwbAe++9R//+/YmKiioyjlOnTrFq1Sp0Oh1+fn7ExsbSoUMHZs6ciaenJ6NHj+b333/Hx8eHjh07ljmnf290XJlVdBNmc9l4z1QkHwUkF8YkH8bMJR+yaWcVlZiYyLhx40hKSsLKyoorV66QnJxMSkoKdnZ22uOynj17UqNGjTKN3bBhQ629i5ubGzt37gTgueeeY86cOVofvB9//JF58+Zx8+ZNfvzxR1JTU7Uxbty4wZkzZ/Dz8yM3N5fJkyfj6elJ165dSx1Hjx49tEbJTZs2JSEhgQ4dOhAfH8+UKVMAePTRR7UZOSGEEKIiSDFlpgICApg4cSI9evTAYDDQokULsrKyTDK2jY2N9rOFhQW5ubkAVKtWje7du7NlyxYgf62Vvb09er0enU7Hhg0bsLa2vmO8r7/+mvj4eGJjY1m4cCEbN24sVRy3CikAS0vLOzYKFUIIIe6FB2NBiCgVBwcH9Ho9AOnp6dSrVw+AiIgI7Vt+jRo1IiMjg4MHDwKwfft2rl+/brIY/Pz82LhxIxs3btR2R3d0dKR169asXLlSO+6PP/4gOTmZ1NRUMjIy8PLyYvz48VSvXp3ExEQcHR3JzMzUCrWy8PDw0AqyP/74g7i4ONPcnBBCCFEImZkyIyNHjmT48OHY2dkRFBTE6NGjqVmzJl5eXtSqVQvIn1VatGgR06ZNA/ILjzp16lC9umkWCLq7u2sF3e3fvFu4cCFz5szBx8cHyC/8Zs2aRWZmJu+++y65ubnk5eXRqVMn3NzcsLCwwMfHBx8fH2rWrEl4eHipY5g8eTITJkwgOjqaevXq0bx5c60hsxBCCGFqOqXUg9EvQ5iMXq/Xiou4uDiCgoKIiYkxmybFmZmZWFlZYWVlxeXLlxkwYACrVq2iUaNGZRrnYtJ1aSfzF3NZRGoqko8Ckgtjkg9j5pIPWYAu7rB161ZWrVqFUgobGxsWLlxoNoUUwPnz5wkMDEQpRW5uLm+99VaZCymAalYWGCwejGJKCCHE/SMzU0Lj7+9/xyLuFi1aEBwcfJ8iur9SUvQYDPJ/DzCf3y5NRfJRQHJhTPJhzFzyITNTotQiIyPvdwhCCCHEA8d8nu0IIYQQQtwH96SYqqgGvBUhPj5e+0q/Kd2PHJT2XuLj4/n+++/vQUQli4yMZMyYMXd9vq+vL5mZmSaMSAghhCjePSmmKqoBb2VS0oaRd5uDu9lnqax+/PFHfvjhhwq/zr0QFRWFnZ3d/Q5DCCFEFVLha6b+3oA3NDSU3bt3m6wJL8DKlSvZunUreXl5uLi4MGPGDKpXr87AgQMZO3YsPXr0YN++fUyfPp0NGzbwyy+/MGvWLJo0acIvv/xCtWrVmDt3Lk8++eQdY2/atInPPvsMgAYNGhAcHEydOnWIjIxk8+bNODg4cOHCBRYsWMC+ffv4+uuvycvLw9bWlmnTpvH0008XmoPs7GymTp1KQkICAK+88gr9+vUDoFu3bvTp04e4uDhcXV25cuUKfn5+9O7dG8j/Nl54eDiff/75HfG+//77fPPNN9SoUQMPDw/t/eTkZAICArhx4wZZWVl07tyZCRMmcPLkScLDwzEYDMTGxtK3b19GjhzJG2+8QVpaGllZWTRv3pzp06cb7Xx+y7x58/jxxx/Jycmhdu3azJ49m0cffZSLFy/Sv39/Bg8ezO7du8nIyGDWrFm4u7uTm5tbqvFff/31Iu976dKlbNmyBVtbW3Q6HatXr6ZGjRo0btyYQ4cOUa1atUIbKAshhBAmp+4BV1dXpdfrtdepqanKYDAopZQ6c+aM8vLyUkoplZWVpby8vNT+/fuVUkpt3bpVubq6qpMnTxY59qZNm9SUKVNUXl6eUkqpL7/8UgUEBCillDp9+rTq3LmzOnLkiOratav65ZdflFJKxcXFKVdXVxUfH6+UUioyMlL5+flpn936+eTJk6pDhw4qKSlJKaXU+++/r8aOHauUUioiIkK5ubmpCxcuaLGkpKRoP//www9q4MCBReZg7Nix6v3331dKKZWUlKQ6dOig3WfXrl3V1KlTtWN3796thg4dqr0ePny42rZt2x25iImJUd7e3kqv16vc3Fz1xhtvaPeSmZmpXT87O1sNGzZM7d69Wyml1JIlS9TcuXO1cQwGg0pNTdV+/u9//6vCwsIKzf/t97xu3Tr1n//8RymlVGJionJ1dVU7duxQSikVFRWlXnzxxRLHj4iIUG+//Xax952WlqZat26tMjIylFJKpaenq5ycHKM8//LLL6pXr17afxdXr14tNH4hhBCivO7Lt/lM2YR3x44d/Pzzz/j5+QH5j9tubUj5xBNPMGbMGAYPHkxQUJDWnBfgscce02ZufH19effdd7Wdu2+Jj4+nc+fOPPzww0D+zJKvr6/2eatWrWjQoIH2+ueff2bFihVcu3YNnU7H+fPni4x73759TJw4EYCHH36Yzp07Ex8fr83A3ZqlAvDy8mL27NmcOXNGy19hDYHj4+Pp06cPDg4OAAwYMIBly5ZpeZk/fz6HDx9GKcWVK1c4ceIEnTp1umMcg8HA559/zp49ezAYDFy7dq3IR2d79uwhLCyMmzdv3vFI0t7eXovTzc2NefPmlWn84u67QYMGTJgwgY4dO9KlS5c7djivX7/+XTdQvkW2RihgLl9vNhXJRwHJhTHJhzFzyUel3BrBlE14lVKMGjWKAQMGFPr58ePHcXJy4s8//yxPyIW6VbQAZGdnM3bsWNasWUOzZs1ISkoqtFApLXt7e+1nnU7H0KFDCQsLA+DFF1/E0rJs+11/8cUXXL9+nfXr12Nra8u7775bZM6jo6M5ePAgX375JY6Ojnz88ceFFoa///47c+bMYcOGDdSvX59Dhw4xfvx47fOiGiKXdvzi7nvdunUcOnSIuLg4/P39+fTTT2nSpIl2bvXq1QttoOzs7FymvAkhhBAluScL0G9vwAumbcLbrVs3wsLCuHbtGpBf1Jw4cQKAbdu2ceDAAbZs2cKuXbvYvXu3dl5CQgIHDhwA8v9xd3V1vWN2w9PTk927d5OcnAzk/wPevn37QuPIzs4mNzeXRx55BEArAIrKQbt27Vi3bh2Qv55p9+7dtG3btsj77NevH9u3b+ebb75h4MCBhR7Ttm1bvv32W27evEleXh4RERHaZ+np6Tg7O2Nra0tSUhIxMTHaZ46OjqSnpxsdW7t2be39LVu2FHo9vV6PtbU1zs7OGAyGUq9JKu34Rd23Xq8nNTUVDw8PxowZg6urK6dOnTI6r6gGykIIIYSp3ZOZqdsb8IaGhpq0CW+/fv24evUqQ4cOBfJnqoYMGYKjoyMzZ85k1apV1KpVi/fff5/XX39d+wff1dWV9evXM23aNOzs7Jg/f/4dY7u6ujJ+/HhGjhwJ5D86Kmo3cEdHR8aMGcOAAQOoVasWzz//fLE5mDJlCu+9957W+Hf8+PE89dRTRd6no6MjXl5eZGZm4uTkVOgxXbt25aeffsLX11dbgJ6UlATAsGHDGDt2LN7e3ri4uGgL/gF69OjBpk2b8PX1pW/fvgwZMoSYmBh69epFnTp1aN26daGzWI0bN6ZXr1706dOH2rVr07lzZ61ALU6/fv1KNX5R963X63n77bfJzMxEKUXTpk157rnnjM77448/Cm2gLIQQQphapWsncy+a8MbHxzNv3rwHasfv3NxcXnjhBebOnUvz5s3vdzj3zP28b1kzVcBc1j2YiuSjgOTCmOTDmLnko1KumSqOuTfhvRsxMTHMnDmTHj16VKlCqqretxBCiAdLpZuZKoo04RX3msxMFTCX3y5NRfJRQHJhTPJhzFzy8cDNTBXlQXokJ8xDRq6BvDyFrbUFZfvupBBCiKqkaj8/E6IYew9fZGvcebJyDPc7FCGEEJWYFFNCCCGEEOUgxdQDLCQkRNuj637GcGtnc1OLjIzk3Llz2uuYmJgKu5YQQghxt6SYeoAtXbpUaxZtjjZu3Gi0M3r37t0JDAy8fwEJIYQQhXhgFqALY9OnTwfy+wVaWFjw6quvsnr1aq24CgwM1DbmPHDggHa8p6cnMTExrFixQusDWJiVK1eydetW8vLycHFxYcaMGTg7O5Oens7kyZP57bffcHZ2pm7dujz00EMATJw4kWeeeUbbQPX21+np6cyePZuff/4ZnU6Hu7s77733Hvv27eODDz4gKyuLvLw83nzzTfr27UtERAQ///wzM2fO5IMPPiAwMJA///yTXbt2sWTJEi3GzZs3A/Dss88yZcoUHBwcCAkJ4dy5c6Snp5OYmEiDBg348MMPqVatWplybF/NFqWzwM7OGmcn+5JPMHPOzkVvnlsVST4KSC6MST6MVYV8SDH1gJo6dSphYWGEh4fj4OBAWloa3t7e6HQ6zp49y4gRI9izZw/Z2dkEBASwePFi3N3d2bZtG6GhocWOHRUVRWJiIuvWrcPCwoKwsDDmzp3LokWL+Oijj3BwcOC7774jNTUVf39/evfuXWK8s2fPxt7enqioKCwsLEhNTQWgadOmhIWFYWlpyZUrV/D396djx47079+fTZs2MXLkSK1J8e3f6Ny9ezebN2/W7j8wMJBly5bx3//+F8hvOr1hwwaqV6/OK6+8QnR0NIMGDSpTjm9mZHHjZg6ZmTlm8dXe8jCXrzebiuSjgOTCmOTDmLnkw2y2RhDFS0xMZNy4cSQlJWFlZcWVK1dITk4mJSUFOzs73N3dAejZsyc1atQodqwdO3bw888/4+fnB0BeXp62K318fDxTpkwBwMnJiZ49e5Yqvp07dxIZGaltwHqrNUxqaiqTJk3iwoULWFpacu3aNc6dO1di65d9+/bRp08fLa5BgwYxe/Zs7fOOHTtq99m8eXMSEhJKFacQQghRVlJMmYmAgAAmTpxIjx49MBgMtGjRosh+dyVRSjFq1CgGDBhQpvMsLS0xGAq2ESjN9adNm0a3bt1YunQpOp2O559//q7jvp2tra1RXKYYUwghhCiMLEB/gDk4OKDX6wFIT0+nXr16AERERGjf8mvUqBEZGRkcPHgQgO3bt3P9+vVix+3WrRthYWFcu3YNgOzsbE6cOAFA27ZttcdtaWlpbN++XTvvscce49ixYwBcvnyZ+Ph47bOuXbvy2WefcWvD/VuP+dLT03n00UfR6XT88MMPXLhwwej+0tMLnx5u164d3377LXq9HqUUGzZsoH379iXmTAghhDA1mZl6gI0cOZLhw4djZ2dHUFAQo0ePpmbNmnh5eVGrVi0AbGxsWLRoEdOmTQPAw8ODOnXqUL160QsC+/Xrx9WrV7WF5EophgwZQpMmTRg9ejSTJk2iV69eODs7a48PAQYOHMiYMWPo06cPjz/+uFE/vaCgIGbPno23tzeWlpZ4eHgwZcoUxo0bx/Tp0wkJCeHZZ5+lcePG2jkvvvgic+fO5bPPPrvjW3ydO3fm5MmTDB48GIBnnnmGUaNGlSufQgghxN14YHrzibun1+u1tUVxcXEEBQURExNT5RtIl+Ri0nVpJ/MXc1lEaiqSjwKSC2OSD2Pmkg9ZgC7YunUrq1atQimFjY0NCxculEKqFKpZWWCwkN81hBBCFE+KqSrA398ff3//Qt/Py8szeq9FixYEBwffq9CEEEKIB54UU1XY7fs2CSGEEOLuyLMeIYQQQohykGLqb+5l8+DIyEjGjBlT4nHbt2/n6NGjJr322rVrWbVqlUnHvKVx48bcuHEDgNdee+2uN8y8ePEiX331ldF75RlPCCGEqAhSTP1NZWweXBHF1JAhQxgxYoRJxyzMJ598QoMGDe7q3N9///2OYqo84wkhhBAVQYqp29zePNjX15fr168THR3NwIED6devH/369WPfvn3a8QcOHMDHxwcfHx9mzpxJ165d+e2334ocPzs7m/fee4/nnnuOF1980ahAOnnyJP/85z/x8/OjT58+2qzR3r172bFjBytXrsTX15dNmzaRnJzMsGHD8Pf3p2/fvsyfP7/Q6y1btsyoxUpaWhqenp7cvHmTkJAQ5s2bB8ChQ4fw8/PD19eXvn37smXLFgCGDRvGzp07tfNvf/3555/Tv39/+vXrx4svvsivv/5aaAzdunXjt99+IyUlBV9fX+1/Hh4eWmzjxo3D398fHx8f/v3vf2ubhQYHB3PmzBl8fX21Gbxb4wFcuHCBl156CR8fH/z8/NizZ4923caNG/Pxxx/Tv39/unfvzv/93/8V+ecihBBClIsSRlxdXZVer9dep6amKoPBoJRS6syZM8rLy0sppVRWVpby8vJS+/fvV0optXXrVuXq6qpOnjxZ5NirV69WL7/8ssrOzlY3b95Ufn5+6u2331ZKKZWenq6ysrKUUkrp9XrVu3dvdfr0aaWUUoGBgSo0NFQbJzMzU4sxOztbDRs2TO3evfuO6/3++++qQ4cOKicnR7v+xIkTlVJKLVmyRM2dO1cppdSbb76poqOjlVJKGQwGde3aNaWUUkOHDlU7duzQxrv9dUpKivb+Dz/8oAYOHFhoDrt27XpHTn755RfVrVs3deHChTvGWrx4sVqwYIFSSqm4uDjl5+dndO7t4w0YMECtW7dOKaXUqVOnlIeHhzaWq6urlrMDBw6ojh073pEfIYQQwhTk23wlMGUD4fj4ePr164e1tTXW1ta88MILHDp0CIDMzEymTZvGyZMn0el0XL58mRMnTvDEE0/cMU5eXh7z58/n8OHDKKW4cuUKJ06coFOnTkbH/eMf/+DJJ59k9+7ddO/enY0bNxIUFHTHeJ6enixfvpyEhAQ6dOhAixYtSszLzz//zIoVK7h27Ro6nY7z58+XeA7AH3/8wZgxY1i4cKH2uC4qKoro6GhycnK4efMmjz/+eInj6PV6fv31V/r37w/Ak08+ydNPP81PP/1Et27dAOjTpw8Abm5uXL58maysLKOefSVJSdFjMMg+U2A+G++ZiuSjgOTCmOTDmLnkQzbtLCdTNhAuzuLFi3F2dmbu3LlYWVkxcuTIIq/zxRdfcP36ddavX4+trS3vvvtukcf6+fmxadMm6tWrR3p6ulH7l1tGjBhBt27diI2NZcaMGXTo0IF33nmnyMbF2dnZjB07ljVr1tCsWTOSkpLuKOQKo9freeONNxg/fjwtW7YE8h+Vrl27lvDwcJycnIiOjmbdunUljlUatwonS8v8/ctzc3PLVEwJIYQQpSFrpv7m9ubBYNoGwm3btiUqKorc3FwyMzO1tUm3rlO3bl2srKz47bffOHDggPaZo6OjUcPf9PR0nJ2dsbW1JSkpiZiYmCKv+dxzz7F//36++OIL/Pz80Ol0dxxz7tw5GjRowODBgxk+fLjWrLhBgwbaz6dPn9bWRWVnZ5Obm8sjjzwCQFhYWLH3DfmFzJgxY/D19aVXr17a+9evX8fR0ZFatWqRnZ1NRESE0X3f/mdxO0dHR55++mk2btwIwJkzZzhx4gRubm4lxiKEEEKYksxM/c3tzYNDQ0NN2kB40KBBnDx5kj59+lC7dm2effZZUlJSABg1ahQTJkxgw4YNNGzYkDZt2mjnvfDCCwQFBfHdd9/x8ssvM2zYMMaOHYu3tzcuLi60a9euyGtWq1aN7t27ExkZWWTRFRoaSnx8PNbW1tjY2DBlyhQgfxuCsWPHEhMTQ9OmTWnatCmQX8iMGTOGAQMGUKtWLZ5//vkS83ro0CH27dtHSkoKmzdvBsDHx4eXXnqJzZs38/zzz1O7dm3c3d21Aq5x48Y0bNgQb29vGjVqxJIlS4zGXLhwIe+99x6rVq3CysqK+fPn4+TkVGIsQgghhClJo+NykAbC5k3WTBUwl3UPpiL5KCC5MCb5MGYu+ZA1UxVIGggLIYQQQoqpcpAGwkIIIYSQYqoCSANh85CRayAvr2If89laW2BZoVcQQghR0aSYEqIIew9fRH+zYlsLPdf2ceyt5dGwEEI8yORvcTNTUY2aS9uUuSwqstmyEEIIca9IMWVmKmOj5qLcq2bLAAaDAfniqhBCiIogj/nMyO2Nmi0sLHj11VdZvXq1VlwFBgZqe1IdOHBAO97T05OYmBhWrFiBq6trkeOnp6fz5ptvkpCQwEMPPcSCBQtwcXEBYOXKlWzdupW8vDxcXFyYMWMGzs7OpKenM2nSJE6dOoWLiwsuLi7UqVOHwMBAQkJCuHnzJoGBgURGRrJlyxZq1KjBqVOnqF69OiEhITg7Oxf7WXHXDgkJ4dSpU+j1ei5dusRXX31FzZo1Kyz/QgghqiYppszI1KlTCQsLIzw8HAcHB9LS0vD29kan03H27FlGjBjBnj17yM7OJiAggMWLF+Pu7s62bdsIDQ0tcfyDBw+yadMmGjVqxNKlS5k1axZLliwhKiqKxMRE1q1bh4WFBWFhYcydO5dFixbx0UcfUaNGDb777juuXr2Kv79/kZt8Hjt2jM2bN/PII48wZcoU1qxZwzvvvFPsZ8VdG+Do0aNERkbe1Wae9tVsUbqKnby1s7PG2cm+Qq9hKs7ORW9IWxVJPgpILoxJPoxVhXxIMWXGTNmkGaB169Y0atQIgIEDB+Lj4wPAjh07+Pnnn/Hz8wPyGzHf2sw0Pj5e21G9Vq1a9OjRo8jxW7VqpbWoadGiBbGxsSV+Vty1ATp16nTXu6LfzMjiRgUvQM/MzHkgNrQzl433TEXyUUByYUzyYcxc8iGbdlZh96pJs1KKUaNGMWDAgHKNc3sTYktLS6O9uor6rKRrOzg4lCsmIYQQoiSyAN3M3N6o2ZRNmiG/v9758+e18dq2bQtAt27dCAsL49q1a0B+I+QTJ04A+T0Lo6KigPymxsU1Zb4bxV1bCCGEuBdkZsrM3N6o2ZRNmiH/Udu8efO4cOGCtgAdoF+/fly9epWhQ4cC+bNFQ4YMoUmTJvz73/8mKCiIXr164ezszDPPPGP0GK68iru2EEIIcS9Io+Mq6l41ac7JycFgMGBra4ter2fIkCEEBQXRvn17k16nIlxMui47oP/FXNY9mIrko4Dkwpjkw5i55EPWTIlC3asmzdevX+e1114jLy+PrKwsvL29H4hCCqCalQUGC/ldQwghRPFkZkoYkSbNBVJS9BgM8n8PMJ/fLk1F8lFAcmFM8mHMXPIhM1OiTKRJc4GKanT8oDzaE0IIUTpSTAlRhIpqdCzNjYUQwrzI3+hCCCGEEOUgxVQVEBISou0xVZk1btyYGzdu3O8whBBCiDKRYqoKWLp0qdbs2Nz9ffG8EEIIUdFkzZSZmz59OgCDBw/GwsKCV199ldWrV2vFVWBgIO3atQPgwIED2vGenp7ExMSwYsUKXF1dCx370KFDzJgxA4PBQG5uLqNGjcLb2xu9Xs+cOXM4efIkWVlZeHp6EhQUhKWlJZ9//jlff/01eXl52NraMm3aNJ5++mltzM8++4yYmBgyMzMJCAjQmiLv2bOHxYsXk5eXh5OTE8HBwTz22GPEx8czc+ZMnnnmGY4fP85//vMfZsyYga+vL7GxsSQnJzNy5EhtU8+yqKhGxw9Sc+PbVYVmpWUh+SgguTAm+TBWFfIhWyNUAY0bN+bQoUM4ODiQlpZGrVq10Ol0nD17lhEjRrBnzx6ys7Pp0aMHixcvxt3dnW3btvHWW28RHR1dZDE1atQo+vbti7e3N0op0tPTqVGjBpMnT6ZNmzb069cPg8HA+PHjadu2LYMGDSI1NVVrPBwbG8sHH3zAunXrtDjffvtt3nrrLc6ePcuQIUP45ptvAOjbty9r1qzhySefZP369axbt47169cTHx/PiBEjCAsLo2XLlkB+i5nnn3+ewMBALl68iI+PD99//32Z+/St/e64LED/i7l8vdlUJB8FJBfGJB/GzCUfsjWCMJKYmMi4ceNISkrCysqKK1eukJycTEpKCnZ2dri7uwPQs2dPatSoUexYnp6eLF++nISEBDp06ECLFi0A2LFjB0ePHuWLL74AIDMzExcXFwB+/vlnVqxYwbVr19DpdFqvv1sGDhwI5PcPbNq0KT/99BM6nY4mTZrw5JNPAtC/f3+mT5+u9SB87LHHtELqlj59+gBQr149atSowZ9//skTTzxxt2kTQgghiiTFVBUTEBDAxIkT6dGjBwaDgRYtWpCVlXVXY40YMYJu3boRGxvLjBkz6NChA++88w5KKZYtW0b9+vWNjs/Ozmbs2LGsWbOGZs2akZSURKdOncp9T/b2dz4ys7W11X62tLSUtVRCCCEqzIP1rEHcFQcHB20WJz09nXr16gEQERGhfcuvUaNGZGRkcPDgQQC2b9/O9evXix333LlzNGjQgMGDBzN8+HCOHTsG5D9mW7lypVbApKamkpiYSHZ2Nrm5uTzyyCMAhIWF3TFmREQEAOfPn+f48eO4ubnh5ubGiRMnOHPmDAAbN26kadOmJm2YLIQQQtwtmZmqAkaOHMnw4cOxs7MjKCiI0aNHU7NmTby8vKhVqxYANjY2LFq0iGnTpgHg4eFBnTp1qF696IWDoaGhxMfHY21tjY2NDVOmTAFg0qRJLFiwAF9fX3Q6HdbW1kyaNIn69eszZswYBgwYQK1atbTF5bfLy8ujX79+ZGRkEBwcTJ06dQCYP38+48ePJzc3FycnJxYsWGDaJAkhhBB3SRagC41er9dme+Li4ggKCiImJqZCGiA/CC4mXZd2Mn8xl0WkpiL5KCC5MCb5MGYu+ZAF6KLUtm7dyqpVq1BKYWNjw8KFC6tsIQVQzcoCg4X8riGEEKJ4UkwJjb+/P/7+/oW+//cF3C1atCA4OPhehSaEEEJUWlJMiRJFRkbe7xDui4xcQ7GP+R7Ex3VCCCFMT4opQUhICG+88QY2Njb3O5RKZe/hi8Vu2vkgbr4phBDC9ORfAlGlevcJIYQQpiYzU1VcRfbuy87OZsaMGfz44484OTnx9NNPc+XKFZYsWcLJkyeZPn06GRkZZGVlMWjQIEaMGAHAxIkTsbGx4fz58yQmJtKzZ0+6du1KSEgIf/75Jy+99BIvvfQSAGfPnmX27NmkpaWRk5PDSy+9RP/+/cnIyCAwMJDTp09jZWVFw4YN+fDDDysylUIIIaooKaaquKlTpxIWFkZ4eLjWu8/b27vQ3n0BAQFGvftCQ0OLHfurr77i0qVLWmPjYcOGUbduXQAeffRRVq1ahY2NDTdu3GDgwIF4eXlpLV9OnTrF//73P/Ly8ujWrRvp6emsWbOG5ORkevXqxYABA7C1tWX8+PEsWLCAJ554Ar1eT//+/XFzc+Ps2bPcuHFD6+137dq1ik2kEEKIKkuKKWHElL374uPj8fX1xcrKCisrK/r27avtsJ6Zmcm0adM4efIkOp2Oy5cvc+LECa2Y6tGjh7aGq2HDhnTu3BkLCwtcXFy0XntKKc6cOUNAQIB2zZycHM6ePUuTJk04c+YM06dPx8PDgy5dupQ5F/bVbFG6op+E29lZ4+x0Zysbc1UVOr+XheSjgOTCmOTDWFXIhxRTwogpe/cVZ/HixTg7OzN37lysrKwYOXKk0XX+3luvsF57Op2O2rVrExUVVeg1tmzZQlxcHHv27OH9998nOjraaJyS3MzI4kYxC9AzM3PMYjO60jCXjfdMRfJRQHJhTPJhzFzyUdKmnbIAXVRY7z4PDw+io6PJzc0lKyuLb7/9VvssPT2dunXrYmVlxW+//caBAwfKHHfDhg2xs7Nj06ZN2ntnzpxBr9fz559/YmlpSY8ePQgKCiI1NZWrV6+W+RpCCCFESWRmSlRY777Bgwdz4sQJ+vbtS+3atWnUqJH22ahRo5gwYQIbNmygYcOGtGnTpsxxW1lZ8fHHHzN79mw+++wzDAYDderU4YMPPuDkyZMsWrQIAIPBwOuvv46Li0uZryGEEEKURHrziVK7m959t87Jzs5m1KhR9OrVi4EDB96rkMtl7XfHZZ+pv5jLVL2pSD4KSC6MST6MmUs+pDefMJm76d338ssvk52dTVZWFu3bt8fPz+8eRVt+Xi3rlbgDuhBCCCEzU6LczLV3X0qKHoNB/u8B5vPbpalIPgpILoxJPoyZSz5kZkpUuKrau08IIYQAKaaEKFJJjY5LSxoiCyGEeZNiSogilNTouLSq0kJ1IYSoiuRveCGEEEKIcpBiysyFhIRoG2+ag2PHjjFu3Lj7HYYQQgihkWLKzC1dupScnPI/qrqXcnNzi/zs2Wef1TbjFEIIISoDWTNlxqZPnw7k70RuYWHBq6++yurVq7XiKjAwkHbt2gFw4MAB7XhPT09iYmJYsWIFrq6uRY6/cuVKtm7dSl5eHi4uLsyYMYPq1aszcOBAxo4dS48ePdi3bx/Tp09nw4YNAMyZM4eTJ0+SlZWFp6cnQUFBWFpaMmzYMJo0acKRI0eoWbMmn3zyCStWrGDLli3odDrs7e0JCwtj//79zJs3j8jISFJSUhg3bhwpKSkAtGvXjkmTJhUZm7Ozc5nyV1Kj49Iyl4bIVaFZaVlIPgpILoxJPoxVhXxIMWXGpk6dSlhYGOHh4Tg4OJCWloa3tzc6nY6zZ88yYsQI9uzZQ3Z2NgEBASxevBh3d3e2bdtGaGhosWNHRUWRmJjIunXrsLCwICwsjLlz57Jo0SI++OADXnnlFR5++GEmT57M0qVLcXR0ZPLkybRp04ZZs2ZhMBgYP348ERERDBo0CIDExETCwsKwsrJi48aN7Nixg7Vr1+Lo6EhaWtodG4RGR0fToEEDVq1aBcC1a9dKjK0sSmp0XFrm0BDZXPaKMRXJRwHJhTHJhzFzyYfsMyU0iYmJjBs3jqSkJKysrLhy5QrJycmkpKRgZ2eHu7s7AD179qRGjRrFjrVjxw5+/vlnbUfzvLw8rdXME088wZgxYxg8eDBBQUE0bdpUO+fo0aN88cUXAGRmZhr1y/Px8cHKKv8/yZ07dzJkyBBtzNq1a98RQ4sWLVi1ahXz5s3Dw8ODjh07lhibEEIIYWpSTFUhAQEBTJw4kR49emAwGGjRogVZWVl3NZZSilGjRjFgwIBCPz9+/DhOTk78+eefRucsW7aM+vXrF3qOvX3ZHoW1bNmSjRs3EhsbS1RUFCtXrmTt2rUlxiaEEEKYkixAN3MODg7o9XoA0tPTqVevHgARERHat/waNWpERkYGBw8eBGD79u1cv3692HG7detGWFiY9mgtOzubEydOALBt2zYOHDjAli1b2LVrF7t379bOWblypdZ6JjU1lcTExELH79q1K2vXrtViT0tLu+OYxMREHB0d6du3L0FBQfzyyy8YDIZiYxNCCCFMTWamzNzIkSMZPnw4dnZ2BAUFMXr0aGrWrImXlxe1atUCwMbGhkWLFjFt2jQAPDw8qFOnDtWrF71osF+/fly9epWhQ4cC+bNOtx7LzZw5k1WrVlGrVi3ef/99Xn/9dcLDw5k0aRILFizA19cXnU6HtbU1kyZNKnSmql+/fiQlJfHiiy9iZWWFvb09X375pdExP/74I6tWrcLCwgKDwcD06dOxsLAoMrYmTZqYIKNCCCGEMWl0LADQ6/XauqK4uDiCgoKIiYm5Y9F3VXIx6bq0k/mLuSwiNRXJRwHJhTHJhzFzyYcsQBelsnXrVlatWoVSChsbGxYuXFilCymAalYWGCzkdw0hhBDFk2JKAODv74+/v3+h799a43RLixYtCA4OvlehCSGEEJWaFFOiWJGRkfc7BCGEEKJSq9rPcYQQQgghyqnSFVOVqTHv/Yxl4sSJrFmzxmTjXbx4ka+++uquz//111/55ptvSnVsZWpGHB8fz/fff3+/wxBCCGHGKl0xVRka895qtFsZYjGV33///a6LqdzcXH799Ve+++67Uh1fmZoR//jjj/zwww/3OwwhhBBmrFKtmfp7Y97Q0FB2795tsua8J0+eZPr06WRkZJCVlcWgQYMYMWIEkD8TZGlpyblz57hx4watWrW6I5Zvv/2WVatWYWNjg8Fg4IMPPuCJJ54o0zVsbGw4f/48f/75J25ubsybNw+dTkdSUhITJkwgOTmZRx99tNhv0u3evZvly5eTnZ2NtbU1QUFBuLm5ER8fz+zZs2nRogWHDx9Gp9Px/vvv88QTTxAcHMzFixfx9fXlscceY8mSJZw9e5bZs2eTlpZGTk4OL730Ev379wegcePGvPXWW+zatQs3NzdiYmLQ6/X4+vrSpk0bpkyZwrhx4zh37hw5OTk0aNCA2bNnU7NmTeLj47VmxBcvXqR///4MHjyY3bt3k5GRwaxZs3B3d9c+GzRoEHv37iUzM5OFCxcSHh7OkSNHsLOzY9myZVqD4qKaF4eEhHDu3DnS09NJTEykQYMGfPjhhyQkJBAeHo7BYCA2Npa+ffvy+uuvl/U/SyGEEKJ4qpJxdXVVer1ee52amqoMBoNSSqkzZ84oLy8vpZRSWVlZysvLS+3fv18ppdTWrVuVq6urOnnyZJFjp6enq6ysLKWUUnq9XvXu3VudPn1aKaVUYGCg8vPzUzdu3CgyllatWqmkpCTt+jdv3izzNQYPHqwyMzNVVlaW6tOnj/r++++VUkq99dZbKiQkRCmlVEJCgnJzc1OhoaF3jH/hwgU1aNAglZ6erpRS6rffflOdO3dWSikVFxenmjZtqn755RellFLLli1TAQEB2md+fn7aODk5OcrPz0+LLT09XT333HPaa1dXV7VixQrt+IiICPX2228bxZKSkqL9vHjxYrVgwYI7rpWYmKhcXV3Vjh07lFJKRUVFqRdffNHos507dyqllPrkk09U69at1fHjx5VSSk2dOlUtXrxYKaXUpk2b1JQpU1ReXp5SSqkvv/xSu7clS5aonj17qmvXrimDwaBefvll9dVXX2mfzZ079448CiGEEKZSqWamCmPK5ryZmZlMmzaNkydPotPpuHz5MidOnNBml3r16lVsf7i2bdsyceJEunbtSpcuXQrdubuka/To0QNbW1sAmjZtSkJCAh06dCA+Pp4pU6YAUL9+fW327e/27t1LQkIC//rXv7T3cnNzuXLlCgANGzbUGgu7ubmxc+fOQsc5f/48Z86cISAgQHsvJyeHs2fParHeahRclKioKKKjo8nJyeHmzZs8/vjjhR5nb29P165dtZjmzZtn9FmXLl0AaNasGXXr1uXpp5/WXsfGxgIlNy/u2LGj9uffvHlzEhISio29NFJS9BgMss8UmM/Ge6Yi+SgguTAm+TBmLvl44DftNGVz3sWLF+Ps7MzcuXOxsrJi5MiRRmOV1Gh36dKlHDt2jLi4OIYPH860adPo3Llzma5xq5ACsLS0vGMPp9Lw8vJi/vz5d7x/5swZbGxstNcWFhba+q+/U0pRu3ZtoqKiirxOcfk4cOAAa9euJTw8HCcnJ6Kjo1m3bl2hxxYX098/u/317flRJTQv/nte7/a/ESGEEKKsKt0C9Nsb84Jpm/Omp6dTt25drKys+O233zhw4ECpY8nNzSUxMZHmzZvz+uuv06FDB3799ddyX+OWtm3bEhERAeTPxu3bt6/Q4zp06MDevXs5deqU9t7Ro0dLHN/R0dEorw0bNsTOzo5NmzZp7505c8bomL+fn55e8NvF9evXcXR0pFatWmRnZ2uxV5S7bV7897iFEEIIU6t0M1O3N+YNDQ01aXPeUaNGMWHCBDZs2EDDhg1p06ZNqWP57LPPmDhxIunp6eh0Oh555JFCv/5f1mvcMnnyZCZMmMCWLVuoV68enp6ehR73+OOPs2DBAiZPnkxmZiY5OTm0atWK5s2bFzt+48aNadiwId7e3jRq1IglS5bw8ccfM3v2bD777DMMBgN16tThgw8+KPT8du3a8fnnn/PCCy/g4eFBYGAgmzdv5vnnn6d27dq4u7tz7NixUt3r3bjb5sU9evRg06ZN+Pr6ygJ0IYQQFeKBbnQszXlFRZI1UwXMZd2DqUg+CkgujEk+jJlLPh74NVPFkea8QgghhLjfHuhiSprzCiGEEOJ+e6CLqaJIc15hChm5BvLyCn/MZ2ttgeU9jkcIIUTlZJbFlLg/QkJCeOONN4y2NzCFX3/9lXPnztGnTx+TjluSvYcvor9ZeDuh59o+jr21PFIWQghRCbdGEA+uiuplWJa+gEIIIcS9JsWUMInb+yr6+voSHR3NwIED6devH/369TPaN+vAgQP4+Pjg4+PDzJkz6dq1K7/99luh46alpbFkyRJiY2Px9fVl5syZQP5WD0uWLMHX15fnn3+e//u//wPg4sWLRttK/P21EEIIYWrymE+YxNSpUwkLCyM8PBwHBwfS0tLw9vZGp9Nx9uxZRowYwZ49e8jOziYgIIDFixfj7u7Otm3bCA0NLXLc2rVrM2bMGHbt2sWSJUuMPrOwsCAqKoqzZ88yZMgQrbWQEEIIcS9JMSUqhCl7KhZl4MCBQP5u+E2bNuWnn36icePGJrsH+2q2KF3hk7d2dtY4OxXffsjcODsXvSFuVST5KCC5MCb5MFYV8iHFlKgQpuypWBZWVlbcvg9tea55MyOLG0UsQM/MzDGLjehKy1w23jMVyUcByYUxyYcxc8lHSZt2ypopYTK39zI0ZU/Fovrr3eoHeP78eY4fP46bmxsPPfQQOTk5XLhwAYAtW7aY5uaEEEKIIsjMlDCZ23sZmrKn4t/7Ak6ZMgWAvLw8+vXrR0ZGBsHBwdSpUwfI73P48ssv4+TkRJcuXSryloUQQogHuzefeDCZoqdi48aNOXToEA4ODhUVJmu/Oy77TP3FXKbqTUXyUUByYUzyYcxc8mHWvfnEg0l6KgohhDAnMjMlKo3K1lPxYtJ1aSfzF3P57dJUJB8FJBfGJB/GzCUfMjMlHhiVradiNSsLDBbyu4YQQojiybMVIYqQkWsgr+TDhBBCVHFSTAlRhL2HL5KVY7jfYQghhKjkpJgSQgghhCgHKabMVEhIiLZRpilFRkYyZswYk403efJkDhw4YLLxhBBCiHtNiikztXTpUnJyCt8jqTKZNWuWNCgWQgjxQJNv85mh6dOnAzB48GAsLCx49dVXWb16tVZcBQYG0q5dOwAOHDigHe/p6UlMTAwrVqzA1dW1yPHT09N58803SUhI4KGHHmLBggW4uLgQEhLCzZs3CQwMBDB6vX37dj788EMsLCzIy8vj3XffxdPTk2HDhjFy5Ei6du3KxIkTsbGx4fz58/z555+4ubkxb948dDoder2eOXPmcPLkSbKysvD09CQoKAhLS0uWLl3Kli1bsLW1RafTsXr1aqytrQkMDOT06dNYWVnRsGFDPvzww4pMuxBCiCpKiikzNHXqVMLCwggPD8fBwYG0tDS8vb3R6XScPXuWESNGsGfPHrKzswkICGDx4sW4u7uzbds2QkNDSxz/4MGDbNq0iUaNGrF06VJmzZrFkiVLij1nyZIlBAcH07JlS/Ly8sjIyCj0uFOnTrFq1Sp0Oh1+fn7ExsbSoUMH5syZQ5s2bZg1axYGg4Hx48cTERHBc889x6pVq/j++++xs7NDr9djZ2fHzp07uXHjBt988w0A165dK3Me7avZYmdnjbOTfZnPNUdVofN7WUg+CkgujEk+jFWFfEgxVQUkJiYybtw4kpKSsLKy4sqVKyQnJ5OSkoKdnZ32mK1nz57UqFGjxPFat25No0aNABg4cCA+Pj4lntO2bVvmzJnDc889R6dOnYqc+erRowe2trYANG3alISEBDp06MCOHTs4evQoX3zxBQCZmZm4uLhQvXp1GjRowIQJE+jYsSNdunTB0dGRJk2acObMGaZPn46Hh8dd9ei7mZFFZmaOWWw4V17msvGeqUg+CkgujEk+jJlLPmTTTkFAQAATJ06kR48eGAwGWrRoQVZWlsmvY2lpicFQsJXA7deYNGkSJ0+eJC4ujrFjx/Lyyy8zaNCgO8a4VUjdGu/WjuhKKZYtW0b9+vXvOGfdunUcOnSIuLg4/P39+fTTT2nSpAlbtmwhLi6OPXv28P777xMdHW00vhBCCGEKsgDdTDk4OKDX64H8NU716tUDICIiQvuWX6NGjcjIyODgwYMAbN++nevXr5c49qFDhzh//rw2Xtu2bQF47LHH+OWXXzAYDOj1enbt2qWdc/bsWRo3bsxLL73ECy+8wLFjx8p0P926dWPlypVacZWamkpiYiJ6vZ7U1FQ8PDwYM2YMrq6unDp1ij///BNLS0t69OhBUFAQqampXL16tUzXFEIIIUrjrmemEhMT0el02j/SonIZOXIkw4cPx87OjqCgIEaPHk3NmjXx8vKiVq1aANjY2LBo0SKmTZsGgIeHB3Xq1KF69eKfb7dq1Yp58+Zx4cIFbQE65D8m/Oabb+jduzf/+Mc/aNasmXbOokWLuHDhApaWltSoUYNZs2aV6X4mTZrEggUL8PX1RafTYW1tzaRJk7C2tubtt98mMzMTpRRNmzblueeeIy4ujkWLFgFgMBh4/fXXcXFxKdM1hRBCiNIodaPjgIAAhg4dSqtWrYiIiGD69OlYWFgwefJkBg4cWNFxigqi1+txdMx/DhwXF0dQUBAxMTFYWMik5cWk61hZ6KpUQ+OimMu6B1ORfBSQXBiTfBgzl3yYbM3Uvn37mDt3LgCrVq3iiy++oEaNGvz73/+WYuoBtnXrVlatWoVSChsbGxYuXCiF1F+qWVlgMEijYyGEEMUrdTGVk5ODjY0NSUlJXL16ldatWwNw5cqVCgtOVDx/f3/8/f0Lff/W+qRbWrRoQXBw8L0KTQghhHgglLqYevrpp1mxYgW///679jXzpKQk7RGRMC+RkZH3O4T7LiPXQF6eaWembK0t5LGhEEKYmVIXU7NmzeLDDz/EysqKCRMmAHD48OFS7TEkKq+QkBDeeOMNbGxs7ncolc7ewxfR3zRtS57n2j6OvbU8RhVCCHNS6gXowjw1btyYQ4cO4eDgcL9DqXTWfndciqm/mMsiUlORfBSQXBiTfBgzl3yYbAG6Uor169fz9ddfk5qaSnR0NPv37yc5OZk+ffqYJFhxb1VkD7/IyEi2bNlCjRo1OHXqFNWrVyckJARnZ+die/iFhIRw9uxZ9Ho958+fp1mzZrz++uvMnTuXS5cu0bNnT+28YcOG0aRJEw4fPsy1a9fo3bs3AQEBHD16lEmTJrFlyxYtnhdeeIFp06bRqlWrikmmEEKIKqvUvyJ/+OGHbNiwgUGDBvHHH38AULduXT799NMKC05UrKlTpwIQHh5OVFQUHTt2ZN26dWzatInFixdrRcutHn5Tp04lOjoaT09PLl26VOL4x44dIzAwkK+//ponn3ySNWvWlCquX375hcWLF/Pdd99x9uxZFi1axKeffsrmzZvZtGmTtmEowJkzZwgPD2fTpk3s3LmTnTt30rx5c+zt7fnxxx+B/ELQwsJCCikhhBAVotQzUxs3bmTjxo04OTlpmzzWq1ePxMTEiopN3GOm7uHXqlUrHnnkESD/m4CxsbGliqNjx47axqGNGzemSZMm2NjYYGNjQ8OGDUlISODxxx8HoF+/flhZWWFlZUWfPn2Ii4uja9euDBs2jLCwMDw8PPjyyy/517/+VeZ82FezRelM+0juQW6cXBWalZaF5KOA5MKY5MNYVchHqYupvLw8bV2NTqcD4MaNG9jbP5j/MIg7mbqHX1F99orr4VfYeUWNU5xevXqxePFijh8/Tnx8PLNnzy5z/Dczsrhh4jVTD2rjZHNZ92Aqko8Ckgtjkg9j5pKPktZMlfrX7k6dOjFnzhytr5tSig8//JCuXbuWP0px31RkD7+iFNfDr6w2b95Mbm4uN2/e5Ntvv9X6BFpbW9O/f39GjRqFj48P1apVu+trCCGEEMUp9czUpEmTmDhxIq1btyY3N5eWLVvSoUMH5s2bV5HxiQpWkT38ilJcD7+yatSoEYMHD9YWoN9e3A8cOJClS5cyZMiQux5fCCGEKEmptkbIy8tj48aN+Pj4oNfr+f3333nkkUdwdna+FzGKSqAy9vAbNmwYI0eOLHJ2NCoqiq+//pqVK1fe1fiyNUIBc5mqNxXJRwHJhTHJhzFzyYdJtkawtLRk7ty5DBgwAFtbW+rUqWOyAMWD4UHr4ffKK6+QkJDA8uXL73oMr5b1KmQHdCGEEOal1Jt2/ve//6V3795069atomMSDxBz7uGXkqKXRsd/MZffLk1F8lFAcmFM8mHMXPJhsk07s7KyGDNmDC1btqRu3braN/oA5s+fX74oxQNLevgJIYSo6kpdTLm6uha527UQ5qgiGh3/nTQ+FkKIB1+pi6m33nqrIuMQotKpiEbHf/egLkgXQghRoNTF1L59+4r87Fb/NiGEEEKIqqbUxdTkyZONXqelpZGTk4OLiwsxMTEmD0wULSQkhDfeeAMbG5v7HUqhVq1ahY+PT6m+9fn3psdF2b59Ow8//DDNmzcHID4+nnnz5hEZGUlSUhLjx48nNDQUyG9Bc+jQIW3HfiGEEKIilbqY2rFjh9HrvLw8li9fLv9g3QdLly5l5MiRlbaYWr16Ne3btzfpFhrbt2/nmWee0Yqp27m4uGiFlBBCCHGvlbqY+jtLS0vefPNNOnfuzMsvv2zKmEQxpk+fDsDgwYOxsLDg1VdfZfXq1eTk5K/tCQwM1B67HjhwQDve09OTmJgYVqxYUeQXCSIjI9myZQvVq1fn5MmTuLi48O677zJv3jwSEhJ45plnWLhwITqdjujo6EKvu3z5ci5fvsyYMWOwtbVl0aJFfPvtt5w+fZq0tDQuX77MU089xezZs+/YQT0vL4+FCxeyd+9eALy8vBg/fjyxsbHs2LGD2NhY1q9fz8svv6w1UAa4ePEi/fv3Jz4+Xnvvs88+IyYmhszMTAICAnj++efLnOuKaHT8dw9S4+Oq0Ky0LCQfBSQXxiQfxqpCPu66mAL44YcfjLZIEBVv6tSphIWFER4ejoODA2lpaXh7e6PT6Th79iwjRoxgz549ZGdnExAQwOLFi3F3d2fbtm2lmr05duwY0dHR1K1blzfeeINx48YRGhqKvb09fn5+7Nu3j/bt29OxY8dCrztq1CjWr1/PkiVLjIq2gwcPsmnTJh566CGCgoJYtmzZHY/2vvrqK3799Vdtu4XXXnuNr776in/+859069aNZ555hqFDhwIYFU6FsbCwICoqirNnzzJkyBDc3d3LPFNWEY2O/+5BaXxsLnvFmIrko4Dkwpjkw5i55MNk+0x17tzZqHDKyMggOzub9957r3wRinJJTExk3LhxJCUlYWVlxZUrV0hOTiYlJQU7Ozvc3d2B/H54NWrUKHG8Vq1aUbduXQCefvppHn30Ue28Jk2acOHCBdq3b1/kdYtqMdSlSxceeughAAYMGMDMmTPvOGbfvn34+flpjy/9/f3Zvn07//znP8ucl4EDBwL5vfuaNm3KTz/9RPfu3cs8jhBCCFGSUhdTCxYsMHpdrVo1GjZsqPVrE/dHQEAAEydOpEePHhgMBlq0aEFWVtZdj2dra6v9bGlpecfrW7udm/q6QgghxIOq1AtCjh07hoeHh/a/Z599FkdHR7744ouKjE8UwsHBAb1eD0B6ejr16tUDICIiguzsbCB/RiYjI4ODBw8C+Qu4r1+/brIYirrurfjS042ndXft2kVqaiqQvzarbdu2d4zZrl07Nm3aRE5ODjk5OWzatIn27dsD4OjoeMeYxYmIiADg/PnzHD9+HDc3tzLdnxBCCFFapZ6Z+uijj3jllVfueH/58uWyAP0eGzlyJMOHD8fOzo6goCBGjx5NzZo18fLyolatWgDY2NiwaNEipk2bBoCHhwd16tS5Y9H33SrqugDDhw9n0qRJ2NnZsWjRIgDc3d155513SEpK4sknn2TixIl3jPniiy+SkJCAn58fAB07dmTQoEEAvPDCCwQFBfHdd9/dsQC9MHl5efTr14+MjAyCg4OlObcQQogKU2Kj41ubdY4aNYqPP/6Y2w+/ePEiy5YtY+fOnRUbpbgrer1eewwbFxdHUFAQMTExWFjc2x23S7uXVGVzMem6tJP5i7ksIjUVyUcByYUxyYcxc8lHuReg39qsMysri0mTJmnv63Q6nJ2dmTJlignCFBVh69atrFq1CqUUNjY2LFy48J4XUg+yalYWGCwqtpgSQgjx4CtxZuqWCRMmMH/+/IqOR9wD/v7+2kLyW1q0aEFwcPB9iqhySknRYzBIMQXm89ulqUg+CkgujEk+jJlLPkqamSp1MSVEVXMvHvM9KOzsrMnMrNg9tx4kko8Ckgtjkg9jlSEfplhOYbJ9pvR6PSEhIezfv5+0tDSjtVO7du0qV5BCVEZ7D19EX8Gbdj4oHBxsuXFDtr64RfJRQHJhTPJhrDLk47m2j2NvXbFLXEo9+rRp0zh+/DijR4/m6tWrTJkyhUceeYQRI0ZUYHiiIoSEhBhtZfAgmThxImvWrLnfYQghhBCaUhdTP/zwA0uWLKFHjx5YWlrSo0cPPvjgA6KioioyPlEBli5dqvXUE0IIIUT5lPoxn8Fg0PYosre3Jz09HWdnZy5cuFBhwQnTq+hGydHR0dja2pKQkMBDDz3EggULcHFx4eTJk0yfPp2MjAyysrIYNGiQNqs5ceJEbGxsOH/+PH/++Sdubm7MmzcPnU5HUlISEyZMIDk5mUcffdTo24hFNVs2GAwEBwcTFxeHjY0N9vb2hIeHV0g+hRBCiFIXU02aNGH//v20a9cOd3d3pk2bhoODA48//ngFhidMraIbJd9qaNyoUSOWLl3KrFmzWLJkCY8++iirVq3CxsaGGzduMHDgQLy8vHjiiScAOHXqFKtWrUKn0+Hn50dsbCwdOnRg5syZtGnThrfeeovExEReeOEFvLy8AIpstnzixAni4+P55ptvsLCw4Nq1a3eVK/tqtiidbCVxi4ODbckHVSGSjwKSC2OSD2P3Ox92dtY4O9lX6DVKXUzNnDlTW3Q+efJkFi9ezPXr12W7hAecqRslt27dmkaNGgH5zYZ9fHwAyMzMZNq0aZw8eRKdTsfly5c5ceKEVkz16NFD6wPYtGlTEhIS6NChA/Hx8dpeZvXr19dmzYqLvX79+uTm5jJ58mQ8PT3p2rXrXeXmZkYWN2QBOlA5FpFWJpKPApILY5IPY5UhH5mZOeXensFk3+arX7++9nOdOnWYNWtWuQITlcO9ali8ePFinJ2dmTt3LlZWVowcOdLoOkU1VL6b2J2dnfn666+Jj48nNjaWhQsXsnHjRpydnU1+X0IIIUSpn2EopVi3bh3Dhw/XZhv279/PN998U2HBiYpRkY2SDx06xPnz57XxbjU0Tk9Pp27dulhZWfHbb79x4MCBUsXatm1brWlxYmKi1t6ouNhTU1PJyMjAy8uL8ePHU716dRITE0t1PSGEEKKsSj0z9eGHHxIbG8tLL73E1KlTAahbty5z5syhT58+FRagML2KbJTcqlUr5s2bx4ULF7QF6JDf23HChAls2LCBhg0b0qZNm1LFOnnyZCZMmMCWLVuoV68enp6e2mdFxf7HH3/w7rvvkpubS15eHp06dcLNza1MORJCCCFKq9Q7oHfu3JmNGzfi5OREmzZt2L9/P0opPDw82L9/f0XHKe6DsjZKjoyMZNeuXSxZsuRehllhZAf0ApVhF+PKRPJRQHJhTPJhrDLko1LtgJ6Xl4eDgwOQ3+QY4MaNG9jbV+wKeXH/VPVGydLouICzk71Z9NcyFclHAcmFMcmHsaqSj1IXU507d2bOnDlMmjQJyF9D9eGHH971N6VE5efv74+/v3+h7xfVKLmw44UQQghzVmIxlZycjLOzM0FBQQQGBuLu7k5OTg4tW7akQ4cOzJs3717EKSqRyMjI+x3CPZGRayjTYz5TTCULIYR48JRYTD3//PMcOnQIR0dHPvroI1577TXefvttHnnkEfmquTBrZW10fC+aaQohhKh8Siym/r4+/ciRIzRv3rzCAhJCCCGEeJCU+Gv0rcXmovxCQkK0vZBE2cXExMhjZSGEEJVOiTNTeXl5xMXFaTNUubm5Rq8BoxYfomhLly5l5MiR2NjY3LcY8vLysLQs/8oeU41TFt27d6d79+739JpCCCFESUrcZ6pbt27FD6DTERMTY9KgzNH06dMJCwvD1dUVCwsLXn31VVavXk1OTv6anMDAQK0oPXDgANOnTwfA09OTmJgYVqxYgaura6FjR0ZGEh0dja2tLQkJCdpmmS4uLkRGRrJ582YcHBy4cOECCxYsIDk5mcWLF5OXl4eTkxPBwcE89thjALz//vt888031KpVCw8PD/bt20dkZCTx8fHMnDmTZ555huPHj/Of//wHvV5f5D1069YNHx8f4uLiSEpKYty4caSkpLBlyxauXbvG7NmzadOmDRcvXqR///4MGjSIvXv3kpmZycKFCwkPD+fIkSPY2dmxbNkynJ2djfaxio+PZ/bs2bRo0YLDhw+j0+l4//33tV5/Rd1HWUTtPsONMuyP0r1NA1wquJmmEEKIyqfEmakdO3bcizjM3tSpUwkLCyM8PBwHBwfS0tLw9vZGp9Nx9uxZRowYwZ49e8jOziYgIIDFixfj7u7Otm3bCA0NLXH8gwcPsmnTJho1asTSpUuZNWuWtnnmkSNHiIqKokGDBqSkpPDyyy+zZs0annzySdavX8/48eNZv349O3bsYOfOnURFRWFnZ8eYMWOMrnH69GmCg4Np2bIlQJH3cEt2djZfffUVR48eZfjw4fz3v/9lw4YNfPPNNyxevJi1a9cCcPXqVVq3bs24ceP49NNPGTFiBKGhocycOZNp06axZs0a3nnnnTvu+fTp08yZM4fg4GCWL1/OsmXLWLRoUYn3UVplbXRsimaalZWzc3Wzvbe7IfkoILkwJvkwZi75KGnTTvnq0X2SmJjIK6+8Qt++fXnnnXe4cuUKycnJnD17Fjs7O9zd3QHo2bMnNWrUKHG81q1b06hRIwAGDhxIXFyc9lmrVq1o0KABkF9YNWnShCeffBKA/v378+uvv6LX64mPj6d3797Y29tjYWFBv379jK7x2GOPaYVUcfdwy602Q82aNSMjI4PevXsD8Mwzz5CQkKAdZ29vT5cuXbRj69aty9NPP629vv3Y2zVs2JCmTZsC4ObmpvXfK+k+hBBCCFMq9aadwrQCAgKYOHEiPXr0wGAw0KJFC7KysirkWrd2ri+vv+92X9I92NraAmhrq269trCwIDc3Vzvu9jVkFhYWRq8tLS3v2CC0qPNuH1MIIYS4V2Rm6h5ycHBAr9cDkJ6eTr169QCIiIjQvuXXqFEjMjIyOHjwIADbt2/n+vXrJY596NAhzp8/r43Xtm3bQo9zc3PjxIkTnDlzBoCNGzfStGlTHB0d8fDw4P/+7//IyMjAYDCwefPmYq9Z1D3cb2W9DyGEEKI8ZGbqHho5ciTDhw/Hzs6OoKAgRo8eTc2aNfHy8qJWrVpA/mzLokWLmDZtGpBfGNSpU4fq1asXO3arVq2YN28eFy5c0BagF8bJyYn58+czfvx4cnNzcXJy0o7t3r07hw8f5oUXXqBmzZq4ublx7dq1Iq9Z1D3cb2W9DyGEEKI8Svw2n7j39Ho9jo75C93i4uIICgoiJiamyCbDt3/LzVTXNhgMTJ48mYcffrjQxd+VnSnu42LSdWkn8xdzWURqKpKPApILY5IPY+aSj5IWoMvMVCW0detWVq1ahVIKGxsbFi5cWGQhZWqBgYH8/vvvZGZm0qxZM1577bV7cl1TM8V9VLOywGAhv2sIIYQonsxMPUD8/f3vWIzdokULgoOD71NE5k1mpgqYy2+XpiL5KCC5MCb5MGYu+ZCZKTNS1k0nRflIo2MhhBClIX/zi3KTnoNCCCGqMimmRLktXbpUaykjhBBCVDXymE+Uy60egoMHDzZ5z8Hs7GxmzJjBjz/+iJOTE08//TRXrlxhyZIlxfYjDAkJ4ebNmwQGBgLc8VoIIYQwJSmmRLlUZM/Br776ikuXLvH111+Tl5fHsGHDqFu3rvZ5cf0IhRBCiHtFiilhUomJiYwbN46kpCSsrKy0fn0pKSll7jkYHx+Pr68vVlZWWFlZ0bdvX21neLizH6GPj49J78W+mi1KV/on4XZ21jg72Zd84APK2bn4jWOrGslHAcmFMcmHsaqQDymmhEndy56DRbG0tMRgMGiv7/b6NzOyuFGGb/NlZuaYxVeAC2MuX282FclHAcmFMcmHMXPJR0lbI8gCdFFuFdVz0MPDg+joaHJzc8nKyuLbb781+ryofoSPPfYYv/zyCwaDAb1ez65du0x1q0IIIcQdZGZKlFtF9RwcPHgwJ06coG/fvtSuXVt7pHdLUf0Ie/bsyTfffEPv3r35xz/+QbNmzSrkvoUQQgiQHdDFPVTWnoO3n5Odnc2oUaPo1asXAwcONGk/wqKs/e64bNr5F3OZqjcVyUcByYUxyYcxc8mH7IAuKo276Tn48ssvk52dTVZWFu3bt8fPz+8eRQteLeuVuZ2MEEKIqkdmpsR9V1l7Dqak6DEY5P8eYD6/XZqK5KOA5MKY5MOYueRDZqZEpSc9B4UQQjzIpJgSoggZuYYyPeYzZ0mpN8nMMRT5ua21BZb3MB4hhKhMpJgSogh7D18s0wJ0c+bgYMuNG0Xv12XOi++FEKIk8refEEIIIUQ5SDElhBBCCFEO8phP3OHw4cPMnz+fGzduADBhwgRq1KjBrFmzuHnzJvb29kyePJnmzZtz8eJF+vfvz6BBg9i7dy+ZmZksXLiQ8PBwjhw5gp2dHcuWLcPZ2ZnIyEiio6OxtbUlISFB22jTxcWlyFi6deuGr68vsbGxJCcnM3LkSIYOHQrAvHnz+PHHH8nJyaF27drMnj2bRx99VItp8ODB7N69m4yMDGbNmqX1BRRCCCFMSbZGEEauXr1K3759CQkJoVWrVuTl5ZGWlsaAAQOYM2cO7dq1IzY2lkmTJrF161YuX75M9+7dWbFiBV26dOHTTz/l448/JjQ0lKeffppp06ZRs2ZN3nnnHSIjI5k2bRqbNm2iUaNGLF26lN9++63YjTe7devG888/T2BgIBcvXsTHx4fvv/8eBwcHUlNTcXJyAmD9+vXExsby/vvvc/HiRbp3787HH39M165d2bx5M2FhYYSHh5cpF1G7z3AjU9ZMlUb3Ng1wMeMmz0IIURyZmRJGfvrpJ5544glatWoF5DcNTklJwdramnbt2gHQvn17rK2tOXfuHA4ODtjb29OlSxcAmjVrRt26dXn66ae117Gxsdr4rVu31trCDBw4EB8fnxJj6tOnDwD16tWjRo0a/PnnnzzxxBPs2bOHsLAwbt68SW5urtE59vb2dO3aFQA3NzfmzZtX5lyUtdGxOStpAbo5N3kujLnsnWMKkgtjkg9j5pIP2WdKVDgbGxvtZwsLC6PXlpaWd2zIWVa2trZ3jPf7778zZ84cNmzYQP369Tl06BDjx48vMqa/F1tCCCGEqcgCdGHEzc2NM2fOcPjwYQDy8vKoU6cOOTk5xMXFAbBv3z5yc3Np2LBhmcc/dOgQ58+fByAiIoK2bdveVZx6vR5ra2ucnZ0xGAxlfoQnhBBCmIrMTAkjtWrVIiQkhLlz53Lz5k0sLCwIDAxkyZIlRgvQP/zwQ6PZn9Jq1aoV8+bN48KFC9oC9LvRuHFjevXqRZ8+fahduzadO3fmwIEDdzWWEEIIUR6yAF3cM5GRkezatavYBeeVydrvjsumnX+RTTuNmcs6EFOQXBiTfBgzl3zImikh7pJXy3rSTuYvdnbWZBbzzUbbKlRICSHE30kxJe4Zf39//P3973h//fr1rFmz5o73586dq30r8H6oZmWBwUKKKQBnJ3uz+O1SCCEqghRT4r4bOHAgAwcOvN9h3KEsjY6l0a8QQlRdUkwJUYSyNDquamuGhBBCFJC//YUQQgghykFmpkSpDBs2jEuXLuHo6EhmZia+vr6MHj36fodVpMaNG3Po0CEcHBzudyhCCCHMnBRTotSmTJlC165duXz5Mn369KFDhw60aNHivsRiMBjQ6XTodLr7cn0hhBDiFimmKrHDhw8zf/58bty4AcCECROoUaOG0eaZkydPpnnz5ly8eJH+/fszaNAg9u7dS2ZmJgsXLiQ8PJwjR45gZ2fHsmXLcHZ2JjIykujoaGxtbUlISNA2z3RxcSlVXA8//DANGzbk0qVLtGjRgsuXLzNz5kwuXbpEVlYWffv25c0338RgMBAcHExcXBw2NjbY29trO5Xv3r2b5cuXk52djbW1NUFBQbi5uZGcnExAQAA3btwgKyuLzp07M2HCBABCQkI4deoUer2eS5cu8dVXX3Ho0CFCQkLIzc3FwsKCuXPn0qRJEwBCQ0PZtm0bV69eZcKECTz//PMV8KckhBCiylOiUkpLS1Pt27dXBw8eVEoplZubq5KTk1Xnzp1VbGysUkqpH374QXXu3FllZWWpxMRE5erqqnbu3KmUUuqTTz5RrVu3VsePH1dKKTV16lS1ePFipZRSERER6tlnn1VnzpxRSikVEhKi3n777WLjGTp0qNqxY4dSSqmzZ8+qHj16qJSUFKWUUiNGjFA//vijUkqprKwsNWTIEPX999+rX375RfXq1Uvl5eUppZS6evWqUkqpCxcuqEGDBqn09HSllFK//fab6ty5s1JKqczMTKXX65VSSmVnZ6thw4ap3bt3K6WUWrJkiercubN23bNnz6r27durc+fOade+Naarq6sKDQ1VSil14MAB1bFjxzJkP9+mXafVl9/9Wqr//Zlyo8zjCyGEMA8yM1VJ/fTTTzzxxBO0atUKyG/wm5KSgrW1Ne3atQOgffv2WFtbc+7cORwcHLC3t6dLly4ANGvWjLp162r7NDVr1ozY2Fht/NatW9OoUSMgf2sCHx+fEmOaOXMmCxcu5OzZswQGBuLk5MTNmzf58ccfSU1N1Y67ceMGZ86cwc/Pj9zcXCZPnoynpyddu3YFYO/evSQkJPCvf/1LOyc3N5crV65gb2/P/PnzOXz4MEoprly5wokTJ+jUqRMAnTp1wsnJCYDY2Fg6derE448/DuQ3N769xU2fPn2A/H6Dly9fJisry6hpckluZmRxo5Tf5svMzDHrfZjMZRdjU5F8FJBcGJN8GDOXfMgO6FXI7YWEhYWF0WtLS0vy8vLKNf6tNVMHDx5k5MiRtGvXjkcffRSdTseGDRuwtra+45yvv/6a+Ph4YmNjWbhwIRs3bgTAy8uL+fPn33H8Rx99xPXr11m/fj22tra8++67ZGUVtDEpy4LyW4WTpWX+DlC5ubllKqaEEEKI0pCtESopNzc3zpw5w+HDhwHIy8ujTp065OTkEBcXB8C+ffvIzc2lYcOGZR7/0KFDnD9/HoCIiAjatm1b6nNbt27NP//5Tz788EMcHR1p3bo1K1eu1D7/448/SE5OJjU1lYyMDLy8vBg/fjzVq1cnMTGRDh06sHfvXk6dOqWdc/ToUQDS09NxdnbG1taWpKQkYmJiioyjQ4cO7NmzR7uP7Oxs9Hp9GbIghBBClJ/MTFVStWrVIiQkhLlz53Lz5k0sLCwIDAxkyZIlRgvQP/zwQ6MZqNJq1aoV8+bN48KFC9oC9LIYNWoUPXv25Pjx4yxcuJA5c+ZojwodHByYNWsWmZmZvPvuu+Tm5pKXl0enTp1wc3PDwsKCBQsWMHnyZDIzM8nJyaFVq1Y0b96cYcOGMXbsWLy9vXFxcdEeaRbm8ccfZ8aMGbzzzjvk5eVhaWnJ3Llzady4cZnzIYQQQtwtnVJKmo9VMZGRkezatYslS5bc71AqtYtJ16WdzF/MZd2DqUg+CkgujEk+jJlLPmTNlBB3SRodCyGEKA0ppqogf39//P3973h//fr1rFmz5o73586dq30rUAghhBDGpJgSmoEDBzJw4MD7HYYQQgjxQJFv8wkhhBBClIMUU2Zu2LBhdO/eHV9fX55//nmWLVt2z6597Ngxxo0bB8D169f55JNP7tm179c1hRBCVD1STFUBU6ZMISoqitDQUD7//HOOHDliknFzc3OL/fzZZ59l0aJFQH5h8+mnn5rkuqV1P64phBCi6pE1UxXgQW9QDHDmzBlmzZpFcnIyACNHjsTPz49hw4bRpEkTjhw5Qs2aNXn11VeZN28ekZGRAMTHx2uvb/85ODiY9PR0fH19qVatGuHh4QwbNoxmzZpx9OhRfv/9d4YPH46Liwtr1qzh8uXL/Pe//6V3794AHDlyhIULF2o5HTNmDF26dNHyN3jwYHbv3k1GRgazZs3C3d290GsKIYQQJnefewOaHXNoUJyTk6Oee+459c0332jjpKamauO98cYbKicnRymlVFxcnPLz89OOu/317T8nJiYqDw+PO2IbO3asysvLU3/++adq3ry5dq9HjhxRXl5eSimlrl27pnx9fVVSUpJSSqmkpCTl5eWlrl27puXv1j1GRUWpF198schrCiGEEKYmM1MmZg4Nih9++GFyc3O1WSGA2rVraz/7+PhgZWWa/3R69eqFhYUFLi4u1KpVix49egD5952UlERWVhaHDx/m4sWLvPbaa9p5Op2OCxcuULt2bezt7bUmym5ubsybN88ksaWk6DEYZJ8pMJ+N90xF8lFAcmFM8mHMXPIhm3Y+ACpbg+Lbe+YVxt7e3ig+ddsm+rc3JS6N2xsPW1paFtqcWClF48aN+fLLL+84/+LFi3fkr6S1XEIIIYQpyQJ0EzOHBsUNGzbEysqKb7/9VvssLS2t0DHr169PYmIi165dQynF119/Xehxjo6OZGZm3lWh07JlSy5cuKDlD/IbI6sSOiGV55pCCCFEacnMlImZQ4NiZ2dnli1bRnBwMMuWLUOn0zFy5Ej69et3x3guLi68/PLL+Pv789BDD9GmTZtCZ7Zq1aqFj48PPj4+1KxZs0yLwWvWrMmyZctYsGABs2fPJicnh/r16/Pxxx8Xe155rimEEEKUljQ6foBIg+J7S9ZMFTCXdQ+mIvkoILkwJvkwZi75KGnNlDzmE0IIIYQoB3nM9wCRBsVCCCFE5SPFlBmQBsVCCCHE/SOP+YQQQgghykGKKSGEEEKIcpDHfKLMhg0bxqVLl3B0dCQrK4shQ4bw0ksv3e+wCrV9+3Yefvhhmjdvfr9DEUIIYaakmBJ35dau6n/88Qfe3t54enrSpEkT7XODwYBOp0On093HKPOLqWeeeUaKKSGEEBVGiqkHxOHDh5k/fz43btwAYMKECdSoUcNoI9DJkyfTvHlzLl68SP/+/Rk0aBB79+4lMzOThQsXEh4ezpEjR7Czs2PZsmU4OzsTGRlJdHQ0tra2JCQkaBuBuri4lCquRx55hIYNG3Lu3Dm2bdvGqVOn0Ov1XLp0ia+++oqdO3fy2WefAdCgQQOCg4OpU6cOhw4dYsaMGRgMBnJzcxk1ahTe3t5MnDgRKysrTp8+TVpaGm3atOG9997DxsYGvV7PnDlzOHnyJFlZWXh6ehIUFISlpSVJSUnMnDlT2x3e29ubpk2bsmPHDmJjY1m/fj0vv/xyoRuPCiGEEOVyP7ssi9JJS0tT7du3VwcPHlRKKZWbm6uSk5NV586dVWxsrFJKqR9++EF17txZZWVlqcTEROXq6qp27typlFLqk08+Ua1bt1bHjx9XSik1depUtXjxYqWUUhEREerZZ59VZ86cUUopFRISot5+++1i4xk6dKjasWOHUkqpU6dOqZYtW6ozZ86oJUuWqM6dO6uUlBSllFInT55UHTp0UElJSUoppd5//301duxYpZRSb775poqOjlZKKWUwGNS1a9eUUkoFBgYqb29vpdfrVU5Ojnr55ZdVaGioUkqpSZMmqY0bNyqllMrLy1PvvPOO+uqrr7SYPvnkEy3GWzEEBgZq5wshhBAVQWamHgA//fQTTzzxBK1atQLymwCnpKRgbW1Nu3btAGjfvj3W1tacO3cOBwcH7O3t6dKlCwDNmjWjbt262p5TzZo1IzY2Vhu/devWNGrUCMjfZuFWe5nizJw5kw8++ABbW1uCg4O18zt16oSTkxMA8fHxdO7cmYcffhiAwYMH4+vrC4CnpyfLly8nISGB/2/vvsOiuraHj39nkCIgKqhookYxAWOHKIixhRglKqJgTaKvYjTqzSW2iC2xhliwYostJjYsNMHyM3Yjilflil41MRaUqNgFRGCYmfcP4tERaYKisD7Pk+dhzpyyz2KCa/beZ68PP/yQhg0bKudu3749FhYWAHTu3JmdO3fyxRdfsGfPHmJjY/n5558BSE1NxdbWlocPHxITE6NsB5Q2FISsgP5EcVnFuLBIPJ6QWBiSeBgqLvHIbQV0SaaKqafr/qnVaoPXRkZGaLXaAp3/8ZypZz1OgnLTt29f3NzciIqKYsqUKXz44YcMGzYsx2P0ej2LFi2iWrVqBtsfD30KIYQQRUGWRngDNGrUiAsXLhATEwOAVqvFxsYGjUbDkSNHADh8+DAZGRnUrFkz3+c/ceKEMtcoODiYpk2bFkq7XVxc2L9/P7du3QJg48aNNGvWDIBLly5RvXp1evbsSZ8+fTh16pRy3I4dO0hJSSEjI4Pw8HClPW5ubixdulRJBO/evcvVq1exsLDA0dGRVatWKee4e/cuAJaWliQlvfnfioQQQry+pGfqDVCuXDkCAwOZNm0aKSkpqNVq/Pz8mD9/vsEE9Hnz5hn0QOWVk5MT06dPJy4uTpmAXhjs7e0ZOXIkPj4+AFSrVo3JkycDsHr1aqKjozE2NsbExITx48crx9WvXx8fHx/u3r2Ls7Mz3bt3B2Ds2LHMnDkTT09PVCoVxsbGjB07lmrVqhEQEMCkSZPo2LEjarWajh07MnDgQDp16sSYMWPYsWOHTEAXQgjxUqj0er1MCinBQkJC2LdvH/Pnzy/qpgAwevRo6tWrxxdffFHUTZE5U08pLvMeCovE4wmJhSGJh6HiEo/c5kzJMJ8QQgghRAFIz5R4rk2bNrFmzZos26dNm6Y8FVjcSc/UE8Xl22VhkXg8IbEwJPEwVFziIU/ziRfSrVs3unXrVtTNKFKPMnRotSUrmTI1VmNU1I0QQog3jCRTQmTjYEw8ySmaom7GK9W2aQ3MjWX0Xwgh8kP+apZgvXv35uOPP8bT0xN3d3d++eUXIHOxTS8vryJuXeFyc3Pjzz//LOpmCCGEKIakZ6qEe17B4lcpIyODUqXkYyiEEOLNJf+KvWJvQsHip0uxZGRk8NVXX3Hv3j3S0tJo0KABkyZNwsTEhI4dO+Lv70+DBg0A+Pnnn7l48SJTpkzh2LFjTJo0CchcvHP37t389NNP2Nvb4+bmRvv27Tly5Aj29vaMGzeOqVOnKgt3enp6MmDAACCzR2nJkiXY29tnee3m5oanpydRUVHcunULHx8fZUmFp6/fpEkT5DkLIYQQL4skU6/Q/fv3+frrrwkMDMTJyQmtVsu9e/fo2rUrP/74I66urkRFReHr68vOnTuVYz744ANGjBjB8uXL6du3L6tXr2bq1KlMnDiRNWvWKGVYjh8/TlhYGHZ2dixYsIAffvghz+tH/fXXX1y8eBEHBwdlxXLILD0TEBBA+fLl0ev1+Pn5ERwcTK9evfj8889Zv349DRo0QK/Xs379eubPn096ejrDhw9n9uzZNG7cmN9++43Vq1cbXC85OZnNmzcDMHPmTHQ6HRERETx8+JAePXpgb29Pq1atcm13amoqGzZsID4+Hg8PD7p06YKxsTHDhg0jICAAFxcXtm3bxtq1a/MUh6eZlzZFrypZI+FmZsZUtDZ/7nsVK5Z5xa15vUk8npBYGJJ4GCoJ8ZBk6hV6UwoWP51M6XQ6Vq5cyYEDB9DpdDx48AAzMzMgswdp4cKF3L9/n9jYWGxsbKhduzbnzp3DzMyMxo0bA/DJJ59gZWVlcN2nVyI/fPgwY8eORaVSYWlpSYcOHTh8+HCekqn27dsDULVqVaysrLhx4wYajYbSpUsrQ5bt27fn+++/z/Vcz0p5lMbDEjYBPTVV89zHmIvL482FReLxhMTCkMTDUHGJhyyN8IYrqoLFj0VERHD8+HHWrl2LpaUlS5YsUer4mZub4+HhQUhICEePHuXzzz/P83XNzZ/f+/EsIyMjdDqd8jotLc3gfVNTU4N9s4uHSqXKc9uEEEKI/ChZYxhF7E0sWJyUlET58uWVgsGRkZEG73/22Wf88ssvnD59mrZt2wJgZ2fHo0ePOH78OAC7du0iMTEx22u4uroSHByMXq8nOTmZbdu2KQWRq1evrsylOnz4MLdv3861zXZ2dqSmpnLs2DEgs3ByTtcXQgghCkJ6pl6hN7FgcefOndm9ezfu7u7Y2NjwwQcfGPQOVatWDTs7Oxo0aKC02cTEhFmzZjFx4kQAnJ2dsbGxoUyZ54+bDxkyhClTpijDkp06daJly5YAfPPNN4wePZo1a9bQtGlT3nrrrVzbbGJiwuzZsw0moOflOCGEEOJFSDmZYqKoChYnJyfj7u5OcHCwwZODycnJWFpmji8fOXKEMWPGsHv3btTqN6czND4hUVZA/0dxmfdQWCQeT0gsDEk8DBWXeMicKfHSrF+/nsWLF+Pj45NlCYadO3eyatUq9Ho9JiYmBAQEvFGJFEDpUmp06pKVTAkhhMg/6Zkq5qRg8YuTQsdPFJdvl4VF4vGExMKQxMNQcYmH9EyVcFKwWAghhHi53qxxFyGEEEKI14wkU0IIIYQQBSDJlCh0vXv3Zu/evXna9+DBg/Ts2ZO2bdvi5eXFV199xR9//AGAg4ODUsNQCCGEeF3JnClRZH7//XfGjRvHwoULqV+/PgBnz57l1q1bODg4FHHrhBBCiLyRZKqYiImJYcaMGUpPzqhRo7CysjJYDHTcuHE0aNCA+Ph4vL296d69OwcPHiQ1NZWAgACCgoI4efIkZmZmLFq0iIoVKxISEkJERASmpqZcuXJFWQz02aUQsjN69GhMTEy4fPkyN27coFGjRkyfPh2VSsXChQsZMmSIkkgB2T5hOH36dI4ePYpGo6F8+fL4+/vz9ttvc+fOHUaMGMGdO3eAzNXUx44dy4kTJ5gyZQo6nY6MjAwGDx5Mx44d8xXTnJ7cKIlKQrHS/JB4PCGxMCTxMFQS4iHJVDFw//59vv76awIDA3FyckKr1XLv3j26du3Kjz/+iKurK1FRUfj6+rJz507lmA8++IARI0awfPly+vbty+rVq5k6dSoTJ05kzZo1DBs2DIDjx48TFhaGnZ0dCxYs4IcffsjX4qDnz59n1apVqFQqunTpQlRUFB9++CFnzpzJcwHiAQMG4OfnB2Qu9xAQEMCcOXOIiIigevXqrFq1CoAHDx4AsGzZMvr370/Hjh3R6/UkJeX/0VxZGuGJ4vJ4c2GReDwhsTAk8TBUXOIhSyOUAP/973+pVasWTk5OQGbB3zt37mBsbIyrqysAzZo1w9jYmEuXLmFhYYG5uTmtW7cGoG7dulSuXFnpFapbty5RUVHK+T/44APs7OyAzKUWHpd9yas2bdooBYnr1KnDlStX+PDDD/N1jgMHDrBu3TpSUlLIyMhQtjds2JBVq1Yxffp0nJ2dad68OQAuLi4sXrxYuVbDhg3zdT0hhBAir2QCegn1dO0/tVpt8NrIyAitVlto13qcSD177jp16hAbG5vr8X///Tc//vgjs2bNIjIyEn9/f9LT0wFwdHQkNDSUevXqER4eTp8+fQDo27cvixcvxtramilTpjBnzpxCux8hhBDiaZJMFQONGjXiwoULxMTEAKDVarGxsUGj0XDkyBEADh8+TEZGBjVr1sz3+U+cOMHly5cBCA4OpmnTpoXS7sGDB7No0SL+97//KdvOnTvH77//brBfcnIyxsbGVKxYEZ1OR1BQkPLe1atXsbS0pEOHDowZM4b//e9/6HQ6Ll26RPXq1enZsyd9+vTh1KlThdJmIYQQ4lkyzFcMlCtXjsDAQKZNm0ZKSgpqtRo/Pz/mz59vMAF93rx5Bj1QeeXk5MT06dOJi4tTJqAXhpYtWzJ58mQmT57M/fv3KVWqFFWrVmXEiBEG+zk4OODu7k779u0pX748rVq14tixYwAcPXqUVatWoVar0el0TJo0CbVazerVq4mOjsbY2BgTExPGjx9fKG0WQgghniW1+USOQkJC2LdvX74mnBcXMgH9ieIyibSwSDyekFgYkngYKi7xyG0CugzzCSGEEEIUgAzziRx5eXnh5eWVZfumTZtYs2ZNlu3Tpk3Ldq2oN82jDB1arfRMASTcTSFVo8uy3dRYjVERtEcIIV4nkkyJF9KtWze6detW1M14qQ7GxJOcoinqZrwWLCxMefgwLcv2tk1rYG4sHdxCiJJN/goKIYQQQhSAJFOvMb1ez+rVq+nYsSPu7u507tyZ/v37c+LEiVyPjY6ONlhiIDExkWXLlhnsM27cOOWpuMKydu1aPDw86NSpE+7u7kyfPj3b6+fH2bNn2bZt2wsdGx8fj4uLywtfWwghhMiJDPO9xubOnct//vMfVqxYodTCO3HiBBcvXlRWO8/O0aNHSUlJUVYET0xMZPny5QwYMEDZ54cffijU9sbGxvLLL7+wefNmrKys0Gq1nD9/Ptvr51VGRgZnz55l3759tG/fvlDbLIQQQhSUJFNPeV6x4ObNmxMbG/vKCwY/fPiQlStXEh4ebrCPk5OTQSK1dOlSdu7ciVarxdbWlilTpnD37l2CgoLQ6XRERUXRoUMHjh07RlJSEp6enpQuXZqgoCB69+6Nj48PH330UY4FiRMSEhg1ahS3b9+mWrVqADRv3pwvvvjCoM0JCQlYWlpibm4OZK52Xrt2bQAmT56c5forV65k69ataLVaTE1NmThxojJ53cHBga+//pp9+/bRqFEjdu/eTXJyMp6enjRp0oTx48dz8uRJAgIClN+Xr6+vUiJn7dq1rFq1CktLS1q1alXQj4YQQgiRLUmm/vG8YsHJycmkp6fj6+v7ygsGX7hwAVNTU6Um3vOEh4dz9epVNm7ciFqtZt26dUybNo1Zs2bRs2dPUlJSlOLA7du3x9vbm/Dw8GzPl11B4qlTp+Li4sKQIUP4+++/8fDwUHq8nvbhhx+ybNkyPvroI5ydnXF2dqZTp06ULl2a77//Psv1O3fujI+PDwBRUVFMmDCBjRs3Ku+bmpoSHBwMZNYLfHq9q8TERCZMmMDSpUupVKkSN2/epGvXrkRGRnLt2jUWL15MWFgYFSpUYOLEidnec07MS5uiV8lI+GMWFqZZtpmZGVPR2rwIWlP0KlYsU9RNeG1ILAxJPAyVhHhIMvWP5xULLlu2LH/88cdrUTA4MTGR3r17k56eTq1atViwYAF79uzh9OnTdOnSBcgsI2Npmf2iYrnJriBxdHS0soL422+/rcTiWebm5mzYsIFTp05x/PhxNm3axNq1a9m8efNz9z99+jQ//fQTDx48QKVSKSVrHnt8X88TExNDfHy8wbChSqUiLi6OmJgYWrduTYUKFQDo0aMH27dvz3McHkt5lMZDeZoPyP5pvtRUTbFYkC+/istChIVBYmFI4mGouMQjt0U7JZkqoJdVMLhWrVqkpaVx+fJlatSogZWVFeHh4ezdu5eVK1cCmRPUBw8eTNeuXQt2E//IriBxfqhUKho0aECDBg34/PPPadasGefPn6ds2bIG+6Wnp/PNN9+wZs0a6tatS0JCAi1btjTY5/Fw4fPo9XocHBxYu3Ztlvce1ygUQgghXgUZw/jH84oFP3jwgJo1axZJwWALCwv69evH+PHjSUhIULY/evRI+dnNzY1169bx4MEDIDNBOXfuHACWlpYkJT35NmBpaUlqaioZGRn5brezszOhoaEAXL9+XYnFsy5cuMCff/6pvL506RIajYbKlStnuX56ejoZGRlUqVIFgHXr1uXYhmfvx9HRkbi4OIO2xMbGotfrcXZ2Zv/+/dy5cwcg254xIYQQojBIz9Q/sisW3KxZsyIrGDxs2DB++eUXfHx80Gq1lC9fHisrK/71r38BmXOO7t+/r0wE1+v19OrVi9q1a9OmTRvCwsLw9PSkQ4cODBw4EA8PDzw8PChbtixBQUF5bve4ceMYNWoUERERVK1alQYNGjx3ODE1NRV/f3/u3LmDqakpRkZGzJw5ExsbG4As1/f19aVr166UK1eOdu3a5dgGV1dXVq5cSadOnXB2dmb8+PEsWrSImTNn4u/vj0ajoVq1aixZsoTatWszaNAgevXqhaWlZZYeLyGEEKIwSaHjV+RNLhicmppKqVKlKFWqlDLRe9WqVTlOji8O4hMSpZzMP8zMjElNzTp/rKSWkyku80AKg8TCkMTDUHGJh8yZEgV2+fJl/Pz80Ov1ZGRk8PXXXxf7RAqgdCk1OrUkUwAVrc2LxR9EIYR4GaRnqoiVhILBb6o7d5LR6eR/Dyg+3y4Li8TjCYmFIYmHoeISD+mZes2VhILBb6pHGbo8DfOV1KEuIYQQmSSZEiIbB2PiSc7DOlNtm9bA3FgejBVCiJJK/gUQQgghhCgA6ZkS+dK7d2+uXbumLI1Qs2ZN5s6d+0Lnio6ORqPRPLc0TWFzcHDgxIkTWFhYvPRrCSGEKFkkmRL5Nn78eD766KMCn+fo0aOkpKS8UDKl1WoxMpKZSkIIIYqeJFNvgJiYGGbMmMHDhw8BGDVqFFZWVgYLiY4bN44GDRoQHx+Pt7c33bt35+DBg6SmphIQEEBQUBAnT57EzMyMRYsWUbFiRUJCQoiIiMDU1JQrV64oC4na2trmu4379+9n8eLFpKenY2xszJgxY2jUqBG3bt1i+PDhPHz4kLS0NFq1asWoUaP4448/CAoKQqfTERUVRYcOHWjYsCHTp08nJCQEyOy5evw6OjqaqVOnUq9ePc6cOcPQoUN555138Pf35969e2g0Gv7f//t/eHt7A7Bz505mz56Nqakpbdu2faG457XQcUkp9lsSipXmh8TjCYmFIYmHoZIQD0mmXnP379/n66+/JjAwECcnJ7RaLffu3aNr1678+OOPuLq6EhUVha+vLzt37lSO+eCDDxgxYgTLly+nb9++rF69mqlTpzJx4kTWrFnDsGHDADh+/DhhYWHY2dmxYMECfvjhh1wXFp06daoytNenTx+aNGnCokWLWLFiBZaWlpw/f54BAwawb98+rKysWLJkCRYWFmg0Gvr378+BAwdo2bIlPXv2JCUlBT8/PyAzecrJX3/9xeTJk3F0dCQjI4Pu3bszc+ZMatWqRXJyMt7e3jRq1IiyZcvy3XffsX79euzs7Fi2bNkLxT6vhY5LQrHf4vJ4c2GReDwhsTAk8TBUXOIhSyO84f773/9Sq1YtnJycgMwCxHfu3MHY2BhXV1cAmjVrhrGxMZcuXcLCwgJzc3Nat24NQN26dalcubKyZlXdunWJiopSzv/BBx8oC3B269YNDw+PXNv07DDf2rVruXLlCp9//rmyLSMjg9u3b2Nubs6MGTOIiYlBr9dz+/Ztzp0790IlXt555x0cHR2BzIVEL1y4wPDhw5X3NRoNFy9eRK1WU6dOHeW+evToQUBAQL6vJ4QQQuSFJFPF0NN1A9VqtcFrIyMjtFptoV+zRYsWzJgxI8v2hQsXkpiYyKZNmzA1NeW7774jLS3tuecwMjLi6TVkn93P3PzJUJper6d8+fKEh4dnOc/u3btf9DaEEEKIfJOlEV5zjRo14sKFC8TExACZE69tbGzQaDQcOXIEgMOHD5ORkUHNmjXzff4TJ05w+fJlAIKDg2natGm+z/Hhhx9y8OBBzp8/r2yLjY0FICkpiYoVK2JqakpCQoJBomNpaUlS0pPu32rVqnH16lUePHiAXq9n69at2V6zZs2amJmZERYWpmy7cOECycnJNGrUiDNnzij3tWnTpnzfkxBCCJFX0jP1mitXrhyBgYFMmzaNlJQU1Go1fn5+zJ8/32AC+rx58wx6oPLKycmJ6dOnExcXp0xAz68aNWowc+ZMxo0bR2pqKhqNBicnJxo0aEDv3r355ptv6NixI7a2tsrQJECbNm0ICwvD09OTDh06MHDgQPr164eXlxcVKlSgSZMmBgna00qVKsWSJUvw9/dnxYoV6HQ6bGxsmDt3LjY2NkyZMoVBgwZhZmb2whPQhRBCiLyQ2nwlWEhICPv27ct1wnlJFZ+QKOVk/lFcJpEWFonHExILQxIPQ8UlHjIBXYgXVLqUGp1avmsIIYTImSRTJZiXlxdeXl5Ztm/atIk1a9Zk2T5t2jTlqcCSIK+FjgtTSejlEkKI4kaSKZFFt27d6NatW1E3o8jltdBxYZKiyUII8eYpMclUt27dSE9PR6PRcPnyZd577z0A6tSpw48//miw79mzZ7l06RLt27fP9bxPr9INmTXg7O3tAUhPT6du3boMGTKEd999N9dzrVq1Cg8PD2xsbPJ7e7lKTExkw4YNDBgwQNk2evRooqKisLa2JiUlBRsbG3r06EHnzp0L/frPCgwMNFiwszC4ubmxZMkSJf5CCCHEq1BikqnHj8c/LrfyvPWJHjt79iz79u3LUzL1PEFBQVhYWKDT6diwYQO9evUiJCSEatWq5Xjcr7/+SrNmzV5aMrV8+XKDZApg4MCBfPHFF0DmfQ8dOpR79+7Rr1+/Qm+DEEIIURyVmGTqecLCwlixYgUA1atXZ/LkyajVaubPn09ycjKenp40adKE8ePHM2LECC5duoRGo6F69er4+/tTtmzZHM+vVqvp1asXR48eZd26dfj5+REREcGvv/6KRpM5fOTn54erqyuLFy/m5s2b+Pr6YmpqyqxZs7h16xZz584lLS0NrVbLoEGD6NChAwALFiwgMjISU1NTVCoVv/76K1ZWVpw8eZKAgACljp+vry+tW7dm8uTJJCUl4enpSenSpQkKCsrS3vfff59x48YxevRo+vbti0qlYunSpWzZsgWA+vXrM378eCwsLGjRogVhYWHY2NgwYMAAZd87d+7QpUsXDhw4QGBgIJcuXSIpKYmrV69SvXp15s2bR+nSpQ2uq9VqCQgI4ODBg0DmAqAjR47EyMgo23gBHDt2jEmTJgHQpEkTZcFPnU7H5MmTOXLkCCYmJpibmz/3foUQQojCUGKTqT///JOAgABCQkKoVKkSc+fOZcqUKcydOxdfX98sSwaMGzcOa2trAObMmcOyZcsYOXJknq7VsGFDDh06BEDz5s3p2LEjKpWKixcv0rdvXw4cOMDgwYPZtGkT8+fPV4apKlasyLp16zAyMuL27dt4eXnRvHlz9Ho9q1at4vfff8fMzIzk5GTMzMxITExkwoQJLF26lEqVKnHz5k26du1KZGQk33//fa49co/beufOHe7evcvp06fZsmWL0tPm5+fHokWL+Pbbb3FxceHIkSO0bduW+Ph4VCoVGo2Gw4cP4+Liopzv9OnTbN68mTJlytC/f38iIiLo3r27wTU3bNjA2bNnlaHSAQMGsGHDBj777LNs45Wens6wYcMICAjAxcWFbdu2sXbtWgDOnTtHdHQ027ZtQ61W8+DBgzz9noQQQogXUWKTqejoaFq1akWlSpUA6NmzJ56entnuHx4eTkREBBqNhpSUFGrUqJHnaz29lNfVq1cZMWIECQkJlCpVitu3b3Pr1i0qVqyY5bi7d+8yduxY4uLiMDIy4sGDB1y6dIn69etTvXp1Ro0aRfPmzWndujWWlpbExMQQHx9vMJSnUqmIi4ujfPny+W7r4cOHad++PZaWmWtrdO/eHX9/fwClwLKtrS2NGjVCr9dz8uRJoqKiDFZRb968OVZWVgA0aNCAK1euZLnm4cOH6dKli7LoqJeXF7t27eKzzz7LNl537tyhdOnSSuLWvn17vv/+eyBzJfWMjAzGjRuHi4uLQR3B/DAvbYpe9Wong5uZGVPR2jz3HYtASaj8nh8SjyckFoYkHoZKQjxKbDKVH8eOHWP9+vUEBQVhbW1NREQEGzduzPPxp06dUia8Dx8+nNGjR9OmTRt0Oh0NGzbMtlbdxIkTcXNzY8GCBahUKtq1a0daWhpGRkZs3LiREydOcOTIEby8vFi+fDl6vR4HBwelh+Zp8fHxeW6rjY1NrvO2mjZtysKFC6lcuTJNmzZFr9dz5MgRjhw5wtdff63sZ2pqqvxsZGSU7b1mJz/xUqlUAJQpU4atW7cSHR1NVFQUAQEBhIaGPjdhzUnKozQevuKn+VJTNa/lAnfFZeG9wiLxeEJiYUjiYai4xCO3RTtL7DPYLi4u7N+/n1u3bgGwceNGmjVrBmStGZeYmIilpSXlypUjPT2d4ODgPF1Dp9OxceNGDh48SK9evYDMWnVVq1YFMmvhpaenK/tbWFgYXDcpKYm3334blUrFoUOHiIuLAyA5OZm7d+/i7OyMr68v9vb2nD9/HkdHR+Li4pSafZBZI0+v12NpaUlqaioZGRnZtvfcuXP4+/srPVuurq5s376d5ORk9Ho9mzdvVmL09ttvY2RkRGhoKK6urri6uhISEkKpUqV466238hSfx1xdXQkLC0Oj0aDRaAgLC1Ouk1287OzsSE1N5dixYwDs2LGDxMREILNH79GjR8rcqzJlynD16tV8tUkIIYTIqxLbM2Vvb8/IkSPx8fEBMoeGJk+eDGT+475y5Uo6deqEs7Mzfn5+bNmyhXbt2lG+fHkaN27MqVOnsj13z549gcylEerUqcP69euVJ/nGjBnDkCFDKFu2LC1atKBcuXLKcX369GHs2LGYmZkxa9YsRowYwaRJkwgMDKR+/fo4ODgAmcnUv//9b1JTU9Hr9dSpU4e2bdtiamrKokWLmDlzJv7+/mg0GqpVq8aSJUsoV64cHh4eeHh4ULZsWWVC9tKlS9m0aROpqalYW1vz1VdfKUsjtGrVij/++EO5n3r16jF48GClva6urhw/flwZKjUzM6Nx48b5/l306NGDK1eu0KVLFyBzaPDxvKrs4mViYsLs2bMNJqA/TuKuX7/Od999R0ZGBlqtlpYtW9KoUaN8t0sIIYTIC6nNJ0Q21u84I4t2/qO4dNUXFonHExILQxIPQ8UlHlKbT4gX1MKxapGUkxFCCPFmkWRKiGxIoWMhhBB5IV+DhRBCCCEKQHqmhMjGowzdKx/me5apsRqjIm2BEEKI3EgyJUQ2DsbEv/IJ6M96XSekCyGEeEL+Sr9k3bp1w9PTk/bt21OnTh08PT3x9PRkzJgxWfY9e/Ys27Zty9N5o6Oj8fLyUl47ODgoSx+0a9eO4cOH89dff+XpXKtWreLOnTt5u6F8SkxMZNmyZcrrpUuXMmzYsCz7jR49mgULFmR7nvzERgghhHiVJJl6yTZt2kR4eDhLly6lTJkyhIeHEx4ezo8//phl37Nnz7Jjx44XvlZQUBARERFs376dJk2a0KtXrzwtVvnrr7++1GRq+fLlyuvOnTuzb98+ZYFNgIcPH7Jz505lnannKWhshBBCiJdFhvmKQFhYGCtWrACgevXqTJ48GbVazfz580lOTsbT05MmTZowfvx4RowYwaVLl9BoNFSvXh1/f3/Kli2b4/nVajW9evXi6NGjrFu3Dj8/PyIiIvj111/RaDKHrfz8/HB1dWXx4sXcvHkTX19fTE1NmTVrFrdu3WLu3LmkpaWh1WoZNGgQHTp0AGDBggVERkZiamqKSqXi119/xcrKipMnTxIQEMDDhw8B8PX1pXXr1kyePJmkpCQ8PT0pXbo0QUFBuLi4EBkZyWeffQbA9u3badiwIW+//Xa+YpPdNe/cucOIESOUBNHV1ZWxY8cW8m9RCCGEyCSLdr4i8fHxeHt7s3r1anx8fAgJCaFSpUrMnTuXy5cvM3fuXEJCQti3bx/z589Xjrt79y7W1tYAzJkzB61Wy8iRI4mOjmb69OmEhIQAmcN8J06cwMLCQjl21apVHDp0iGXLlnHv3j3KlSuHSqXi4sWL9O3blwMHDgDg5ubGkiVLsLe3B+DBgwdYWlpiZGTE7du38fLyIiIiAr1eT5s2bfj9998xMzMjOTkZMzMzUlJS6NOnD0uXLqVSpUrcvHmTrl27EhkZSWJiIt7e3kRHRyvt+u233/jpp5/YvHkzAJ999hmfffYZ9vb2eY5NYmJittcMCQnh4sWLyor2Dx48yDUBfZ7w/Rd4mFq0c6Y+blId29e08LEQQohM0jP1ikVHR9OqVSulBEvPnj3x9PTMdv/w8HAiIiLQaDSkpKRQo0aNPF/r6Tz56tWrjBgxgoSEBEqVKsXt27e5devWc4v/3r17l7FjxxIXF4eRkREPHjzg0qVL1K9fn+rVqzNq1CiaN29O69atsbS0JCYmhvj4eKWmH2QWHY6Li6N8+fJZzt+6dWsmTJjA+fPnMTY25q+//uKTTz5h48aNeY5NTtds2LAhq1atYvr06Tg7O9O8efM8x+xpRVHo+FmvS+Hj4rKKcWGReDwhsTAk8TBUXOIhK6C/wY4dO8b69esJCgrC2tqaiIgINm7cmOfjT506xXvvvQfA8OHDGT16NG3atEGn09GwYUPS0tKee9zEiRNxc3NjwYIFqFQq2rVrR1paGkZGRmzcuJETJ05w5MgRvLy8WL58OXq9HgcHB9auXZvlXPHx8Vm2GRsb06lTJ0JCQjAxMaFDhw6Ymprm+b6AHK8JEBoaSlRUlDJfbf369fk6vxBCCJFXMgH9FXNxcWH//v3cunULgI0bN9KsWTMALC0tSUp6ksEnJiZiaWlJuXLlSE9PJzg4OE/X0Ol0bNy4kYMHD9KrVy8AkpKSqFq1KgDBwcGkp6cr+1tYWBhcNykpibfffhuVSsWhQ4eIi4sDMgss3717F2dnZ3x9fbG3t+f8+fM4OjoSFxfHkSNHlHPExsai1+uxtLQkNTWVjIwMgzZ6e3uzZcsWwsLC8Pb2zndscrrm1atXsbS0pEOHDowZM4b//e9/6HS6PMVOCCGEyC/pmXrF7O3tGTlyJD4+PgBUq1ZNmdvj6urKypUr6dSpE87Ozvj5+bFlyxbatWtH+fLlady4MadOncr23D179gQgPT2dOnXqsH79eqpVqwbAmDFjGDJkCGXLlqVFixaUK1dOOa5Pnz6MHTsWMzMzZs2axYgRI5g0aRKBgYHUr18fBwcHIDOZ+ve//01qaip6vZ46derQtm1bTE1NWbRoETNnzsTf3x+NRkO1atVYsmQJ5cqVU5ZsKFu2LEFBQQC89957vPXWW6SlpVGvXr18x2b8+PHZXvPo0aOsWrUKtVqNTqdj0qRJqNXyvUEIIcTLIRPQhchGfEKirID+j+Iy76GwSDyekFgYkngYKi7xkDlTQrwgKXQshBAiL2TsQwghhBCiAKRnSohsvA6Fjl8XCXdTSNXIJP7HijIer8vQrxDiCUmmhMjG61Do+HVhYWHKw4fPX0qjJCrKeEjxayFeP/J/5Gvsyy+/zLI+kl6v5+OPP+bo0aMFOndCQgK9e/cu0Dni4+NxcHBgwoQJBttcXFwKdN5nhYSE4OvrW6jnFEIIIQqLJFOvMW9vb0JDQw22RUdHo1aradKkSa7H63Q6sntY09bWltWrVxe4jebm5uzevZsrV64U+FwvU06xEEIIIQpChvleYx9//DETJ07kwoUL1KpVC8jspfHy8mLZsmXs3LkTrVaLra0tU6ZMoWLFigQGBnL+/HmSk5O5du0a69evZ968eRw5cgQTExPMzc0JCgpSagU+rpl34MABZs+ejVarxdramsmTJ/POO+8QHR2Nv78/DRs2JCYmBpVKxZw5c5T2mJiY4OPjw9y5c5k9e7ZB+5+9xtOvH//cvXt3Dh48SGpqKgEBAQQFBXHy5EnMzMxYtGiRUu4mKSmJQYMGceXKFSpUqMDMmTOxtbUFYOnSpXmKxYYNG16oRp8QQgiRE+mZeo2ZmJjg4eGhrHyenJzMrl27eOutt7h69SobN24kNDSUli1bMm3aNOW42NhYAgIC2LFjB9evXyc6Oppt27axZcsWfvrppyzXuXPnDqNGjSIgIICIiAg6duzIyJEjlff/+usvevbsSUREBJ9++imLFi0yOP7zzz8nJiaGs2fP5uv+7t+/zwcffEBYWBhdu3alb9++fP7550RERFC3bl3WrFmj7Hv8+HFGjRrFtm3bcHZ25ocffgAyaxfmNRaSSAkhhHgZpGfqNde1a1e+/PJLRowYwfbt23FycmLPnj2cPn2aLl26AKDVarG0fLKYWMuWLbG2tgYyVxHPyMhg3LhxuLi48NFHH2W5xsmTJ6lduzbvvvsukDm8OGnSJJKTkwGoWbMmderUAaBRo0bs3bvX4HhTU1OGDBnC7NmzDeZP5cbc3JzWrVsDULduXSpXrsz777+vvI6KilL2/eCDD7CzswOgW7dueHh4AOQrFvllXtoUvUq+bzxmYZG/+onFXVHFw8zMmIrW5kVy7exUrFimqJvwWpF4GCoJ8ZBk6jVXu3ZtKlWqxIEDBwgODub//b//x/bt2xk8eDBdu3Z97jEWFhbKz2XKlGHr1q1ER0cTFRVFQEBAlnlYuTExMVF+VqvVWersAXh5ebFixQqOHTumbCtVqpTBPKVnCys/e96nXxsZGaHVanNtm16vz3Ms8ivlURoP5Wk+QJ7me1ZRxiM1VfNarShdXFa4LiwSD0PFJR65rYAuX7vfAN7e3gQGBnL58mU+/vhj3NzcWLduHQ8ePAAya/GdO3fuucfevXuXR48e0aJFC0aOHEmZMmW4evWqwT6NGjXi3LlzXLhwAYDQ0FDq1Klj0MOTGyMjI4YOHcr8+fOVbRUqVECj0SiFkiMjI/N13087ceIEly9fBjILNTdt2hQgX7EQQgghXgbpmXoDdOzYkenTp9O9e3dMTEzo3Lkz9+/f54svvgAye2d69epF7dq1sxx7/fp1vvvuOzIyMtBqtbRs2ZJGjRpx7do1ZR9ra2tmzJjByJEjycjIwNrampkzZ+a7ne7u7ixbtoyHDx8CmT1T48aNo1+/flhbWytDei/CycmJ6dOnExcXp0xAB/IVCyGEEOJlkELHQmRj/Y4zsmjnP2SYz5As2vlEcRnGKSwSD0PFJR5S6FiIF9TCsaqUk/mHmZkxqamSWD5WlPEwfY0SKSFEJkmmhMhG6VJqdGpJpgAqWpsXi2+XhUXiIYR4mnzFEUIIIYQoAOmZEiIbjzJ0RTLMZ2qsxuiVX1UIIcSLkmSqCGzfvp2ffvoJvV5PWloadevWZdasWXk+/tSpU6xatSrXYxITE9mwYQMDBgxQto0ePZp69eopT78VF9HR0UyfPp2QkJAs7z1b1iavDsbEF8kE9NdtgrEQQoicSTL1it28eZNJkyYRGhpKlSpV0Ov1+SrDkpGRQf369fOUfCUmJrJ8+XKDZEoIIYQQhUuSqVfs9u3blCpVinLlygGgUqmUUi0xMTHMmDFDWadp1KhRNG/eHDc3N9q3b8+RI0ewt7fH09NT6YV53OvSpUsXDh06BMCECRNo3LgxkydPJikpCU9PT0qXLk1QUFC27YqIiODXX39Fo8nsifHz88PV1RXIXBjT09OTqKgobt26hY+Pj9KzdezYMSZNmgSAi4sLu3fv5qeffsLe3h4HBwdOnDihrEL+9OsRI0Zw6dIlNBoN1atXx9/fX6mdN2fOHLZt20a5cuVwdnbm8OHDSo9TaGgo69atU8rGTJw4USkz87S1a9eyatUqLC0tadWq1Yv/woQQQohcSDL1itWuXZsGDRrQunVrXFxccHJywtPTE5VKxddff01gYCBOTk5otVqlNh5kFjnevHkzQJbhqvv371O7dm1Gjx5NdHQ0w4cPZ9euXXz//fd4e3sTHh6ea7uaN29Ox44dUalUXLx4kb59+3LgwAHl/dTUVDZs2EB8fDweHh506dIFY2Njhg8fzuzZs2ncuDG//fYbq1evzlMcxo0bp9TMmzNnDsuWLWPkyJHs2bOHvXv3Eh4ejpmZGb6+vsoxx44dY/v27axduxYTExP279/P2LFjsySJ586dY/HixYSFhVGhQgUmTpyYpzYJIYQQL0KSqVdMrVazaNEi/vzzT/7zn/+wa9cuVqxYwbfffkutWrVwcnICMsuzPO6pgcyVvrNjbGxMp06dgMzeITMzMy5evJivcjBXr15lxIgRJCQkUKpUKW7fvs2tW7eoWLEiAO3btwegatWqWFlZcePGDTQaDWZmZjRu3BiATz75BCsrqzxdLzw8nIiICDQaDSkpKdSoUQPITBQ//fRTzM3NlftetGgRkFnU+Ny5c3Tr1g3IXO08MTExy7mPHj1K69atqVChAgA9evRg+/bteY7FY0VV6Ph1LGQLJaNYaX5IPJ6QWBiSeBgqCfGQZKqI2NvbY29vz+eff64kKjl5nFy8LMOHD2f06NG0adMGnU5Hw4YNDQoTm5qaKj/ntQixkZGRUuj46XMdO3aM9evXExQUhLW1NREREWzcuDHX8+n1ery9vfnmm2/yc2svrKgKHb9uhWyh+KxiXFgkHk9ILAxJPAwVl3hIoePXTEJCAjExMcrrGzducPfuXezs7Lhw4YLynlarVYr35kaj0RAREQFkJiqpqanY2dlhaWlJamoqGRkZuZ4jKSmJqlWrApmFhNPT03M9xs7OjkePHnH8+HEAdu3aZdBTVL16dU6dOgWgtA8yJ8ZbWlpSrlw50tPTCQ4OVt5zdnbm//7v/3j06BE6nY4tW7Yo77m5uREeHs6NGzeAzBidPn06S7ucnZ3Zv38/d+7cAVCGR4UQQoiXQXqmXrGMjAwCAwP5+++/MTMzQ6fTMXToUOrVq0dgYCDTpk0jJSUFtVqNn58fzZo1y/Wc5cqV49y5cyxfvhyA2bNnY2JigomJCR4eHnh4eFC2bFllbtG8efNYunSpcvyUKVMYM2YMQ4YMoWzZsrRo0UKZIJ8TExMTZs2apcxJcnZ2xsbGhjJlMrt0x4wZw/fff0+ZMmVwd3dXjmvRogVbtmyhXbt2lC9fnsaNGytJ18cff0xMTAydOnWibNmyNGrUSEkqmzRpwtChQxk8eDBarRaNRoO7uzv16tUzaFft2rUZNGgQvXr1wtLSkpYtW+Z6L0IIIcSLkkLHb7gXXUOpsCQnJytzs44cOcKYMWPYvXs3avWLd3o+PqdOp2PcuHFUqlSJYcOGFVaT86yoCh2/jutMFZeu+sIi8XhCYmFI4mGouMRDCh2Ll2rnzp2sWrUKvV6PiYkJAQEBBUqkIHNZhr///pvU1FTq1q1bZOtkFVWhYylkK4QQbxbpmRIiG3fuJKPTyf8eUHy+XRYWiccTEgtDEg9DxSUeMgFdCCGEEOIlkmE+IbJRVIWOcyJFkIUQ4vUjyZQQ2SiqQsc5eR0npwshREknyRSZ6xc9Xkrg0aNHvPvuuwwYMEBZjbywPHjwgEmTJvHnn3+iUqlQq9WMHj0aV1dX1q9fT1paGn379i3Ua0LmquIDBw6kRo0aaLVaypUrx6RJk6hVq9YLn3P37t0cO3YMPz+/HK+r0Who3rz5C10jJCQER0dHatasmedrCiGEEK+aJFP/mD9/Pvb29kDmE2oDBw5kxYoVNGzYsNCuMXfuXGxtbZk1axYqlYp79+7x6NEjAHr16lVo13meWrVqKcWCZ86cyY8//qisS/UiPv74Yz7++OMc9zl69CgpKSnZJlMZGRmUKpX9RzA0NJTy5csryVRerimEEEK8apJMPUfbtm2JjY1lxYoV9OrVi7lz55KWloZWq2XQoEF06NCB2NhYxo4dS2RkpHJcp06dmDhxIuXKlWPMmDHKKt5dunShf//+3LhxAxcXF1QqFQDly5enfPnyAAQGBpKSkoKfnx8hISFERkZiZWXF+fPnKVOmDIGBgUqdvJ9++onIyEhUKhXm5uasW7cOtVpNaGgo69atQ6vVYmlpycSJE7Gzs8tyf87Ozuzbt095nd1x6enpTJkyhaNHj2Jtbc3777/P7du3mT9/PiEhIezbt4/58+dz8eLFLPfbvHlzgoKC0Ol0REVF0aFDB9q3b4+3tzdeXl4cOXKE7t27U6NGjefGNzg4mNOnTzN16lTmzp2Ln58fN27cUK4JsHTpUmWF9Pr16zN+/HgsLCwIDAzk0qVLJCUlcfXqVapXr868efMoXbr0S/m8CCGEKNkkmcpGw4YN2bNnD3Xq1GHdunUYGRlx+/ZtvLy8aN68OQ0aNMDc3JyjR4/i7OzMsWPHUKvVODk5MXXqVNzc3Pjqq68AlBW8+/Tpg6+vL5GRkTg6OuLm5oarq+tzr3/q1Cm2bNlClSpVGD9+PGvWrGHYsGGEhoayZ88e1q9fj6WlJffu3UOtVnPs2DG2b9/O2rVrMTExYf/+/YwdO1ZZ9fwxnU7H7t27lXqAOR23YcMGrl27xtatW9FqtfTu3ZvKlStnaeu6deuy3G/ZsmXp2bOnkiBC5gKj9+/fp379+sq2Bw8ePDe+3t7ehIWF4ePjw0cffQSg9KwB7N+/ny1bthAUFISFhQV+fn4sWrSIb7/9FoDTp0+zefNmypQpQ//+/YmIiKB79+75+gwUVaHjnBRlEeSSUKw0PyQeT0gsDEk8DJWEeEgylY3Hy2/dvXuXsWPHEhcXh5GREQ8ePODSpUs0atSI3r17s27dOpydnVm7di2ff/45kFn2ZObMmTx69AgXFxeaNm0KgKurK3v37iU6Oprjx48zdOhQ+vfvz8CBA7Nc38nJiSpVqgCZiV1UVBQAe/fuVcqkAErP1p49ezh37hzdunVT2v90nbwLFy7g6elJQkIClpaWbNq0KdfjoqOj8fT0pFSpUpQqVYoOHToodfielt39Po+pqSmffvqp8jqn+Obk8OHDtG/fXolD9+7d8ff3V95v3rw5VlZWADRo0IArV67keL7nKapCxzkpqiLIxWWtmMIi8XhCYmFI4mGouMRD1pl6QadOneK9995j4sSJODs7ExERQXh4OJUrVyYtLQ0Ad3d3Tp48yZkzZ4iOjqZjx44AtGvXjrVr11K9enWWLVum9JYAWFpa8vHHHzNq1CgmTJhgUAD4aaampsrPRkZGaLXaHNur1+vx9vYmPDyc8PBwtmzZYjCUV6tWLcLDwzlw4AC1a9dW6unldlxe5HS/zypdurQyzAnkGN+CyG/8hBBCiBclydRz7Nq1i/Xr1+Pj40NSUhJvv/02KpWKQ4cOERcXp+xnbGyMt7c3gwcPxsPDQ5mTExcXR8WKFfHy8uJf//qXUsT30KFDJCcnA5lJzJkzZ6hatWq+2vbRRx+xfv165Tz37t0DMp9IDA8P58aNGwBotVpOnz6d5XgTExMmTpzIwYMHOXPmTI7HPU5yMjIySEtLY/v27c9tU3b3a2lpSVJSzt9IcoqvhYVFtse7urqyfft2kpOT0ev1bN68OU9FoYUQQojCJsN8//D19VWWRqhVqxZLly6lYcOGjBgxgkmTJhEYGEj9+vVxcHAwOK5bt24sWLDA4Gm87du3ExERgbGxMSqVirFjxwLwxx9/MG3aNGUI8Z133uH777/PVzs7d+5MQkICPXr0oFSpUpibm7N27VqaNGnC0KFDGTx4MFqtFo1Gg7u7O/Xq1ctyjgoVKuDj48OCBQtYtGhRtsf17NmTc+fO0aFDB8qXL//cyew53W+bNm0ICwvD09NTmYD+rJzi26NHD6ZNm8aKFSuyLIfQqlUr/vjjD3r27AlAvXr1GDx4cL5iKYQQQhQGqc1XQOHh4WzdupWlS5cWdVNeiuTkZCwtLUlPT2fw4MG4u7sr86uKu/U7zsiinf8oLvMeCovE4wmJhSGJh6HiEo/c5kxJz1QB9O/fnytXrrB48eKibspL069fP9LT00lLS6NZs2Z06dKlqJv0yrRwrPpalpMRQgjxepFkqgBWrFhR1E146R4/9VcSlS6lRqd+vZIpIYQQrx9JpoTIxutY6LioJNxNIVWjy9O+UoxZCFHSSDIlRDZex0LHRcXCwpSHD/O2ZIUUYxZClDQl9i/egwcPaNCgAVOnTs3T/rt27SI2NlZ5HR0djZeXV6G0pXfv3nz88cd4enri7u7OL7/88kLnWbduHe7u7nTu3FlZOuFNFRwcjKenJ56enjg7O9OyZUvl9dGjR+ndu3dRN1EIIYQASnDPVGRkJA0bNmTr1q2MGjUKExOTHPfftWsX9erVo0GDBoXWBp1OpyxgOX78eD766COuX79Ox44dcXFxoXbt2nk6z+OCwatXr2bGjBn5bqNWq8XI6PUamPH29sbb2xuA0aNHU69ePb744gvl/dWrV+f7nLkVVhZCCCFeRIn9lyU4OJhvv/2Wn376id27d/Ppp59m+Uf78et33nmHPXv2EBUVxaZNm+jXrx9VqlRBq9Xy/fffExMTg0qlYs6cOdSqVQvIuQjv+fPnSU5O5tq1a2zYsMGgXVWqVKFmzZpcunSJqlWr8uOPP/LHH3+QlpaGi4sLY8aMwcjIiN69e1O7dm1OnjxJ2bJlsbCw4OrVq4waNYq6desya9YswsLClEny1atXZ/LkydjY2BASEsKWLVuwsLAgLi6OmTNn0rlzZ4YOHcquXbu4f/8+U6dOJSoqioMHD5KRkcG8efOoVasWt27dYvjw4Tx8+JC0tDRatWrFqFGjAHIsMJyens6cOXM4ePAgarWaatWqsXDhQiVWO3fuRKvVYmtry5QpU5Sizs8THx+Pt7c30dHRAJw8eZKAgAAePnwIZK4Z1rp1a2W/pwsrP70emBBCCFEYSuQw37lz57h//z5NmzbFy8uL4ODgHPdv0aIFbm5uDBw4kPDwcDp37gzAX3/9Rc+ePYmIiODTTz9l0aJFgGER3oiICLRarfIeQGxsLAEBAezYsYOyZcsaXOuvv/7i4sWLODg48OOPP9KkSRM2b95MeHg4d+/eNWjr1atXWbduHcuWLWPu3LlUqlSJ+fPnM2vWLP78808CAgJYsWIFERERvPfee0yZMkU59uTJk/j5+REZGcn7778PgJWVFcHBwYwcOZIhQ4bg5OSkLLr5ePkHKysrlixZQkhICGFhYZw+fZoDBw4o5z19+jSzZs1i+/btZGRkKOVyli5dytWrV5VE7nFbwsPDuXr1Khs3biQ0NJSWLVsybdq0PP8uExMTmTBhArNmzSIkJIQlS5bw/fffK/UFHxdWDg0NlURKCCHES1Eie6Y2b96Mp6cnKpWKtm3bMnXqVBISEvJ9npo1a1KnTh0AGjVqxN69e4Hci/C2bNkSa2trg3NNnTqVuXPnYmpqyuTJk7Gzs2PPnj3Exsby888/A5Camoqtra1yjIeHR7bDVtHR0bRq1YpKlSoB0LNnTzw9PZX3nZycqF69usExjwsQ161bF8gsXQOZq4v/9ttvQOaQ4IwZM4iJiUGv13P79m3OnTtHy5YtgewLDO/du5fRo0crw6mP73/Pnj2cPn1aWb9Kq9UqccuLmJgY4uPjGTBggLJNpVIRFxdH+fLlsxRWzg/z0qboVSXy+8ZzWViY5r4TYGZmTEVr85fcmqJXsWKZom7Ca0NiYUjiYagkxKPEJVPp6elERkZiYmJCeHg4ABqNhpCQEIyMjNDpnjz+nVvB3afnWanVajIyMvLUBgsLiyzbHs+Zepper2fRokVUq1btuecxN3/xf7Ce14bHxYHVanW29/bzzz+TmJjIpk2bMDU15bvvvjOI07MFhnOLoV6vZ/DgwXTt2vWF7kOv1+Pg4MDatWuzvBcfH5+lsHJ+pDxK46E8zQfk72m+1FRNsVjxOCfFZVXnwiCxMCTxMFRc4pHbCugl7mv37t27qVmzJgcOHGDPnj3s2bOHlStXEhoayjvvvKMU6b1586YyJwfyVrT3scIqwuvm5sbSpUvRarUA3L17l6tXr+bpWBcXF/bv38+tW7cA2LhxY6EUAk5KSqJixYqYmpqSkJDA7t2783TcRx99xC+//EJ6ejqQeS+QeY/r1q3jwYMHQGaye+7cuTy3x9HRkbi4OI4cOaJsi42NRaokCSGEeFVKXM9UcHAwHh4eBtscHR3R6XQ0atSIgwcP0r59e2rUqGHwVFynTp0YM2YMO3bsUCagZ6ewivCOHTuWmTNnKkOSxsbGjB07NtueqqfZ29szcuRIfHx8AKhWrRqTJ0/Odxue1bt3b7755hs6duyIra0trq6ueTpu4MCBzJo1i86dO2NsbMw777zD/Pnz6dy5M/fv31cm/ev1enr16pXnJxnLli3LokWLmDlzJv7+/mg0GqpVq8aSJUte+B6FEEKI/JBCx0Jk43UsdFxUZNFOQ8Vl6KIwSCwMSTwMFZd4SKFjIV7Q61jouKiYmRmTmpq3xFKKMQshShpJpoTIhhQ6fqKitXmx+HYphBAvg3yFFEIIIYQoAOmZEiIbjzJ0JW6Yz9RYzetVWEgIIV5/kkwJkY2DMfElbgJ6SZg8LoQQhU3+aoos3Nzc+PPPPwF49OgR/fv3Z8yYMcp6Vy9LfHw8Li4uBtsePHhAgwYNmDp16ku9thBCCPGiJJkS2UpMTKRfv37Y2dnh7++PkVHeBoAKM+mKjIykYcOGbN26VVnwUwghhHidyDCfeK47d+4watQo3Nzc8PX1BSA0NJR169Yp9fMmTpyInZ2dUrzYwsKCuLg4Zs6cSefOnRk2bBi//fYb9+/fZ9SoUbRr1w7ILLIcEBDAw4cPAfD19aV169bPbUdwcDDffvstP/30E7t371bq7CUlJTF27FjOnz+Pra0ttra22NjY4OfnR3p6OnPmzOE///kP6enpODg4MHHixOeW0BFCCCEKSpIp8VxDhw7ls88+UxKpY8eOsX37dtauXYuJiQn79+9n7NixBAUFAZkJUnh4uEHxZEtLS4KDgzl+/DhDhw6lXbt2JCYmMmHCBJYuXUqlSpW4efMmXbt2JTIyMksbzp07x/3792natCm3bt0iODhYSaYWLlyIlZUVO3bs4P79+3h5eSnJ2vLlyylTpgybN28GYObMmSxdupRhw4blKwYlsdBxTkWKS0Kx0vyQeDwhsTAk8TBUEuIhyZR4rlatWrFt2zZ69uyJra0te/bs4dy5c3Tr1g3ILPuSmJio7O/k5GSQSAG0b98egEaNGnHz5k3S0tKIiYkhPj6eAQMGKPupVCri4uIoX768wfGbN29WSum0bduWqVOnkpCQgK2tLdHR0YwfPx6AcuXK0aZNG+W4PXv2kJyczP/93/8BmfX+8lqe5mklsdBxdkWKi8sqxoVF4vGExMKQxMNQcYmHrIAuXsiXX37J3r176dOnD7/++it6vR5vb2+++eab5+7/vCE0U1NTAGWuVUZGBnq9HgcHB9auXZtl//j4eOXn9PR0IiMjMTExITw8HACNRkNISEiudQ71ej0TJkzIc91AIYQQoiBK1hiGyJevvvqKLl260KdPH9zc3AgPD+fGjRtA5iTz06dP5/ucjo6OxMXFceTIEWVbbGwsz5aI3L17NzVr1uTAgQPs2bOHPXv2sHLlSkJDQwFwdnZWkqzExER2796tHOvm5saqVatITU0FIDk5mQsXLuS7rUIIIUReSM+UyNGgQYPQ6/WMHz+eoUOHMnjwYLRaLRqNBnd3d+rVq5ev85UtW5ZFixYxc+ZM/P390Wg0VKtWjSVLlhjsFxwcjIeHh8E2R0dHdDodR48e5V//+hdjxozB3d2dihUrUq9ePSwtM7tgBw4cyIIFC+jatSsqlQqVSsXXX39NrVq1ChYMIYQQ4jlU+me7BIR4A2g0GnQ6HaampiQnJ9OrVy/GjBlDs2bNCu0a63eckUU7/1Fc5j0UFonHExILQxIPQ8UlHjJnShRLiYmJDBgwAK1WS1paGh07dizURAqghWPVEllORgghRP5IMiXeSDY2NoSEhLzUa5QupUanLlnJlBBCiPyTr6FCCCGEEAUgyZQQQgghRAFIMiWEEEIIUQCSTAkhhBBCFIAkU0IIIYQQBSDJlBBCCCFEAUgyJYQQQghRAJJMCSGEEEIUgCRTQgghhBAFIMmUEEIIIUQBSDIlhBBCCFEAkkwJIYQQQhSAJFNCCCGEEAUgyZQQQgghRAFIMiWEEEIIUQCSTAkhhBBCFIAkU0IIIYQQBSDJlBBCCCFEAUgyJYQQQghRAJJMCSGEEEIUgCRTQgghhBAFUKqoGyDE60qtVhV1E14rEg9DEo8nJBaGJB6GikM8crsHlV6v17+itgghhBBCFDsyzCeEEEIIUQCSTAkhhBBCFIAkU0IIIYQQBSDJlBBCCCFEAUgyJYQQQghRAJJMCSGEEEIUgCRTQgghhBAFIMmUEEIIIUQBSDIlhBBCCFEAkkyJEuPSpUv06NGDdu3a0aNHDy5fvpxlH61Wy6RJk2jTpg2ffPIJmzZtytN7b6KCxiMwMBBXV1c8PT3x9PRk0qRJr7D1hS8v8fj999/x8vKiXr16TJ8+3eC9kvj5yCkeJfHzsXDhQjp06ICHhwdeXl4cPHhQee/Ro0cMHTqUTz75BHd3d/bu3fsKW1/4ChqP0aNH07JlS+XzsXjx4lfY+pdAL0QJ0bt3b31YWJher9frw8LC9L17986yT2hoqN7Hx0ev1Wr1d+7c0bdo0UJ/9erVXN97ExU0HvPnz9dPmzbtlbb5ZcpLPC5fvqw/c+aMfvbs2VnuvSR+PnKKR0n8fBw4cECfkpKi1+v1+rNnz+o/+OAD/aNHj/R6vV4fGBioHzdunF6v1+svXbqkb9asmT45OfkVtb7wFTQefn5++tWrV7+6Br9k0jMlSoQ7d+5w5swZOnbsCEDHjh05c+YMd+/eNdhv27ZtdOvWDbVajbW1NW3atGHHjh25vvemKYx4FCd5jcc777zD+++/T6lSWWvEF6dYFUY8ipO8xqNFixaULl0aAAcHB/R6Pffv3wdg+/bt9OjRA4AaNWpQr149Dhw48OpuohAVRjyKG0mmRIlw/fp1bG1tMTIyAsDIyIhKlSpx/fr1LPu99dZbyusqVapw48aNXN970xRGPAC2bt2Kh4cHPj4+xMTEvJrGvwR5jUdu5yhpn4/clOTPR1hYGNWrV6dy5coAXLt2jbffflt5v6R9Pp6NB8DPP/+Mh4cHQ4YM4cKFCy+93S9T8f46IYR4aXr27MmgQYMwNjbm0KFDDBkyhG3btlG+fPmibpp4DZTkz8fRo0eZN28eK1euLOqmvBaeF49hw4ZRsWJF1Go1YWFhfPnll+zatUtJ0N400jMlSoQqVaqQkJCAVqsFMicL37x5kypVqmTZ79q1a8rr69evK9+kcnrvTVMY8ahYsSLGxsYAfPjhh1SpUoXz58+/ojsoXHmNR27nKGmfj5yU1M9HTEwM3377LQsXLsTOzk7Z/tZbb/H3338rr0vK5yO7eNja2qJWZ6YgnTt3JiUl5Y3tqQNJpkQJYWNjw/vvv09kZCQAkZGRvP/++1hbWxvs5+7uzqZNm9DpdNy9e5ddu3bRrl27XN970xRGPBISEpT9zp49y99//03NmjVf3U0UorzGIycl8fORk5L4+YiNjWXYsGHMnz+funXrGrzn7u7Ohg0bALh8+TKnTp2iRYsWr+YGCllhxOPpz8fBgwdRq9XY2tq+/Ma/JCq9Xq8v6kYI8SpcuHCB0aNHk5iYiJWVFdOnT8fOzo4BAwbg6+tL/fr10Wq1TJ48mUOHDgEwYMAAZdJoTu+9iQoaDz8/P/73v/+hVqsxNjbG19eXVq1aFeUtFUhe4nHs2DGGDx9OcnIyer2eMmXK8MMPP9CiRYsS+fnIKR4l8fPh7e3N33//bZAUzJgxAwcHB1JSUhg9ejRnz55FrVbz7bff0qZNmyK8o4IpaDz69u3LnTt3UKlUWFpaMmrUKBo1alR0N1RAkkwJIYQQQhSADPMJIYQQQhSAJFNCCCGEEAUgyZQQQgghRAFIMiWEEEIIUQCSTAkhhBBCFIAkU0IIIYQQBSDJlBBCvAA3NzcaNGiAo6Oj8t/TCxG+6DmjoqIKqYW5CwwMZOTIka/sejkJCQmhV69eRd0MIV6I1OYTQogXtGTJEpo1a1bUzVBkZGRQqtSb92c9IyOjqJsgRIFIz5QQQhSipKQkxo4dS/PmzWnRogVz5sxRaphduXKFPn364OLigouLCyNGjCAxMRGAb7/9lmvXrjFo0CAcHR1ZtmwZ0dHRtGzZ0uD8T/deBQYG4uvry8iRI3FyciI0NDTH6+fGwcGBtWvX0rZtWxwdHZk7dy5XrlyhZ8+eODk58c0335Ceng6gtG3JkiW4uLjg5ubGli1bDOIwatQomjZtykcffcSiRYvQ6XRAZi9Uz5498ff3x8XFhWHDhjFhwgT++9//4ujoSOPGjQHYt28fnTt3xsnJiVatWhEYGKicPz4+HgcHB0JDQ2ndujUuLi4sXrxYeV+r1bJkyRLatGmDo6MjXl5eXL9+Hchcvbtfv344OzvTrl07tm3blvdfsBDP8eZ9hRFCiNfY6NGjsbGxYefOnTx69IivvvqKKlWq0LNnT/R6PV999RVNmjQhOTmZf//73wQGBjJu3DhmzpzJ8ePHmTp1qtLbFR0dnev1du/ezbx585gxYwbp6emMGDEi2+vnxe+//05ISAjXr1+nS5cuxMTEMHPmTMqVK0ePHj3YunUrXbp0AeD27dvcu3ePgwcP8t///peBAwdSr1497OzsmDJlCklJSezatYv79+/Tv39/KlasSLdu3YDMum0dOnTg0KFDZGRksG3bNjZt2sT69euVtpQuXZrp06fz3nvv8eeff+Lj48P7779vUIbl+PHj7Nixg8uXL9O1a1fatm1LrVq1+Pnnn9m6dStLly6lZs2a/PHHH5iZmZGSkoKPjw++vr4sW7aMP//8k379+mFvb8+7776b59+zEE+TnikhhHhB//rXv2jcuDGNGzdmyJAh3L59m/379zN27FjMzc2xsbGhb9++bN26FYB33nmHDz/8EBMTE6ytrenXrx//+c9/CtSGRo0a0aZNG9RqNcnJyTlePy++/PJLLC0tee+997C3t+fDDz+kWrVqlClThpYtW3LmzBmD/b/55htMTExwdnamVatWbN++Ha1Wy7Zt2xgxYgSWlpZUrVqVfv36GfRcVapUid69e1OqVCnMzMye2xYXFxccHBxQq9XUrl2bDh06cPToUYN9vv76a8zMzKhduza1a9fm3LlzAGzatIlvvvkGOzs7VCoVtWvXpnz58uzbt4+3334bb29vSpUqRZ06dWjXrh07duzIc4yEeJb0TAkhxAtauHChwZyp2NhYMjIyaN68ubJNp9NRpUoVILMn54cffuDYsWM8fPgQvV6PlZVVgdpQuXJl5edr167leP28qFChgvKzqalplte3b99WXltZWWFubq68fuutt7h58yb37t1Do9Hw1ltvGbz39AT9p9udnZMnTxIQEMD58+fRaDSkp6fj7u6ebXtLly5NSkoKADdu3KB69epZzvn3338TGxurDCVC5pBgp06dcm2PENmRZEoIIQpJ5cqVMTEx4ciRI8+dCD579mxUKhURERGUK1eOXbt2MXny5GzPV7p0aVJTU5XXWq2Wu3fvGuyjUqnyfP3ClpiYSEpKipJQXb9+nffee4/y5ctjbGzMtWvXlKGz69evY2tr+9x2P+81wIgRI/jiiy9Yvnw5pqam/PDDD9y7dy9PbatcuTJXrlzB3t7eYHuVKlVo0qQJP//8c77uVYicyDCfEEIUkkqVKvHhhx8ybdo0kpOT0el0XLlyRRmaevjwIebm5pQpU4aEhASWL19ucHyFChW4evWq8rpmzZqkpaWxb98+NBoNixcvViaAv8j1X4bAwEDS09M5duwY+/btw93dHSMjI9zd3ZkzZw7Jycn8/fff/Pzzzzn2/tjY2JCQkGBwfw8fPqRs2bKYmpoSGxtLZGRkntvVrVs35s2bx+XLl9Hr9Zw7d4579+7RunVrLl++TFhYGBqNBo1GQ2xsLBcuXChQHETJJsmUEEIUohkzZqDRaGjfvj1NmjTB19eXW7duAZnze86cOUPjxo0ZOHAgbdu2NTh24MCBLF68mMaNG7NixQrKlCnDhAkTGD9+PC1btqR06dK5Do/ldP3CVqFCBaysrGjRogUjR45k4sSJ1KpVC4DvvvuO0qVL06ZNGz777DM6duyIt7d3tudq2rQp7777Ls2bN8fFxQWACRMmMH/+fBwdHVm4cCGffvppntvWr18/Pv30U3x8fHBycmLcuHGkpaVhaWnJihUr2LZtGy1atKB58+YEBATkmKQKkRuVXq/XF3UjhBBCvFmio6P59ttvOXDgQFE3RYgiJz1TQgghhBAFIMmUEEIIIUQByDCfEEIIIUQBSM+UEEIIIUQBSDIlhBBCCFEAkkwJIYQQQhSAJFNCCCGEEAUgyZQQQgghRAFIMiWEEEIIUQD/H/cacWV4Fus6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = np.array([30, 2, 365, 8, 1, 30, 2070, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
        "columns = X_train.columns.values\n",
        "\n",
        "bm_kernel = pd.DataFrame(data = [data], columns = columns)"
      ],
      "metadata": {
        "_uuid": "9a0f1a152dce34c0aaef96428e91ee64e72b9b76",
        "execution": {
          "iopub.status.busy": "2022-02-02T19:18:43.723908Z",
          "iopub.execute_input": "2022-02-02T19:18:43.724690Z",
          "iopub.status.idle": "2022-02-02T19:18:43.733396Z",
          "shell.execute_reply.started": "2022-02-02T19:18:43.724650Z",
          "shell.execute_reply": "2022-02-02T19:18:43.732358Z"
        },
        "trusted": true,
        "id": "zL8uye7ABlBm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_votes = xgb.predict(bm_kernel)\n",
        "predict_votes"
      ],
      "metadata": {
        "_uuid": "c985ea4e6dfd01bcb56869b083af670a29f2e15f",
        "execution": {
          "iopub.status.busy": "2022-02-02T19:18:47.257271Z",
          "iopub.execute_input": "2022-02-02T19:18:47.258192Z",
          "iopub.status.idle": "2022-02-02T19:18:47.274978Z",
          "shell.execute_reply.started": "2022-02-02T19:18:47.258115Z",
          "shell.execute_reply": "2022-02-02T19:18:47.274301Z"
        },
        "trusted": true,
        "id": "NYzODqXQBlBm",
        "outputId": "22c98880-b0f1-4ed9-b03b-b8dca40183fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 85,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([6.038366], dtype=float32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion"
      ],
      "metadata": {
        "_uuid": "65918894856aa7b279fea48fe80af88e43252bc4",
        "id": "SSMKC07ABlBn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In conclusion, I would like to summarize all the findings and recommendations from this analysis:\n",
        "\n",
        "1. __It is hard to create a really helpful kernel, which will be appreciated and upvoted by Kagglers__: only 38% of kernels have upvotes and only 9% of kernels have awards (have more than 5 upvotes).\n",
        "2. __Views and comments bring upvotes__: consider adding a captivating title to the kernel and sharing the link to the kernel with others, the more people will view the kernel - the more people will find it useful.\n",
        "3. __Active authors have more votes__: try to be an active author and gain visibility, experience in writing kernels and feedback from the others will eventually help to get votes.\n",
        "4. __It doesn't really matter what topic the kernel is related to, but it matters how the kernel material is presented__: notebooks tend to be more appreciated by Kagglers."
      ],
      "metadata": {
        "_uuid": "4fff15f009ead95ccc5d30b7531ece3b27c2c095",
        "id": "xrn7ARvkBlBn"
      }
    }
  ]
}