{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A look into kiva.org" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Kiva is a non-profit organization that hosts a platform, kiva.org, to crowdfund loans to individuals and groups who otherwise may not have had access to capital. They provide a rich dataset of historical loans from 2006 to 2019.\n", "\n", "In this notebook, we will first explore the dataset to glean insights and deeper understanding about the platform.\n", "\n", "We will then consider the expected personal loss of making a single loan, and then withdrawing any repayment as soon as possible.\n", "\n", "Finally, we will attempt to use machine learning models to predict which loans will get funded, and which will get fully repaid." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.colors as mplc\n", "import matplotlib.patches as patches\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as ticker\n", "from scipy import stats\n", "import numpy as np\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "%matplotlib inline\n", "# activate plot theme\n", "import qeds\n", "qeds.themes.mpl_style();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Exploration\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "# Source: https://www.kiva.org/build/data-snapshots\n", "loans_raw = pd.read_csv(\"datasets/loans_big.csv\", parse_dates=[\"POSTED_TIME\",\"PLANNED_EXPIRATION_TIME\",\"DISBURSE_TIME\",\"RAISED_TIME\",])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above dataset was provided by Kiva as a snapshot of their historical loans. Below is an example row:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
LOAN_IDLOAN_NAMEORIGINAL_LANGUAGEDESCRIPTIONDESCRIPTION_TRANSLATEDFUNDED_AMOUNTLOAN_AMOUNTSTATUSIMAGE_IDVIDEO_IDACTIVITY_NAMESECTOR_NAMELOAN_USECOUNTRY_CODECOUNTRY_NAMETOWN_NAMECURRENCY_POLICYCURRENCY_EXCHANGE_COVERAGE_RATECURRENCYPARTNER_IDPOSTED_TIMEPLANNED_EXPIRATION_TIMEDISBURSE_TIMERAISED_TIMELENDER_TERMNUM_LENDERS_TOTALNUM_JOURNAL_ENTRIESNUM_BULK_ENTRIESTAGSBORROWER_NAMESBORROWER_GENDERSBORROWER_PICTUREDREPAYMENT_INTERVALDISTRIBUTION_MODEL
77657807094GUSTAVOSpanishGustavo es soltero y vive con sus padres en La...Gustavo is single and lives with his parents i...500.0500.0funded1745738.03002.0Higher education costsEducationto pay university tuitionBOBoliviaLa Pazshared0.1USD48.02014-11-27 15:25:02+00:002015-01-25 02:20:02+00:002014-11-21 08:00:00+00:002014-12-21 15:17:44+00:0020.01721user_favorite, user_favorite, user_favoriteGUSTAVOmaletruemonthlyfield_partner
\n", "
" ], "text/plain": [ " LOAN_ID LOAN_NAME ORIGINAL_LANGUAGE \\\n", "77657 807094 GUSTAVO Spanish \n", "\n", " DESCRIPTION \\\n", "77657 Gustavo es soltero y vive con sus padres en La... \n", "\n", " DESCRIPTION_TRANSLATED FUNDED_AMOUNT \\\n", "77657 Gustavo is single and lives with his parents i... 500.0 \n", "\n", " LOAN_AMOUNT STATUS IMAGE_ID VIDEO_ID ACTIVITY_NAME \\\n", "77657 500.0 funded 1745738.0 3002.0 Higher education costs \n", "\n", " SECTOR_NAME LOAN_USE COUNTRY_CODE COUNTRY_NAME \\\n", "77657 Education to pay university tuition BO Bolivia \n", "\n", " TOWN_NAME CURRENCY_POLICY CURRENCY_EXCHANGE_COVERAGE_RATE CURRENCY \\\n", "77657 La Paz shared 0.1 USD \n", "\n", " PARTNER_ID POSTED_TIME PLANNED_EXPIRATION_TIME \\\n", "77657 48.0 2014-11-27 15:25:02+00:00 2015-01-25 02:20:02+00:00 \n", "\n", " DISBURSE_TIME RAISED_TIME LENDER_TERM \\\n", "77657 2014-11-21 08:00:00+00:00 2014-12-21 15:17:44+00:00 20.0 \n", "\n", " NUM_LENDERS_TOTAL NUM_JOURNAL_ENTRIES NUM_BULK_ENTRIES \\\n", "77657 17 2 1 \n", "\n", " TAGS BORROWER_NAMES \\\n", "77657 user_favorite, user_favorite, user_favorite GUSTAVO \n", "\n", " BORROWER_GENDERS BORROWER_PICTURED REPAYMENT_INTERVAL DISTRIBUTION_MODEL \n", "77657 male true monthly field_partner " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('display.max_columns', 40)\n", "loans_raw.dropna().head(1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The dataset contains 1682790 loans in the date range 2006-04-16 07:10:50+00:00 to 2019-02-25 04:12:27+00:00.\n" ] } ], "source": [ "min_date = loans_raw[\"POSTED_TIME\"].min()\n", "max_date = loans_raw[\"POSTED_TIME\"].max()\n", "print(f\"The dataset contains {len(loans_raw)} loans in the date range {min_date} to {max_date}.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will first explore the quality of the data and see if there will be any issues with missing data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "is_executing": false }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LOAN_ID 0\n", "LOAN_NAME 48555\n", "ORIGINAL_LANGUAGE 44209\n", "DESCRIPTION 44244\n", "DESCRIPTION_TRANSLATED 453635\n", "FUNDED_AMOUNT 0\n", "LOAN_AMOUNT 0\n", "STATUS 0\n", "IMAGE_ID 44209\n", "VIDEO_ID 1681943\n", "ACTIVITY_NAME 0\n", "SECTOR_NAME 0\n", "LOAN_USE 44232\n", "COUNTRY_CODE 29\n", "COUNTRY_NAME 0\n", "TOWN_NAME 163515\n", "CURRENCY_POLICY 0\n", "CURRENCY_EXCHANGE_COVERAGE_RATE 337326\n", "CURRENCY 0\n", "PARTNER_ID 18325\n", "POSTED_TIME 0\n", "PLANNED_EXPIRATION_TIME 371834\n", "DISBURSE_TIME 3189\n", "RAISED_TIME 85150\n", "LENDER_TERM 24\n", "NUM_LENDERS_TOTAL 0\n", "NUM_JOURNAL_ENTRIES 0\n", "NUM_BULK_ENTRIES 0\n", "TAGS 831171\n", "BORROWER_NAMES 48555\n", "BORROWER_GENDERS 44209\n", "BORROWER_PICTURED 44209\n", "REPAYMENT_INTERVAL 0\n", "DISTRIBUTION_MODEL 0\n", "dtype: int64\n" ] } ], "source": [ "print(loans_raw.isnull().sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are some missing cells. However, the core data relating to the loans themselves are complete. Although cells relating to currencies are listed, manually investigating a sample revealed that all loan amounts are in USD.\n", "\n", "We will proceed by asking some basic questions about the loans.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (10, 5)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "What are these loans going towards?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Loans by Sector')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sectors = loans_raw[\"SECTOR_NAME\"].value_counts().sort_values()\n", "ax = sectors.plot.barh()\n", "ax.set_title(\"Loans by Sector\")" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "What about if we get slightly more granular?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Loans by Activity')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwsAAAFOCAYAAADJrIFMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde1zO9//48UdXJ0mnKyolpxwSo6wPOc55TjnOJhpWYQ5jbN9N9hmzMT5Y5hAzcz4zjM9QJJs5Jm2knAqLORSJpHS6fn/4Xe+Pax0UWul63m+33T5d78Pr9Xq+L5/b7f28XieD9PR0DUIIIYQQQgjxN6rSboAQQgghhBCibJJkQQghhBBCCJEvSRaEEEIIIYQQ+ZJkQQghhBBCCJEvSRaEEEIIIYQQ+ZJkQQghhBBCCJEvSRaEEEKIEhQWFoa3tzcbNmwo7aaUig0bNuDt7U10dPRzl3H79m28vb2ZN2/eS2yZEKIoJFkQQghRYry9vfH29i7tZoj/b9myZXh7e9O7d2/u3r37UsrUJgNhYWEvpbziCAwMxNvbm9u3b//jdQuhL4xKuwFCCCGEKHmZmZkcPHgQAwMDcnNz2b9/PwMHDizxenv06EGbNm2oUqXKc5dha2vL4sWLMTc3f4ktE0IUhfQsCCGEEHrg8OHDpKam4u3tjZmZGfv27SM3N7fE67WyssLZ2ZkKFSo8dxlGRkY4OzujVqtfYsuEEEUhPQtCCCHKjPj4eLZu3UpMTAwPHz7E2toad3d33nnnHRwcHHSuvXv3Lvv27SMqKopbt27x8OFDLC0tadSoEW+//TY1atTQuf727dsEBATQqFEjJk2axNq1a4mIiCA1NZWqVavSp08funTponOPRqPhwIEDhIaGcuPGDdLT07G0tMTJyYm2bdvy5ptvFiu+8+fPs27dOi5evAhAgwYNePfdd6lTp45yzYoVK9ixYwcffvghHTt2zFPGjRs3eP/996lXrx5z584tct2hoaHAk1/609PT2b9/P1FRUXh6euZ7fU5ODmFhYRw8eJCrV6+SmZmJjY0Nrq6u9OnTh7p16xIYGMjZs2cBmD9/PvPnz1fu/+GHH7C3t2fDhg1s3LiRr7/+mtdee427d+/i5+eHs7MzixYtyrfu2bNn89tvvzF9+nSaNGmifHcdOnRgwoQJADrD2wICApS/7ezsWL58ORMmTODy5cssXbo0z78d7fNYtGgRb731FkOHDi3ycxRC30iyIIQQokyIiIhg5syZaDQaWrRoQdWqVbly5QphYWEcO3aMGTNm4OLiolwfExPDjz/+SOPGjWnZsiUVKlTgxo0bHDlyhBMnTjB79mxq166dp560tDQ+/fRTjI2NadWqFZmZmRw5coSFCxeiUqno1KmTcu3q1avZtm0bdnZ2tGrVikqVKnHv3j2uXLlCeHh4sZKFCxcusHXrVtzd3enZsyc3btzg2LFjnD17lunTp9OgQQMAunfvzs6dO9m7d2++ycLevXvRaDR069atyHVfu3aN2NhY3NzccHR0pGPHjuzfv5+QkJB8k4WsrCymT59OVFQUarWa1q1bY2FhQVJSEtHR0Tg5OVG3bl2lfWfPnqV58+Y6z7ugIUO2tra4u7sTFRVFXFycTqIET76fEydOUKVKFV577bUCY/Lx8eHAgQMkJibSq1cvpT7t//bo0YP58+cTGhqabzIQEhKCSqUqdsInhL6RZEEIIUSpS09P59tvvyUnJ4fp06fTuHFj5dy+fftYuHAhQUFBLFq0CAMDAwAaN27M2rVrqVixok5ZcXFxTJo0idWrVzNt2rQ8dV25coU333yTUaNGYWhoCEDv3r354IMP2LZtm06yEBoailqtJjg4OM8wmvv37xcrxqioKN5//3169OihHDty5AizZs1i/vz5LFmyBAMDAxwcHGjatCmRkZFcuXKFWrVqKddnZWURHh6OhYUFbdq0KXLdISEhAEpsDRs2xNHRkZMnT3L37l1sbW11rt+4cSNRUVG4u7vz2Wef6cSek5OjxN6pUycSExM5e/YsXl5eOs+uMJ06dSIqKooDBw7kSRZ+++03MjMz6dChAypVwaOlBw0aRHR0tJIs2Nvb65xv27YtK1asICwsjEGDBmFsbKyci4uLIy4ujtdffz3fXgchxP/InAUhhBCl7sSJE6SmptKyZUudRAGgS5cu1KlTh4SEBM6fP68ct7a2zpMoANSpU4fGjRsTHR1NdnZ2nvOmpqb4+/sriQJA9erVcXNz4/r16zx69Eg5bmBggJGRkc61WlZWVsWKsWrVqnl6A1q1akWDBg3466+/OHfunHJcm1Ds3btX5/ojR47w4MEDOnTogImJSZHqzczMJDw8nAoVKtCqVSvleMeOHcnNzWXfvn061+fk5LBnzx6MjY0ZO3ZsniTJ0NDwhecOeHl5YW5uzq+//kpWVpbOuQMHDgDQoUOHF6rDxMSEzp07k5KSwvHjx3XOaZ9rcXpnhNBXkiwIIYQodfHx8QA0adIk3/Pa49rrtE6ePMm0adMYMmQIffr0UZZqPXnyJFlZWTx48CBPWU5OTpiZmeU5XrlyZeDJMBit9u3bk5iYyKhRo1i1apUyx+F5NGzYMN9fyhs2bAjA5cuXlWNNmzbFwcGBX375hfT0dOW4toega9euRa738OHDPHz4kJYtW+okV9pf7vfv368z0fn69eukpaVRvXr1PL/WvyzGxsa0bduW1NRUTp48qRy/ceMG58+fV4ZLvahu3bqhUql0kq5Hjx5x6NAhKleuXOB8DSHE/0iyIIQQotRpX9Ctra3zPW9jY6NzHcCuXbv48ssvOX/+PA0bNqR3794MHDgQHx8fZejO33+1BvLtjQCUF/mnX5z9/PwYOXIk5ubmbN++na+++gpfX18+//xzrly5UqwYC4pNe/zp2FQqFd26dSM9PZ1ffvkFgISEBGJiYmjcuDHVqlUrcr1/H4KkVblyZdzd3UlKSuLUqVPKcW07tMlTSdG2R9uT8PTf+c3VeB7aIV3R0dFcu3YNgIMHD5KRkcGbb76Zb4+REEKXzFkQQghR6rSTUlNSUvI9f+/ePZ3rcnJy2LBhAzY2Nnz77bd5hsU8PVzpRRgaGtKzZ0969uzJgwcPiI2N5dixYxw8eJDPP/+cxYsXY2lpWaSyCopNe/zvE4I7d+7M+vXrCQkJoVu3bspLf3GGziQkJCjDmyZPnlzgdSEhIfzrX//SacfL2rStIPXq1cPZ2ZlTp06RkpKClZUVBw8exNTUlNatW7+0enr06EFkZCQhISEMHz6ckJAQDA0N86x8JYTInyQLQgghSp12laMzZ87k+zJ8+vRpAGUy7IMHD0hLS6Nx48Z5EoX09PQ8w5VeBktLS7y8vPDy8iI7O5tDhw5x7tw5mjdvXqT7Y2Njyc3NzTMUKSYmBiDPyk0WFha0bduWsLAwoqOjCQ8Px8bGBi8vryK3WZtguLm54eTklO81R48eJTIykjt37lC5cmWqVauGubk5f/75J4mJidjZ2RVaR349MkXVsWNHVq1axS+//EKtWrVISkqiXbt2Bfb+PE/dr7/+OlWrViU8PJxmzZpx9epVWrZsKXs2CFFEMgxJCCFEqfPy8sLCwoKjR48q6/ZrhYWFERcXR/Xq1alfvz7wZHKxqakpcXFxOmP6s7OzWbZsWb5zFYorKyuLP/74I8+LqEajUVYDenqFnWe5ceNGvhOWz507h5OTk7J06tO0E53nzp1LWloanTt3xsioaL/zaXdsVqlUfPTRR4wbNy7f/zp16qTs6AxPelN69OhBVlYWwcHBPH78WKfcnJwckpOTlc/anpWkpKQiPwut9u3bo1KpOHDggDIEqagrKhW1bgMDA7p27crDhw8JCgoCZGKzEMUhPQtCCCFK3Lx58wo85+fnh5WVFR9++CEzZ87k888/p2XLltjb23P16lUiIyMxNzdnwoQJyrKpKpUKb29vfvzxR8aOHYuXlxdZWVlER0fz8OFDGjduzJkzZ16ozY8fP+bzzz+nSpUq1K9fHzs7O7Kzszl79iyXL1+mfv36BU7Izk/Tpk1Zvnw5kZGR1KpVS9lnwcTEhHHjximxPa1OnTrUq1ePixcvFntPAO3E5qZNmxbaO/Dmm2+yc+dO9u3bx9tvv42hoSEDBw4kLi6OqKgoRowYQbNmzbCwsODu3bucOXOGzp07M2jQIODJ5HOVSsWuXbuUjfQAevbsWeBeC1pqtVpZJvb69evP3Fvh7zw8PDh8+DCLFi2iZcuWmJmZYW5uTs+ePXWu0w7pSk5OxtHRsVjfmxD6TpIFIYQQJS48PLzAc4MGDcLKyopmzZoxZ84ctm7dyunTp3n48CFWVlZ06NCBgQMH5lkP39fXFysrK/bt20dISAgVK1bE3d2dd999l/Xr179wmytUqMB7773HmTNnuHDhAhEREZiammJvb4+/vz9du3Yt1gTZ+vXr4+Pjw9q1a/n5558BlPb+fa+Bp3Xq1ImLFy/y+uuvP3NI0NO0Q5CelWA4Ozvj5uZGbGwsp06dolmzZhgbGzNlyhT27dvHgQMH+PXXX8nJycHGxoZGjRrRrFkz5X4nJyc+/vhjtm/fzr59+8jMzASgXbt2z0wWtPFFRkaSnZ39zL0V8rv3zp07/PLLL+zcuZPs7Gzs7OzyJAsWFhZ4eXlx6NAhunbtmm9iJoTIn0F6erqmtBshhBBCiPwtWrSI0NBQpk6dKkt9PieNRsOoUaNISkpi5cqVRZ6ULoSQOQtCCCFEmXXnzh0OHjxI1apVadq0aWk355V19OhR/vrrL9q2bSuJghDFJMOQhBBCiDImPDycGzducPjwYTIzM/H19S3W8BzxxObNm0lNTSUsLAwTExPeeeed0m6SEK8cSRaEEEKIMmb//v3ExMRga2uLv78/bdu2Le0mvZLWrVuHoaEhzs7OvPfee3nmvQghnk3mLAghhBBCCCHyJX2aQrxkiYmJJCYmlnYzhBBCCCFemAxDEqKEZGRklHYTSkVqaioWFhal3YxSI/Hrb/z6HDtI/Pocvz7HDuUj/goVKhR4TnoWhBBCCCGEEPmSZEEIIYQQQgiRL0kWhBBCCCGEEPmSOQui3Pjmm29IT0/n3//+d2k3BYD6Y0JKuwmlYk+gJ56TjpR2M0qNxK+/8etz7CDx63P8+hw7lFz8F4K7vvQyn4ckC+KFzJs3j/Dw8DzH58+fT+3atf/Rtrz//vtoNLISsBBCCCHEyyLJgnhh7u7uTJw4UeeYpaXlc5WVnZ2NkdHz/bM0Nzd/rvuEEEIIIUT+JFkQL8zIyAgbG5s8xyMjI9myZQsJCQkYGBhQr149hg8fTrVq1QC4ceMGI0eO5JNPPmHv3r2cP3+e4cOHo1KpWLlyJRMnTmTFihUkJSXh4eHBxIkTOXXqFGvXruX+/ft4eXkxZswYTExMgLzDkD755BNcXFwwNTVl3759GBoa0rFjR4YMGYJK9WS6TnJyMgsXLuTMmTNYW1szePBgtmzZQvv27XnnnXf+oScohBBCCFE2SbIgSkxGRgZ9+vShZs2aPH78mI0bN/LVV18RHBys03uwatUq/P39GTduHMbGxkRGRvL48WP++9//8vHHH5OZmcnMmTOZOXMmpqamTJ48mfv37/P111/j4uJCr169CmxDeHg4ffr0Yc6cOcTFxREUFESdOnVo3bo1AEFBQaSmpjJjxgyMjY1Zvnw5d+/eLfFnI4QQQgjxKpBkQbywqKgoBgwYoHx2c3Nj2rRpygu51ocffsjAgQOJi4vD1dVVOd67d29atmypc212djajR4+matWqALRt25bdu3ezdu1aZeOTZs2acebMmUKThZo1a+Lj4wOAk5MToaGhnD59mtatW/Pnn39y+vRpgoKCqFu3LgDjx49n+PDhBZYXEhJCaGhooc8jMDAQeDLhSR+pKxnrbewg8etz/PocO0j8+hy/PscOJRd/amrqSy+zIIVtyibJgnhhjRo1YsyYMcpnU1NT4Mkwo/Xr13PhwgUePHiARqNBo9GQlJSkkyzUqVMnT5mmpqZKogBgbW2NWq3W2SHR2tqaW7duFdq2mjVr6nxWq9Xcv38fgOvXr2NoaIiLi4ty3t7eHmtr6wLL69q1K127Fr46QWJiIgDdZ0YWel15tSfQU29jB4lfn+PX59hB4tfn+PU5dii5+GU1JFFumJiY4OjomOf4tGnTsLe354MPPkCtVmNgYMCYMWPIzs7WuS6/bDa/Sc75HXvW6kd/v8fAwIDc3NxC7xFCCCGEEE/IpmyiRNy7d48bN27wzjvv0KRJE5ydnXn06FGZelGvVq0aOTk5XL58WTmWmJhISkpKKbZKCCGEEKLskJ4FUSIsLS2xsLAgJCQEGxsb7t69y4oVK5RViMqCGjVq0KRJExYtWsSoUaOUCc7aYVQvqqx0H/7TUlNT9TZ2kPj1OX59jh0kfn2OX59jh/Iff9l5cxPliqGhIZ988gnx8fGMHTuWpUuXMnToUAwNDUu7aTomTpyIjY0NgYGBTJ8+nY4dO2JhYaEsxyqEEEIIoc8M0tPTZctbIf6/e/fuMWzYMAIDA/Hy8nquMrQTnJ93Y7pXXWpqqs5EdH0j8etv/PocO0j8+hy/PscO5SN+WQ1JiAL88ccfPH78mBo1apCSksKaNWuwsbHBw8OjtJsmhBBCCFHqJFkQei07O5s1a9Zw+/ZtKlSoQP369Zk1a9ZLm7cghBBCCPEqk2RB6DVPT088PfV3IxkhhBBCiMLIBGchhBBCCCFEviRZEEIIIYQQQuRLkgUhhBBCCCFEvmTOgvjHhIWFsXTpUrZu3VraTflH1B8TUtpNKBV7Aj3xnHSktJtRaiR+/Y1fn2MHiV+f4y/J2MvzZmevCkkWXgH37t3jxx9/5OTJk9y5c4eKFStStWpV2rZtS6dOnTAzMyvtJr40V65cYf369Vy4cIG0tDSsrKyoW7cuAQEB2NnZcfv2bQICAggKCqJu3bql3VwhhBBCiHJNkoUy7vbt23zyySdUrFgRX19fatasiUaj4a+//iI8PBwLCwvatWtXqm3MysrC2Nj4hcu5f/8+n332GU2bNmXq1KlYWFiQmJhIZGQkjx49egkt1ZWdnY2RkfxfQAghhBCiIPKmVMYtXrwYlUrFvHnzdHbXq1GjBi1btkSj+d8G3GlpaaxcuZLjx4+TmZlJ7dq18ff3V36B1w4D+ve//83333/P7du3qVevHuPGjcPBwUEpJyIigg0bNpCQkICNjQ1vvPEGPj4+SkLg7+9Px44dSUpK4tixY7i7uzNp0iRWrVrF8ePHSUpKwtramtatWzN48GBMTEyKFGtsbCxpaWmMHz9eqcve3p7XXntNuSYgIACAiRMnAtCoUSNmzpxJbm4uW7ZsITQ0lJSUFJycnPD19VV2Ydb2SHz88cfs27eP8+fP895779GzZ0/OnTvH6tWruXTpEpUqVaJ58+YMGzaMihUrFvv7EkIIIYQoT2SCcxmWmprK77//Tvfu3QvchtvAwAAAjUbDtGnTuHv3LlOmTOHbb7+lUaNGfPbZZyQnJyvXZ2VlsXXrVsaPH8+cOXNIS0tj8eLFyvmoqCjmzp1Lz549CQ4OZvz48Rw9epQ1a9bo1PvTTz9RrVo1goKCGDJkCPBkq/Bx48axePFiRo0axaFDh9iyZUuR47WxsSE3N5cjR47oJEFP++abbwCYNm0aa9asYfLkyQDs2rWL7du3M3ToUBYtWoSXlxczZ87k8uXLOvevWbOG7t27ExwcjJeXF1evXmXKlCk0b96chQsXMnnyZC5fvsz8+fOL3G4hhBBCiPJKehbKsBs3bqDRaKhWrZrO8WHDhpGWlgZAu3btGDNmDGfOnOHKlSusW7dO2X3Y19eXiIgIDh48SP/+/QHIycnh/fffV8rs27cv8+fPJzc3F5VKxZYtW+jXrx+dOnUCoGrVqgwdOpSgoCD8/PyU5KRRo0ZKmVoDBw5U/ra3t+ftt99mx44d+Pr6FileV1dXBgwYwLfffst3331H3bp1ee2112jXrh12dnYAWFlZAWBhYYGNjY1y744dO+jbt68yJMvX15eYmBh27NjBRx99pFzXs2dPWrVqpXxes2YNbdq0oW/fvsqx0aNHM378eFJSUrC2ttZpY0hICKGhoYXGERgYCDyZ8KWP1JWM9TZ2kPj1OX59jh0kfn2OvyRjT01NLZFyXyaNRvNKtLMwBf0oDZIsvJJmzZpFbm4uwcHBZGZmAhAfH8/jx4/zvJhnZmZy8+ZN5bOxsbFO8qFWq8nOziYtLQ0LCwvi4uK4ePEi27ZtU67Jzc0lMzOTe/fuoVarAfKdXHzkyBF27tzJzZs3ycjIIDc3l9zc3GLFNmTIEPr06cOZM2e4cOEC+/fvZ8uWLXz++ec0adIk33sePXpEcnIybm5uOsfd3NyIjIzUOVanTh2dz3Fxcdy8eZPffvtNOabt1bh161aeZKFr16507Vr4ygyJiYkAdJ8ZWeh15dWeQE+9jR0kfn2OX59jB4lfn+MvydhfhdWQUlNTsbCwKO1mlBhJFsqwqlWrYmBgwPXr13WOa+cXPD0XIDc3F2tra2bNmpWnnKfH3hsaGuqc0/YUaF/qNRoNPj4+Or++a2l/1QeU3gut8+fPM3v2bHx8fGjatCmVKlXixIkTrFixokixPs3S0pLWrVvTunVrhgwZwvjx49m0aVOByUJhtPFp/T1z1mg0dOnShd69e+e519bWttj1CSGEEEKUJ5IslGGWlpZ4eHjw888/07Nnz0KXSHVxcSElJQWVSqUzWbm4XFxcuH79Oo6OjsW679y5c9ja2uoMRdL+wv4ijI2NqVq1qjLvQrt60dM9FhUrVkStVhMbG6uTUMTGxuLs7Fxo+S4uLiQkJBQ7XiGEEEIIfSDJQhk3atQoPvnkEyZMmICPjw+1atXC0NCQuLg4rl69ioeHBwDu7u40aNCA6dOnM2zYMKpVq0ZKSgqnTp3C3d2dhg0bFqm+gQMH8uWXX1KlShXatGmDSqUiISGBixcv8t577xV4n5OTE3fv3uWXX37B1dWVqKgoDh06VKxYIyIi+O2332jTpg1OTk5oNBoiIiKIjIxk0KBBAFhbW2NiYkJUVBR2dnaYmJhgbm5Ov379WL9+PY6OjtSpU4eDBw8SGxvLvHnzCq2zf//+fPzxxwQHB9O1a1fMzMy4fv06ERERjB07tljt/7tXoeu0JKSmpupt7CDx63P8+hw7SPz6HL8+x64PJFko4xwcHJg/fz5bt25l/fr1JCUlYWRkRLVq1ejevTs9evQAngy3mTp1KuvWrWPRokXcv38fa2trGjRoQIcOHYpcX9OmTZkyZQqbN29mx44dGBoa4uTkRMeOHQu9r1mzZvTr149ly5aRmZmJh4cHgwcPZsmSJUWuu3r16lSoUIEVK1Zw584dDA0Nsbe3x8/Pj169egFPhlGNGDGCTZs2sWnTJtzc3Jg5cybe3t6kp6ezatUqZenUSZMmUbt27ULrrFWrFrNmzWLdunUEBgaSm5uLg4ODsuSqEEIIIYQ+M0hPT89/jUohxHPRDr+ytLQs5ZaUjvI+0etZJH79jV+fYweJX5/j1+fYoXzEX9hqSLLPghBCCCGEECJfkiwIIYQQQggh8iXJghBCCCGEECJfkiwIIYQQQggh8iXJghBCCCGEECJfkiwIIYQQQggh8iX7LAi9dvv2bQICAggKCqJu3bp5Pr+I+mNCXlIrXy17Aj3xnHSktJtRaiR+/Y0/clar0m6CEEK8dJIsiFI3b948wsPDAVCpVKjVav71r38xZMgQKlWqVKQywsLCWLp0KVu3bi1W3ZUrV2bNmjV6uyeCEEIIIURhJFkQZYK7uzsTJ04kJyeHhIQEFixYQFpaGv/3f/9XovUaGhpiY2NTonUIIYQQQryqJFkQZYKRkZHy0l65cmXatGnDgQMHlPNpaWmsXLmS48ePk5mZSe3atfH396du3bpER0czf/58ALy9vQHw8fFh0KBBHDx4kF27dvHXX39hYmJCo0aNGD58OLa2tkDeYUhCCCGEEOJ/JFkQZc6tW7c4deoUhoaGAGg0GqZNm4a5uTlTpkyhUqVKhIeH89lnn/Hdd9/h6urK8OHDWbNmDcuWLQP+t215dnY2gwcPplq1ajx48IBVq1YxZ84cZs2aVWrxCSGEEEK8KiRZEGVCVFQUAwYMIDc3l8zMTAD8/f0BOHPmDFeuXGHdunWYmpoC4OvrS0REBAcPHqR///5UrFgRAwODPEOKOnfurPzt4ODAqFGjGD16NHfu3KFy5crFbmdISAihoaGFXhMYGAg8meipj9SVjPU2dpD49Tl+jUZDampqaTej1Ej8+hu/PscO5SN+7Y+s+ZFkQZQJjRo1YsyYMWRmZhIaGsqtW7eUIUXx8fE8fvwYX19fnXsyMzO5efNmoeXGxcWxadMmLl++zMOHD9FoNAAkJSU9V7LQtWtXunbtWug1iYmJAHSfGVns8suDPYGeehs7SPz6HH/krFZYWFiUdjNKTWpqqsSvp/Hrc+xQ/uOXZEGUCSYmJjg6OgIwcuRIJk+ezObNmxk0aBC5ublYW1vnO3SoYsWKBZaZkZHB1KlTlcnTVlZWPHjwgEmTJpGdnV1isQghhBBClBeSLIgyycfHhy+++II333wTFxcXUlJSUKlUODg45Hu9kZERubm5OseuX7/OgwcPePfdd5X7jh49WuJtF0IIIYQoLyRZEGXSa6+9RvXq1dm8eTOjRo2iQYMGTJ8+nWHDhlGtWjVSUlI4deoU7u7uNGzYEHt7ezIzM/n999+pXbs2pqamVKlSBWNjY3bv3k2PHj24du0a69at+8diuBBc+HCl8io1NVVvYweJX5/jf9XHLAshRH5Upd0AIQrSu3dv9u/fT1JSElOnTqVx48YsWrSIUaNG8Z///Ie//voLtVoNQIMGDejWrRtz5szB19eX7du3Y2VlxYQJEzh+/DijR49m48aNBAQElHJUQgghhBCvDoP09HRNaTdCiPJEO8FZX3eFLu8TvZ5F4tff+PU5dpD49Tl+fY4dykf8ha2GJD0LQgghhBBCiHxJsiCEEEIIIYTIlyQLQgghhBBCiHxJsiCEEEIIIYTIl9Jp5DAAACAASURBVCQLQgghhBBCiHxJsiCEEEIIIYTIl2zKJkQJqT8mpLSbUCr2BHriOelIaTej1Ej85Sd+fd1cTgghniY9C8U0bdo05s2bV9rNUHz33XcEBgaWeD0bNmxgzJgxJV7Py+Lv78/27dtLuxlCCCGEEK+0V7ZnYd68eYSHh+c5Pn/+fGrXrl0KLXoiOjqayZMnK58tLS2pU6cOw4YNo1atWqXWrqK6ffs2AQEBBAUFUbduXeV437596dmzZ4nUmZycjL+/P6tXr8bc3JwdO3Zw4MABEhMTMTY2pmrVqrRv355evXqVSP1CCCGEECJ/r2yyAODu7s7EiRN1jpWVXXODg4OxsLAgKSmJ77//nqlTp7JkyRLMzc3zXJuVlYWxsXEptLLozMzMMDMzK5GyT5w4gaurK5aWlqxbt449e/bw/vvvU69ePdLT07l8+TJJSUklUrcQQgghhCjYK50sGBkZYWNjk++5rKwsVq1axaFDh0hLS6N27dq89957NGzYULnm7NmzrFy5kitXrmBubk7btm0ZNmyY8uKekZHBkiVLOHr0KBUqVMDb27vIbbOyssLKygobGxv8/Pz49NNPuXDhAk2bNsXf35+OHTuSlJTEsWPHcHd3Z9KkSVy9epUffviBc+fOYWJiQrNmzRgxYoSSYOTk5LBq1Sr2798PQMeOHcnNzdWpNzAwkBo1avD+++8rx+bNm8eDBw+YOnUqABqNhp9++om9e/eSlJSElZUV7du3Z+jQoQQEBAAoSVijRo2YOXMmGzZs4MiRIwQHBwOQm5vLli1bCA0NJSUlBScnJ3x9ffHy8gL+10MxadIkQkJCiI2Nxd7enuHDh+Ph4aHT5hMnTtC8eXMAIiIi6NatG23btlXO/71H5uLFi6xdu5b4+Hiys7OpWbMmfn5+uLq6Fvh9pKWlsXLlSo4fP05mZia1a9fG399f6T1JS0vju+++4/fff+fRo0eo1Wq8vb3p3bv3M79rIYQQQojy6pVOFgqzcuVKDh8+zLhx43BwcOCnn37iiy++YOnSpajVau7evcsXX3xB+/bt+fDDD7l58yYLFy5EpVLh7+8PwIoVK/jjjz8IDAzE1taWjRs3EhMTQ4sWLYrVFhMTEwCys7OVYz/99BPvvPMOQUFBwJPEZOrUqdStW5dvvvmG1NRUFi1axPz585VhTT/99BP79u1j7Nix1KxZkz179vDLL7/g4uJSrPasWbOGvXv34u/vT8OGDXnw4AHx8fEAfPPNN3z00UdMmzaNWrVqYWSU/z+RXbt2sX37dkaPHk3dunU5ePAgM2fOZN68eTrDwNauXYufnx+jRo1i8+bNzJkzh+XLlyu9FI8ePeLMmTNKcmNjY0N0dDT37t0rMBFMT0+nffv2jBgxAoDdu3cr362VlVWe6zUaDdOmTcPc3JwpU6ZQqVIlwsPD+eyzz/juu+9Qq9WsW7eOP//8kylTpmBlZUViYiL379/PU1ZISAihoaGFPl/tHJI9gZ6FXldeqSsZ623sIPGXp/hTU1OLdb1Goyn2PeWJxK+/8etz7FA+4q9QoUKB517pZCEqKooBAwYon93c3Jg2bRoZGRns3buXDz74gH/9618AjB49mjNnzrB7927effdddu/ejVqtZtSoUahUKpydnRk6dCjBwcEMHjwYjUbD/v37GT9+PE2bNgVg/PjxvPfee8Vq44MHD9i0aRNmZmbUq1dPOd6oUSP69++vfA4NDSUjI4OJEydSsWJFAMaOHcvkyZO5ceMGjo6O7Nq1i379+tGmTRsAhg8fTlRUVLHak56ezs6dOxk+fDidO3cGwNHRUflVXvuybWFhUeDLOsCOHTvo27cv7dq1A8DX15eYmBh27NjBRx99pFzXu3dvmjVrBsCQIUMIDw/n8uXLSg9PVFQUTk5OODg4AE8mJs+aNYuhQ4dSrVo1XF1d8fT0pEWLFhgYGADQpEkTnbaMHDmSo0ePEhUVRfv27fO09cyZM1y5coV169ZhamqqtDciIoKDBw/Sv39/EhMTqV27tvId2dvb5xt3165d6dq18BVSEhMTAeg+M7LQ68qrPYGeehs7SPzlKf7iroaUmpqKhYVFCbWm7JP49Td+fY4dyn/8r3Sy0KhRI50VerQvgjdv3iQ7O5sGDRoo5wwNDXF1deXatWsAXL9+nfr166NS/W9BKDc3N7Kzs7l58ybwpCfg6aEtZmZm1KhRo0ht0w7nycjIwNHRkUmTJmFtba2cf3ryMMC1a9eoWbOmkigAuLq6olKpuHbtGlZWViQnJ+u0R6VSUa9ePe7cuVOkNmnrycrKyvPCXRyPHj0iOTkZNzc3neNubm5ERuq+JDw9hEitVgPo/GL/9BAkgOrVq7No0SLi4uKIjY0lJiaG//znP3h4eDBlyhRUKhUpKSmsW7eO6OhoUlJSyM3NJTMzs8B5DfHx8Tx+/BhfX1+d45mZmcp33a1bN2bNmkV8fDzu7u40a9aM11577TmejhBCCCFE+fFKJwsmJiY4OjoWeF77S3R+NBpNgecNDAzyzAUorhkzZmBhYYGVlZVOAqClTWyK2p6iUqlUaDQanWM5OTk69ZSkv7fV0NAwzzltG3JycoiMjOTLL7/UuUebBNWrV48+ffpw8OBBgoKCiImJ4bXXXmPevHmkpKQQEBCAnZ0dxsbG/Pvf/9YZ5vW03NxcrK2tmTVrVp5z2u/G09OT5cuXc+rUKU6fPs2XX35Jq1at+PDDD5//YQghhBBCvOJe6WShIFWrVsXIyIjY2FhleEtOTg7nz5/njTfeAMDZ2ZnDhw+Tm5ur9C7ExsZiZGSEg4MDGo0GIyMjzp8/r5SRkZHBn3/+qXwujL29fb7j5wtSvXp1wsLCePTokfICe/78eXJzc6lWrRrm5uao1WouXLig9ApoNBouXbqkM1zI0tKSe/fu6ZR95coV7OzslLiNjY05ffp0vomWdo5CYclSxYoVUavVxMbG6vRQxMbG4uzsXOSYz549S4UKFfL0svydtsz09HQAzp07x4gRI5QhZvfu3csT89NcXFxISUlBpVIV+t1ZWVnRoUMHOnTogKenJ3PmzGHMmDHPvVKVvm7olJqaqrexg8Sv7/ELIUR5Uy6ThQoVKtC9e3dWr16NpaUl9vb27Ny5k5SUFLp37w5Ajx492LVrF0uWLKFXr17cunWL1atX07NnT2WSR+fOnVm9ejVWVlao1Wo2bdr0wj0OBXnjjTfYsGED8+bNY/DgwTx8+JDg4GBatGihvNR7e3vz448/4uTkRI0aNdizZw/Jyck6yULjxo354YcfOHHiBE5OToSEhHDnzh0lWahYsSK9evVi9erVGBsb07BhQ1JTU4mLi6N79+5YW1tjYmJCVFQUdnZ2mJiY5Lvca79+/Vi/fj2Ojo7UqVOHgwcPEhsbW6wN644fP67MZ9CaOXMmDRo0oEGDBtjY2HD79m1Wr16NtbW1MqzM0dGRgwcPUq9ePTIyMli1alWBE7HhyRK7DRo0YPr06QwbNoxq1aqRkpLCqVOncHd3p2HDhqxbtw4XFxdq1KhBTk4OR48excHBocwvaSuEEEIIUZLKZbIAMGzYMODJJm0PHz7ExcWFL774Qhk3b2tryxdffMHKlSsZN24clSpVom3btgwZMkQpw8/Pj4yMDL7++mtMTU3p2bMnGRkZJdLeChUqMG3aNJYtW8ZHH32EsbExzZs3V1b8gScbo927d4+FCxcC0L59e9q1a6fMw4AnCc7Vq1eZP38+AN27d8fLy4sHDx4o1wwZMgRzc3M2bdrE3bt3sba2ViYGGxoaMmLECDZt2sSmTZtwc3Nj5syZedrr7e1Neno6q1atUpZOnTRpUrE2xIuIiMizK3TTpk357bff2LZtGw8fPlSShA8++ECZPDR+/HgWLVrEhAkTUKvV+Pj45LtykZaBgQFTp05l3bp1LFq0iPv37yvldujQAQBjY2PWrl3L7du3MTExoX79+nz++edFjkUIIYQQojwySE9PL9lB7ELkIz4+nsmTJ7N+/fpCewVeRdrVkMrKBoH/tPK+KsSzSPz6G78+xw4Svz7Hr8+xQ/mIv7ClU1UFnhGiBOXk5DBy5MhylygIIYQQQpQn8qYmSoV2tSMhhBBCCFF2Sc+CEEIIIYQQIl+SLAghhBBCCCHyJcmCEEIIIYQQIl96nyyEhYUxYMCA0m5GkW3YsCHPcqNCCCGEEEKUhGdOcJ43bx7h4eHAkzX4K1euTIsWLRg8eHChyyyVJ/7+/vTo0YN+/frpHN++fTu7d+9m+fLl/1hb+vbtS8+ePUu8nujoaCZPnpzvucWLFxdrp2Z9VX9MSGk3oVTsCfTEc9KR0m5GqZH4y0b8sou0EEK8HEVaDcnd3Z2JEyeSnZ1NTEwMCxcu5PHjx4wePfq5Ks3KypKdcZ+TmZkZZmZm/1h9wcHBedYO1tf9A4QQQggh9E2RkgUjIyNsbGwAaNeuHdHR0Rw/flxJFhISEli5ciUxMTGYmJjQpEkTAgIClHvmzZvHgwcPaNiwIT///DPZ2dmsW7eOo0ePsnHjRm7cuIGJiQk1atTg008/Ve7bu3cvO3bsICkpiSpVqtC/f3/efPNNpV3e3t6MGTOGP/74g8jISKytrRk8eLCyGzHAqlWrOH78OElJSVhbW9O6dWsGDx6MiYnJy3mCf1OUNk+aNIlWrVopx/7ec7F3715++uknkpKSMDMzw8XFhalTp2JoaMiGDRs4cuQIwcHBOs/Ww8ODbdu28fjxY7y8vHj//feVnp+MjAwWL17MsWPHqFChAr169SI2NhZLS0smTJhQaDxWVlZYWVnlOZ6ZmcmECROoV68e48ePB+Du3bt88MEHvPXWW/Tr14+wsDCWLl3K//3f/7F8+XKSkpJwdXVl3LhxODg4KGVFRESwYcMGEhISsLGx4Y033sDHx0dJKP39/enSpQtJSUkcOnSIihUr0qtXL52ensKeGTwZbrZ9+3Zu3bpFlSpV6NatG7169UKlUhXpfiGEEEIIffRc+yyYmJiQnZ0NQHJyMpMmTaJLly74+fmRnZ3N2rVr+eqrr5g7d67yMhYTE4O5uTnTpk1Do9Fw79495syZw5AhQ2jZsiUZGRmcP39eqePYsWMsXbqUgIAAPDw8iIqKYsmSJdjY2NCsWTPluk2bNjF06FCGDBnC/v37WbBgAQ0bNsTOzg54siPduHHjsLW15dq1awQHB2NsbIyvr+9zP7SCFLXNhbl06RLfffcdEyZMwM3NjbS0NE6fPl3oPbGxsajVaqZPn05SUhKzZ8/GyclJmYuxfPlyzp49y+TJk1Gr1WzevJnY2Fi8vLyeO1YTExM+/vhjPvroI15//XVatWrFvHnzqFWrFn379lWuy8rKYuPGjYwfPx5TU1OWLVvGjBkzWLBgAQYGBkRFRTF37lxGjBhBw4YNSUpKYvHixWRlZeHv76+Us3PnTgYNGkS/fv04deoU33//PW5ubri6uj7zmYWGhrJ+/XpGjhyJi4sLCQkJLFy4ECMjI3r27Plcz1wIIYQQQh8UO1m4ePEiv/76K02aNAFgz5491KpVi2HDhinXTJw4ER8fH+Li4pSNt4yNjRk/frzya3FcXBzZ2dm0atVKebGvUaOGUsaOHTto3769Mj7fycmJuLg4fvzxR50X7/bt2ys9Cb6+vuzatYuYmBilzIEDByrX2tvb8/bbb7Njx45iJwtr165l48aNOseys7NRq9XFbnNhkpKSqFChAs2aNaNixYoA1KpVq9B7KlasyOjRozE0NMTZ2ZlWrVpx+vRpBgwYQHp6OmFhYUyYMAEPDw8Axo0bp/N9FSYgIEDns7m5OatWrVLaNXToUBYtWsT58+e5fPkyCxcuxMDAQLk+JyeH4cOH4+bmBjz5tzF8+HBOnz6Nu7s7W7ZsoV+/fnTq1AmAqlWrMnToUIKCgvDz81PK8vDwUJ6ro6Mj//3vfzl9+jSurq7PfGabNm1i2LBhSm+Og4MDb731Fnv27KFnz57FeuYhISGEhoYW+swCAwOBJ2O39ZG6krHexg4Sf1mJPzU19R+vU6PRlEq9ZYXEr7/x63PsUD7iL2wecpGShaioKAYMGEBOTg45OTk0b96ckSNHAhAfH09MTEy+KwrdvHlTSRZq1KihM0+hVq1auLu7M3bsWNzd3XF3d6dVq1bKkJdr164pL5Babm5uRERE6ByrWbOm8rehoSFWVlbcv39fOXbkyBF27tzJzZs3ycjIIDc3l9zc3KKEraNPnz507txZ59j+/fs5dOiQ8rmobS6Mu7s7dnZ2BAQE0LRpUzw8PGjRooXyEpsfZ2dnneEyarWaixcvAnDr1i2ys7N1dkuuUKGCTmJWmBkzZlCpUiXls7anSKtXr15ERESwc+dOPv30U2xtbXXOq1Qqnbrt7OxQq9UkJCTg7u5OXFwcFy9eZNu2bco1ubm5ZGZmcu/ePSUZe/p71saYkpICFP7M7t+/z507dwgODmbJkiXK/Tk5OWg0mmfe/3ddu3ala9fCJ04mJiYC0H1mZKHXlVd7Aj31NnaQ+MtK/KUxwTk1NTXPHC99IvHrb/z6HDuU//iLlCw0atSIMWPGYGRkhFqtxsjof7fl5ubi6emJn59fnvusra2Vv01NTXXOGRoa8uWXX3LhwgV+//139u/fz5o1a5g5c6byq+7Tv1Br/f3Y023RntcmA+fPn2f27Nn4+PjQtGlTKlWqxIkTJ1ixYkVRwtZhYWGBo6NjnmPPat/fjxkYGCgvqVraIV3wpJfg22+/5ezZs/zxxx9s3bqVNWvWEBQUlOdFXOvv4+qffgZ/r6u47O3t852zoPXgwQOuXbuGSqXi5s2bxS5fo9Hg4+OjM4dD6+l684tRG1thz0yb3IwZMwZXV9d82/A8z1wIIYQQQh8UaZ8FExMTHB0dsbOzy/Nyrh0Dbmdnh6Ojo85/hf0aDk9e+FxdXfHx8SEoKAi1Ws1vv/0GPPm1PDY2Vuf62NjYYi3Zee7cOWxtbRk4cCD16tXD0dFR+dW3JBSlzVZWViQnJyuf7927x71793TuMTQ0pEmTJgwdOlRZeerkyZPP1aaqVatiZGTEpUuXlGMZGRn8+eefz1Xe3y1cuBAHBwc++eQTNmzYQFxcnM753NxcnboTExNJTk5WnomLiwvXr1/P82/H0dGxWJOLC3pmNjY22NracvPmzXzreNb9QgghhBD67LkmOD+tR48e7Nu3j9mzZ9O/f3+srKy4desWhw8fxs/Pr8CE4fz585w+fRoPDw+sra25fPkyd+7cUV4i+/bty3/+8x/q1KmDh4cHp06d4tdffy1w7f/8ODk5cffuXX755RdcXV2JiorSGTb0shWlzY0bN2bPnj00aNAAlUrFmjVrdIZnRUREcOvWLRo2bIiFhQVnzpwhPT39ufc1MDMzo1OnTqxatQpLS0tsbGzYvHkzGo0m316Qv7t//36eYVuVKlXC2NiYvXv3Eh0dzfz583FwcOD3339n7ty5fPvtt8rYN0NDQ5YtW8aIESMwMTHhhx9+oHr16ri7uwNP5pR8+eWXVKlShTZt2qBSqUhISODixYu89957RYrxWc/Mx8eH77//HnNzczw9PcnJySE+Pp67d+8yYMCAl/7MhRBCCCHKixdOFmxtbZk9ezarV69m6tSpZGVlUaVKFTw8PArdS8Hc3JzY2Fh+/vlnHj58SJUqVXjnnXeUycotWrRg5MiR7Nixg2XLlmFnZ8eoUaOKPFEYoFmzZvTr149ly5aRmZmJh4cHgwcP1hm7/jIVpc1+fn4sWLCAyZMnY21tzbBhw7h+/bpy3tzcnOPHj7Np0yYeP36Mg4MDH3zwAQ0bNnzudvn5+ZGRkcFXX32FmZkZvXr1IiUlpUjLx+a3W/RXX31F5cqVWb58OaNHj1aWQQ0ICODDDz/khx9+YOzYscCTie1vv/02QUFBJCUlUb9+fQIDA5VEpWnTpkyZMoXNmzezY8cODA0NcXJyomPHjkWO71nP7M0336RChQps376dNWvWYGJiQvXq1ZUJ0yXxzEF/N4VKTU3V29hB4tf3+IUQorwxSE9Pf7FB7eKVk5WVhZ+fH/369dNZ5vRl0+6zsHXr1hKroyzSDnXT183ryvtEr2eR+PU3fn2OHSR+fY5fn2OH8hH/C6+GJF5t8fHxXLt2jXr16pGens62bdtIT0+nTZs2pd00IYQQQghRhkmyoCd27tzJX3/9hUqlonbt2syaNYvKlSuXdrOEEEIIIUQZJsOQhHjJZBjSq98d+yIkfv2NX59jB4lfn+PX59ihfMRf2DCkIi2dKoQQQgghhNA/kiwIIYQQQggh8iXJghBCCCGEECJfkiy8wvz9/dm+ffsLl+Pt7c2RI0de+JqyIjo6Gm9vb+7fv1/aTRFCCCGEeKXJakhl1L1799i6dSsnT57kzp07WFpaUrNmTby9vfH09HyuMjds2MCRI0cIDg4u9r1r1qyhUqVKz1Vvcdy6dYv169cTHR3N/fv3sbS0pHbt2vj6+uLi4lLi9b9M9ceElHYTSsWeQE88J70aiWVJkPj/ufhl8zchhCh5kiyUQbdv3+aTTz7BzMyMIUOGUKtWLTQaDadPnyY4OJiVK1f+422ysbEp8Tqys7OZMmUKDg4OfPrpp9ja2nL37l3++OMPHj58WOL1CyGEEEIIXZIslEFLliwBYN68eZiZmSnHnZ2dadeuXYH3JSYmsmzZMk6fPg2Au7s7I0aMoHLlyoSFhbFx40bgyZAigPHjx9OpUyfgybJfs2bNIjIyEmtrawYPHkz79u2Vsr29vZk0aRKtWrXi9u3bBAQEMGnSJEJCQoiNjcXe3p7hw4fj4eGh3HPy5EmWL19OYmIi9erVo3v37syZM4cffvgBe3v7PO1PSEjg5s2bfPHFFzg6OgJgZ2dHgwYNdK776aefOHDgADdv3sTc3JzXX38dPz+/Qns+zp07x+rVq7l06RKVKlWiefPmDBs2jIoVKwJw9uxZVq1axZ9//olKpaJatWqMGzeOGjVqFFimEEIIIUR5J3MWypjU1FSioqLo0aOHTqKgVdALsUajYcaMGaSkpDB9+nRmzJhBcnIyM2bMQKPR0KZNG/r06YOTkxNr1qxhzZo1Ojs4b9q0iebNm7NgwQLatGnDggULlP0CCrJ27Vq8vb1ZuHAhdevWZc6cOaSnpwNPEpevv/4aT09PFixYgLe39zN7RKysrFCpVBw9epScnJwCrzMwMCAgIIDg4GA+/vhjLl26xNKlSwu8/urVq0yZMoXmzZuzcOFCJk+ezOXLl5k/fz4AOTk5TJ8+nQYNGrBgwQLmzp2Lt7c3KpX830MIIYQQ+k16FsqYmzdvotFocHZ2LtZ9f/zxB1evXuX7779XfrX/+OOPGTFiBKdPn8bd3R0zMzMMDQ3zHVLUvn17pSfB19eXXbt2ERMTg52dXYF19u7dm2bNmgEwZMgQwsPDuXz5Mg0bNmTv3r04ODjg7++PgYEB1apV46+//mLt2rUFlmdra8uIESNYuXIlmzdvxsXFhUaNGtGmTRudX/h79+6t/G1vb8+wYcOYPn06EyZMyPcFf/v27bRp04a+ffsqx0aPHs348eNJSUnB0NCQtLQ0mjVrRtWqVQEKfP4hISGEhoYWGANAYGAg8GTstj5SVzLW29hB4v8n409NTf1H6ikqjUZT5tr0T5L49Td+fY4dykf8hW3KJslCGaPRPN+G2teuXUOtVusM73FwcECtVpOQkIC7u3uh99esWVP529DQECsrq2euJlSrVi3lb7VaDaDcc/36derWrYuBgYFyTf369Z8ZR48ePWjfvj3R0dFcuHCBEydO8OOPPzJu3Dg6dOgAwOnTp/nxxx+5du0ajx49Iicnh+zsbO7du4etrW2eMuPi4rh58ya//fabckz7nG/duoWrqysdO3Zk6tSpNGnShCZNmtCqVSuqVKmSp6yuXbvStWvhkyq1PTLdZ0Y+M97yaE+gp97GDhL/Pxl/WZvgXB52cX0REr/+xq/PsUP5j1+ShTLG0dERAwMDrl27RosWLV5KmU+/sBfEyEj3n4KBgQG5ubmF3mNoaJinDu1LuEajKVK9+alYsSLNmzenefPmvPvuu0yZMoX169fToUMHEhMT+fLLL+nSpQuDBw/GwsKC+Ph45syZQ3Z2dr7laTQaunTpotMjoaVNLj788EN69+7NqVOnOHHiBGvXruWzzz6jadOmzxWDEEIIIUR5IIOyyxgLCws8PDzYvXu3Mv7/aQWtCuTs7ExycjK3b99Wjt26dYvk5GSqV68OPEkInpUAvCzOzs5cunRJ59jFixeLXY52CJP2WVy6dIns7GwCAgJwdXXFycmJ5OTkQstwcXEhISEBR0fHPP+Zmpoq19WqVYu33nqLmTNn0qhRIw4cOFDs9gohhBBClCeSLJRBo0aNQqPRMGHCBA4fPsz169e5du0ae/bs4YMPPsj3Hnd3d2rWrMk333xDXFwcly5dYu7cubi4uNC4cWPgycpCiYmJxMXFcf/+fbKyskoshm7dunHz5k2WL1/O9evXOXr0KCEhT/YdKKjH4fLly0yfPp0jR46QkJDAjRs32LdvH2FhYUovi6OjI7m5uezatYtbt27x66+/snPnzkLb0r9/fy5evEhwcDDx8fHcuHGDiIgIFi1aBDxJqlatWsW5c+dITEzkzJkzXL16VUmyhBBCCCH0lQxDKoMcHBz49ttv2bp1K6tWreLu3bvKpmxjxozJ9x4DAwM+++wzvv/+eyZPngxAkyZNGDlypPJy3qpVK44dO8a///1v0tLSdJZOfdns7OwIDAxk+fLl7N69m7p16+Lj48P8+fMxNjbO9x5bW1vs7e3ZtGkTt2/fRqPRUKVKFfr27ctbb70FPPn1f/jw4Wzbto1169bh6uqKn58fs2fPLrAttWrVYtasWaxbt47AwEByc3NxcHDAy8sLu6pBQQAAIABJREFUAFNTU27cuMGsWbN48OAB1tbWtGvXjv79+7/QMyhr46n/KampqXobO0j8+h6/EEKUNwbp6enPN6NWiGLatWsX69evZ+PGjeV6WVLtBGdLS8tSbknpKO8TvZ5F4tff+PU5dpD49Tl+fY4dykf8shqSKBXaHgVLS0suXLjApk2b6NixY7lOFIQQQgghyhNJFkSJuXHjBlu2bCE1NZXKlSvTrVs3Bg4cWNrNEkIIIYQQRSTJgigxw4cPZ/jw4aXdDCGEEEII8ZxkPIgQQgghhBAiX5IsCCGEEEIIIfIlyYIQQgghhBAiX5IsiJcmLCyMAQMGvNQyAwMD+e67715qmUIIIYQQomhkgrOeio+PZ+LEidSvX7/QDc2Ko02bNnh6er6Usgri7+9Pjx496NevX4nW8zLUHxNS2k0oFXsCPfGcdKS0m1FqJP7nj182cxNCiLJHehb0VGhoKN27d+fPP//k2rVrL1xednY2pqamWFtbv4TWlbzc3FxycnJKuxlCCCGEEGWaJAt66PHjxxw6dIguXbrQqlUr9u3bp3P+woULjB8/nn79+jF+/HgiIyPx9vYmOjoagOjoaLy9vYmMjGTixIn07duXqKiofIch/T/27j2u5/v///itencuqUhFUiFyioVZzocpypwt51Fja2Rsn8kHc5qYqTnkMFrCyIxhQw6zYYxF+DjMcVZaOji3RKf3749+vb7eOjhOvN+P6+Xi8vm8X6fn8/4yl8v78X4+n69XfHw848aNo1evXvTv359p06aRk5MDFI4SbNy4UeP4sqYdhYaGkp6eTnR0NP7+/vj7+wMlT38q6uPt27c1jjly5AjBwcH06NGD5ORkZd/7779Pz549GTFiBJs2baKgoOBpbq0QQgghhFaRaUg66MCBA1SuXBkXFxfatWvH7NmzGTJkCCqViuzsbKZNm4anpydjx47lxo0bLFu2rMTrrFixgmHDhuHo6IipqSnx8fEa+48ePcqMGTPo3bs3ISEh5Ofnc+zYsaf+Ij5hwgRGjx5Nx44d6dKlyxOfn5OTw7p16wgODsbKygpra2t27NjBN998w4gRI3BzcyMpKYkFCxagUqnw8/N7qn4KIYQQQmgLKRZ00M6dO2nXrh0A9evXx9jYmMOHD+Pt7c0vv/xCQUEBo0ePxtjYGGdnZ/r27cvcuXOLXScgIIAmTZqU2s66devw9vZm0KBByjYXF5en7relpSX6+vqYmppibW39xOcXFBQwYsQIatasqWyLjY1l6NCheHt7A2Bvb0/v3r3Ztm1bicVCXFwcO3bsKLOd0NBQoHDuti6ysTDU2ewg+Z8lf2Zm5nPuzYulVqtf+QzPQvLrbn5dzg7akd/ExKTUfVIs6JiUlBT++OMPPv74YwD09PRo06YNO3fuxNvbm+TkZJydnTE2NlbOcXd3L/FatWrVKrOtS5cu0aFDh+fX+WdkYGCgUazcvn2ba9euERkZyeLFi5Xt+fn5qNXqEq/h4+ODj0/ZizDT09MB6BJ25Dn0+tWzLdRLZ7OD5H+W/K/6AufMzEwsLS3LuxvlRvLrbn5dzg7an1+KBR2zc+dOCgoKGDZsWLF9GRkZT3StBwuKp6Gnp1ds29MsOtbX1y/25T4vL6/YcYaGhhgYGCifi6ZDBQcHU6dOnSduVwghhBBC20mxoEPy8/PZs2cPgwcPplmzZhr7wsPD2b17N9WqVWPPnj3cv39fKQbOnz//VO25ublx4sQJOnfuXOJ+Kysrbty4oXzOyckhOTkZV1fXUq+pUqmKrXmoUKEC9+/f5+7du5iZmQFw+fLlR/bP2toaW1tbrl69Svv27R8nkhBCCCGETpGnIemQ+Ph47ty5Q+fOnXF2dtb406pVK3bv3k2bNm3Q19dn4cKFJCUlcfz4cdavX/9U7fXt25cDBw6watUqkpKSSExMZNOmTdy7dw+Ahg0bsnfvXk6ePEliYiLz5s0rcUTgQXZ2dpw+fZrr168rTzpyd3fHxMSEmJgYUlJSOHDgAFu3bn2sPgYEBLBx40Y2bdpEcnIyiYmJ7Nmz56kzCyGEEEJoExlZ0CG7du2iQYMGVKhQodi+li1bEhMTw9mzZ5k0aRKLFy8mJCSE6tWrExAQwKxZszAyMnqi9ry8vJgwYQJr165l48aNmJqaUrduXeVJRn369CE9PZ0ZM2ZgYmJC3759NUYaSjJgwAAiIyMJCgoiNzeXH374AUtLS8aNG0d0dDS7d++mXr16DBw4kPDw8Ef2sXPnzpiYmLBx40ZWrlyJkZER1atXfy5PQnrV518/rczMTJ3NDpJf1/MLIYS20cvOzi55JacQ/9+hQ4eYOXMmq1atwsrKqry789IrWuBcUlGmC7R9odejSH7dza/L2UHy63J+Xc4O2pFfnoYknshPP/2Evb09lSpVIjExkWXLltGsWTMpFIQQQgghdIwUC6KYW7dusWbNGm7cuIG1tTVeXl4MHTq0vLslhBBCCCFeMCkWRDG9evWiV69e5d0NIYQQQghRzuRpSEIIIYQQQogSSbEghBBCCCGEKJEUC0IIIYQQQogSSbEghBBCCCGEKJEscBZaJS0tjcDAQMLDw6lVq1a59sU9OK5c2y8v20K98Bp/oLy7UW60Kb+8XE0IIYQUC+KVExERwZ49ewAwMDCgUqVKtGjRggEDBlCpUiVWrlypsy9EE0IIIYR4nqRYEK8kT09Pxo4dS15eHqdPn2bBggXcv3+f999/H2tr6/LunhBCCCGEVpA1C+KVpFKpsLa2pnLlyrRt25a2bdty6NAh0tLS8Pf358KFC8qx8fHxjBw5kp49ezJ+/Hj27duHv78/aWlpAGRlZTF37lwGDhxIz549CQwMZPPmzeUVTQghhBDipSEjC0IrGBkZkZeXV2x7eno6M2fOpGvXrvj4+JCYmMjy5cs1jlm9ejWJiYlMnjwZKysr0tPTuX37dontxMXFsWPHjjL7EhoaChTOXddFNhaGOpsdtCt/ZmbmE5+jVquf6jxtoMvZQfLrcn5dzg7akd/ExKTUfVIsiFfe+fPn2bt3L40aNSq2b/v27djb2zN8+HD09PSoVq0af//9N6tWrVKOSU9Px9XVldq1awNQpUqVUtvy8fHBx6fsRZ/p6ekAdAk78jRxXnnbQr10NjtoV/6nWeCcmZmJpaXlv9Cbl58uZwfJr8v5dTk7aH9+KRbEKykhIYE+ffqQn59Pfn4+zZs3Z8SIEdy/f1/juOTkZGrVqoWenp6yzd3dXeMYX19fZs2axaVLl/D09KRZs2Y0aNDgheQQQgghhHiZSbEgXkn169cnODgYlUqFjY0NKlXhf8pF6xCKqNVqjUKhJF5eXkRFRXH06FFOnDjBtGnT8Pb2ZsyYMf9a/4UQQgghXgWywFm8koyMjHB0dMTOzk4pFEri5OSksdgZCqctPczKyor27dvz4YcfMnr0aPbs2UNubu5z77cQQgghxKtERhaEVvP19WXTpk1ERUXRuXNnkpKSiIsrfFla0YjD6tWrcXNzw9nZmfz8fA4ePIi9vT2GhobP1LauvtAqMzNTZ7OD5BdCCKFdpFgQWs3Ozo7Q0FCioqLYunUrtWrVIiAggHnz5inFgKGhIatWrSItLQ0jIyPc3d2ZNGlSOfdcCCGEEKL86WVnZ6vLuxNCvEhbtmzhm2++Ye3atejrP/+ZeEVPQ9LVt0hr+1MhHkXy625+Xc4Okl+X8+tydtCO/PLoVKHTikYUKlSowLlz54iNjaVDhw7/SqEghBBCCKFNpFgQWi8lJYVvv/2WzMxMKlWqhK+vL2+//XZ5d0sIIYQQ4qUnxYLQekFBQQQFBZV3N4QQQgghXjkyD0MIIYQQQghRIikWhBBCCCGEECWSYkEIIYQQQghRIlmzIMS/xD04rry7UC62hXrhNf5AeXej3JRHfnkJnBBCiH+LFAuvgJs3b/Ldd98RHx/PtWvXMDMzw8HBgdatW9OxY0dMTU3Lu4vPzeXLl/nmm284d+4cWVlZWFlZUatWLQIDA7GzsyMtLY3AwEDCw8OpVatWeXdXCCGEEEKrSbHwkktLS+M///kPZmZmDBw4kBo1aqBWq/n777/Zs2cPlpaWtG3btsRzc3NzlbcUv0j5+fno6+ujp6f3ROfdvn2b//73vzRp0oRPP/0US0tL0tPTOXLkCHfv3n3u/czLy0Olkn8CQgghhBClkW9KL7lFixahr69PRESExtv1nJ2deeONN1Cr/+8F3P7+/owcOZITJ06QkJCAr68vw4cP59SpU0RHR3P58mXMzc1p3bo1Q4cOVQoJtVrNpk2b2L59OxkZGVhZWdGuXTuGDBkCwPXr14mKiiIhIQGAunXrEhQUhKOjIwBr1qzhwIED9OjRg3Xr1pGenk5ISAjLly8nJiZGo2D54osvyM7OZtKkScWynjlzhqysLEJCQpRzqlSpQoMGDZRjAgMDARg7diwA9evXJywsjIKCAr799lt27NjBrVu3qFq1KgMHDuT1118HUEYkPvroI3bu3MnZs2d555138PPz448//iAmJoYLFy5gYWFB8+bNGTp0KGZmZs/4tyeEEEII8WqTBc4vsczMTI4dO0aXLl1KfQ33w7/er127ltdee42FCxfStWtXrl+/zpQpU3B1dWXevHmMGjWKffv2sXLlSuWclStXsm7dOvr06UNkZCTjx4+nUqVKANy7d48JEyZgaGhIWFgYc+bMwdramokTJ3Lv3j3lGmlpaezdu5dPPvmE+fPn8/rrr1NQUMChQ4eUY7Kysvjtt9/o1KlTiVmsra0pKCjgwIEDGkXQg+bOnQvA1KlTWblyJRMmTABgy5YtbNy4kSFDhrBw4UJef/11wsLC+PPPPzXOX7lyJV26dCEyMpLXX3+dv/76i8mTJ9O8eXMWLFjAhAkT+PPPP5k3b16J7QshhBBC6BIZWXiJpaSkoFarqVatmsb2oUOHkpWVBUDbtm0JDg5W9rVq1YrOnTsrn1euXImNjQ3vvfce+vr6ODk5MWTIECIjIxkwYABqtZrNmzcTFBSkfIl3dHSkTp06AOzfvx+1Ws2YMWOUwiQ4OJhBgwYRHx9Pq1atgMIpPWPHjsXa2lppu23btuzevVs5Zu/evZiZmdG0adMS89apU4c+ffrw5ZdfsmTJEmrVqkWDBg1o27YtdnZ2AFhZWQFgaWmp0db3339Pjx49lClZAwcO5PTp03z//feMGzdOOc7Pzw9vb2+N+9OqVSt69OihbHv//fcJCQnh1q1bVKxYUaOPcXFx7Nixo8T+FwkNDQUKF7rqIhsLQ53NDuWTPzMz84W2Vxa1Wv1S9edF0uXsIPl1Ob8uZwftyF/aj9IgxcIradasWRQUFBAZGUlOTo7Gvpo1a2p8Tk5Oxt3dHX39/xtE8vDwIC8vj6tXr5Kbm0tubi6NGjUqsa2LFy+SlpZG3759Nbbfv3+f1NRU5bOtra3Gl3eAzp07M2bMGK5du0alSpXYtWsXHTp0wMDAoNRsgwcPpnv37vzvf//j3Llz7Nq1i2+//ZZJkyaV2se7d+9y48YNPDw8NLZ7eHhw5MgRjW0P35+LFy9y9epV9u/fr2wrGtVITU0tViz4+Pjg41P2k2fS09MB6BJ2pMzjtNW2UC+dzQ7lk/9lehpSZmYmlpaW5d2NcqHL2UHy63J+Xc4O2p9fioWXmIODA3p6eiQnJ2tst7e3B8DIyKjYOQ9Xhmq1utSFxnp6eqVO93nwfFdXVz7++ONi+x78h1FSReri4oKrqys//fQTr7/+OhcvXtT4lb80FSpUoGXLlrRs2ZLBgwcTEhJCbGxsqcVCWR7OXtL9efPNN3nrrbeKnWtra/vE7QkhhBBCaBMpFl5iFSpUoHHjxvz444/4+fk91SNSnZyc+PXXXykoKFBGF86cOYNKpcLe3p6CggIMDQ05ceKEsmD5QW5ubuzbt48KFSpgYWHxxO137tyZjRs3cufOHerWrVtsStWjGBoa4uDgwI0bNwCUpxcVFBQox5iZmWFjY8OZM2c0CoozZ87g5ORU5vXd3NxISkoqMbsQQgghhK6TYuEl99577/Gf//yHDz/8kICAAFxcXDAwMODixYv89ddfNG7cuMzzu3btypYtW1i8eDHdunUjNTWVmJgY/Pz8lF/Zu3Xrpjy1qF69emRmZnLx4kW6dOlCmzZt+P7775kxYwYDBgygcuXKXLt2jcOHD+Pr6/vIL9mtW7cmKiqKbdu2aaytKMnvv//O/v37adWqFVWrVkWtVvP7779z5MgR+vfvD0DFihUxMjIiISEBOzs7jIyMMDc3p2fPnnzzzTc4OjpSs2ZNfv75Z86cOUNERESZbfbq1YuPPvqIyMhIfHx8MDU1JTk5md9//50PPvigzHMf5WWaGvIiZWZm6mx2kPxCCCG0ixQLLzl7e3vmzZvH+vXr+eabb8jIyEClUlGtWjW6dOlC165dyzzf1taWKVOmEB0dzejRo7GwsKB169YMHjxYOWbw4MGYm5sTGxvL9evXqVixIu3atQMKp+2EhYURExPD7NmzycrKwsbGhoYNG2Jubv7I/puZmdGyZUsOHDhAy5Ytyzy2evXqmJiY8PXXX3Pt2jUMDAyoUqUKw4YNo1u3bgAYGBjw7rvvEhsbS2xsLB4eHoSFheHv7092djYrVqxQHp06fvx4XF1dy2zTxcWFWbNmsXr1akJDQykoKMDe3l555KoQQgghhC7Ty87OLnvSuhDP6NNPP6VSpUqMGjWqvLvyQhQtcK5QoUI596R8aPtCr0eR/LqbX5ezg+TX5fy6nB20I39ZT0OS9yyIf01mZib79+/n+PHjysiAEEIIIYR4dcg0JPGvGTNmDJmZmQwaNAhnZ+fy7o4QQgghhHhCUiyIf01UVFR5d0EIIYQQQjwDmYYkhBBCCCGEKJEUC0IIIYQQQogSSbEghBBCCCGEKJGsWfiXDB06lB49evDWW2+Vd1e0yvbt21m1ahVr1qx5pmNeBPfguHJtv7xsC/XCa/yB8u5GuXnc/PLiNiGEEK8CnS4WIiIi2LNnT7Ht8+bNe+TLvB5l3rx5GBsbA5Cfn0/37t2ZMGECLVq0eKbrpqSksGTJEs6ePYuBgQG1a9cmJCQEGxubUs85d+4cH330EXPmzKFOnTrF9s+cOZM7d+4wa9asZ+rbk1ixYgUnTpx45BuWhRBCCCFE+dHpYgHA09OTsWPHamwr7WVaubm5GBoaPtZ1raysnrlvJZk/fz737t1j5syZmJqacu7cOdTqst+r5+7uTo0aNdi1a1exYuH27dvEx8frzAvThBBCCCHE49P5YkGlUmFtbV3ivv/85z+4urqiUqn4+eefcXBwYPbs2SWOEjw87ejBz8OHDwcKf8EHcHBw4KuvviI9PZ2lS5dy5swZcnJysLOzY8CAAbRs2bLU/urp6dGkSRNq1qwJQNWqVR8rZ6dOnVi9ejVBQUEab+nbs2cPxsbGeHt7K9vi4uLYtGkT6enp2NnZ4efnR9euXdHT0wMgKSmJhQsXcvHiRezt7QkKCmLKlCmMGzeO1q1bA4VvMf766685fvw4+vr61K1bl6CgIOzt7dm+fTsbNmwAwN/fH4CPP/6Y1q1b89133/Hzzz+TlpaGpaUlTZs2ZejQoZiZmWnkOXDgADExMVy7do169eoxatQo7OzsSs1/8OBB1q1bx5UrV7CxsaF9+/b07dsXlarwn8D+/fuJjY0lNTUVY2NjatSowfjx43X2LcxCCCGEECDFwiPt2bMHX19fZs+e/chf8EsTHh7OkCFDCAkJ4bXXXkNfv3BdeWRkJIAySpCcnKzsK03z5s2JjY3ljTfeUAqGx9G2bVtWrFjBgQMH6NChg7J9165dtG7dWpky9cMPP7BhwwZGjBiBi4sLly9fZuHChRgaGtK5c2fy8/OZMWMGDg4OzJ07l7t377J8+XKNe3P37l1CQ0Np3Lgxs2bNwsDAgPXr1zN58mQiIyNp3749SUlJnD59mqlTpwJgbm4OgIGBASNHjsTOzo60tDSWLFlCVFSUxshHdnY2GzZsYOzYsahUKpYsWcKsWbMIDw8vMfvhw4eZP38+7777Lh4eHqSmphIZGUl+fj6DBg0iPT2dL774gqCgIJo2bUp2djZ//PHHY99bIYQQQghtpfPFQkJCAn369FE+e3h4KF9goXAU4J133lE+5+fnP3EbRVOSzM3NNUYxMjIyaNOmDS4uLgDY29uXeZ1jx46xevVqBgwYwPTp0/noo49o0KABAEePHmX69OmsX7++xKlSFSpU4PXXX2fXrl1KsXD27FmuXLmiTMNSq9V8++23jBgxQhk1sbe3Jzk5mW3bttG5c2fi4+PJyMjg888/p2LFikDhKMrEiROVtn7++WdMTEz44IMPlG2jR4+mf//+JCQk0Lx5c4yNjTEwMCg2qtOjRw/l/1epUoVBgwYRERGhUSzk5eXx/vvvK8XS2LFjGTFiBKdPn6ZevXrFsq9bt46+ffvSvn17JdOgQYNYvHgxgwYN4vr166jVary9vZX+1KhRo8S/g7i4OHbs2FHiviKhoaFA4UJXXWRjYaiz2eHx82dmZr6A3rx4arVaa7M9ii5nB8mvy/l1OTtoR/4HZ508TOeLhfr16xMcHKx8LvqFvciT/Hr/pPz9/Vm6dClHjhyhUaNGtGjRAjc3t1KPX7FiBV27dqVHjx64uLjw2WefERISQosWLUhMTKR27dplrqno1KkTkydPJiUlBUdHR3bt2oWLi4uS8dq1a9y6dYuIiAjmzZunnJefn69cNzk5GTs7O6VQgMI1EQ+6dOkSycnJGkUYwP3797l69WqZ9yQhIYENGzaQnJxMdnY2+fn55OTkkJmZiaWlJVA4dezB++To6EiFChW4cuVKsWJBrVbz559/kpiYyNq1a5XtBQUF5OTk8M8//1CrVi08PDwYOXIkjRs3xtPTkzfeeKPEKUg+Pj74+JT9FJv09HQAuoQdKfM4bbUt1Etns8Pj59fWpyE9+G9V1+hydpD8upxfl7OD9ufX+WLByMgIR0fHUveXVWk96GlGHHx9ffHy8uLIkSOcOHGCjz76iLfffpt+/fqVePxff/1Fz549gcKF2R9++CGzZ88mKCiInTt30qtXrzLb8/T0xM7Ojt27d9O3b1/279/P4MGDlf1FU4nGjBlTrGgpmh6lVquVtQulKSgowN3dnTFjxhTbV9YagJSUFKZPn46fnx+DBg3C0tKSc+fOERERQV5ennLco9p/kFqtRq1WM3DgQJo3b15sv6mpKQYGBsycOZOzZ89y7Ngxtm/fTkxMDJ9//jlOTk6P3ZYQQgghhLbR+WLhSRkYGGBpacmNGzeUbdevX+fWrVulnqOvr4++vj4FBQXF9lWuXBlfX198fX1Zt24dcXFxpRYLtra2nD59mjZt2gCF6xfGjBnD3LlzcXBwUKbZlEZPT49OnTqxfft2qlSpQn5+Pm3btlX2V6pUiQoVKpCamkqrVq1KvIaTkxNpaWncvn1bmV51/vx5jWPc3NyIj4+nYsWKxRYmF1GpVMXux/nz59HT01MWhEPhQuaH5ebmcunSJWVE5OrVq9y5c6fEL/b6+vq4uLjw999/l1kU6uvr4+HhgYeHBwEBAYwYMYJff/2VgICAUs8RQgghhNB2Uiw8hYYNG7J161Zl+s3KlSsxMjIq9Xg9PT0qV67MiRMn8PDwwNDQEAsLC5YuXUrTpk1xdHQkKyuLY8eOUb169VKv06dPH5YsWYK1tTUtW7YkOzubY8eOYWRkRGpqKidOnKBJkyZl9r1jx46sXbuWFStW0KJFCywsLJR9+vr6BAQEsGLFCkxMTGjSpAm5ublcvHiRO3fu0LNnT5o2bUrlypWJiIhgyJAhZGdnExMTg56envKLf4cOHdi8eTOfffYZ/fv3x9bWloyMDH777Te6d++OnZ0dVapU4erVq1y+fBkbGxvMzMxwdHQkNzeXH3/8ES8vL86cOcO2bduKZSha1BwUFISBgQFfffUVNWvWLHG9AkBAQABhYWHY2tryxhtvoK+vz19//cXly5cZPHgwp0+f5syZM3h6emJlZcWFCxe4ceNGmX8Xj0Nbp5k8SmZmps5mB8kvhBBCu0ix8BQCAwOZP38+oaGhWFtb884775CUlFTmOcOHDyc6OpqdO3diZ2fHV199RX5+PkuWLOHatWuYmZnRqFEjjV/VH+br60uFChX47rvv2LhxI8bGxrz22mssXLiQLVu2MGvWLGbPnq0smC5JpUqVaNy4MUePHuXNN98stt/Pzw8zMzM2bdrE119/jYmJCc7OzsojTg0MDJg4cSILFixg7Nix2NvbM2zYMKZPn66sazAzM2P27NmsWLGCmTNnkp2djY2NDY0aNVJGGlq1asXhw4cJDQ0lKytLeXTqO++8w/r164mOjqZevXoMHTqUuXPnavTR1NSU7t27M2fOHK5fv46Hhwfjxo0rNXPz5s2ZOHEi69at47vvvkOlUlG1alU6deoEFC48P3nyJJs3b+bu3btUrlyZQYMGaTxOVgghhBBCF+llZ2c/3fNAhfj/it4QvXDhQpydncu7O+WuaIGzrr6jQdsXej2K5Nfd/LqcHSS/LufX5eygHfnlaUjiufr111+xsLDAwcGB1NRUli1bRu3ataVQEEIIIYTQMlIsiCeWlZXFypUruXbtGpaWlo+cPiWEEEIIIV5NUiyIJ9a5c2c6d+5c3t0QQgghhBD/Mv3y7oAQQgghhBDi5STFghBCCCGEEKJEUiwIIYQQQgghSqQ1axbS0tIIDAwkPDycWrVqPdY5u3fvZunSpaxfv/5f7t2z6dOnDyNGjKBjx47l2o+TJ08yYcIEVq9erby9WZTOPTiuvLtQLraFeuE1vvibt1928iI1IYQQoriXfmQhIiKCqVOnFtt+4cIF/P39SUtLAwpfNrZy5UpcXV1fdBe10vDhw9m4caPGtjoxx8o8AAAgAElEQVR16rBy5cpX/v0B/v7+HDjw6n2ZFUIIIYR40V76YuFxGRgYYG1tjYGBQXl3hdzc3PLuwr/C0NAQa2tr9PT0yrsrQgghhBDiBdDqaUjx8fFERUWRnp5O7dq16dKlC3PmzGH58uVUqVJFOffEiRN89dVXpKWlUbt2bUaPHo29vb2y//fff2fNmjUkJSVhbW1NmzZtCAgIwNDQECj8Fb5Dhw5kZGTw22+/4enpyfjx40vs5+7du9m4cSOpqalUrlwZX19funXrhr5+Yd2WkpLCggULOHfuHHZ2dgwbNuyROaHw1/Lx48fj7e0NwPXr14mOjiYhIYH79+9TtWpVAgMDadiwIVevXiUqKopz586RnZ1N1apVGTBgAM2aNQMgNDSU9PR0oqOjiY6OBuCHH34ocRrSwYMHWbNmDX///TcVK1bEx8eHvn37KgXF8OHDefPNN8nIyGDfvn2YmZnRrVs3evbsWebfZ3x8PLGxsfz1118YGxtTp04dxo8fj5GREf/88w/Lli3j8OHD5ObmUrduXYKCgpSXwmVlZbFkyRKOHTvG3bt3sbGxwd/fn7feekt5H8SsWbMAsLOzIyoqioyMDJYuXcrp06fJycmhcuXK9O/fn9atW5fZTyGEEEIIbaY1xcLD0tPTmTlzJl27dsXHx4fExESWL19e7Ljc3FzWr19PSEgIhoaGfPnllyxatIhp06YBkJCQwBdffMG7775LvXr1yMjIYNGiReTm5mq8iGzTpk3069eP8PDwUvu0Y8cOvvnmG0aMGIGbmxtJSUksWLAAlUqFn58fBQUFzJw5EwsLC+bMmcP9+/dZtmzZE49U3Lt3j9DQUCpWrMiECROwtbXl8uXLGvtfe+01Bg4ciJGREfv37ycsLIz58+fj5OTEhAkTGD16NB07dqRLly6ltnPx4kVmz55N3759adu2LRcuXCAyMhIzMzP8/f2V4zZv3kz//v3p2bMnR48e5auvvsLDw4M6deqUeN2jR48yY8YMevfuTUhICPn5+Rw7doyCggIAvvzyS5KTk5k4cSIWFhasWrWKKVOmsGTJEoyNjVm9ejWJiYlMnjwZKysr0tPTuX37NgDh4eEMHDiQDz74gGbNmilF2uLFi8nNzWXmzJmYmpry999/P9E9F0IIIYTQRq9EsZCQkECfPn00thV9cSzN9u3bsbe3Z/jw4ejp6VGtWjX+/vtvVq1apXFcfn4+I0eOpFq1agD06NGDefPmUVBQgL6+Pt9++y09e/ZUFhc7ODgwZMgQwsPDGTZsmPILev369enVq1eZfYqNjWXo0KHKr//29vb07t2bbdu24efnx/Hjx7ly5QrLli3Dzs4OgMDAwFJHKUqzd+9ebt26xZw5c5QRAAcHB2W/i4sLLi4uyud+/foRHx/PwYMH6devH5aWlujr62Nqaoq1tXWp7WzatIn69eszYMAAAKpWrUpKSgobNmzQKBYaN26Mn58fAI6Ojvzwww+cOHGi1GJh3bp1eHt7M2jQII0+Q+HIy+HDhwkLC6N+/foAjB07lmHDhvHLL7/QuXNn0tPTcXV1pXbt2gAao0hF98PCwkIjW0ZGBm+88YbSzoMjSw+Ki4tjx44dpd4TKByZgcKFvrrIxsLwlcyemZn5XK6jVquf27VeRbqcX5ezg+TX5fy6nB20I7+JiUmp+16JYqF+/foEBwdrbEtMTGTmzJmlnpOcnEytWrU05te7u7sXO87Q0FApFABsbGzIy8sjKysLS0tLLl68yPnz59mwYYNyTEFBATk5Ody8eRMbGxuARz6B6fbt21y7do3IyEgWL16sbM/Pz0etVit9trGxUQqFoj4X/fr9uC5dukSNGjVKfWLRvXv3WLt2LfHx8dy4cYP8/HxycnKoUaPGE7Vz5coVmjZtqrHNw8ODtWvXcvfuXczMzACKXdfGxoZbt26V2f8OHTqU2qa+vr5GoWFubo6zszNXrlwBwNfXl1mzZnHp0iU8PT1p1qwZDRo0KDOLv78/ixYt4ujRozRq1IgWLVpQs2bNYsf5+Pjg41P2U3PS09MB6BJ2pMzjtNW2UK9XMvvzehpSZmYmlpaWz+VaryJdzq/L2UHy63J+Xc4O2p//lSgWjIyMcHR01NiWlZVV5jlqtfqxFuI+vCC66JyikQu1Wk1AQIAyGvCgB7+MGxsbl9lO0fWCg4NL/UW9qGgoS1H/Hjw2Ly/vkec96Ouvv+bo0aMMGzYMR0dHjI2NiYiI+NcWZpd0jx8na0nKOq/o3nh5eREVFcXRo0c5ceIE06ZNw9vbmzFjxpR67ptvvkmTJk04cuQIx48f5+OPP6ZPnz7079//qfophBBCCKENtOZpSA9zcnLiwoULGtvOnz//xNdxc3MjOTkZR0fHYn+e5MlL1tbW2NracvXq1RKvVdTnGzdukJGRodHnB6dcFRUoN2/eVLb9+eefxfr8119/KfP0H3bmzBnat2+Pt7c3Li4uVKpUidTUVI1jVCrVI6d6OTk58ccffxS7dqVKlZRRhafh5ubGiRMnStxXvXp1CgoKOHv2rLLt7t27JCYm4uTkpGyzsrKiffv2fPjhh4wePZo9e/YoxVBp2SpVqoSPjw/jx49nwIABj5xuJIQQQgih7V6JkYWn4evry6ZNm4iKiqJz584kJSURF1f4kqwnefTn22+/zbRp06hcuTKtWrVCX1+fpKQkzp8/zzvvvPNEfQoICOCrr77C3NwcLy8v8vPzuXTpEtevX6dPnz54enpStWpVIiIiCAwMJCcnh+XLl2sUJcbGxri7u7Nhwwbs7e25e/cuMTExGu20adOG7777js8++4whQ4Zga2tLYmIipqamNGzYEEdHRw4dOkTz5s1RqVSsXbuWnJwcjWvY2dlx+vRp2rVrh0qlKnFKU/fu3Rk3bhxr1qyhTZs2XLhwgU2bNmmsNXgaffv2Zfr06axatYo2bdqgVqs5duwYPj4+ODo60rx5cyIjI/nggw8wNzdn1apVmJmZ0aZNGwBWr16Nm5sbzs7O5Ofnc/DgQezt7ZWnV9nZ2XHixAnq16+PoaEhFhYWfPXVV7z22mtUrVqVu3fvkpCQoFF8PA1dfclXZmamzmYXQgghtI3WFgt2dnaEhoYSFRXF1q1bqVWrFgEBAcybN0/50vg4mjRpwuTJk1m3bh3ff/89BgYGVK1atdQ59WXp3LkzJiYmbNy4kZUrV2JkZET16tWVxb/6+vr897//ZcGCBYwbN47KlSszfPhwvvjiC43rhISEsGDBAsaOHYuDgwPvvfeexiJoExMTwsLCiIqKYvr06eTm5lKtWjUCAwOBwkXT8+fPZ/z48VhYWNCtW7dixcKAAQOIjIwkKCiI3Nxcfvjhh2J5atasySeffMKaNWtYv349FStWpFevXkqep+Xl5cWECRNYu3YtGzduxNTUlLp16ypPZhozZgzLli1TstWtW5cpU6YoU8EMDQ1ZtWoVaWlpGBkZ4e7uzqRJk5TrDx8+nOXLl7N7925sbW2JiopCrVazdOlSrl27hqmpKY0aNdJ42pUQQgghhC7Sy87OfrrJ46+gLVu28M0337B27donXjQsxOMqWuD8qr/p+mlp+0KvR5H8uptfl7OD5Nfl/LqcHbQj/yv/NKSnVTSiUKFCBc6dO0dsbCwdOnSQQkEIIYQQQojHoNXFQkpKCt9++y2ZmZlUqlQJX19f3n777fLulhBCCCGEEK8ErS4WgoKCCAoKKu9uCCGEEEII8UqS+ThCCCGEEEKIEkmxIIQQQgghhCiRFAtCCCGEEEKIEmn1mgXxeObOnUt2djYTJ0585LE7duwgOjqa2NjYF9Czstt+1Ofy5h4cV95dKBfbQr3wGn+gvLtRjLwoTgghhHhyUizogIiICPbs2VNs+7x583B1dWXkyJGo1f/u6zb+/PNPVq9ezYULF8jKyqJixYrUqlWLwMBAKleu/FzaaNu2Lc2bN38u1xJCCCGEEFIs6AxPT0/Gjh2rsa3opWHm5ub/ats3b95k4sSJeHl58emnn2JpaUl6ejq///472dnZz60dY2Nj5S3OQgghhBDi2UmxoCNUKhXW1tYl7nt4GtLJkydZsWIFiYmJqFQqqlatypgxY3ByclLOOXbsGMuWLSM9PR13d3dCQkKws7Mr8fpnzpzh7t27jB49GpWq8D+5KlWq0KBBA43jMjIyiIqK4vjx4+jp6VGnTh3effddHBwcHivjw9OQVq1aRXx8PD179mT16tXcuXMHT09PRo0apbxpMT8/n+XLl/Pzzz+jp6dHx44dyc7OJjU1lRkzZjxWu0IIIYQQ2koWOAsNeXl5zJgxgwYNGrBgwQLmzJmDn58fenp6yjH3799n48aNjBkzhjlz5nDnzh0WL15c6jUrVqxIfn4+v/32W6nTnbKzs5kwYQJmZmaEhYXx+eefY2VlxcSJE7l///5T50lNTeXgwYNMnDiRKVOmcOHCBVavXq3s/+6779i7dy8hISF8/vnn5Obmsn///qduTwghhBBCm8jIgo5ISEigT58+ymcPDw+mTp1a7Lh//vmHu3fv0qxZM+UX/QdHFKCwoHj//feV/d27d2fRokWltl2vXj169erFF198QWRkJLVr16ZBgwa0adNGGY3Yu3cvBgYGjBo1SilMRo0axYABAzh69ChvvPHGU+UuKChgzJgxmJmZAfDmm2+yd+9eZf+WLVvo3bs3LVq0AODdd98lISGh1OvFxcWxY8eOMtsMDQ0FChf66iIbC8OXMntmZuYLaUetVr+wtl5Gupxfl7OD5Nfl/LqcHbQjv4mJSan7pFjQEfXr1yc4OFj5XNrc/ooVK9K2bVsmTZpEw4YNadSoES1btqRSpUoa5z44NcjGxoacnBzu3r2rfCl/2NChQ+nRowf/+9//OHfuHDt27GDdunV8+umnNGjQgIsXL3L16lX69u2rcd79+/dJTU196tx2dnYafbKxseH27dsA3Llzhzt37lC7dm1lv76+PrVq1VKOeZiPjw8+PmU/VSc9PR2ALmFHnrrfr7JtoV4vZfYX9TSkzMxMZZqbLtLl/LqcHSS/LufX5eyg/fmlWNARRkZGODo6Ptax48aNo0ePHhw9epRDhw6xatUqJk2ahKenJ4Cy7qBI0UhAQUFBmde1srKiVatWtGrViiFDhjBq1CjWrVtHgwYNUKvV1KxZk3HjxhU7r2gh9tN4uK+AMhWq6H8fnGIlhBBCCCH+j6xZECVydXWlT58+zJo1Cw8PjxIfvfosDA0Nsbe35969ewC4ubmRkpJCxYoVcXR01PhjYWHxXNsuYmVlRYUKFTh//ryyraCggAsXLvwr7QkhhBBCvGpkZEFoSElJYdeuXTRv3hwbGxuuXr1KYmIiDRs2fOpr/vbbbxw8eJDWrVtTtWpVCgoKOHToEMeOHWPQoEEAtGvXjk2bNjFjxgwGDBiAra0tGRkZHDp0CH9/f+zt7Z9XRA3dunVj/fr1ODg4UK1aNbZt28bt27epUqXKM19bV18ClpmZqbPZhRBCCG0jxYLQYGJiQnJyMnv27OHOnTtYW1vToUMHevbs+dTXdHZ25ujRo0RFRXHt2jVUKhVVqlRh+PDh+Pv7A2BqasqsWbOIiYkhLCyMrKwsbG1tadiw4b/6HojevXtz69YtIiIi0NPTo1OnTjRr1oysrKx/rU0hhBBCiFeFXnZ29r/76l4hXjGjRo2iYcOGBAUFPdX5RQucn2WtxatM2xd6PYrk1938upwdJL8u59fl7KAd+eVpSEKUIjU1lRMnTlCvXj3y8/PZvn07SUlJjBkzpry7JoQQQghR7qRYEDpNX1+fn376ia+//hq1Wk316tWZNm0abm5u5d01IYQQQohyJ8WC0Gl2dnZ8/vnn5d0NIYQQQoiXkjw6VQghhBBCCFEiKRaEEEIIIYQQJZJiQQghhBBCCFEiWbMg/lVr1qzhwIEDREZGlvhZm7kHx5V3F8rFtlAvvMYfKO9uyIvhhBBCiOdARhaeQUREBFOnTi22/cKFC/j7+5OWllYOvXp8mzZt4q233mLlypUvrM0ePXoQFhb2wtoTQgghhBBPT4oFHbZz50569erFTz/9RH5+/gtp09TUVGdfViaEEEII8aqRaUgvyKlTp4iOjuby5cuYm5vTunVrhg4diqGhIQChoaE4OTlhbGzM7t270dfXp1+/fvj6+rJ8+XL27t2LqakpgwYNon379sp1r1+/TlRUFAkJCQDUrVuXoKAgHB0dy+zP2bNnyczMpH///vz6668cPXqUZs2aKft3797N0qVL+fjjj4mKiiIjI4M6deowevRo7O3tgf+bUtStWzdiY2O5c+cOjRs3ZtSoUVhZWZXY7sPTkM6fP8+qVau4dOkSeXl51KhRg2HDhlGnTh3lHH9/f4KDgzl+/DhHjhyhYsWKDBgwgHbt2mnch+joaBISErh//z5Vq1YlMDCQhg0bAvD777+zZs0akpKSsLa2pk2bNgQEBCj3/+DBg6xdu5aUlBSMjIxwdnbmk08+wdra+vH+goUQQgghtJCMLLwA169fZ8qUKbi6ujJv3jxGjRrFvn37ik3/+eWXXzA1NWXu3Ln07t2bZcuWMWPGDKpWrUp4eDgdOnRgwYIFXL9+HYB79+4xYcIEDA0NCQsLY86cOVhbWzNx4kTu3btXZp927txJq1atUKlUtG3blp07dxY7Jjc3l7Vr1xISEsKcOXMoKCjgs88+Q61WK8ekp6fzyy+/MHHiRKZPn05KSgrz589/7HuTnZ1Nu3btmD17NnPnzsXV1ZUpU6Zw+/ZtjeNiY2Np3rw58+fPp1WrVsyfP5/09HTlPoSGhpKens6ECRNYuHAhb7/9tnJuQkICX3zxBX5+fkRGRhISEsLBgweV+3/z5k3mzJlD+/btWbRoEbNmzdIoRIQQQgghdJWMLDyjhIQE+vTpo7GtoKBA4/PWrVuxsbHhvffeQ19fHycnJ4YMGUJkZCQDBgzAxMQEgOrVq9O/f38AunfvznfffYdKpaJbt24AvP3222zYsIGzZ8/i7e3N/v37UavVjBkzBj09PQCCg4MZNGgQ8fHxtGrVqsQ+Z2dn8+uvv/LZZ58B0L59e9avX8/Nmzc1fknPz88nKCgIDw8PAMaOHUtQUBAnTpzA09MTgJycHD788EPs7OyU9sePH09KSsojRzcAGjVqpPF5xIgRHDx4kISEBI0v7O3atVM+Dxw4kC1btnD69Gns7OzYu3cvt27dYs6cOcqIhoODg3Lut99+S8+ePenYsaOyb8iQIYSHhzNs2DCuX79OXl4e3t7eSg5nZ+cS+xsXF8eOHTvKzBQaGgoULvTVRTYWhi9F9szMzHJpV61Wl1vbLwNdzq/L2UHy63J+Xc4O2pG/6LtoSaRYeEb169cnODhYY1tiYiIzZ85UPicnJ+Pu7o6+/v8N5Hh4eJCXl8fVq1dxcXEBoEaNGsp+PT09rKysNL60qlQqLCwsuHXrFgAXL14kLS2Nvn37arR///59UlNTS+3zvn37sLW1pVatWgDY29tTq1YtfvrpJ3r37q0cp6+vT+3atZXPdnZ22NjYkJSUpBQLNjY2yhdsQMl55cqVxyoWbt26xerVqzl58iS3bt2ioKCAnJwcMjIyNI578N4YGBhgZWWljD5cunSJGjVqlDr16eLFi5w/f54NGzYo24rauXnzJi4uLnh6evLBBx/g6emJp6cn3t7eJV7Px8cHH5+yn7JTNOLRJezII/Nro22hXi9F9vJ6GlJmZiaWlpbl0vbLQJfz63J2kPy6nF+Xs4P255di4RkZGRkV+1KclZWl8VmtViu//D/swe0qlarYvoe3FV2v6H9dXV35+OOPix1T1n+0u3bt4u+//+att97SuObt27c1ioUXISIiglu3bhEYGIidnR2GhoZMnDiRvLw8jeNKujcPj+CURq1WExAQgLe3d7F9VlZWGBgYMG3aNM6dO8exY8fYtWsXK1euJCwsTCnkhBBCCCF0kRQLL4CTkxO//vorBQUFyujCmTNnUKlUymLhp+Hm5sa+ffuoUKECFhYWj3VOYmIi586dY/r06RpTju7fv88nn3zCqVOnqF+/PlD46/uFCxeoW7cuUPiL+Y0bN3ByclLOu3HjBhkZGVSuXBkoXLBcUFCgcUxZ/vjjD959912aNm0KFK4fuHnz5mOdW8TNzY1ffvmF27dvlzga4ObmRnJycpkjHXp6etSpU4c6derw9ttvExwczP79+6VYEEIIIYROkwXOL0DXrl25ceMGixcv5sqVK8THxxMTE4Ofn1+Zc8QepU2bNlSsWJEZM2Zw8uRJUlNTOXXqFFFRUaSkpJR4zs6dO3F1dcXT0xNnZ2flT+3atWnYsKHGQmcDAwOWLVvG2bNn+fPPP/nyyy+pXr26MgUJCkdWvvzyS/7880/Onj3LokWL8PLyeqwpSACOjo78/PPPJCUlcf78eebMmVPiaMqj7oOVlRWfffYZp0+fJjU1lcOHD/O///0PKFzrsXfvXlavXk1iYiJXrlzhwIEDREdHA4VPhlq3bh3nz58nPT2dw4cPc+3atccueIQQQgghtJWMLLwAtra2TJkyhejoaEaPHo2FhQWtW7dm8ODBz3RdExMTwsLCiImJYfbs2WRlZWFjY0PDhg0xNzcvdnxubi6//PKLxvSjB7Vs2ZIlS5YwYsQIAAwNDenbty/h4eFkZGTg7u5OaGioxtQpOzs7WrVqxfTp0zUenfq4QkJCWLhwIR9++CE2NjYEBAQUexLS496HqKgopk+fTm5uLtWqVSMwMBCAJk2aMHnyZNatW8f333+PgYEBVatWpUOHDgCYm5tz5swZfvzxR/755x8qV65Mv379nvmJSLr6BuHMzEydzS6EEEJoG73s7Gz1ow8TuqboPQvr168v9ZiH35kgChUtcNbVl89p+0KvR5H8uptfl7OD5Nfl/LqcHbQjf1kzXWQakhBCCCGEEKJEUiwIIYQQQgghSiTTkIR4zmQa0qs/HPssJL/u5tfl7CD5dTm/LmcH7cgv05CEEEIIIYQQT0yKBSGEEEIIIUSJpFgQQgghhBBClEiKBfFMpk6dSkRERHl3A4Dhw4ezcePG8u6GEEIIIYTWkJeyaYmIiAj27NlTbPu8efNwdXUthx4VOnnyJBMmTMDMzIyYmBiNBTRXrlzh/fffB2D16tVYWVmVVzcBSEtLIzAwkPDwcGrVqvXM13MPjnsOvXr1bAv1wmv8gXJpW14GJ4QQQjxfUixoEU9PT8aOHaux7WV5Io+ZmRkHDhxQ3poMsHPnTipXrkxGRsYzXTs3NxdDQ8Nn7aIQQgghhHiIFAtaRKVSYW1tXeK+3NxcVqxYwb59+8jKysLV1ZV33nmHevXqKcecOnWK6OhoLl++jLm5Oa1bt2bo0KHKF/F79+6xePFiDh48iImJCf7+/o/dtw4dOrBr1y6lWMjLy+OXX37Bx8eH2NhY5bj8/HwiIyM5ceIEt27dwtbWls6dO9OjRw/09QtnzUVERHDnzh3q1avHjz/+SF5eHqtXry7W5s8//8zixYsZN24czZs3R61Ws3HjRuLi4rhx4wYODg706tWLdu3aARAYGAigFFz169cnLCzssTMKIYQQQmgbKRZ0RHR0NL/++iujR4/G3t6eTZs2MWXKFJYuXYqNjQ3Xr19nypQptGvXjjFjxnD16lUWLFiAvr4+w4cPB+Drr7/m+PHjhIaGYmtry9q1azl9+jQtWrR4ZPvt2rXj+++/5+rVqzg4OBAfH4+JiQkNGjTQKBbUajU2NjZ88sknWFlZcf78eSIjI7G0tOTNN99Ujjt9+jTm5uZMnToVtbr4q0K2bNnCmjVrmDx5MvXr1wdg1apVHDx4kJEjR1K1alXOnj3LwoULsbCwoGnTpsydO5dx48YxdepUXFxcUKnkn4cQQgghdJt8G9IiCQkJ9OnTR/ns4eHB1KlTuXfvHtu3b2fUqFE0bdoUgPfff5///e9/bN26lUGDBrF161ZsbGx477330NfXx8nJiSFDhhAZGcmAAQNQq9Xs2rWLkJAQmjRpAkBISAjvvPPOY/XNwsKCZs2asWvXLgYPHszOnTvp2LEjenp6GsepVCoGDhyofK5SpQqXLl1i3759GsWCoaEhISEhJU4/Wr16NTt27OCzzz7Dzc0NKBwV2bx5M9OmTVNGU+zt7blw4QJbt26ladOmypoJS0vLUkdo4uLi2LFjR5lZQ0NDgcK5+7rIxsKw3LJnZmaWS7sPUqvVL0U/yosu59fl7CD5dTm/LmcH7chf1kvZpFjQIvXr1yc4OFj5bGxsDMDVq1fJy8ujbt26yj4DAwPq1KnDlStXAEhOTsbd3V2Z6gOFxUZeXh5Xr14FCqcO1alTR9lvamqKs7PzY/evU6dOzJ8/H19fX44fP05wcLBy7Qdt376dnTt3kp6eTk5ODnl5edjZ2Wkc4+zsXGKh8MMPP5CdnU14eDiOjo7K9qSkJHJycvj00081CpS8vDyqVKny2Bl8fHzw8Sl7EW3RG5y7hB157Otqk22hXuWW/WVY4KwNb/J8FrqcX5ezg+TX5fy6nB20P78UC1rEyMhI4wvywx7+Ff9BarW61P16enoUFBQ8c/88PT3R19cnIiKChg0bUqlSpWLFwv79+1m2bBnDhg2jTp06mJmZsXXrVg4dOqRxXFEh9DAPDw+OHj3K3r17CQgIULYXTVWaNGkSlStX1jhHphsJIYQQQpRM3rOgAxwcHFCpVJw5c0bZlp+fz9mzZ6levToATk5OnD17VqMoOHPmDCqVCnt7e+UaZ8+eVfbfu3ePxMTEx+6Hvr4+HTp04OTJkxpTih505swZateujZ+fHzVr1sTR0ZHU1NTHbsPNzY1p06axefNmjbUQTk5OGBoakpGRgaOjo8afolGLoqLheRRGQgghhBDaQH5S1QEmJiZ06dKFmJgYKlSoQJUqVdi8eTO3bgW2cr8AABbDSURBVN2iS5cuAHTt2pUtW7awePFiunXrRmpqKjExMfj5+Snz2Dp16kRMTAxWVlbY2NgQGxv7xF+s+/Xrh5+fX6nDdY6Ojvz0008cOXIER0dH9u3bx6lTp7CwsHjsNmrXrs20adOYPHkyenp69OvXDzMzM3r06MHXX3+NWq2mXr163Lt3j3PnzqGnp4ePjw8VK1bEyMiIhIQE7OzsMDIywtzc/InyCSGEEEJoEykWdMTQoUOBwpe0/fPPP7i5uTFlyhRsbGwAsLW1ZcqUKURHRzN69GgsLCxo3bo1gwcPVq4xbNgw7t27x8yZMzE2NsbPz4979+49UT9UKlWZL1/z8fHh8uXLfPHFFwC88cYbdO/end27dz9ROw8WDFBYpAwcOJCKFSvy/fffs2jRIszMzHB1daVnz55A4TqOd999l9jYWGJjY/Hw8HimR6e+DPPny0NmZqbOZhdCCCG0jV52dnbx504KIZ5a0QLnl+WFeC+ati/0ehTJr7v5dTk7SH5dzq/L2UE78pf1NCRZsyCEEEIIIYQokRQLQgghhBBCiBJJsSCEEEIIIYQokRQLQgghhBBCiBJJsSCEEEIIIYQokRQLQgghhBBCiBJJsSCe2vDhw9m4cWN5d+OZpKWl4e/vz4ULF8q7K0IIIYQQLx15KZsoU0REBHfu3OHTTz8tti88PBxjY+MX1pfQ0FCcnZ0ZOXLkC2vzWbgHx5V3F8rFtlAvvMYfeGHtyQvghBBCiH+PFAviqZX1JmYhhBBCCPHqk2JBPLXhw4fTtWtXevbsyZw5c8jNzWXChAnK/oKCAoYPH85bb71F9+7dUavVbNy4kbi4OG7cuIGDgwO9evWiXbt2yjlr165l165d3Lx5EwsLCxo3bszYsWOJiIjg1KlTnDp1iq1btwKwfPlyqlSpwqlTp4iOjuby5cuYm5vTunVrhg4diqGhIQBqtZpNmzaxfft2MjIysLKyol27dgwZMqRYpoKCApYuXcqRI0eYPn06jo6O//JdFEIIIYR4eUmxIJ6Ltm3bEhYWxj///IOFhQUAp06d4saNG7Rp0waAVatWcfD/tXf3QVHd9x7H3yzLQ3iSxQBbYcUQRJQaBgYhhlGRKqJEfJ4YTcAEH4ujhZiktHZiYsiNba0zhgRjohasMQkaIxUD1kYJo7GaRgQfQG3iIIiigoC6yOP9g9lzXVyRe++qsHxfM8xkzv529/s5a845v9/5nXMOH2bx4sV4eXlRWlpKeno6Tk5OjBgxgkOHDrFr1y5ef/11fHx8qKuro6ysDICFCxdy6dIlvL29iY+PB8DFxYXr16+zatUqxo4dy29+8xuqqqr44IMPUKlUJCYmApCVlcU333xDYmIigYGB1NfX85///OeeDC0tLaxbt46ff/6ZP/7xj/Tv3/9RrDohhBBCiB5LOgvCLEJCQnBwcODw4cNER0cDcPDgQYKCgtBoNDQ2NrJ7927eeecdAgMDAdBqtZw7d47c3FxGjBjB1atXcXNzIzg4GLVajYeHB4MHDwbA0dERtVqNnZ0dGo1G+d7c3Fzc3NxYsmQJKpUKnU5HQkICH374IXPnzqW9vZ3du3ezYMECxo8fD8CAAQMICAgwqv/OnTusXr2aW7dusWbNGpydnU3mzMvLIz8/v8t1kZqaCnTM3e+L3JxsHmn2hoaGR/Zd3dHe3t7janqU+nL+vpwdJH9fzt+Xs4Nl5Le3t7/va9JZEGZhbW3NqFGjKCgoIDo6mubmZg4fPszChQsBKC8vp6mpibfeegsrKyvlfS0tLXh6egIQERFBTk4O8+fPJyQkhJCQEMLDw5XpRKZUVFQwZMgQVKr/ubHXsGHDaGlpoaqqiubmZpqbmwkKCuqy/rVr16LRaHjvvfe6/B8mJiaGmJiuL6itrq4GYNJ//dBlO0u1NzX0kWbvaRc4NzQ03Lez2Rf05fx9OTtI/r6cvy9nB8vPL50FYTaRkZG88cYbXL9+nbKyMlpaWhg5ciTQ0esG+MMf/oC7u7vR+9Tqjn+G7u7ubNiwgRMnTlBUVMSmTZvYvn07a9euve8BfHt7u1Hn425WVlbK9z5IaGgo3377LadPnyYkJKRb7xFCCCGEsHTynAVhNkOGDEGr1VJQUMDBgwd59tlneeKJJwDQ6XTY2Nhw9epVBgwYYPTn4eGhfIatrS0jRoxgwYIF/OUvf6G8vJzTp08DHZ2KtrY2o+/U6XSUlpYaLT99+jRqtRqtVqt874kTJ7qsPTo6mgULFpCWlsaPP/5orlUihBBCCNGryZkF8UB6vZ6ffvrJaJmjo6PJtpGRkezbt4/q6mqjOyM5ODgwbdo0Nm/eTHt7O4GBgTQ2NlJWVoaVlRUxMTHs37+f1tZWhgwZgr29PYWFhajVauWORJ6enpw9e5YrV65gb2+Ps7MzsbGx5OTkkJGRQVxcHJcvXyYzM5Pnn39eORsRFxdHZmYmNjY2BAYG0tDQwPnz55k0aZJR7TExMbS3t5OWlsbKlSsJDg4252oUQgghhOh1pLMgHujUqVMsX77caNlzzz1nsm1kZCSfffYZrq6u9xxsv/TSS7i6urJr1y4++ugjHBwc8PX1Zfr06UBHB2Tnzp1s2bKFlpYWdDodqampaLVaAKZNm8a6dev49a9/TVNTk3Lr1FWrVrFlyxaWLVuGk5MTo0ePVu6YBBAfH4+joyOff/45169fx9XV1eh2rXebOHEiAGlpafz+97//f3UYetpc+keloaGhz2YXQgghLI2VXq/v3qRuIUS3GC5wdnFxecyVPB6WfqHXg0j+vpu/L2cHyd+X8/fl7GAZ+bu6uYtcsyCEEEIIIYQwSc4sCGFmhjMLQgghhBC9xd03nLmbnFkQQgghhBBCmCRnFoR4CJKTk1m3bt3jLuOx6MvZQfL35fx9OTtI/r6cvy9nB8vPL2cWhBBCCCGEECZJZ0EIIYQQQghhknQWhBBCCCGEECZJZ0EIIYQQQghhknQWhBBCCCGEECZJZ0EIIYQQQghhknQWhBBCCCGEECZJZ0EIIYQQQghhkvXKlStXPe4ihLBEfn5+j7uEx6YvZwfJ35fz9+XsIPn7cv6+nB0sO788wVkIIYQQQghhkkxDEkIIIYQQQpgknQUhhBBCCCGESdJZEEIIIYQQQpikftwFCGFJcnNz+eqrr6itrWXgwIEsWLCAwMDAx11Wl7Kzszl8+DCVlZXY2NgwZMgQEhIS8PHxUdq0t7ezfft28vPzuXnzJv7+/ixevNiozc2bN/n44485evQoAGFhYSxatAgnJyelzYULF9iwYQPnzp3DycmJmJgYZs+ejZWVldLm0KFDbNu2jaqqKn7xi1/w8ssvM3LkyEewJuDLL79k69atxMbGsnjx4j6RvaamhszMTH744Qf0ej1arZYlS5YwfPhwi87f2trK9u3bOXDgALW1tWg0GiIjI5kzZw7W1tYWl/3kyZPs2rWL8+fPU1NTw/Llyxk3bpzyek/K2p1azJm/paWFv/3tb/z73/+mqqoKBwcHhg8fTkJCAh4eHspnNDc3s3nzZgoKCmhqaiIoKIglS5bw5JNPKm2qq6vZsGEDxcXF2NraMmbMGF599VVsbGyUNiUlJWzatIny8nLc3NyYMWMGEydONKrXnPuSB/32d0tPTyc/P59XXnmF6dOn9/rs3c1fWVlJZmYmxcXFNDc34+3tzYoVK9DpdL0+vznI3ZCEMJPCwkLS09OZN28e8fHx3Lhxg82bNxMZGYmjo+PjLu++vvjiC8aPH8/s2bOJiori5MmTfPnll4wfPx47OzsAdu7cyc6dO0lKSmLWrFmcO3eO7OxsJkyYoGwI09LSqKio4M0332Ts2LHk5eVRUlLCmDFjALh9+zYpKSkMHDiQFStWMHjwYP76179iY2PD0KFDASgtLeXtt99m8uTJLFy4EGtrazIyMggJCTHaKD8MpaWlZGVl4e7ujoeHB6GhoRaf/ebNm7z22mu4u7uzcOFCpk+fTkBAABqNhn79+ll0/h07dpCTk8PSpUuZO3cuTz/9NFlZWbS1tfHLX/7S4rKXl5fT3NzM+PHjOXr0KCNGjMDX11d5vSdl7U4t5syv1+v5+9//TlxcHC+++CIjR47k0KFD7Nu3j5iYGFSqjkkYH3/8MUeOHOG1114jNjaWo0ePsn//fqKjo1GpVLS2tpKamopKpeKNN94gNDSU7Oxsrl27pmxPLl++zJtvvkl4eDjLli3Dw8ODDRs2oNPpGDhwIGD+fcmDfnuDQ4cOUVhYiJWVFQEBAcpv1puzdyf/5cuXef311xk+fDivvvoq06ZN46mnnuLJJ59UvrM35zcHmYYkhJl8/fXX/OpXv2LChAnodDoWLVqERqPhm2++edyldemdd95h3Lhx+Pj4MGjQIFJSUqivr+fMmTNAxyhfTk4OM2bMICIiAh8fH5KTk9Hr9RQUFABw8eJFfvzxR5YuXcrQoUMJCAggKSmJY8eOUVFRAcDBgwe5c+cOycnJ+Pj4EBERwYwZM/j6669pb++4Kdvu3bt55plneOGFF9DpdLzwwgsMHz6cnJych7oObt26xdq1a1m2bJnRKKmlZ//qq69wc3MjJSUFf39/tFotQUFBymiaJec/c+YMYWFhhIWF4enpSXh4OOHh4ZSVlVlk9tDQUOLj44mIiFAOfg16Utbu1GLu/I6OjqxevZpRo0bh7e2Nv78/SUlJXLx4kYsXLwId24h//OMfvPLKKwQHB+Pn50dKSgoXLlzgxIkTABw/fpzy8nJSUlLw8/MjODiYefPmkZ+fz+3btwHIy8vDzc2NRYsWodPpmDBhAlFRUezatUupx9z7kq6yG1RXV7Nx40ZWrFiBWm086aQ3Z+9O/q1btxIcHExiYiJ+fn5otVpCQ0Nxd3e3iPzmIJ0FIcygubmZ8+fPExwcbLQ8ODhYOejuLfR6PW1tbcooxpUrV6itrTXKZmdnR2BgIKWlpUDHaOETTzxhNBI1bNgw7O3tjdoEBgYqZyugY/3U1NRw5coVpU3ndRgSEvLQ12F6ejoREREEBQUZLbf07EeOHMHf3581a9bw0ksvsWzZMvbs2aMc2Fly/mHDhlFcXKwcDJaXl1NcXKyMAlpy9s56Utbu1PIoGA7wDIMH58+fp6Wlxagud3d3vL29ldpLS0vx9vZWDjKhI5th/2BoYyq/4fMfx76ktbWVP/3pT0oHrjNLzt7W1saxY8fQ6XS89dZbzJ07l+TkZAoLC5U2lpy/u6SzIIQZ1NfX09bWhqurq9FyV1dXbty48Ziq+r/ZuHEjvr6+BAQEAFBbWwtgMpvhtdraWlxcXIzmJVtZWdGvXz+jNqY+A1DW0Y0bN7r8nochPz+fqqoq5s6de89rlp798uXL7N27F61Wy9tvv01cXByZmZnk5uYqdd9dq6m6emv+mTNnMnbsWJKSkpg6dSpJSUlERUURGxur1Hx3naZq6q3ZO+tJWbtTy8NmmJ8eFhamTI2qra1FpVLh4uJi1Faj0Sh13bhxA41GY/S6i4sLKpXqgeuotbWV+vr6x7Iv2bZtG87OzkyaNMnk65acva6uDr1eT3Z2NsHBwaxevZoxY8bw5z//Wbk2x5Lzd5dc4CyEGd29I+2NPv30U86cOcOaNWuUizwNOmdrb2+/58ChM8MIdVdtHqTz95hTRUUFWVlZvP/++13OhbbE7IbP9/PzIyEhAYCnn36aS5cukZuby/PPP6+0s8T8hYWFHDhwgBUrVjBw4EB++uknPvnkEzw9PYmOjlbaWWL2++lJWR9Uy8PS2trK2rVruXnzJitXrnxg++7W1dV6NKxDKysro/9+FEpKSvjnP//J+vXr/9fv7e3ZoePMAkB4eDhTp04FwNfXl3PnzpGbm0tYWNh932sJ+btLziwIYQadRxAMTI2i9VSffPIJ3333He+++y5arVZZbhgt6Zytrq5OyabRaKirqzM6cGhvb6e+vl55/92jMAaG0RLD55gaQbn7e8yttLSU+vp6li5dypQpU5gyZQonT55k7969TJkyBWdnZ8Aysxvq6jztwNvbm6tXryqvg2Xm37JlC9OmTWP06NEMGjSIqKgopk6dyo4dO5SawTKzd9aTsnanlofFMB3nwoULpKWlGY0kazQa2traqK+vN3rP3dt4U2c/Oo8Wm1pHdXV1WFtb4+zs/Mj3JSUlJdTW1hIfH69sA6urq8nMzGTevHlKzZaYHTr23dbW1soFxgY6nc5oO2ip+btLOgtCmIGNjQ1+fn4UFRUZLS8qKjKa49tTbdy4ke+++460tLR7Dh49PT3RaDRG2Zqamjh16pQyVSkgIAC9Xm80p7i0tJTGxkajNqdOnaKpqUlpU1RUhJubG56enkqb48ePG33/8ePHH9o6fPbZZ0lPT2f9+vXKn5+fH6NGjWL9+vV4eXlZbHaAoUOHUllZabTs0qVLyu0iLfm3v3Pnzj0XO6pUKmWk0ZKzd9aTsnanloehpaWFNWvWcOHCBd577717ppT4+fmhVquNar927RoVFRVK7QEBAVRUVHDt2jWjbIb9g6GN4aJYg6KiIuXzH/W+ZNKkSXzwwQdG20A3Nzfi4uJ49913LTo7dOy7Bw8erFykb1BZWalsBy05f3fJrVOFMBMHBwc+++wzNBoNdnZ2fPHFF5w6dYrly5f36FunZmRk8O233/Lb3/4Wd3d3GhsbaWxsBDo2pFZWVrS2tpKdnY2XlxdtbW1s2rSJ2tpakpKSsLGxoV+/fpSVlVFQUICvry/Xrl3jww8/xN/fn8mTJwMwYMAA8vLy+Pnnn/H29ub06dNs3ryZWbNmKRvC/v37s23bNtRqNS4uLuzbt4/9+/ezdOnSh3L7TFtbW1xdXY3+CgoK8PDwYNy4cRadHTou0vv888+xsrLCzc2NEydOsHXrVmbNmoW/v79F5y8vL+fAgQN4eXmhVqspKSkhKyuL0aNHExISYnHZ9Xo9Fy9epLa2ln379jFo0CAcHR1pbm7Gycmpx2Ttzno3d357e3vef/99zp49S2pqKg4ODsp2UKVSoVarsbW1paamhj179vDUU09x69YtPvroIxwcHEhISEClUuHp6cn333/P8ePHGTRoEOXl5WRkZDB27FjlORJarZYdO3ZQV1eHh4cHR44cITs7m8TERGV029z7kq6y9+/f/55t4J49e3jmmWcIDw8H6NXZH5Tf0dERZ2dntm/fjqurKw4ODhw+fJidO3eSmJiIl5dXr89vDlZ6vb79wc2EEN1heJhKTU0NPj4+zJ8/X7lne09l2NF39uKLLzJnzhzgfx6SlJeXpzwkacmSJUYPSWpoaGDjxo3861//AjrmgN7vgU1nz57FycmJiRMnmnxg09atW7ly5QparZaXX36Z55577mFENyk1NRUfH597HspmqdmPHTtGVlYWlZWVuLu7Exsby+TJk5W6LDX/7du32bZtG99//z11dXVoNBpGjx7N7NmzsbW1tbjsJSUl/O53v7tneVRUFMnJyT0qa3dqMWf+OXPmMH/+fJPvu/sBXk1NTWzZsoWCggLu3LmjPJjr7jvgVFdXk5GRQXFxMXZ2dvd9MNenn36qPJhr5syZ930wlzn2JQ/67TtLTEwkNjbW6KFsvTV7d/Pv379feS7CgAEDmDlzpvL8kN6e3xyksyCEEEIIIYQwSa5ZEEIIIYQQQpgknQUhhBBCCCGESdJZEEIIIYQQQpgknQUhhBBCCCGESdJZEEIIIYQQQpgknQUhhBBCCCGESdJZEEIIIYQQQpgknQUhhBBCCCGESdJZEEIIIYQQQpj03+XHK0jGSV44AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "activities = loans_raw[\"ACTIVITY_NAME\"].value_counts().nlargest(15).sort_values()\n", "ax = activities.plot.barh()\n", "ax.set_title(\"Loans by Activity\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAApcAAAFOCAYAAADTtTbrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde1zO9//48UfnVKKicmhUDlGU8zDnOczkODPmfNq+zjZDNoexsRNmw5jTxBhG2ByLOR+TKKScs44iks5dvz/6dX1cuqS3dbrqeb/ddpvex9f7eb1dnr3er/frqZeUlKRCCCGEEEKIfKBf1A0QQgghhBAlhySXQgghhBAi30hyKYQQQggh8o0kl0IIIYQQIt9IcimEEEIIIfKNJJdCCCGEECLfSHIphBA6ztPTEy8vrxzLHz16xOLFixk2bBg9evTA09OT6Oho/Pz88PT0xM/Pr1DbGR0d/dK26orCar+Xlxeenp4Ffp6SrCTcb7rKsKgbIMTreP5Ld8WKFVSpUkXrdjNnziQwMBCAMWPG8M477xRa+2xtbVmzZk2hnE8IbX788UcCAgJo1qwZHTp0QF9fH3Nz86JuliAreQwODmb16tXY2dkVdXOEyFeSXAqdZWBgQEZGBgcPHmTYsGE51kdFRXHp0iX1dkKUVMuXL8fExERjWVpaGoGBgVSpUoUvvvhCY13z5s2pXbs21tbWhdnMEkFbrAvC5MmTSUlJKfDzCFEQ5LG40Flly5aldu3aHD58mPT09BzrDx48iEqlomnTpkXQOiEKj4ODA7a2thrLHj16RGZmJlZWVjm2Nzc3x8HBQXoxX4O2WBcEW1tbHBwcCvw8QhQE6bkUOq1z58789NNPnD17lpYtW6qXZ2RkcOjQIWrVqkX16tU5ffq01v0jIiLYunUrgYGBPH78GAsLC1xdXenXrx+Ojo4a26alpbF3714OHz5MTEwMqampWFpa8sYbb9C5c2datGhBUFAQM2bMACAmJkbj8X379u2ZPHnyK68pNTWVPXv2cPz4ce7fv09GRgY2Nja4ubnx3nvvUblyZfW2z549488//+TUqVPExMRgbGyMk5MTnp6eNG/eXOO40dHRjBw5Ejc3N6ZOnYq3tzf+/v4kJSXh6OjIkCFDcHNzIykpid9//52TJ08SHx9PpUqVGDBgAG+99ZbG8fz8/FiyZAn9+/enSZMmbNy4kevXrwNQv359Ro0aRcWKFYmIiMDb25ugoCCSk5OpVasWo0ePzhFfyEqItm7dyvnz54mLi6NMmTLUrl2b3r17U69ePY1ts2Pdvn17BgwYwPr16wkMDCQ5OZk33niD/v3706xZs1fG+/nj+fj4cPv2beLj4zE3N6dixYq4ubkxfPhw9PT0ANi0aRObN29m4sSJWFpasnXrVu7cuYORkRHu7u4MGTKESpUqaf1c//77b44dO8a///6LSqXCwcGBTp060aVLF/Xxn3fjxg18fHy4evUq8fHxWFhYUKVKFVq3bk3Xrl3V23l6euLm5saCBQsAGDFiBDExMQAEBwer78PsezD7s5s4cSJvv/22xjnj4uLw8fHB39+f2NhYjIyMsLW1pVGjRvTr1w9TU9NXxvLZs2ds2rSJEydO8OTJE2xtbencuXOOe/J5mZmZ+Pr6cujQIe7evUtaWhqVKlWibdu29OzZEyMjo9f6vCDr+8DPz49//vmHO3fukJqaipWVFS4uLvTs2ZOaNWsCmvd0gwYN2Lx5M6GhoSQmJrJ582YsLCxyxBqU3xPPfy+MHDlS/efnh9JkPzb/66+/NK5bpVJx4MABfH19CQ8PJyMjgypVqtC2bVs8PT1zxCn7Xti5cyfbt2/Hz8+P2NhYypcvT6tWrRg0aFCOfbRZv349f/75J7Nnz6Zx48bq5du3b+e3337DwsKC33//HX39//VZTZw4kfDwcDZv3qzR23vy5En+/vtvbt++TWpqKnZ2drRs2ZLevXtjZmamcd7sOKxatYrTp0/j6+tLVFQUDRs2VPfIK73fEhMT2b17NydOnCA2NhaVSoWlpSXOzs54enrm+K4RyklyKXRaq1atWLVqFQcPHtRILs+fP8/Dhw8ZMGAAcXFxWvcNCwtj5syZJCYm0rhxYxwdHYmMjOT06dOcO3eOGTNmaHyJLlq0iBMnTuDg4EDbtm0xNTXl4cOHhIaGcvr0aVq0aIGtrS39+/dn8+bNmJub0717d/X+2pKpFz19+pQvvviCmzdvUqlSJdq3b4+pqSlRUVGcPn2aunXrqpPLp0+fMnXqVMLDw3FycqJ79+48ffqUkydPMn/+fD744AM+/PDDHOdITExk2rRpWFhY0KZNGx48eMCpU6eYPXs233//PUuXLiU5OZk333yTZ8+ecezYMb777jsqVKiAi4uL1jhu374dd3d3OnbsqI7H3bt3+fzzz5k2bRpOTk60b9+e8PBwLly4wMyZM1m1ahVlypRRHyc6Oppp06YRFxeHm5sbrVq14uHDh5w4cYKAgADGjh1Lp06dcpw/NjaWTz/9FHt7e9q1a8fTp085fvw4X3/9NfPmzcPd3f2Vcff392fu3LmYmZnRtGlTKlSowNOnT4mIiOCvv/5i6NChGBgYaOxz+vRpLly4QPPmzalfvz43b97k5MmTBAUF8f333+f4JWDmzJmEhobi5OREhw4dAAgICGD58uWEhITk+MXD19eXZcuWAdC4cWMcHBxISEjgzp07bN++XSO5fFH37t2JiYlh9+7d2Nraqs/3qnvwxo0bzJ49mydPnuDi4kLTpk3JyMjg/v377Ny5ky5durwyuUxLS+OLL74gLCyM6tWr07ZtWxITE9m6dSvBwcFa98nIyGD+/PmcO3dOnTwbGxsTHByMt7c3ly5d4ssvv1R/Bko+r7S0NL766isCAgKwtrbmrbfeomzZssTGxhIUFESVKlXUyWW2a9eusW3bNlxdXenUqRNxcXEaSdPL5PWe6N+/P4cOHSImJobu3bure5Dz0pO8cOFCjh49io2NDR06dMDQ0JBz586xbt06AgICNOL0vO+//56rV6/SqFEjzMzM8Pf3x8fHh8ePH+fpl14PDw/+/PNPLl26pPG9eOnSJSDr++jGjRvUqlULgCdPnnDnzh3c3Nw0Ektvb2+2bdtG2bJleeutt7CwsODixYts2bKFs2fP8u233+ZIMAFWrlzJtWvXaNKkCU2aNFF/dyi931QqFbNnz+b69evUqlWLjh07YmRkRFxcHFeuXCEwMFCSy3wgyaXQaaamprRp04aDBw8SExOjflx14MABypQpQ+vWrfHx8cmxn0qlYvHixSQmJjJp0iT1P74AgYGBzJo1i8WLF7NmzRpMTU1JTEzk5MmTODs7s3Dhwhxf3o8fPwbAzs6OAQMGqJPLAQMGKLqeFStWcPPmTdq3b8+ECRM0zpOamkpSUpL6599++43w8HDefvttJkyYoO6p+eCDD/jkk0/YsmULTZo0UX/ZZ7t9+zaenp6MGjVKvc+2bdvw9vZmxowZeHh4MGXKFAwNs74eGjRowMKFC9m+fTuff/55jjb7+/szffp0dXKvUqmYM2cOAQEBTJ06lf79+9OjRw/19kuXLlX3vDyffC9btoy4uDgGDBhA//791ct79erFp59+yooVK2jQoAEVK1bUOH9QUBADBw6kX79+6mVt2rRh9uzZ7NixI0/JZfYQiq+//hpnZ2eNdU+ePNH6j/W5c+eYNWsWTZo0US/z8fFh7dq1rFixgrlz56qXr169mtDQUIYMGcJ7772nXp6Wlsb8+fM5fPgwLVq0UPe03rt3j2XLlmFsbMz8+fOpUaOGxrljY2NzvZ4ePXoQHR2tTi7zch+mpaWxYMECnjx5wrhx4+jcubPG+sePH2v8MvAyPj4+hIWF0axZM2bMmKFOyvr27cukSZO07vPnn39y7tw53n33XUaNGqWOd2ZmJsuXL+fAgQPs2bNHfb8o+bw2b95MQEAAHh4efP755xrJcUZGhvrv7vMCAwMZO3YsXbp0eeX1Pi+v98SAAQMICgpSJ5d5faHn6NGjHD16lOrVq2skYYMHD2bOnDlcunSJXbt20bt37xz7RkdHs3z5ciwsLAAYNGgQEyZM4MiRIwwZMuSV42/r1KmDsbGxOpmErHvm6tWrNGjQgIsXL3Lp0iX1901QUBCZmZnUr19fvX1ISAjbtm3DxsaGhQsXYmNjA8CQIUP48ccfOXz4MOvXr+f//u//cpz/1q1bLFmyJEeslN5vd+7c4fr16zRr1izHWGSVSkVCQkKucRB5I2Muhc7r3LkzmZmZHDx4EIAHDx4QEBBA69atX/qP4bVr1wgPD6dmzZoaiSVk/Yb+5ptv8uTJE86cOQOAnp4eKpUKIyMjrT0Y5cqV+8/XER8fz/HjxylXrhwfffRRjoTG2NhYfZ709HSOHDmCiYkJQ4cO1XgEWKFCBfr27YtKpVLH5HmmpqYMHjxYY5+2bdsCWb2aw4cPVyeWkNU7bGhoyK1bt7S2u169ehq9xnp6erRp0wYAS0tLjQQSoF27dgAax3vw4AEXL17ExsZGI/kCqF69Ol27diUtLY1//vknx/ltbW1z7NOwYUNsbW0JCwvT2uYXZcdC24salpaWWvepX7++RhIBWT2GFStW5OLFi+oe84SEBA4fPoyTk1OOdhoZGTF48GAAjWvbt28fGRkZ9O3bN0diCeRIsPPDuXPniImJoWHDhjkSS8i6x42NjV95HD8/P/T09Bg6dKjG3xVbW1utU+tkZmaye/duypcvr5FYAujr6zNs2DD09PQ04pPXzysjI4O9e/diZGTEuHHjcvS6GhgYaE2qHB0dFSeWkPd74nVlTx01ZMgQjd49IyMj9eP1AwcOaN136NCh6sQSsr4H2rZtS2ZmJjdu3HjluY2NjXFxceHOnTvqhDwkJISUlBQ6dOhA5cqVNRLP7D8//8udr68vkJX4ZSeWgPp+MTY2fukY+t69e2tNwpXeb9nbaLt39PT0Xvr3XSgjPZdC59WoUQMnJyf8/Pzo378/vr6+ZGZman2Emu3mzZsAGr9VP8/Dw4PTp09z8+ZN2rZti5mZGc2aNePs2bOMHz+e5s2bU7duXVxcXPLUm5MXYWFhZGZmUrduXa2PhZ53//59UlJSqF27ttbE1sPDA/jfdT6vcuXKOf6Rzf4H1sLCIsfLCgYGBpQrV+6l/zA6OTnlWJZ9vOrVq+cYS5j9gsnzx8tONOvWrat1/Je7uzs7d+7Uej1OTk5aexZtbGzUY0BfpW3btpw6dYopU6bw1ltvUa9ePVxcXHLtUXJzc8uxzMDAgDp16hAbG8vNmzexsbEhNDSUjIwM9PX12bRpU459sv8hvX//vnpZSEgIQI5EpSBlx+r5R55KPXv2jMjISKytralatWqO9dpi9u+///LkyRMqVarEli1btB7X2NiYf//9V/1zXj+v+/fvk5iYiLOzs6Lpfl7s7c+rvN4Tryv7/tf22NbR0ZHy5csTERFBUlJSju8lbb+kZLfl6dOneTq/h4cHly9f5tKlS7Ru3ZpLly6hp6eHu7s7V65c4dChQ6Smpqp7OM3MzDSGHOT2vWtlZUX16tUJDQ3l33//pVq1ahrra9eunWOf17nfqlatSo0aNTh27BjR0dE0a9aMunXrUrNmzTz98iTyRpJLUSJ07tyZX375BX9/f3x9fXF0dMz1H4hnz54BaH2T9vnl2dsBTJ06FR8fH44cOcIff/wBgKGhIU2aNGHEiBH/ea66xMREIKvnMa/bli9fPtf2Z2/3PG2Ja3Zy9rKkNrfpnHI7nrbEO3vd870T2e182eeRnaw+/3nkdv7s82RmZmpd96LmzZvz5Zdf4uPjw6FDh9S9P9WqVaN///4aPbPZXhb77OXZbc1+zHbjxo1ce4ieH/Kg5F7IL/lxzuxrflVsnpcdn8jISDZv3pyn8+T183rda3rZffgqeb0nXldiYiLm5uYvnQrJysqK+Ph4nj17luPvnrbxnM8PP8iL7F7I7OTy8uXLVKtWjfLly+Pu7s6+ffu4du0alStXJiIigmbNmmn84pfX711t31vaYvs695uBgQHz5s1j69atnDx5Em9vbyCrJ7NVq1YMHTo0X55ElXaSXIoSoW3btupxTQ8ePKBPnz65bp+dkDx69Ejr+uzlzycuxsbG9OvXj379+hEXF8fVq1c5cuQIp0+f5t69eyxdulTjcbJS2V/+eXl0lr1tfHx8ru3Xlalmstv5ss/j4cOHwMsTyfzQsGFDGjZsSEpKCqGhoVy4cIG9e/fy7bff8vXXX+foLXpZ7LOXZ7c1+//dunXjo48+ylNbnr8Xnn+UWZCU3H8vk32tr4qNtvM2bdqUmTNn5vlcefm8XveatL25nxd5vSdel7m5OQkJCaSkpGhNMLV9b+UnZ2dnzM3NCQwM5NmzZ4SGhqofPdevXx99fX0CAwPVY4Jf7KF8/ntX232d2/eWts/kde43yHpCM3z4cIYPH05UVBRXrlzB19cXPz8/YmJi+Prrr7XuJ/JOxlyKEsHMzIxWrVrx4MEDjI2N1WMIXyb7JYCgoCCt67PHC2l7lARZj5NatWrFzJkzcXFx4d9//yU8PFy9Xl9fP8+9Adlq1aqFvr4+V69efWUPR9WqVTExMdEY/6St/S++7FBcZT9av3btGmlpaTnWv+rzyE8mJibUq1ePoUOHMnz4cFQqFWfPns2xnbY3UTMyMrh27Rrwv9jXrl1b/bnmVfZb+efPn3+dS3gt+XFOMzMzKlWqxKNHjzQeY2fTFrOqVatibm5OaGio1s/+VXL7vLKPfffuXfXUTAUpr/cE/G/sn5Lvidy+t+7evUt8fDxVqlTJt6E6LzIwMMDNzY2YmBj8/PzIyMhQ92aWLVsWJycnLl26pHW85ava//jxY+7evYupqelLK6696HXutxfZ29vToUMHvv76aypUqMDly5e19pwKZSS5FCXGhx9+yIwZM5g7d+4re+zq1KmDg4MDoaGhOV4SuXTpEqdPn8bS0lL99u7jx4/V4+Cel5aWpv4ien68jqWlJY8fP1ZUYaNcuXK0bt2a+Ph4Vq1aleMxdFpamjqRNDQ0pF27dqSkpODt7Y1KpVJvFxcXx7Zt29DT06Njx455Pn9RqlChAg0bNuTBgwfs2LFDY93du3fZt28fRkZGr/yl4XVlz4/5ouyeFG1jsS5fvpwjEdu9ezexsbF4eHiox7OVK1eOtm3bcuvWLTZt2qR1eMGDBw80fjnp2rUrhoaGbN26ldu3b2vdPr81bdoUOzs7AgIC1C9ePO/Jkyekpqa+8jhvv/02KpWKdevWaSROMTExOeZshKyEpXv37sTHx7NixQqtn8Pjx481XgDL6+dlYGDAu+++S1paGsuWLcvx9zEjI0PdK54f8npPwP9ePHrVm//Py/77vGHDBo1hFOnp6axevRog17Hm+SF7PPe2bdswNDTUGNfo7u7OzZs3uXDhAlZWVjnGTWa3f9u2bRpPKbLvl5SUFNq3b6/oCZDS+y0qKoq7d+/mWJ6UlERKSgoGBgZax3ALZeSxuCgxKlSokOexVXp6ekyaNImZM2eyePFiTpw4QbVq1YiKiuLUqVMYGhoyefJk9YsvcXFxfPbZZ1SpUoUaNWpQoUIFUlJSCAgIICIigubNm2v8tu3h4cGRI0eYPXs2bm5uGBkZ4ejo+MpqQR999BH37t3Dz8+PK1eu0KhRI0xMTIiNjeXixYsMHz5cPen1kCFDuHLlCgcPHuTWrVu4u7urp0xKSEjggw8+0DoIvrgaM2YMU6dOZePGjVy+fJnatWur57lMTU1l3LhxBfKWNMDatWuJjo6mXr162NraYmxszO3bt7l48SJly5bV+vZ0s2bN+Prrr2nRogX29vbcvHmTgIAAypYtm2MqlY8++kg9pvCff/7B1dUVKysrdY/L9evXGTFihLoii4ODA2PGjGHp0qVMnjyZJk2a4ODgwNOnT7lz5w5xcXH5Xrfe0NCQ6dOnM2vWLH766Sd8fX2pU6cOGRkZREREEBgYyC+//PLKscW9evXizJkznD17lokTJ9KoUSMSExM5ceIErq6uWnuB+/Xrx507dzh48CD+/v7Ur1+fChUq8PjxY6Kiorh69Srvvvuuuodbyef1wQcfcOPGDQICAhg9ejRNmzalbNmyxMXFcfnyZTp27Kh4yrCXUXJPNGjQgBMnTrB06VJatGhBmTJlMDc3p1u3bi89fuvWrTl37hxHjx5lzJgxNG/eHAMDA86fP8+///6Lu7t7jtkZ8lt2b2R8fDyurq4aLwe6u7uzfft2EhIStP4i6OLiQt++fdm2bRtjx47lrbfewszMjMDAQG7evEn16tXVsyfkldL77fbt28yfPx8nJyeqV6+OtbU1T58+5fz58yQkJNCzZ888FQoQuZPkUpRatWrVYvHixWzZsoXAwEACAgIwNzfnzTff5P3339d4C9rW1pYPP/yQoKAggoOD1dV8KlWqRO/evXNUORk1ahT6+vpcvHiRa9eukZmZSfv27V+ZXFpYWPDdd9/x119/cfz4cXUPko2NDS1atKBu3boa237//fds376dU6dOsWvXLoyMjNRVJlq0aJGP0Sp4dnZ2/Pjjj2zdupVz585x9epVTE1NcXNzo0+fPgU6sXHfvn05c+YMN27c4PLly0BWzLt3706PHj20JrVvvvkmnTt3ZsuWLZw7dw5DQ0NatmzJ4MGDNSZQh6zHd/Pnz8fX15cjR45w5swZUlJSKF++PHZ2dgwaNChHBaSOHTtSrVo1duzYwZUrV/D398fCwoKqVavSt2/fAolDjRo1+Omnn9i+fTv+/v7s3r0bExMT7Ozs6NmzZ55edDAyMuKrr75i06ZNHD9+XD3X5vvvv0/z5s21JpcGBgZ4eXlx7Ngx/Pz8uHDhAklJSZQtW1Y91VT79u3V2yv5vIyMjJg1axYHDx7k0KFDHD16lIyMDKysrHBzc8vX8rBK7om3336bBw8ecOTIEXbt2kV6ejq2tra5JpcAn3zyCa6urvj6+qrn+6xcuTLDhg3D09PzP437zgsHBwesra15+PChuhczW/ZsD2lpaS+dX3bw4ME4OTnx999/c+TIEdLS0rCzs+P999+nT58+iseLKr3fatasSd++fQkODubixYskJCRgaWlJ1apVGTlyZI6/h+L16CUlJalevZkQQgjQLPX34i8VonSSe0IITTLmUgghhBBC5BtJLoUQQgghRL6R5FIIIYQQQuQbGXNZDGTPv/Zi2T0hhBBCCF0jPZdCCCGEECLfyFRExYi2SYGFdgkJCZQtW7aom6EzJF7KSLyUkXgpI/FSRuKlTGHFK7f5QKXnUgghhBBC5BtJLoUQQgghRL6R5FIIIYQQQuQbSS6FEEIIIUS+keRSCCGEEELkG0kuhRBCCCFEvpHkUgghhBBC5BtJLoUQQgghRL6R5FIIIYQQQuQbSS6FEEIIIUS+keRSCCGEEELkmwKvLe7l5UW1atX4+OOPC+T4fn5+rFy5km3bthXI8QtT7bH7i7oJOmOvV2MaTz9Z1M3QGRIvZSReyki8lJF4KSPxUsb/m5ZF3YSCTy4LWqtWrWjcuHFRN0MIIYQQQlACkksTExNMTEyKuhlCCCGEEIJCSi4zMjL49ddfOXz4MACdOnVi6NCh6OvrM2LECN5991169+6t3v7FR+mnTp1i8+bNREREYGxsTLVq1Zg2bRpWVlY5Hotv2rSJkydP0q9fPzZs2MDjx4+pX78+48ePp1y5cupz+Pn5sWPHDqKioqhYsSLvvPMO3bt3R18/axjqvn372LlzJ7GxsZQpUwZnZ2dmz56NgYEBd+7cYdWqVYSFhQFgZ2fHqFGjqF+/fmGEUwghhBCi2CqU5PLo0aN06NCB77//njt37rB06VKsra3p2bPnK/d99OgR33//PYMHD6ZFixYkJycTEhKS6z4xMTEcP36cGTNmkJKSwnfffceGDRsYN24cAAcOHOD333/no48+wtnZmXv37vHzzz9jaGhIt27dCAsLY8WKFUyePJm6deuSmJjIpUuX1Mf/4YcfcHR0ZOHChRgYGHD37l2MjY3/W5CEEEIIIUqAQkkuraysGD16NHp6ejg4OBAREcHOnTvzlFzGxcWRnp5Oy5YtsbW1BaBatWq57pORkcGkSZMwNzcHoHPnzhw6dEi9/o8//mDo0KG0bJk16NXe3p733nuPvXv30q1bN2JjYzE1NaVp06aYmZkB4OjoqN4/JiaGXr164eDgAEDlypVf2pb9+/dz4MCBXNvr5eUFZA1aFnljbWEk8VJA4qWMxEsZiZcyEi9lJF7KqFQqEhISCvw8pqamL11XKMll7dq10dPTU//s4uLCxo0befbs2Sv3dXR0xMPDg3HjxuHh4YGHhwctW7bUeMT9IltbW3ViCWBjY0N8fDwAjx8/5sGDByxbtoxffvlFvU1GRgYqlQoADw8PbG1tGTlyJA0bNqRBgwY0b95cnWj27NmTn3/+mcOHD1O/fn1atGihTjRf1KVLF7p06ZLrNcbExADQdYH/K+Mhsuz1aizxUkDipYzESxmJlzISL2UkXsr4f9OSsmXLFmkbivyFnueTzmwZGRnqPxsYGDB37lyuX7/OxYsX8fX1xdvbmwULFmj0Jj7PwMAgx7LsxDEzMxOAsWPH4uLionV/MzMzfvzxR4KDgwkMDGTbtm14e3uzaNEibGxsGDBgAG3btsXf35+LFy/yxx9/MGbMGDp27Kj4+oUQQgghSpJCmUQ9NDRUndwBhISEYG1tjZmZGeXKlePhw4fqdampqdy/f19jfz09PVxcXOjfvz+LFi3C2tqa48ePv1ZbrKyssLGxITIyksqVK+f4L5uBgQHu7u4MGTKEn3/+mZSUFM6fP69eX7lyZbp3787s2bPp2LEjBw8efK32CCGEEEKUJIXSc/nw4UNWrVpF165duXv3Lj4+Prz//vsA1K9fHz8/P5o1a4alpSVbt24lPT1dvW9ISAiXLl2iQYMGlC9fnlu3bvHgwYOXPobOi/79+/Prr79ibm5O48aNycjI4ObNm8TFxdG3b1/OnTtHVFQUrq6ulC1blsuXL5OUlISDgwMpKSmsXbuWt956C1tbW8JY8dMAACAASURBVOLj47l69Sq1atX6z3ESQgghhNB1hZJctmnThszMTKZMmQJAx44d6dGjBwB9+/YlJiaGr776ClNTU95//32Nnkxzc3OuXr3K33//zdOnT6lYsSL9+vWjXbt2r92ezp07Y2pqyo4dO/D29sbY2Jg33niDbt26qc955swZ/vjjD1JSUrC3t2f8+PG4urqSlpbG06dPWbx4MY8ePcLS0pImTZowfPjw/xChLNeX5T42U/xPQkKCxEsBiZcyEi9lJF7KSLyUkXgpUxgv87yKXlJSkurVm4mClP1Cj6WlZRG3RHckJCQU+YBlXSLxUkbipYzESxmJlzISL2UKK15F/ra4yBupLZ53UmtWGYmXMvkZL+lxEUKUNoXyQo8QQgghhCgdJLkEFi9ezJdfflnUzRBCCCGE0HmSXOaj5ydiF0IIIYQojXRyzKW/vz/fffcdmzdvxsDAgIiICD766CPeeecdxowZA4C3tzdhYWHMmTOHZcuWcenSJeLj47GxsaFz58706tULfX19Nm3axOHDhwHw9PQEYP78+dSrV4+4uDjWrFlDQEAAAHXq1GHUqFHq+TA3bdrEyZMn6dWrF1u2bCEmJoY//viDMmXKFEFUhBBCCCGKnk4ml66urqSmphIWFoaLiwtBQUFYWlpy+fJl9TbBwcE0atQIlUqFtbU106ZNo1y5coSGhrJs2TLKli1Lp06d6NWrF+Hh4Tx9+pRPPvkEAAsLC5KTk5kxYwYuLi4sWLAAQ0NDfHx8+OKLL1i+fLn6Lano6GiOHj3KtGnTMDIywtjYWKOtUlu8YEitWWUkXsrkZ7yKw7QgBa2wahmXFBIvZSReypSa2uL5rUyZMjg7OxMUFKROLrt168aff/7Jw4cPMTMzIywsjKFDh2JoaMjAgQPV+9rZ2XHz5k2OHTtGp06dKFOmDMbGxhgaGmJlZaXe7siRI6hUKiZNmqQuUTl27FgGDRrE+fPnadWqFQDp6el88sknGvs+T2qLFwypNauMxEuZ/IxXaXhbXKaKUUbipYzES5niEC+dTC4B6tWrR1BQEH379iU4OJju3btz6dIldS+mgYGBumrOvn37OHjwIDExMaSmppKeno6trW2ux79x4wbR0dHqSkLZUlJSiIqKUv9sY2Pz0sRSCCGEEKK00dnk0s3NjT179nDv3j2SkpJwdnZWJ5yWlpbUqVMHQ0NDjh8/zqpVqxg+fDguLi6YmZmxZ88ezpw5k+vxVSoVTk5OfPbZZznWPf8bQW7dwkIIIYQQpY3OJpfZpRi3b99O3bp1MTAwoF69eixdupTy5cvTqFEjAHXd7+zSjoBGzyOAkZERmZmZGsucnZ05duwYlpaWWFhYFPwFCSGEEEKUADqbXGaPuzxy5AhDhgwBwMXFhQcPHhAdHc3QoUMBqFy5MocOHcLf35/KlStz7NgxgoODNRJGW1tbLly4wP379ylbtizm5ua0adMGHx8fvvrqKz788EMqVqzIgwcPOHv2LO+88476jfH8VBrGZuUXqTWrjMRLGYmXEEK8Pp1NLiFr3GVoaCj16tUDwNjYmNq1axMWFqYeb9mlSxdu377NDz/8AECLFi3o2bMnfn5+6uN07tyZoKAgPvnkE5KSktRTES1YsID169fz7bffkpiYiLW1NfXr18fc3LzwL1YIIYQQQgfoJSUlyazfRSz7bXFLS8sibonuKA5vw+kSiZcyEi9lJF7KSLyUkXgpU1jxKnFTEeW3ESNG8O6779K7d+8ibUftsfuL9Py6ZK9XYxpPP1nUzdAZEi9lXhUveWQuhBAvJ8klsGjRIkxMTIq6GUIIIYQQOq/UJ5dpaWmUK1euqJshhBBCCFEilLrk0svLCwcHB0xMTDh8+DC2trY8efJE47H4s2fP+O233zhz5gxPnz7Fzs6OAQMGqKvyXLt2jfXr1xMWFoaFhQXNmjVj6NChmJmZFeWlCSGEEEIUOf2ibkBROHLkCADffPONup54NpVKxZw5cwgODmbixIksX76cESNGYGiYlYffuXOHWbNm0axZM37++WdmzJjBrVu3WLJkSWFfhhBCCCFEsVPqei4hq774iBEjtK4LDAwkJCSEZcuW4eDgAIC9vb16/Y4dO2jVqhW9evVSLxszZgwTJ04kPj6e8uXLaxxv//79HDhwINf2eHl5AVkvEYi8sbYwkngpIPFS5lXxSkhIKMTWFH8qlUpiooDESxmJlzKFFS95W/wFzs7OL11369YtrKys1Inli27cuEFkZCTHjx9XL1OpsmZzioqKypFcdunShS5dcn+zNHsqoq4L/PPUfpGViEu88k7ipcyr4iVvi2uSqWKUkXgpI/FSpjjEq1Qml7ll29mJYm7rO3XqRI8ePXKss7Gx+c9tE0IIIYTQZaUyucyNs7Mzjx49Ijw8XGvvpbOzM/fu3SuQ8o9CCCGEELquVL7Qkxt3d3dq1arFggULCAgIICoqiosXL3L69GkA+vTpQ2hoKMuWLePmzZtERERw7tw5li5dWsQtF0IIIYQoetJz+QJ9fX3mzJnDunXrWLhwIUlJSdjb29O/f38AHB0d+eabb9i4cSNeXl5kZmZib2/Pm2+++Z/PLeO48i4hIUHipYDESxmJlxBCvD6pLV4MSG1x5YrDgGVdIvFSRuKljMRLGYmXMhIvZUpdbfHMzEyWL1/OqVOnSEhIYP78+dSrVy/fju/l5UW1atX4+OOP8/RzcSO1xfNOamUrI/FSRmqLCyHE6yvU5NLf359Dhw4xf/587O3tsbCweK3j+Pn5sXLlSrZt26axfMaMGRgYGLx0v1etF0IIIYQQ/02hJpeRkZFYWVlRp06d1z5Genr6S9e9qhu4oLuJ09LSMDIyKtBzCCGEEEIUZ4WWXC5evJjDhw8D4Onpia2tLatXr2bHjh3s37+fhw8fUqlSJfr06UO7du0AiI6OZuTIkUyZMoWDBw8SEhLCsGHDWLlypfo4AP3792fAgAGvfOz9/Ho/Pz+tJRvbt2/P5MmTATh37hybNm3i3r17WFlZ0aZNG/r3769OIEeMGEGHDh2IjY3l9OnTeHh4MH369PwNnBBCCCGEDim05HL06NHY2tri5+fHokWL0NfXZ8OGDZw6dYqPP/6YKlWqEBISwtKlS7GwsKBJkybqfb29vRk+fDjjx49HX1+fzMxMvL29WbVqFZD7oNKXadWqFY0aNVL/fPv2bebNm6ceAxoQEMAPP/zA6NGjcXV1JTY2luXLl5OWlqZROnLnzp3069ePRYsWvW5ohBBCCCFKjEJLLs3NzSlTpgz6+vpYWVmRnJzMrl27mDt3Lq6urkBWDe+wsDD27NmjkVx269aNli1bqn82MzNDT08PKyur126PiYkJJiYmADx+/Jhly5bRtWtX3n77bQC2bt1K79691T9XqlSJIUOGsGjRIoYPH46enh4Abm5u9OnT56XnkdriBUNqZSsj8VJGaosrI7WflZF4KSPxUqZU1xa/d+8eqampzJ49W52oQdaYSjs7O41ta9SoUWDtSEtL4+uvv6Zq1aoMHz5cvfzGjRuEhoayfft29bLMzExSU1N59OgR1tbWANSsWTPX40tt8YIhtbKVkXgpI7XFlZGpYpSReCkj8VKmOMSryJLL7BreM2fOpGLFihrrDA01m/U6j73zavny5Tx9+pQ5c+ZovEmuUqno37+/Ro9ptnLlyqn/nN37KYQQQgghijC5dHBwwMjIiNjYWNzd3RXta2hoSGZm5n9uw44dOzh37hwLFy7EzMxMY52zszP379+XGuJCCCGEEAoUWXJpZmZGr169WLt2LSqVCldXV5KTk7l+/Tp6enq5Pkq2s7MjNTWVixcv4uTkhImJieLezcDAQDZs2MCnn36KiYkJjx49AsDY2Bhzc3M++OAD5s6dS8WKFWnVqhX6+vrcu3eP0NBQhg0b9p+uXQghhBCipCrS2uIDBw6kfPny+Pj4sHz5cszMzHBycqJ379657lenTh3eeecdvv/+exISEtRTESlx9epV0tPT+fbbbzWWZ09F1LBhQ2bNmsWWLVvw8fHBwMCAKlWq0KFDB8XXmVcyjivvpPazMhIvZSReQgjx+qS2eDEgtcWVKw4DlnWJxEsZiZcyEi9lJF7KSLyUKQ61xfUL/OxCCCGEEKLUKNLH4rrk8ePHDBw4kPnz56snWs9vtcfuL5DjlkR7vRrTePrJom6GzpB4aZJH3kIIUXCk51IIIYQQQuQbSS6FEEIIIUS+0dnH4sHBwfz222/cvXsXfX19qlatyoQJE6hWrRrXrl1j/fr1hIWFYWFhQbNmzRg6dKh6LkuVSsWOHTvYv38/Dx8+pFKlSvTp04d27dqpjx8aGsry5cu5d+8eDg4ODBw4UOP86enprFmzhlOnTvHkyRPKly9PmzZtGDp0aGGGQQghhBCiWNHJ5DIjI4OvvvqKjh078umnn5Kens7NmzfR19fnzp07zJo1iwEDBjBhwgQSEhJYtWoVS5YsUdfw3rBhA6dOneLjjz+mSpUqhISEsHTpUiwsLGjSpAnJycnMnTsXNzc3Jk+eTFxcHKtWrdJow19//cWZM2f47LPPsLW1JS4ujn///bcowiGEEEIIUWzoZHL57NkzEhMTadq0KZUqVQKyKv4ALFq0iFatWtGrVy/19mPGjGHixInEx8djamrKrl27mDt3Lq6urgDY29sTFhbGnj17aNKkCUeOHCE9PZ2JEydSpkwZqlWrxvvvv8+iRYvUx4yJiaFy5cq4urqip6eHra0tderUydHW/fv3c+DAgVyvJzvp3evV+L8FphSxtjCSeCkg8dKUkJCQ63qVSvXKbcT/SLyUkXgpI/FSprDildtURDqZXJYtW5YOHTowe/Zs3N3dcXd3p2XLllSsWJEbN24QGRnJ8ePH1dtn1zGPiopCX1+f1NRUZs+ejZ6ennqb9PR07OzsAAgPD6d69eqUKVNGvd7FxUWjDR06dGDWrFl89NFHNGjQgMaNG9OoUSP09TWHsXbp0iXXakPwv3kuuy7wf41olE57vRpLvBSQeGl61dviMq+eMhIvZSReyki8lCkO8dLJ5BJg0qRJ9OjRgwsXLnD27Fk2bNjA559/jkqlolOnTvTo0SPHPjY2Nty5cweAmTNnUrFiRY31hoZ5D0eNGjVYvXo1AQEBXL58mcWLF+Po6Mi8efNyJJhCCCGEEKWFziaXAI6Ojjg6OvLee+8xe/ZsDh06hLOzM/fu3aNy5cpa93FwcMDIyIjY2Fjc3d1fus2hQ4dITk5Wd/tev349x3ZmZma89dZbvPXWW3To0IEpU6YQGRlJlSpV8u8ihRBCCCF0iE52sUVFRfHbb79x7do1YmJiuHz5Mnfu3OGNN96gT58+hIaGsmzZMm7evElERATnzp1j6dKlQFZC2KtXL9auXYuvry8RERHcunWLffv2sX9/1iTmbdq0wcDAgCVLlnD37l0uXrzI1q1bNdqwc+dOjh49Snh4OBERERw9ehQzMzNsbGwKPR5CCCGEEMWFTtYWf/ToEb/88gvXr19XTwPUunVrBg0ahKGhIWFhYWzcuJFr166RmZmJvb09b775pno6IZVKxd9//82+ffuIjIzEzMwMJycnevfuTYMGDYCsnsrsqYiqVq3KoEGDmDdvnrpCz4EDB9i7dy+RkZEAODk5MWTIEK0v9byK1BZXrjiMKdElEi9lJF7KSLyUkXgpI/FSpjjUFtfJ5LKkkeRSOfmyUUbipYzESxmJlzISL2UkXsoUh+RSp8dcljRSWzzvpFa2MroQL6n3LYQQJYNOjrkUQgghhBDFkySX+SwtLa2omyCEEEIIUWR0+rH4y+qLh4WFsXLlSrZt26beNigoiBkzZrBx40bKlSsHgK+vL5s2beLJkye4u7vTqFEjVqxYwV9//QVAZGQka9as4fr16yQlJVGlShU+/PBDmjZtqj7uiBEj6NChA7GxsZw+fRoPDw+mT59euIEQQgghhCgmdDa5zK2+eF6EhITw888/M3jwYJo3b05wcDDe3t4a2yQnJ9OoUSMGDhyIsbExx48fZ8GCBfz000/qcpOQNS1Rv379NMpDCiGEEEKURjqbXOZWX1zbhOcv+uuvv2jQoAHvvfceAFWqVCEsLEyjDnj2JO3Z+vXrx/nz5zl16hT9+vVTL3dzc6NPnz5azyO1xQuG1MpWRhfiVZxqB0stY2UkXspIvJSReCkjtcX/g9zqi+fF/fv3adKkicayWrVqaSSCycnJbN68mfPnz/Pw4UMyMjJITU2levXqGvvVrFnzpeeR2uIFQ2plK6ML8SpOb4vL1CfKSLyUkXgpI/FSpjjES2eTS3h5fXF9fX1UKs3pO9PT0zV+VqlU6Onp5Xr8tWvXcuHCBYYPH07lypUxMTFh8eLFOV7aMTExyZ8LEkIIIYTQcTr/tnh2bfEFCxbg5ubGoUOHsLS0JCUlhWfPnqm3u337tsZ+Dg4OhIaGaix78eerV6/Svn17WrZsiaOjIxUqVCAqKqrgLkYIIYQQQsfpbHKZW33x2rVrY2pqyvr164mIiODkyZPs2bNHY39PT08CAwPZsWMHERERHDx4kDNnzmhsU7lyZc6cOcONGze4c+cOCxcuJDU1tTAvUwghhBBCp+jsY3ETExMiIiL45ptv1PXF27ZtS58+fTA0NOTTTz9l3bp1+Pn54erqysCBAzXe5nZxcWHcuHFs2rSJ33//HXd3d/r06cPGjRvV24wcOZKffvqJ6dOnY2FhQffu3Qs0uSxOY86Ku4SEBImXAhIvIYQQhUVqiz9n1apVXLp0iaVLlxbqeaW2uHLFYcCyLpF4KSPxUkbipYzESxmJlzJSW7yI7dixAw8PD0xNTbl06RL79+9n0KBBRd0sIYQQQgidVSKTy8zMTJYvX86pU6dISEhg/vz51KtXL8d2YWFh+Pj4kJiYiJ2dHYMHD6Z79+5F0OIstcfuL7Jz65q9Xo1pPP1kUTdDZxRVvORRvBBClD4lMrn09/fn0KFDzJ8/H3t7eywsLLRuN23atEJumRBCCCFEyVYik8vIyEisrKyoU6fOax8jPT0dQ8MSGR4hhBBCiAJT4rKnxYsXc/jwYSBruiFbW1vGjBnD1q1buXv3Lnp6etSsWZNRo0apy0VGR0czcuRIpkyZwsGDBwkJCWHYsGGYmpqycuVKpk2bxurVq4mNjcXDw4NPPvmEwMBA1q9fz+PHj2natCnjxo2TydSFEEIIUeqVuORy9OjR2Nra4ufnx6JFi9DX1yc4OJju3bvj6OhISkoKW7ZsYd68eSxbtgwjIyP1vt7e3gwfPpzx48djaGhIYGAgaWlp7Ny5kylTppCens6CBQv45ptvMDIywsvLSz2mc+/evfTq1asIr1wIIYQQouiVuOTS3NycMmXKoK+vj5WVFQAtW7bU2GbSpEn069eP0NBQXF1d1cu7deuWY9uMjAw+/vhjqlatCkDr1q3ZvXs33t7elCtXDoBmzZpx+fJlrcnl/v37NeqVa+Pl5QVkvXQh8sbawkjipUBRxSshIaHQz5kfVCqVzra9KEi8lJF4KSPxUqaw4lXqpyKKjIxk48aNhIaG8vjxY1QqFZmZmcTGxmpsV6NGjRz7GhkZqRNLACsrK8qXL69OLLOXhYeHaz13ly5d6NIl9zdms+e57LrAP8/XVNrt9Wos8VKgqOKlq2+Ly7x6yki8lJF4KSPxUqY4xKtUJJfz5s3DxsaGsWPHYmNjg4GBAWPGjCE9PV1jO21ZuIGBQY5l2l70UalkLnohhBBCCJ2tLZ5XT548ITw8nL59++Lh4YGDgwPPnj0jIyOjqJsmhBBCCFHilPieSwsLCywtLTlw4AAVKlQgLi6OdevWae2RFEIIIYQQ/02JTy719fWZOnUqv/76K+PGjaNSpUqMGDGCBQsWFHXTctDV8WlFISEhQeKlgMRLCCFEYdFLSkqSwYJFLPuFHktLyyJuie4oDgOWdYnESxmJlzISL2UkXspIvJQprHiV2rfF81pj/L/68ssvsbS0ZPLkyf/pOFJbPO+ktrgyUltcCCFEYSnRyWVea4wLIYQQQoj8UaKTy/yoMS6EEEIIIfKuxCaX2mqMr1ixgt9++41jx46RmJiIk5MTw4YN06jSExwczLp167h9+zbm5ua0bt2aoUOHqstEJicn88svv3Dq1ClMTU3x9PQskusTQgghhCiOSmxyqa3G+Lp16zhx4gQTJkzA3t6enTt3MmfOHFauXIm1tTVxcXHMmTOHdu3aMWnSJCIjI/n555/R19dnxIgRAKxdu5bAwEC8vLywsbFh8+bNXLlyhebNmxfxFQshhBBCFL0Sm1y+WGM8OTmZffv2MX78eJo0aQLAmDFjuHz5Mnv27GHQoEHs2bMHa2tr/u///g99fX0cHBwYMmQIy5Yt48MPP0SlUuHr68vEiRNp2LAhABMnTmTYsGEvbYfUFi8YUltcGaktrozUMlZG4qWMxEsZiZcyUlu8EEVGRpKenq4x/tLAwAAXFxd1XfD79+9Tu3Zt9PX/V7iobt26pKenExkZCUB6ejouLi7q9WXKlKFatWovPa/UFi8YUltcGaktroxMfaKMxEsZiZcyEi9likO8Snz5xxfp6em9dJ1KpXrpej09PakfLoQQQgjxCqUmuaxUqRKGhoZcvXpVvSwjI4OQkBDeeOMNABwcHAgJCSEzM1O9zdWrVzE0NMTe3l59jJCQEPX65ORk7t69W3gXIoQQQghRjJWax+KmpqZ07dqV9evXY2lpiZ2dHbt27SI+Pp6uXbsC8O6777J7925++eUXunfvTlRUFOvXr6dbt27qsQUdO3Zk/fr1lCtXDmtra/744w+NZFQIIYQQojQrNcklwNChQwFYsmQJT58+xdnZmTlz5mBtbQ2AjY0Nc+bMYd26dUyYMAELCwtat27N4MGD1ccYPnw4ycnJzJ8/HxMTE7p160ZycnK+tE9Xx6cVBamVrYzESwghRGGR2uLFgNQWV644DFjWJRIvZSReyki8lJF4KSPxUqY41BYvNWMuhRBCCCFEwdOZx+KZmZksX76cU6dOkZCQwPz586lXr16+n8fPz4+VK1eybds2rT8XpNpj9xf4OUqKvV6NaTz9ZFE3Q2cUZrzk8bsQQpRuOpNc+vv7c+jQIebPn4+9vT0WFhYFcp5WrVrRuLFMzi2EEEII8Tp0JrmMjIzEyspKYxL0gmBiYoKJiUmBnkMIIYQQoqTSiTGXixcvZvXq1cTGxuLp6cmIESO4cOEC06ZN44MPPqB///7MmjVLXWkHIDo6Gk9PT44dO8b06dPp06cPEydO5Pbt29y9e5fPPvuM9957j6lTpxIVFaXez8/Pj759+2ptR3R0ND169CAsLExj+YEDBxgwYABpaWkFEwAhhBBCCB2hE8nl6NGj+eCDD6hQoQLe3t4sWrSI5ORkunfvzqJFi5g/fz5mZmbMmzcvR4K3adMm3nvvPX788UfMzc354YcfWLlyJYMGDWLhwoWkpaXx66+/5qkddnZ2uLu74+vrq7Hc19eXdu3aYWRklG/XLIQQQgihi3Tisbi5uTllypRBX18fKysrAFq2bKmxzaRJk+jXrx+hoaG4urqql/fo0UM9hrJnz57MmzcPLy8v6tevD2RNnL5y5co8t6Vz5878/PPPjBw5EmNjY8LDw7l+/Trjx4/Xuv3+/fs5cOBArsf08vICsl66EHljbWEk8VKgMOOVkJBQKOcpSCqVqkRcR2GReCkj8VJG4qVMYcUrt6mIdCK51CYyMpKNGzcSGhrK48ePUalUZGZmEhsbq7Gdo6Oj+s/ly5cHoHr16hrLkpOTSU5OzjVQ2Zo1a8aKFSs4deoUbdu2xdfXl1q1alGtWjWt23fp0oUuXXJ/ezZ7nsuuC/xfeX6RZa9XY4mXAoUZr5LwtrjMq6eMxEsZiZcyEi9likO8dDa5nDdvHjY2NowdOxYbGxsMDAwYM2YM6enpGtsZGBio/6ynp/fSZSpV3uaSNzQ0pF27dvj5+dGqVSv++ecfPvzww/96OUIIIYQQJYJOjLl80ZMnTwgPD6dv3754eHjg4ODAs2fPyMjIKJTzd+7cmaCgIPbs2UNSUhKtW7culPMKIYQQQhR3OtlzaWFhgaWlJQcOHKBChQrExcWxbt06jR7JglSlShXq1KnDunXraN26NWZmZoVyXiGEEEKI4k4nk0t9fX2mTp3Kr7/+yrhx46hUqRIjRoxgwYIFhdaGTp06ceXKFTp27JhvxywJY9UKS0JCgsRLAYmXEEKIwqKXlJSUt8GGQsOff/6Jr6+vojfNXyb7hR5LS8v/fKzSojgMWNYlEi9lJF7KSLyUkXgpI/FSprDiVSLfFs+LgqhHnpSUxP379/nrr794//33AdixYwd79uxhzZo1/+nYUls876S2uDL5FS/p/RRCCPEqJTq5LIh65CtWrODYsWM0a9bslVMMCSGEEEKUNiU6uSyIeuSTJ09m8uTJ+XY8IYQQQoiSpMQml4sXL+bw4cMAeHp6Ymtry+rVq9mxYwf79+/n4cOHVKpUiT59+tCuXTv1fnFxcaxZs4aAgAAA6tSpw6hRo6hcubJ6m+3bt7Nz506Sk5Np3rw59vb2hXtxQgghhBDFVIlNLkePHo2trS1+fn4sWrQIfX19NmzYwKlTp/j444+pUqUKISEhLF26FAsLC5o0aUJycjIzZszAxcWFBQsWYGhoiI+PD1988QXLly/H1NSU48ePs3HjRkaPHk39+vU5ceIE27dvl8HGQgghhBCU4OTyxXrkycnJ7Nq1i7lz56prj9vb2xMWFsaePXto0qQJx48fR6VSMWnSJHXlnrFjxzJo0CDOnz9Pq1at2L17N+3bt+edd94BoF+/fgQFBREZGam1HVJbvGBIbXFl8itepaW+r9QyVkbipYzESxmJlzJSW7wQ3bt3j9TUVGbPnq1OHAHS09Oxs7MD4MaNluvufwAAIABJREFUG0RHR6vfAs+WkpJCVFQUAOHh4XTq1EljvYuLy0uTS6ktXjCktrgy+RWv0vK2uEx9oozESxmJlzISL2WKQ7xKTXKZXTt85syZVKxYUWOdoaGhehsnJyc+++yzHPsX9QclhBBCCKELSk1y6eDggJGREbGxsbi7u2vdxtnZmWPHjmFpafnSaYscHBy4fv26RmWe69evF0ibhRBCCCF0jX5RN6CwmJmZ0atXL9auXYuvry8RERHcunWLffv2sX9/1uTlbdq0oXz58nz11VcEBQURFRVFcHAwa9asISIiAoDu3btz6NAhDhw4QEREBNu2bZPkUgghhBDi/ys1PZcAAwcOpHz58vj4+LB8+XLMzMxwcnKid+/eQNbg1AULFrB+/Xq+/fZbEhMTsba2pn79+pibmwPQqlUroqKi2LBhAykpKTRt2pSePXty6NCh/9y+0jKeLT9IrWxlJF5CCCEKi9QWLwaktrhyxWHAsi6ReCkj8VJG4qWMxEsZiZcyxaG2eKl5LC6EEEIIIQpeqXosnl+io6MZOXIkixYtombNmjl+fl21x+7Px1aWbHu9GtN4+smibobOyI94yWN1IYQQeSHJ5WuoUKEC3t7e8hhbCCGEEOIFkly+BgMDA6ysrIq6GUIIIYQQxY6MuXyJtLQ0Vq1axaBBg+jduzdTpkzhypUrQNZjcU9PT8LCwoq4lUIIIYQQxYskly+xbt06jh8/zoQJE1iyZAnVqlVjzpw5PHz4sKibJoQQQghRbMljcS2Sk5PZt28f48ePp0mTJgCMGTOGy5cvs2fPnhy1xXOzf/9+Dhw4kOs2Xl5eQNZLFyJvrC2MJF4K5Ee8EhIS8qk1xZ9KpSpV1/tfSbyUkXgpI/FSprDildtURJJcahEZGUl6ejp16tRRLzMwMMDFxYXw8HBFx+rSpQtduuT+lm32PJddF/grb2wptderscRLgfyIV2l6W1zm1VNG4qWMxEsZiZcyxSFe8lg8F3p6ekXdBCGEEEIInSLJpRaVKlXC0NCQq1evqpdlZGQQEhLCG2+8UYQtE0IIIYQo3uSxuBampqZ07dqV9evXY2lpiZ2dHbt27SI+Pp6uXbuSlpZW1E0UQgghhCiWJLl8iaFDhwKwZMkSnj59irOzM3PmzMHa2pro6OgCOWdpGtP2XyUkJEi8FJB4CSGEKCx6SUlJqqJuRGmX/UKPVPzJu+IwYFmXSLyUkXgpI/FSRuKljMRLmcKKl7wtriOktnjeSW1xZf5rvKTXUwghRF6Vihd6/Pz86Nu3b1E3QwghhBCixCsVyWV+yczMJCMjo6ibIYQQQghRbJWox+LBwcH89ttv3L17F319fapWrUq7du1YuXIlAJ6engD079+fAQMG8PTpU1atWsXZs2dJS0ujTp06jBo1imrVqgFZPZ4rV65k2rRprFu3jvv37/PTTz9RrVo1/Pz82LFjB1FRUfy/9u48Lqp6/+P4i4Fhl1UWlcVdDAsh1FvkT826LlftmlZqpuZaaa4toiauaZq44pI7uObPrXtTLDStn5ZmuSvuCCQoKpvKyDL8/vDByRFETgEzwOf5eNzHI85855zvvO8wfJxzzvfj5uZGhw4d6NKlCxqN1OtCCCGEqLoqTXGZl5fHtGnTePXVVxkzZgy5ublcvnwZX19fBg0aRGRkJMuXLwf+vAh13rx5JCYmMmHCBOzt7YmKimLSpEksXboUKysrALKzs9m8eTNDhw7F0dERZ2dn9uzZw/r16xkyZAj16tUjPj6ehQsXYmFhQadOnYyWgRBCCCGEsVWa4vL+/fvcu3eP5s2bU6NGDQC8vb0BuHz5MmZmZjg7Oyvjr1+/zuHDh5kxYwZNmjQBYPTo0fTv35/9+/fTrl074OGp8CFDhlC/fn3luZs2baJfv36EhIQA4OnpSffu3dm1a1eh4lJ6i5cN6S2uzt/Nq6r19ZVexupIXupIXupIXupIb/FSVK1aNdq2bUtYWBgBAQEEBAQQEhKCm5tbkeMTEhLQaDT4+fkp2+zs7PD19TXoH25ubk6dOnWUn9PT07l16xYREREsWbJE2Z6Xl0d+fuFVnaS3eNmQ3uLq/N28qtrd4rL0iTqSlzqSlzqSlzqmkFelKS4BRo4cyWuvvcZvv/3G4cOHiYqKYvz48UWOLaoQLPBoT3GtVou5ubnys16vB2Do0KEGhakQQgghhKiEd4vXqVOH7t27K6e79+7di4WFhVIUFvDx8UGv1xMbG6tsu3//PteuXVNOpxfF2dkZV1dXkpKSqFmzZqH/CSGEEEJUZZXmm8vk5GSio6Np0aIFrq6uJCcnExcXR8eOHfHw8CA7O5tjx45Rt25drKysqFmzJi1atCAiIoJhw4ZhZ2dHVFQUtra2tGrVqthj9ezZk6+++go7OzuCg4PJy8vj8uXL3L59W9bTFEIIIUSVVmmKSysrK65fv87MmTPJyMjAycmJ1q1b061bNywsLOjQoQOzZ88mMzNTWYpo5MiRLF++nKlTpypLEU2aNEm5U/xJ2rVrh7W1Ndu2bSMyMhJLS0t8fHz+9p3iVe26tr9DemWrI3kJIYQoL9Jb3ARIb3H1TOGC5YpE8lJH8lJH8lJH8lJH8lLHFHqLV7prLoUQQgghhPGU+mlxvV7P4sWLOXToEJmZmXz++ec8++yzpbb/0NBQfH19ee+990r0c0XSaGi0sadQYewKDSZ47EFjT6PCODozxNhTEEIIUUWUenF59OhR9u7dy+eff46npyf29vZ/aT8FrRe3bNlisH3cuHEGSwM97mmPCyGEEEKIslPqxWVSUhLOzs40btz4L+8jNzf3iY897TqCsr7OICcnB61WW6bHEEIIIYSoqEq1uJw7dy779u0DoHPnzri7u7NixQq2bdtGdHQ0d+7coUaNGnTr1o02bdoAcOPGDQYOHMhHH33Ed999R2xsLO+++y7Lli1T9gMod3g/7bT3o4/HxMQwf/78QmNefvllRo0aBcCRI0fYsGED8fHxODs706pVK3r27KkUkAMGDKBt27akpKTw888/07RpU8aOHcvGjRv5/vvvSU1Nxd7ensDAQEaPHl2acQohhBBCVDilWlwOHjwYd3d3YmJiCA8PR6PREBUVxaFDh3jvvfeoVasWsbGxLFq0CHt7e5o1a6Y8NzIykv79+/Phhx+i0WjQ6/VERkayfPlyoPi7kp6kZcuWPP/888rPV69eZerUqco1oL///jtffvklgwcPxt/fn5SUFBYvXkxOTg4DBgxQnrdjxw7eeustwsPDATh48CDbt2/n448/xtfXl/T0dM6fP/+XMhNCCCGEqExKtbi0s7PDxsYGjUaDs7MzOp2OnTt3MmXKFPz9/QHw9PTk4sWLfPvttwbFZadOnQgJ+fOmA1tbW8zMzHB2dv7L87GyslLWrExPTyciIoKOHTvyyiuvAPD111/z+uuvKz/XqFGDvn37Eh4eTv/+/ZU2kE2aNKFbt27Kfo8cOYKLiwuBgYFYWFjg7u5OgwYNipxDdHQ0e/bsKXaeoaGhwMObVETJuNhrJS8V8vPzyczMNPY0KgzJSx3JSx3JSx3JS53yyqu4L/3KdBH1+Ph4srOzCQsLM+jXnZubi4eHh8HY+vXrl9k8cnJymD59Ol5eXvTv31/ZfunSJS5cuMDWrVuVbXq9nuzsbFJTU3FxcQEoVDiGhITwzTffMHDgQIKCgggKCqJFixZFXovZvn172rcvfvHqgnUuO844+pdfY1WzKzRY8lLh6MwQWSdOBVlXTx3JSx3JSx3JSx1TyKtMi8v8/Ifrs3/22We4ubkZHtjC8NB/5bR3SS1evJi7d+8yadIkgzvJ8/Pz6dmzp8E3pgUcHR2V/368Y4+bmxtLly7lxIkTHD9+nJUrV7Jx40bmzJlTpq9DCCGEEMLUlWlx6e3tjVarJSUlhYCAAFXPtbCwQK/X/+05bNu2jSNHjjBnzhxsbW0NHqtXrx6JiYnUrFlT9X4tLS1p1qwZzZo1o3v37vTp04ezZ88SFBT0t+cshBBCCFFRlWlxaWtrS9euXVm1ahX5+fn4+/uj0+k4f/48ZmZmxZ4u9vDwIDs7m2PHjlG3bl2srKxUfyt4/PhxoqKiGDNmDFZWVqSmpgIPC0M7Ozt69OjBlClTcHNzo2XLlmg0GuLj47lw4QLvvvvuE/cbExNDXl4ejRo1wtramp9++gkLC4u/VKQKIYQQQlQmZVpcAvTu3RsnJye2b9/O4sWLsbW1pW7durz++uvFPq9x48Z06NCB2bNnk5mZqSxFpMbZs2fJzc3liy++MNhesBRRUFAQEydOZPPmzWzfvh1zc3Nq1apF27Zti92vnZ0dW7duZfXq1eTm5uLt7U1oaCienp6q5ve48xHFX5sp/pSZmSl5qSAXwwshhCgvZllZWfnGnkRVV3BDj4ODg5FnUnGYwgXLFYnkpY7kpY7kpY7kpY7kpU555WW0u8WrivT0dHr37v23+6hLb/GSk97ihck3uUIIIUyBxtgTEEIIIYQQlYcUl0IIIYQQotSY1Gnx/Px8duzYwe7du0lJScHR0ZE2bdrQt29f1qxZwy+//EJKSgpOTk689NJLvP3221haWgKwYcMGDh48yFtvvUVUVBTp6ek899xzfPjhh8qalXPnziUjI4PAwEC2bt3KgwcP+Mc//sF7772nXDuQn59fbC90gAsXLrB48WLi4+Px9vamd+/e5R+WEEIIIYQJMqniMjIykt27dzNgwAD8/f3JyMjg8uXLwMMLR4cPH46rqysJCQlERESg1WoNCrubN2/y008/MW7cOB48eMCsWbOIiopi2LBhypizZ8/i4uLCtGnTSElJYdasWdSqVYs33ngD4Km90HU6HVOmTKFJkyaMGjWK27dvK/3PhRBCCCGqOpMpLrOysti5cyeDBg3i1VdfBaBmzZr4+fkB0KNHD2Wsh4cHb775Jtu3bzcoLvPy8hg5ciR2dnYAtGvXjr179xocx9bWlg8++ABzc3O8vb0JCQnhxIkTvPHGGyXqhb5//35yc3MZMWIENjY2+Pr68uabbxIeHl7k65Le4mVDeosXVtxyQ9KbVx3JSx3JSx3JSx3JS51K31tcjYSEBHJycp7YyefgwYPs3LmTpKQkdDoder2+UAcfd3d3pbAEcHV1JS0tzWCMt7e3QQtIFxcXLly4AJSsF3pCQgK1a9fGxsZGebygAC6K9BYvG9JbvLDi7haXpTzUkbzUkbzUkbzUkbzUMYW8TKa4LOhDXpTY2FhmzZpFz549CQoKwt7ensOHD7Nq1SqDcY8WjU/a7+NjzMzMlCJVTS90IYQQQghRmMlUTAV9yE+cOFGojeK5c+dwdXU1ODVe8G1fWcyhuF7o3t7e7N27F51Op3wlfP78+VKfixBCCCFERWQyxaWtrS1dunRh7dq1aLVa/P39yczM5NKlS9SqVYvbt2+zf/9+/Pz8+P333/nxxx/LZA5P64XeqlUroqKimD9/Pj169ODOnTt8/fXXpT4XIYQQQoiKyGSKS4A+ffpgZ2fHpk2buH37Nk5OTrRp04aOHTvy+uuvs3z5crKzswkMDOTtt99myZIlpT6Hp/VCt7GxYeLEiSxevJiRI0fi5eVFv379mDp16t8+tnRYKTnpLS6EEEKYJuktbgKkt7h6pnDBckUieakjeakjeakjeakjealjCr3FpUOPEEIIIYQoNSZ1Wry86PV6Fi9ezKFDh8jMzMTd3R0fHx/CwsKe+tyYmBiWLVvGli1bSn1ejYZGl/o+K6tdocEEjz1o7GkYnVwaIIQQwtRUyeLy6NGj7N27l88//xxPT08sLS2LXQpJCCGEEEKUTJUsLpOSknB2dqZx48bGnooQQgghRKVS5YrLuXPnsm/fPgA6d+6Mu7s7TZo0ISMjQzktfvr0adasWcO1a9fQaDR4eXkxfPhwfH19lf2cOHGCr776ihs3btCwYUOGDx+Op6enUV6TEEIIIYSpqHLF5eDBg3F3dycmJobw8HA0Go1Bp5+8vDymTZvGq6++ypgxY8jNzeXy5ctoNH/e+5STk8OWLVsYMWIEWq2WefPmsXjxYqZMmWKMlySEEEIIYTKqXHFpZ2eHjY0NGo0GZ2fnQo/fv3+fe/fu0bx5c2rUqAE87MrzqLy8PN577z28vLwA6Nq1K/Pnz0ev1xsUoQDR0dHs2bOn2DmFhoYCD29SESXjYq+VvHi45ERJ5Ofnl3iskLzUkrzUkbzUkbzUKa+8iluKqMoVl09TrVo12rZtS1hYGAEBAQQEBBASEmLQa1yr1SqFJYCLiwu5ubncu3ev0NpS7du3p3374u/oLVjnsuOMo6X4Siq3XaHBkhclv1tc1olTR/JSR/JSR/JSR/JSxxTyknUuizBy5EjmzJmDv78/hw8f5r333uP3339XHjc3NzcYb2ZmBjxc4kgIIYQQoiqT4vIJ6tSpQ/fu3ZkxYwZNmjRh7969xp6SEEIIIYTJk+LyMcnJyaxZs4Zz585x8+ZNTp48SVxcHD4+PsaemhBCCCGEyZNrLh9jZWXF9evXmTlzJhkZGTg5OdG6dWu6detW5seWbisll5mZKXkJIYQQJsgsKytLWtMYWcENPQ4ODkaeScVhChcsVySSlzqSlzqSlzqSlzqSlzrllVeVvFv87/QPNxbpLV5yVaW3uHw7K4QQoqKptMXl4/3DV6xYwf379409LSGEEEKISq3SFpeP9w+3sCifl5qbm1tuxxJCCCGEMDWV8m7xuXPnsmLFClJSUujcuTMDBgwoNCYnJ4fly5fzzjvv8Prrr/PRRx9x5swZ5fFTp07RuXNn0tPTlW03btygc+fOXLx40WDM0aNHGT16NF27djVYD1MIIYQQoqqplF+xPa1/OMDq1av5v//7P4YPH46npyc7duxg0qRJLFu2DBcXF1XHW7NmDf3796dmzZrY2NiU5ksRQgghhKhQKmVx+bT+4Tqdjt27d/Phhx/SrFkzAD744ANOnjzJt99+yzvvvKPqeD179iQoKKjIx6S3eNmoKr3FS6s/rPTmVUfyUkfyUkfyUkfyUkd6ixtJUlISubm5yvWY8LClo5+fHwkJCar316BBgyc+Jr3Fy0ZV6S1eWneLy1Ie6khe6khe6khe6khe6phCXpXymsuSKugJXtLH8vLyihxrZWVVanMSQgghhKjIqmRxWaNGDSwsLDh79qyyLS8vj9jYWKXNo6OjIwB37txRxly5cqV8JyqEEEIIUcFUydPi1tbWdOzYkbVr1+Lg4ICHhwc7d+4kLS2Njh07Ag8L0OrVq7Nx40b69u3LjRs32Lx5s5FnLoQQQghh2qpkcQnQr18/AObPn8/du3epV68ekyZNUu4Ut7Cw4JNPPmHJkiUMHz6cOnXq0KdPH6ZMmVJmc5JuLCUnvcWFEEII0yS9xU2A9BZXzxQuWK5IJC91JC91JC91JC91JC91TKG3eJW85lIIIYQQQpQNkzwt3rlzZ8aOHUtISMhf3seNGzcYOHAg4eHhT1wqqCRjylOjodHGnkKFsSs0mOCxB409jVInp/qFEEJUdCZZXKo1d+5cMjIyCAsLU/W86tWrExkZKaejhRBCCCFKSaUoLv8qc3PzIjv4CCGEEEKIv8ZoxWV+fj47duxg9+7dpKSk4OjoSJs2bejbt2+hsXFxcaxYsYJz585haWlJ8+bNGTx4MHZ2dmzYsIF9+/YBD0+nA3z++ee4u7sDD2+WiYyM5OzZs3h4eDBo0CACAwOBwqfFT506xbhx45g2bRqRkZHExcXh4+PD0KFDqV+/vjKf77//ng0bNpCRkUFAQADPP/88S5cu5T//+U9ZxyaEEEIIYdKMdkNPZGQkmzdv5o033iAiIoKxY8dSvXr1QuN0Oh1hYWFYW1szZ84cxo0bR2xsLPPnzwega9euvPTSSzRt2pTIyEgiIyPx8/NTnh8VFUXnzp1ZuHAhDRo0YPbs2WRlZRU7t7Vr19K3b1/mzZtHtWrVmDNnDvn5D2+qj42NZeHChfzrX/9iwYIFtGjRgg0bNpRiMkIIIYQQFZdRvrnMyspi586dDBo0iFdffRWAmjVrGhSFBQ4cOIBOp2P06NHY2toCMGzYMMaNG8f169epWbMmlpaWWFhYFHmK+7XXXqN58+YA9OnTh3379nHlyhX8/f2fOL/evXvz3HPPAdCjRw8+/fRTbt++TfXq1fnPf/5DYGAg3bt3B6BWrVpcvHiRPXv2FLmv6OjoJz5WIDQ0FHh4k4ooGRd7baXMKzMzs0z2m5+fX2b7rowkL3UkL3UkL3UkL3XKK6/iliIySnGZkJBATk4OAQEBJRpbu3ZtpbAE8PPzQ6PRkJCQQM2aNYt9fp06dZT/LlggPT09vdjn1K5du9Bz0tLSqF69OomJiTRr1sxgfMOGDZ9YQLZv35727Yu/A7hgncuOM44WO078aVdocKXMq6zuFpd14tSRvNSRvNSRvNSRvNQxhbyMclq84BRzSceamZkV+diTtj/K3Ny80PinHb+45xQ3HyGEEEKIqs4oxaW3tzdarZYTJ048dayPjw9Xr17l/v37yrbY2Fj0ej1eXl4AaLVa9Hp9mc33Ud7e3ly4cMFg2+M/CyGEEEJUVUYpLm1tbenSpQtr164lJiaGpKQkLly4wK5duwqNbdWqFdbW1sydO5e4uDhOnz5NREQEL7zwgnJK3N3dnfj4eBITE0lPTyc3N7fM5t65c2eOHz/Otm3buH79Ot999x2//PJLmR1PCCGEEKIiMdpSRH369MHOzo5NmzZx+/ZtnJycaNOmTaFx1tbWTJ48meXLlzNmzBi0Wi0tWrRg8ODByph27dpx6tQpRo8eTVZWlsFSRKXNz8+PYcOGsWHDBtavX09AQADdunVj3bp1f3vf0p2l5DIzMyUvIYQQwgSZZWVllfwCSFGk5cuXc+LECRYtWvSXnl9wQ490Cio5U7hguSKRvNSRvNSRvNSRvNSRvNQpr7xM7m7x8lSwMPq6detwdHT8y2MetW3bNpo2bYq1tTUnTpwgOjqad95552/PVXqLl1xF7i0u37gKIYSozCpMcRkTE8OyZcvYsmWLsafCxYsX2b59O/fu3cPDw4M+ffrQpUsXY09LCCGEEMLoKkxxaUo+/fRTY09BCCGEEMIkPbW43L17Nxs2bGDNmjUG6z/Onj2bBw8eMGHCBI4cOcKGDRuIj4/H2dmZVq1a0bNnT7RaLQCpqaksWrSI48eP4+joSK9evdi+fTshISH06tULgHv37rF69Wp++eUXsrOzqVu3LgMGDFB6fhe0eyzoH96zZ0969erFDz/8wDfffMMff/yBpaUlTZo0YdCgQbi6uhq8jvPnz7Nu3ToSExPx8fFh2LBhBv3CH3fu3DnWrl3LxYsXsbe3p0WLFvTr109ZzP306dOsWbOGa9euodFo8PLyYvjw4fj6+qrJXwghhBCiUnnqUkQtW7bk7t27HD9+XNmm0+k4fPgwrVu35vfff+fLL7+kU6dOREREMGLECA4dOkRkZKQyft68edy8eZPp06czYcIEfvjhB1JSUpTH8/PzmTx5Mrdv32bixInMmzePJk2aMH78eO7cuYOfnx+DBg3CyspK6R/etWtXAHJzc3n77bdZsGABEydOJCMjg9mzZxd6HatWraJfv37MnTsXT09PJk+ejE6nK/I1x8XFMXHiRFq0aMHChQsZN24cV65cUQrcvLw8pk2bRuPGjVmwYAFffvklnTt3RqMxWqt2IYQQQgiT8NRvLu3t7QkODmb//v08//zzAPz888+Ym5vTvHlzJk6cyOuvv84rr7wCQI0aNejbty/h4eH079+fP/74g99//53Zs2crvcNHjhzJwIEDlWOcPHmSq1evsm7dOqysrICH/b2PHDnCDz/8QLdu3bC1tcXMzKxQ//CC3uQAnp6evP/++3zwwQfcunWL6tWrK4/16NGDoKAgAEaMGMG7777LgQMHaNeuXaHXvG3bNlq2bKkUsAAffPABI0aMIC0tDXNzc+7du0fz5s2pUaMG8HBx9aJIb/GyUZF7ixujR6705lVH8lJH8lJH8lJH8lKnwvQWb926NfPnz0en02Ftbc3+/fsJCQnB0tKSS5cuceHCBbZu3aqM1+v1ZGdnk5qaSmJiIhqNhgYNGiiPu7m5KT27AS5fvsyDBw/o3bu3wXGzs7NJSkoqdm6XLl1i06ZNXLlyhbt37yptGlNSUgyKy4LCFsDGxgZfX18SEhKeuM+kpCR++uknZVvBfpOTk/Hz86Nt27aEhYUREBBAQEAAISEhuLm5FdqX9BYvGxW5t7gx7haXpTzUkbzUkbzUkbzUkbzUMYW8SlRcNm/eHI1Gw+HDhwkICODEiRNMmTIFeFh09ezZk5CQkELPc3R0LFEfcb1ej5OTEzNnziz0WME1jkXR6XSEhYXRtGlTRo8ejaOjIxkZGYwdO/ZvdenJz8/nn//8J6+99lqhxwqu5Rw5ciSvvfYav/32G4cPHyYqKorx48cr344KIYQQQlRFJSoutVotISEh7N+/n4yMDJydnWnSpAkA9erVIzExUWnF+Dhvb2/0ej2XLl2iUaNGANy6dYs7d+4oY+rVq0daWhoajQZPT8+iJ2phUah/eGJiIhkZGbzzzjvK8w4dOlTk82NjY5UxOp2Oa9eu8fLLLxc5tl69esTHxz/xNRWoU6cOderUoXv37oSFhbF3714pLoUQQghRpZX4DpTWrVtz7Ngxdu/eTatWrZSbV3r06MGBAwdYt24d165dIyEhgYMHD7J69WoAvLy8CAoKYvHixcTGxio3xlhZWWFmZgZA06ZNady4MdOmTePo0aMkJycTGxvL+vXrOXPmDAAeHh5kZ2dz7Ngx0tPT0el0uLm5odVq+fbbb0lOTubXX399YhvGr7/+mmPHjnHt2jXmz5+PVqulVatWRY7t1q0bFy5cICIigsuXL3P9+nWOHDmidOCdbBRtAAAZWElEQVRJTk5mzZo1nDt3jps3b3Ly5Eni4uLw8fEpaZxCCCGEEJVSide5bNKkCa6uriQkJPDJJ58o24OCgpg4cSKbN29m+/btmJubU6tWLdq2bauMGTlypHLXtaOjI2+//TbJycnKUkVmZmaEhYWxbt06Fi1aRHp6Ok5OTjRu3Fj5drFx48Z06NCB2bNnk5mZqSxFNGrUKCIjI/n222+pXbs2AwcOJCwsrND8+/bty6pVq5SliD777LMnXoxap04dZs6cybp16wgNDUWv1+Pp6ck//vEPAKysrLh+/TozZ84kIyMDJycnWrduTbdu3UoaZ5Gkc0vJSW9xIYQQwjQZpbd4eno6/fr146OPPiryWs2qRnqLq2cKFyxXJJKXOpKXOpKXOpKXOpKXOlWmt/iJEyfIysqidu3apKWlERUVhYODg7K0kXhIeouXXEXtLS7ftgohhKjsyqW4zMvLY926dSQnJ2NlZUXDhg2ZMWNGsVVvedqwYQMHDx4kIiLC2FMRQgghhKjQyqW4DAoKMum7qLt27UqnTp2MPQ0hhBBCiAqvXIrLp8nJyVFu7ilPer2e/Px8bGxssLGxKffjCyGEEEJUNmVSXOp0OhYvXszPP/+MtbU1Xbp04ezZszg4ODBq1CgGDBhA27ZtSUlJ4eeff6Zp06aMHTuWuLg4VqxYwblz57C0tKR58+YMHjwYOzs7Zd979+5l+/bt/PHHH9jb2xMUFMSoUaMAuHfvHqtXr+aXX34hOzubunXrMmDAAKU7UExMDMuWLePTTz9l9erVJCYmsmDBAg4ePFjotHhMTAzbtm0jOTkZNzc3OnToQJcuXZQlmHbv3s2OHTtISUnBxsaGevXqERYWhrm5eVlEKoQQQghRIZRJcbly5UpOnz7NuHHjcHFxYfPmzZw9e1ZZygdgx44dvPXWW4SHhwN/dttp0KABc+bMITMzk0WLFjF//nzGjRsHPCzoli9fTp8+fQgODkan03Hy5EngYVedyZMnY2dnx8SJE7G3t2ffvn2MHz+epUuXKu0ms7Oz2bx5M0OHDsXR0bFQr3KAPXv2sH79eoYMGaIsqL5w4UIsLCzo1KkTFy9eZOnSpYwaNYpnnnmGe/fuceLEiSKzkN7iZaOi9hY3Vn9c6c2rjuSljuSljuSljuSlToXpLa5GVlYWMTExjBo1isDAQACGDx9Ov379DMY1adLEYF3IPXv2oNPpGD16tNLycdiwYYwbN47r169Ts2ZNNm/eTJcuXfj3v/+tPK9+/foAnDx5kqtXr7Ju3TqsrKwA6N27N0eOHOGHH35QjqXX6xkyZIjyvKJs2rSJfv36KcskeXp60r17d3bt2kWnTp1ISUnB2tqa5s2bK3OtU6dOkfuS3uJlo6L2FjfW3eKylIc6kpc6kpc6kpc6kpc6ppBXqReXycnJ5Obm0rBhQ2WbtbU1vr6+BuMKTlUXSEhIoHbt2ga9xP38/NBoNCQkJGBra8vt27cJCAgo8riXL1/mwYMH9O7d22B7dnY2SUlJys/m5uZPLATh4Rqct27dIiIigiVLlijb8/LylD7pTZs2xd3dnYEDBxIUFERgYCAvvPBCsX3QhRBCCCGqglIvLgsKsKcp+Hbx0ecVtIN8nJmZ2VP3q9frcXJyYubMmYUee7To02q1xV4XWdC/fOjQofj5+RU5xtbWlnnz5nH69GmOHz/Oli1biIyMJDw8HFdX12LnKYQQQghRmZW4t3hJ1ahRAwsLCy5evKhs0+l0XLt2rdjn+fj4cPXqVe7fv69si42NRa/X4+XlhbOzM66urk+8trFevXqkpaWh0WioWbOmwf+cnJxKPP+C4yQlJRXaT82aNZVx5ubmBAQE0LdvXxYuXMiDBw/49ddfS3wcIYQQQojKqNS/ubSxseGVV15hzZo1ODg44OzszObNm4v9ZhKgVatWbNiwgblz5/L2229z9+5dIiIieOGFF5Si7s0332TFihU4OTnRrFkzHjx4wIkTJ+jatStNmzalcePGTJs2jX79+uHl5UVaWhq//fYbTZs2xd/fv8SvoWfPnnz11VfY2dkRHBxMXl4ely9f5vbt27zxxhscOXKE5ORk/P39qVatGidPniQrKwtvb++/lZ10byk56S0uhBBCmKYyuVu8f//+6HQ6pk6dio2NDV26dCEtLQ1LS8snPsfa2prJkyezfPlyxowZg1arpUWLFgwePFgZ07FjRywsLNixYwdr167F3t6e4OCHdwybmZkRFhbGunXrWLRoEenp6Tg5OdG4cWNefvllVfNv164d1tbWbNu2jcjISCwtLfHx8VEWWrezs+OXX35h06ZNPHjwAE9PTz788ENVBawQQgghRGVklpWVVbKLJP+GnJwc+vfvz+uvv07Xrl3L+nAVTsHd4g4ODkaeScVhCnfDVSSSlzqSlzqSlzqSlzqSlzrllVe5LkUED+/cTkhIoGHDhmRlZbF161aysrJo2bJlqR9r8uTJyuLsT9K5c2fGjh2rLC30NKdOnWLcuHGsW7cOR0fH0prqUzUaGl1ux6rodoUGEzz2oLGnoYqcxhdCCFEVlFn7x507d/LHH3+g0WioW7cuM2fOpHr16mV1uGJFRkZib29vlGMLIYQQQlQlZVJc1qtXj7lz55bFrlUp6FleVBceIYQQQghR+srsm8uyoNPpWLJkCYcOHcLa2prOnTsbPP6knuWPnha/ceMGAwcOZOzYsURHR3P27Fk8PDwYNGiQ0lHocTk5OcyaNYubN28yefJknJyc2LhxI99//z2pqanY29sTGBjI6NGjyyMGIYQQQgiTVaGKy1WrVnH8+HFCQ0NxdXVl48aNnDlzhhdeeEEZ83jP8ieJioqif//+vP/++2zevJnZs2ezcuVKbGxsDMbdv3+fadOmodfrmTFjBra2thw8eJDt27fz8ccf4+vrS3p6OufPny+T1yyEEEIIUZFUmOIyKyuL77//nhEjRhAUFATAiBEjePfddw3GPd6z/Elee+01mjdvDkCfPn3Yt28fV65cMVhOKCMjgzlz5uDq6sqnn36qLKWUkpKCi4sLgYGBWFhY4O7uXqidZYHo6Gj27NlT7FxCQ0OBhzepiJJxsddWuLwyMzONduz8/HyjHr+ikbzUkbzUkbzUkbzUKa+8yv1u8bJQ0LP80ZaMNjY2T+1Z/iSP9hd3cXEBHvYVf9TEiROpV68eoaGhBi0jQ0JC+Oabb5Te4kFBQbRo0QKtVlvoOO3bt6d9++LvEi5YiqjjjKMlmrt4WIhXtLyMebe4LOWhjuSljuSljuSljuSljinkVertH8vKX+1Z/iSPFosFnYMeP0azZs04e/YscXFxBtvd3NxYunQpQ4cOxcbGhpUrVzJy5Eh0Ol2Jji2EEEIIUVlVmOKyoGd5bGyssq0kPcv/jrfffpv27dvz2WefceXKFYPHLC0tadasGYMGDSI8PJz4+HjOnj1bZnMRQgghhKgIKsxpcRsbG1599VXWrl2Lo6MjLi4ubNq0Cb1eX6bH7dOnDwATJkxg+vTp1KlTh5iYGPLy8mjUqBHW1tb89NNPWFhYKD3QhRBCCCGqqgpTXMKfPcs///xzrKys6NSpU7mciu7Tpw/5+fmMHz+e6dOnY2dnx9atW1m9ejW5ubl4e3sTGhqKp6fn3zqOdHApuczMTMlLCCGEMEHl0ltcFE96i6tnChcsVySSlzqSlzqSlzqSlzqSlzqm0Fu8wlxzKYQQQgghTJ8Ul0IIIYQQotRIcSmEEEIIIUqNFJdCCCGEEKLUSHEphBBCCCFKjRSXQgghhBCi1EhxKYQQQgghSo0Ul0IIIYQQotRIcSmEEEIIIUqNdOgxAQUdeoQQQgghKgp3d/cit8s3l0IIIYQQotTIN5cmYtSoUcydO9fY06gwJC91JC91JC91JC91JC91JC91TCEv+eZSCCGEEEKUGikuhRBCCCFEqZHiUgghhBBClBopLoUQQgghRKmR4lIIIYQQQpQaKS6FEEIIIUSpkeJSCCGEEEKUGikuhRBCCCFEqTGfMGHCJGNPQjxUv359Y0+hQpG81JG81JG81JG81JG81JG81DF2XtKhRwghhBBClBo5LS6EEEIIIUqNFJdCCCGEEKLUSHEphBBCCCFKjYWxJyDg22+/Zdu2baSmpuLj48OgQYPw9/c39rRKzZYtWzh06BB//PEHWq2WRo0a0bdvX3x9fZUxc+fOZd++fQbPa9SoEV9++aXyc05ODqtWreLAgQNkZ2cTEBDA+++/T/Xq1ZUxN2/eZOnSpZw8eRJLS0tatWpF//790Wq1yphTp06xcuVK4uPjcXFxoVu3bnTo0KEME1Bnw4YNbNy40WCbk5MTUVFRAOTn57Nx40b27NnD3bt3adiwIe+9955Bnnfv3mXZsmUcOXIEgObNmzNkyBDs7e2VMXFxcSxdupSLFy9ib29P+/bt6dGjB2ZmZsqYgwcPsn79epKSkqhRowbvvPMOL7zwQlm+fNUGDBjAzZs3C20PDg4mLCzsqXlC+WZa3k6fPs327du5dOkSd+7cYcSIEbzyyivK46b0firJXMpacXnl5uaybt06fvvtN5KSkrC1teXZZ5+lb9++uLu7K/sIDQ3l9OnTBvtt2bIln3zyifJzZfkdfdr7y9Q+24399/ZpeXXu3LnI53Xs2JH3338fML1MiyLFpZH99NNPLF++nPfff59nnnmGXbt2MWnSJCIiIgw+rCqyU6dO8a9//YsGDRqQn5/P+vXrmTBhAosXL6ZatWrKuKZNmzJ69GjlZwsLw7fn8uXLOXz4MB9//DHVqlVj5cqVTJkyhblz52Jubk5eXh5TpkyhWrVqzJw5k8zMTObNmwfAkCFDAEhOTmby5Mm8+uqrjBkzhrNnz7JkyRIcHBwICQkphzRKplatWsyYMUP5WaP58yTD1q1b2bFjByNGjMDLy4uNGzcyceJElixZgq2tLQCzZ88mJSWFSZMmYWZmxoIFCwgPD2fixIkA3L9/n88++wx/f3/Cw8NJTExk/vz5WFtb07VrVwBiY2OZNWsWvXr14sUXX+TQoUPMnDmTWbNm0ahRo3JMo3jh4eHo9Xrl5zt37jBq1CheeuklZVtxeUL5ZWoMOp0OX19fXn75ZcLDwws9bkrvp5LMxZh5PXjwgMuXL/Pmm29Sp04d7t+/z8qVK5k0aRILFy7E3NxcGfvKK6/Qp08f5WdLS0uDfVWW39Gnvb/AdD7bTeHv7dPyioyMNPj54sWLTJ061eDzDEwn0yeR0+JGtmPHDtq2bUu7du3w9vZmyJAhODs7s3v3bmNPrdRMmTKFV155BV9fX2rXrs3o0aPJyMjg3LlzBuMsLCxwdnZW/vdo4Xnv3j2+//573n33XQIDA6lfvz6jR48mLi6OEydOAHDs2DHi4+MZPXo09evXJzAwkH79+rFnzx7u378PQHR0NC4uLgwZMgRvb2/atWvHyy+/zPbt28svkBIwNzc3yMLR0RF4+M3ON998Q7du3QgJCcHX15dRo0aRlZXFgQMHAEhISOD3339n2LBhNG7cGD8/P4YOHcqvv/5KYmIiAPv37+fBgweMGjUKX19fQkJC6NatGzt27CA//+ECEjt37uS5557jrbfewtvbm7feeotnn32Wb775xjihPIGjo6NBVkePHsXW1tbgw+9JeUL5ZmoMwcHB9OnTh5CQkEJFtSm9n0oyF2PnZWdnx9SpU2nZsiVeXl40bNiQoUOHkpCQQEJCgsFYKysrg/ecnZ2d8lhl+h0tLq8CpvLZbgp/b5+W16M5OTs7c/jwYWrVqsWzzz5rMM5UMn0SKS6NKCcnh0uXLhEYGGiwPTAwsFDhVZlkZWWh1+sNPmwBzp07R+/evRkyZAgLFy4kLS1NeezSpUvk5uYaZOXm5oaXl5eSVWxsLF5eXri5uSljgoKClJwLxjyed1BQkLJ/U5GcnEzfvn0ZMGAAs2bNIjk5GYAbN26Qmppq8BqsrKzw9/cnNjYWePgabWxsaNy4sTLmmWeewdra2mCMv78/VlZWypjAwEDu3LnDjRs3lDFFZWXK7838/Hy+//57WrdujbW1tbL9SXlC+WZqakzp/VSSuZiigj/Ej57OBvjxxx/p1asXH3zwAStXrlTGQdX7HTWFz/aK+Pf2/v37/PTTT/zzn/8s9JgpZFocOS1uRBkZGej1epycnAy2Ozk5Kf+6qIy++uor6tati5+fn7Lt+eef58UXX8TDw4ObN28SFRXF+PHjmTdvHlqtltTUVDQaDQ4ODgb7cnZ2JjU1FYC0tDScnZ0NHndwcECj0ShjUlNTCQgIMBjj5OREXl4eGRkZuLi4lMVLVqVhw4aMHDkSLy8v0tPT2bx5Mx9//DERERHK6yjqPXP79m3g4Wt0cHAwuC7LzMwMR0dHgxwevfbm0X2mpaXh6elJWlpakccp2IcpOnbsGDdu3DD4MC4uTwcHh3LN1NSY0vupJHMxNQXXtTVv3tzg9bdq1Qp3d3dcXFyIj49n7dq1xMXFMXXqVKBq/Y6aymd7fn5+hft7++OPP5KTk0Pbtm0NtptKpsX9vZTi0gQY82L/8rZixQrOnTvHF198YXB90v/8z/8o/127dm3q1avHgAED+PXXX3nxxRefuL/8/PwS5ff4h/jj+yhqu7EEBwcb/NyoUSMGDRrEvn37lOuoinoNxb3GgjGP+iuvt6R5G8t3331HgwYNqFu3rrKtuDz//e9/K9uNlakpMKX309PmYiry8vKYM2cOd+/eZcKECQaPtW/fXvnv2rVr4+npyZgxY7h06ZLSOaWq/I6ayme7qX3Ol8SePXto0aKFwWU8YDqZFkdOixvR4/9KKFDUv0Yrg+XLl/Pjjz8ybdq0p36L4+rqiqurK9evXwce/otLr9eTkZFhMO7RrIr6F/vj3w4/+i+3Aunp6Zibmxtcs2JKbGxs8PHx4fr168q/NIt6DY++xvT0dIM/VPn5+WRkZCjPLyqHgtMqj+b56KmWx49jatLS0jh8+DDt2rUrdtyjeQLlmqmpMaX3U0nmYiry8vKYPXs2cXFxTJ8+vdA3RI+rX78+Go2GpKQkoOr+joLxPtsr2t/bK1eucOnSpad+noFp/r2U4tKItFot9evX5/jx4wbbjx8/bnAtTmXw1Vdf8eOPPzJ9+nS8vb2fOj49PZ07d+4oX7vXr18fCwsLjh07poy5desWiYmJSlZ+fn4kJiZy69YtZcyxY8eUnAvGPH4K5Pjx48r+TVF2djaJiYk4Ozvj4eGBs7OzwXsmOzubM2fOKJcZ+Pn5kZWVZXCdWmxsLDqdzmDMmTNnyM7OVsYcP34cFxcXPDw8lDGP5g0P8zTV92ZMTAxarZaWLVsWO+7RPIFyzdTUmNL7qSRzMQW5ubl88cUXxMXF8fnnnxc6tViUa9euodfrlbFV9XcUjPfZXtH+3kZHR+Pu7k7Tpk2fOtYU/16aT5gwYVKJXqkoE7a2tmzYsAFnZ2esrKzYvHkzZ86cYcSIEYVueKmolixZwr59+xg7dixubm7odDp0Oh3wsMDOysoiMjISW1tb8vLyuHr1KgsWLECv1zNkyBC0Wi2WlpbcuXOH//73v9SpU4d79+6xePFibG1t6du3LxqNBg8PD37++WeOHTtG7dq1iY+PZ8mSJbRp00ZZ983T05P//d//JT09HXd3d3755Re2bNnCgAED8PHxMWZMipUrV6LVasnPz+ePP/5g6dKlJCUlMWzYMOzt7cnLy2PLli3UqlULvV7PypUrSU1NZejQoWi1WhwdHTl//jwHDhygbt263Lp1i4iICBo2bKisoVazZk2io6O5evUqXl5enD17llWrVvHGG28oHz6urq6sX78eCwsLHBwc+O6774iJiWHYsGGFrgUztvz8fObPn0+zZs0KLdlRXJ52dnaYmZmVW6bGkJWVRUJCAqmpqXz33XfUrl0bOzs7cnJyTOr9VJL/H4ydl7W1NTNnzuTChQuEhoZia2urfJ5pNBosLCxISkriv//9L9bW1uTm5hIbG8uiRYuoXr06vXv3RqPRVKrf0eLy0mg0JvXZbgp/b4vLq2AOOp2OefPm0alTJ5o0aVLo+aaU6ZOYZWVlGW+NDAH8uajrnTt38PX1ZeDAgYXeUBXZkxaF7dmzJ7169eLBgwdMnz6dK1eucO/ePZydnXn22Wfp3bu3wZ1s2dnZrF69mgMHDvDgwQNlUdhHx9y8eZMlS5Zw8uRJrKysnrgo7IoVK5RFYbt3725Si6jPmjWLM2fOkJGRgYODA40aNaJ3797KL3PBQtPR0dHKQtPvv/++wULTmZmZfPXVVxw+fBiAFi1aPHGB5gsXLmBvb0+HDh2KXKA5KiqKGzdu4OnpyTvvvFPsNT3GcvLkScaPH8+cOXNo2LChwWNPyxPKN9PydurUKcaNG1do+8svv8yoUaNM6v1UkrmUteLy6tWrFwMHDizyeQWLYaekpDBnzhzi4+PJysrCzc2N4OBgevbsaXAqsbL8jhaX1wcffGByn+3G/nv7tN9HeHgWZuHChaxatQpXV1eDcRXl76UUl0IIIYQQotTINZdCCCGEEKLUSHEphBBCCCFKjRSXQgghhBCi1EhxKYQQQgghSo0Ul0IIIYQQotRIcSmEEEIIIUqNFJdCCCGEEKLUSHEphBBCCCFKjRSXQgghhBCi1Pw/emIyWE5J8MgAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.feature_extraction import text\n", "\n", "additional_stopwords = ['buy', 'purchase', 'sell', 'pay', 'additional', 'items', 'materials', 'stock', 'like', 'goods',\n", " 'increase', 'new', 'improve', 'build', 'make', 'products', 'supplies']\n", "\n", "stop_words = text.ENGLISH_STOP_WORDS.union(additional_stopwords)\n", "\n", "vectorizer = CountVectorizer(stop_words=stop_words)\n", "words = vectorizer.fit_transform(loans_raw[\"LOAN_USE\"].dropna())\n", "words_sum = words.sum(axis=0)\n", "\n", "words_freq = [(word, words_sum[0, idx]) for word, idx in vectorizer.vocabulary_.items()]\n", "words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)\n", "\n", "# The double sorting is because barh puts the first element at the bottom\n", "top_20_words = sorted(words_freq[0:20], key=lambda x:x[1])\n", "\n", "y = [word[0] for word in top_20_words]\n", "width = [freq[1] for freq in top_20_words]\n", "\n", "fig, ax = plt.subplots()\n", "ax.set_title(\"Most common specific description words\")\n", "ax.barh(y, width=width)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the most common reasons for loans are to help small businesses, such as farming and local retail." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "What does the distribution of loan amounts look like?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Loan Amounts in USD')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "amounts = loans_raw['LOAN_AMOUNT']\n", "\n", "# Remove outliers to be able to view amounts at a reasonable scale.\n", "# These outliers are quite rare, but some have extremely large amounts, skewing the image.\n", "def filter_outliers(data, crit=4):\n", " \"\"\"Removes data points with a zscore magnitude greater than the critical value crit.\"\"\"\n", " z = np.abs(stats.zscore(data))\n", " return data[(z < crit)]\n", "\n", "filtered_amounts = filter_outliers(amounts)\n", "\n", "ax = sns.distplot(filtered_amounts)\n", "ax.set_xticks(np.arange(0, np.max(filtered_amounts) + 1, 500))\n", "ax.set_xlabel(\"Loan Amounts in USD\")" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We can see that the most common loan amount is 250 USD, though multiples of 500 are generally more popular than surrounding amounts." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "How many people tend to contribute to each loan?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 78933\n", "1 72962\n", "9 72353\n", "7 72134\n", "5 69761\n", "Name: NUM_LENDERS_TOTAL, dtype: int64\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filtered_lenders = filter_outliers(loans_raw[\"NUM_LENDERS_TOTAL\"])\n", "\n", "ax = sns.distplot(filtered_lenders)\n", "ax.set_xticks(np.arange(0, 141, 10))\n", "ax.set_xlabel(\"Number of lenders per loan\")\n", "\n", "print(filtered_lenders.value_counts().head())" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We see that the distribution is centered around 8 lenders, with a long right tail. However, we can also see that a significant number of loans were funded by only a single person." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The loans are not guaranteed to get fully funded. How many do not?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.1% of loans do not get fully funded.\n", "Of those, the mean amount funded is 56.4% of the ask.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAE5CAYAAADSsJtbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de1SVZf7//9dGEFQOe5MHUnA7RgyIh8ExzfgWM+YkZjo2HscypzTUn64anJwVZqZOB8sZ0kYnz06ZOTNmkukInRk/2sEKEs1taplSCiqbg7IRg/37w8WtWwTBuIHg+VirtfK+3vd9vWEt9nqt677ue1tcLpdbAAAAgIm8GroBAAAANH2ETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfobMRyc3OVm5vb0G0AAAD8aN4N3QCurqSkpKFbAAAAuCo/P78qx1jpBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGC6Bn1P5969e7V582YdOnRIeXl5evjhhzVw4ECPmu+++04vvfSS9uzZo/Pnzys0NFSPPPKIwsLCJEnnz5/XmjVrlJ6ertLSUvXq1UtTp05V27ZtjWvk5uZq2bJl2rNnj1q2bKm4uDg98MAD8vHxMWqysrK0evVqHT16VMHBwRoxYoQGDx7s0cu2bdv0+uuvy+l0qnPnznrwwQcVHR1tjNekFwAAgOaoQVc6S0pKZLfblZCQoJYtW1YaP3HihP785z+rQ4cOevLJJ7V06VKNHz/e48WjK1eu1K5duzRz5kwtWLBAxcXFmj9/vsrKyiRJZWVlmj9/vlwulxYsWKCZM2dq165dWrNmjcc88+bNU1RUlBYvXqxRo0Zp+fLl2rlzp1GzY8cOrVy5UqNHj9bixYsVFRWluXPnenxj0NV6AQAAaK4aNHT26dNH9913n2JjY+XlVbmVdevWKSYmRhMnTlR4eLhCQkLUp08ftWvXTpJ09uxZvf3227r//vsVExOj8PBwzZgxQ0eOHNEXX3whScrIyNDRo0c1Y8YMhYeHKyYmRn/4wx+Ulpam4uJiSVJqaqqCg4M1efJkhYWFadCgQRowYIA2b95s9JKSkqLbb79dgwYNUlhYmCZPniybzabt27fXuBcAAIDmqtHu6SwvL9fu3bsVFhamJ554Qvfcc48SExO1Y8cOo+bQoUP64YcfFBMTYxxr166dQkNDtX//fkmSw+FQaGioEVQlqXfv3jp//rwOHTpk1Fx6jYqaiutX1F5eExMTY8xTk14AAACaq0b73esFBQVyuVzauHGj7r33Xk2YMEF79uzRX//6V/n6+qpv375yOp3y8vJSYGCgx7k2m01Op1OSlJ+fL5vN5jEeGBgoLy8vo8bpdKpXr14eNVarVWVlZSosLJTb7VZ5ebmsVmulmopVzJr0cqnU1FSlpaVV+ztISkqSJBUVFVVbV5dOFbjqbS4AF7UNatXQLQDAj1bdd6832tBZXl4uSerXr5+GDx8uSeratasOHjyobdu2qW/fvlWe63a7ZbFYrjrHpTWX17vdbuP4pf9fW1X1Eh8fr/j4+GrPrdgvGhAQUOt5r9UdSW/W21wALvpwybiGbgEATNVob68HBgaqRYsW6ty5s8fxsLAwnTx5UtKFVcTy8nIVFhZ61OTn5xurklartdJKY2FhocfK5ZVWIwsKCtSiRQsFBARUWhm90jw16QUAAKC5arSh08fHRzfeeKOys7M9jn/33Xdq3769JCk8PFze3t7KyMgwxk+dOqXs7GxFRUVJkiIjI5Wdna1Tp04ZNRkZGfLx8VF4eLhRc/nDPpmZmcb1K2ozMzMr1VTMU5NeAAAAmqsWs2fPnttQk7tcLh07dkxOp1NvvfWWunTpojZt2uj8+fNq06aNAgICtGHDBlmtVrVu3Vq7du3Spk2bNHHiRHXq1EktW7ZUXl6etm7dqp/97Gc6e/as/vGPf6h169aaMGGCvLy81KFDB3344YfKyMhQly5ddPToUb344ov69a9/rf79+0uSQkJC9Nprr6mgoEDt27fXRx99pI0bN2rixInGSmvr1q316quvymazydfXV//+97+1b98+Pfzww2rTpk2Neqmts2fPSpJ8fX3r7pd+Fav/m1VvcwG4aNKdPRq6BQD40by9q965aXG5XO567MVDVlaWZs2aVen4gAEDlJiYKEl65513tHHjRp06dUodO3bUyJEjFRcXZ9SWlpZq7dq1Sk9P17lz54wXsl/6tHpubq5efPFF7dmzR76+vlW+HH7VqlXGy+FHjhxZ5cvh8/LyZLfbNWnSJHXv3r1WvdRGxZ7Oyx9OMlP/6a/W21wALmJPJ4CmoLoHiRo0dKJ6hE6g+SB0AmgKqgudjXZPJwAAAJoOQicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdN4NOfnevXu1efNmHTp0SHl5eXr44Yc1cODAK9YuWbJEaWlpuv/++/W73/3OOH7+/HmtWbNG6enpKi0tVa9evTR16lS1bdvWqMnNzdWyZcu0Z88etWzZUnFxcXrggQfk4+Nj1GRlZWn16tU6evSogoODNWLECA0ePNijh23btun111+X0+lU586d9eCDDyo6OrpWvQAAADRHDbrSWVJSIrvdroSEBLVs2bLKup07d+rgwYMKDg6uNLZy5Urt2rVLM2fO1IIFC1RcXKz58+errKxMklRWVqb58+fL5XJpwYIFmjlzpnbt2qU1a9YY1zhx4oTmzZunqKgoLV68WKNGjdLy5cu1c+dOo2bHjh1auXKlRo8ercWLFysqKkpz585Vbm5ujXsBAABorho0dPbp00f33XefYmNj5eV15VZyc3O1YsUKPfLII/L29lyYPXv2rN5++23df//9iomJUXh4uGbMmKEjR47oiy++kCRlZGTo6NGjmjFjhsLDwxUTE6M//OEPSktLU3FxsSQpNTVVwcHBmjx5ssLCwjRo0CANGDBAmzdvNuZKSUnR7bffrkGDBiksLEyTJ0+WzWbT9u3ba9wLAABAc9Wgt9evpqysTAsXLtSYMWMUFhZWafzQoUP64YcfFBMTYxxr166dQkNDtX//fvXu3VsOh0OhoaFq166dUdO7d2+dP39ehw4dUs+ePeVwODyuUVHz3nvv6YcffpDb7dahQ4d09913e9TExMRo//79Ne7lUqmpqUpLS6v2509KSpIkFRUVVVtXl1599MrbGwCYqz7/zgHALH5+flWONerQuX79egUEBOjOO++84rjT6ZSXl5cCAwM9jttsNjmdTklSfn6+bDabx3hgYKC8vLyMGqfTqV69ennUWK1WlZWVqbCwUG63W+Xl5bJarZVqKlYxa9LLpeLj4xUfH1/tz19x6z4gIKDaurp0R9Kb9TYXgIs+XDKuoVsAAFM12qfXs7Ky9O677+rhhx+u9blut1sWi+WqdZfWXF7vdruvWlOXvQAAADRljXalMysrS06nU/fdd59xrLy8XC+99JK2bNmif/7zn7LZbCovL1dhYaGCgoKMuvz8fOOpcqvVqi+//NLj2oWFhR4rl1dajSwoKFCLFi0UEBAgt9vtsTJ66TyXXuNqvQAAADRXjTZ03nnnnYqNjfU4NmfOHN12220aNGiQJCk8PFze3t7KyMjQr371K0nSqVOnlJ2draioKElSZGSk/vOf/+jUqVPGq4syMjLk4+Oj8PBwo+ajjz7ymCszM9O4fsVcmZmZ+n//7/951Nxyyy017gUAAKC5atDQ6XK5dPz4cUkXVjFPnjypr7/+Wv7+/mrfvn2lPZTe3t6y2WwKDQ2VJLVp00a/+c1vtHbtWlmtVgUEBGj16tXq0qWLsUczJiZGnTt31vPPP68HHnhARUVFWrt2rQYNGqTWrVtLurC/cuvWrVq5cqXi4+P15Zdf6t1339UjjzxizD18+HAlJyfrxhtvVLdu3bR9+3bl5eUZ7/KsSS8AAADNlcXlcrkbavKsrCzNmjWr0vEBAwYoMTGx0vGJEydqyJAhHi+HLy0t1dq1a5Wenq5z584ZL2S/9Gn13Nxcvfjii9qzZ498fX2rfDn8qlWrjJfDjxw5ssqXw+fl5clut2vSpEnq3r17rXqpjYoHiS5/OMlM/ae/Wm9zAbiIB4kANAXVPb3eoKET1SN0As0HoRNAU1Bd6Gy0T68DAACg6SB0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDrvhpx879692rx5sw4dOqS8vDw9/PDDGjhwoCTphx9+0CuvvKLPPvtMx48fV+vWrdWjRw9NmDBB7du3N65x/vx5rVmzRunp6SotLVWvXr00depUtW3b1qjJzc3VsmXLtGfPHrVs2VJxcXF64IEH5OPjY9RkZWVp9erVOnr0qIKDgzVixAgNHjzYo99t27bp9ddfl9PpVOfOnfXggw8qOjq6Vr0AAAA0Rw260llSUiK73a6EhAS1bNnSY+zcuXM6fPiwRo8erUWLFmn27Nk6deqU5s6dq7KyMqNu5cqV2rVrl2bOnKkFCxaouLhY8+fPN2rKyso0f/58uVwuLViwQDNnztSuXbu0Zs0a4xonTpzQvHnzFBUVpcWLF2vUqFFavny5du7cadTs2LFDK1eu1OjRo7V48WJFRUVp7ty5ys3NrXEvAAAAzVWDhs4+ffrovvvuU2xsrLy8PFtp06aN/vKXv+jWW29VaGioIiIiNG3aNB07dkzHjh2TJJ09e1Zvv/227r//fsXExCg8PFwzZszQkSNH9MUXX0iSMjIydPToUc2YMUPh4eGKiYnRH/7wB6Wlpam4uFiSlJqaquDgYE2ePFlhYWEaNGiQBgwYoM2bNxv9pKSk6Pbbb9egQYMUFhamyZMny2azafv27TXuBQAAoLn6Se3prAiJ/v7+kqRDhw7phx9+UExMjFHTrl07hYaGav/+/ZIkh8Oh0NBQtWvXzqjp3bu3zp8/r0OHDhk1l16joqbi+hW1l9fExMQY89SkFwAAgOaqQfd01kbFfsm+ffsaeySdTqe8vLwUGBjoUWuz2eR0OiVJ+fn5stlsHuOBgYHy8vIyapxOp3r16uVRY7VaVVZWpsLCQrndbpWXl8tqtVaqqVjFrEkvl0pNTVVaWlq1P3NSUpIkqaioqNq6uvTqowPrbS4AF9Xn3zkAmMXPz6/KsZ9E6CwrK9Pf/vY3nTlzRrNnz75qvdvtlsViuWrdpTWX17vdbuP4pf9fW1X1Eh8fr/j4+GrPrdgvGhAQUOt5r9UdSW/W21wALvpwybiGbgEATNXob6+XlZVp4cKFOnLkiJ566imPlUSbzaby8nIVFhZ6nJOfn2+sSlqt1korjYWFhR4rl1dajSwoKFCLFi0UEBBQaWX0SvPUpBcAAIDmqlGHzh9++EHPPvusjhw5oqeffrrSbfLw8HB5e3srIyPDOHbq1CllZ2crKipKkhQZGans7GydOnXKqMnIyJCPj4/Cw8ONmssf9snMzDSuX1GbmZlZqaZinpr0AgAA0Fw16O11l8ul48ePS5LKy8t18uRJff311/L399d1112nBQsW6ODBg3r88cdlsViMlcbWrVvL19dXbdq00W9+8xutXbtWVqtVAQEBWr16tbp06WLs0YyJiVHnzp31/PPP64EHHlBRUZHWrl2rQYMGqXXr1pIu3OreunWrVq5cqfj4eH355Zd699139cgjjxi9Dh8+XMnJybrxxhvVrVs3bd++XXl5eca7PGvSCwAAQHNlcblc7oaaPCsrS7Nmzap0fMCAARo3bpwmTZp0xfMufYl8aWmp1q5dq/T0dJ07d854IfulT6vn5ubqxRdf1J49e+Tr61vly+FXrVplvBx+5MiRVb4cPi8vT3a7XZMmTVL37t2N8Zr0UhsVezovfzjJTP2nv1pvcwG4iD2dAJqC6h4katDQieoROoHmg9AJoCmoLnQ26j2dAAAAaBoInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJjOuyEn37t3rzZv3qxDhw4pLy9PDz/8sAYOHGiMu91ubdiwQWlpaTpz5owiIiI0ZcoU2e12o+bMmTNavny5PvnkE0lS3759NXnyZPn7+xs1R44c0bJly3Tw4EH5+/srPj5eY8eOlcViMWp27typ9evX6/jx47r++us1fvx49e/fv857AQAAaI4adKWzpKREdrtdCQkJatmyZaXxTZs2KSUlRQkJCUpOTlZQUJDmzJmj4uJio2bhwoU6fPiw5s6dq3nz5unw4cNKTk42xouLi/X444/LarUqOTlZCQkJ2rx5s1JSUowah8Oh5557TnFxcXrhhRcUFxenBQsW6MCBA3XaCwAAQHPVoKGzT58+uu+++xQbGysvL89W3G63tmzZohEjRig2NlZ2u12JiYlyuVxKT0+XJB07dkyff/65pk+frqioKEVGRmratGnavXu3srOzJUkffPCBzp07p8TERNntdsXGxmrEiBFKSUmR2+2WJL3xxhvq2bOnxowZo7CwMI0ZM0Y9evTQli1b6rQXAACA5qrR7unMycmR0+lUTEyMcczX11fR0dFyOBySLqxQtmrVSlFRUUZNt27d5Ofn51ETHR0tX19foyYmJkZ5eXnKyckxai6dR5J69+6t/fv312kvAAAAzVWD7umsjtPplCRZrVaP41arVadPnzZqAgMDPfZmWiwWBQUFGec7nU61bdu20jUkKT8/XyEhIcrPz7/iPJdeoy56uVRqaqrS0tKq/R0kJSVJkoqKiqqtq0uvPjrw6kUA6lx9/p0DgFn8/PyqHGu0obPCpSFOunCr+/Jgd7mK2+bV1VzN5fPUVS8V4uPjFR8fX20Pubm5kqSAgIAa9VwX7kh6s97mAnDRh0vGNXQLAGCqRnt73WazSVKlVcKCggJjxdFms6mgoMAj2LndbhUWFhrn22y2StfIz8+XdHHl0mq1GseqmqcuegEAAGiuGm3o7NChg2w2mzIzM41jpaWl2rdvnyIjIyVJkZGRcrlcHnsmHQ6HSkpKPGr27dun0tJSoyYzM1PBwcHq0KGDUZORkeExf0ZGhrE/s656AQAAaK5azJ49e25DTe5yuXTs2DE5nU699dZb6tKli9q0aaPz58/L399fZWVl2rhxozp16qTy8nKtXr1aTqdT06ZNk4+Pj4KCgnTgwAGlp6era9euOnXqlJYuXaqIiAgNHTpUktSxY0elpqbqm2++UWhoqL788kutWbNGo0aNMkLlddddp/Xr18vb21uBgYF666239M4772j69Olq27atLBZLnfRSW2fPnpUkj4egzLb6v1n1NheAiybd2aOhWwCAH83bu+qdmxaXy3XlTYf1ICsrS7Nmzap0fMCAAUpMTDReyJ6ammq8kH3q1KkeL2QvKirSihUr9PHHH0uS+vXrV+XL4b/66iv5+/tr8ODBV3w5/Lp165STk6OQkBCNHz9et9xyizFeV73URsWezsDAwGs6/1r0n/5qvc0F4CL2dAJoCqp7kKhWoXPSpEl68MEH1a9fvyuOf/LJJ1qxYoVWrVpV+y5RCaETaD4InQCagupCZ632dObm5srlclU5XlJSopMnT9bmkgAAAGgGav0gUXWvH/ruu+/UqlWrH9UQAAAAmp6rvqfz3Xff1XvvvWf8+9///rfeeuutSnVnzpzRkSNHdNNNN9VthwAAAPjJu2roLCkpUV5envHvs2fPXvHl676+vho0aJB+//vf132XAAAA+Em7augcMmSIhgwZIkmaOHGiEhISqnyQCAAAALiSWn0N5urVq83qAwAAAE3YNX33enFxsU6ePKkzZ85c8bvFu3fv/qMbAwAAQNNRq9BZ8fLz//u//1N5eXmlcbfbLYvFojfeeKPOGgQAAMBPX61C59KlS/XRRx9pyJAh6t69+zV/0w4AAACal1qFzs8//1xDhw7VxIkTzeoHAAAATVCtXg7v7e2tjh07mtULAAAAmqhahc7Y2Fh99tlnZvUCAACAJqpWofPuu+/W6dOn9fzzz8vhcCgvL0/5+fmV/gMAAAAuVas9nVOmTJHFYtHhw4f1wQcfVFnH0+sAAAC4VK1C59ixY2WxWMzqBQAAAE1UrULnuHHjzOoDAAAATVit9nQCAAAA16JWK50bNmy4ao3FYtHYsWOvuSEAAAA0PXUWOi0Wi/E1mIROAAAAXKpWoXPLli2VjpWXlys3N1dvvvmm9u/fr7lz59ZVbwAAAGgifvSeTi8vL4WEhOjBBx9Uhw4dtHLlyrroCwAAAE1InT5I1LNnT+3evbsuLwkAAIAmoFa3168mOztbbre7zq5XVlamDRs26P3335fT6ZTNZtOvfvUrjRs3Ti1atJAkud1ubdiwQWlpaTpz5owiIiI0ZcoU2e124zpnzpzR8uXL9cknn0iS+vbtq8mTJ8vf39+oOXLkiJYtW6aDBw/K399f8fHxld5LunPnTq1fv17Hjx/X9ddfr/Hjx6t///7GeE16AQAAaI5qFTr37t17xeNnz57Vnj17tG3bNsXGxtZJY5K0adMmbdu2TYmJibLb7Tpy5Iief/55+fj4GA8rbdq0SSkpKXr44YcVGhqqDRs2aM6cOXrxxRfVunVrSdLChQt18uRJzZ07VxaLRS+88IKSk5M1Z84cSVJxcbEef/xxRUdHKzk5WdnZ2Vq8eLH8/Px09913S5IcDoeee+45jRs3Trfccot27dqlBQsW6LnnntPPf/7zGvcCAADQHNUqdM6aNeuK30jkdrvl5eWl2267TQkJCXXW3P79+9W3b1/17dtXktShQwf169dPBw4cMObdsmWLRowYYYTdxMREjR8/Xunp6Ro8eLCOHTumzz//XM8++6yioqIkSdOmTdOjjz6q7OxshYaG6oMPPtC5c+eUmJgoX19f2e12ZWdnKyUlRcOHD5fFYtEbb7yhnj17asyYMZKkMWPGKCsrS1u2bNHMmTNr1AsAAEBzVavQ+dRTT1U6ZrFY5O/vr/bt29f5al63bt303//+V8eOHVNYWJiOHj2qPXv2aOTIkZKknJwcOZ1OxcTEGOf4+voqOjpaDodDgwcPlsPhUKtWrYzAWXFdPz8/ORwOhYaGyuFwKDo6Wr6+vkZNTEyMXnnlFeXk5CgkJEQOh0NDhw716K93797aunVrjXsBAABormoVOnv06GFWH1c0cuRIuVwuTZs2TV5eXiorK9Po0aM1ZMgQSZLT6ZQkWa1Wj/OsVqtOnz5t1AQGBnqs0FosFgUFBRnnO51OtW3bttI1JCk/P18hISHKz8+/4jyXXuNqvVwqNTVVaWlp1f78SUlJkqSioqJq6+rSq48OrLe5AFxUn3/nAGAWPz+/Kseu6UGi4uJi7d27Vzk5OZIu3Pbu3r17na907tixQ++//74eeeQRde7cWV9//bVWrlypDh066I477jDqLr/lX/GS+qrGK2oudaWaq7l8npr0UiE+Pl7x8fHVXj83N1eSFBAQUOvertUdSW/W21wALvpwybiGbgEATFXr0Pnmm29q3bp1OnfunEdw8/Pz0/jx4yvdgv4x1q5dq7vvvlu33XabJKlLly46efKkXnvtNd1xxx2y2WySLqwytmvXzjivoKDAWHG02WwqKCjwCH9ut1uFhYXG+TabzViprJCfny/p4sql1Wo1jlU1z9V6AQAAaK5q9Z7O9957TytXrlTnzp31pz/9SYsXL9bixYuNlchVq1bpgw8+qLPmzp07Jy8vzxa9vLxUXl4u6cIKq81mU2ZmpjFeWlqqffv2KTIyUpIUGRkpl8slh8Nh1DgcDpWUlHjU7Nu3T6WlpUZNZmamgoOD1aFDB6MmIyPDo5eMjAxjr2hNegEAAGiuWsyePXtuTYsXLVqkkJAQLViwQD/72c9ks9lks9lkt9t1++23KzMzU3v37q2zh2aOHj2q999/X506dZK3t7eysrL08ssv67bbblPv3r1lsVhUVlamjRs3qlOnTiovL9fq1avldDo1bdo0+fj4KCgoSAcOHFB6erq6du2qU6dOaenSpYqIiDBWZTt27KjU1FR98803Cg0N1Zdffqk1a9Zo1KhRRqi87rrrtH79enl7eyswMFBvvfWW3nnnHU2fPl1t27atUS+1dfbsWUnyeMDJbKv/m1VvcwG4aNKd9btnHgDM4O1d9U10i8vlqvHb3EeMGKH7779fd9111xXHt27dqrVr12rTpk217/IKiouLtX79en344YcqKCiQzWbTbbfdprFjx6ply5aSLr6QPTU11Xgh+9SpUz1eyF5UVKQVK1bo448/liT169evypfDf/XVV/L399fgwYOv+HL4devWGU+0jx8/XrfccosxXpNeaqNiT2dgYOA1nX8t+k9/td7mAnARezoBNAXVPUhUq9B5zz33aNCgQbrvvvuuOP7yyy8rLS1N69evr32XqITQCTQfhE4ATUF1obNWezpjYmK0detWZWVVvgWblZWlrVu3qnfv3rXvEAAAAE1arZ5enzBhgvbt26fZs2era9euCg0NlXThO9e//vprBQcHa8KECaY0CgAAgJ+uWoXOdu3aafHixdq4caM+/fRT7dq1S5LUvn17/fa3v9XIkSPr9VYwAAAAfhpqFTpLSkpUUlKiiRMnauLEiZXGc3Nz1bJly2rv5wMAAKD5qdWezlWrVl3x+9crPPXUU1qzZs2PbgoAAABNS61CZ0ZGhm6++eYqx/v371/pBeoAAABArcFIVY8AABqISURBVEKn0+lUcHBwleM2m015eXk/uikAAAA0LbUKnUFBQTp69GiV40ePHlWbNm1+dFMAAABoWmoVOn/5y18qNTVV+/fvrzTmcDiUmpqqPn361FlzAAAAaBpq9fT6Pffco88++0yPPvqofvnLX8put8tisejIkSP67LPPZLPZdM8995jVKwAAAH6iahU6bTabkpOT9c9//lMff/yxPv30U0lS69at9etf/1oTJkyQzWYzpVEAAAD8dNUqdEoXgmdiYqLcbrcKCgrkdrtltVplsVjM6A8AAABNQK1DZwWLxSKr1VqXvQAAAKCJqtWDRAAAAMC1IHQCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAACA6QidAAAAMN01vxy+vuTl5emll17Sp59+KpfLpZCQEE2dOlU9evSQJLndbm3YsEFpaWk6c+aMIiIiNGXKFNntduMaZ86c0fLly/XJJ59Ikvr27avJkyfL39/fqDly5IiWLVumgwcPyt/fX/Hx8Ro7dqzHNy3t3LlT69ev1/Hjx3X99ddr/Pjx6t+/vzFek14AAACao0a90nnmzBn9+c9/ltvt1hNPPKF//OMfmjx5ssc3IW3atEkpKSlKSEhQcnKygoKCNGfOHBUXFxs1Cxcu1OHDhzV37lzNmzdPhw8fVnJysjFeXFysxx9/XFarVcnJyUpISNDmzZuVkpJi1DgcDj333HOKi4vTCy+8oLi4OC1YsEAHDhyoVS8AAADNUaMOna+//rqCg4M1Y8YMRUREKCQkRL169VJYWJikCyuLW7Zs0YgRIxQbGyu73a7ExES5XC6lp6dLko4dO6bPP/9c06dPV1RUlCIjIzVt2jTt3r1b2dnZkqQPPvhA586dU2Jioux2u2JjYzVixAilpKTI7XZLkt544w317NlTY8aMUVhYmMaMGaMePXpoy5YtNe4FAACguWrUofOjjz5SRESEnn32Wd1777166KGHtHXrViMI5uTkyOl0KiYmxjjH19dX0dHRcjgcki6sULZq1UpRUVFGTbdu3eTn5+dREx0dLV9fX6MmJiZGeXl5ysnJMWounUeSevfurf3799e4FwAAgOaqUe/pPHHihP773//qt7/9rUaOHKlvvvlGy5cvlyTdddddcjqdkuRxu73i36dPn5YkOZ1OBQYGeuzNtFgsCgoKMs53Op1q27ZtpWtIUn5+vkJCQpSfn3/FeS69xtV6uVRqaqrS0tKq/fmTkpIkSUVFRdXW1aVXHx1Yb3MBuKg+/84BwCx+fn5VjjXq0Ol2uxUeHq4JEyZIkm644QZ9//332rZtm+666y6j7tJAWXHe5SHzSte+1JVqatLf5eddrZcK8fHxio+Pr/b6ubm5kqSAgIBa93at7kh6s97mAnDRh0vGNXQLAGCqRn173WazGfs3K4SGhurkyZPGuHRxlbFCQUGBseJos9lUUFDgETLdbrcKCwuN8202W6Vr5OfnS7q4cmm1Wo1jVc1ztV4AAACaq0YdOqOiovTdd995HPv+++/Vvn17SVKHDh1ks9mUmZlpjJeWlmrfvn2KjIyUJEVGRsrlcnnsq3Q4HCopKfGo2bdvn0pLS42azMxMBQcHq0OHDkZNRkaGRy8ZGRnGXtGa9AIAANBctZg9e/bchm6iKu3atdO//vUvWSwWBQcH64svvtC6des0atQoRUREyGKxqKysTBs3blSnTp1UXl6u1atXy+l0atq0afLx8VFQUJAOHDig9PR0de3aVadOndLSpUsVERGhoUOHSpI6duyo1NRUffPNNwoNDdWXX36pNWvWaNSoUUaovO6667R+/Xp5e3srMDBQb731lt555x1Nnz5dbdu2rVEvtXX27FlJ8njAyWyr/5tVb3MBuGjSnT0augUA+NG8vaveuWlxuVzuKkcbgd27d+vll1/Wd999p3bt2mnIkCEaOnSosU+y4oXsqampxgvZp06d6vFC9qKiIq1YsUIff/yxJKlfv35Vvhz+q6++kr+/vwYPHnzFl8OvW7dOOTk5CgkJ0fjx43XLLbcY4zXppTYq9nQGBgZe0/nXov/0V+ttLgAXsacTQFNQ3YNEjT50NmeETqD5IHQCaAqqC52Nek8nAAAAmgZCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdAIAAMB03g3dQG385z//0bp16zRkyBBNmTJFkuR2u7VhwwalpaXpzJkzioiI0JQpU2S3243zzpw5o+XLl+uTTz6RJPXt21eTJ0+Wv7+/UXPkyBEtW7ZMBw8elL+/v+Lj4zV27FhZLBajZufOnVq/fr2OHz+u66+/XuPHj1f//v2N8Zr0AgAA0Bz9ZFY6HQ6H0tLS1KVLF4/jmzZtUkpKihISEpScnKygoCDNmTNHxcXFRs3ChQt1+PBhzZ07V/PmzdPhw4eVnJxsjBcXF+vxxx+X1WpVcnKyEhIStHnzZqWkpHjM/9xzzykuLk4vvPCC4uLitGDBAh04cKBWvQAAADRHP4nQefbsWf3tb3/TQw895LE66Xa7tWXLFo0YMUKxsbGy2+1KTEyUy+VSenq6JOnYsWP6/PPPNX36dEVFRSkyMlLTpk3T7t27lZ2dLUn64IMPdO7cOSUmJsputys2NlYjRoxQSkqK3G63JOmNN95Qz549NWbMGIWFhWnMmDHq0aOHtmzZUuNeAAAAmqufxO31JUuWKDY2Vr169dK//vUv43hOTo6cTqdiYmKMY76+voqOjpbD4dDgwYPlcDjUqlUrRUVFGTXdunWTn5+fHA6HQkND5XA4FB0dLV9fX6MmJiZGr7zyinJychQSEiKHw6GhQ4d69NW7d29t3bq1xr1cKjU1VWlpadX+3ElJSZKkoqKimv6qfrRXHx1Yb3MBuKg+/84BwCx+fn5VjjX60JmWlqbjx49rxowZlcacTqckyWq1ehy3Wq06ffq0URMYGOixN9NisSgoKMg43+l0qm3btpWuIUn5+fkKCQlRfn7+Fee59BpX6+VS8fHxio+Pr/Znz83NlSQFBARUW1eX7kh6s97mAnDRh0vGNXQLAGCqRn17PTs7Wy+//LL+9Kc/ycfHp8q6SwOldOFW9+Uh83IVt82rq7may+epSS8AAADNUaNe6XQ4HCosLNT06dONY+Xl5dq3b5+2b9+upUuXSrqwytiuXTujpqCgwFhxtNlsKigo8Ah/brdbhYWFstlsRk3FSmWF/Px8SRdXLq1Wq3Gsqnmu1gsAAEBz1ahD580336wbb7zR49iiRYvUsWNHjR49Wp06dZLNZlNmZqYiIiIkSaWlpdq3b5/uv/9+SVJkZKRcLpccDoexr9PhcKikpESRkZFGzT//+U+VlpaqZcuWkqTMzEwFBwerQ4cORk1GRoZ+97vfGb1kZGQY1+zQocNVewEAAGiuGvXtdX9/f9ntdo///Pz8FBAQILvdLovFomHDhum1117Trl279O2332rRokVq1aqV4uLiJElhYWHq3bu3li5dKofDIYfDoaVLl+qmm25SaGioJCkuLk6+vr5atGiRvv32W+3atUuvvfaahg8fbqyODhs2THv27NHGjRt17Ngxbdy4UVlZWRo2bJgk1agXAACA5sricrncVy9rPJKSkmS32yu9HD41NdV4IfvUqVM9XsheVFSkFStW6OOPP5Yk9evXr8qXw3/11Vfy9/fX4MGDr/hy+HXr1hlPtI8fP1633HKLMV6TXmqj4kGiwMDAazr/WvSf/mq9zQXgIh4kAtAUVPf0+k8udDYnhE6g+SB0AmgKqgudjfr2OgAAAJoGQicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACm827oBqqzceNG7dq1S9999518fHz085//XBMmTJDdbjdq3G63NmzYoLS0NJ05c0YRERGaMmWKR82ZM2e0fPlyffLJJ5Kkvn37avLkyfL39zdqjhw5omXLlungwYPy9/dXfHy8xo4dK4vFYtTs3LlT69ev1/Hjx3X99ddr/Pjx6t+/f616AQAAaI4a9UpnVlaWhgwZooULF+qpp55SixYtNHv2bBUVFRk1mzZtUkpKihISEpScnKygoCDNmTNHxcXFRs3ChQt1+PBhzZ07V/PmzdPhw4eVnJxsjBcXF+vxxx+X1WpVcnKyEhIStHnzZqWkpBg1DodDzz33nOLi4vTCCy8oLi5OCxYs0IEDB2rVCwAAQHPUqEPn/PnzNXDgQNntdnXp0kUzZsxQYWGh9u/fL+nCyuKWLVs0YsQIxcbGym63KzExUS6XS+np6ZKkY8eO6fPPP9f06dMVFRWlyMhITZs2Tbt371Z2drYk6YMPPtC5c+eUmJgou92u2NhYjRgxQikpKXK73ZKkN954Qz179tSYMWMUFhamMWPGqEePHtqyZUuNewEAAGiuGnXovJzL5VJ5ebnatGkjScrJyZHT6VRMTIxR4+vrq+joaDkcDkkXVihbtWqlqKgoo6Zbt27y8/PzqImOjpavr69RExMTo7y8POXk5Bg1l84jSb179zYCcE16AQAAaK4a9Z7Oy61YsUJdu3ZVZGSkJMnpdEqSrFarR53VatXp06eNmsDAQI+9mRaLRUFBQcb5TqdTbdu2rXQNScrPz1dISIjy8/OvOM+l17haL5dKTU1VWlpatT9vUlKSJHlsJzDbq48OrLe5AFxUn3/nAGAWPz+/Ksd+MqFz1apV2r9/v5599lm1aNHCY+zSQClduNV9eci8XMVt8+pqrubyeWrSS4X4+HjFx8dXe/3c3FxJUkBAQK17u1Z3JL1Zb3MBuOjDJeMaugUAMNVP4vb6ypUr9b///U9PPvmkQkJCjOM2m03SxVXGCgUFBcaKo81mU0FBgUfIdLvdKiwsNM632WyVrpGfny/p4sql1Wo1jlU1z9V6AQAAaK4afehcsWKF/ve//+mpp55SWFiYx1iHDh1ks9mUmZlpHCstLdW+ffuMW/CRkZFyuVwe+yodDodKSko8avbt26fS0lKjJjMzU8HBwerQoYNRk5GR4TF/RkaGsVe0Jr0AAAA0V406dL744ot655139Mgjj8jf319Op1NOp1Mul0vShVvZw4YN02uvvaZdu3bp22+/1aJFi9SqVSvFxcVJksLCwtS7d28tXbpUDodDDodDS5cu1U033aTQ0FBJUlxcnHx9fbVo0SJ9++232rVrl1577TUNHz7cuDU+bNgw7dmzRxs3btSxY8e0ceNGZWVladiwYTXuBQAAoLmyuFwu99XLGsbQoUOvePz3v/+9xo27sP+p4oXsqampxgvZp06d6vFC9qKiIq1YsUIff/yxJKlfv35Vvhz+q6++kr+/vwYPHnzFl8OvW7dOOTk5CgkJ0fjx43XLLbcY4zXppTYq9nQGBgZe0/nXov/0V+ttLgAXsacTQFNQ3YNEjTp0NneETqD5IHQCaAqqC52N+vY6AAAAmgZCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADCdd0M3AABo+j79/5IaugWgWerzj2caugUDK50AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOl4ObwJtm3bptdff11Op1OdO3fWgw8+qOjo6IZuCwAAoMGw0lnHduzYoZUrV2r06NFavHixoqKiNHfuXOXm5jZ0awAAAA2G0FnHUlJSdPvtt2vQoEEKCwvT5MmTZbPZtH379oZuDQAAoMFwe70OnT9/XocOHdLdd9/tcTwmJkb79++/5usWFhb+2NZqLO3pu+ptLgAX1effeUOIWMB3rwMNob4/WwoLC9W+ffsrjhE661BhYaHKy8tltVo9jlutVn3xxRcex1JTU5WWllbt9ZKS+JAGAABNA6HTBBaL5ao18fHxio+Pr4du0JwkJibq+eefb+g2ADQxfLagLrCnsw4FBgbKy8tLTqfT43h+fn6l1U8AAIDmhNBZh3x8fBQeHq7MzEyP45mZmYqKimqgrgAAABoet9fr2PDhw5WcnKwbb7xR3bp10/bt25WXl6fBgwc3dGsAAAANhtBZx2699VYVFhbqP//5j/Ly8mS32/XEE09U+SQXAABAc0DoNMGQIUM0ZMiQhm4DAACg0WBPJwAAAExH6AQAAIDpCJ0AAAAwHaETaEIGDRrU0C0AaIL4bEFdsLhcLndDNwEAAICmjZVOAAAAmI7QCQAAANMROgEAAGA6QidQj8rLy7VkyRKNGzdOQ4cOVVZWlmlzTZs2Ta+++uqPusbOnTs1dOjQOuoIQEMpKSnRM888ozFjxmjo0KHKyckxdb5Ro0bpnXfe+VHXeP311zVx4sQ66giNAd9IBNSjTz/9VO+++66efvpphYSEyN/fv6FbAtAMvPPOO9q3b5+effZZBQUFKTAwsKFbQjNE6ATq0fHjx2Wz2RQVFdXQrQBoRo4fP67Q0FB16dKloVtBM0boBOrJ888/r/fee0+SNHToULVv317t27eX3W7XlClTPOoKCwv1xBNPSJKSkpIUFhYmf39/paamysvLS7/+9a91//33y8vrwg6Z/Px8LVmyRBkZGQoKCtLvf//7SvOfPXtWa9eu1UcffaTS0lJ17dpVEydO1I033mjUvPfee3rllVdUUFCgnj17qk+fPmb+SgDUQFJSUrWfE1f7jEhKStLevXslXfjs6d69u5555hlNnDhRQ4YM0e9+97sq55o4caLuuOMOnTx5Uv/73//UunVrDRs2zOOc77//Xn//+9914MABtW/fXg888ECln+H06dNavXq1Pv/8c0lSVFSUHnzwQXXs2NGo2bRpk1JSUlRSUqL+/fsrJCSkbn+RaHDs6QTqSUJCgsaOHau2bdvq5ZdfVnJyco3PTU9Pl5eXlxYuXKjJkydry5Yt2rFjhzG+aNEiff/99/rLX/6ixx57TO+9955yc3ONcbfbrXnz5un06dOaM2eOFi1apO7du+uxxx5TXl6eJOnAgQNatGiRBg0apBdeeEF9+/bV+vXr6+4XAMA01X1GzJo1SwMHDlRkZKRefvllzZo1q1bXfuONN9SlSxctWrRII0aM0Nq1a+VwOCRd2Kf+9NNPy+12a+HChXrooYe0YcMGnT9/3ji/pKREs2bNko+Pj5555hktXLhQNptNs2fPVklJiSRpx44deuWVVzRu3DgtWrRInTp1UkpKSh39dtBYEDqBetKmTRu1atVKXl5estlsCgoKqvG5YWFhuvfee9WpUyfdeuut6tmzp7744gtJ0nfffafPPvtM06dPV7du3XTDDTcoMTFRpaWlxvl79uzRN998o0cffVQRERHq2LGj7r33XoWEhOj999+XJG3ZskW9evXSmDFj1KlTJw0ePFg333xz3f4SAJiius+IgIAA+fr6ytvbWzabTQEBAbW6dkxMjO666y517NhRQ4cO1fXXX29cOzMzU8eOHdOMGTN0ww03qFu3bpo0aZLKysqM83fs2CG3260//vGP+tnPfqawsDBNmzZNJSUl2r17t6QLnz8DBgzQ4MGD1alTJ40ZM0YRERF19NtBY8HtdeAn4PJ9WMHBwSooKJAkHTt2TF5eXh4f0O3bt1dwcLDx78OHD+vcuXO69957Pa5TWlqq48ePS5Kys7N10003eYxHRkbq7bffrssfBYAJqvuMMOPa+fn5ki58bgQHB6t9+/bG+M9//nNj648kHTp0SDk5ORo9erTHdc6dO6cTJ05IuvA5dscdd3iMR0ZGGp9PaBoInUAD8vLyktvt+U20l64QVPD2rvynWl5eLkmVzr+S8vJyWa1WLViwoNJY69ata3wdAPWvJp8T1X1GVMVisVQ6dqXPnxYtWlQ6r6KfmnxuuN1ude3aVTNnzqw0VttVV/y0cXsdaECBgYFyOp0ex7755ptaXSMsLEzl5eU6ePCgcSw3N9fYqylJN9xwg/Lz8+Xl5aWOHTt6/Ge1Wo3rHDhwwOPal/8bQP2ri8+JKwkKCvL4nCgtLVV2dnatrhEWFqa8vDydPHnSOPbVV195BN4bbrhBx48fV2BgYKXPn4rQyedP80DoBBpQz5499dlnn+njjz9Wdna2Vq1apVOnTtXqGqGhoerdu7eWLl0qh8Ohr7/+WosXL1bLli2Nml/84heKiorSk08+qU8//VQnTpyQw+HQ+vXrtW/fPkkXnmr94osvtHHjRn3//fdKS0vThx9+WKc/L4Daq4vPiaqum56erqysLH377bdavHixfvjhh1pd4xe/+IU6deqk559/Xl9//bUcDodWrVrlsToaFxcnq9WqJ598UllZWTpx4oT27t2r1atX6/vvv5ckDRs2TO+++67S0tL0/fffa+PGjYTOJojb60AD+s1vfqMjR45o8eLFkqQ777xTN998swoLC2t1nT/+8Y9asmSJHnvsMQUGBmrs2LHGnivpwu2wJ554Qq+88oqWLFmigoICWa1WRUVFacCAAZIu7J966KGHtH79ev3rX/9S9+7dNW7cOC1fvrzufmAAtVZXnxOXGzVqlHJzc/Xkk0/Kz89Po0eP9lj5rAkvLy899thj+vvf/64//elPateunSZOnKi//vWvRo2fn5+eeeYZvfTSS3r22Wd19uxZBQcHq2fPnmrTpo0k6dZbb9WJEye0bt06nTt3Tn379tXw4cP17rvv/qifEY2LxeVysZELAAAApuL2OgAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABguv8f3VAVscH9BccAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "loan_diffs = amounts - loans_raw[\"FUNDED_AMOUNT\"]\n", "funding_status = [\"funded\" if diff == 0 else \"unfunded\" for diff in loan_diffs]\n", "\n", "loan_diffs = loan_diffs[loan_diffs != 0]\n", "percent_unfunded = ((loan_diffs.count() / amounts.count()) * 100).round(1)\n", "print(f\"{percent_unfunded}% of loans do not get fully funded.\")\n", "\n", "unfunded_asking_amounts = amounts[loan_diffs.index]\n", "unfunded_ratios = loan_diffs / unfunded_asking_amounts\n", "how_much_funded = (unfunded_ratios.mean() * 100).round(1)\n", "print(f\"Of those, the mean amount funded is {how_much_funded}% of the ask.\")\n", "\n", "sns.countplot(funding_status)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "loans_raw[\"TIME_TO_FUND\"] = loans_raw[\"RAISED_TIME\"] - loans_raw[\"POSTED_TIME\"]\n", "loans_raw[\"DAYS_TO_FUND\"] = [time.days for time in loans_raw[\"TIME_TO_FUND\"]]\n", "days_to_fund = filter_outliers(loans_raw[\"DAYS_TO_FUND\"].dropna())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.distplot(days_to_fund)\n", "ax.set_xticks(np.arange(0, 71, 5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is there a relationship between the loan amount and the number of days it takes to fund the loan?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Need to filter outliers but don't want to filter by different amounts\n", "amounts_and_days = loans_raw[[\"LOAN_AMOUNT\", \"DAYS_TO_FUND\"]]\n", "amounts_and_days = amounts_and_days[(amounts_and_days[\"LOAN_AMOUNT\"].isin(filtered_amounts))\n", " & (amounts_and_days[\"DAYS_TO_FUND\"].isin(days_to_fund))]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sample = amounts_and_days.sample(250)\n", "sns.lmplot(x=\"LOAN_AMOUNT\", y=\"DAYS_TO_FUND\", data=sample, fit_reg=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There does not seem to be a clear relationship." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Once the loans are distributed, there is no guarantee that they will be repaid. How many do not?\n", "\n", "Unfortunately, there was no information to answer this question in the original dataset. We instead turn to a previously supplied dataset of 5000 randomly sampled loans with their repayment details, taken from https://stat.duke.edu/resources/datasets/kiva-loans" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Source: https://stat.duke.edu/resources/datasets/kiva-loans\n", "repayments = pd.read_excel(\"datasets/loan_repayment_samples.xlsx\")\n", "\n", "# basket_amount was all n/a, video.youtube_id was not used\n", "repayments = repayments.drop([\"basket_amount\", \"video.youtube_id\"], axis=1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# This dataset has a row for each individual payment; aggregate these into a single row per loan\n", "grouped_repayments = repayments.groupby(\"id\", as_index=False).agg(lambda x: x.tolist())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.0 % of loans in this sample have defaulted.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get the payment status of the loan\n", "grouped_repayments[\"status\"] = grouped_repayments[\"status\"].agg(lambda x: \"defaulted\" if \"defaulted\" in x else \"paid\")\n", "\n", "status_counts = grouped_repayments[\"status\"].value_counts()\n", "default_ratio = ((status_counts[\"defaulted\"] / len(grouped_repayments)) * 100).round(1)\n", "print(f\"{default_ratio} % of loans in this sample have defaulted.\")\n", "\n", "fig, ax = plt.subplots()\n", "ax.pie(x=status_counts, labels=[\"paid\", \"defaulted\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "What is the gender ratio of those receiving loans?" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loan recipients are 80.0% female and 20.0% male.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "genders = loans_raw[\"BORROWER_GENDERS\"].dropna()\n", "\n", "# The gender entries include all recipients as a comma separated string, so a simple value_counts will not work.\n", "females = 0\n", "males = 0\n", "for entry in genders:\n", " recipients = entry.split(\", \")\n", " for gender in recipients:\n", " if gender == \"female\":\n", " females += 1\n", " else:\n", " males += 1\n", " \n", "fig, ax = plt.subplots()\n", "ax.pie(x=[males, females], labels=[\"males\", \"females\"])\n", "\n", "female_ratio = round((females / (females + males)) * 100, 1)\n", "male_ratio = round(((males / (females + males)) * 100), 1)\n", "\n", "print(f\"Loan recipients are {female_ratio}% female and {male_ratio}% male.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What about the lenders themselves? What can we determine about them?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PERMANENT_NAMEDISPLAY_NAMEMAIN_PIC_IDCITYSTATECOUNTRY_CODEMEMBER_SINCEPERSONAL_URLOCCUPATIONLOAN_BECAUSEOTHER_INFOLOAN_PURCHASE_NUMINVITED_BYNUM_INVITED
11476barketingErika Godwin2899846.0OttawaOntarioCA1528393535www.barketing.co/Business OwnerI loan because I want to help fellow female en...I am co-founder of ProPet Software and owner o...5Bella0
\n", "
" ], "text/plain": [ " PERMANENT_NAME DISPLAY_NAME MAIN_PIC_ID CITY STATE COUNTRY_CODE \\\n", "11476 barketing Erika Godwin 2899846.0 Ottawa Ontario CA \n", "\n", " MEMBER_SINCE PERSONAL_URL OCCUPATION \\\n", "11476 1528393535 www.barketing.co/ Business Owner \n", "\n", " LOAN_BECAUSE \\\n", "11476 I loan because I want to help fellow female en... \n", "\n", " OTHER_INFO LOAN_PURCHASE_NUM \\\n", "11476 I am co-founder of ProPet Software and owner o... 5 \n", "\n", " INVITED_BY NUM_INVITED \n", "11476 Bella 0 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lenders_raw = pd.read_csv(\"datasets/lenders.csv\")\n", "lenders_raw.dropna().head(1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "map_loans_to_lenders = pd.read_csv(\"datasets/loans_lenders.csv\")\n", "loans_raw = pd.merge(loans_raw, map_loans_to_lenders, on=\"LOAN_ID\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What are the nationalities of the lenders?" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "import squarify" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "pycharm": { "is_executing": false }, "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lenders_by_country = lenders_raw[\"COUNTRY_CODE\"].value_counts()\n", "ax = squarify.plot(\n", " sizes=lenders_by_country.values[0:20],\n", " label=lenders_by_country.index[0:20],\n", " alpha=.7\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evidently the majority of the users live in the US. Which states in particular?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "state_mappings = pd.read_csv(\"datasets/states.csv\")\n", "state_pops = pd.read_csv(\"datasets/us_state_populations.csv\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "import plotly.express as px" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CA 57270\n", "NY 20708\n", "TX 16410\n", "WA 15102\n", "IL 12441\n", "dtype: int64\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "geo": "geo", "hovertemplate": "locations=%{location}
color=%{z}", "locationmode": "USA-states", "locations": [ "CA", "NY", "TX", "WA", "IL", "FL", "MA", "PA", "CO", "VA", "OR", "MN", "NC", "MI", "OH", "NJ", "AZ", "GA", "MD", "WI", "MO", "IN", "CT", "TN", "DC", "UT", "KS", "NM", "IA", "SC", "KY", "OK", "NV", "NH", "ME", "ID", "LA", "HI", "AL", "AK", "AR", "NE", "MT", "VT", "RI", "DE", "MS", "WV", "WY", "SD", "ND" ], "name": "", "type": "choropleth", "z": [ 57270, 20708, 16410, 15102, 12441, 11132, 11056, 9359, 8882, 8411, 7971, 7422, 6999, 6840, 6803, 6623, 6416, 6032, 5883, 5080, 4045, 3959, 3878, 3526, 3351, 2967, 2088, 2032, 2002, 1917, 1863, 1821, 1791, 1702, 1562, 1478, 1465, 1462, 1460, 1311, 1259, 1124, 1021, 969, 897, 460, 435, 434, 372, 315, 249 ] } ], "layout": { "coloraxis": { "colorbar": { "title": { "text": "color" } }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "geo": { "center": {}, "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] }, "scope": "usa" }, "legend": { "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Number of lenders per US state" } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "state_values = lenders_raw[\"STATE\"].value_counts()\n", "num_lenders_per_state = {}\n", "for state in state_values.index[0:200]: # ignore the irrelevant state entries from other countries\n", " # Some entries have state as its full name, others its code; combine the two\n", " if state in state_mappings[\"State\"].values:\n", " code = state_mappings[state_mappings[\"State\"] == state][\"Abbreviation\"].iat[0]\n", " num_lenders_per_state[code] = num_lenders_per_state.get(code, 0) + state_values[state]\n", " elif state in state_mappings[\"Abbreviation\"].values:\n", " num_lenders_per_state[state] = num_lenders_per_state.get(state, 0) + state_values[state]\n", "\n", "us_lenders_by_state = pd.Series(num_lenders_per_state).sort_values(ascending=False)\n", "print(us_lenders_by_state.head())\n", "\n", "fig = px.choropleth(\n", " locations=us_lenders_by_state.index,\n", " color=us_lenders_by_state.values,\n", " locationmode=\"USA-states\",\n", " title=\"Number of lenders per US state\",\n", " scope=\"usa\",\n", " color_continuous_scale=px.colors.sequential.Plasma\n", ")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of users in California more than doubles the next state. However, the above plots were with raw numbers, but what about adjusting for population?\n", "Let's again look at countries first:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "populations = pd.read_csv(\"datasets/world_pop.csv\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# Load a dictionary to convert iso2 to iso3 for other datasets and plotly\n", "import urllib.request, json\n", "with urllib.request.urlopen(\"http://country.io/iso3.json\") as url:\n", " iso2_to_3_dict = json.loads(url.read().decode())" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "country_codes = []\n", "per_capita_values = []\n", "for country in lenders_by_country.index:\n", " code = iso2_to_3_dict.get(country)\n", " pop = populations[populations[\"Country Code\"] == code][\"2016\"]\n", " \n", " if pop is None or len(pop) == 0:\n", " continue\n", " \n", " val = lenders_by_country[country] / pop.iat[0]\n", " country_codes.append(code)\n", " per_capita_values.append(val)\n", " \n", "\n", "lenders_per_capita = pd.Series(per_capita_values, index=country_codes).sort_values(ascending=False)\n", "\n", "ax = squarify.plot(\n", " sizes=lenders_per_capita.values[0:20],\n", " label=lenders_per_capita.index[0:20],\n", " alpha=0.7,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per-capita, user nationality is much more evenly distributed, with Canada winning out. What about states?" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DC 0.004748\n", "WA 0.001983\n", "OR 0.001890\n", "AK 0.001792\n", "MA 0.001604\n", "dtype: float64\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "geo": "geo", "hovertemplate": "locations=%{location}
color=%{z}", "locationmode": "USA-states", "locations": [ "DC", "WA", "OR", "AK", "MA", "VT", "CO", "CA", "MN", "NH", "ME", "CT", "NY", "HI", "VA", "IL", "MD", "NM", "MT", "UT", "AZ", "WI", "RI", "ID", "NJ", "PA", "KS", "MI", "NC", "MO", "WY", "IA", "IN", "OH", "NV", "NE", "GA", "TX", "FL", "TN", "DE", "OK", "AR", "KY", "SC", "SD", "ND", "LA", "AL", "WV", "MS" ], "name": "", "type": "choropleth", "z": [ 0.004748147004104859, 0.0019832189368911685, 0.0018898760164514762, 0.0017920975469725034, 0.0016040616884751447, 0.0015529119904357288, 0.001542352349543372, 0.001449424903276133, 0.0013160433163014892, 0.0012517365822590242, 0.0011620190862750815, 0.001087710470433376, 0.0010644837724054737, 0.0010325792162003346, 0.000985411666238456, 0.000981784701662058, 0.0009730915298196398, 0.0009690823619856459, 0.0009552966097730305, 0.0009254644009684469, 0.0008814740290081343, 0.0008724873480747055, 0.000846736853631576, 0.0008270544160397076, 0.0007456494400592647, 0.0007310582753976745, 0.0007167095616881668, 0.000684900164285921, 0.0006673287513715566, 0.0006590708681226078, 0.0006427545835140361, 0.0006345342575600541, 0.0005880676193094728, 0.0005819951921020438, 0.0005814640557166585, 0.0005810563231748421, 0.0005681227921313863, 0.000565942452309002, 0.000518304139770405, 0.0005163142716820511, 0.00047239372168205026, 0.00046020049173976763, 0.0004171907784600988, 0.00041699560375166225, 0.00037232598275996684, 0.0003560694007521542, 0.00032674506798659425, 0.00031513549535643005, 0.00029776563601006283, 0.00024216763468621716, 0.00014616203691414644 ] } ], "layout": { "coloraxis": { "colorbar": { "title": { "text": "color" } }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "geo": { "center": {}, "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] }, "scope": "usa" }, "legend": { "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Number of per-capita lenders per state" } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "us_per_capita_lenders = {}\n", "\n", "for state in us_lenders_by_state.index:\n", " full_name = state_mappings[state_mappings[\"Abbreviation\"] == state][\"State\"].iat[0]\n", " pop = state_pops[state_pops[\"NAME\"] == full_name][\"POPESTIMATE2019\"].iat[0]\n", " us_per_capita_lenders[state] = us_lenders_by_state[state] / pop\n", " \n", "us_per_capita_lenders = pd.Series(us_per_capita_lenders).sort_values(ascending=False)\n", "print(us_per_capita_lenders.head())\n", "\n", "fig = px.choropleth(\n", " locations=us_per_capita_lenders.index,\n", " color=us_per_capita_lenders.values,\n", " locationmode=\"USA-states\",\n", " title=\"Number of per-capita lenders per state\",\n", " scope=\"usa\",\n", " color_continuous_scale=px.colors.sequential.Plasma\n", ")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After adjusting for population, California is no longer even in the top 5. Though difficult to see on the map, the number one spot goes to the District of Columbia. Another surprise is that Alaska comes in at number 4." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many available loans are there at once? How has this changed over time as the organization has grown?" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Number of available loans on the site')" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "timeseries = loans_raw.set_index(\"POSTED_TIME\").sort_index()\n", "timeseries = timeseries.set_index(timeseries.index.to_pydatetime())\n", "ax = timeseries[\"LOAN_ID\"].rolling(\"30d\").count().plot() # Loans are available for 30 days once posted\n", "ax.set_title(\"Number of available loans on the site\")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Amount Funded in 10s of millions')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = timeseries[\"FUNDED_AMOUNT\"].rolling(\"30d\").sum().plot() # Loans are available for 30 days once posted\n", "ax.set_title(\"Amount Funded in 10s of millions\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which countries receive the most loans?" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "geo": "geo", "hovertemplate": "locations=%{location}
color=%{z}", "locations": [ "PHL", "KEN", "PER", "KHM", "SLV", "UGA", "PAK", "TJK", "COL", "NIC", "ECU", "PRY", "VNM", "BOL", "LBN", "GHA", "NGA", "TZA", "IND", "MEX", "WSM", "TGO", "RWA", "HND", "SLE", "PSE", "ARM", "GTM", "LBR", "MLI", "SEN", "KGZ", "IDN", "JOR", "AZE", "MNG", "ZWE", "USA", "MDG", "MOZ", "GEO", "HTI", "COD", "BEN", "TLS", "SSD", "CMR", "CRI", "UKR", "DOM", "BFA", "YEM", "ALB", "MMR", "IRQ", "EGY", "TUR", "XKX", "MWI", "NPL", "AFG", "LAO", "BDI", "ZMB", "SLB", "FJI", "MDA", "BRA", "COG", "CHL", "LSO", "TON", "ZAF", "BIH", "ISR", "THA", "BGR", "SUR", "CIV", "LKA", "PAN", "SOM", "BLZ", "CHN", "PRI", "TCD", "PNG", "VCT", "VUT", "GUM", "CAN", "BTN", "BGD", "MRT", "BWA", "URY" ], "name": "", "type": "choropleth", "z": [ 301308, 175978, 89973, 88771, 72975, 58293, 54789, 53064, 46485, 45538, 40679, 28393, 27204, 26671, 26507, 24687, 23257, 22055, 21507, 21001, 19553, 19409, 18468, 18266, 17920, 15820, 15411, 14791, 14262, 13716, 13256, 13062, 12811, 12228, 10138, 9655, 9430, 9390, 8563, 8128, 7631, 6347, 5986, 5925, 5819, 5805, 5791, 5154, 5117, 5001, 4471, 4229, 3962, 3934, 3566, 3528, 2567, 2549, 2466, 2453, 2335, 1942, 1727, 1618, 1326, 1300, 1074, 988, 952, 892, 855, 693, 634, 607, 515, 366, 293, 292, 281, 264, 235, 227, 219, 134, 106, 61, 19, 16, 4, 4, 2, 2, 1, 1, 1, 1 ] } ], "layout": { "coloraxis": { "colorbar": { "title": { "text": "color" } }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "geo": { "center": {}, "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] } }, "legend": { "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Number of loans per country" } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "loans_raw[\"ISO3_CODES\"] = [iso2_to_3_dict.get(code, np.nan) for code in loans_raw[\"COUNTRY_CODE\"]]\n", "countries = loans_raw[\"ISO3_CODES\"].dropna()\n", "loans_per_country = countries.value_counts()\n", "\n", "fig = px.choropleth(\n", " locations=loans_per_country.index,\n", " color=loans_per_country.values,\n", " title=\"Number of loans per country\",\n", " color_continuous_scale=px.colors.sequential.Plasma\n", ")\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = squarify.plot(sizes=loans_per_country.values[0:20], label=loans_per_country.index[0:20], alpha=.7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that two countries have received significantly more loans than others: the Phillipines and Kenya, with the Phillipines receiving more than 300 thousand loans and Kenya receiving more than 170 thousand. After that, Peru, Cambodia, and El Salvador have each received close to 90 thousand loans." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What about the actual amounts received, rather than number of loans?" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": true }, "outputs": [], "source": [ "amounts_by_country = loans_raw.groupby(\"ISO3_CODES\")[\"FUNDED_AMOUNT\"].agg(sum)\n", "sorted_amounts = amounts_by_country.sort_values(ascending=False)\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "geo": "geo", "hovertemplate": "locations=%{location}
color=%{z}", "locations": [ "AFG", "ALB", "ARM", "AZE", "BDI", "BEN", "BFA", "BGD", "BGR", "BIH", "BLZ", "BOL", "BRA", "BTN", "BWA", "CAN", "CHL", "CHN", "CIV", "CMR", "COD", "COG", "COL", "CRI", "DOM", "ECU", "EGY", "FJI", "GEO", "GHA", "GTM", "GUM", "HND", "HTI", "IDN", "IND", "IRQ", "ISR", "JOR", "KEN", "KGZ", "KHM", "LAO", "LBN", "LBR", "LKA", "LSO", "MDA", "MDG", "MEX", "MLI", "MMR", "MNG", "MOZ", "MRT", "MWI", "NGA", "NIC", "NPL", "PAK", "PAN", "PER", "PHL", "PNG", "PRI", "PRY", "PSE", "RWA", "SEN", "SLB", "SLE", "SLV", "SOM", "SSD", "SUR", "TCD", "TGO", "THA", "TJK", "TLS", "TON", "TUR", "TZA", "UGA", "UKR", "URY", "USA", "VCT", "VNM", "VUT", "WSM", "XKX", "YEM", "ZAF", "ZMB", "ZWE" ], "name": "", "type": "choropleth", "z": [ 1964475, 5193725, 22142800, 14157775, 5119300, 3870450, 4949400, 50000, 373725, 476275, 150675, 45846225, 2336375, 15625, 8000, 100000, 2549525, 373475, 343150, 2520300, 21411825, 2256100, 26903900, 6036675, 9791325, 43386675, 2071450, 950075, 10047000, 17358350, 22069650, 8700, 13764975, 5537025, 9038775, 11115950, 8708700, 1957000, 12174650, 76845780, 16133575, 56028750, 1337025, 33705150, 4833275, 74800, 603775, 1812825, 2498425, 25920325, 16006225, 5120850, 14893925, 4646150, 15000, 3649700, 7814875, 31340450, 887575, 28762450, 383675, 84685225, 103347175, 87700, 575300, 63420650, 25029475, 33731000, 20919250, 1175100, 13124300, 43366400, 308725, 2411725, 738600, 20075, 10786675, 916775, 42600175, 4806725, 786150, 1098475, 21676775, 42525225, 7203350, 8000, 43310620, 49225, 29128100, 9250, 13650725, 3264225, 3355600, 1011525, 2230500, 8282925 ] } ], "layout": { "coloraxis": { "colorbar": { "title": { "text": "color" } }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "geo": { "center": {}, "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] } }, "legend": { "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Dollars received in loans per country" } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.choropleth(\n", " locations=amounts_by_country.index,\n", " color=amounts_by_country,\n", " title=\"Dollars received in loans per country\",\n", " color_continuous_scale=px.colors.sequential.Plasma\n", ")\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAE/CAYAAAApN5W5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeXxc9X3v/9c5Z3bNSDPaV8vyIi/yijfAAUNCiIEEEkxJ4iyUtCx9hCSXJDf3krS0JTdt0tzyyL23KZD0VxLaQiGQUAjETpNQs4UABuNV3i3b2vddmu38/hCMGUYWBo80Hun9fDz0sPU93/Odz5FszVvf8z3nGMPDwzYiIiIiklXMTBcgIiIiIu+dQpyIiIhIFlKIExEREclCCnEiIiIiWUghTkRERCQLKcSJiIiIZCGFOBEREZEs5DiTTrt37+YXv/gFhw4doquri6985Stcdtllie22bfPQQw+xdetWBgYGqK2t5dZbb6W6ujrRZ2BggPvuu4+XX34ZgLVr13LLLbfg9/vTfEgiIiIi098ZzcSNjIxQXV3NzTffjMvlStn+2GOP8fjjj3PzzTdz9913k5eXx5133snQ0FCiz/e//30OHz7MX/3VX/HXf/3XHD58mLvvvjt9RyIiIiIyg5xRiFu9ejWf//znWb9+PaaZvItt2zzxxBNs2rSJ9evXU11dze23387w8DDbtm0D4MSJE7z22mvcdtttLFq0iIULF/LFL36RV155hZMnT6b/qERERESmubNeE9fa2kp3dzcrV65MtLndburq6qivrwegvr4er9fLokWLEn0WL16Mx+NJ9BERERGRM3fWIa67uxuAYDCY1B4MBhPburu7yc3NxTCMxHbDMMjLy0v0ea/a2tpoa2t7n1WLiIiIZLczurDhTLw9oMHYadZ3hrZ3sm173LG2bNnC1q1bJ3y9O+64A4Ajx3Q6Nlv5PE6GRiKZLiNtjHA00yVMGW+uh+G+kUyXMS7blbYfa9PWufZ/z4jEMl1C1vAGPAz3T+L/PVM3rZhMNXOr0jreWf+0C4VCwNhsW1FRUaK9t7c3MTsXCoXo7e1NCna2bdPX15fY/+02btzIxo0bJ3zdt2bhfvzI9rM9BMmQm65fNa2+f+6XD2e6hClzw99cy0+/+fNMlzGu4QvnZbqEc94t157HfT9/LdNlJPhePpbpErLG5799DQ/8xX9M3guUF0/e2MJf3v2ptI531pG7pKSEUCjEjh07Em3hcJg9e/awcOFCABYuXMjw8HDS+rf6+npGRkYSfURERETkzJ3RTNzw8DDNzc0AxONx2tvbOXLkCH6/n+LiYq6++moeeeQRKisrqaio4OGHH8br9bJhwwYAqqqqOO+88/jhD3/IbbfdBsAPf/hD1qxZQ2Vl5SQdmoiIiMj0dUYh7tChQ3zzm99MfP7ggw/y4IMP8sEPfpDbb7+dTZs2EQ6HuffeexM3+73rrrvw+XyJfb7+9a/zox/9iDvvvBOAdevWccstt6T5cERERERmhjMKcUuXLuXJJ5887XbDMNi8eTObN28+bZ9AIMDXvva1916hiIiIiKTQZSgiIiIiWUghTkRERCQLKcSJiIiIZCGFOBEREZEspBAnIiIikoUU4kRERESykEKciIiISBZSiBMRERHJQgpxIiIiIlnojJ7YICIiIjKZhka62HtiCzmeAhZVfSRp26sH/y3xd9Nw4Hb6KQktpDB3bqK9b6iVA42/wTKdLKu5Fss8FXGGw73safglAMvnbMJpeSb5aKaGQpyISAY07vsvelsOjn1iGDjdfgJFsymuWYVpORP9ju/cykDnCWYt34g/vzJpjLaj2+lvP8rctdcl2ob72ji+cwv+glmUL7gYw9QJl/fi6OhuOqPNFDrKme2uS9p2InyA1kgDeVYh8z0raQwfpjlyJGUMh+FihW8DMTvG3uGXyLMKmOVemNgesUfZM/R7SpzVlLlqJv2YskV73yGK8+bT2X+U4XAvXlde0vbq4nUEcyqIxaN0DzRwrPUlnJaXvJzypH6W6aR74DiFuXMSbR29h3E5fISjQ1NyLFNFIU5EJENyQhVULLoE244z1NtCU/2z2LEoZQs+AEBkdIjB7ibyq5bQ07w/JcS900BXIyd3/yfB8oWUzF2HYRhTcRjTjsvw0BVtpcq1EMuwALDtOF3RZlxG8gyOx/CxwLM6eYA3v+6WYVHjrmP/yKsEHUXkWgUAHBvdi9v0UeqcPenHki3i8Shd/cdYUPlh4naMjt7DVBWdl9THMl04HV6cQFn+Elq699E31JwS4gpy59DRdzgR4uJ2nM7+oxTlzae5a9dUHdKU0K9oIiIZYpgmDrcPp8dPXsk88krm0d9xLLG9t+UA/oJKCiqX0N/RQDQyctqx+tqOcGLXVgqrV1A673wFuLPgNf14TB/d0ZZEW0+sAwOTgBV6R28Dp+lO/jBcia1+K0iJs5pjo3uJ2hE6Io30x7qocS/R9+htugeO43Lk4HOHyA/U0Nl/hLgdH7evbcfp6m8gFg9jGKkxpiBQw+BIJyPhfgB6BxuxTAcBb/GkHkMmaCZOROQcYZoO7DffuGzbpqd5P8Vz1+L0+PHmFtPbcpCCqqUp+3U37qP54IuU1a4nVL4wZbu8d4WOCjqiTRQ6KwDojDZR6Chn1B5+z2OVO+fSG+3g6OhuBmI9VLpq8Zi+dJec1dr7DlOQO3ZqOeAtxjQc9A6cJBSYlehztPVFjrX+nrgdA2wcppvC3HkpY1mWi2BOBR19h6ksXEFH7yEKcucA0y80ayZOROQcMNzXRm/bIXJCY6FhqKeZWGSUQMHYm1he6Xx6mven7Bce6qX5wPOUzjtfAS6N8h2lDMb7GIkPEomP0hvroMBRntJvxB7ktcHfJX0cGdmZ1Mc0TKrcC+iNdeA1/RQ5Jj4tPtOMhPsZGG4nPzAbAMMwyA/Mpr3vUFK/qsKVLJ51JbUVH8TrDlFVtAqPKzDumIW58+jsP0I4MkjfcAuFgbnj9st2mokTEcmQga6T7Hv2frBt7HicQGE1pfMvBKCneT+5xXMwzLE1WblFNbQceIGh3jZ8eadOCzncPiynl86TuwkUVuP0+DNyLNONw3ASsorpiDZh4SBg5eM2vSn93IaP+Z6VSW0WVkq/jkgTJiYj8UGiRHDiSukzU3X0HQJsdh59PGVbODKIy5kDgMPy4nEF8LgCzC29iL0nnsbnyU+5AAIg11eKgcHR1t8T8JbgcvoYifRP9qFMOYU4EZEM8eWVUr7gIjBNnK6cxJWkscgofe1HseNxupvrT+1g2/Q01yeFONNyUr3iSo6/8SuOvf5Lqldehcsz/uyEvDeFznKOju7BxKLCNf5MjoHxrqdGu6NtdMdaWOhZQ0N4Hw2j+5jnWT4ZJWcd247T2XeEioIVBHMqkrYdaX2Rjr4jlBekLiHwuAKEcmZxsuN15pdfkrLdMAwKcufS3LWLOaUXTVb5GacQJyKSIablwOVLnUXobT2E5fQwa9nGpPbhvjZaD71E6fwLkm5DYjlczFp+Bcd3bqHh9V9SveIqXN7cSa9/uguY+RgYRO0IQev9LYqP2GEaRvdS5pxDjpVHjXsJe4dfojPaTIGjLM0VZ5+ewUaisVGK8ubhsNxJ2/L91bT3HqQsf8m4+5aEFrL3+NMMjHTg9xSmbC/LX0JxsBaH6R5n7+lBa+JERM4xPc37yS2qwePPT/oIls4Hw6C3LfXeZJbDRfXyK3B6Ahx7/ZeEh/syUPn0YhgGdd4LWOb7AOY4V0GOsYnER1M+3tIwug+36aXMObZo32v6KXfO5fjo/qR+M1VH32ECvpKUAAeQH5hFODpI31DLOHuCzx0i11dKU+cb4243DROn5ZnWVwFrJk5E5Bwy3N/ByEAnpbXrU7YZpkWgYBY9TfWEyhakbDctJ7OWbeTErq1jp1ZXXIV7nJk+OXOWMfHb5Ig9xBvDz6a0r/J9iK5oC72xDhZ7k2/5UuqcTU+snYbwPuZ5VqS95mwy3qnQt7idAVbP/wxA4s93qq34UOLvub6S0/Y7k+3ZSCFORCQDKhZdMm67N1DI4ktvOv1+iy9N/L24ZhXFNauStpuWg+oVV6Wlxpmoxj3+qbvxtle45p52rRxAgbOcAmfqFa2GYbDIu/b9FynyJp1OFREREclCCnEiIiIiWUghTkRERCQLaU2cyDS0P/w6XiOHWc7apPaOWDMnogdZ6b4YgJgdpTV2gu54O6P2MCYmbsND0CyiyCrHYSTfkLQ71s6R6G74135A9yITEckkhTiRGSpqR9gfeZ2YHaXcMRufkYuJyag9RFe8nY5YM6WO6qR9OuJNlFqz2LVnJ4tYi8NwnmZ0ERGZbApxIjNUY/QIYXuEOtc6XMapezR58JFnFWLbdlL/sD1Cf7yHGtdicmd56DrWSrGlZ0CKiGSKQtw0c3L/NmLREarrPpJo6+s8zon631JYsQQMk/bjr6fs53B6WXj+2P1zjuz8JUO9LVQu2ECweH6iT3frAZoPvcji9X886cchk8u2bbrjbeSbJUkB7u3eeYPMzlgLuWY+DsPJmlVr+Pnh/1CIExHJIIW4aa679SBNB5+jpGYthRVLaG3YjsubR82y5PtIGSS/YRumReux7eQWzsE0Ux/mLNktSoQYUTxG8jMf68PbGbYHAfAbecx3jT3f0bZtOmLNVDrG7om1YvlKHnr4IQbj/eSYWhsnIpIJujp1Guto3E3Twecon3/R2CzcmwzDxOnyJX04XN6kffMK52DHY3Q17Z3qsiWD5jjrWORaTdAsJE480d5vdxMjSp459nxCt9tN0CyiI9aUqVJFRGY8zcRNU63HXqWjcRezFl9GIH/We97ftJwUzVpJW8N2QqW1WI7p+wDh6cgyLGJEU9pjdhQLCwdOLByM2ENJ212GZ2x/HMCp5zp2xJqJEeX18NjjhV7/79uIx+NYOKiy52Eamq0VEZlqCnHT0EB3I/1dJ6iuu3zcADc61MPeF36S1BYomEXVwg8mteWXLaSzaTftJ96gtEaPiMkmHsNHb7wT27aT1rYN2f14DB+GYRAyi+mKt1BmVyfC23iidoSeeAezHYvwGX4Arv7KZTzxf37DgcgOuuPtFFilk35MIiKSTCFuGnLnhIhHI7Q1vI4vtyRlFs3lDSRd+ABjM2/vZBgmJdWrOXlgGwXldZNas6RXkVVBW6yRE9GDFFrlmJj0xjvpircxz7EUgArHHAYiPdSHt1PuqMFn5GIZFsPxQfrtHpyM/ZvojLVgYZFvliQCYXlZOV7TnzilqhAnIjL1tCZuGnK6fNQsu4pYLMzRXb8iFhlN2m4YFm5vXtKH0+Ubd6y8ojl4cvJpbdg+FaVLmrgNLwucKxm1hzgY2cG+yKt0xVuZ66gjzyoAwGE4WehcRYFVRmvsBPWR7ewJv0xT7AhBs4Aa51hw74w3EzQLU65WBQhZxQzYvYzEh1K2iYjI5NJM3DTldOdQs+wqju18iqO7nmb20itwOE9/ymwipbPXcnTX0zi0Li6r5Ji5zHetmLCPZTiocMyhgjmn7bPYdfpT6blmiFXuS993jSIi8v5pJm4ae2tGzrbjHNv1NNHICAC2HScSHkr5OJ2cYBmB/Eo6m3WlqoiIyLlCM3HTnMPlo2bplRzb/SuO7nyKnLxSwsO97P/Dgyl96z7wBQxj/FxfMnsN/a/9AkP3jBMRmbbs47ptUDZRiJtmKhdsSGlzuLzMO+/axOfl89ZPOMacZR9NafPk5LPkoj85+wJFREQkLRTiRGRaGS7SKpF3Y5vn1tfJPV+PbztjlklsEr9e5r5jkza2pN+5879YRERERM6YQpyIiIhIFlKIExEREclCCnEiIiIiWUghTkRERCQLpeXq1FgsxkMPPcQzzzxDd3c3oVCISy65hM2bN2NZY/cVs22bhx56iK1btzIwMEBtbS233nor1dXV6ShBREREZEZJy0zcY489xlNPPcUtt9zCPffcw80338xTTz3Fz372s6Q+jz/+ODfffDN33303eXl53HnnnQwN6ZmLIiIiIu9VWkLcvn37WLt2LWvXrqWkpIR169axbt069u/fD4zNwj3xxBNs2rSJ9evXU11dze23387w8DDbtm1LRwkiIiIiM0paQtzixYvZuXMnJ06cAOD48ePs3LmT1atXA9Da2kp3dzcrV65M7ON2u6mrq6O+vj4dJYiIiIjMKGlZE3fdddcxPDzMF7/4RUzTJBaLcf3113PVVVcB0N3dDUAwGEzaLxgM0tnZmTLeli1b2Lp164SveccddwBw0/Wr0nEIkgE+j3Naff+Mjy/PdAlTxpvr4Ya/ufbdO2ZA3KMH0bybHLeTr2xYkekyEsz1dqZLyBpen4sbb1wzeS8QPW/yxpa0S8tPu+eee45nnnmGr3/968yaNYsjR47w4x//mJKSEi6//PJEP8MwkvazbTulDWDjxo1s3Lhxwtdsa2sD4MePbE/DEUgm3HT9qmn1/XO/fDjTJUyZG/7mWn76zZ9nuoxxdV1Tm+kSznlf2bCC/7NtR6bLSAgeCGe6hKxx441ruP/+VyZtfD12a3L91aNfTet4aQlx999/P5/4xCe4+OKLAZg9ezbt7e08+uijXH755YRCIWBsRq6oqCixX29vb8rsnIiIiIi8u7SsiRsdHcU0k4cyTZN4PA5ASUkJoVCIHTtO/eYXDofZs2cPCxcuTEcJIiIiIjNKWmbi1qxZw6OPPkpJSUnidOrjjz/OBz/4QWDsNOrVV1/NI488QmVlJRUVFTz88MN4vV42bNiQjhJEREREZpS0hLhbbrmFf/u3f+Oee+6ht7eXUCjERz7yET71qU8l+mzatIlwOMy9996buNnvXXfdhc/nS0cJIiIiIjNKWkKcz+fjpptu4qabbjptH8Mw2Lx5M5s3b07HS4qIiMg0cyyyj854y5ufGbhwE7KKKLNqsAyLnlg7LbHjjNhD2Ng4DTd+I4/ZzrGlWf3xbg5Exr9op865Fo+ZM0VHMjV0Lb6IyBRoee5X2LEYZZd8NGVbuK+bxi2PUHrxVbQ8+xSYJpUbP4nTn5vo0/7yM8RGRyi96AoAune/Ss/e7XiKK1LG7Du0h87XnseZG6Jy4/WTe2DTWCQyTGvrG/T2nSQSGcTh8ODxhCgqWkRebhUAg4PttLS+weBgK/F4FJcrQChUQ0nxUkzz1Fvsnj0/o7BoISXFSzN1OFkjYISocS7CxmYg3suxaD0xO0bIKuJwdA/l1mxmmwsBgxF7iJ54R8oYi51rcRjJEceBa4qOYOooxImITIHAnEW0vbCVyGA/zpxA0rb+I/U4fH48JRXA2JmL7l0vU3zBZROOaXl8jHQ0p4zZf7Qey+dP/0HMIKOj/Rw89DSm6aS8bBVebz5g09/fxIkTvyevrore3uMcPfYModAc5s39CA6Hh4HBNpqaXqG/v5l5cz+CaVqZPpSsYxomTsMNQL7loT/eTU+8A9MwyTFyKXPMTvT14CNoFaaM4TScOIzpF9reKS1Xp4qIyMR8ZbOwPD4GjiY/pcaOxxhoOIi/ZmHivpm585YwePIIo13tE45putz4ymYljRnu6STS30NO5Zz0H8QMcvLk77FtmwW1HyMUqsHjycPjCVJUtJiFC64hHo9y/Pjz5AYqqZ51ET5fIS6Xn/zQHObUfIjBwVbaO/Zm+jCmBcMwx06d4mLEHmIoPpDpks4ZCnEiIlPAME38s2vpP3YA2z71hIKhpgbi4RECNQsSbe78YnIqauja+dK7jhuoWcjA28bsP1pPTuVcTIcz/QcxQ0Sjo/T1N1JUtAjLSv06Ohxu+voaicZGKS5ZkrLd5ysk4C+nu/vIVJQ7rQ3G++iKtZFrhiiyKskxA+yLvMLO0Rc5HNlNe6yRmB1N2W9X+CVeH3028bFz9MUMVD/5dDpVRGSKBGoW0lu/g+HWk/hKx9ZU9R/dj7ekEsc7Tn+Glq7l5NZHGGo+jq9s1mnH9JZWYdtxRlob8RSVMdBwiJL1lzPc2jipxzKdjY72AeBxn/5m9KOjvRP28Xjy6OhsTX9xM0BvvIvXR5/FxsYmTtAspMoxH8uwmO9czqg9TH+8m4F4H43RI7TQwELXapxvO31a61yB9bY1cQapT4eaDhTiRESmiDOQNxa0ju7HV1pFdHiQ4ZYT4659cwbyCNQspHvXy3jfDHzjMUwTf3Ut/UfriYVHsNxuPEVlCnHngPEeKynvLmDkMcu5AAMTFy4MI/mkodvw4ra8FFrljNrV7A7/gfZYI+WOmrf18WhNnIiIpJe/ZiFDjceIjY4wcHT/2Lq28upx+4bqVhMZ6GOg4eCEYwZqFjLU1EDfwV34Z+spOGfL7R67KnhktGeCPnljfUbG7zMy0ovblTvuNpmYaVh4DB9uw5MS4N7JhQcTkzixKaru3KIQJyIyhXIq52BYFgMNB+k/th9/dS3Gaa5gtDxe8hYso2f3K9ix079JOQN5uPOLGO3qwD97/mSVPmM4HG4CgQra2/cRi0VStkejowQC5ViWm7a23Snbh4Y66B9oIpSvi0vSqSl6lJPRQ/THuxm1hxmK99MQrSdOjDwz+QrViB0hYo8mfcTteIYqnzw6nSoiMoVMh4OcWfPo2budeHiUwJyJZ87yapfTf3gvQ03H8BRXnLZfyUVXYMfjWC53ukuekaoqL+DAwafYf+BJyspW4vW8eYuRgRZaW3eypO56ZlWt5+ixZ2g4/jxFhYtwONwMDrbR2PQKOTklFBUuThozEhlmaKgzqc3lysHh8EzhkWWvgBmkLdbIscg+IkSwsPAYOcxzLCVgJq9N3Bt5OWX/+c7l5Br5U1XulFCIExGZYoE5C+k/vBd3QQmu3NCEfU2nk+DiVXS+9vzE/XQ1alq53QEWLrialtadNDW9SiQyhGW58Xrzqaq6EIBgsJra+VfS0voGhw5vefNmv34K8mspKVmaco+49vY9tLfvSWqrrFhHUVFy2JvJZjsXnXZbwAwRMCf+/xIwQ6xyX5russ5ZCnEiIlPMHSqi5vpbxt02XnvuvDpy59UltYWWrCa0ZPVpX+Pdtsu7czp9VFWeD5x/2j45OcXMnfPhdx2rru6P0liZyBiFOJE0MStLM13C1LGsmXW8IiLnIF3YICIiIpKFFOJEREREspBCnIiIiEgWUogTERERyUIKcSIiIiJZKOuvTnW/NPHjaOTcZXx8+fT6/s0qz3QFIiIyg2gmTkRERCQLKcSJiIiIZCGFOBEREZEspBAnIiIikoUU4kRERESykEKciIiISBZSiBMRERHJQgpxIiIiIllIIU5EREQkCynEiYiIiGShrH/slojI21152R8yXcI5z20untKv07Z/WT3h9kjAmqJKsp9tGmn5evXMO80YK2vPemyZOpqJExEREclCCnEiIiIiWUghTkRERCQLKcSJiIiIZCGFOBEREZEspBAnIiIikoUU4kRERESykEKciIiISBZSiBMRERHJQgpxIiIiIllIIU5EREQkCynEiYiIiGQhhTgRERGRLOTIdAEiMrleOfzQhNsLAjXMKT6fVw4/xNyS9eT7ZwHwRsMTFOfNpyy4KNG3sWsnzT31rNidN6k1i4jIu0tbiOvq6uKnP/0pr776KsPDw5SWlvJnf/ZnLF26FADbtnnooYfYunUrAwMD1NbWcuutt1JdXZ2uEkRkHCuqP574e89QE8faX05qMwzrXcewbZuGjlfpGmhgQdklLF2yjB3PPDcp9YqIyJlJS4gbGBjgG9/4BosXL+Yv//Ivyc3NpbW1lWAwmOjz2GOP8fjjj/OVr3yFyspKHnroIe68807uuecefD5fOsoQkXE4Hd7E3y3TmdL2buJ2nKNtv6dvuI2F5R/C5w6lvUYREXnv0rIm7uc//zn5+fl89atfpba2ltLSUpYvX05VVRUw9lv8E088waZNm1i/fj3V1dXcfvvtDA8Ps23btnSUICKTIB6PcrB5G4MjnSyquEwBTkTkHJKWEPfSSy9RW1vL9773PT772c/y5S9/mV/+8pfYtg1Aa2sr3d3drFy5MrGP2+2mrq6O+vr6dJQgIpOguXsvg6OdLKy4DI8zkOlyRETkbdJyOrWlpYWnn36aa665huuuu46jR49y3333AfDRj36U7u5ugKTTq2993tnZmTLeli1b2Lp164SveccddwBww3evS8chSAZ4cz3T6/tnvfvaskx7fcdrHP7JC3zuSxelbHvlvz3ExVcsYuWK8wA4/NdbKSuv4cCB/eSUd/LJ6y/HMAwAvDmuccc4FzjN8zNdwjnPiY8l5o1T9nq117um7LWmO5/HyU3XrzrrceLn/o8rOQNpCXG2bTNv3jxuuOEGAObOnUtTUxNPPfUUH/3oRxP93noDePt+72wD2LhxIxs3bpzwNdva2gD46f989GzLlwy54bvXTavvnzmrPNMlvKuugeMA/Mv/G/+ihGd/tY/dzw0CMNA/Sm+LgzlFF/HSS89yYHcTs4vWYhgGn/vSRacdI9PW/o99mS7hnLfEvJHd8fun7PW2PbJ6yl5rurvp+lX8+JHtZz1OzzyluEy4p6oyreOl5XRqKBRKrH97S2VlJe3t7YntQGJG7i29vb0ps3Micm7J9ZZQW7aBroHjHGv/Q2KZhIiIZFZaZuIWLVpEY2NjUltTUxPFxcUAlJSUEAqF2LFjB7W1tQCEw2H27NnDjTdO3ZS+iLw/AW8xteWXcLB5G7ZtE49/INMlZYXf3t9A/e/Hfnk1TMjJc1K9LJfzP16GJ8fB1h8dY2QwyjW3z0vs03RwgF98/xBLLy3k4k+f+q19z3OdPPvQSW76wVIcrlO/fz/77yfZ/UwHGz5bRd1FBVN3cNPAyf3biEVHqK77SMq2/S//O5HRgTc/M3C4vATyZ1FasxbLMXZ6eKCniWO7nsblzWP+qk0Yhpm0f0H5Ygorl03FoWS9rueeYejQAXJXriH3zSUdACPNTXRseZKyT38eOxKl5dEHKf7YtbgKi5L6DOx+g3B7G/FoBIc/gLu8gkDdchyB6b2WNy0zcddccw379+/n4Ycfpqmpieeff54nn3ySq666Chg7jXr11Vfz6KOP8uKLL9LQ0MAPfvADvF4vGzZsSEcJInIW3ppde/ub0LW0cEAAACAASURBVDsFPEXUll1Kz1Aj//KvP8G241NVXlarXOTnj79fx+f/djGXfr6KY2/08eyDJ8e2LfTTcmiQWPTU17Jx/wD+fCeN+weSxmncP0BpjS8pwMUicQ78oZvzNpaw97nU9cVydopmrWTBus0sWPspKms3MNB9gtajL6f0i4wO0N2yPwMVTjOWRf/uHcRGhs94l4H6vXRs/SWm20P+pZdR+olPElq/AWybvjdem8Rizw1pmYmrra3lW9/6Fg888AAPP/wwRUVFfOYzn+HKK69M9Nm0aRPhcJh77703cbPfu+66S/eIE5lC+f5ZiScyvF0kNvZD02l5Em3Lq69O6ef3FHBezXV87vPn7pq4c43lMMnJG7s/nz/kYt6aIPUvdgFQsSBANGLTenSI8vl+YCysrby8mBcebWKoL4Ivd2zfpgMDLH7HTNvh13vJLXCx6soSdj7TTmfjMAUVZ34PQJmYZTlxusbeo5zuHHIL5zDU15LSr6C8jraG1wgWz8O0nFNd5rThKS0nNjRI/47XCJ6//l37RwcH6PnDC/gX1iX1dwQCuEtKiY+OTma554S0PbFhzZo1rFmz5rTbDcNg8+bNbN68OV0vKSJnybbjhKNDNPfsw2G58bm0RnUy9baPcnx3P6Y1dkFXsMRNTnBs1q18vp9YJE7LkUEu/VwVB1/pofHAAPNXh+huGWGwJ0LlAn/SeHuf76R2XQin22Tuyjz2Pt/JRZ9M78JpGRMeGWCg+yQ5wbKUbQXldfS2H6ajcTfFs1aOs7ecEcMgd9U6On+3Ff/iJThyJ3683/CxIxCPE1i2Ytztpts9GVWeU/TsVJEZLBwdYteJp/A685hb8gFMUz8S0u34nj7u+9JO7LhNLDJ22nr9H526krligZ/G+gHWfBRajgzi9TvIK3ZTXptD4/6xENe4fwCHy6RkzqkzF73tozQfHOTDfzL26MIFF+Sz9b5jXHhtOZYzLStlZrzWY6/S1vAaNjZ2PIYvt5SS2amTFYZpUVy9iubDvye/dCEOl2ZD3y9v1SzcxaX0vvYKBZdcNmHfaF8vhtOF5cuZourOPfqJPQ0di9bTabe++ZmBCzchs5AyczZRIuyO/mHc/eZZS8kz8+mIt9AQO7W+w4GTHCNAhTUHrzFz/7NMR26nn9VzPpnpMqa18vl+LvlcFbFwnD3Pd9LXFmbZh04tyq5Y4OfZB08SjcQ5uX+A8tqx2baKWj/P/fvYBWON+wcom5uD5TgVzva90EXlQn/iVG1FrR+Hy+TIjl7mr9GTNdKhoGIpodKxi/Eio4O0HnuFht1bqVl2VcrtsYLF8+k4uYu2E69TPvfCTJQ7beStXkfbU48T7mifuKMNpN6lbEZRiJumAkaQGmsRNnEG7F6OxQ4QI0apObYeap61FJ+RfGrGets/BxOTJY51gE3YDtMYP8Kh6C7qHGsxJ1j8LiLJHC6TYPHYaZ2LP1XJ4//7EK/+soW1V4+dlqtc6CcWtWk9MkjT/gEWXJAPQNm8HPo6wgz2RGg8MMDytwW/eNym/sUuBnsj/OOtOxLttj12ilUhLj0cTjdu79gpPbc3D3POBRx54wkGe5rwhyqS+hqGQWnNWo7v/U8Kypdkotxpw1VUjLe6ht5XXyKw/PQ3Nnbk5WGHw8SGBmfsbJzejacpExOn4cJleMg3SygwiumJn7p6zYETp+FK+nhnOBtrd5NjBig2KwgzyihDU30oItPKmo+V8NrWNgZ7IgDkFroJFLho2NVP69EhKt6ciXO6LYqqvez6rw6G+6JUvG093PHdfYwMRrn+m7V88i8WJD6uum0OJ+sH6OuY/gu6M+LN2bd4PDru5kB+Fb7cEtqOvTqVVU1LeavWMtrawmjjidP28VbPAdOkf+eOcbfrwgaZNgzDet+3hIjaUbriY0/IMJT7Rc5KxYIA+eUeXn2qhQ2fqXqzzc/uZzvwBsbWwyX61vrZ+bt2nB6T4upT6+H2Pt9F9ZJciqqTr+4vqPASKnGz74Uu1l2TugBfUsWiEYYHkm/P8tZ94GKxCJHw2C+ukdEBWo6+jOXw4MstOe14JTVrObLjiQlv1yPvzpGbR07tIvr37jp9H7+f4NoL6XnpeeLhML75C3AEAsSGBhk6cghisbHbjUxjCnEzwGC8j654K7nGqVMs+2M7MGLJ/ZY5LsAyxv5JxInzeuS5xN8B8owCPIZuCSNytlZcVsxvf3qclRtLyC1wUbnAT/2LXdQsT74ar7zWz2tb2qhempu4onWoL0LDzl4+dGP1uGPPXTV2C5O1HyvFMGf4gqEzMNTXwuHXf5HUllswG4D246/Tfvx1ACynB6+/iNlLr8Dh9LxzmARfoIjcwtn0dRydtJpnitwVqxg6dACb2Gn7+BfV4cjLY2D3Trqe+TXxaBRHjh93RSWB5eeddr/pQiFumuq1u3g98tzYVVXYBI1Cqqx5iUA2x1qE5x0XKZhYb/u7ySLHasCm3+6hNXaSaqt2Kg9BJOudLmjVrgtRu+7UL1ULLshPrIV7u+oluXzxR8m3T/DlOvmze8e/pQLAumvKNAt3hioXbKBywfufqfEHy1ly0Z+mtM9a9KGzKWtGyr/o0pQ2y+ul4nNfONXggcobb0np5ymvxFM+M2+toxA3TQWMILOsWgwMXLgSU/uj9ggATtx4jIkvg39ru8fwEbHDHIntZYHj9G8eIiIiMnV00n6aMjHxGF7chictazNKzEqG7AG64+9yybeIiIhMCYW4GSpKhIgdTvqI26dfd2AZDgrNMppiDYnnbIqIiEjm6HTqDHUolnrFT7VVS6Fx+rU0JWYFbfFGuu028o3TX50lIiIik08hbhqa7Vh42m1uw8Mq58QLeQvNUgrN0pR2l+FhlfPis65PREREzp5Op4qIiIhkIYU4ERERkSykECciIiKShRTiRERERLKQQpyIiIhIFlKIExEREclCCnEiIiIiWUghTkRERCQLKcSJiIiIZCGFOBEREZEspBAnIiIikoUU4kRERESykEKciIiISBZSiBMRERHJQgpxIiIiIllIIU5EREQkCynEiYiIiGQhhTgRERGRLOTIdAEi08XJK3MyXcKUiXmMc/Z4T+5anekS+OzyFzNdwoQMwGeGp+z1rrjh3P56TLbf/MO6tI1lxG2c/bGzHqfo9bMfQ96Ha9M7nGbiRERERLKQQpyIiIhIFlKIExEREclCCnEiIiIiWUghTkRERCQLKcSJiIiIZCGFOBEREZEspBAnIiIikoUU4kRERESykEKciIiISBZSiBMRERHJQgpxIiIiIlloUkLcI488wsc+9jHuvffeRJtt2zz44IPccMMNbNq0iTvuuIOGhobJeHkRERGRaS/tIa6+vp6tW7cye/bspPbHHnuMxx9/nJtvvpm7776bvLw87rzzToaGhtJdgoiIiMi0l9YQNzg4yN///d/z5S9/Gb/fn2i3bZsnnniCTZs2sX79eqqrq7n99tsZHh5m27Zt6SxBREREZEZIa4j7h3/4B9avX8/y5cuT2ltbW+nu7mblypWJNrfbTV1dHfX19eksQURERGRGSFuI27p1K83NzXzmM59J2dbd3Q1AMBhMag8Gg4ltIiIiInLmHOkY5OTJkzzwwAN897vfxel0nrafYRhJn9u2ndIGsGXLFrZu3Trha95xxx0A3PDd695HxXIu8OZ6ptX3L5aTlv9OWcHvcPKNulWZLuOc5WVppkuYkAMfc7kl02XMGFU3nv598b3y+lzceOOatI0n2S0t7zr19fX09fVx2223Jdri8Th79uzhV7/6FT/84Q+BsRm5oqKiRJ/e3t6U2TmAjRs3snHjxglfs62tDYCf/s9H03EIkgE3fPe6afX9a7p1fqZLmDLfqFvF3+3ZnukyzlmfXf5ipkuY0Fxu4TD3ZbqMGeM3969L21g33riG++9/JW3jydS669tVaR0vLSHu/PPPZ/785DewH/zgB5SXl3P99ddTUVFBKBRix44d1NbWAhAOh9mzZw833njjWb32bf/fnrPaXzInHvn4tPr+ffOVmRPiREQk89IS4vx+f9LVqAAej4dAIEB1dTUAV199NY888giVlZVUVFTw8MMP4/V62bBhQzpKEBEREZlRpmwRz6ZNmwiHw9x7770MDAxQW1vLXXfdhc/nm6oSROQ96vnPbQzXH0xp/0HNb+CjlwEQae9k4NUdhJtaiI+Gsfw5uMpKyFm5FGdhPqMnm+j6xdOU/OlnMb2epHHafvLv+JYtxn/esik5Hjl7Q/1RXviPdo7sGmCwN4rba1JY4WbdFYXMrvPz7393jMIKN5d9pixpv4GeCPd+4yBX/WkFi9bmpYz7u4dbOPhaPzd/d964a6Xl3HHw4K/weINUVV6Q1N7ZeZCTjS+xfNnniMejtLS8QXfPUSKRIUzTgceTR2HhIvJDc5L2GxrqZP+BJ8nJKaJ2/lVTeShZb9JC3N/+7d8mfW4YBps3b2bz5s2T9ZIiMglcVeUEP3xJUttNdefxw2P1jBw9TvfTv8FdVUHwwxuw8vKIj4wwcvgY/S++Qv7VH8lM0TJp/uMfTxIJx9n4x2UEi10M9cU4cWCQ4cHYhPv5g07mLPGz67melBAXi9rse6mXlR/MV4CbJo6feJHBwTYqK9bh8QSJxUYZHOogFh1N6dvZeYDCwoV0dR1iZKQHjyd1rbyMb+ZcTici74thWVg5yTPmOTk52JEovb95Fnd1JfkfvfzUxrwArpIi4qOpP6wlu40MxTh5cIg/+uosqheNLaHJK4CyGu8Z7b/0ohCP/+MJejvD5BW4Eu2H3uhneCDGkvV6854u+npPUF6xhry8txbyB/D5ClP6xeNRuruPMH/+FcTjUTo7D1BRsXZqi81ik/LsVBGZ/kaPnyQ+MoJ/1fJxt5tu9xRXJJPN5TZxuk0O7xggGom/5/3nLvOTk+tg9wu9Se27nuthdl0OufnpuxWHZJbD6aW/7ySxWHjCfj09x3C6cvB688kPzaWr+zC2/d7/bc1UmokTkQmNNpyk5d6fJLU9eelxogNjb8SOkGZPZgrTMrjiC+X8+qdNvPFsN8WzPFTM87JgdS7lc959fbNpGdRdkMfuF3q48GOFGIZBf3eEY3sH+NgtlVNwBDJVZlVdyLGGbezc9SBeb4icnGLy8maRG6hI6tfZeYD8/LkA+P2lmIaD3t7jBIOzM1B19lGIE5EJuSpKybv0A0ltly5fyys/fyRDFUkmLViVy9xlfk4eGKLpyDBHdw/w6q+7uOgTRZx/VdG77r/soiAvb+mkYd8gsxf72f1CD94ci3nLA1NQvUwVv7+UusV/xOBgGwODbQwMNHP48K8pKKhlVtV6AEZH+xgYbGP27EuAsbXzofw5dHQeUIg7QwpxIjIhw+HAEUxeiO73+xNt0e4eXGUlp93fdI2tfYqHwylXp8ZHw4ntkj0cTpPZdX5m1/m58GNFbPlJEy880c6aj6SueXqnUImbylofu5/voXpRDrtf6GHxBXlYDl3QkC1My0ksFklpj8XCWNap/8+GYeL3l+L3l0LJMlpadtDc8jolxctwuwN0dB4AbHbvSf2FMBwewOXyp7RLMoU4EXlfXLMqMD0eBra/kXxhw5vio6OYbjdWMBcMg0hbB4683MT2aG8fdjiMFUq93YRkl4JyN/E4Z7xObtlFQX79QDMHX+unpz3Csg+EJrlCSSePO4++/pMpj84cGu7E7c49/X5vXnUaj0ex7ThdXYcoK1tFXm7yUwwajj9LZ9chykpXTM4BTCMKcSIyITsWIzY4lNQ20N+P6XSS96GL6P7Vb+l6Yis5K+qwgnnYI6OMHDlGpK2T/Ks/guly4Vu8gP4XXsawLBwF+cQGBuh/8RWcJUW4ykszdGTyXg0PRHni3pMsWR+kqNKDy2PS2jDCy1s6qF6Yg9trvdkvRuvxkaR9c3It/MGxCxdqV+Xy2wdb2PrTJsrneiko10Uw2aSwcCHtHfs42fgSBQULMA2Lvr6TdHcfYc6cDwFj95ILhWrw+QqxLDcjoz00Nb+G252Hx5NHb98JotERCgtqcTiSZ+hDwRo6OvZTWrJct5x5FwpxIjKh8Ikm2v75waS2/x18Gt/nrsMzp5rCP7qage1v0PPr/zp1s9/yUgLrT90mIPfi8xnYvpO+F18m1j+A5fPiqqog94I1+iGdRZxuk7I5Xl77bRfdbWFiURt/0MmidXlc8Lb1cPWv9FH/Sl/Svqsvz+fS68cCu9NlsmhdHjv+q5tlF2kWLtu43QHmz7+C5ubXOHx4K/F4DI8nj5rZlyZm1QK5FXR1H6ap+TXi8ShOh5dAoJzS0uUYhkln50ECgbKUAAcQDNbQ1Lyd/v4mcnMrUrbLKcbw8LCd6SLej7a2NgBM5//IcCXyfsUj38J0fifTZaTNN1/5eKZLmDLfqFvF3+3ZnukyzlmfXf5ipkuY0Fxu4TD3ZbqMGeM3/7AubWPdeOMa7r//lbSNJ1Prrm9fm9bxdJ84ERERkSykECciIiKShRTiRERERLKQQpyIiIhIFlKIExEREclCCnEiIiIiWUghTkRERCQLKcSJiIiIZCGFOBEREZEspBAnIiIikoUU4kRERESykEKciIiISBZSiBMRERHJQgpxIiIiIllIIU5EREQkCynEiYiIiGQhhTgRERGRLKQQJyIiIpKFFOJEREREspBCnIiIiEgWUogTERERyUIKcSIiIiJZSCFOREREJAs5Ml2AyLThime6gqk10473PfjXfeendbyKe0+kdbyqb1s88xcVaR1TTs/iZPoGi63COjg23jfv25W+cWWKXJvW0TQTJyIiIpKFFOJEREREspBCnIiIiEgWUogTERERyUIKcSIiIiJZSCFOREREJAspxImIiIhkIYU4ERERkSykECciIiKShRTiRERERLKQHrs1Df3Tj/p54flRACwL8vNNzlvt5hPX+ujvi/Pfv9bNnX+VR80cZ9J+P/j7XvwBkz+9OZAYZ6A/zn/7Wt6UH4OIiIhMLC0h7mc/+xkvvvgijY2NOJ1OFixYwA033EB1dXWij23bPPTQQ2zdupWBgQFqa2u59dZbk/pI+iyuc3LzLQGiMZsD+yP85J8HCI/aXHmVN9OliYiISBqk5XTqrl27uOqqq/j+97/Pd77zHSzL4s///M/p7+9P9Hnsscd4/PHHufnmm7n77rvJy8vjzjvvZGhoKB0lyDs4HZAXNCkosLjgQg/nX+Dmte2jmS5LRERE0iQtIe6uu+7isssuo7q6mtmzZ/PVr36Vvr4+9u3bB4zNwj3xxBNs2rSJ9evXU11dze23387w8DDbtm1LRwnyLlwug1gs01WIiIhIukzKmrjh4WHi8Tg5OTkAtLa20t3dzcqVKxN93G43dXV11NfXc8UVV0xGGfKmI4cjvPT7URYtPrUG7rt/04thJPeLROCCC91TXJ2IiIi8H5MS4n70ox8xZ84cFi5cCEB3dzcAwWAwqV8wGKSzszNl/y1btrB169YJX+OOO+4AIB75VjpKnlbs+IPs2rWdW2/qIxaLE4vFWLlyCZs//UeEwxHg29x00xeoqChN2u+BBx7BjoeIRzYnxrHtQeKRmyapUv+0+v59Y74n0yVMGb/DyTfmr8l0GTOG9e3z0jqeN+Dh89++Jq1jytR4+/euZ/TKDFcj71VRmsdLe4j7p3/6J/bt28f3vvc9LMtK2ma8Y+rHtu2UNoCNGzeycePGCV+nra0NANP5nbOsePoxzH5qF1j88RdysCwIBk0cjmbg/9LRPnZOtaDoMcoqk69Odbt7McwTmM6jiXEMIz5pX+N45FvT6vv3d3uvznQJU+Yb89fwdwdfyXQZM0bFvSfSOt7nv30ND/zFf6R1TJkab//effO+XRmuRt67H6d1tLTeJ+7HP/4xzz77LP/rf/0vSktPzfKEQiHg1IzcW3p7e1Nm5yQ93C4oKbEoLLRwOFKDsoiIiGS3tIW4H/3oRzz77LN85zvfoaqqKmlbSUkJoVCIHTt2JNrC4TB79uxJnHKVc9PwiM3xhmjSx1uzeSIiIpI5aTmdes899/DMM8/wrW99C7/fn5hx83g8eL1eDMPg6quv5pFHHqGyspKKigoefvhhvF4vGzZsSEcJMkkO7I/yl3/Rk9S2eo2LL34pN0MViYiICKQpxD399NMA/Pmf/3lS+6c//Wk2bx5bJL9p0ybC4TD33ntv4ma/d911Fz6fLx0lyNu89cSF8RQWWdz/QOG42975ZIY/vTkw4VgiIiKSOWkJcU8++eS79jEMg82bNydCnYiIiIi8f2m9sEFEREREpoZCnIiIiEgWUogTERERyUIKcSIiIiJZSCFOREREJAspxImIiIhkobQ/O1VEZqaeLc8yvOfQ2CemgRXIwTNvNv71K4kPjdD+Tz9L9DVcThwFQfzrluOZOwuAoZ376fuvP1D4+Y/jCJ66mXRscJiOn/6CnNVL8K9dNqXHJJPj6OhuonaE+Z6VibaeaDtHRndS7KzGwKA72soS34VJ+0XsMG8MbaPWs4pcKx+AVwf/E4AFntUErFCir23b7Bx+logdZo57GfmOkik4MhnP/7uvh2eeG0l8HvAb1M5zccPmAJXlp2LI6ztHefyXgxw+GiEatSkrc/Chi71cebkP0zz1+MhrP9vC178c5MK1nik9jnORQpyIpI1rVjnBKy+GWJxwYys9v34eOxolZ81SAPI3XY6jKB97JMzgG/V0P/E7Cj93Dc7CEL5lCxg5fJzeLc+R/8krMYyxH9q9v3kBK5SbGEOmn85IE8fCe6l01VLinEVj+PB72t9leOiINiWFuN5YB4ZONp0zli1x8ZVbx24o39Ud54GH+vneD7r5f39XBMCW3wzxTz/t4+orc/jC5wJ43AY7doX5l4f72X8owtdu03PWx6N/4SKSNobDwsrxYeX68S6ai3fRXEYONZza7vFg5fhwFAQJfGAVxOOEjzcntudd/gGiXb0Mbt8NwNDeQ4QbmghecXEi1Mn00ho5zrHwXma7FlPinPW+xihwlNEdbSVmRxNtHdFGChzl6SpTzpLTYRAKWoSCFnNrnHzsCh+NTTFGwzYdnTH++V/7uOJyH5//dIDqKiclxQ4+8iEfX7o5jxdeGuHFP4y8+4vMQApxIjJpDIcDOxZPabdjcYZ27h/7xDr1Y8jK8ZL74Qvpf+E1Rhsa6fvdS+Resi7p9KpMH43hQzSGDzLXvZwC5/sPXF4zgMfMoSvaCoyddu2NdVCoEHdOGh6O88JLI1RXOXC7DH7/8gjRKHziozkpfdet9lBWavHsi8MZqPTcp9OpIjIpws3tDO87jHvWqTfSzoefwjAM7GgMbBsrz4+3tiZpP+/82YzWNtD12K9xz67At2zBVJcuU6Av1klvrIN57hUEHUUp20fsQV4b/N0Zj1foKKcj2kiRs4LOaBN+M4Tb9KazZDkLr+8cZfOfjIXskVGbwgKTb3197PR3U0sUn9cgP2SNu29luYOm5ui422Y6hTgRSZvRoydp+b8PYMdtiMdxz51F7ofOx46M/QAOXXkJjsIg0e4++v7rD+R9eD2m150yjv/8lQzvPYz//BVTfQgyRbymn5gdpSlyBL8VxGE4k7a7DV/ShQ8AMTvCvpGXxx0v31HKyfABRuKDdESaKHPVjNtPMmPxQhd/9oWxGfWBwThbfjPEXd/r5nt/XTDWYYLVEraNllOchkKciKSNq7KUvA+vB8vEyvFhvHmqNNrbD4AZyMERysMRysNwOuh58hmK/vhaTN87rjJ760o0Uys+piun4WaeewX7R7ZzYGQ7tZ5VSUHOwMBj+pL2idjh047nMJwErWIaRvcRsUcJWcWTVru8d26XQVnpqcgxp8bJ525q49e/G6K81MHQkE1nV4yC/NTZuMamKFWViivj0U9IEUkbw+nAEcrFketPBLjTcVeV4SgI0v/SjimqTs41LtPDAs9q4nacAyPbiU4Q0s5EobOC/ng3+Y4yTGP8U3NybjAAw4DRsM0Faz04LHj8qcGUfi+9MkJza4yLL9Sp8fEo2opIxuSsXkL3k8/gX70EK9ef6XIkA1ymmwXeVewf3s7+N2fk3q9cK5/lvg1Yems750SiNt09MQAGB22e/s8hRkZt1qx0U1hgccNnAtz/L/04HQaXXOTB7TJ4Y3eYB/69n/Xne7hwXfJsfVt7jKMNkaS2kiILn29mzU3pX7qIZIx7ThVWrp/+l3YQvPwDmS5HMsRpuFngXc2Bke0cGN6O33r/9wRzGq40VibpsnN3mD+5rR0Ar8egotzB178UZMnisTWxV12eQ2mxg/94apAtvx0iFrMpL3XwqWv9XHm5L2W8Bx7qT2n75teCrF45s24ArBAnImkR3Hjxabc58gKUfe0LKe2GYVD8hU1n3F+mhxr3kpQ2p+GiznvBhPs5DRercz6c1PbOz9/p3bbL5PvSLUG+dMu791u1ws2qFakXOr3Tz/+1NA1VTQ8za95RRET+//buPyTu+oHj+Ot0btWG3g38AaYn67o8LcZiaGCTaLItbqMikxrR/mnehmNDqWghzQJhY2RsbMm0iBy1ms0kupz9kZjQr0XoTLzBVjTnqq28ebOdJd59/4jdd/e1pl+yu8+7ng/wn/fn7X3e8prsdZ/P+z4C+IegxAEAABiIEgcAAGAgShwAAICBKHEAAAAGosQBAAAYiBIHAABgIEocAACAgShxAAAABqLEAQAAGIgSBwAAYCBKHAAAgIEocQAAAAaixAEAABiIEgcAAGAgShwAAICBKHEAAAAGosQBAAAYiBIHAABgIEocAACAgShxAAAABqLEAQAAGIgSBwAAYCBKHAAAgIEocQAAAAaixAEAABhoQaJP6Pf71dHRoWAwqPz8fG3evFnFxcWJXgYAAIDREnolrq+vT62traqqqtK+ffvk8XjU0NCgCxcuJHIZAAAAxktoievs7NTq1au1du1a6EwX6gAACJ5JREFU5eXlyefzyeFwqKurK5HLAAAAMJ4tHA5HE3GiqakpVVZW6qmnntLdd98dG29ubtZ3332n3bt3/1+vx9U7AABgmqysrHl7rYTtiQuFQopEIrLb7XHjdrtdAwMDcWPHjx9Xd3f3dV9v586d875GAAAAUyT8gw02m23WOevWrdO6detmnVdbW6uXXnppPpaFJCA/c5Gd2cjPXGRntvnOL2F74tLT05WSkqJgMBg3funSpRlX5wAAAHB9CStxaWlpcrlc6u/vjxvv7++Xx+NJ1DIAAAD+ERJ6O/WBBx5QU1OTbr31VhUVFamrq0tjY2O67777ErkMAAAA4yW0xK1atUqhUEhHjx7V2NiYnE6ndu3aNa+f1AAAAPg3SPgHG7xer7xeb6JPCwAA8I/C304FAAAwECUOAADAQJQ4AAAAA6XW19c3JHsRf4XL5Ur2EvAXkJ+5yM5s5GcusjPbfOaXsL+dCgAAgPnD7VQAAAADUeIAAAAMRIkDAAAwUMIf9jtf/H6/Ojo6FAwGlZ+fr82bN6u4uDjZy8I12tvb9cknn2h0dFRpaWm67bbbtGnTJjmdzticaDSqI0eOqLu7WxMTE3K73dqyZUvcHCTf0aNHdfjwYXm9Xm3ZskUS2Vnd2NiYXn/9dX355ZcKh8PKycnR1q1bdccdd0giPyubnp7WkSNH1NPTo2AwKIfDoXvuuUcbN25UamqqJPKziq+//lrvvvuuTp8+rbGxMe3YsUMVFRWx43PJaWJiQocOHdIXX3whSSopKZHP59OSJUtmPb+RV+L6+vrU2tqqqqoq7du3Tx6PRw0NDbpw4UKyl4ZrDA4Oyuv1au/evWpsbFRqaqrq6+t1+fLl2Jxjx46ps7NT1dXVampqUkZGhp577jlduXIliSvHtQKBgLq7u1VQUBA3TnbWNTExoaefflrRaFS7du3Syy+/LJ/PJ7vdHptDftZ17Ngx+f1++Xw+NTc3q7q6Wn6/X+3t7XFzyC/5Jicn5XQ6VV1drYULF844Ppec9u7dqzNnzqihoUHPP/+8zpw5o6ampjmd38gS19nZqdWrV2vt2rXKy8uTz+eTw+FQV1dXspeGa7zwwguqqKiQ0+lUQUGB6urqFAqFNDw8LOn3dyjvvfeeHnroIZWVlcnpdKq2tlbhcFi9vb1JXj0k6ZdfftGLL76o7du3x70rJDtr6+jo0NKlS1VXVye3262cnBwtX75ceXl5ksjP6oaHh1VSUqKSkhJlZ2ertLRUpaWlOnXqlCTys5KVK1fq8ccfV1lZmVJS4ivVXHIaGRnRV199pW3btsnj8aiwsFA1NTU6ceKEzp07N+v5jStxU1NTOn36tFasWBE3vmLFilg5gDWFw2FFIhEtXrxYkvTjjz8qGAzGZblo0SIVFxcrEAgka5m4xoEDB1RWVqbly5fHjZOdtX322Wdyu93as2ePHnvsMW3fvl3vv/++otHfnyhFftZWVFSkkydPamRkRJJ09uxZnTx5UitXrpREfqaYS06BQEA33nijPB5PbE5RUZFuuOGGOWVp3J64UCikSCQSd1tAkux2uwYGBpK0KsxFS0uLli1bpsLCQklSMBiUpD/M8ueff074+hCvu7tb33//verq6mYcIztr++GHH/TBBx/o/vvvV2Vlpb799lsdOnRIkrR+/Xrys7jKykqFw2HV1NQoJSVF09PTqqqqktfrlcTvnynmklMwGFR6erpsNlvsuM1mU0ZGRuz7r8e4EnfVtT8wrO+VV17R8PCw9uzZE9uYe9X/ZhmNRsk3yc6dO6e2tjbt3r1baWlpfzqP7KwpGo3K5XJp06ZNkqRbbrlF58+fl9/v1/r162PzyM+a+vr61NPToyeffFL5+fn65ptv1NraquzsbK1ZsyY2j/zMMFtOf5TZ1avmszGuxKWnpyslJWVGQ7106dKMtgtraG1tVV9fnxobG5WTkxMbdzgckn5/J5KZmRkbHx8fJ8skCwQCCoVC2rZtW2wsEoloaGhIXV1dOnjwoCSysyqHwxHb/3bVzTffrIsXL8aOS+RnVa+99poefPBBlZeXS5IKCgp08eJFvfPOO1qzZg35GWIuOTkcDo2Pj8cVu2g0qlAoFPv+6zFuT1xaWppcLpf6+/vjxvv7++PuKcMaWlpa9PHHH6uxsXHGfyrZ2dlyOBxxWf72228aGhqK3XJFctx11106cOCA9u/fH/tyuVxatWqV9u/fr9zcXLKzMI/Ho9HR0bix8+fPKysrSxK/e1b366+/ztgkn5KSokgkIon8TDGXnAoLCxUOh+P2vwUCAU1OTs4py9T6+vqGeV/53+ymm27Sm2++KYfDoUWLFuntt9/W0NCQduzYEds0j+Rrbm7WRx99pGeeeUaZmZmanJzU5OSkpN/LuM1m0/T0tNrb25Wbm6tIJKJXX31VwWBQNTU1172Nh7/XwoULZbfb4756e3uVlZWliooKsrO4zMxMvfXWW7LZbFq6dKkGBgZ0+PBhPfzww3K73eRncWfPnlVPT49yc3O1YMECDQ4Oqq2tTeXl5brzzjvJz0LC4bBGRkYUDAb14YcfqqCgQIsXL9bU1JSWLFkya04ZGRk6deqUent7tWzZMv300086ePCg3G63NmzYMOv5beFweG43Xi3m6sN+x8bG5HQ69cQTT+j2229P9rJwjT/7B/joo49q48aNkv77IMTjx4/HHoS4detWHlhpQTt37pTT6ZzxsF+ys6YTJ06ora1No6OjyszMlNfr1YYNG+Ju2ZCfNV25ckVvvPGGPv30U42Pj8vhcKi8vFyPPPJI7Flk5GcNg4ODevbZZ2eM33vvvaqtrZ1TTpcvX1ZLS4s+//xzSVJpaemcH/ZrbIkDAAD4NzNuTxwAAAAocQAAAEaixAEAABiIEgcAAGAgShwAAICBKHEAAAAGosQBAAAYiBIHAABgIEocAACAgf4DsudxceVySVcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = squarify.plot(sizes=sorted_amounts.values[0:20], label=sorted_amounts.index[0:20], alpha=.7)\n", "ax.set_title(\"Dollars received in loans per country\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When sorting by amount received rather than number of loans, the United States appear in the top 10. Why might that be?" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'US loans by Activity')" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "us_loans = loans_raw[loans_raw[\"COUNTRY_NAME\"] == \"United States\"]\n", "us_activities_by_amount = us_loans.groupby(\"ACTIVITY_NAME\")[\"FUNDED_AMOUNT\"].agg(sum).sort_values(ascending=False)\n", "ax = us_activities_by_amount[0:20].sort_values().plot.barh()\n", "ax.set_title(\"US loans by Activity\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although I originally hypothesized that perhaps the loans were for personal health care, Kiva focuses its within-US efforts on supporting entrepreneurs, leading to the activity breakdown above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compared to the rest of the countries that receive significant amounts of money through Kiva, the US seems like an outlier. How does its distribution of lenders compare to other countries?" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Lender countries for US loans')" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "us_lenders = us_loans[\"LENDERS\"]\n", "\n", "made_us_loan = set()\n", "def get_lender_names(lenders_csv):\n", " lender_usernames = lenders_csv.split(\", \")\n", " for name in lender_usernames:\n", " made_us_loan.add(name)\n", " \n", "us_lenders.apply(get_lender_names)\n", "\n", "lenders_by_username = lenders_raw.set_index(\"PERMANENT_NAME\")\n", "\n", "filtered_us_lenders = lenders_raw[lenders_raw[\"PERMANENT_NAME\"].isin(made_us_loan)]\n", "filtered_us_lenders = filtered_us_lenders[filtered_us_lenders[\"COUNTRY_CODE\"].notna()]\n", "\n", "country_codes = lenders_raw[\"COUNTRY_CODE\"]\n", "lender_countries = {country: 0 for country in country_codes.unique()}\n", "def count_lender_countries(lender):\n", " country_code = lender[\"COUNTRY_CODE\"]\n", " lender_countries[country_code] += 1\n", "\n", " \n", "filtered_us_lenders.apply(count_lender_countries, axis=1)\n", "\n", "codes_df = pd.DataFrame(lender_countries.values(), index=lender_countries.keys(), columns=[\"LENDER_COUNT\"])\n", "us_lender_countries = codes_df.nlargest(20, columns=\"LENDER_COUNT\")\n", "ax = squarify.plot(sizes=us_lender_countries.values[0:20], label=us_lender_countries.index[0:20], alpha=.7)\n", "ax.set_title(\"Lender countries for US loans\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As one might expect, the percentage of US loans that are funded by US citizens is larger than the baseline for all loans, going from roughly 70% to over 80%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking into the United States led to the question: what are the largest average loan amounts per country?" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Average loan amounts by country')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def get_large_sample_size_mean(country_loans):\n", " \"\"\"Returns the mean of a country's loans if the sample size is at least 500, otherwise 0.\"\"\"\n", " return 0 if len(country_loans) < 500 else np.mean(country_loans)\n", "\n", "avg_amt_by_country = loans_raw.groupby(\"COUNTRY_NAME\")[\"FUNDED_AMOUNT\"].agg(get_large_sample_size_mean).sort_values(ascending=False)\n", "ax = avg_amt_by_country[0:20].sort_values().plot.barh()\n", "ax.set_title(\"Average loan amounts by country\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Costs of Lending\n", "\n", "Using Duke's sample dataset, we previously estimated that the loan default rate was roughly 2%. We will proceed using this estimate to calculate the expected personal loss in present value when making a single loan.\n", "\n", "We will make the following simplifying assumptions:\n", "1. Only loans with monthly repayment schemes (which make up 87% of the dataset) are considered\n", "1. The loans are made uniformly and randomly, without any selection from the lender\n", "2. Currency exchange rates are held constant, removing the risk of gain/loss due to dollar fluctuations\n", "3. Defaulted loans do not return any invested money whatsoever\n", "4. Returns are immediately withdrawable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to compute the expected loss, we must figure out the expected repayment intervals." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13.0\n", "2.0\n" ] } ], "source": [ "monthly_loans = loans_raw[loans_raw[\"REPAYMENT_INTERVAL\"] == \"monthly\"]\n", "num_repayments = monthly_loans[\"LENDER_TERM\"].mean().round()\n", "print(num_repayments)\n", "print(default_ratio)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def NPV(pay_per_period, discount_rate, n_periods):\n", " \"\"\"Return the Net Present Value of an asset.\"\"\"\n", " result = 0\n", " for t in range(1, int(n_periods) + 1):\n", " result += pay_per_period / (1 + discount_rate)**t\n", " \n", " return result\n", "\n", "\n", "def expected_loss(initial_amount, discount_rate=0.035, num_repayments=13):\n", " pay_per_period = initial_amount / num_repayments\n", " \n", " # default ratio was computed as an integer, get the probability that we get our money back\n", " chance_to_repay = (100 - default_ratio) / 100\n", " \n", " expected_discounted_value = chance_to_repay * NPV(pay_per_period, discount_rate, num_repayments)\n", " \n", " # discounted value - current value is how much we're losing\n", " return expected_discounted_value - initial_amount\n", "\n", "\n", "x = np.linspace(0, 2500, 50) # amounts of money we could loan\n", "\n", "discount = 0.0052 # current long-term average S&P 500 monthly return\n", "# (according to https://ycharts.com/indicators/sp_500_monthly_total_return)\n", "y = expected_loss(x, discount, num_repayments)\n", "\n", "fig, ax = plt.subplots(1)\n", "ax.set_xlabel(\"Amount loaned\")\n", "ax.set_ylabel(\"Change in present value\")\n", "ax.set_title(\"Expected result with 0.52% monthly discount rate\")\n", "\n", "ax.plot(x, y, 'r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assuming that one would've otherwise put the money into an index fund, if one is willing to accept the variance of these loans, they can provide \\\\$1000 worth of capital while only losing roughly \\$55 in present value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting funding\n", "\n", "We now turn to the task of attempting to use the dataset to predict whether a loan will be fully funded or not. Because the dataset has multiple different data types, missing values, and superfluous features, it requires quite a bit of preprocessing before we can pass it to machine learning models.\n", "\n", "First, we will split the features into 3 sets and ensure that each set can be trained on:\n", "1. numerical; these are the easiest to work with, we just need to interpolate missing values.\n", "2. categorical; these are text features that we expect to have a limited range of values. We apply one-of-K encoding to turn these features into dummy variables.\n", "3. descriptive; these are text features that are full English sentences. We apply bag-of-words to vectorize and transform these features into more convenient forms for learning.\n", "\n", "Second, we will try learning on these sets of features individually to try to see if any are more predictive than others. We will construct an ensemble of classification models to first benchmark each model's individual performance, and then the performance of the models together.\n", "\n", "Finally, we will combine all that we have done previously into one large benchmark. If we get any promising preliminary results for a particular model, we may spend more time optimizing that final model by tuning scikit-learn parameters and model hyperparameters." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "# For the sake of the exercise, ignore features that: \n", "# 1) are not populated upon the loan first being available\n", "# 2) are irrelevant ids unique to the loan\n", "\n", "# Note that disbursed dates are typically available when first viewing the loan,\n", "# either as pre-dispursal from the partner or as the expected date\n", "\n", "number_features = [\n", " 'LOAN_AMOUNT',\n", " 'DISBURSE_TIME',\n", " 'LENDER_TERM',\n", " 'CURRENCY_EXCHANGE_COVERAGE_RATE',\n", " 'POSTED_TIME'\n", "]\n", "\n", "categorical_features = [\n", " 'ACTIVITY_NAME',\n", " 'SECTOR_NAME',\n", " 'COUNTRY_CODE',\n", " 'COUNTRY_NAME',\n", " 'TOWN_NAME',\n", " 'CURRENCY_POLICY',\n", " 'CURRENCY',\n", " 'TAGS',\n", " 'BORROWER_GENDERS',\n", " 'BORROWER_PICTURED',\n", " 'PARTNER_ID',\n", "]\n", "\n", "description_features = [\n", " 'LOAN_USE',\n", " 'DESCRIPTION',\n", " 'DESCRIPTION_TRANSLATED'\n", "]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import logging\n", "import sys\n", "from time import time\n", "\n", "from sklearn.linear_model import RidgeClassifier\n", "from sklearn.linear_model import RidgeCV\n", "from sklearn.svm import LinearSVC\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.linear_model import Perceptron\n", "from sklearn.linear_model import PassiveAggressiveClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.utils.extmath import density\n", "from sklearn import metrics\n", "from sklearn.pipeline import make_pipeline, Pipeline\n", "from sklearn.feature_selection import SelectFromModel\n", "from sklearn.feature_selection import SelectKBest, chi2\n", "from sklearn.ensemble import StackingClassifier, VotingClassifier\n", "from sklearn.neural_network import MLPClassifier\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "# Source: forked from https://scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py\n", "# License: BSD 3 clause\n", "\n", "# Display progress logs on stdout\n", "logging.basicConfig(level=logging.INFO,\n", " format='%(asctime)s %(levelname)s %(message)s')\n", "\n", "def benchmark(clf, name, X_train, X_test, y_train, y_test):\n", " \"\"\"Trains and tests a model. Prints information about its performance.\"\"\"\n", " print('=' * 80)\n", " print(name)\n", " print('_' * 80)\n", " print(\"Training: \")\n", " print(clf)\n", " t0 = time()\n", " clf.fit(X_train, y_train)\n", " train_time = time() - t0\n", " print(\"train time: %0.3fs\" % train_time)\n", "\n", " t0 = time()\n", " pred = clf.predict(X_test)\n", " test_time = time() - t0\n", " print(\"test time: %0.3fs\" % test_time)\n", "\n", " score = metrics.accuracy_score(y_test, pred)\n", " print(\"accuracy: %0.3f\" % score)\n", "\n", " if hasattr(clf, 'coef_'):\n", " print(\"dimensionality: %d\" % clf.coef_.shape[1])\n", " print(\"density: %f\" % density(clf.coef_))\n", " print()\n", " \n", " print(\"confusion matrix:\")\n", " print(metrics.confusion_matrix(y_test, pred))\n", " \n", " print()\n", " clf_descr = str(clf).split('(')[0]\n", " return clf_descr, score, train_time, test_time\n", " \n", "\n", "def rank_classifiers(X_train, X_test, y_train, y_test, n_iters=100, njobs=-1):\n", " \"\"\"Creates, trains, and evaluates many classification models.\"\"\"\n", " results = []\n", " classifiers = []\n", " \n", " # Baseline\n", " mode = y_train.value_counts().index[0]\n", " score = len(y_test[y_test.values == mode]) / len(y_test)\n", " results.append(\n", " (\"Predict Mode\",\n", " score,\n", " 0,\n", " 0)\n", " )\n", " \n", " print('=' * 80)\n", " print(\"Predict the Mode\")\n", " print('_' * 80)\n", " print(\"Score:\", score)\n", " print()\n", " \n", " for clf, name in (\n", " (RidgeClassifier(tol=1e-2, solver=\"auto\"), \"Ridge Classifier\"),\n", " (Perceptron(max_iter=n_iters, n_jobs=-1), \"Perceptron\"),\n", " (MLPClassifier(max_iter=n_iters, early_stopping=True), \"Neural Network\"),\n", " (PassiveAggressiveClassifier(max_iter=n_iters * 10, n_jobs=njobs), \"Passive-Aggressive\"),\n", " (KNeighborsClassifier(n_neighbors=5, n_jobs=njobs), \"kNN\"),\n", " (RandomForestClassifier(max_depth=n_iters, n_jobs=njobs), \"Random forest\")):\n", " \n", " results.append(benchmark(clf, name, X_train, X_test, y_train, y_test))\n", " classifiers.append((name, clf))\n", " \n", "\n", " for penalty in [\"l2\", \"l1\"]:\n", " name = f\"Liblinear with {penalty.upper()} penalty\"\n", " clf = LinearSVC(penalty=penalty, dual=False, tol=1e-3, max_iter = n_iters * 50)\n", " results.append(benchmark(clf, name, X_train, X_test, y_train, y_test))\n", " classifiers.append((name, clf)) \n", "\n", " name = f\"Ordinary SGD with {penalty.upper()} penalty\"\n", " clf = SGDClassifier(alpha=.0001, max_iter=n_iters, penalty=penalty, n_jobs=njobs)\n", " results.append(benchmark(clf, name, X_train, X_test, y_train, y_test))\n", " classifiers.append((name, clf)) \n", "\n", " name = \"SGD with Elastic Net penalty\"\n", " clf = SGDClassifier(alpha=.0001, max_iter=n_iters, penalty=\"elasticnet\", n_jobs=njobs)\n", " results.append(benchmark(clf, name, X_train, X_test, y_train, y_test))\n", " classifiers.append((name, clf))\n", "\n", " name = \"LinearSVC with L1-based feature selection\"\n", " # The smaller C, the stronger the regularization.\n", " # The more regularization, the more sparsity.\n", " clf = Pipeline([\n", " ('feature_selection', SelectFromModel(LinearSVC(penalty=\"l1\", dual=False, tol=1e-3))),\n", " ('classification', LinearSVC(penalty=\"l2\"))\n", " ])\n", " results.append(benchmark(clf, name, X_train, X_test, y_train, y_test))\n", " classifiers.append((name, clf))\n", " \n", " # Ensemble methods were causing intermittent mmap file errors on windows\n", " try:\n", " name = \"Voting classifier with all previous estimators\"\n", " voting_classifier = VotingClassifier(estimators=classifiers)\n", " results.append(benchmark(voting_classifier, name, X_train, X_test, y_train, y_test))\n", "\n", " name = \"Stacking classifier with all previous estimators\"\n", " stacking_classifier = StackingClassifier(estimators=classifiers)\n", " results.append(benchmark(stacking_classifier, name, X_train, X_test, y_train, y_test))\n", " \n", " except:\n", " pass\n", " \n", " \n", " return results\n", "\n", "\n", "def plot_rankings(results):\n", " \"\"\"Given tuples of a model name, score, training time, and testing time, produces a plot of each model's performance.\"\"\"\n", " indices = np.arange(len(results))\n", "\n", " results = [[x[i] for x in results] for i in range(4)]\n", "\n", " clf_names, score, training_time, test_time = results\n", " training_time = np.array(training_time) / np.max(training_time)\n", " test_time = np.array(test_time) / np.max(test_time)\n", "\n", " plt.figure(figsize=(14, 10))\n", " plt.title(\"Score\")\n", " plt.barh(indices, score, .2, label=\"score\", color='navy')\n", " plt.barh(indices + .3, training_time, .2, label=\"training time\", color='c')\n", " plt.barh(indices + .6, test_time, .2, label=\"test time\", color='darkorange')\n", " plt.yticks(())\n", " plt.legend(loc='best')\n", " plt.subplots_adjust(left=.25)\n", " plt.subplots_adjust(top=.95)\n", " plt.subplots_adjust(bottom=.05)\n", "\n", " for i, c in zip(indices, clf_names):\n", " plt.text(-.3, i, c)\n", "\n", " plt.show()\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the sake of time, we only run the models on a very small subset of the dataset. Previous trial runs that used the full dataset have never shown significant differences in performance." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Predict the Mode\n", "________________________________________________________________________________\n", "Score: 0.947670864408852\n", "\n", "================================================================================\n", "Ridge Classifier\n", "________________________________________________________________________________\n", "Training: \n", "RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False, random_state=None,\n", " solver='auto', tol=0.01)\n", "train time: 2.647s\n", "test time: 0.007s\n", "accuracy: 0.944\n", "dimensionality: 185898\n", "density: 0.046068\n", "\n", "confusion matrix:\n", "[[ 20 0 731 0]\n", " [ 0 0 34 0]\n", " [ 76 2 15424 0]\n", " [ 0 0 68 3]]\n", "\n", "================================================================================\n", "Perceptron\n", "________________________________________________________________________________\n", "Training: \n", "Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=100, n_iter_no_change=5, n_jobs=-1,\n", " penalty=None, random_state=0, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.125s\n", "test time: 0.011s\n", "accuracy: 0.924\n", "dimensionality: 185898\n", "density: 0.013029\n", "\n", "confusion matrix:\n", "[[ 162 1 588 0]\n", " [ 1 0 33 0]\n", " [ 545 8 14942 7]\n", " [ 0 0 53 18]]\n", "\n", "================================================================================\n", "Neural Network\n", "________________________________________________________________________________\n", "Training: \n", "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=True, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_fun=15000, max_iter=100,\n", " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", " power_t=0.5, random_state=None, shuffle=True, solver='adam',\n", " tol=0.0001, validation_fraction=0.1, verbose=False,\n", " warm_start=False)\n", "train time: 1375.956s\n", "test time: 0.061s\n", "accuracy: 0.947\n", "confusion matrix:\n", "[[ 9 0 742 0]\n", " [ 0 0 34 0]\n", " [ 18 0 15484 0]\n", " [ 0 0 71 0]]\n", "\n", "================================================================================\n", "Passive-Aggressive\n", "________________________________________________________________________________\n", "Training: \n", "PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,\n", " early_stopping=False, fit_intercept=True,\n", " loss='hinge', max_iter=1000, n_iter_no_change=5,\n", " n_jobs=-1, random_state=None, shuffle=True,\n", " tol=0.001, validation_fraction=0.1, verbose=0,\n", " warm_start=False)\n", "train time: 0.226s\n", "test time: 0.011s\n", "accuracy: 0.934\n", "dimensionality: 185898\n", "density: 0.021068\n", "\n", "confusion matrix:\n", "[[ 83 1 667 0]\n", " [ 1 0 33 0]\n", " [ 303 6 15175 18]\n", " [ 0 0 47 24]]\n", "\n", "================================================================================\n", "kNN\n", "________________________________________________________________________________\n", "Training: \n", "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n", " weights='uniform')\n", "train time: 0.029s\n", "test time: 11.451s\n", "accuracy: 0.943\n", "confusion matrix:\n", "[[ 47 0 704 0]\n", " [ 0 0 34 0]\n", " [ 124 1 15374 3]\n", " [ 2 0 67 2]]\n", "\n", "================================================================================\n", "Random forest\n", "________________________________________________________________________________\n", "Training: \n", "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n", " criterion='gini', max_depth=100, max_features='auto',\n", " max_leaf_nodes=None, max_samples=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100,\n", " n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)\n", "train time: 24.794s\n", "test time: 0.217s\n", "accuracy: 0.948\n", "confusion matrix:\n", "[[ 0 0 751 0]\n", " [ 0 0 34 0]\n", " [ 2 0 15500 0]\n", " [ 0 0 71 0]]\n", "\n", "================================================================================\n", "Liblinear with L2 penalty\n", "________________________________________________________________________________\n", "Training: \n", "LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l2', random_state=None, tol=0.001,\n", " verbose=0)\n", "train time: 1.388s\n", "test time: 0.003s\n", "accuracy: 0.939\n", "dimensionality: 185898\n", "density: 0.046068\n", "\n", "confusion matrix:\n", "[[ 58 0 693 0]\n", " [ 0 0 34 0]\n", " [ 205 1 15293 3]\n", " [ 0 0 54 17]]\n", "\n", "================================================================================\n", "Ordinary SGD with L2 penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=100, n_iter_no_change=5, n_jobs=-1, penalty='l2',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.122s\n", "test time: 0.011s\n", "accuracy: 0.946\n", "dimensionality: 185898\n", "density: 0.015328\n", "\n", "confusion matrix:\n", "[[ 20 0 731 0]\n", " [ 0 0 34 0]\n", " [ 45 0 15457 0]\n", " [ 0 0 69 2]]\n", "\n", "================================================================================\n", "Liblinear with L1 penalty\n", "________________________________________________________________________________\n", "Training: \n", "LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l1', random_state=None, tol=0.001,\n", " verbose=0)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 3.318s\n", "test time: 0.006s\n", "accuracy: 0.942\n", "dimensionality: 185898\n", "density: 0.003877\n", "\n", "confusion matrix:\n", "[[ 26 0 725 0]\n", " [ 0 0 34 0]\n", " [ 128 4 15370 0]\n", " [ 0 0 65 6]]\n", "\n", "================================================================================\n", "Ordinary SGD with L1 penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=100, n_iter_no_change=5, n_jobs=-1, penalty='l1',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.129s\n", "test time: 0.012s\n", "accuracy: 0.946\n", "dimensionality: 185898\n", "density: 0.000769\n", "\n", "confusion matrix:\n", "[[ 3 0 748 0]\n", " [ 0 0 34 0]\n", " [ 27 1 15473 1]\n", " [ 0 0 69 2]]\n", "\n", "================================================================================\n", "SGD with Elastic Net penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=100, n_iter_no_change=5, n_jobs=-1, penalty='elasticnet',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.233s\n", "test time: 0.011s\n", "accuracy: 0.947\n", "dimensionality: 185898\n", "density: 0.005589\n", "\n", "confusion matrix:\n", "[[ 4 0 747 0]\n", " [ 0 0 34 0]\n", " [ 26 0 15476 0]\n", " [ 0 0 68 3]]\n", "\n", "================================================================================\n", "LinearSVC with L1-based feature selection\n", "________________________________________________________________________________\n", "Training: \n", "Pipeline(memory=None,\n", " steps=[('feature_selection',\n", " SelectFromModel(estimator=LinearSVC(C=1.0, class_weight=None,\n", " dual=False,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l1',\n", " random_state=None,\n", " tol=0.001, verbose=0),\n", " max_features=None, norm_order=1, prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0, class_weight=None, dual=True,\n", " fit_intercept=True, intercept_scaling=1,\n", " loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l2', random_state=None,\n", " tol=0.0001, verbose=0))],\n", " verbose=False)\n", "train time: 3.752s\n", "test time: 0.030s\n", "accuracy: 0.942\n", "confusion matrix:\n", "[[ 37 0 714 0]\n", " [ 0 0 34 0]\n", " [ 144 1 15355 2]\n", " [ 0 0 56 15]]\n", "\n", "================================================================================\n", "Voting classifier with all previous estimators\n", "________________________________________________________________________________\n", "Training: \n", "VotingClassifier(estimators=[('Ridge Classifier',\n", " RidgeClassifier(alpha=1.0, class_weight=None,\n", " copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False,\n", " random_state=None, solver='auto',\n", " tol=0.01)),\n", " ('Perceptron',\n", " Perceptron(alpha=0.0001, class_weight=None,\n", " early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=100,\n", " n_iter_no_change=5, n_jobs=-1,\n", " penalty...\n", " verbose=0),\n", " max_features=None,\n", " norm_order=1,\n", " prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0,\n", " class_weight=None,\n", " dual=True,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l2',\n", " random_state=None,\n", " tol=0.0001,\n", " verbose=0))],\n", " verbose=False))],\n", " flatten_transform=True, n_jobs=None, voting='hard',\n", " weights=None)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 1170.299s\n", "test time: 12.258s\n", "accuracy: 0.945\n", "confusion matrix:\n", "[[ 20 0 731 0]\n", " [ 0 0 34 0]\n", " [ 68 0 15434 0]\n", " [ 0 0 68 3]]\n", "\n", "================================================================================\n", "Stacking classifier with all previous estimators\n", "________________________________________________________________________________\n", "Training: \n", "StackingClassifier(cv=None,\n", " estimators=[('Ridge Classifier',\n", " RidgeClassifier(alpha=1.0, class_weight=None,\n", " copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False,\n", " random_state=None,\n", " solver='auto', tol=0.01)),\n", " ('Perceptron',\n", " Perceptron(alpha=0.0001, class_weight=None,\n", " early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=100,\n", " n_iter_no_change=5, n_jobs=...\n", " norm_order=1,\n", " prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0,\n", " class_weight=None,\n", " dual=True,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l2',\n", " random_state=None,\n", " tol=0.0001,\n", " verbose=0))],\n", " verbose=False))],\n", " final_estimator=None, n_jobs=None, passthrough=False,\n", " stack_method='auto', verbose=0)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 6410.119s\n", "test time: 10.902s\n", "accuracy: 0.948\n", "confusion matrix:\n", "[[ 10 0 741 0]\n", " [ 0 0 34 0]\n", " [ 23 0 15471 8]\n", " [ 0 0 51 20]]\n", "\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.model_selection import train_test_split\n", "\n", "X_cat = loans_raw[categorical_features]\n", "X_cat = X_cat.fillna(\"\")\n", "\n", "y = loans_raw[\"STATUS\"]\n", "\n", "categories = [X_cat[column].unique() for column in X_cat]\n", "\n", "cat_pipe = make_pipeline(\n", " SimpleImputer(missing_values=None,\n", " strategy='constant',\n", " fill_value='missing'),\n", " OneHotEncoder(categories=categories)\n", ")\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X_cat, y, train_size=0.01, test_size=0.01)\n", "\n", "cat_pipe.fit(X_train)\n", "X_train = cat_pipe.transform(X_train)\n", "X_test = cat_pipe.transform(X_test)\n", "\n", "cat_results = rank_classifiers(X_train, X_test, y_train, y_test, 100)\n", "plot_rankings(cat_results)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Predict the Mode\n", "________________________________________________________________________________\n", "Score: 0.9461425602151853\n", "\n", "================================================================================\n", "Ridge Classifier\n", "________________________________________________________________________________\n", "Training: \n", "RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False, random_state=None,\n", " solver='auto', tol=0.01)\n", "train time: 0.106s\n", "test time: 0.001s\n", "accuracy: 0.947\n", "dimensionality: 5\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 4 0 15473 0]\n", " [ 7 0 62 0]]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "================================================================================\n", "Perceptron\n", "________________________________________________________________________________\n", "Training: \n", "Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=1000, n_iter_no_change=5, n_jobs=-1,\n", " penalty=None, random_state=0, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.124s\n", "test time: 0.001s\n", "accuracy: 0.947\n", "dimensionality: 5\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 0 0 15477 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "Neural Network\n", "________________________________________________________________________________\n", "Training: \n", "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=True, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_fun=15000, max_iter=1000,\n", " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", " power_t=0.5, random_state=None, shuffle=True, solver='adam',\n", " tol=0.0001, validation_fraction=0.1, verbose=False,\n", " warm_start=False)\n", "train time: 1.418s\n", "test time: 0.045s\n", "accuracy: 0.947\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 0 0 15477 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "Passive-Aggressive\n", "________________________________________________________________________________\n", "Training: \n", "PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,\n", " early_stopping=False, fit_intercept=True,\n", " loss='hinge', max_iter=10000, n_iter_no_change=5,\n", " n_jobs=-1, random_state=None, shuffle=True,\n", " tol=0.001, validation_fraction=0.1, verbose=0,\n", " warm_start=False)\n", "train time: 0.122s\n", "test time: 0.001s\n", "accuracy: 0.947\n", "dimensionality: 5\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 0 0 15477 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "kNN\n", "________________________________________________________________________________\n", "Training: \n", "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n", " weights='uniform')\n", "train time: 0.079s\n", "test time: 0.825s\n", "accuracy: 0.944\n", "confusion matrix:\n", "[[ 35 0 731 0]\n", " [ 0 16 30 0]\n", " [ 72 15 15390 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "Random forest\n", "________________________________________________________________________________\n", "Training: \n", "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n", " criterion='gini', max_depth=1000, max_features='auto',\n", " max_leaf_nodes=None, max_samples=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100,\n", " n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)\n", "train time: 0.526s\n", "test time: 0.107s\n", "accuracy: 0.944\n", "confusion matrix:\n", "[[ 54 0 712 0]\n", " [ 0 20 26 0]\n", " [ 94 10 15370 3]\n", " [ 4 0 62 3]]\n", "\n", "================================================================================\n", "Liblinear with L2 penalty\n", "________________________________________________________________________________\n", "Training: \n", "LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=10000,\n", " multi_class='ovr', penalty='l2', random_state=None, tol=0.001,\n", " verbose=0)\n", "train time: 0.050s\n", "test time: 0.000s\n", "accuracy: 0.947\n", "dimensionality: 5\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 0 0 15477 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "Ordinary SGD with L2 penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=1000, n_iter_no_change=5, n_jobs=-1, penalty='l2',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.323s\n", "test time: 0.003s\n", "accuracy: 0.947\n", "dimensionality: 5\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 0 0 15477 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "Liblinear with L1 penalty\n", "________________________________________________________________________________\n", "Training: \n", "LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=10000,\n", " multi_class='ovr', penalty='l1', random_state=None, tol=0.001,\n", " verbose=0)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 10.301s\n", "test time: 0.003s\n", "accuracy: 0.946\n", "dimensionality: 5\n", "density: 0.600000\n", "\n", "confusion matrix:\n", "[[ 0 0 766 0]\n", " [ 0 0 46 0]\n", " [ 6 0 15471 0]\n", " [ 1 0 68 0]]\n", "\n", "================================================================================\n", "Ordinary SGD with L1 penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=1000, n_iter_no_change=5, n_jobs=-1, penalty='l1',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.525s\n", "test time: 0.002s\n", "accuracy: 0.947\n", "dimensionality: 5\n", "density: 0.900000\n", "\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 0 3 15474 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "SGD with Elastic Net penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=1000, n_iter_no_change=5, n_jobs=-1,\n", " penalty='elasticnet', power_t=0.5, random_state=None,\n", " shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n", " warm_start=False)\n", "train time: 0.625s\n", "test time: 0.003s\n", "accuracy: 0.947\n", "dimensionality: 5\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 0 0 15477 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "LinearSVC with L1-based feature selection\n", "________________________________________________________________________________\n", "Training: \n", "Pipeline(memory=None,\n", " steps=[('feature_selection',\n", " SelectFromModel(estimator=LinearSVC(C=1.0, class_weight=None,\n", " dual=False,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l1',\n", " random_state=None,\n", " tol=0.001, verbose=0),\n", " max_features=None, norm_order=1, prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0, class_weight=None, dual=True,\n", " fit_intercept=True, intercept_scaling=1,\n", " loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l2', random_state=None,\n", " tol=0.0001, verbose=0))],\n", " verbose=False)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 5.417s\n", "test time: 0.005s\n", "accuracy: 0.946\n", "confusion matrix:\n", "[[ 0 0 766 0]\n", " [ 0 0 46 0]\n", " [ 0 3 15474 0]\n", " [ 0 1 68 0]]\n", "\n", "================================================================================\n", "Voting classifier with all previous estimators\n", "________________________________________________________________________________\n", "Training: \n", "VotingClassifier(estimators=[('Ridge Classifier',\n", " RidgeClassifier(alpha=1.0, class_weight=None,\n", " copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False,\n", " random_state=None, solver='auto',\n", " tol=0.01)),\n", " ('Perceptron',\n", " Perceptron(alpha=0.0001, class_weight=None,\n", " early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=1000,\n", " n_iter_no_change=5, n_jobs=-1,\n", " penalt...\n", " verbose=0),\n", " max_features=None,\n", " norm_order=1,\n", " prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0,\n", " class_weight=None,\n", " dual=True,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l2',\n", " random_state=None,\n", " tol=0.0001,\n", " verbose=0))],\n", " verbose=False))],\n", " flatten_transform=True, n_jobs=None, voting='hard',\n", " weights=None)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 21.122s\n", "test time: 1.331s\n", "accuracy: 0.947\n", "confusion matrix:\n", "[[ 14 0 752 0]\n", " [ 0 0 46 0]\n", " [ 0 0 15477 0]\n", " [ 6 0 63 0]]\n", "\n", "================================================================================\n", "Stacking classifier with all previous estimators\n", "________________________________________________________________________________\n", "Training: \n", "StackingClassifier(cv=None,\n", " estimators=[('Ridge Classifier',\n", " RidgeClassifier(alpha=1.0, class_weight=None,\n", " copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False,\n", " random_state=None,\n", " solver='auto', tol=0.01)),\n", " ('Perceptron',\n", " Perceptron(alpha=0.0001, class_weight=None,\n", " early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=1000,\n", " n_iter_no_change=5, n_jobs...\n", " norm_order=1,\n", " prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0,\n", " class_weight=None,\n", " dual=True,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l2',\n", " random_state=None,\n", " tol=0.0001,\n", " verbose=0))],\n", " verbose=False))],\n", " final_estimator=None, n_jobs=None, passthrough=False,\n", " stack_method='auto', verbose=0)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 102.763s\n", "test time: 0.276s\n", "accuracy: 0.047\n", "confusion matrix:\n", "[[ 766 0 0 0]\n", " [ 46 0 0 0]\n", " [15477 0 0 0]\n", " [ 69 0 0 0]]\n", "\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "loans_raw['DISBURSE_TIME'] = pd.to_numeric(loans_raw['DISBURSE_TIME'])\n", "loans_raw['POSTED_TIME'] = pd.to_numeric(loans_raw['POSTED_TIME'])\n", "\n", "X_num = loans_raw[number_features]\n", "y = loans_raw[\"STATUS\"]\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X_num, y, train_size=0.01, test_size=0.01)\n", "\n", "num_pipe = make_pipeline(SimpleImputer(strategy='mean'))\n", "\n", "X_train = num_pipe.fit_transform(X_train)\n", "X_test = num_pipe.transform(X_test)\n", "\n", "number_results = rank_classifiers(X_train, X_test, y_train, y_test, 1000)\n", "plot_rankings(number_results)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Predict the Mode\n", "________________________________________________________________________________\n", "Score: 0.9466316175571585\n", "\n", "================================================================================\n", "Ridge Classifier\n", "________________________________________________________________________________\n", "Training: \n", "RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False, random_state=None,\n", " solver='auto', tol=0.01)\n", "train time: 2.500s\n", "test time: 0.069s\n", "accuracy: 0.945\n", "dimensionality: 87527\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 13 0 754 0]\n", " [ 1 0 35 0]\n", " [ 35 0 15450 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Perceptron\n", "________________________________________________________________________________\n", "Training: \n", "Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=1000, n_iter_no_change=5, n_jobs=-1,\n", " penalty=None, random_state=0, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.358s\n", "test time: 0.072s\n", "accuracy: 0.928\n", "dimensionality: 87527\n", "density: 0.192006\n", "\n", "confusion matrix:\n", "[[ 54 0 699 14]\n", " [ 3 0 32 1]\n", " [ 220 4 15126 135]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Neural Network\n", "________________________________________________________________________________\n", "Training: \n", "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=True, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_fun=15000, max_iter=1000,\n", " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", " power_t=0.5, random_state=None, shuffle=True, solver='adam',\n", " tol=0.0001, validation_fraction=0.1, verbose=False,\n", " warm_start=False)\n", "train time: 546.074s\n", "test time: 0.903s\n", "accuracy: 0.947\n", "confusion matrix:\n", "[[ 0 0 767 0]\n", " [ 0 0 36 0]\n", " [ 0 0 15485 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Passive-Aggressive\n", "________________________________________________________________________________\n", "Training: \n", "PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,\n", " early_stopping=False, fit_intercept=True,\n", " loss='hinge', max_iter=10000, n_iter_no_change=5,\n", " n_jobs=-1, random_state=None, shuffle=True,\n", " tol=0.001, validation_fraction=0.1, verbose=0,\n", " warm_start=False)\n", "train time: 0.482s\n", "test time: 0.070s\n", "accuracy: 0.936\n", "dimensionality: 87527\n", "density: 0.502382\n", "\n", "confusion matrix:\n", "[[ 59 0 708 0]\n", " [ 3 0 33 0]\n", " [ 238 1 15246 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "kNN\n", "________________________________________________________________________________\n", "Training: \n", "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n", " weights='uniform')\n", "train time: 0.171s\n", "test time: 13.992s\n", "accuracy: 0.921\n", "confusion matrix:\n", "[[ 1 0 746 20]\n", " [ 0 0 35 1]\n", " [ 6 0 15069 410]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Random forest\n", "________________________________________________________________________________\n", "Training: \n", "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n", " criterion='gini', max_depth=1000, max_features='auto',\n", " max_leaf_nodes=None, max_samples=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100,\n", " n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)\n", "train time: 3.194s\n", "test time: 0.473s\n", "accuracy: 0.946\n", "confusion matrix:\n", "[[ 3 0 764 0]\n", " [ 0 0 36 0]\n", " [ 7 0 15478 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Liblinear with L2 penalty\n", "________________________________________________________________________________\n", "Training: \n", "LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=10000,\n", " multi_class='ovr', penalty='l2', random_state=None, tol=0.001,\n", " verbose=0)\n", "train time: 2.864s\n", "test time: 0.057s\n", "accuracy: 0.942\n", "dimensionality: 87527\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 39 0 728 0]\n", " [ 2 0 34 0]\n", " [ 114 0 15371 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Ordinary SGD with L2 penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=1000, n_iter_no_change=5, n_jobs=-1, penalty='l2',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.459s\n", "test time: 0.069s\n", "accuracy: 0.946\n", "dimensionality: 87527\n", "density: 0.307094\n", "\n", "confusion matrix:\n", "[[ 11 0 756 0]\n", " [ 1 0 35 0]\n", " [ 21 0 15464 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Liblinear with L1 penalty\n", "________________________________________________________________________________\n", "Training: \n", "LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=10000,\n", " multi_class='ovr', penalty='l1', random_state=None, tol=0.001,\n", " verbose=0)\n", "train time: 5.252s\n", "test time: 0.057s\n", "accuracy: 0.942\n", "dimensionality: 87527\n", "density: 0.011739\n", "\n", "confusion matrix:\n", "[[ 19 0 748 0]\n", " [ 1 0 35 0]\n", " [ 92 7 15386 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Ordinary SGD with L1 penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=1000, n_iter_no_change=5, n_jobs=-1, penalty='l1',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.569s\n", "test time: 0.070s\n", "accuracy: 0.947\n", "dimensionality: 87527\n", "density: 0.000654\n", "\n", "confusion matrix:\n", "[[ 0 0 767 0]\n", " [ 0 0 36 0]\n", " [ 0 0 15485 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "SGD with Elastic Net penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=1000, n_iter_no_change=5, n_jobs=-1,\n", " penalty='elasticnet', power_t=0.5, random_state=None,\n", " shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n", " warm_start=False)\n", "train time: 0.675s\n", "test time: 0.068s\n", "accuracy: 0.947\n", "dimensionality: 87527\n", "density: 0.027354\n", "\n", "confusion matrix:\n", "[[ 0 0 767 0]\n", " [ 0 0 36 0]\n", " [ 0 0 15485 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "LinearSVC with L1-based feature selection\n", "________________________________________________________________________________\n", "Training: \n", "Pipeline(memory=None,\n", " steps=[('feature_selection',\n", " SelectFromModel(estimator=LinearSVC(C=1.0, class_weight=None,\n", " dual=False,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l1',\n", " random_state=None,\n", " tol=0.001, verbose=0),\n", " max_features=None, norm_order=1, prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0, class_weight=None, dual=True,\n", " fit_intercept=True, intercept_scaling=1,\n", " loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l2', random_state=None,\n", " tol=0.0001, verbose=0))],\n", " verbose=False)\n", "train time: 5.187s\n", "test time: 0.056s\n", "accuracy: 0.943\n", "confusion matrix:\n", "[[ 30 0 737 0]\n", " [ 1 0 35 0]\n", " [ 97 0 15388 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Voting classifier with all previous estimators\n", "________________________________________________________________________________\n", "Training: \n", "VotingClassifier(estimators=[('Ridge Classifier',\n", " RidgeClassifier(alpha=1.0, class_weight=None,\n", " copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False,\n", " random_state=None, solver='auto',\n", " tol=0.01)),\n", " ('Perceptron',\n", " Perceptron(alpha=0.0001, class_weight=None,\n", " early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=1000,\n", " n_iter_no_change=5, n_jobs=-1,\n", " penalt...\n", " verbose=0),\n", " max_features=None,\n", " norm_order=1,\n", " prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0,\n", " class_weight=None,\n", " dual=True,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l2',\n", " random_state=None,\n", " tol=0.0001,\n", " verbose=0))],\n", " verbose=False))],\n", " flatten_transform=True, n_jobs=None, voting='hard',\n", " weights=None)\n", "train time: 571.932s\n", "test time: 16.037s\n", "accuracy: 0.946\n", "confusion matrix:\n", "[[ 11 0 756 0]\n", " [ 1 0 35 0]\n", " [ 24 0 15461 0]\n", " [ 0 0 70 0]]\n", "\n", "================================================================================\n", "Stacking classifier with all previous estimators\n", "________________________________________________________________________________\n", "Training: \n", "StackingClassifier(cv=None,\n", " estimators=[('Ridge Classifier',\n", " RidgeClassifier(alpha=1.0, class_weight=None,\n", " copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False,\n", " random_state=None,\n", " solver='auto', tol=0.01)),\n", " ('Perceptron',\n", " Perceptron(alpha=0.0001, class_weight=None,\n", " early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=1000,\n", " n_iter_no_change=5, n_jobs...\n", " norm_order=1,\n", " prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0,\n", " class_weight=None,\n", " dual=True,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l2',\n", " random_state=None,\n", " tol=0.0001,\n", " verbose=0))],\n", " verbose=False))],\n", " final_estimator=None, n_jobs=None, passthrough=False,\n", " stack_method='auto', verbose=0)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 3007.199s\n", "test time: 13.469s\n", "accuracy: 0.946\n", "confusion matrix:\n", "[[ 8 0 759 0]\n", " [ 0 0 36 0]\n", " [ 18 0 15467 0]\n", " [ 0 0 70 0]]\n", "\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.feature_extraction.text import TfidfTransformer\n", "from sklearn.decomposition import TruncatedSVD\n", "from scipy.sparse import coo_matrix, hstack\n", "\n", "description_pipe = make_pipeline(\n", " TfidfVectorizer(),\n", " TfidfTransformer()\n", ")\n", "\n", "X_desc = loans_raw[description_features].fillna(\"\")\n", "y = loans_raw[\"STATUS\"]\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X_desc, y, train_size=0.01, test_size=0.01)\n", "\n", "# was too annoying to setup the initial matrix to do it in a loop\n", "def get_description_features(X_train, X_test, description_pipe):\n", " sparse_X_train = description_pipe.fit_transform(X_train[\"LOAN_USE\"])\n", " sparse_X_test = description_pipe.transform(X_test[\"LOAN_USE\"])\n", "\n", " sparse_X_train = hstack( [sparse_X_train, description_pipe.fit_transform(X_train[\"DESCRIPTION\"])] )\n", " sparse_X_test = hstack( [sparse_X_test, description_pipe.transform(X_test[\"DESCRIPTION\"])] )\n", "\n", " sparse_X_train = hstack( [sparse_X_train, description_pipe.fit_transform(X_train[\"DESCRIPTION_TRANSLATED\"])] )\n", " sparse_X_test = hstack( [sparse_X_test, description_pipe.transform(X_test[\"DESCRIPTION_TRANSLATED\"])] )\n", " \n", " return (sparse_X_train, sparse_X_test)\n", "\n", "sparse_X_train, sparse_X_test = get_description_features(X_train, X_test, description_pipe)\n", "\n", "description_results = rank_classifiers(sparse_X_train, sparse_X_test, y_train, y_test, 1000)\n", "plot_rankings(description_results)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": true }, "outputs": [], "source": [ "from sklearn.compose import make_column_transformer\n", "\n", "feature_transformer = make_column_transformer(\n", " (cat_pipe, categorical_features),\n", " (num_pipe, number_features),\n", " remainder=\"drop\"\n", ")\n", "\n", "X = pd.concat([X_num, X_cat, X_desc], axis=1)\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.005, test_size=0.01)\n", "\n", "sparse_X_train, sparse_X_test = get_description_features(X_train, X_test, description_pipe)\n", "\n", "X_train = feature_transformer.fit_transform(X_train)\n", "X_test = feature_transformer.transform(X_test)\n", "\n", "sparse_X_train = hstack([sparse_X_train, X_train])\n", "sparse_X_test = hstack([sparse_X_test, X_test])" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Predict the Mode\n", "________________________________________________________________________________\n", "Score: 0.9463259567184252\n", "\n", "================================================================================\n", "Ridge Classifier\n", "________________________________________________________________________________\n", "Training: \n", "RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False, random_state=None,\n", " solver='auto', tol=0.01)\n", "train time: 3363.492s\n", "test time: 0.067s\n", "accuracy: 0.047\n", "dimensionality: 246749\n", "density: 1.000000\n", "\n", "confusion matrix:\n", "[[ 763 0 0 0]\n", " [ 39 0 0 0]\n", " [15480 0 0 0]\n", " [ 76 0 0 0]]\n", "\n", "================================================================================\n", "Perceptron\n", "________________________________________________________________________________\n", "Training: \n", "Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=100, n_iter_no_change=5, n_jobs=-1,\n", " penalty=None, random_state=0, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 0.288s\n", "test time: 0.081s\n", "accuracy: 0.947\n", "dimensionality: 246749\n", "density: 0.111970\n", "\n", "confusion matrix:\n", "[[ 9 0 754 0]\n", " [ 0 0 39 0]\n", " [ 2 0 15478 0]\n", " [ 2 0 74 0]]\n", "\n", "================================================================================\n", "Neural Network\n", "________________________________________________________________________________\n", "Training: \n", "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=True, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_fun=15000, max_iter=100,\n", " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", " power_t=0.5, random_state=None, shuffle=True, solver='adam',\n", " tol=0.0001, validation_fraction=0.1, verbose=False,\n", " warm_start=False)\n", "train time: 881.287s\n", "test time: 1.271s\n", "accuracy: 0.946\n", "confusion matrix:\n", "[[ 0 0 754 9]\n", " [ 0 0 39 0]\n", " [ 0 0 15478 2]\n", " [ 0 0 74 2]]\n", "\n", "================================================================================\n", "Passive-Aggressive\n", "________________________________________________________________________________\n", "Training: \n", "PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,\n", " early_stopping=False, fit_intercept=True,\n", " loss='hinge', max_iter=1000, n_iter_no_change=5,\n", " n_jobs=-1, random_state=None, shuffle=True,\n", " tol=0.001, validation_fraction=0.1, verbose=0,\n", " warm_start=False)\n", "train time: 0.199s\n", "test time: 0.085s\n", "accuracy: 0.947\n", "dimensionality: 246749\n", "density: 0.141683\n", "\n", "confusion matrix:\n", "[[ 9 0 754 0]\n", " [ 0 0 39 0]\n", " [ 2 0 15478 0]\n", " [ 2 0 74 0]]\n", "\n", "================================================================================\n", "kNN\n", "________________________________________________________________________________\n", "Training: \n", "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n", " weights='uniform')\n", "train time: 0.103s\n", "test time: 8.126s\n", "accuracy: 0.944\n", "confusion matrix:\n", "[[ 19 0 741 3]\n", " [ 0 3 36 0]\n", " [ 45 9 15426 0]\n", " [ 0 0 74 2]]\n", "\n", "================================================================================\n", "Random forest\n", "________________________________________________________________________________\n", "Training: \n", "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n", " criterion='gini', max_depth=100, max_features='auto',\n", " max_leaf_nodes=None, max_samples=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100,\n", " n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)\n", "train time: 2.957s\n", "test time: 0.494s\n", "accuracy: 0.946\n", "confusion matrix:\n", "[[ 0 0 763 0]\n", " [ 0 0 39 0]\n", " [ 3 0 15477 0]\n", " [ 0 0 76 0]]\n", "\n", "================================================================================\n", "Liblinear with L2 penalty\n", "________________________________________________________________________________\n", "Training: \n", "LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l2', random_state=None, tol=0.001,\n", " verbose=0)\n", "train time: 0.523s\n", "test time: 0.068s\n", "accuracy: 0.947\n", "dimensionality: 246749\n", "density: 0.268694\n", "\n", "confusion matrix:\n", "[[ 9 0 754 0]\n", " [ 0 0 39 0]\n", " [ 2 0 15478 0]\n", " [ 2 0 74 0]]\n", "\n", "================================================================================\n", "Ordinary SGD with L2 penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=100, n_iter_no_change=5, n_jobs=-1, penalty='l2',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 0.791s\n", "test time: 0.082s\n", "accuracy: 0.947\n", "dimensionality: 246749\n", "density: 0.187064\n", "\n", "confusion matrix:\n", "[[ 9 0 754 0]\n", " [ 0 0 39 0]\n", " [ 2 0 15478 0]\n", " [ 2 0 74 0]]\n", "\n", "================================================================================\n", "Liblinear with L1 penalty\n", "________________________________________________________________________________\n", "Training: \n", "LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l1', random_state=None, tol=0.001,\n", " verbose=0)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 114.357s\n", "test time: 0.058s\n", "accuracy: 0.941\n", "dimensionality: 246749\n", "density: 0.002444\n", "\n", "confusion matrix:\n", "[[ 44 0 719 0]\n", " [ 1 0 38 0]\n", " [ 142 4 15332 2]\n", " [ 0 0 53 23]]\n", "\n", "================================================================================\n", "Ordinary SGD with L1 penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=100, n_iter_no_change=5, n_jobs=-1, penalty='l1',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 1.808s\n", "test time: 0.072s\n", "accuracy: 0.946\n", "dimensionality: 246749\n", "density: 0.002931\n", "\n", "confusion matrix:\n", "[[ 9 0 754 0]\n", " [ 0 0 39 0]\n", " [ 2 6 15470 2]\n", " [ 2 1 73 0]]\n", "\n", "================================================================================\n", "SGD with Elastic Net penalty\n", "________________________________________________________________________________\n", "Training: \n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=100, n_iter_no_change=5, n_jobs=-1, penalty='elasticnet',\n", " power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)\n", "train time: 2.404s\n", "test time: 0.065s\n", "accuracy: 0.947\n", "dimensionality: 246749\n", "density: 0.020402\n", "\n", "confusion matrix:\n", "[[ 9 0 754 0]\n", " [ 0 0 39 0]\n", " [ 2 0 15478 0]\n", " [ 2 0 74 0]]\n", "\n", "================================================================================\n", "LinearSVC with L1-based feature selection\n", "________________________________________________________________________________\n", "Training: \n", "Pipeline(memory=None,\n", " steps=[('feature_selection',\n", " SelectFromModel(estimator=LinearSVC(C=1.0, class_weight=None,\n", " dual=False,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l1',\n", " random_state=None,\n", " tol=0.001, verbose=0),\n", " max_features=None, norm_order=1, prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0, class_weight=None, dual=True,\n", " fit_intercept=True, intercept_scaling=1,\n", " loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l2', random_state=None,\n", " tol=0.0001, verbose=0))],\n", " verbose=False)\n", "train time: 117.112s\n", "test time: 0.060s\n", "accuracy: 0.946\n", "confusion matrix:\n", "[[ 0 0 763 0]\n", " [ 0 0 39 0]\n", " [ 0 0 15480 0]\n", " [ 0 0 76 0]]\n", "\n", "================================================================================\n", "Voting classifier with all previous estimators\n", "________________________________________________________________________________\n", "Training: \n", "VotingClassifier(estimators=[('Ridge Classifier',\n", " RidgeClassifier(alpha=1.0, class_weight=None,\n", " copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False,\n", " random_state=None, solver='auto',\n", " tol=0.01)),\n", " ('Perceptron',\n", " Perceptron(alpha=0.0001, class_weight=None,\n", " early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=100,\n", " n_iter_no_change=5, n_jobs=-1,\n", " penalty...\n", " verbose=0),\n", " max_features=None,\n", " norm_order=1,\n", " prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0,\n", " class_weight=None,\n", " dual=True,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l2',\n", " random_state=None,\n", " tol=0.0001,\n", " verbose=0))],\n", " verbose=False))],\n", " flatten_transform=True, n_jobs=None, voting='hard',\n", " weights=None)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train time: 4395.905s\n", "test time: 9.928s\n", "accuracy: 0.947\n", "confusion matrix:\n", "[[ 9 0 754 0]\n", " [ 0 0 39 0]\n", " [ 2 0 15478 0]\n", " [ 2 0 74 0]]\n", "\n", "================================================================================\n", "Stacking classifier with all previous estimators\n", "________________________________________________________________________________\n", "Training: \n", "StackingClassifier(cv=None,\n", " estimators=[('Ridge Classifier',\n", " RidgeClassifier(alpha=1.0, class_weight=None,\n", " copy_X=True, fit_intercept=True,\n", " max_iter=None, normalize=False,\n", " random_state=None,\n", " solver='auto', tol=0.01)),\n", " ('Perceptron',\n", " Perceptron(alpha=0.0001, class_weight=None,\n", " early_stopping=False, eta0=1.0,\n", " fit_intercept=True, max_iter=100,\n", " n_iter_no_change=5, n_jobs=...\n", " norm_order=1,\n", " prefit=False,\n", " threshold=None)),\n", " ('classification',\n", " LinearSVC(C=1.0,\n", " class_weight=None,\n", " dual=True,\n", " fit_intercept=True,\n", " intercept_scaling=1,\n", " loss='squared_hinge',\n", " max_iter=1000,\n", " multi_class='ovr',\n", " penalty='l2',\n", " random_state=None,\n", " tol=0.0001,\n", " verbose=0))],\n", " verbose=False))],\n", " final_estimator=None, n_jobs=None, passthrough=False,\n", " stack_method='auto', verbose=0)\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.seterr('ignore')\n", "description_results = rank_classifiers(sparse_X_train, sparse_X_test, y_train, y_test, 100)\n", "plot_rankings(description_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, although we achieved slight improvements when using all features, we were not able to produce significantly better performing predictions than simply always guessing that the loans will be fully funded. While it might be possible with better tuned models, using more of the dataset, better feature selection, and running models for more iterations, there was no evidence found for this throughout the course of development.\n", "\n", "Ultimately, it just may not be possible to predict given the information available when the loans are first posted, and has more to do with which loans get presented to users by Kiva." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Kiva has grown significantly over the past 15 years. Despite the majority of loans still focusing on poorer countries, there are many options for the average user living in the US to help domestically if they so choose. Relative to ordinary loans, the default rates here are extremely good, implying that the loans tend to be successful. As shown in the section on the expected costs of loaning, if one has any amount of disposable income, wishes to help others, and believes in the power of investing in small businesses, then these microloans can be a worthwhile charitable option to consider without needing to give up much in the long run." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }