25.2a_Take_Home_Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1320a6f6-e7c8-49dd-959e-7807b202b33e",
   "metadata": {},
   "source": [
    "# Ultimate Technologies Inc. Take-Home Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6be6b86-ce32-430a-8d07-cbacf0919a72",
   "metadata": {},
   "source": [
    "## Part 1\n",
    "\n",
    "The attached logins.json file contains (simulated) timestamps of user logins in a particular\n",
    "geographic location. Aggregate these login counts based on 15-minute time intervals, and\n",
    "visualize and describe the resulting time series of login counts in ways that best characterize the\n",
    "underlying patterns of the demand. Please report/illustrate important features of the demand,\n",
    "such as daily cycles. If there are data quality issues, please report them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d369f296-0549-40c7-8d4d-a4faee264ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9deb4108-5db4-4fef-9f7a-7b0ebfb44067",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data\n",
    "df = pd.read_json('logins.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0706fd6c-5e8a-46f6-bc2a-449a06e8cbf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>login_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970-01-01 20:13:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-01-01 20:16:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-01-01 20:16:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-01-01 20:16:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-01-01 20:26:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           login_time\n",
       "0 1970-01-01 20:13:18\n",
       "1 1970-01-01 20:16:10\n",
       "2 1970-01-01 20:16:37\n",
       "3 1970-01-01 20:16:36\n",
       "4 1970-01-01 20:26:21"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "c967dfae-6329-4e6d-83c1-1ba655eae833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>login_time</th>\n",
       "      <th>num_logins</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day_name</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20:15:00</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20:30:00</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20:45:00</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  login_time  num_logins  month  day  day_name  day_of_week\n",
       "0   20:00:00           2      1    1  Thursday            3\n",
       "1   20:15:00           6      1    1  Thursday            3\n",
       "2   20:30:00           9      1    1  Thursday            3\n",
       "3   20:45:00           7      1    1  Thursday            3\n",
       "4   21:00:00           1      1    1  Thursday            3"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate based on 15-minute time intervals\n",
    "df_15min = pd.DataFrame(df.resample('15min', on='login_time').login_time.count()).rename(columns={'login_time':'num_logins'}).reset_index()\n",
    "# Add columns for month, day of month, day name, and day of week\n",
    "df_15min['month'] = df_15min['login_time'].dt.month\n",
    "df_15min['day'] = df_15min['login_time'].dt.day\n",
    "df_15min['day_name'] = df_15min['login_time'].dt.day_name()\n",
    "df_15min['day_of_week'] = df_15min['login_time'].dt.dayofweek\n",
    "# Make login time label only show time, not date\n",
    "df_15min['login_time'] = df_15min['login_time'].dt.time\n",
    "df_15min.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "6e906942-5cbb-4cca-bbb4-1e9688df0a5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1970-01-01 20:12:16\n",
      "1970-04-13 18:57:38\n"
     ]
    }
   ],
   "source": [
    "# Earliest and latest login recorded\n",
    "print(min(df.login_time))\n",
    "print(max(df.login_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f10884-5f2b-4ea6-9689-2882bfb218d7",
   "metadata": {},
   "source": [
    "### Average Login Counts Per Time of Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "8be8c8dc-2abb-4e55-bed5-5942dfa24dba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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YPE+41rZtW8TExKBly5aIiIjAiBEj4ODggM8//xx//vknVq1apfsQHjNmDDZs2IDmzZtjzJgxcHJywoIFC3SHlRb2EM+CHDt2DIMHD0bXrl1RqVIlODg4IDY2FseOHcvzS8xQPXr0wNdff422bdvinXfewfPPPw97e3tcvnwZ27dvR8eOHdG5c2c0atQInp6eGDhwICZMmAB7e3t8/fXXOHr0aIH1q1atit9++w0tWrTAiy++iF9//RUBAQF5Luvu7o4XX3wRM2fOROnSpREcHIy4uDgsWrQIpUqV0lu2Ro0aAICFCxfCzc0Njo6OCAkJyXMzCAB8/PHHGD9+PNq0aYOoqKhch7/nHK5u7PM0Vs6moMKe/O9R7u7uGDNmDIYNG5Zr3rBhw7Bs2TJERUVh8uTJCAoKwqZNm/D555/jzTff1DXkvXv3xmeffYY+ffrgwoULqFmzJn7//XdMnToVbdu2RYsWLYx6XhqNBk2bNtXb5+px5nite/fujTlz5qB3796YMmUKKlWqhM2bN+Pnn38udI24uDjdaQmysrJw8eJFfP/99wAe/oApU6YMAGDEiBFYvny57jXWarWYNm0a7t+/j4kTJ+aqa2tri5YtW2L48OHIzs7G9OnTkZqaikmTJhn9fEkBFt39mEqsnKOZ8rvlHIH022+/SbNmzcTFxUWcnJykQYMGsmHDhlz1fvvtN6lfv75otVrx9fWV9957T3ckyK1bt3TL5Xc008yZM3PVBCATJkwQEZFr165J3759JTQ0VFxcXMTV1VVq1aolc+bM0TsKKi85R0Y8erRVXjIyMmTWrFkSFhYmjo6O4urqKqGhoTJgwAA5ffq0brndu3dLw4YNxdnZWcqUKSOvv/66HDp0KNdRIo8eeZPj8uXLEhoaKsHBwXL27Nl8s1y+fFn+85//iKenp7i5uUmbNm3kzz//lKCgoFxHlcydO1dCQkLE1tb2iUeqNG3atMB/90cV5XnmrKt69eq5pgcFBUlUVJTetNKlS0uDBg3yzf2kdaWnp0tISEiuo5lERC5evCjR0dHi7e0t9vb2UqVKFZk5c6ZkZWXpLZecnCwDBw4UPz8/sbOzk6CgIBk9erTcv39fb7m81pHzvB79t7l9+7YAkB49ejzxeRX1tc75G39Uzt+Qq6uruLm5yX/+8x/ZvXt3oY9mKuhv5fGj586cOSOdOnUSd3d3cXZ2lubNm8vBgwf1lsl5r0+fPl0mTZokAQEB4uDgIHXq1JGff/45z+fz+HvW0L81sh4aEYXPtU5PtVatWuHChQs4deqUpaOQFTtx4gSqV6+OjRs3GnyOHGu2efNmtGvXDkePHkXNmjUtHcfiLly4gJCQEMycORMjRoywdBwqZtzMREoYPnw46tSpg8DAQNy4cQNff/01tm7dqtthlyg/27dvR8OGDUtUIwM8fF49evRgI0MENjOkiKysLIwfPx6JiYnQaDSoVq0ali9fjp49e1o6Glm5QYMGYdCgQZaOYXIzZ860dAQiq8HNTERERKQ0HppNRERESmMzQ1SAnCvx5tzs7e3h7e2NevXqYdiwYfjrr79yPWbHjh3QaDQFHi5rjLFjx6J8+fKws7PLdQi1tcm5KvHjt8fPrJqf+Ph4dO7cGRUqVICLiws8PDxQp04dzJs3T3eV5EedO3cOXbp0QalSpeDq6oqWLVua5PxApnTixAlMnDjRqMPDn2Ts2LFo164dypUrB41Go7tMQl5UeK2IDMVmhqgQhgwZgj179iAuLg7Lly9Hp06dsH79eoSFheXad+HZZ5/Fnj178Oyzz5ps/T/++COmTJmC3r17Iy4uDr/++qvJapvTli1bdFfb3rNnD3bu3Fmox925cwfu7u4YN24c1q9fj2+++QZNmjTBkCFDMHDgQL1lr1+/jhdeeAGnTp3C4sWL8e233+L+/fsIDw/XXd/LGpw4cQKTJk0ySzMzZ84cJCcno0OHDvleMgNQ57UiMphljwwnsm4Fnafm7t270qZNGwEgmzdvNmuOjz76SADItWvXnrjs3bt3zZqlMAp77h1DdevWTezs7PTOzfLee++Jvb29XLhwQTctJSVFSpcuLd26dTPp+ovCFFcgz8+j57TJ7wrUIuq8VkSG4sgMkZGcnJywaNEi2Nvb643O5LWZqW/fvnB1dcVff/2F5s2bw8XFBWXKlMHgwYNx9+7dAtcTHByMsWPHAnh4cU2NRqM782lwcDDatWuHNWvWoE6dOnB0dNSd6fTPP/9Ex44d4enpCUdHR9SuXTvXxRJzsq5cuRIjR46En58fXF1d0b59e1y7dg23b9/GG2+8gdKlS6N06dJ49dVXkZaWZoJXzzhlypSBjY2N3mnp165di2bNmiEoKEg3zd3dHV26dMGGDRv0NktNmjQJ9evXh5eXF9zd3fHss89i0aJFkMeOg8h5XTdu3Ig6derAyckJVatWxcaNGwEAS5cuRdWqVeHi4oLnn38eBw4cKDD30qVL0bVrVwAPLwWQs9lt6dKlumUWL16MsLAwODo6wsvLC507dy70FcwLexZsFV4rIqNYupsismYFjczkaNCggWi1WsnIyBARke3bt+f6Bd6nTx9xcHCQ8uXLy5QpU+SXX36RiRMnip2dnbRr167ADIcOHZLXXntNAMiWLVtkz549Eh8fLyIPzwrr5+cnFSpUkMWLF8v27dtl3759cvLkSXFzc5NnnnlGli1bJps2bZKXX35Zd4bUHDlZg4KCpG/fvrJlyxZZsGCBuLq6SkREhLRs2VJGjBghv/zyi0yfPl1sbW1lyJAhT3zdckZmfH19xcbGRsqWLSu9evWSixcv5lq2T58+emeFflR2drZkZGTIjRs35JtvvhEXFxcZPXq0bv7du3dFo9HIe++9l+ux8+bNEwDyzz//6Kb17dtXFi1aJFu3bpWtW7fKhx9+KE5OTjJp0iS9xwYFBUlAQIDUqFFDVq1aJZs3b5b69euLvb29jB8/Xho3bixr1qyRtWvXSuXKlcXHx6fAEbGkpCSZOnWqAJDPPvtM9uzZI3v27JGkpCQREd28l19+WTZt2iTLli2TChUqiIeHh5w6deqJr/ej8huZUeW1IjIGmxmiAhSmmenevbveJqD8mhkA8sknn+g9dsqUKQJAfv/99wJz5LfZJigoSGxtbfW+hEREevToIVqtVi5duqQ3PTIyUpydnXWXgMjJ2r59e73lhg4dKgDk7bff1pveqVMn8fLyKjCriMiyZctkypQpsnnzZomNjZVp06aJl5eX+Pj4yOXLl/WW7devn9ja2upt+sgRExOjO8W9RqORMWPG6M2/cuWKAJCYmJhcj125cqUAkN27d+eZMSsrSzIyMmTy5Mni7e0t2dnZunlBQUHi5OSkl/XIkSMCQPz8/OTOnTu66evWrRMAsn79+gJfk/w2M928eVOcnJykbdu2etMvXbokWq1WoqOjC6z7uPyaGZVeKyJDcTMTURGJAadqeuWVV/TuR0dHA3h4Nldj1apVS3dBwxyxsbFo3rw5AgMD9ab37dsXd+/exZ49e/Smt2vXTu9+1apVASDXWXOrVq2KGzduPHFTU69evfDBBx8gMjISERERGDlyJH766Sdcv34dM2bM0Ft20aJFyMzM1Nv08Wje/fv34+eff8b777+PmTNnYsiQIbmWy7koaV4enRcbG4sWLVrAw8MDtra2sLe3x/jx45GcnJzrauO1a9dGuXLl9J47AISHh8PZ2TnX9IsXLxb0kuRrz549uHfvXq4jkAIDA9GsWTNs27bNqLr5Ufm1IsoPmxmiIrp48SK0Wi28vLwKXM7Ozi7X1aZ9fX0BAMnJyUav38/PL9e05OTkPKf7+/vnub7Hs+ccEZPf9Pv37xuc8/nnn0flypVzXUm7IL6+vnjuuefQqlUrTJs2DZMnT8a8efNw+PBhAICnpyc0Gk2er9+NGzf0nsO+ffvQqlUrAMCXX36JXbt2Yf/+/RgzZgwA4N69e3qPL47XBPi/f4v8/r2K8rfxqJLwWhHlh5czICqCK1eu4ODBg2jatCns7Ap+O2VmZiI5OVmvoUlMTASAXE2OIfL6pe3t7Y2rV6/mmp6QkAAAKF26tNHrKwoRKfTOqnl5/vnnAQCnTp3S7WxasWJFHD9+PNeyx48fh5OTEypUqAAA+Oabb2Bvb4+NGzfqne9m3bp1RucxhZx/+/z+vUz1b1USXiui/HBkhshI9+7dw+uvv47MzEy8//77hXrM119/rXd/5cqVAB4Ox5tS8+bNERsbq2tecixbtgzOzs5o0KCBSddXGHv37sXp06eLtO6czXEVK1bUTevcuTNiY2MRHx+vm3b79m2sWbMGHTp00DWZGo0GdnZ2ekdC3bt3D8uXLzc6jyG0Wq1unY9q2LAhnJycsGLFCr3ply9f1m0uNBVVXisiQ3FkhqgQLl26hL179yI7OxspKSk4fPgwFi9ejIsXL+Ljjz/WDckXxMHBAR9//DHS0tJQr1497N69Gx999BEiIyPRpEkTk+adMGECNm7ciIiICIwfPx5eXl74+uuvsWnTJsyYMQMeHh4mXd/jwsLC0LNnT1StWhWOjo7Yt28fZs6cCV9f31yN32uvvYavvvoKZ8+e1e03M2HCBFy7dg0vvvgiypUrh1u3bmHLli348ssv0bVrV9StW1f3+BEjRmD58uWIiorC5MmTodVqMW3aNNy/f193CDvwcP+f2bNnIzo6Gm+88QaSk5Mxa9YsXZNhbjVq1AAALFy4EG5ubnB0dERISAi8vb0xbtw4fPDBB+jduzdefvllJCcnY9KkSXB0dMSECROeWDsuLg7Xr18H8PCirBcvXsT3338PAGjatCnKlCkDQJ3Xishglt4Dmcia5RzNlHOztbUVT09PqVu3rgwdOlT++uuvXI/J72gmFxcXOXbsmISHh4uTk5N4eXnJm2++KWlpaU/MUdDRTFFRUXk+5vjx49K+fXvx8PAQBwcHCQsLkyVLluSZ9bvvvtObvmTJEgEg+/fvL1SOx/Xo0UMqVqwoLi4uYm9vL0FBQTJw4EBJSEjItWxeh2avX79eWrRoIT4+PmJnZyeurq7y/PPPy3//+1/dIfCPOnPmjHTq1Enc3d3F2dlZmjdvLgcPHsy13OLFi6VKlSqi1WqlQoUKEhMTI4sWLcq1/vxeVwAyaNAgvWmFOeItx9y5cyUkJERsbW0FgN6/x//+9z+pVauWODg4iIeHh3Ts2DHPv6+8NG3aVO/v9NHb40dPqfJaERmCV80mKgZ9+/bF999/b9ETzhERlVTcZ4aIiIiUxmaGiIiIlMbNTERERKQ0jswQERGR0tjMEBERkdLYzBAREZHSSvxJ87Kzs5GQkAA3N7cCL7BGRERE1kNEcPv2bfj7+z/xMiglvplJSEjIdeVgIiIiUkN8fDwCAgIKXKbENzNubm4AHr4Y7u7uFk5DREREhZGamorAwEDd93hBSnwzk7Npyd3dnc0MERGRYgqziwh3ACYiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqXZWToAERERWbe67y3Tu39wZm8LJckbR2aIiIhIaRyZKQbW3tESERGpjCMzREREpDQ2M0RERKQ0NjNERESkNDYzREREpDSLNjM7d+5E+/bt4e/vD41Gg3Xr1uVa5u+//0aHDh3g4eEBNzc3NGjQAJcuXSr+sERERE+Buu8t07upwKLNzJ07dxAWFoZ58+blOf/s2bNo0qQJQkNDsWPHDhw9ehTjxo2Do6NjMSclIiIia2XRQ7MjIyMRGRmZ7/wxY8agbdu2mDFjhm5ahQoViiMaERERKcJqzzOTnZ2NTZs24f3330fr1q1x+PBhhISEYPTo0ejUqVO+j0tPT0d6errufmpqajGkJSIierpY0znUrHYH4KSkJKSlpWHatGlo06YNfvnlF3Tu3BldunRBXFxcvo+LiYmBh4eH7hYYGFiMqYmIiNSg4r4x+bHaZiY7OxsA0LFjRwwbNgy1a9fGqFGj0K5dOyxYsCDfx40ePRopKSm6W3x8fHFFJiIiIguw2s1MpUuXhp2dHapVq6Y3vWrVqvj999/zfZxWq4VWqzV3PCIyM2sawiYi62a1IzMODg6oV68e/vnnH73pp06dQlBQkIVSERERkbWx6MhMWloazpw5o7t//vx5HDlyBF5eXihfvjzee+89dO/eHS+++CIiIiKwZcsWbNiwATt27LBcaCIiIrIqFm1mDhw4gIiICN394cOHAwD69OmDpUuXonPnzliwYAFiYmLw9ttvo0qVKvjhhx/QpEkTS0UmIiIiK2PRZiY8PBwiUuAy/fr1Q79+/YopEREREanGancAVhV3WiQiIipeVrsDMBEREVFhsJkhIiIipbGZISIiIqWxmSEiIiKlcQdgIiIiMglLHQTDZoaIlMIjBonocdzMREREREpjM0NERERK42YmIiKiEq6kb55lM2Okkv6HQUREZArF8X35VDczbEiIiIjUx31miIiISGlsZoiIiEhpbGaIiIhIaU/1PjN5sbb9aKwtDxERkbXhyAwREREpjc0MERERKY3NDBERESmN+8xYEe4fQ0REZDiOzBAREZHSODJjIRyFISIiMg02M0RERCXI0/hjmZuZiIiISGkcmSmEp7HLJSIiUgWbGSIiIrIKjw4eZKXfK/TjuJmJiIiIlMZmhoiIiJTGZoaIiIiUxmaGiIiIlMYdgImIiKhYmfooYYuOzOzcuRPt27eHv78/NBoN1q1bl++yAwYMgEajwdy5c4stHxEREVk/izYzd+7cQVhYGObNm1fgcuvWrcMff/wBf3//YkpGREREqrDoZqbIyEhERkYWuMyVK1cwePBg/Pzzz4iKijJ6XS+OXQVbrZPuPk98R0REVDJY9Q7A2dnZ6NWrF9577z1Ur17d0nGIiIjICln1DsDTp0+HnZ0d3n777UI/Jj09Henp6br7qamp5ohmUby8AhER0f+x2pGZgwcP4pNPPsHSpUuh0WgK/biYmBh4eHjoboGBgWZMSURERJZmtc3Mb7/9hqSkJJQvXx52dnaws7PDxYsX8e677yI4ODjfx40ePRopKSm6W3x8fPGFJiIiomJntZuZevXqhRYtWuhNa926NXr16oVXX30138dptVpotVpzxyMiIiIrYdFmJi0tDWfOnNHdP3/+PI4cOQIvLy+UL18e3t7eesvb29vD19cXVapUKe6oREREZKUs2swcOHAAERERuvvDhw8HAPTp0wdLly61UCoiIiJSiUWbmfDwcIhIoZe/cOGC+cJQseCRWJQX/l0QUVFY7Q7ARERERIXBZoaIiIiUxmaGiIiIlMZmhoiIiJTGZoaIiIiUxmaGiIiIlMZmhoiIiJTGZoaIiIiUxmaGiIiIlMZmhoiIiJTGZoaIiIiUxmaGiIiIlMZmhoiIiJRm0atmk2lZ25WHrS0PEf8miUomjswQERGR0jgyQ0REpCiONj7EkRkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGncAfgpxhzEqafg3TfR048gMERERKY3NDBERESmNzQwREREpjc0MERERKY3NDBERESmNRzOVcDzKg55W/NsnenpwZIaIiIiUxmaGiIiIlMZmhoiIiJTGfWaIiIgUwP3A8seRGSIiIlKaRZuZnTt3on379vD394dGo8G6det08zIyMjBy5EjUrFkTLi4u8Pf3R+/evZGQkGC5wERERGR1LNrM3LlzB2FhYZg3b16ueXfv3sWhQ4cwbtw4HDp0CGvWrMGpU6fQoUMHCyQlIiIia2XRfWYiIyMRGRmZ5zwPDw9s3bpVb9qnn36K559/HpcuXUL58uWLIyIRERFZOaV2AE5JSYFGo0GpUqXyXSY9PR3p6em6+6mpqcWQjIiIiCxFmR2A79+/j1GjRiE6Ohru7u75LhcTEwMPDw/dLTAwsBhTEhERUXFTopnJyMhAjx49kJ2djc8//7zAZUePHo2UlBTdLT4+vphSEhERkSVY/WamjIwMdOvWDefPn0dsbGyBozIAoNVqodVqiykdERGR6fGcMoax6mYmp5E5ffo0tm/fDm9vb0tHIiIiIitj0WYmLS0NZ86c0d0/f/48jhw5Ai8vL/j7++Oll17CoUOHsHHjRmRlZSExMREA4OXlBQcHB0vFJiIiIiti0WbmwIEDiIiI0N0fPnw4AKBPnz6YOHEi1q9fDwCoXbu23uO2b9+O8PDw4opJRCbE4XMiMjWLNjPh4eEQkXznFzSPiIiICFDkaCYiIiKi/LCZISIiIqWxmSEiIiKlsZkhIiIipVn1eWbo6cCjW4iIqCg4MkNERERK48gMASj66AhHV4iIyFI4MkNERERKYzNDRERESmMzQ0REREozWTNz69YtU5UiIiIiKjSjmpnp06dj9erVuvvdunWDt7c3ypUrh6NHj5osHBEREdGTGHU00xdffIEVK1YAALZu3YqtW7fip59+wrfffov33nsPv/zyi0lDkvXgUUtERGRtjGpmrl69isDAQADAxo0b0a1bN7Rq1QrBwcGoX7++SQMSERERFcSozUyenp6Ij48HAGzZsgUtWrQAAIgIsrKyTJeOiIiI6AmMGpnp0qULoqOjUalSJSQnJyMyMhIAcOTIEVSsWNGkAYmIiIgKYlQzM2fOHAQHByM+Ph4zZsyAq6srgIebn9566y2TBiQiIiIqiFHNjL29PUaMGJFr+tChQ4uah4iIiMggRl+b6dSpU9ixYweSkpKQnZ2tN2/8+PFFDkZERERUGEY1M19++SXefPNNlC5dGr6+vtBoNLp5Go2GzQwREREVG6OamY8++ghTpkzByJEjTZ2HiIiIyCBGNTM3b95E165dTZ2FrAxPkEdERCow6jwzXbt25Vl+iYiIyCoYNTJTsWJFjBs3Dnv37kXNmjVhb2+vN//tt982STgiIiKiJzGqmVm4cCFcXV0RFxeHuLg4vXkajYbNDBERERUbo5qZ8+fPmzoHERERkVGMPs8MERERFQ0PtDCNQjczw4cPx4cffggXFxcMHz68wGVnz55d5GBEREREhVHoZubw4cPIyMjQ/X9+Hj2BHhEREZG5FbqZ2b59e57/T0SkMg7zE6nPqPPMEBEREVkLo3YA7ty5c56bkzQaDRwdHVGxYkVER0ejSpUqRQ5IREREVBCjRmY8PDwQGxuLQ4cO6Zqaw4cPIzY2FpmZmVi9ejXCwsKwa9euAuvs3LkT7du3h7+/PzQaDdatW6c3X0QwceJE+Pv7w8nJCeHh4fjrr7+MiUxEREQllFHNjK+vL6Kjo3Hu3Dn88MMPWLNmDc6ePYuePXvimWeewd9//40+ffo88UKUd+7cQVhYGObNm5fn/BkzZmD27NmYN28e9u/fD19fX7Rs2RK3b982JjYRERGVQEZtZlq0aBF27doFG5v/64VsbGwwZMgQNGrUCFOnTsXgwYPxwgsvFFgnMjISkZGRec4TEcydOxdjxoxBly5dAABfffUVfHx8sHLlSgwYMMCY6ERERFTCGNXMZGZm4uTJk6hcubLe9JMnTyIrKwsA4OjoWKTDtM+fP4/ExES0atVKN02r1aJp06bYvXt3vs1Meno60tPTdfdTU1ONzkBERcMjhYioOBjVzPTq1QuvvfYaPvjgA9SrVw8ajQb79u3D1KlT0bv3ww+ruLg4VK9e3ehgiYmJAAAfHx+96T4+Prh48WK+j4uJicGkSZOMXi8RERGpxahmZs6cOfDx8cGMGTNw7do1AA+bjGHDhun2k2nVqhXatGlT5ICPj+6ISIEjPqNHj9Y7Q3FqaioCAwOLnIOIiIisk1HNjK2tLcaMGYMxY8boNuO4u7vrLVO+fPkiBfP19QXwcITGz89PNz0pKSnXaM2jtFottFptkdZNRGQp3DRHZLginTTv+vXrOHbsGI4fP45///3XVJkAACEhIfD19cXWrVt10x48eIC4uDg0atTIpOsiIiIidRk1MnPnzh0MGTIEy5YtQ3Z2NoCHozW9e/fGp59+Cmdn50LVSUtLw5kzZ3T3z58/jyNHjsDLywvly5fH0KFDMXXqVFSqVAmVKlXC1KlT4ezsjOjoaGNiExERUQlk1MjM8OHDERcXhw0bNuDWrVu4desWfvzxR8TFxeHdd98tdJ0DBw6gTp06qFOnjq5unTp1MH78eADA+++/j6FDh+Ktt97Cc889hytXruCXX36Bm5ubMbGJiIioBDJqZOaHH37A999/j/DwcN20tm3bwsnJCd26dcP8+fMLVSc8PBwiku98jUaDiRMnYuLEicbEJCIioqeAUSMzd+/ezXMn3LJly+Lu3btFDkVERERUWEY1Mw0bNsSECRNw//593bR79+5h0qRJaNiwocnCERERET2JUZuZPvnkE7Rp0wYBAQEICwuDRqPBkSNH4OjoiJ9//tnUGYmIiIjyZVQzU6NGDZw+fRorVqzAyZMnISLo0aMHXnnlFTg5OZk6IxERkfJ4DiHzMaqZAQAnJyf079/flFmIiIiIDFboZmb9+vWFLtqhQwejwhAREREZqtDNTKdOnQq1nEaj0V05m4iIiMjcCt3M5Jzpl4iIiMiaFOnaTE9Ss2ZNxMfHm3MVRERE9JQzegfgwrhw4QIyMjLMuQoiIoviESpElmfWkRkiIiIic2MzQ0REREpjM0NERERKYzNDRERESmMzQ0REREozazPzxRdfwMfHx5yrICIioqec0Ydm79u3Dzt27EBSUlKuE+rNnj0bABAdHV20dERERERPYFQzM3XqVIwdOxZVqlSBj48PNBqNbt6j/09ERERkbkY1M5988gkWL16Mvn37mjgOEZHlFfVEeDyRHlHxMqqZsbGxQePGjU2dhYiI8sEGiSh/RjUzw4YNw2effYa5c+eaOA4REZFa2GhanlHNzIgRIxAVFYVnnnkG1apVg729vd78NWvWmCQcERER0ZMY1cwMGTIE27dvR0REBLy9vbnTLxEREVmMUc3MsmXL8MMPPyAqKsrUeYiIiIgMYlQz4+XlhWeeecbUWYiIrBr3jSCyTkY1MxMnTsSECROwZMkSODs7mzoTESmIX/REZClGNTP//e9/cfbsWfj4+CA4ODjXDsCHDh0ySTgiIiKiJzGqmenUqZOJYxAREREZx6hmZsKECabOQUREZPW4OdU6GX2hSSIiIsobm57iZfTlDAo6t0xWVpbRgYiIiIgMYVQzs3btWr37GRkZOHz4ML766itMmjTJJMFyZGZmYuLEifj666+RmJgIPz8/9O3bF2PHjoWNjY1J10VERETqMaqZ6dixY65pL730EqpXr47Vq1fjtddeK3KwHNOnT8eCBQvw1VdfoXr16jhw4ABeffVVeHh44J133jHZeoiIiEhNJt1npn79+ujfv78pS2LPnj3o2LGj7mzDwcHBWLVqFQ4cOGDS9RARUcnHfVlKJpNtp7l37x4+/fRTBAQEmKokAKBJkybYtm0bTp06BQA4evQofv/9d7Rt29ak6yEiIiI1GTUy4+npqbcDsIjg9u3bcHZ2xooVK0wWDgBGjhyJlJQUhIaGwtbWFllZWZgyZQpefvnlPJdPT09Henq67n5qaqpJ8xAREZF1MaqZmTt3rt59GxsblClTBvXr14enp6cpcumsXr0aK1aswMqVK1G9enUcOXIEQ4cOhb+/P/r06ZNr+ZiYGJPvhExERETWy6hmJq8mwlzee+89jBo1Cj169AAA1KxZExcvXkRMTEyeOUaPHo3hw4fr7qempiIwMLDY8hIREVHxMnoH4Fu3bmHfvn1ISkpCdna23rzevU23Q9Xdu3dzHYJta2uba505tFottFqtydZPRERE1s2oZmbDhg145ZVXcOfOHbi5uentP6PRaEzazLRv3x5TpkxB+fLlUb16dRw+fBizZ89Gv379TLYOIiIiUpdRzcy7776Lfv36YerUqXB2djZ1Jj2ffvopxo0bh7feegtJSUnw9/fHgAEDMH78eLOul4jI3HiYMJFpGNXMXLlyBW+//bbZGxkAcHNzw9y5c3PtdExEREQEGHmemdatW/OkdURERGQVjBqZiYqKwnvvvYcTJ06gZs2asLe315vfoUMHk4QjIqL8cTMV0UNGNTM5lyyYPHlyrnkajYZXzSYiImWwKVSfUc1MfodFE5kKP1yIiKiwTHqhycfVrFkTmzdv5knriIgs6Gn9cfC0Pu+nkckuNJmXCxcuICMjw5yrICIioqecWZsZIiIiInMz62YmIiIiVXEzlTrYzBARPYX4RU0lCZsZIqIShE0KPY24zwwREREpzawjM1988QV8fHzMuQoiIiVwxITIfIxqZv773//mOV2j0cDR0REVK1bEiy++iOjo6CKFIyIiy2MjRtbOqGZmzpw5uH79Ou7evQtPT0+ICG7dugVnZ2e4uroiKSkJFSpUwPbt23nCPCIiIjIro/aZmTp1KurVq4fTp08jOTkZN27cwKlTp1C/fn188sknuHTpEnx9fTFs2DBT5yUiIiLSY9TIzNixY/HDDz/gmWee0U2rWLEiZs2ahf/85z84d+4cZsyYgf/85z8mC0pERNaNm6PIUowambl69SoyMzNzTc/MzERiYiIAwN/fH7dv3y5aOiIiIqInMKqZiYiIwIABA3D48GHdtMOHD+PNN99Es2bNAADHjx9HSEiIaVISERER5cOozUyLFi1Cr169ULduXdjb2wN4OCrTvHlzLFq0CADg6uqKjz/+2HRJiYjIalhykxI3Z9HjjGpmfH19sXXrVpw8eRKnTp2CiCA0NBRVqlTRLRMREWGykEREZH5sEkhVRjUzcXFxaNq0KUJDQxEaGmrqTERERAAK32CxEXu6GbXPTMuWLVG+fHmMGjUKf/75p6kzERERERWaUc1MQkIC3n//ffz222+oVasWatWqhRkzZuDy5cumzkdERERUIKOamdKlS2Pw4MHYtWsXzp49i+7du2PZsmUIDg7WHc1EREREVByKfNXskJAQjBo1CtOmTUPNmjURFxdnilxEREREhVKkZmbXrl1466234Ofnh+joaFSvXh0bN240VTYiIiKiJzLqaKYPPvgAq1atwpUrV9CyZUvMnTsXnTp1grOzs6nzERERERXIqGZmx44dGDFiBLp3747SpUubOhMRERFRoRnVzOzevRsAcOLECRw4cAAPHjzQm9+hQ4eiJyMiIiIqBKOamfPnz6Nz5844duwYNBoNRAQAoNFoAABZWVmmS0hERERUAKOambfffhvBwcHYunUrKlSogH379iE5ORnvvvsuZs2aZeqMRGRleLZVMjX+TVFRGNXM7NmzB7GxsShTpgxsbGxgY2ODJk2aICYmBm+//bbe1bSJiOjpxSaFioNRh2ZnZWXB1dUVwMMT6CUkJAAAgoKC8M8//5gu3f935coV9OzZE97e3nB2dkbt2rVx8OBBk6+HiIiI1GPUyEyNGjVw7NgxVKhQAfXr18eMGTPg4OCAhQsXokKFCiYNePPmTTRu3BgRERH46aefULZsWZw9exalSpUy6XqIiIhITUY1M2PHjsWdO3cAAB999BHatWuHF154Ad7e3li9erVJA06fPh2BgYFYsmSJblpwcLBJ10FERETqMqqZad26te7/K1SogBMnTuDGjRvw9PTUHdFkKuvXr0fr1q3RtWtXxMXFoVy5cnjrrbfQv3//PJdPT09Henq67n5qaqpJ8xAREZF1MaqZyYuXl5epSuk5d+4c5s+fj+HDh+ODDz7Avn378Pbbb0Or1aJ379w7ksXExGDSpElmyUJEREXHnYLJ1Ip8oUlzy87OxrPPPoupU6eiTp06GDBgAPr374/58+fnufzo0aORkpKiu8XHxxdzYiIiIipOVt/M+Pn5oVq1anrTqlatikuXLuW5vFarhbu7u96NiIiISi6rb2YaN26c63DvU6dOISgoyEKJiIiIyJpYfTMzbNgw7N27F1OnTsWZM2ewcuVKLFy4EIMGDbJ0NCIiIrICVt/M1KtXD2vXrsWqVatQo0YNfPjhh5g7dy5eeeUVS0cjIiIiK2Cyo5nMqV27dmjXrp2lYxAREZEVsvqRGSIiIqKCsJkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlKXGeGSKAV9olIqK8cWSGiIiIlMaRGSLKF0fDiEgFHJkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipfFyBkQEgJcuICJ1cWSGiIiIlMZmhoiIiJTGZoaIiIiUxmaGiIiIlMZmhoiIiJTGo5lIeTwKh4jo6cZmhugpxAaQiEoSbmYiIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipSnVzMTExECj0WDo0KGWjkJERERWQplmZv/+/Vi4cCFq1apl6ShERERkRZRoZtLS0vDKK6/gyy+/hKenp6XjEBERkRVRopkZNGgQoqKi0KJFiycum56ejtTUVL0bERERlVxWf9K8b775BocOHcL+/fsLtXxMTAwmTZpk5lRERERkLax6ZCY+Ph7vvPMOVqxYAUdHx0I9ZvTo0UhJSdHd4uPjzZySiIiILMmqR2YOHjyIpKQk1K1bVzctKysLO3fuxLx585Ceng5bW1u9x2i1Wmi12uKOSkRERBZi1c1M8+bNcfz4cb1pr776KkJDQzFy5MhcjQwRERE9fay6mXFzc0ONGjX0prm4uMDb2zvXdCIiIno6WfU+M0RERERPYtUjM3nZsWOHpSMQERGRFeHIDBERESlNuZEZosKo+94yvfsHZ/a2UBIiIjI3jswQERGR0tjMEBERkdLYzBAREZHS2MwQERGR0rgDMFEJx52hiaik48gMERERKY3NDBERESmNzQwREREpjc0MERERKY07ANNTgzvCEhGVTByZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqXxqtlEJQivDE5ETyOOzBAREZHS2MwQERGR0tjMEBERkdK4zww91biPCRGR+tjMEBUBmyEiIsuz+mYmJiYGa9aswcmTJ+Hk5IRGjRph+vTpqFKliqWjEVkUGykiooesvpmJi4vDoEGDUK9ePWRmZmLMmDFo1aoVTpw4ARcXF0vHo6eEJRsHNi1ERAWz+mZmy5YteveXLFmCsmXL4uDBg3jxxRctlIpKMjYPRERqsfpm5nEpKSkAAC8vrzznp6enIz09XXc/NTW1WHIRERGRZSh1aLaIYPjw4WjSpAlq1KiR5zIxMTHw8PDQ3QIDA4s5JRERERUnpZqZwYMH49ixY1i1alW+y4wePRopKSm6W3x8fDEmJCIiouKmzGamIUOGYP369di5cycCAgLyXU6r1UKr1RZjMiIiIrIkq29mRARDhgzB2rVrsWPHDoSEhFg6ElGBCrsDMXc0JiIyDatvZgYNGoSVK1fixx9/hJubGxITEwEAHh4ecHJysnA6IiIisjSr32dm/vz5SElJQXh4OPz8/HS31atXWzoaERERWQGrH5kREUtHICoybnoiIjIfqx+ZISIiIioImxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSGpsZIiIiUhqbGSIiIlIamxkiIiJSmhLNzOeff46QkBA4Ojqibt26+O233ywdiYiIiKyE1Tczq1evxtChQzFmzBgcPnwYL7zwAiIjI3Hp0iVLRyMiIiIrYPXNzOzZs/Haa6/h9ddfR9WqVTF37lwEBgZi/vz5lo5GREREVsDO0gEK8uDBAxw8eBCjRo3Sm96qVSvs3r07z8ekp6cjPT1ddz8lJQUAkPXgnt5yqampyEo37TQAJq/JdXPdXDfXzXVz3U/junO+t0UETyRW7MqVKwJAdu3apTd9ypQpUrly5TwfM2HCBAHAG2+88cYbb7yVgFt8fPwT+wWrHpnJodFo9O6LSK5pOUaPHo3hw4fr7mdnZ+PGjRvw9vbG7du3ERgYiPj4eLi7uwN42A2acpo5anLdXDfXzXVz3Vz307buS5cuQaPRwN/fH09i1c1M6dKlYWtri8TERL3pSUlJ8PHxyfMxWq0WWq1Wb1qpUqUA/F9T5O7urvfCmWNaca2H6+a6uW6um+vmukviuj08PPKcnher3gHYwcEBdevWxdatW/Wmb926FY0aNbJQKiIiIrImVj0yAwDDhw9Hr1698Nxzz6Fhw4ZYuHAhLl26hIEDB1o6GhEREVkBq29munfvjuTkZEyePBlXr15FjRo1sHnzZgQFBRlcS6vVYsKECXqboUw9rbjWw3Vz3Vw31811c91Py7qfRCNSmGOeiIiIiKyTVe8zQ0RERPQkbGaIiIhIaWxmiIiISGlsZoiIiEhpbGaIiIhIaWxmiIiISGlWf56Zojp9+jR2796NxMREaDQa+Pj4oFGjRqhUqZJV1FOlpgoZzVFThYzmqklEhlPh/a1CRkOV2GYmJSUFvXv3xoYNG+Dh4YGyZctCRHD9+nWkpqaiffv2WLZsWaGv+2DqeqrUVCEjn7dpa+ZQ4QNPhYzmqKlCRnPUtOaMKry/VchotCdeV1tRvXr1kpo1a8revXtzzdu7d6/UqlVLevfubbF6qtRUIaM5aqqQ0Vw1b926JR06dBCNRiOlSpWSypUrS6VKlaRUqVJiY2MjHTt2lJSUFIvWVCEjn/fT9bxVeH+rkNFYJbaZ8fDwyPPFzbFnzx7x8PCwWD1VaqqQ0Rw1VchorpoqfOCpkNEcNVXIaI6aKmRU4f2tQkZjlehm5o8//sh3/t69ew3+IzBlPVVqqpDRHDVVyGjOmtb+gadCRnPUVCGjOWqqktHa398qZDRWiT2aqX379ujfvz8OHDiQa96BAwcwcOBAdOjQwWL1VKmpQkZz1FQho7lqAoBGozFqXnHWVCGjOWqqkNEcNa09owrvbxUyGs3s7ZKF3Lx5U9q0aSMajUY8PT2lSpUqEhoaKp6enmJjYyORkZFy8+ZNi9VTpaYKGfm8TVuzZ8+eUqtWLdm/f3+uefv375fatWtLr169LFpThYzmqKlCRnPUVCGjCu9vFTIaq8RfNfvvv//G3r17kZiYCADw9fVFw4YNERoaahX1VKmpQkZz1FQho6lr3rp1Cy+//DJ+/vlnlCpVCmXLloVGo8G1a9eQkpKC1q1bY+XKlShVqpTFaqqQkc/76XvegPW/v1XJaKgS38wQkXFU+MBTIaM5aqqQ0Rw1VchIllGimxkRwa+//prrHAKNGzdG8+bNDd4maup6qtRUIaM5aqqQ0Vw1ichwKry/VchojBLbzFy5cgXt2rXD8ePHUaNGDfj4+EBEkJSUhD///BNhYWFYv349ypUrZ5F6qtRUISOft2lrAmp84KmQ0Rw1VchojprWnlGF97cKGY1mjh1xrEGHDh2kWbNmkpCQkGteQkKCNGvWTDp27GixeqrUVCGjOWqqkNFcNS9fviy1a9cWW1tbCQsLk1atWknLli0lLCxMbG1t5dlnn5XLly9btKYKGfm8n67nrcL7W4WMxiqxzYyLi4scOXIk3/mHDh0SFxcXi9VTpaYKGc1RU4WM5qqpwgeeChnNUVOFjOaoqUJGFd7fKmQ0Vom9NpOTkxNu3LiR7/ybN2/CycnJYvVUqalCRnPUVCGjuWpu27YNu3btgp+fX655fn5+mDVrFl544QWL1lQhozlqqpDRHDVVyKjC+1uFjMYqsSfN69GjB/r06YPvv/8eKSkpuukpKSn4/vvv8eqrryI6Otpi9VSpqUJGc9RUIaO5aqrwgadCRnPUVCGjOWqqkFGF97cKGY1m9rEfC0lPT5eBAweKg4OD2NjYiKOjozg6OoqNjY04ODjIm2++Kenp6Rarp0pNFTLyeZu25uDBgyUwMFC+++47uXXrlm76rVu35LvvvpPy5cvL22+/bdGaKmQ0R00VMpqjpgoZVXh/q5DRWCX2aKYcqampOHjwoN45BOrWrWv05chNXU+VmipkNEdNFTKauuaDBw/wzjvvYPHixcjMzISDg4Nuup2dHV577TXMnTtXN90SNVXIyOf99D1vwPrf36pkNFSJb2aIyDgqfOCpkNEcNVXIaI6aKmQky3gqmplLly7B3t5eb0evq1evIiMjA+XLl7d4PVVqqpDRHDVVyGiumkRkOBXe3ypkNIjZN2RZAY1GI1WrVtWbFhoaKjY2NlZRT5WaKmQ0R00VMpqr5sWLF3MdupqQkCAXL160mpoqZDRHTRUymqOmChlVeH+rkNEQJfbQ7Edt374dzs7OetOWLVuGu3fvWkU9VWqqkNEcNVXIaK6awcHBCA0NxYkTJ3TTmjVrhlOnTiErK8sqaqqQ0Rw1VchojpoqZFTh/a1CRkM8FZuZiMg4cXFxcHZ2Rr169XTT9u/fj7t376Jp06ZWUVOFjOaoqUJGc9RUISMVv6eimcnKysK///4LjUYDb29v2NraWlU9VWqqkNEcNVXIaK6aRGQ4Fd7fKmQ0iNk3ZFnQmjVrpFGjRrrj33OOe2/UqJGsXbvW4vVUqalCRnPUVCGjuWrmyMzMlMTERLl27ZpkZmYWqZa5aqqQ0Rw1VchojprWnFGF97cKGY1RYpuZBQsWiIODgwwcOFDWrl0ru3fvll27dsnatWtl4MCBotVqZeHChRarp0pNFTLyeZu2pogaH3gqZDRHTRUymqOmtWdU4f2tQkZjldhm5plnnpH//e9/+c5ftGiRVKhQwWL1VKmpQkZz1FQho7lqqvCBp0JGPu+n63mr8P5WIaOxSmwz4+joKCdPnsx3/t9//y2Ojo4Wq6dKTRUymqOmChnNVVOFDzwVMpqjpgoZzVFThYwqvL9VyGisEnuhyerVq2PhwoX5zv/yyy9RvXp1i9VTpaYKGc1RU4WM5qp55coVNGnSJN/5jRo1QkJCgkVrqpDRHDVVyGiOmipkVOH9rUJGY5XY88x8/PHHiIqKwpYtW9CqVSv4+PhAo9EgMTERW7duxcWLF7F582aL1VOlpgoZ+bxNWzPnw+njjz/Oc35RPvBMVVOFjOaoqUJGc9RUIaMK728VMhqrRB+afeHCBcyfPx979+7Vu+5Gw4YNMXDgQAQHB1u0nio1VchojpoqZDRHzbi4OERFRSEoKKjAD6cXXnjBYjVVyMjn/fQ9bxXe3ypkNEaJbmaIyDgqfOCpkNEcNVXIaI6aKmQky3kqmpmLFy8iMTERGo0GPj4+CAoKsqp6qtRUIaM5aqqQ0Vw1ichwKry/VchoELPvYmxBs2fPloCAALGxsRGNRiMajUZsbGwkICBA5syZY/F6qtRUIaM5aqqQ0Vw1c1y4cEH27t0rf/zxh1y4cKFItcxVU4WM5qipQkZz1LTmjCq8v1XIaIwS28xMnjxZ3N3dZdq0aXL48GFJSEiQK1euyOHDh2XatGni4eEhH374ocXqqVJThYx83qatKaLGB54KGc1RU4WM5qhp7RlVeH+rkNFYJbaZCQgIKPAMjmvWrBF/f3+L1VOlpgoZzVFThYzmqqnCB54KGfm8n67nrcL7W4WMxiqxzYyTk5OcOHEi3/l//vmnODk5WayeKjVVyGiOmipkNFdNFT7wVMhojpoqZDRHTRUyqvD+ViGjsUpsM9O0aVN55ZVXJCMjI9e8jIwMiY6OlqZNm1qsnio1VchojpoqZDRXTRU+8FTIaI6aKmQ0R00VMqrw/lYho7FK7NFMx48fR6tWrZCeno6mTZvqnUNg586d0Gq12Lp1a6FPimTqeqrUVCEjn7dpa4aHhyMgIABLly6FnZ3+eTUzMzPRp08fXLlyBTt27LBYTRUymqOmChnNUVOFjCq8v1XIaKwS28wAwO3bt7FixYo8zyEQHR0Nd3d3i9ZTpaYKGc1RU4WM5qipwgeeChn5vJ++563C+1uFjMYo0c0MERlHhQ88FTKao6YKGc1RU4WMZDlsZoiIiEhpJfaq2Y+KiIhA37599ab16dMHzZo1s4p6qtRUIaM5aqqQ0Vw1ichwKry/VchoiKeimQkODoa/v7/etHLlyhl9qmVT11OlpgoZzVFThYzmqqnCB54KGc1RU4WM5qipQkYV3t8qZDSE3ZMXUd+SJUtyTZs6darV1FOlpgoZzVFThYzmqhkcHAw/Pz+9aeXKlYONjfG/g0xdU4WM5qipQkZz1FQhowrvbxUyGoL7zBAREZHSSvTIzJ07d7By5Urs3r1b70qejRs3xssvvwwXFxeTrevatWv44osvMH78eIMfe/nyZZQqVQqurq560zMyMrBnzx68+OKLBtVLTk7GsWPHEBYWBi8vL/z7779YtGgR0tPT0bVrV1StWtXgjI+rUKECfv75Z1SqVKnItTIyMrBp0yacPn0afn5+6Ny5s8H/NpcvX4ajoyNKly4NAPjtt9+wYMECXLp0CUFBQRg0aBAaNmxY6Hoff/wxXnrpJZMPj27YsAEHDhxAmzZt0LBhQ8TGxmLWrFnIzs5Gly5d8MYbbxhc8969e1i1ahV+//13XL16Fba2tggJCUGnTp3QvHlzk+YnovwV53cOYPz3jorfOU9k9tPyWchff/0l/v7+UqpUKenYsaO88cYb0r9/f+nYsaOUKlVKypUrJ3/99ZfJ1nfkyBGxsbEx6DEJCQlSr149sbGxEVtbW+ndu7fcvn1bNz8xMdHgmn/88Yd4eHiIRqMRT09POXDggISEhEilSpWkYsWK4uTkJAcPHix0vU8++STPm62trYwePVp33xANGzaUmzdviohIUlKS1KxZUxwcHKRSpUri6Ogo5cuXl8uXLxtcc/PmzSIism7dOrGxsZEOHTrIyJEjpXPnzmJvby8bNmwodD2NRiO2trbSokUL+eabbyQ9Pd2gPHmZP3++2NnZSd26dcXd3V1WrFghbm5u8vrrr8uAAQPEyclJ5s6da1DN06dPS1BQkHh7e4ufn59oNBqJioqS+vXri62trXTt2jXPM3M+SVpamixcuFD69u0rbdq0kcjISOnbt698+eWXkpaWZnC9J0lMTJRJkyYZ/Lj4+Hi990yOBw8eSFxcnMH1/v33X4mNjZXk5GQREbl+/bpMmzZNJk2aVODZYg0VEhIip06dKnKdBw8eyNq1a2XGjBmyfPlyo/5t4uPj5fr167r7O3fulOjoaGnSpIm88sorsnv3boNrzpo1y2RXtM6xfv16GT9+vC7Ptm3bJDIyUlq3bi1ffPGFUTXv3r0rixYtkldffVXatGkjUVFRMnjwYPn1118NrlXc3zkihn/vqPCdY6wS28yEh4dLjx498vwSSk9Pl5dfflnCw8MLXe/o0aMF3lavXm3wH0Hv3r2lQYMGsn//ftm6das899xzUrduXblx44aIPPzD0mg0BtVs0aKFvP7665KamiozZ86UgIAAef3113XzX3vtNenUqVOh62k0GgkICJDg4GC9m0ajkXLlyklwcLCEhIQYlFGj0ci1a9dERKR///5Su3ZtuXr1qog8/DJp1KiR9OvXz6Cabm5ucv78eRERqV+/vkybNk1v/qeffip16tQxKOOSJUukY8eOYm9vL97e3vLOO+/I8ePHDcr1qKpVq8rChQtFRCQ2NlYcHR3ls88+081fsmSJVK1a1aCakZGRMmDAAMnKyhIRkZiYGImMjBQRkVOnTklwcLBMmDDBoJr8UDbdh7Kpfwyo8ENAxPQ/BlT4IWDq7xwR03/vqPCdY6wS28w4OTkV+IF7/Phxg667kXNp+JzLxD96y5lu6Ienv7+//PHHH7r79+/fl44dO0rt2rUlOTnZqA9kT09P3S/IBw8eiI2Njd46Dh06JOXKlSt0vTfeeENq166d61epnZ2d0V9ojzYzlStXlo0bN+rN3759uwQHBxtU08PDQ44ePSoiImXLltX9f44zZ86Is7OzURmvXbsm06dPl9DQULGxsZF69erJwoULJTU11aCMTk5OcvHiRd19e3t7vebo/PnzBmUUEXF2dtb7hZ+eni729vby77//isjDLydDX0t+KJvuQ9nUPwZU+CGQk9OUPwZU+CFg6u8cEdN/76jwnWOsEtvM+Pv7y7p16/Kdv3btWoOuiFq6dGlZtGiRXLhwIc/bpk2bDP4jcHFxyTXUnJGRIZ06dZJatWrJsWPHjKqZ88EkIuLq6ipnz57V3b948aI4OjoaVHPt2rUSGBgon376qW5aUZuZpKQkEXnYeDxe58KFC6LVag2q2aFDBxk1apSIiLRu3TrXr90vv/xSKlWqZFDGnC+NR+3cuVP69OkjLi4u4uLiYlDGgIAA2blzp4iIXLlyRTQajWzatEk3f8eOHRIQEGBQTX9/f73Rgps3b4pGo9E1WufOnTP4teSHsuk+lE39Y0CFHwKP5zTFjwEVfgiY+jtHxPTfO6p85xijxDYzEyZMEA8PD5k5c6YcOXJErl69KomJiXLkyBGZOXOmeHp6GrSNvnXr1vLhhx/mO//IkSMG/xKsWbOmfP/997mm5/xxlS9f3uA/rNDQUNm2bZvu/saNG+Xu3bu6+3v37jX4C1NE5PLly9KsWTNp06aNXL16tcjNTNu2baVz587i6empG+LOsWfPHvHx8TGo5okTJ8Tb21t69+4tH374obi6ukrPnj1lypQp0rt3b9FqtbJkyZJC17OxscmzmcmRkpKi+6VYWIMGDZJKlSrJRx99JM8//7z06dNHQkND5aeffpItW7ZIzZo1Df5V3adPH2natKn8/fffcu7cOenevbver+gdO3ZIYGCgQTX5ofyQqT6UTfljQIUfAjk5TfljQIUfAqb+zhEx/feOSt85hiqxzYyIyLRp03TbQm1sbHS/AP38/GT69OkG1VqzZo0sX7483/k3btyQpUuXGlTz/fffl1atWuU5LyMjQzp06GDwH9bEiRNl1apV+c7/4IMPpEuXLgbVzJGdnS1Tp04VX19fsbW1NbqZ6du3r97t22+/1Zs/YsQIad26tcF1z5w5I927dxc3Nzfdr357e3tp1KiRrF271qBa+X0YF0VaWpq8/vrrUqNGDRk4cKA8ePBAZs6cKQ4ODqLRaCQ8PNzgdV67dk0aNGige77BwcF6H9Dfffed/Pe//zWoJj+UHzLlh7Kpfgyo8ENAxPQ/BlT5IWDK7xwR03/vqPadY4gS3czkOHfunOzevVt2794t586ds3QcnYyMDElJScl3fmZmpsmPCLhz547cv3+/SDUOHjwoc+fO1e2fYGppaWly7949ox+fnZ0tiYmJkpCQIA8ePDBhMvO4d++ewfvfPO7UqVNy/PhxyczMNEmmkvChbOhIqbk/lE3xY8CcPwR69Ohhkh8CIqb/MWDuHwI2NjYSHBwshw4d0s035odADn7n/B9TfOcURok+ad7ly5cxf/78PI/5HzBgAAIDA01Sr1GjRhg4cKDB9Z5U880330RAQIBJaxqTs7ift6lrqvDv3bhxYwwcONAq/r0fdf78eb2rCYeEhBhdy5QyMzNx9+7dfK9qnJWVhcuXL5v0PEF3796Fra0ttFptkeocPHgQv//+O3r37g1PT08TpXvozp07sLW1haOjo1GPFxEkJSUhOzsbpUuXhr29vUnzmdr9+/eRkZEBNzc3o2ucPn0a6enpCA0NhZ1diT71WolWYpuZ33//HZGRkQgMDESrVq3g4+Oje6Nu3boV8fHx+Omnn9C4cWOL1FOlpgoZ+bxNW/NJ4uPjMWHCBCxevNhqa6qQ0Rw1VchojprWkvHevXs4ePAgvLy8UK1aNb159+/fx7fffovevXsblMPUNVXIaBSzj/1YyHPPPSdDhw7Nd/7QoUPlueees1g9VWqqkNEcNVXIaK6aT2LMCSKLu6YKGc1RU4WM5qhpDRn/+ecfCQoK0m22atq0qSQkJOjmG3OEnalrqpDRWCV2ZMbJyQlHjhxBlSpV8px/8uRJ1KlTB/fu3bNIPVVqqpDRHDVVyGiumuvXry9w/rlz5/Duu+8iKyvLYjVVyGiOmipkNEdNFTJ27twZmZmZWLJkCW7duoXhw4fjzz//xI4dO1C+fHlcu3YN/v7+BmU0dU0VMhrN7O2ShYSEhMjixYvznb948WKDTlZl6nqq1FQhozlqqpDRXDULOifMo+eGsWRNFTLyeT9dz7ts2bJy7NgxvWlvvfWWlC9fXs6ePWvUCIWpa6qQ0Vgltpn57LPPxMHBQQYNGiTr1q2TPXv2yN69e2XdunUyaNAg0Wq1Mn/+fIvVU6WmChn5vE1b09/fv8AjWA4fPmzU2a5NWVOFjOaoqUJGc9RUIaObm1ue1+8aPHiw7jw5hmY0dU0VMhqrxDYzIiLffPON1K9fX+zs7HSdtp2dndSvX19Wr15t8Xqq1FQhozlqqpDRHDXbt28v48aNy3e+MSeINHVNFTKao6YKGc1RU4WM9erVk2XLluU5b9CgQVKqVCmDv9RNXVOFjMYq0c1MjgcPHkhCQoLJzjti6nqq1FQhozlqqpDRlDV37twpP/30U77z09LSZMeOHRatqUJGc9RUIaM5aqqQcerUqbprO+XlzTffNLjhMnVNFTIaq8TuAExERERPBxtLByAiIiIqCjYzREREpDQ2M0RERKQ0NjNEZLDw8HAMHTrUZPUmTpyI2rVrm6xeXpYuXYpSpUqZdR1EZBlsZojI4kaMGIFt27aZrF5wcDDmzp2rN6179+44deqUydZBRNaDlwglIotzdXWFq6urWdfh5OQEJycns66DiCyDIzNEVCQ3b95E79694enpCWdnZ0RGRuL06dN6y3z55ZcIDAyEs7MzOnfujNmzZ+tt8nl8M1Pfvn3RqVMnzJo1C35+fvD29sagQYOQkZHxxDzh4eG4ePEihg0bBo1GA41GAyD3ZqacdS5evBjly5eHq6sr3nzzTWRlZWHGjBnw9fVF2bJlMWXKFL36KSkpeOONN1C2bFm4u7ujWbNmOHr0qOEvHBGZDJsZIiqSvn374sCBA1i/fj327NkDEUHbtm11jceuXbswcOBAvPPOOzhy5AhatmyZq0HIy/bt23H27Fls374dX331FZYuXYqlS5c+8XFr1qxBQEAAJk+ejKtXr+Lq1av5Lnv27Fn89NNP2LJlC1atWoXFixcjKioKly9fRlxcHKZPn46xY8di7969AAARQVRUFBITE7F582YcPHgQzz77LJo3b44bN24U7gUjIpPjZiYiMtrp06exfv167Nq1C40aNQIAfP311wgMDMS6devQtWtXfPrpp4iMjMSIESMAAJUrV8bu3buxcePGAmt7enpi3rx5sLW1RWhoKKKiorBt2zb079+/wMd5eXnB1tYWbm5u8PX1LXDZ7OxsLF68GG5ubqhWrRoiIiLwzz//YPPmzbCxsUGVKlUwffp07NixAw0aNMD27dtx/PhxJCUlQavVAgBmzZqFdevW4fvvv8cbb7xR2JeOiEyIIzNEZLS///4bdnZ2qF+/vm6at7c3qlSpgr///hsA8M8//+D555/Xe9zj9/NSvXp12Nra6u77+fkhKSnJRMkfCg4Ohpubm+6+j48PqlWrBhsbG71pOes9ePAg0tLS4O3trdvPx9XVFefPn8fZs2dNmo2ICo8jM0RktPyuhiIiun1VHv3/Jz3uUfb29nr3NRoNsrOzjUxa+HUUtN7s7Gz4+flhx44duWrxsG8iy2EzQ0RGq1atGjIzM/HHH3/oNjMlJyfj1KlTqFq1KgAgNDQU+/bt03vcgQMHzJrLwcEBWVlZJq/77LPPIjExEXZ2dggODjZ5fSIyDjczEZHRKlWqhI4dO6J///74/fffcfToUfTs2RPlypVDx44dAQBDhgzB5s2bMXv2bJw+fRpffPEFfvrpp1yjNaYUHByMnTt34sqVK/j3339NVrdFixZo2LAhOnXqhJ9//hkXLlzA7t27MXbsWLM3aESUPzYzRFQkS5YsQd26ddGuXTs0bNgQIoLNmzfrNtc0btwYCxYswOzZsxEWFoYtW7Zg2LBhcHR0NFumyZMn48KFC3jmmWdQpkwZk9XVaDTYvHkzXnzxRfTr1w+VK1dGjx49cOHCBfj4+JhsPURkGI0UZuM1EZEJ9e/fHydPnsRvv/1m6ShEVAJwnxkiMrtZs2ahZcuWcHFxwU8//YSvvvoKn3/+uaVjEVEJwZEZIjK7bt26YceOHbh9+zYqVKiAIUOGYODAgUbV+u233xAZGZnv/LS0NGNjEpGi2MwQkVLu3buHK1eu5Du/YsWKxZiGiKwBmxkiIiJSGo9mIiIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKlsZkhIiIipbGZISIiIqWxmSEiIiKl/T+AQgqMlaklBQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot average number of login counts per login time\n",
    "avg_logins_per_15min = round(df_15min.groupby('login_time')['num_logins'].mean(),2)\n",
    "ax = sns.barplot(avg_logins_per_15min);\n",
    "xticks_labels = plt.xticks()[1]\n",
    "# Filter x labels to keep only every hourly label\n",
    "hour_labels = [label if i % 4 == 0 else '' for i, label in enumerate(xticks_labels)]\n",
    "plt.xticks(ticks=plt.xticks()[0], labels=hour_labels, rotation=90);\n",
    "# Set y label and title\n",
    "ax.set_ylabel('avg_num_logins');\n",
    "ax.set_title('Logins Peak at 2am, Noon, and 10pm\\n Dip from 5:30am to 10am');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460550a9-1381-4269-877e-28ae47132ecb",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">We can see some clear peaks around 2am, noon, and 10pm. The largest dip occurrs between about 5:30am and 10am, with another, less-significant, dip occurring between about 3pm and 8pm. This highlights average daily demand for rides, and provides Ultimate Technologies with insights as to when they have the greatest opportunities for customer engagement.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81ebc2e-d47f-4e08-9f98-6e8f48d4b70c",
   "metadata": {},
   "source": [
    "### Average Login Counts Per Time of Day Per Day of Week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "6c029867-5859-4f13-b560-4eb9246f1624",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "avg_login_by_day = pd.DataFrame(round(df_15min.groupby(['day_of_week', 'day_name',\n",
    "                   'login_time'])['num_logins'].mean(),2)).rename(columns={'num_logins':'avg_num_logins'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "874e867a-0979-4b46-8960-395f8b86c49b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x900 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=7, figsize=(10,9), sharex=True, sharey=True);\n",
    "\n",
    "for i in range(7):\n",
    "    data = avg_login_by_day[avg_login_by_day['day_of_week']==i]\n",
    "    ax = sns.barplot(data=data, x=data['login_time'], y=data['avg_num_logins'], ax=axes[i])\n",
    "    xticks_labels = plt.xticks()[1]\n",
    "    hour_labels = [label if i % 4 == 0 else '' for i, label in enumerate(xticks_labels)]\n",
    "    plt.xticks(ticks=plt.xticks()[0], labels=hour_labels, rotation=90)\n",
    "    axes[i].set_ylabel(data['day_name'].iloc[0])\n",
    "\n",
    "axes[0].set_title('Logins Per Time Per Day of Week');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38428ec2-1566-4631-b3cb-b2276da7efa7",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">This expands upon the analysis of demand throughout a day, by looking at how it varies by day of week. The most obvious difference between days of the week indicates that Saturday and Sunday have much higher peaks in demand between midnight and 5am, while weekdays have higher peaks in demand around noon.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f74e383-6f9b-483e-9259-d702ad1c9dfb",
   "metadata": {},
   "source": [
    "### Additional Summary Statistics: Total Logins Per Month and Total Logins Per Weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "bfe72f82-b64c-4a3b-9f2d-cc1ffb617d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Review total logins per month, drop April since it only includes 13 days of login data\n",
    "logins_per_month = df_15min.groupby('month')['num_logins'].sum().drop(4)\n",
    "logins_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "baedb66a-8ece-4b5d-9197-bcdb552fe948",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(logins_per_month);\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.set_title('Total Logins Increasing');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "1ea6914d-9f4a-434f-a352-c647cc425f2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>num_logins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>Monday</th>\n",
       "      <td>8823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>Tuesday</th>\n",
       "      <td>9268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>Wednesday</th>\n",
       "      <td>10339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>Thursday</th>\n",
       "      <td>11960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>Friday</th>\n",
       "      <td>15208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>Saturday</th>\n",
       "      <td>19377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>Sunday</th>\n",
       "      <td>18167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       num_logins\n",
       "day_of_week day_name             \n",
       "0           Monday           8823\n",
       "1           Tuesday          9268\n",
       "2           Wednesday       10339\n",
       "3           Thursday        11960\n",
       "4           Friday          15208\n",
       "5           Saturday        19377\n",
       "6           Sunday          18167"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review total logins per day of week\n",
    "logins_per_day = pd.DataFrame(df_15min.groupby(['day_of_week', 'day_name'])['num_logins'].sum())\n",
    "logins_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "dc19eea0-dc7f-44fc-b104-b671989c679f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(logins_per_day, x='day_name', y='num_logins');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "plt.xticks(rotation=45);\n",
    "ax.set_title('Weekends Most Popular');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2edeeca6-7cf8-4bac-977b-7ab0b6a1e667",
   "metadata": {},
   "source": [
    "## Part 2\n",
    "\n",
    "The neighboring cities of Gotham and Metropolis have complementary circadian rhythms: on weekdays, Ultimate Gotham is most active at night, and Ultimate Metropolis is most active during the day. On weekends, there is reasonable activity in both cities.\n",
    "However, a toll bridge, with a two way toll, between the two cities causes driver partners to tend to be exclusive to each city. The Ultimate managers of city operations for the two cities have proposed an experiment to encourage driver partners to be available in both cities, by reimbursing all toll costs.\n",
    "\n",
    "1) What would you choose as the key measure of success of this experiment in encouraging driver partners to serve both cities, and why would you choose this metric?\n",
    "\n",
    "2) Describe a practical experiment you would design to compare the effectiveness of the proposed change in relation to the key measure of success. Please provide details on:\n",
    " - How you will implement the experiment\n",
    " - What statistical test(s) you will conduct to verify the significance of the observation\n",
    " - How you would interpret the results and provide recommendations to the city operations team along with any caveats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "fe1b2aae-23ce-458b-91c8-af4bce6ec7fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weekday_night</th>\n",
       "      <th>Weekday_day</th>\n",
       "      <th>Weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gotham</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metropolis</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Weekday_night  Weekday_day  Weekend\n",
       "Gotham                  1            0        1\n",
       "Metropolis              0            1        1"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gm = pd.DataFrame({'Weekday_night':[1,0], 'Weekday_day':[0,1], 'Weekend':[1,1]}, index=['Gotham','Metropolis'])\n",
    "gm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b0796b-a49d-4f6d-9b9f-a2bebc422944",
   "metadata": {},
   "source": [
    "As a success metric, I would examine the difference between the number of drivers available in both cities before and after the implementation. The greater the change in dual-city drivers after the implementation, the more successful the implementation.\n",
    "\n",
    "To test the effectiveness of the proposed change, I would first calculate the average number of drivers per day that are available in both cities, before the implementation, over the span of a month. This could be computed by counting the number of unique driver clock-ins per day that also have a toll-bill that day, and taking the average count over a 30-day span. After making drivers aware of the toll-bill reimbursement, I would then calculate the average number of drivers available across both cities per day, with the implementation, over the span of a month. I would then compute the difference to determine how many more (or less) drivers were available across both cities after the implementation. \n",
    "\n",
    "To verify the significance of this observation, I would conduct a paired t-test with the null hypothesis (H0) that there is no difference between dual-city driver counts before and after the implementation, and the alternative hypothesis (H1) that there was a change in the number of dual-city drivers after the implementation. If the null is rejected (with a p-value < 0.05 deeming the change in dual-city drivers to be statistically signficant), and the results show an increase in dual-city drivers after the implementation, the implementation can be deemed successful.\n",
    "\n",
    "I would express the findings to the city operations with a bar chart to simply visualize dual-city driver counts before and after the implementation. I would also analyse driver counts across days of the week to see if there are certain days that have higher numbers of dual-city drivers. This could lead to insights as to whether the reimbursement-policy is most effective on specific days of the week. In communicating these conclusions, it's important to mention other confounding factors such as seasonality or changes in traffic patterns. A longer-term study would provide greater verifiabilty of the findings.\n",
    "\n",
    "I also would like to convey that while using dual-city driver count as a success metric indicates achievement of the the goal of having more dual-city drivers available, a caveat might come with the toll-reimbursement costs. If drivers are frequently shuttling back and forth between cities, the cost of toll reimbursement might offset net profits, even if the drive demand increases. In this case, it would be important to conduct a similar experiment, using net profit to Ultimate Technologies as a success metric, to determine if having more dual-city driver availability actually improves profit, or contrarily decreases it by accruing more expenses from the toll reimbursements."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa547a00-b52b-4a90-975d-e5efd90b49bb",
   "metadata": {},
   "source": [
    "## Part 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be94c7e9-dc06-43b6-8104-9c12c35c2f50",
   "metadata": {
    "tags": []
   },
   "source": [
    "Predictive modeling\n",
    "\n",
    "Ultimate is interested in predicting rider retention. To help explore this question, we have provided a sample dataset of a cohort of users who signed up for an Ultimate account in January 2014. The data were pulled several months later; we consider a user retained if they were “active” (i.e. took a trip) in the preceding 30 days.\n",
    "We would like you to use this dataset to help understand what factors are the best predictors for retention, and offer suggestions to operationalize those insights to help Ultimate.\n",
    "The data are in the attached file ultimate_data_challenge.json. See below for a detailed description of the dataset. Please include any code you wrote for the analysis and delete the dataset when you have finished with the challenge.\n",
    "1. Perform any cleaning, exploratory analysis, and/or visualizations to use the provided data for this analysis (a few sentences/plots describing your approach will suffice). What fraction of the observed users were retained?\n",
    "2. Build a predictive model to help Ultimate determine whether or not a user will be active in their 6th month on the system. Discuss why you chose your approach, what alternatives you considered, and any concerns you have. How valid is your model? Include any key indicators of model performance.\n",
    "3. Briefly discuss how Ultimate might leverage the insights gained from the model to improve its long term rider retention (again, a few sentences will suffice).\n",
    "\n",
    "Data description\n",
    " - city: city this user signed up in\n",
    " - trips_in_first_30_days: the number of trips this user took in the first 30 days after signing up\n",
    " - signup_date: date of account registration; in the form ‘YYYYMMDD’\n",
    " - avg_rating_of_driver: the rider’s average rating of their drivers over all of their trips\n",
    " - avg_surge: The average surge multiplier over all of this user’s trips\n",
    " - last_trip_date: the last time this user completed a trip; in the form ‘YYYYMMDD’\n",
    " - phone: primary device for this user\n",
    " - surge_pct: the percent of trips taken with surge multiplier > 1\n",
    " - ultimate_black_user: TRUE if the user took an Ultimate Black in their first 30 days; FALSE otherwise\n",
    " - weekday_pct: the percent of the user’s trips occurring during a weekday \n",
    " - avg_dist: the average distance in miles per trip taken in the first 30 days after signup\n",
    " - avg_rating_by_driver: the rider’s average rating over all of their trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "id": "bc45a263-9e5f-40fe-a1a9-9a25b6e40f7c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "from datetime import timedelta\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "4f84cdec-2799-40d7-ab90-de2fb66f52a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read in data\n",
    "df = pd.read_json('ultimate_data_challenge.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "d1e3b2ec-4ea4-4928-a842-7c4fb1c04667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>last_trip_date</th>\n",
       "      <th>phone</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>ultimate_black_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>King's Landing</td>\n",
       "      <td>4</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>15.4</td>\n",
       "      <td>True</td>\n",
       "      <td>46.2</td>\n",
       "      <td>3.67</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Astapor</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.26</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Astapor</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>King's Landing</td>\n",
       "      <td>9</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>20.0</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Winterfell</td>\n",
       "      <td>14</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>11.8</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city  trips_in_first_30_days signup_date  avg_rating_of_driver  \\\n",
       "0  King's Landing                       4  2014-01-25                   4.7   \n",
       "1         Astapor                       0  2014-01-29                   5.0   \n",
       "2         Astapor                       3  2014-01-06                   4.3   \n",
       "3  King's Landing                       9  2014-01-10                   4.6   \n",
       "4      Winterfell                      14  2014-01-27                   4.4   \n",
       "\n",
       "   avg_surge last_trip_date    phone  surge_pct  ultimate_black_user  \\\n",
       "0       1.10     2014-06-17   iPhone       15.4                 True   \n",
       "1       1.00     2014-05-05  Android        0.0                False   \n",
       "2       1.00     2014-01-07   iPhone        0.0                False   \n",
       "3       1.14     2014-06-29   iPhone       20.0                 True   \n",
       "4       1.19     2014-03-15  Android       11.8                False   \n",
       "\n",
       "   weekday_pct  avg_dist  avg_rating_by_driver  \n",
       "0         46.2      3.67                   5.0  \n",
       "1         50.0      8.26                   5.0  \n",
       "2        100.0      0.77                   5.0  \n",
       "3         80.0      2.36                   4.9  \n",
       "4         82.4      3.13                   4.9  "
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "719c0cc5-bd72-497a-a2a9-4f651183af9a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   city                    50000 non-null  object \n",
      " 1   trips_in_first_30_days  50000 non-null  int64  \n",
      " 2   signup_date             50000 non-null  object \n",
      " 3   avg_rating_of_driver    41878 non-null  float64\n",
      " 4   avg_surge               50000 non-null  float64\n",
      " 5   last_trip_date          50000 non-null  object \n",
      " 6   phone                   49604 non-null  object \n",
      " 7   surge_pct               50000 non-null  float64\n",
      " 8   ultimate_black_user     50000 non-null  bool   \n",
      " 9   weekday_pct             50000 non-null  float64\n",
      " 10  avg_dist                50000 non-null  float64\n",
      " 11  avg_rating_by_driver    49799 non-null  float64\n",
      "dtypes: bool(1), float64(6), int64(1), object(4)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "52b8f318-f9ca-4141-b062-e9f22603c8cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Impute missing values with mean for both of the 'rating' columns\n",
    "df['avg_rating_of_driver'].fillna(df['avg_rating_of_driver'].mean(), inplace=True)\n",
    "df['avg_rating_by_driver'].fillna(df['avg_rating_by_driver'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "102871a9-cb80-48d6-a3bf-4b3fa0c6450c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert 'date' columns to datetime objects\n",
    "df['signup_date'] = pd.to_datetime(df['signup_date'])\n",
    "df['last_trip_date'] = pd.to_datetime(df['last_trip_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "f6293794-3c3c-40f5-ab9f-91c319eea26f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert 'city' and 'phone' column to numeric\n",
    "dummies = pd.get_dummies(df[['city', 'phone']], prefix=['city', 'phone'], dtype='int')\n",
    "df = pd.concat([df.drop(columns=['city', 'phone']), dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "f971fbf1-c36f-47ee-98a3-23941a5a640b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert 'ultimate black user' column to integer\n",
    "df['ultimate_black_user'] = df['ultimate_black_user'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "4141dea0-f39b-428e-aaca-9c9f09ab71fe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Determine if user was 'active' in preceding 30 days, e.g. retained\n",
    "cutoff = max(df['last_trip_date']) - timedelta(days=30)\n",
    "df['Active'] = np.where(df['last_trip_date'] > cutoff, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "id": "5d954ac6-6b67-4ed3-b7cf-98f63e4a69a8",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4.9</td>\n",
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      "text/plain": [
       "   trips_in_first_30_days signup_date  avg_rating_of_driver  avg_surge  \\\n",
       "0                       4  2014-01-25                   4.7       1.10   \n",
       "1                       0  2014-01-29                   5.0       1.00   \n",
       "2                       3  2014-01-06                   4.3       1.00   \n",
       "3                       9  2014-01-10                   4.6       1.14   \n",
       "4                      14  2014-01-27                   4.4       1.19   \n",
       "\n",
       "  last_trip_date  surge_pct  ultimate_black_user  weekday_pct  avg_dist  \\\n",
       "0     2014-06-17       15.4                    1         46.2      3.67   \n",
       "1     2014-05-05        0.0                    0         50.0      8.26   \n",
       "2     2014-01-07        0.0                    0        100.0      0.77   \n",
       "3     2014-06-29       20.0                    1         80.0      2.36   \n",
       "4     2014-03-15       11.8                    0         82.4      3.13   \n",
       "\n",
       "   avg_rating_by_driver  city_Astapor  city_King's Landing  city_Winterfell  \\\n",
       "0                   5.0             0                    1                0   \n",
       "1                   5.0             1                    0                0   \n",
       "2                   5.0             1                    0                0   \n",
       "3                   4.9             0                    1                0   \n",
       "4                   4.9             0                    0                1   \n",
       "\n",
       "   phone_Android  phone_iPhone  Active  \n",
       "0              0             1       1  \n",
       "1              1             0       0  \n",
       "2              0             1       0  \n",
       "3              0             1       1  \n",
       "4              1             0       0  "
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "8e35ec19-8948-4294-a5e4-1a3db0a02d4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3662"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate % of retained users: users with 'last trip' within 30 days of last trip in entire dataframe\n",
    "df[df['Active'] == 1].shape[0] / df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089f91b5-2dc4-48cc-8f9a-e2f0bd6d8d60",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">36.62% (a little over 1/3) of observed users were retained.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "35e105ef-7fbd-42f3-9655-10dd8812fe69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Review histograms of variables\n",
    "fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(12, 10))\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5);\n",
    "\n",
    "for (col, ax) in zip(df.columns, axes.flatten()):\n",
    "    df[col].hist(ax=ax, grid=False)\n",
    "    ax.set_title(col)\n",
    "    if col in ['signup_date']:\n",
    "        ax.set_xticks([min(df['signup_date']), max(df['signup_date'])])\n",
    "    if col in ['last_trip_date']:\n",
    "        ax.set_xticks([min(df['last_trip_date']), max(df['last_trip_date'])])\n",
    "    if col in list(df.columns)[-6:]:\n",
    "        ax.set_xticks([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "id": "ca795154-dc42-4aa2-b593-b8d3d3088170",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Train/test split\n",
    "X = df.drop(columns=['signup_date', 'last_trip_date', 'Active'])\n",
    "y = df['Active']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "id": "b191e34d-31fd-415f-a330-f9691657ff86",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Build predictive model\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "train_pred = cf.predict(X_train)\n",
    "test_pred = cf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "id": "60e56a79-15db-4391-a1e3-d6cdff258849",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.948875\n",
      "Test accuracy: 0.9506\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score\n",
    "print('Train accuracy:', accuracy_score(y_train, train_pred))\n",
    "print('Test accuracy:', accuracy_score(y_test, test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "id": "4b6cf748-5444-448a-bd1e-599c35a7adca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6116  222]\n",
      " [ 272 3390]]\n",
      "\n",
      "True negatives: 6116\n",
      "False negatives: 272\n",
      "True positives: 3390\n",
      "False positives: 222\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, test_pred)\n",
    "print(cm)\n",
    "print('\\nTrue negatives:', cm[0,0])\n",
    "print('False negatives:', cm[1,0])\n",
    "print('True positives:', cm[1,1])\n",
    "print('False positives:', cm[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "id": "5de6b456-a0aa-4dd7-98f4-a554e30a8973",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96      6338\n",
      "           1       0.94      0.93      0.93      3662\n",
      "\n",
      "    accuracy                           0.95     10000\n",
      "   macro avg       0.95      0.95      0.95     10000\n",
      "weighted avg       0.95      0.95      0.95     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(y_test, test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "id": "2eea8fe3-d1a8-4ca3-9a7a-984b0adab6f3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>avg_dist</td>\n",
       "      <td>0.293702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>weekday_pct</td>\n",
       "      <td>0.130147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>avg_rating_by_driver</td>\n",
       "      <td>0.118125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>avg_rating_of_driver</td>\n",
       "      <td>0.081026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>trips_in_first_30_days</td>\n",
       "      <td>0.079402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>surge_pct</td>\n",
       "      <td>0.078421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>avg_surge</td>\n",
       "      <td>0.068525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>city_King's Landing</td>\n",
       "      <td>0.046925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ultimate_black_user</td>\n",
       "      <td>0.032928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>phone_iPhone</td>\n",
       "      <td>0.021491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>phone_Android</td>\n",
       "      <td>0.019349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>city_Astapor</td>\n",
       "      <td>0.017582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>city_Winterfell</td>\n",
       "      <td>0.012377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Feature  Importance\n",
       "0                 avg_dist    0.293702\n",
       "1              weekday_pct    0.130147\n",
       "2     avg_rating_by_driver    0.118125\n",
       "3     avg_rating_of_driver    0.081026\n",
       "4   trips_in_first_30_days    0.079402\n",
       "5                surge_pct    0.078421\n",
       "6                avg_surge    0.068525\n",
       "7      city_King's Landing    0.046925\n",
       "8      ultimate_black_user    0.032928\n",
       "9             phone_iPhone    0.021491\n",
       "10           phone_Android    0.019349\n",
       "11            city_Astapor    0.017582\n",
       "12         city_Winterfell    0.012377"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importance\n",
    "df_fi = pd.DataFrame({'Feature': X_test.columns, 'Importance': cf.feature_importances_}).sort_values('Importance', ascending=False).reset_index(drop=True)\n",
    "df_fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "id": "d7893f29-d929-40d5-905e-dd81f9898973",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hn3/+GdOmTUNERIT685RRJUuWRJ48edT3LSwsULBgwRQ/AymZO3cuTE1NYWZmhiJFiuD48eMYN24cevXq9cnXfPz4cTx+/BgdOnTQeM2JiYmoU6cOTp48iZcvX+Lly5c4efIkmjZtqtHfxcbGBg0bNvxsfH/88QcKFiyIr7/+Ok2v53PCwsLw+vVrrXPa3d0d1atX1zqnVSqVVpzFixdP8/t7/vx5mJqawtTUFDly5MC4ceMwbNgwdO/eXb1Nes731KT32vtvz5uPHThwAID2taJFixawsrLSel8z+/j/BjtB69DKlStRpEgRPH/+HOvXr8eCBQvQpk0b/PHHHwD+1xfou+++w3fffZfiPj41TDwgIAD79+/HyJEjUaZMGdja2kKlUqFevXp4/fp1huPu1KkTNm3ahJCQENSuXRvBwcF4+/atxgl/7949PH36FGZmZumO+1McHR21yszMzLTKk4/75s0bre3d3Nw07puYmMDJyQmPHj0CAPW/KY0WyZkzp9YHM72jSjZv3oyWLVuiRYsWGDx4MNzc3GBiYoJ58+al2Pbt5OSkVWZubq7+Gz558gQJCQmf7Rh779497NixA6ampik+/qm/SXrfk/QqUaJEip2g7927BxGBq6tris/LmzcvgKT+SbVq1cLdu3cxcuRI+Pj4wMrKComJiShfvvy/Ot8/5eP3I/kzW6ZMmRS3NzJK+o2pUqmwf/9+jBs3DlOmTMGgQYPg6OiItm3bYsKECRoJb1p97jz5nJYtW2Lw4MFQqVSwsbFBvnz5YGxsrLVdaq+5efPmqe778ePHUKlUSExM1Pr8AdqfyZQ8ePBA44vy3/rcOR0SEqJRli1bNq2Oyubm5ileY1KSL18+rFu3DiKCW7duYfz48Zg0aRKKFy+O1q1bA0j7+f4p6b32/tvz5mOPHj2CiYmJ1o8xlUoFNzc39fueVcf/N5gA6VCRIkXUHZ+rVauGhIQELF68GBs3bkTz5s3VXwjDhg3T6Fz8oUKFCqVY/uzZM/z+++8YPXo0hg4dqi5/+/YtHj9+/K/irl27NnLmzIlly5ahdu3aWLZsGcqVKwdvb2/1Ns7OznByctLqiJwsIxf4zBIbG4tcuXKp78fHx+PRo0fqD2LyvzExMVpJxd27d7W+qNM76mf16tXw8vLC+vXrNZ6b0c6Ujo6OMDY21uqg/TFnZ2cUL14cEyZMSPHxD2vQPpbe9ySzODs7Q6VS4ciRIzA3N9d6PLns3LlzOHPmDJYvX44OHTqoH0/uoJtW5ubmKf4dPr5oJ/v4b5/8PmzcuFFd+5QaDw8PLFmyBABw+fJl/PbbbxgzZgzevXuH+fPnpyvuzODi4qK+Hn1Kaq959uzZGqOcPuTq6qoeMRYbG6v1eEplKcX3uXM8PT48pz+WFee0hYWF+v0tU6YMqlWrhqJFi6J///5o0KABrK2t03y+f4rS114nJyfEx8fjwYMHGkmQiCA2NjbVHwf6gAmQgqZMmYJNmzZh1KhRaNq0KQoVKoQCBQrgzJkzmDhxYrr2pVKpICJaH5jFixcjISFBoyx5m7Rm3MbGxmjfvj1mzJiBI0eO4NSpU1iwYIHGNg0aNMC6deuQkJCAcuXKpSv2rLZmzRqNZrTffvsN8fHxqFq1KgCom7NWr16t8WE9efIkoqKiMHz48DQdJ7VfMSqVCmZmZhpfJLGxsSmOAksLS0tLVKlSBRs2bMCECRNSvXA3aNAAu3btQr58+eDg4JCuY2TWe5JeDRo0wE8//YQ7d+6gZcuWqW6X/F5+fL5/fF5+uE1KfxtPT0+cPXtWo+zAgQN48eJFmuKtXbs2TExMcO3aNa2mok8pWLAgRowYgU2bNuH06dNpfp4+8Pf3h729PS5cuPDJOW3MzMxQtmxZbN68GT///LO6NuX58+fYsWPHZ49Tt25djBo1CgcOHEi1yTk917IKFSrA0tISq1ev1phz6p9//sGBAwc+WaOVGZycnPDTTz+hY8eOmD17NoYNG5bm8x1I/fqSFdfe9LyvNWrUwJQpU7B69WoMGDBAXb5p0ya8fPkSNWrUyJSYsgITIAU5ODhg2LBh+P7777F27Vq0a9cOCxYsQN26dVG7dm0EBQUhV65cePz4MaKionD69Gls2LAhxX3Z2tqicuXK+Pnnn+Hs7AxPT0+EhoZiyZIlWjPzFitWDACwcOFC2NjYwMLCAl5eXilWTSbr1KkTJk+ejICAAFhaWqJVq1Yaj7du3Rpr1qxBvXr10K9fP5QtWxampqb4559/cPDgQTRq1AhNmjT5d29YBm3evBkmJiaoWbMmzp8/j5EjR6JEiRLqC06hQoXQrVs3zJ49G0ZGRqhbty5u3ryJkSNHwt3dXeND/Sk+Pj5Yt24d1q9fj7x588LCwgI+Pj5o0KABNm/ejF69eqF58+a4ffs2fvzxR+TIkSPDM4FPmzYNX331FcqVK4ehQ4cif/78uHfvHrZv344FCxbAxsYG48aNQ0hICCpWrIi+ffuiUKFCePPmDW7evIldu3Zh/vz5qTajZdZ7kl7+/v7o1q0bOnbsiFOnTqFy5cqwsrJCTEwMjh49Ch8fH/Ts2ROFCxdGvnz5MHToUIgIHB0dsWPHDq1mDCDp7wIAM2fORIcOHWBqaopChQrBxsYG7du3x8iRIzFq1ChUqVIFFy5cwJw5c2BnZ5emeD09PTFu3DgMHz4c169fV/ftu3fvHk6cOAErKyuMHTsWZ8+eRe/evdGiRQsUKFAAZmZmOHDgAM6ePatRY/slsLa2xuzZs9GhQwc8fvwYzZs3R/bs2fHgwQOcOXMGDx48wLx58wAAP/74I+rUqYOaNWti0KBBSEhIwOTJk2FlZfXZmun+/ftj/fr1aNSoEYYOHYqyZcvi9evXCA0NRYMGDdR95Tw8PLBt2zbUqFEDjo6O6uvfx+zt7TFy5Ej88MMPCAwMRJs2bfDo0SOMHTsWFhYWGD16dFa8XRoCAwMxbdo0TJ06Fd9++22az3cg6TzevHkz5s2bBz8/PxgZGaF06dJZcu1Nz3dEzZo1Ubt2bQwZMgRxcXHw9/fH2bNnMXr0aPj6+qJ9+/b//o3LKjrvdm2AUhsGL5LUAz5PnjxSoEAB9dDIM2fOSMuWLSV79uxiamoqbm5uUr16dY3RAymNAvvnn3+kWbNm4uDgIDY2NlKnTh05d+6c1ugKEZEZM2aIl5eXGBsbawyx/7iH/4cqVqwoAKRt27YpPv7+/XuZOnWqlChRQiwsLMTa2loKFy4s3bt3lytXrnzyPUptFFhKQ0k9PDykfv36WuX4aGhv8j7Dw8OlYcOGYm1tLTY2NtKmTRu5d++exnMTEhJk8uTJUrBgQTE1NRVnZ2dp166d1pDu1GISEbl586bUqlVLbGxs1MPvk/3000/i6ekp5ubmUqRIEVm0aFGKo3s+fg0fvuaP/4YXLlyQFi1aiJOTk5iZmUmePHkkKChIY9jpgwcPpG/fvuLl5SWmpqbi6Ogofn5+Mnz4cHnx4kWKryO970lGRoF9btulS5dKuXLlxMrKSiwtLSVfvnwSGBioMSLwwoULUrNmTbGxsREHBwdp0aKFREdHpzgqaNiwYZIzZ04xMjLS+Ny8fftWvv/+e3F3dxdLS0upUqWKREZGpjoKLKXPsIjI1q1bpVq1amJrayvm5ubi4eEhzZs3Vw8Jv3fvngQFBUnhwoXFyspKrK2tpXjx4jJ9+nSNIdGfes8+lNpnoEqVKmkaiZjaefah5GvMhg0bUnw8NDRU6tevL46OjmJqaiq5cuWS+vXra22/fft2KV68uPoc/emnn1J9TR+f40+ePJF+/fpJnjx5xNTUVLJnzy7169eXixcvqrfZt2+f+Pr6irm5uQBQ7yOlYfAiIosXL1bHY2dnJ40aNdIaEdmhQwexsrLSes0pxZ2ST10ndu7cqZ7KI1lazvfHjx9L8+bNxd7eXlQqlUYcab32pue8Sc93xOvXr2XIkCHi4eEhpqamkiNHDunZs6c8efJEY7t/e95mNpWIiM6yLSIdGjNmDMaOHYsHDx588bMOExFR5uIweCIiIjI4TICIiIjI4LAJjIiIiAwOa4CIiIjI4DABIiIiIoPDeYBSkJiYiLt378LGxibds/4SERGRMkQEz58/R86cOdVL0aSGCVAK7t69C3d3d6XDICIiogy4ffv2Z9dLZAKUguS1U27fvg1bW1uFoyEiIqK0iIuLg7u7e5rWQGMClILkZi9bW1smQERERF+YtHRfYSdoIiIiMjhMgIiIiMjgMAEiIiIig8MEiIiIiAyO4gnQ3Llz4eXlBQsLC/j5+eHIkSOpbnv06FH4+/vDyckJlpaWKFy4MKZPn6613aZNm+Dt7Q1zc3N4e3tjy5YtWfkSiIiI6AujaAK0fv169O/fH8OHD0dERAQqVaqEunXrIjo6OsXtrays0Lt3bxw+fBhRUVEYMWIERowYgYULF6q3CQsLQ6tWrdC+fXucOXMG7du3R8uWLfHXX3/p6mURERGRnlN0MdRy5cqhVKlSmDdvnrqsSJEiaNy4MSZNmpSmfTRt2hRWVlZYtWoVAKBVq1aIi4vDH3/8od6mTp06cHBwQHBwcJr2GRcXBzs7Ozx79ozD4ImIiL4Q6fn+VqwG6N27dwgPD0etWrU0ymvVqoXjx4+naR8RERE4fvw4qlSpoi4LCwvT2mft2rU/uc+3b98iLi5O40ZERET/XYolQA8fPkRCQgJcXV01yl1dXREbG/vJ5+bOnRvm5uYoXbo0vv32W3Tp0kX9WGxsbLr3OWnSJNjZ2alvXAaDiIjov03xTtAfz9YoIp+dwfHIkSM4deoU5s+fjxkzZmg1baV3n8OGDcOzZ8/Ut9u3b6fzVRAREdGXRLGlMJydnWFsbKxVM3P//n2tGpyPeXl5AQB8fHxw7949jBkzBm3atAEAuLm5pXuf5ubmMDc3z8jLICIioi+QYjVAZmZm8PPzQ0hIiEZ5SEgIKlasmOb9iAjevn2rvl+hQgWtfe7duzdd+yQiIqL/NkUXQx04cCDat2+P0qVLo0KFCli4cCGio6PRo0cPAElNU3fu3MHKlSsBAL/++ivy5MmDwoULA0iaF2jq1Kno06ePep/9+vVD5cqVMXnyZDRq1Ajbtm3Dvn37cPToUd2/QCIiItJLiiZArVq1wqNHjzBu3DjExMSgWLFi2LVrFzw8PAAAMTExGnMCJSYmYtiwYbhx4wZMTEyQL18+/PTTT+jevbt6m4oVK2LdunUYMWIERo4ciXz58mH9+vUoV66czl8fERER6SdF5wHSV5wHiIiI6MuTnu9vRWuAviR+g1cqduzwnwMVOzYREdF/keLD4ImIiIh0jQkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFRPAGaO3cuvLy8YGFhAT8/Pxw5ciTVbTdv3oyaNWvCxcUFtra2qFChAvbs2aOxzfLly6FSqbRub968yeqXQkRERF8IRROg9evXo3///hg+fDgiIiJQqVIl1K1bF9HR0Sluf/jwYdSsWRO7du1CeHg4qlWrhoYNGyIiIkJjO1tbW8TExGjcLCwsdPGSiIiI6AtgouTBp02bhs6dO6NLly4AgBkzZmDPnj2YN28eJk2apLX9jBkzNO5PnDgR27Ztw44dO+Dr66suV6lUcHNzS3Mcb9++xdu3b9X34+Li0vlKiIiI6EuiWA3Qu3fvEB4ejlq1ammU16pVC8ePH0/TPhITE/H8+XM4OjpqlL948QIeHh7InTs3GjRooFVD9LFJkybBzs5OfXN3d0/fiyEiIqIvimIJ0MOHD5GQkABXV1eNcldXV8TGxqZpH7/88gtevnyJli1bqssKFy6M5cuXY/v27QgODoaFhQX8/f1x5cqVVPczbNgwPHv2TH27fft2xl4UERERfREUbQIDkpqrPiQiWmUpCQ4OxpgxY7Bt2zZkz55dXV6+fHmUL19efd/f3x+lSpXC7NmzMWvWrBT3ZW5uDnNz8wy+AiIiIvrSKJYAOTs7w9jYWKu25/79+1q1Qh9bv349OnfujA0bNuDrr7/+5LZGRkYoU6bMJ2uAiIiIyLAo1gRmZmYGPz8/hISEaJSHhISgYsWKqT4vODgYQUFBWLt2LerXr//Z44gIIiMjkSNHjn8dMxEREf03KNoENnDgQLRv3x6lS5dGhQoVsHDhQkRHR6NHjx4Akvrm3LlzBytXrgSQlPwEBgZi5syZKF++vLr2yNLSEnZ2dgCAsWPHonz58ihQoADi4uIwa9YsREZG4tdff1XmRRIREZHeUTQBatWqFR49eoRx48YhJiYGxYoVw65du+Dh4QEAiImJ0ZgTaMGCBYiPj8e3336Lb7/9Vl3eoUMHLF++HADw9OlTdOvWDbGxsbCzs4Ovry8OHz6MsmXL6vS1ERERkf5SiYgoHYS+iYuLg52dHZ49ewZbW1sAgN/glYrFE/5zoGLHJiIi+lKk9P2dGsWXwiAiIiLSNSZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHMUToLlz58LLywsWFhbw8/PDkSNHUt128+bNqFmzJlxcXGBra4sKFSpgz549Wttt2rQJ3t7eMDc3h7e3N7Zs2ZKVL4GIiIi+MIomQOvXr0f//v0xfPhwREREoFKlSqhbty6io6NT3P7w4cOoWbMmdu3ahfDwcFSrVg0NGzZERESEepuwsDC0atUK7du3x5kzZ9C+fXu0bNkSf/31l65eFhEREek5lYiIUgcvV64cSpUqhXnz5qnLihQpgsaNG2PSpElp2kfRokXRqlUrjBo1CgDQqlUrxMXF4Y8//lBvU6dOHTg4OCA4ODjFfbx9+xZv375V34+Li4O7uzuePXsGW1tbAIDf4JXpfn2ZJfznQMWOTURE9KWIi4uDnZ2dxvd3ahSrAXr37h3Cw8NRq1YtjfJatWrh+PHjadpHYmIinj9/DkdHR3VZWFiY1j5r1679yX1OmjQJdnZ26pu7u3s6XgkRERF9aRRLgB4+fIiEhAS4urpqlLu6uiI2NjZN+/jll1/w8uVLtGzZUl0WGxub7n0OGzYMz549U99u376djldCREREXxoTpQNQqVQa90VEqywlwcHBGDNmDLZt24bs2bP/q32am5vD3Nw8HVETERHRl0yxBMjZ2RnGxsZaNTP379/XqsH52Pr169G5c2ds2LABX3/9tcZjbm5uGdonERERGQ7FmsDMzMzg5+eHkJAQjfKQkBBUrFgx1ecFBwcjKCgIa9euRf369bUer1ChgtY+9+7d+8l9EhERkWFRtAls4MCBaN++PUqXLo0KFSpg4cKFiI6ORo8ePQAk9c25c+cOVq5MGoEVHByMwMBAzJw5E+XLl1fX9FhaWsLOzg4A0K9fP1SuXBmTJ09Go0aNsG3bNuzbtw9Hjx5V5kUSERGR3lF0HqBWrVphxowZGDduHEqWLInDhw9j165d8PDwAADExMRozAm0YMECxMfH49tvv0WOHDnUt379+qm3qVixItatW4dly5ahePHiWL58OdavX49y5crp/PURERGRflJ0HiB9ldI8ApwHiIiISL99EfMAERERESmFCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwclwArRq1Sr4+/sjZ86cuHXrFgBgxowZ2LZtW6YFR0RERJQVMpQAzZs3DwMHDkS9evXw9OlTJCQkAADs7e0xY8aMzIyPiIiIKNNlKAGaPXs2Fi1ahOHDh8PY2FhdXrp0afz999+ZFhwRERFRVshQAnTjxg34+vpqlZubm+Ply5f/OigiIiKirJShBMjLywuRkZFa5X/88Qe8vb3/bUxEREREWcokI08aPHgwvv32W7x58wYighMnTiA4OBiTJk3C4sWLMztGIiIiokyVoQSoY8eOiI+Px/fff49Xr14hICAAuXLlwsyZM9G6devMjpGIiIgoU2UoAQKArl27omvXrnj48CESExORPXv2zIyLiIiIKMtkKAG6ceMG4uPjUaBAATg7O6vLr1y5AlNTU3h6emZWfERERESZLkOdoIOCgnD8+HGt8r/++gtBQUH/NiYiIiKiLJWhBCgiIgL+/v5a5eXLl09xdBgRERGRPslQAqRSqfD8+XOt8mfPnqlnhSYiIiLSVxlKgCpVqoRJkyZpJDsJCQmYNGkSvvrqq0wLjoiIiCgrZKgT9JQpU1C5cmUUKlQIlSpVAgAcOXIEcXFxOHDgQKYGSERERJTZMlQD5O3tjbNnz6Jly5a4f/8+nj9/jsDAQFy8eBHFihXL7BiJiIiIMlWG5wHKmTMnJk6cmJmxEBEREelEhhOgp0+f4sSJE7h//z4SExM1HgsMDPzXgRERERFllQwlQDt27EDbtm3x8uVL2NjYQKVSqR9TqVRMgIiIiEivZagP0KBBg9CpUyc8f/4cT58+xZMnT9S3x48fZ3aMRERERJkqQwnQnTt30LdvX2TLli2z4yEiIiLKchlKgGrXro1Tp05ldixEREREOpGhPkD169fH4MGDceHCBfj4+MDU1FTj8W+++SZTgiMiIiLKChlKgLp27QoAGDdunNZjKpWKy2EQERGRXstQAvTxsHciIiKiL0mG+gARERERfckyPBHiy5cvERoaiujoaLx7907jsb59+/7rwIiIiIiySoYSoIiICNSrVw+vXr3Cy5cv4ejoiIcPHyJbtmzInj07EyAiIiLSaxlqAhswYAAaNmyIx48fw9LSEn/++Sdu3boFPz8/TJ06NbNjJCIiIspUGUqAIiMjMWjQIBgbG8PY2Bhv376Fu7s7pkyZgh9++CGzYyQiIiLKVBlKgExNTdXrf7m6uiI6OhoAYGdnp/4/ERERkb7KUB8gX19fnDp1CgULFkS1atUwatQoPHz4EKtWrYKPj09mx0hERESUqTJUAzRx4kTkyJEDAPDjjz/CyckJPXv2xP3797FgwYJMDZCIiIgos2WoBqh06dLq/7u4uGDXrl2ZFhARERFRVstQDVD16tXx9OlTrfK4uDhUr17938ZERERElKUylAAdOnRIa/JDAHjz5g2OHDnyr4MiIiIiykrpagI7e/as+v8XLlxAbGys+n5CQgJ2796NXLlyZV50RERERFkgXQlQyZIloVKpoFKpUmzqsrS0xOzZszMtOCIiIqKskK4msBs3buDatWsQEZw4cQI3btxQ3+7cuYO4uDh06tQpXQHMnTsXXl5esLCwgJ+f3yeb0GJiYhAQEIBChQrByMgI/fv319pm+fLl6iTtw9ubN2/SFRcRERH9d6WrBsjDwwPv379HYGAgHB0d4eHh8a8Ovn79evTv3x9z586Fv78/FixYgLp16+LChQvIkyeP1vZv376Fi4sLhg8fjunTp6e6X1tbW1y6dEmjzMLC4l/FSkRERP8d6e4EbWpqim3btmXKwadNm4bOnTujS5cuKFKkCGbMmAF3d3fMmzcvxe09PT0xc+ZMBAYGws7OLtX9qlQquLm5adw+5e3bt4iLi9O4ERER0X9XhkaBNW7cGFu3bv1XB3737h3Cw8NRq1YtjfJatWrh+PHj/2rfL168gIeHB3Lnzo0GDRogIiLik9tPmjQJdnZ26pu7u/u/Oj4RERHptwxNhJg/f378+OOPOH78OPz8/GBlZaXxeN++fT+7j4cPHyIhIQGurq4a5a6urhqjy9KrcOHCWL58OXx8fBAXF4eZM2fC398fZ86cQYECBVJ8zrBhwzBw4ED1/bi4OCZBRERE/2EZSoAWL14Me3t7hIeHIzw8XOMxlUqVpgTow+0/JCJaZelRvnx5lC9fXn3f398fpUqVwuzZszFr1qwUn2Nubg5zc/MMH5OIiIi+LBlKgG7cuPGvD+zs7AxjY2Ot2p779+9r1Qr9G0ZGRihTpgyuXLmSafskIiKiL1uG+gB9SEQgIul+npmZGfz8/BASEqJRHhISgooVK/7bsNREBJGRkerFW4mIiIgynACtXLkSPj4+sLS0hKWlJYoXL45Vq1alax8DBw7E4sWLsXTpUkRFRWHAgAGIjo5Gjx49ACT1zQkMDNR4TmRkJCIjI/HixQs8ePAAkZGRuHDhgvrxsWPHYs+ePbh+/ToiIyPRuXNnREZGqvdJRERElKEmsGnTpmHkyJHo3bs3/P39ISI4duwYevTogYcPH2LAgAFp2k+rVq3w6NEjjBs3DjExMShWrBh27dqlnl8oJiYG0dHRGs/x9fVV/z88PBxr166Fh4cHbt68CQB4+vQpunXrhtjYWNjZ2cHX1xeHDx9G2bJlM/JSiYiI6D9IJRlov/Ly8sLYsWO1amdWrFiBMWPGZEofISXFxcXBzs4Oz549g62tLQDAb/BKxeIJ/znw8xsREREZuJS+v1OToSawmJiYFPvpVKxYETExMRnZJREREZHOZCgByp8/P3777Tet8vXr16c61w4RERGRvshQH6CxY8eiVatWOHz4MPz9/aFSqXD06FHs378/xcSIiIiISJ9kqAaoWbNm+Ouvv+Ds7IytW7di8+bNcHZ2xokTJ9CkSZPMjpGIiIgoU2WoBggA/Pz8sHr16syMhYiIiEgnMpwAJSQkYMuWLYiKioJKpUKRIkXQqFEjmJhkeJdEREREOpGhbOXcuXNo1KgRYmNjUahQIQDA5cuX4eLigu3bt8PHxydTgyQiIiLKTBnqA9SlSxcULVoU//zzD06fPo3Tp0/j9u3bKF68OLp165bZMRIRERFlqgzVAJ05cwanTp2Cg4ODuszBwQETJkxAmTJlMi04IiIioqyQoRqgQoUK4d69e1rl9+/fR/78+f91UERERERZKUMJ0MSJE9G3b19s3LgR//zzD/755x9s3LgR/fv3x+TJkxEXF6e+EREREembDDWBNWjQAADQsmVLqFQqAEDykmINGzZU31epVEhISMiMOImIiIgyTYYSoIMHD2Z2HEREREQ6k6EEqEqVKpkdBxEREZHOZHjWwjdv3uDs2bO4f/8+EhMTNR775ptv/nVgRERERFklQwnQ7t27ERgYiIcPH2o9xn4/REREpO8ylAD17t0bLVq0wKhRo+Dq6prZMVE6+A1eqdixw38OVOzYRERE/0aGhsHfv38fAwcOZPJDREREX6QMJUDNmzfHoUOHMjkUIiIiIt3IUBPYnDlz0KJFCxw5cgQ+Pj4wNTXVeLxv376ZEhwRERFRVshQArR27Vrs2bMHlpaWOHTokHoyRCCpEzQTICIiItJnGUqARowYgXHjxmHo0KEwMspQKxoRERGRYjKUvbx79w6tWrVi8kNERERfpAxlMB06dMD69eszOxYiIiIinchQE1hCQgKmTJmCPXv2oHjx4lqdoKdNm5YpwRERERFlhQwlQH///Td8fX0BAOfOncvUgIiIiIiyGleDJyIiIoOTrgSoadOmn91GpVJh06ZNGQ6IiIiIKKulKwGys7PLqjiIiIiIdCZdCdCyZcuyKg4iIiIineFEPkRERGRwMtQJmuhz/AavVOzY4T8HKnZsIiL6MrAGiIiIiAwOEyAiIiIyOEyAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoPDBIiIiIgMDhMgIiIiMjhMgIiIiMjgMAEiIiIig8MEiIiIiAwOEyAiIiIyOEyAiIiIyOAwASIiIiKDwwSIiIiIDI7iCdDcuXPh5eUFCwsL+Pn54ciRI6luGxMTg4CAABQqVAhGRkbo379/ittt2rQJ3t7eMDc3h7e3N7Zs2ZJF0RMREdGXSNEEaP369ejfvz+GDx+OiIgIVKpUCXXr1kV0dHSK2799+xYuLi4YPnw4SpQokeI2YWFhaNWqFdq3b48zZ86gffv2aNmyJf7666+sfClERET0BVE0AZo2bRo6d+6MLl26oEiRIpgxYwbc3d0xb968FLf39PTEzJkzERgYCDs7uxS3mTFjBmrWrIlhw4ahcOHCGDZsGGrUqIEZM2akGsfbt28RFxencSMiIqL/LsUSoHfv3iE8PBy1atXSKK9VqxaOHz+e4f2GhYVp7bN27dqf3OekSZNgZ2envrm7u2f4+ERERKT/FEuAHj58iISEBLi6umqUu7q6IjY2NsP7jY2NTfc+hw0bhmfPnqlvt2/fzvDxiYiISP+ZKB2ASqXSuC8iWmVZvU9zc3OYm5v/q2MSERHRl0OxGiBnZ2cYGxtr1czcv39fqwYnPdzc3DJ9n0RERPTfolgCZGZmBj8/P4SEhGiUh4SEoGLFihneb4UKFbT2uXfv3n+1TyIiIvpvUbQJbODAgWjfvj1Kly6NChUqYOHChYiOjkaPHj0AJPXNuXPnDlauXKl+TmRkJADgxYsXePDgASIjI2FmZgZvb28AQL9+/VC5cmVMnjwZjRo1wrZt27Bv3z4cPXpU56+PiIiI9JOiCVCrVq3w6NEjjBs3DjExMShWrBh27doFDw8PAEkTH348J5Cvr6/6/+Hh4Vi7di08PDxw8+ZNAEDFihWxbt06jBgxAiNHjkS+fPmwfv16lCtXTmevi4iIiPSb4p2ge/XqhV69eqX42PLly7XKROSz+2zevDmaN2/+b0MjIiKi/yjFl8IgIiIi0jUmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcJgAERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwTFROgAiXfMbvFKR44b/HKjIcYmISBtrgIiIiMjgsAaISE+wZoqISHeYABHRJzExI6L/IjaBERERkcFhAkREREQGhwkQERERGRwmQERERGRwmAARERGRwWECRERERAaHCRAREREZHCZAREREZHCYABEREZHBYQJEREREBocJEBERERkcrgVGRF8kpdYoA7hOGdF/AWuAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoPDBIiIiIgMDhMgIiIiMjhMgIiIiMjgMAEiIiIig8MEiIiIiAwOEyAiIiIyOEyAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoOjeAI0d+5ceHl5wcLCAn5+fjhy5Mgntw8NDYWfnx8sLCyQN29ezJ8/X+Px5cuXQ6VSad3evHmTlS+DiIiIviCKJkDr169H//79MXz4cERERKBSpUqoW7cuoqOjU9z+xo0bqFevHipVqoSIiAj88MMP6Nu3LzZt2qSxna2tLWJiYjRuFhYWunhJRERE9AUwUfLg06ZNQ+fOndGlSxcAwIwZM7Bnzx7MmzcPkyZN0tp+/vz5yJMnD2bMmAEAKFKkCE6dOoWpU6eiWbNm6u1UKhXc3NzSHMfbt2/x9u1b9f24uLgMviIiIiL6EihWA/Tu3TuEh4ejVq1aGuW1atXC8ePHU3xOWFiY1va1a9fGqVOn8P79e3XZixcv4OHhgdy5c6NBgwaIiIj4ZCyTJk2CnZ2d+ubu7p7BV0VERERfAsUSoIcPHyIhIQGurq4a5a6uroiNjU3xObGxsSluHx8fj4cPHwIAChcujOXLl2P79u0IDg6GhYUF/P39ceXKlVRjGTZsGJ49e6a+3b59+1++OiIiItJnijaBAUnNVR8SEa2yz23/YXn58uVRvnx59eP+/v4oVaoUZs+ejVmzZqW4T3Nzc5ibm2cofiIiIvryKFYD5OzsDGNjY63anvv372vV8iRzc3NLcXsTExM4OTml+BwjIyOUKVPmkzVAREREZFgUqwEyMzODn58fQkJC0KRJE3V5SEgIGjVqlOJzKlSogB07dmiU7d27F6VLl4apqWmKzxERREZGwsfHJ/OCJyJKhd/glYodO/znQMWOTfSlUXQY/MCBA7F48WIsXboUUVFRGDBgAKKjo9GjRw8ASX1zAgP/94Hu0aMHbt26hYEDByIqKgpLly7FkiVL8N1336m3GTt2LPbs2YPr168jMjISnTt3RmRkpHqfRERERIr2AWrVqhUePXqEcePGISYmBsWKFcOuXbvg4eEBAIiJidGYE8jLywu7du3CgAED8OuvvyJnzpyYNWuWxhD4p0+folu3boiNjYWdnR18fX1x+PBhlC1bVuevj4iIiPST4p2ge/XqhV69eqX42PLly7XKqlSpgtOnT6e6v+nTp2P69OmZFR4RERH9Bym+FAYRERGRrjEBIiIiIoPDBIiIiIgMDhMgIiIiMjhMgIiIiMjgMAEiIiIig8MEiIiIiAwOEyAiIiIyOEyAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4Ci+GjwREWU9v8ErFTt2+M+Bih2bKDWsASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoPDBIiIiIgMDhMgIiIiMjhMgIiIiMjgMAEiIiIig8MEiIiIiAwOEyAiIiIyOEyAiIiIyOBwMVQiIlIMF2klpbAGiIiIiAwOEyAiIiIyOGwCIyIiSoFSzXNsmtMN1gARERGRwWENEBER0ReENVOZgzVAREREZHCYABEREZHBYQJEREREBocJEBERERkcJkBERERkcDgKjIiIiP61L210GmuAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoPDBIiIiIgMDhMgIiIiMjhMgIiIiMjgMAEiIiIig6N4AjR37lx4eXnBwsICfn5+OHLkyCe3Dw0NhZ+fHywsLJA3b17Mnz9fa5tNmzbB29sb5ubm8Pb2xpYtW7IqfCIiIvoCKZoArV+/Hv3798fw4cMRERGBSpUqoW7duoiOjk5x+xs3bqBevXqoVKkSIiIi8MMPP6Bv377YtGmTepuwsDC0atUK7du3x5kzZ9C+fXu0bNkSf/31l65eFhEREek5RROgadOmoXPnzujSpQuKFCmCGTNmwN3dHfPmzUtx+/nz5yNPnjyYMWMGihQpgi5duqBTp06YOnWqepsZM2agZs2aGDZsGAoXLoxhw4ahRo0amDFjho5eFREREek7xVaDf/fuHcLDwzF06FCN8lq1auH48eMpPicsLAy1atXSKKtduzaWLFmC9+/fw9TUFGFhYRgwYIDWNp9KgN6+fYu3b9+q7z979gwAEBcXpy5LePs6Ta8rK3wYx8cYl7ZPxQUoFxvjSh99jQv4Ms99xqVNX88xxpU+H8aV/H8R+fwTRSF37twRAHLs2DGN8gkTJkjBggVTfE6BAgVkwoQJGmXHjh0TAHL37l0RETE1NZU1a9ZobLNmzRoxMzNLNZbRo0cLAN5444033njj7T9wu3379mfzEMVqgJKpVCqN+yKiVfa57T8uT+8+hw0bhoEDB6rvJyYm4vHjx3Bycvrk89IiLi4O7u7uuH37Nmxtbf/VvjKbvsbGuNKHcaUP40o/fY2NcaWPIcQlInj+/Dly5sz52W0VS4CcnZ1hbGyM2NhYjfL79+/D1dU1xee4ubmluL2JiQmcnJw+uU1q+wQAc3NzmJuba5TZ29un9aWkia2trV6dcB/S19gYV/owrvRhXOmnr7ExrvT5r8dlZ2eXpu0U6wRtZmYGPz8/hISEaJSHhISgYsWKKT6nQoUKWtvv3bsXpUuXhqmp6Se3SW2fREREZHgUbQIbOHAg2rdvj9KlS6NChQpYuHAhoqOj0aNHDwBJTVN37tzBypUrAQA9evTAnDlzMHDgQHTt2hVhYWFYsmQJgoOD1fvs168fKleujMmTJ6NRo0bYtm0b9u3bh6NHjyryGomIiEj/KJoAtWrVCo8ePcK4ceMQExODYsWKYdeuXfDw8AAAxMTEaMwJ5OXlhV27dmHAgAH49ddfkTNnTsyaNQvNmjVTb1OxYkWsW7cOI0aMwMiRI5EvXz6sX78e5cqV0/nrA5Ka10aPHq3VxKYP9DU2xpU+jCt9GFf66WtsjCt9GJcmlUhaxooRERER/XcovhQGERERka4xASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4Ci+FAZRQkICjh49iuLFi8PBwUHpcNTev3+Pbt26YeTIkcibN6/S4XwRVq5ciVatWmkNZ3337h3WrVuHwMBAhSLTT7NmzUqxXKVSwcLCAvnz50flypVhbGys48jov0BEEB0djezZs8PS0lLpcPTPZ1cLo3SrVq2aPHnyRKv82bNnUq1aNd0H9P+8vLzk4cOHWuVPnjwRLy8vBSL6H3Nzc7l+/bqiMaTEzs5Orl27pnQYGpYvXy6///67+v7gwYPFzs5OKlSoIDdv3lQwMhEjIyO5d++eVvnDhw/FyMhIgYj+5/Dhw9K2bVspX768/PPPPyIisnLlSjly5IhiMXl6eoqVlZWoVCpxdHQUBwcHUalUYmVlJa6urqJSqSRfvnwSHR2t07jev38vy5cvl5iYGJ0elzJXQkKCmJqayuXLl5UORUSSvgPTetMFNoFlgUOHDuHdu3da5W/evMGRI0cUiCjJzZs3kZCQoFX+9u1b3LlzR4GI/sfHxwfXr19XNIaUNGnSBFu3blU6DA0TJ05U/5oLCwvDnDlzMGXKFDg7O2PAgAGKxiapLDz8zz//pHl9nqywadMm1K5dG5aWloiIiMDbt28BAM+fP8fEiRMVi2vixIkoU6YMrly5gkePHuHx48e4fPkyypUrh5kzZyI6Ohpubm46/7uamJigZ8+e6vdJ3zx9+hSLFy/GsGHD8PjxYwDA6dOnFb+O6RsjIyMUKFAAjx49UjoUAElrbDo4OHzylryNLrAJLBOdPXtW/f8LFy5oLMqakJCA3bt3I1euXDqPa/v27er/79mzR+OLKCEhAfv374enp6fO4/rQhAkT8N133+HHH3+En58frKysNB5XauG+/Pnz48cff8Tx48dTjKtv3746j+n27dvInz8/AGDr1q1o3rw5unXrBn9/f1StWlXn8QCAr68vVCoVVCoVatSoAROT/11aEhIScOPGDdSpU0eR2ABg/PjxmD9/PgIDA7Fu3Tp1ecWKFTFu3DjF4hoxYgQ2bdqEfPnyqcvy58+PqVOnolmzZrh+/TqmTJmiMdu9rpQrVw6RkZHqmfn1xdmzZ/H111/Dzs4ON2/eRNeuXeHo6IgtW7bg1q1b6qWTdMHBwSHFhD8lyYmark2ZMgWDBw/GvHnzUKxYMUViSHbw4EFFj/8xJkCZqGTJkuovgerVq2s9bmlpidmzZ+s8rsaNGwNI6lfQoUMHjcdMTU3h6emJX375RedxfSj5y/Gbb77RuKAk1yikVHOlC4sXL4a9vT3Cw8MRHh6u8ZhKpVIkAbK2tsajR4+QJ08e7N27V107YGFhgdevX+s8HuB/51hkZCRq164Na2tr9WNmZmbw9PRU5Es82aVLl1C5cmWtcltbWzx9+lT3Af2/mJgYxMfHa5XHx8erf0DlzJkTz58/13Vo6NWrFwYOHIjbt2+nmPwXL15c5zEBSWtIBgUFYcqUKbCxsVGX161bFwEBATqNZcaMGer/P3r0COPHj0ft2rVRoUIFAEk1tHv27MHIkSN1GteH2rVrh1evXqFEiRIwMzPT6guky8SsSpUqOjtWWjABykQ3btyAiCBv3rw4ceIEXFxc1I+ZmZkhe/bsinRmTExMBJC0ltrJkyfh7Oys8xg+R99+GSS7ceOG0iFoqVmzJrp06QJfX19cvnwZ9evXBwCcP39esZq80aNHAwA8PT3RunVrvVtrKEeOHLh69arW+3P06FFFO7hXq1YN3bt3x+LFi+Hr6wsAiIiIQM+ePdU/ov7++294eXnpPLZWrVoB0KzlVKlUiv8oOXnyJBYsWKBVnitXLo1ad1348Adls2bNMG7cOPTu3Vtd1rdvX8yZMwf79u1TrHn6wyRNaR+2knyOLhJsJkCZKLmqODnh0Df6+GWeTN9+GXzs3bt3uHHjBvLly6fRvKOEX3/9FSNGjMDt27exadMmODk5AQDCw8PRpk0bRWPz9vZGZGSk1uLDf/31F4yNjVG6dGlF4urevTv69euHpUuXQqVS4e7duwgLC8N3332HUaNGKRITACxZsgTt27eHn58fTE1NASTV/tSoUQNLliwBkFTjp0QNrb5eLywsLBAXF6dVfunSJY0fnbq2Z88eTJ48Wau8du3aGDp0qAIRJfm41l9Jya0k8pklSHWWYOukq7WB0ddROn369JGZM2dqlc+ePVv69eun+4A+kjxKp0KFCnozSufly5fSqVMnMTY2FmNjY/WIsD59+sikSZMUi0tflSlTRjZs2KBVvmnTJilbtqwCEf3PDz/8IJaWlqJSqUSlUomFhYWMGDFC0ZiSRUVFybZt22Tr1q1y8eJFpcPRa127dpXGjRvLu3fvxNraWq5fvy63bt0SX19fRa9jefLkkSlTpmiVT5kyRfLkyaNARP8THx8vGzdulB9//FHGjx8vmzdvlvj4eJ3HcfPmzTTfdIEJUBYoWLCg7N+/X0REjh8/LpaWlrJgwQJp2LChNGnSRLG4cubMKadOndIqDw8Pl1y5cikQ0f9s3LhRLC0tpUuXLmJubq5ONH799VepW7euYnH17dtX/Pz85MiRI2JlZaWOa9u2bVKyZElFYvLw8JCxY8fqfGh0Wnz4Hn3o+vXrYm1trUBEml6+fCknT56Uv/76S54/f650OHpv5cqVUrFiRcmRI4f6S2n69OmydetWxWJ69uyZ+Pv7i729vRgbG4u7u7uYmppK5cqV5cWLF4rFtWzZMjEyMpJ69erJjz/+KD/++KPUr19fjI2NZdmyZYrFdeXKFSlQoIBky5ZNfH19pWTJkpItWzYpVKiQXL16VbG49AEToCxgaWkpt27dEhGR77//Xtq3by8iIufOnRNnZ2fF4jI3N5crV65olV+5ckXMzc0ViOh/SpYsKStWrBAREWtra/WXaEREhLi6uioWV548eSQsLEwrritXroiNjY0iMc2aNUtKlSolxsbG8vXXX0twcLC8efNGkVg+5ujoKMePH9cqP3bsmNjb2ysQkX6Lj4+XxYsXS5s2baRGjRpSrVo1jZuS5s6dK87OzjJ+/HixtLRUn/vLli2TqlWrKhqbiMj+/fvl559/lsmTJ0tISIjS4YiIyJ9//ikBAQHqRCMgIED+/PNPRWOqW7eu1KlTRx49eqQue/jwodSpU0fq1aunYGTKJ9jsA5QF9HGUDpA0vHb37t0anfQA4I8//lB8pmN9HaXz4MEDZM+eXav85cuXaR7+mtn69OmDPn364MyZM1i6dCn69u2LXr16ISAgAJ06dUKpUqUUiQtI6qA9bNgwbNu2TT3dwtOnT/HDDz+gZs2aisXVpEmTFP9eH864HBAQgEKFCuk0rn79+mH58uWoX78+ihUrptg5lZLZs2dj0aJFaNy4MX766Sd1eenSpfHdd98pGFmS6tWrpzjaVknlypXDmjVrlA5DQ2hoKP788084Ojqqy5ycnPDTTz/B399fsbjmzZuHUaNGoX///pgwYYK6z4+9vT1mzJiBRo0aZXkMTICygD6O0gGSho/27t0bDx48UF849u/fj19++UXxkQL6OkqnTJky2LlzJ/r06QMA6i+oRYsWqYe6KqVEiRKYOXMmpk6dirlz52LIkCHquT769euHjh076vwL9ZdffkHlypXh4eGhHtUUGRkJV1dXrFq1SqexfMjOzg5bt26Fvb09/Pz8ICKIiIjA06dPUatWLaxfvx6TJ0/G/v37dfqlsG7dOvz222+oV6+ezo6ZVjdu3FD/DT9kbm6Oly9fKhDR/+zfvx/79+/H/fv3tQadLF26VGdxxMXFqecoS6lj9oeUmsvM3Nw8xWkUXrx4ATMzMwUiSqIPCTYToCygr6N0OnXqhLdv32LChAn48ccfASQNW543b57iazTp6yidSZMmoU6dOrhw4QLi4+Mxc+ZMnD9/HmFhYQgNDVUsLiBprbItW7Zg2bJlCAkJQfny5dG5c2fcvXsXw4cPx759+7B27VqdxpQrVy6cPXsWa9aswZkzZ2BpaYmOHTuiTZs26lFOSnBzc0NAQADmzJkDI6OkCfATExPRr18/2NjYYN26dejRoweGDBmCo0eP6iwuMzMz9aSW+sbLyyvFiRD/+OMPeHt7KxQVMHbsWIwbNw6lS5dGjhw5FK01c3BwQExMDLJnzw57e/sUYxGFpw1o0KABunXrhiVLlqBs2bIAkkZl9ujRA998840iMQF6kmDrpKGN9M79+/f1rhOovo7SOXv2rAQGBkrRokWlSJEi0rZtWzl79qxi8YSHh0vv3r3FyclJsmfPLoMGDZKoqCiNbU6cOCEWFhYKRah/nJ2d5dKlS1rlly5dEicnJxFJ+jvb2dnpNK6pU6dKr169JDExUafHTYulS5dKrly5ZN26dWJlZSXBwcEyfvx49f+V4ubmJitXrlTs+B86dOiQvH//Xv3/T92U8uTJE/nmm29EpVKJmZmZmJmZiZGRkTRu3FiePn2qWFxFihRR9/X5sH/lzJkzpVSpUjqJgTVAmeTs2bMoVqwYjIyMPjvZk1IzqCa7f/8+Ll26BJVKhUKFCik6d8aHJkyYgOHDh+PChQtITEyEt7e3xozCSvHx8cGKFSuUDkOtTJkyqFmzJubNm4fGjRunWLPi7e2N1q1bKxBdUn+u2bNnIyoqCiqVCoULF0bv3r1RuHBhReIBkubWuXjxIgoWLKhRfvHiRfUvcwsLC53XJhw9ehQHDx7EH3/8gaJFi2r9LTdv3qzTeD7UsWNHxMfH4/vvv8erV68QEBCAXLlyYebMmYqdW0DSnFwVK1ZU7Pgf+nD+Mn2dy8ze3h7btm3DlStXEBUVBSDp+qB0zePgwYPx7bff4s2bNxARnDhxAsHBwZg0aRIWL16skxhUIp+ZkYjSxMjICLGxsciePTuMjIy0JnvShxlU4+Li8O233yI4OFjdbm5sbIxWrVrh119/VXSxyhUrVqB58+Za0+0rrVq1amjXrh2aN2+u6PvzoVu3bund+kzJNm7ciDZt2qB06dLqPlJ//vknTp48ibVr16JFixaKxNW3b18EBwfjhx9+QJkyZaBSqXDixAlMnDgRAQEBmDlzJhYvXozly5frtAmsY8eOn3x82bJlOork0x4+fIjExMQUBwTo2pAhQ2Btba3o8hKpefr0KZYsWaJO/r29vdGpUye9uXYkfyfpS2f7RYsWYfz48bh9+zaApCb0MWPGoHPnzjo5PhOgTHLr1i3kyZMHKpUKt27d+uS2Sn15tWzZEpGRkZg9ezYqVKgAlUqF48ePo1+/fihevDh+++03ReICABcXF7x69QoNGzZEu3btUKdOHcVnXAaSvjg3bNiAp0+fol69emjfvj3q1aunaOdBfZY3b160a9dOa4HR0aNHY9WqVbh+/boicSUkJOCnn37CnDlzcO/ePQCAq6sr+vTpgyFDhsDY2BjR0dEwMjJC7ty5FYlR37x+/RoigmzZsgFIusZt2bIF3t7eqFWrlmJx9evXDytXrkTx4sVRvHhxrVqzadOmKRLXqVOnULt2bVhaWqJs2bIQEZw6dQqvX7/G3r17FR2duWTJEkyfPh1XrlwBABQoUAD9+/dHly5ddBrH9u3bUbduXa2/mVIJNhMgA2JlZYU9e/bgq6++0ig/cuQI6tSpo+jIjvj4eOzevRvBwcHYtm0bLC0t0aJFC7Rr107x6u7ExER1h+ItW7bA2NgYzZs3R9u2bRWp9k5ISMD06dPx22+/ITo6Gu/evdN4XKlVpwEgW7ZsOHv2rFb1+pUrV1CiRAm8evVK5zHFx8djzZo1qF27Ntzc3NSjdZQalfOlqFWrFpo2bYoePXrg6dOnKFSoEMzMzPDw4UNMmzYNPXv2VCSuatWqpfqYSqXCgQMHdBjN/1SqVAn58+fHokWL1D/e4uPj0aVLF1y/fh2HDx9WJK6RI0di+vTp6NOnj8YirXPmzEG/fv0wfvx4ncVibGyM2NhYuLi4wNjYWN2BXClMgDLJ9u3b07ytUj3v8+TJg507d8LHx0ej/OzZs6hXrx7++ecfReL62KtXr7BlyxasXbsW+/btQ+7cuXHt2jWlwwIAvHnzBjt27MCECRPw999/K9KcOWrUKCxevBgDBw7EyJEjMXz4cNy8eRNbt27FqFGjFFmhPlm9evXQokULraadZcuWYd26ddizZ48icWXLlg1RUVF60XRYqlQp7N+/Hw4ODvD19f1kc8Tp06d1GJkmZ2dnhIaGomjRoli8eDFmz56NiIgIbNq0CaNGjVL3J6EklpaWiIiI0OrrduHCBZQuXVqR5B9I+jvOnj1bawRycHAw+vTpg4cPH+osFjc3NyxatAgNGzaEkZER7t27p2gfVOXbGP4jGjdurHE/pT5AyZTqAzRixAgMHDgQK1euRI4cOQAAsbGxGDx4sF61p2fLlg21a9fGkydPcOvWLb250MbGxmLdunVYvXo1zp49izJlyigSx5o1a7Bo0SLUr18fY8eORZs2bZAvXz4UL14cf/75p6IJ0DfffIMhQ4YgPDwc5cuXB5DUB2jDhg0YO3asxg8FXf4QKFeuHCIiIvQiAWrUqBHMzc0BaF839MmrV69gY2MDANi7dy+aNm0KIyMjlC9f/rPN/Lryzz//QKVSIVeuXEqHAltbW0RHR2slQLdv31a/j0pISEhIcRFiPz8/xMfH6zSWHj16oFGjRlCpVFCpVHBzc0t1Wy6G+oUKCQmRUqVKye7du+XZs2cSFxcnu3fvltKlS8vevXsVi6tkyZJibW0tpqamki9fPsmXL5+YmpqKtbW1+Pr6atyU8PLlS1m9erXUrVtXTE1NJW/evDJ8+HC5cOGCIvGIJK07tHTpUvn666/FxMREChYsKGPGjElxSRFdyZYtm3qpFTc3NwkPDxcRkWvXromtra1icYmIegqDz92MjIx0Gtdvv/0mefPmldmzZ8vx48flzJkzGjfS5uPjIzNnzpTo6GixtbVVL3Fy6tQpRZenSUhIkLFjx4qtra0YGRmJkZGR2NnZybhx4yQhIUGxuPr06SO5c+eWdevWSXR0tNy+fVuCg4Mld+7cii7S2rt3bxkwYIBW+aBBg6RXr146jycqKkp27NghKpVKli9fLlu3bk3xpgusAcoC/fv3x/z58zX62tSuXRvZsmVDt27dFKvR0Odfm23atMGOHTuQLVs2tGjRAocOHVK87w+Q1FHWwcEBLVu2xMSJExWr9flQ7ty5ERMTgzx58iB//vzqDpYnT55U1ywo5eNZefVFq1atAECjdkwfRmbqs1GjRiEgIAADBgxAjRo11P1H9u7dm+IEdroyfPhwLFmyRL2Ug4jg2LFjGDNmDN68eYMJEyYoEtfUqVOhUqkQGBiorlkxNTVFz549NWY6VsKSJUuwd+9ejVrZ27dvIzAwEAMHDlRvp4sO5IULF0ahQoUQGBiIpk2bKlo7xj5AWcDS0hInTpxIsa9NuXLlFF0PLC2Cg4PxzTff6HRIekBAANq2bYvatWvrxeivZHv37sXXX3+tnj1YHwwdOhS2trb44Ycf1MPOPT09ER0djQEDBih+sU0LHx8f7Nq1C+7u7jo5nj6NzHRwcEjzMGQlO7QDSc2+MTExKFGihPozcOLECdja2io2r1POnDkxf/58rSbUbdu2oVevXrhz547OY0pISMDRo0fh4+MDCwsLXLt2DSKC/Pnzq0fRKeVTncY/pMsO5ImJibCwsMD58+dRoEABnRwzJUyAskDlypVhamqK1atXa/S1ad++Pd69e6f4EgqfY2tri8jISMUXSKW0+fPPP3H8+HHkz59f0ant08PGxgZnzpwxyHPsw0k1Hz16hPHjx6N27doaI3T27NmDkSNHqhdSpv+xsLDA2bNntSa1vHTpEkqWLKnYD0wLCwtERUXBy8tLkeN/aYoWLYolS5aoa6WUwAQoC1y9ehVNmjTBpUuXkCdPHgBAdHQ0ChYsiK1btyo+A+fn6OrLadasWejWrRssLCwwa9asT26ry469X8oonS+ZrhOglStXfvJxpdbCa9asGapVq4bevXtrlM+ZMwf79u3D1q1bFYkLSKo5+NS5r9Rw83LlyqFcuXJa14w+ffrg5MmT+PPPPxWJq0yZMvjpp59Qo0YNRY7/pdm5cyd++ukn9QLOSmAClEVEBCEhIbh48SJEBN7e3vj666/1ZgbOT9HVl5OXlxdOnToFJycneHp6pvreqFQqnU6gN3bsWAwePBjZsmXD2LFjP7nt6NGjdRLTlzDNQnroOgFycHDQuP/+/Xu8evUKZmZmyJYtm2JNTdbW1oiMjExx3iRfX1+8ePFCkbgAaNU+vX//HpGRkTh37hw6dOiAmTNnKhJXaGgo6tevjzx58mhM6Hr79m3s2rULlSpVUiSuvXv3YsiQIfjxxx/h5+en1YVAyXmnTp48iQ0bNqQ4b5hSy604ODjg1atXiI+Ph5mZGSwtLTUe18VnkgmQgnTdDyKtDLl54kPJ7frFixfX+gLVtY/7IH08zUJyGaDcNAvpoQ/n2JUrV9CzZ08MHjwYtWvXViQGDw8P9O7dG4MHD9Yo//nnnzFnzhy9GW7+oTFjxuDFixeYOnWqYjHcvXsXv/76q8YPzF69eiFnzpyKxfThZ/TDH3NKd7Rft24dAgMDUatWLYSEhKBWrVq4cuUKYmNj0aRJE8WWW/nc+oodOnTI8hiYAClIH74EUqLruN6/f49ChQrh999/h7e3t06OmVb62K6/b98+DBkyBBMnTtT4BTxixAhMnDgRNWvWVDrEz9KXc//UqVNo164dLl68qMjxly9fjs6dO6NOnToaa6ft3r0bixcvRlBQkCJxfcrVq1dRtmxZxTto65vP9e1UarHU4sWLo3v37vj222/VnzsvLy90794dOXLk+Gwt93+Z/gy3IYNlamqKt2/f6mXzoI+PD65fv65XCZC+TrPwJTI2Nsbdu3cVO35QUBCKFCmCWbNmYfPmzerajGPHjqFcuXKKxfUpYWFhsLCwUDSGp0+f4sSJE7h//77W1AtK9efS19Xgr127hvr16wMAzM3N8fLlS6hUKgwYMADVq1dXNAG6du0ali1bhmvXrmHmzJnInj07du/eDXd3dxQtWjTLj88EiLR4eHhoLVaX1fr06YPJkydj8eLFejUMfsKECfjuu+/0ql3/2rVrKa4ubWdnh5s3b+o8ntS8efMm1S/KBQsWwNXVVWexfNyHSkQQExODOXPmwN/fX2dxpKRcuXJYs2aNojGkpGnTphr3k9+zU6dOKTpz/I4dO9C2bVu8fPkSNjY2Gj+ckufh0ZWzZ8+medvixYtnYSSpc3R0xPPnzwEkrbZ+7tw5+Pj44OnTp4otzwEk1ZjVrVsX/v7+OHz4MCZMmIDs2bPj7NmzWLx4MTZu3JjlMbAJTEG6bgYICgpCp06dULlyZZ0cLz2aNGmC/fv3w9raGj4+PlqJhlId9fSxXV+fp1lITEzEhAkTMH/+fNy7dw+XL19G3rx5MXLkSHh6eqJz586KxJVSHyoXFxdUr14dv/zyi/p9VEJiYiKuXr2aYm2Gkp/Vj9dzMzIyUr9nSq4GX7BgQdSrVw8TJ05UfI4dIyMjjQk1P0WpPkABAQEoXbo0Bg4ciAkTJmDmzJlo1KgRQkJCUKpUKcWurRUqVECLFi0wcOBAje/CkydPonHjxjqZz0l/fmpTlnv+/Dlq1aoFd3d3dOzYER06dNCLNXQAwN7eHs2aNVM6DC0HDx5UOgQtS5cuRZMmTeDh4ZHiNAtKGj9+PFasWIEpU6aga9eu6nIfHx9Mnz5dsQRIX2eo/vPPPxEQEIBbt26l2KldyQ7tae0cq+uJU+/cuYO+ffsqnvwAwI0bN9T/j4iIwHfffYfBgwdrzOn0yy+/YMqUKUqFiDlz5uDNmzcAgGHDhsHU1BRHjx5F06ZNFa3J+/vvv7F27VqtchcXFzx69Eg3QehkwQ1KkbW1tVy7dk2nx3z48KHMmDFDSpYsKSYmJlKnTh3ZsGGDvHv3Tqdx0L+TmJgoe/bskZkzZ8qMGTNk7969kpiYqHRYki9fPtm3b5+IaJ7fUVFRYm9vr2RoGuLj4yUiIkIeP36saBwlSpSQFi1ayIULF+TJkyfy9OlTjduXwMbGRqfXsSZNmsj69et1dry0KlOmjOzcuVOrfOfOnVKqVCkFItJvuXLlkmPHjomI5rVi8+bNkjdvXp3EwBogBem6HwQAODk5oV+/fujXrx8iIiKwdOlStG/fHtbW1mjXrh169eql6NTk+uBLaNdXqVSoVavWJ5silJhm4c6dOylO9JmYmIj379/rLI6P9e/fHz4+PujcuTMSEhJQuXJlhIWFIVu2bPj9999RtWpVReK6cuUKNm7cqPeTo36K6KAXxYd9uOrXr4/BgwfjwoUL8PHx0eqvqNQ8WH///XeKgyW8vLxw4cIFBSJKYmxsjJiYGGTPnl2j/NGjR8iePbuiTXNDhgzBhg0boFKpkJiYiGPHjuG7777TWT8uJkBZILVZjVUqFSwsLJA/f35UrlwZAQEBOo7sf2JiYrB3717s3bsXxsbGqFevHs6fPw9vb29MmTJFJ1Pwf26W5Q/pcsblkiVL6n27flrcvHlT50lH0aJFceTIEa21tTZs2KDoApobN25Eu3btACR1or158yYuXryIlStXYvjw4Th27JgicZUrVw5Xr179ohMgXUhpIedx48ZplSnZbFikSBGMHz8eS5YsUXf+f/v2LcaPH48iRYooEhOQeoL69u1bmJmZ6Tia/5kwYQKCgoKQK1cu9ejHhIQEBAQEYMSIETqJgQlQFpg+fToePHiAV69ewcHBASKCp0+fIlu2bLC2tsb9+/eRN29eHDx4UKe/zt+/f4/t27dj2bJl2Lt3L4oXL44BAwagbdu26hV5161bh549e+okAfrwovbmzRvMnTsX3t7eGvOhnD9/Hr169cryWD70JbTr66vRo0ejffv2uHPnDhITE7F582ZcunQJK1euxO+//65YXA8fPoSbmxsAYNeuXWjRogUKFiyIzp07f3YZlqzUp08fDBo0CLGxsSnWZihVw6hv9LUP14fmz5+Phg0bwt3dHSVKlAAAnDlzBiqVSpFzP/m8VqlUWLx4MaytrdWPJSQk4PDhw4otaAskTX+yZs0a/Pjjjzh9+jQSExPh6+ur2xYInTS0GZi1a9dK1apV5erVq+qyK1euSPXq1WXdunVy+/Zt8ff3l2bNmuk0LicnJ3FwcJBevXpJREREits8fvxYPD09dRqXiEjnzp1lxIgRWuWjRo2Sjh076jyeZF9yu74SfcxERHbv3i2VK1cWKysrsbS0FH9/f9mzZ4/O4/hQnjx5ZM+ePRIfHy/u7u6yY8cOERE5d+6con2TVCqV1s3IyEj975dAl+dZYmKiXL58Wc6fPy/v37/XyTHT4+XLl7JgwQIZMGCA9O/fXxYuXCgvXrxQJBZPT0/x9PQUlUol7u7u6vuenp5SsGBBqVWrlvz555+KxCYiMnbsWHn58qVW+atXr2Ts2LE6iYEJUBbImzdvignG6dOnxcvLS0REjh07Jm5ubjqNa+XKlfL69WudHjOtbG1t5fLly1rlly9fFltbWwUiSmJhYSEXLlzQKr9w4YJYWFgoEFHaKZUA6aPRo0eLnZ2dFC5cWPLkySNv3rwREZElS5ZI+fLlFYvr5s2bn7x9CXR1nt24cUOKFy8uRkZGYmRkJB4eHnLq1KksP+6XrmrVqop39k+JkZGR3Lt3T6v84cOHOkv+2QSWBWJiYhAfH69VHh8fj9jYWABAzpw51ZNT6Ur79u11erz0sLS0xNGjR7WqP48eParorLP62q5P6TNmzBgUK1YMt2/fRosWLWBubg4gqYPo0KFDFYvr475SXwr5oH+criZOHTJkCN68eYNVq1bBwsICP//8M3r27IkTJ05k+bHT6vLlyzh06FCKczqNGjVKkZg+nsojISEBf//9Nzw8PBRd41BS6WN55swZODo66iQGJkBZoFq1aujevTsWL16s7vgZERGBnj17onr16gBSHzGQ1fRxVWAgaZROz549ER4ejvLlywNI6gO0dOlSxS4cgP616+s7BweHFC9qHw4ACAoK0ppkTxeaN2+uVfbxgotKLVB84cKFFD+TSo1oAoBJkyZh2LBhWuUJCQlo164dgoODAQDnzp3TSTxHjhxBcHCwesmJsmXLwsPDA69fv9ZaSVwJixYtQs+ePeHs7Aw3NzetGaqVuo7p2wjI5GuESqVCwYIFNd6nhIQEvHjxAj169NBNMDqpZzIwMTEx8vXXX4tKpRIzMzMxMzMTIyMjqVmzpsTGxoqIyIEDB3TeLyI4OFhMTU2lfv36YmZmJg0aNJBChQqJnZ2dBAUF6TSWlKxfv14qVqwoDg4O4uDgIBUrVtSL+T70qV0/PdasWaPzOKdNmyZOTk7Srl07mTVrlsycOVPatWsnzs7OMmHCBOnSpYuYm5vLwoULdRpXWum62fDatWtSvHhxjb4/yf9Xug9Q9uzZZcGCBRpl8fHx0rx5cylcuLDO41GpVOrrZzIrKyu5ceOGzmNJSZ48eeSnn35SOgwtOXPmlJMnT4qIyJYtWyRnzpxy6dIlGT58uFSsWFHn8SxfvlyWLVsmKpVKZs6cKcuXL1ff1q5dK8ePH9dZLEyAslBUVJRs27ZNtm7dKhcvXlQ6HPHx8ZE5c+aIyP8u9ImJidK1a1cZNWqUwtGlzdq1a/Uy+ahXr57cvXs3y4/z4sULWbhwoQQFBUmdOnWkbt26EhQUJIsWLdKL96Vp06Yyb948rfL58+dL06ZNRURk1qxZUqxYMV2Hlia6ToAaNGggjRo1kvv374u1tbVcuHBBjhw5ImXLlpXDhw/rLI6UnDp1Suzt7dU/Qt69eydNmjSRIkWKSExMjM7jMTIykvv372uU2djYyPXr13UeS0p0PSFkWpmbm8vt27dFRKRr167Sr18/ERG5fv262NjYKBbXoUOHFJ+AlwlQFjh06JDSIaQoW7Zs6l9LTk5OcvbsWRFJ6tCr6w7ZGaWvFxldfHGeP39ecubMKfb29tKoUSPp1q2bdO3aVRo1aiT29vaSK1cuOX/+fJbG8DlWVlZy5coVrfIrV66IlZWViIhcvXpVsmXLpuvQ0kTXCZCTk5OcOXNGRJIGAiT/UNq/f7+ULFlSZ3Gk5uDBg2Jraytbt26Vhg0bire3t1YtjK6oVCqxt7dX1xA7ODiISqUSOzs7jTKldOrUKcXkX2n6OgJSRCQhIUEuXbokR44ckdDQUI2bLrAPUBaoWbMm3NzcEBAQgHbt2qFYsWJKhwRAf1cFTg8x4LV7v/32W1SuXBkrVqzQmsDs3bt3CAoKwrfffqvo+mWOjo7YsWOH1jxSO3bsUHdsTF7Fm5L6PCTPz+Ls7Iy7d++iUKFC8PDwwKVLlxSODqhatSpWrVqFZs2aoUiRIggNDYWzs7MisaR1bTKl5M+fHyNHjsSff/6Z4pxOffv2VSSujh07omXLlsiRIwdUKhVq1qwJAPjrr78UnQdIH9bBYwKUBe7evYt169YhODgYU6ZMQbFixdCuXTsEBAQgd+7cisVVqVIlhISEwMfHBy1btkS/fv1w4MABhISEoEaNGorFRWnz119/4dSpUynO3mpmZoYffvgBZcuWVSCy/xk5ciR69uyJgwcPomzZslCpVDhx4gR27dqF+fPnAwBCQkLUHVkNXbFixXD27FnkzZsX5cqVw5QpU2BmZoaFCxcib968Oo+nadOmKZa7uLjA3t4e3bp1U5fpetDExx3W9c3ChQthbW2N0NBQhIaGajymUqkUS4A+NQIypU7uutKjRw+ULl0aO3fuVCdnuqYSQ/5JrQM3btzA2rVrERwcjIsXL6Jy5co4cOCAIrE8fvwYb968Qc6cOZGYmIipU6fi6NGj6l8uSg6JTCsbGxucOXNGkS+HT9FFXLly5cLcuXPRqFGjFB/funUrvv32W9y5cyfLYkiLY8eOYc6cObh06RJEBIULF0afPn1QsWJFReNKC12fX3v27MHLly/RtGlTXL9+HQ0aNMDFixfh5OSEdevW6fyHSXpG5+l7jQx92qNHj7Bq1Sr0799fkeNbWVnhzJkzyi4Do5OGNgMXHx8vO3bskJIlSyo+suNLp6+T++kiruTJ/H7++WeJjIyUmJgYiY2NlcjISPn555/FwcFBZzOopuTdu3cSFBSkl3+ftFJi5NzHHj16JImJiYrGoG8cHBzkwYMHIiJa/YA+vumbs2fPqjseKy0xMVF2794tLVq0EDMzM3F2dlYslmrVqskff/yh2PFF2AcoSx07dgxr1qzBxo0b8ebNG3zzzTeYOHGiTmOIi4tL87a2trZZGAn9W2PGjIGlpSWmTZuG77//Xl1lLCJwc3PD0KFD8f333ysWn6mpKbZs2YKRI0cqFkNqvoQFipM5OjoiKioK9evXx/Xr1xWL48aNG4iPj9eanPTKlSswNTWFp6enzmKZPn26ut/YjBkzdHbcjIqLi0NwcDCWLFmCU6dOKb6m282bN7F06VIsX74cd+7cQdu2bbFz505Uq1ZNsZj0Yh08RdOv/6hhw4aJp6enmJqaSr169WTNmjUprnmiCx/OKfK525egaNGiEh0dnaXH8PX1VU8dn9p6NR+bOHGiPHnyJEvj+tD169fl+PHjcvz4cb0ZBiwiEhQUJL/88ovSYWjx9PQUKysrUalU4ujoqB5BZGVlJa6urqJSqSRfvnxZfm6lVWRkpOKfycqVK8vy5cu1yletWiVVqlTRfUDpNGnSJJ1+JkWSRgC3b99esmXLJkZGRjJkyJAUR0Xqwps3b2Tt2rVSvXp1sbCwkCZNmsiGDRvExMRE8dGiIvqxDh77AGWBihUrom3btmjVqpViIyaSfdgZ7+bNmxg6dCiCgoI0VjZfsWIFJk2apGgnw6CgIHTq1AmVK1dWLIZklpaWuHLlCnLnzg1jY2PExMQge/bsSof1RZgwYQKmTp2KGjVqwM/PD1ZWVhqPK9URNDg4GAsXLsTixYuRL18+AMDVq1fRvXt3dOvWDf7+/mjdujXc3NywceNGRWL80JkzZ1CqVCmdjIRJja2tLU6fPq3VR+Pq1asoXbo0nj59qkxgaWRra4vIyMgs788VExODZcuWYenSpXj58iXatGmDgIAAVKhQAWfOnIG3t3eWHj81zs7O8Pb2Rrt27dCiRQt1H09TU1NF40p269atTz6ui2Vi2ASWBY4fPw4gaXr7U6dOKTq9/YejbcaNG4dp06ahTZs2GrH4+Phg4cKFiiZAz58/R61ateDu7o6OHTuiQ4cOyJUrlyKxlCxZEh07dsRXX30FEcHUqVPVQ5U/puvp7aOiovDnn3+iQoUKKFy4MC5evIiZM2fi7du3aNeunXqpFaUsXrwY9vb2CA8PR3h4uMZjSo6EGTFiBDZt2qROfoCkYctTp05Fs2bNcP36dUyZMgXNmjVTJD59pFKpUlyv8NmzZ4omZmmlq9/2Xl5eaNGiBX799VfUrFkTRkZGOjnu5yQkJKiXnDA2NlY6HC36sA4eE6AscOPGDTRp0gRnz56FSqVSfxCT+2wodfEICwtTD0X+UOnSpdGlSxcFIvqfTZs24dGjR1i9ejWWL1+O0aNH4+uvv0bnzp3RqFEjnSy2mCz5+L///jtUKhX++OMPmJhof1R0vb7P7t270ahRI1hbW+PVq1fYsmULAgMDUaJECYgIateujT179iiaBN24cUOxY3+Kvi5QrM8qVaqESZMmITg4WP0FmpCQgEmTJuGrr75SODr94eHhgaNHjyJPnjzw8PBQdG6dD8XExGDTpk1YsmQJ+vXrh7p166Jdu3aKDDdPtn37dtStWxempqbYvn37J7fVSUWBThraDIy+Tm9fsGBBGThwoFb5wIEDpWDBggpElLrTp09L7969xcLCQpydnaV///5y+fJlncehUqnk3r17Oj9uSipUqCDDhw8XkaR13RwcHOSHH35QP/7DDz9IzZo1lQpPr9WrV09KlSolp0+fVpedPn1a/Pz8pH79+iIisn37dp0t0fG5kUw2NjaK9wE6f/68ODk5Sb58+SQoKEiCgoIkX7584uLiIn///beisaWFLkeMHj16VDp27CjW1tZSqlQpmTZtmpiYmMiFCxd0cvzPuXr1qgwfPlxy584tKpVKAgICZO/evRIfH6/TOD68nqbUB+jDvkA6iUeEfYAym7OzMw4cOIDixYvDzs4OJ06cQKFChXDgwAEMGjQIERERisS1a9cuNGvWDPny5dNYcf3atWvYtGkT6tWrp0hcH4uJicHKlSuxdOlS3LlzB82aNUNMTAwOHjyIKVOmaM0ybCjs7OwQHh6O/PnzIzExEebm5vjrr79QqlQpAEmrcn/99dfqGg0ldOrU6ZOPL126VEeRaIqNjUX79u2xf/9+dW1ifHw8atSogVWrVsHV1RUHDx7E+/fvUatWrSyPZ8WKFWnaTunJ/+7evYs5c+bgzJkzsLS0RPHixdG7d2/1rN76TIk5w168eIHg4GAsXboUf/31F6pUqYKAgAA0btwYLi4uOosjNYmJidizZw+WLFmCHTt2wMbGBg8fPlQ6LMUwAcoCDg4OCA8PR968eZEvXz4sXrwY1apVw7Vr1+Dj46PoshP//PMP5s2bh6ioKIgIvL290aNHD7i7uysWEwC8f/8e27dvx7Jly7B3714UL14cXbp0Qdu2bdXDX9etW4eePXviyZMnOo3t2rVrmDFjBqKioqBSqVCkSBH069dPoz+JLnyYAAHaF/hbt26hcOHCeP36tU7j+lCTJk007r9//x7nzp3D06dPUb16dZ3PHvyxixcv4vLly+oJGgsVKqRoPJR1lJ40NSoqCkuWLMGqVavw+PFjvH//XpE4UvPgwQOsWrUKAwcOBJA0UOCbb77RGriQ2RYvXozq1avrx2S2OqlnMjBfffWVbNmyRURE2rRpI3Xq1JGjR49KYGCgFC1aVLG43r59m+pjyZOMKcXJyUkcHBykV69eEhERkeI2jx8/Fk9PT53GtXv3bjEzM5OyZcvKgAEDpH///lK2bFkxNzeXvXv36jSW4sWLa0wc9vfff8v79+/V948cOSJeXl46jSktEhISpHv37jJ58mTFYtDXBYq/BC9fvpSoqCg5c+aMxk3f1a1bV+7evat0GPL+/XvZtGmT+r4Sw/PTQlcLTSdPEeDu7i6BgYGybNkyuXXrVpYfNyVMgLLA7t271Sf8tWvXpEiRIqJSqcTZ2Vn279+vWFyNGjWShIQErfLY2FhFEzMRkZUrV8rr168VjSElJUuWlCFDhmiVDxkyRHx9fXUay7x58+T3339P9fEffvhBOnfurMOI0u7ixYvi5uam2PFNTU3F3d1dhgwZ8kX0X9EH9+/fl/r16+vlvGHJfVpat26t7lPyxx9/yLlz5xSNKy10lWikl676TL17904OHz4sP/74o1SvXl2dEHl5eUnnzp1l9erVcufOnSyPQ4QJkM7ow/T2ZcuWlaCgII2yu3fvSuHChaVZs2YKRaUtOjpabt++rXQYIiJibm6eYufrS5cuibm5uQIRpd3t27dTTHiVsHPnTkWn3X/w4IHMnj1bKlasKCqVSnx8fGTy5Ml6c57po4CAAKlYsaKcOHFCrKysZO/evbJq1SopVKjQJxPxrHbo0CGxtLSUr7/+WszMzNRf2pMnT9ar61hqDHk5n5QkJ0Rjx46VatWqSbZs2cTY2Fgnx9aPCQsMgKOjo6LDD4GkTtAnTpxQdyK+c+cOqlatCh8fH/z222+KxhYfH4+RI0fCzs4Onp6e8PDwgJ2dHUaMGKFo27mLiwsiIyO1yiMjI/V+ckRvb2/cvHlTp8ccOHCgxm3AgAFo3bo1WrVqhVatWuk0lg85Ozujd+/eOHbsGK5du4ZWrVph5cqV8PT0VHzuJH114MABTJ8+HWXKlIGRkRE8PDzQrl07TJkyBZMmTVIsrqFDh2L8+PEICQmBmZmZurxatWoICwtTLC7KmISEBLx79w5v377F27dvER8fDy8vL50cm/MAGRAnJyfs2bNHPYfHzp07UapUKaxZs0bxybt69+6NLVu2YMqUKRqzVI8ZMwYPHz5Mcf4iXejatSu6deuG69evo2LFilCpVDh69CgmT56MQYMGKRJTWokC4xs+HuFoZGQEFxcX/PLLL58dIaYrXl5eGDp0KEqUKIGRI0dqzJautISEBPz999/w8PBQz9yrlJcvX6qTfEdHRzx48AAFCxaEj48PTp8+rVhcf//9N9auXatV7uLigkePHikQEaXHmzdvcPz4cRw6dAgHDhzAqVOnkDdvXlSuXBm9e/dGlSpVkDNnTt0Eo5N6JtIrly9fluzZs0vbtm0Vb5ZLZmtrK7t27dIq37Vrl9ja2ioQUZLExESZNm2a5MqVSz1HRa5cuWTGjBl6896lRokq7ZcvX2qspn7jxg2ZPn267N69W6dxpObo0aPSs2dPcXFxERsbG2nbtm2K552u9OvXTxYvXiwiIvHx8eLv769eo+zgwYOKxSUiUrp0afXfrVGjRtK+fXv5559/5Pvvv5e8efMqFleuXLnk2LFjIqJ5jm/evFnRuNLK0JvAzM3NJU+ePNK7d2/57bffFJ1njQnQf1xqE66Zm5uLra2tRpmSsmfPnuKkYRcuXFC078iH4uLiJC4uLsXHjh49Km/evNFxRJ+mxIW2Zs2aMm/ePBERefLkibi6ukru3LnFwsJC5s6dq9NYPqRPCxR/KFeuXHLy5EkREdmyZYvkzJlTLl26JMOHD5eKFSsqGtvq1atl2bJlIpI0aaSLi4sYGRmJhYWFrFu3TrG4Bg8eLF999ZXExMSIjY2NXLlyRY4ePSp58+aVMWPGKBZXWulrAqSLhaZFkvqimpmZiY+Pj/Tp00c2btwoDx8+zPLjpoTzAP3HpXXCNUDZSdfGjRuHixcvYtmyZTA3NwcAvH37Fp07d0aBAgUwevRoxWJLC10tvJgeSsyD4uzsjNDQUBQtWhSLFy/G7NmzERERgU2bNmHUqFGIiorSWSwf0qcFij9kYWGBq1evInfu3OjWrRuyZcuGGTNm4MaNGyhRogTi4uKUDlHt1atXuHjxIvLkyaPoe/j+/XsEBQVh3bp1EBGYmJggISEBAQEBWL58uV6ue/WhevXqYcmSJciRI4dOjqdPC00ne/nyJY4cOYKDBw/i0KFDiIiIQMGCBVG1alVUqVIFVapU0UkfS/YB+o9TeibZT2natKnG/X379iF37twoUaIEgKQVsd+9e4caNWooEV666OPvCCU63b969Uo9ceXevXvRtGlTGBkZoXz58p9d/Tkr6dMCxR9ydXXFhQsXkCNHDuzevRtz584FkPQ+6tsXebZs2dSzjivJ1NQUa9aswbhx4xAREYHExET4+vqiQIECSoeGa9euYdmyZbh27RpmzpyJ7NmzY/fu3XB3d0fRokUBJA1G0SV9Wmg6mZWVFerUqYM6deqoYzxy5AhCQkLQtWtXvHjxIsW1+zIbEyADk5YPqK7Y2dlp3P94JW6lZ6f+0imRlOXPnx9bt25FkyZNsGfPHvWIw/v378PW1lbn8STT1wWKO3bsiJYtWyJHjhxQqVSoWbMmAOCvv/5SZFHN5FmB02LatGlZGMnn5cuXT+ezsX9KaGgo6tatC39/fxw+fBgTJkxA9uzZcfbsWSxevBgbN25UJC59Wmj6Y4mJiTh58iQOHTqEgwcP4tixY3j58qXOVopnE5gB+fgDGhUVhbx582LKlCk4ceKEYh/Q9Dh27BhKly6tbibTF7psburUqRNmzpyprmlJ9vLlS/Tp00e93tbt27eRM2dOndYkbNy4EQEBAUhISECNGjWwd+9eAMCkSZNw+PBh/PHHHzqL5UMNGzaEsbExFi1ahLx58+LEiRN49OgRBg0ahKlTp6JSpUqKxAUkvWe3b99GixYtkDt3bgBJTdf29vZo1KiRTmOpVq1amrZTqVQ4cOBAFkeTsoSEBCxfvhz79+/H/fv3kZiYqPG4UnFVqFABLVq0wMCBAzWuBydPnkTjxo1x584dReL6WEREBJYuXYrFixfD2toa7dq1Q69evXRWg3by5El109fRo0fx4sUL5M6dG1WrVkW1atVQrVo1eHp66iQWJkAG5Ev5gH6KPva1AXSbABkbGyMmJkarjfzhw4dwc3PTSdXxp8TGxiImJgYlSpRQT69w4sQJ2NraKlKrAejvAsWUfr1798by5ctRv359dc3Zh6ZPn65IXNbW1vj777/h5eWlcT24efMmChcujDdv3igS14f0YaFpIyMj5MiRQ53wVK1aVb2+oa6xCcyA/Bfmz9DXfF0X/W3i4uIgSSM38fz5c1hYWKgfS0hIwK5du/RickY3Nze4ublplJUtW1ahaJIkJCTA2toaQFIydPfuXRQqVAgeHh64dOmSzuNZuXKlxv3AwECdx5Aet2/fhkqlUtdQKWndunX47bffUK9ePaVD0WBvb4+YmBitSfwiIiIU7XOT0kLTAwYMSHGhaV0kQFFRUelahDgrF2llAmRA9PUD+l+gi8TM3t4eKpUKKpUKBQsW1HpcpVJh7NixWR7Hl6hYsWI4e/Ys8ubNi3LlymHKlCkwMzPDwoULFalNXLZsmfr/KpVKLxOg+Ph4jB07FrNmzcKLFy8AJNVy9OnTB6NHj1as74iZmZliNQafEhAQgCFDhmDDhg1QqVRITEzEsWPH8N133yn6982RIwcSExPRpk0bnDhxAiVLltTapnbt2rC3t9dJPOlJfgCge/fuKFeuXNZ8TpUYe0/K+NLnzxDR3zk0dOHQoUNy8OBBUalUsnnzZjl06JD6dvz4cZ0tIPgl0tcFivVZ9+7dJXv27DJ//nz1CvDz588XNzc36d69u2JxTZ06VXr16qV3E5G+e/dOAgICxMjISFQqlZiamoqRkZG0a9dO4uPjFYtLXxeaTqusvOazD5AB+dLnzwB0P7eNr69vis1bKpUKFhYWyJ8/P4KCgtLceTQz3Lp1C3ny5FF8bbkv3ePHj+Hg4MD3MRV2dnZYt24d6tatq1H+xx9/oHXr1nj27JkicTVp0gQHDx6Eo6MjihYtqlUTtXnzZkXiSnbt2jW9Gp6f1kET+iorr/lMgAyQvn1A00PXnaCHDRuGefPmwcfHB2XLloWI4NSpUzh79iyCgoJw4cIF7N+/H5s3b9bZiJ3du3fD2tpavabbr7/+ikWLFsHb2xu//vqr4mtIUdqlNuz8wwS7UaNGcHR01HFkSXMUHTp0CEWKFNEoj4qKQuXKlfHgwQOdxwQkTR3wKR82L5L+D5r4HCZAlKnevXuHGzduIF++fDAx+bK6gem6Bqhr167IkycPRo4cqVE+fvx43Lp1C4sWLcLo0aOxc+dOnDp1Sicx+fj4YPLkyahXrx7+/vtvlC5dGoMGDcKBAwdQpEgRfgF8QapVq4bTp08jISEBhQoVgojgypUrMDY2RuHChXHp0iX1Arze3t46je1Ln51d1/RteH7yoAkHBwdcuXIFLi4uGrHu2LEDQ4cOxd27d3UaV3pl5TX/y/r2o3/l1atX6NOnj3p5jMuXLyNv3rzo27cvcubMiaFDhyoc4ec9f/5cp8f77bffEB4erlXeunVr+Pn5YdGiRWjTpo1OJ4W7ceOG+stw06ZNaNiwISZOnIjTp0/r3cgY+rTk2p1ly5apJ4qMi4tD586d8dVXX6Fr164ICAjAgAEDsGfPniyP50uanf3BgwfqBLFgwYIaX/BK6Nevn3p4frFixRRvWuWgic9jAmRAhg0bhjNnzuDQoUPqKcgB4Ouvv8bo0aMVTYD0sa8NkLRW0/Hjx7VGnRw/flw9DD0xMVGnEzOamZnh1atXAJK+oJJHmDg6OurV2lH0eT///DNCQkI0Zsm2tbXFmDFjUKtWLfTr1w+jRo1CrVq1dBLPlzA7e3LflZUrV6prWYyNjREYGIjZs2cjW7ZsisSlb8PzDx48CBFB9erVsWnTJo1mVDMzM3h4eCBnzpwKRpg2Hh4eWTbikAmQAdm6dSvWr1+P8uXLayQb3t7euHbtmoKRAXXq1PlsX5uvv/5ap31tAKBPnz7o0aMHwsPDUaZMGahUKpw4cQKLFy/GDz/8AADYs2cPfH19dRaTv78/Bg4cCH9/f5w4cQLr168HkFSjpw/ztFDaPXv2DPfv39dq3nrw4IE6mbW3t9dauyyrZKT5VNezsw8cOBChoaHYsWMH/P39AQBHjx5F3759MWjQIMybN08ncXxM34bnV6lSBUBSjbE+DppI6yKt586dy7ogsmRsGeklS0tL9XDCD4cWRkZGiq2trZKhSZcuXWTcuHFa5T/++KN06dJFRERGjRolfn5+ug5NVq9eLeXLlxcHBwdxcHCQ8uXLy5o1a9SPv3r1SqfDTG/duiUNGjSQ4sWLy+LFi9Xl/fv3lz59+ugsDvr3AgICxMvLSzZv3iy3b9+Wf/75RzZv3ix58+aVdu3aiYhIcHCwIud9WtnY2Oh0agonJyc5ePCgVvmBAwfE2dlZZ3F8TJ+G5585c0YSEhLU///UTSlNmzYVc3NzyZ8/v0yYMEH++ecfncfATtAGpEqVKmjevDn69OkDGxsbnD17Fl5eXujduzeuXr2K3bt3KxabnZ0dwsPDtX5BXb16FX5+fnj27BkuXryIMmXK6LwfkD6Jj4/HmjVrUKtWLeTIkUPpcOhfevHiBQYMGICVK1eqR+OYmJigQ4cOmD59OqysrBAZGQkAKU5gpw90PTAhW7ZsCA8P1xqddv78eZQtWxYvX77USRwf06fh+UZGRoiNjUX27NlhZGSksQDwh1QqlWILAQPQWKT13LlzOl+klU1gBmTSpEmoU6cOLly4gPj4eMycORPnz59HWFgYQkNDFY1NH/vafOjdu3cpjuzIkyePTuMwMTFBz549ERUVpdPjUtawtrbGokWLMH36dFy/fh0ignz58qmX7QD0N/FRSoUKFTB69GisXLlSfW14/fo1xo4diwoVKigWl729PZo0aaLY8T9048YNdafwGzduKBxN6pycnNCvXz/069dPvUhr+/btdbZIKxMgA1KxYkUcP34cP//8M/Lly4e9e/eiVKlSCAsLg4+Pj6Kx6WNfGwC4cuUKOnXqhOPHj2uUi4hiv57KlSuHiIgIeHh46PzYlDWsra1RvHhxpcP4IsycORN16tRRj05TqVSIjIyEhYWFTkbKpUafpp/48Nqwdu1auLq6olOnThrbLF26FA8ePMCQIUN0HZ6WmJgY7N27F3v37oWxsTHq1auH8+fPw9vbO0sXaWUTmAFp27YtqlatiipVqqQ4LFJpa9aswZw5c9SLUxYqVAh9+vRBQEAAgKRfecmjwnTF398fJiYmGDp0aIorTycPD9alDRs2YOjQoRgwYAD8/Py0FgnkF+mX4+XLl/jpp59SnTvm+vXrCkWWdrpuAgOSrgWrV6/GxYsXISLw9vZG27ZtYWlpqbMYUqNvw/M9PT2xdu1aVKxYUaP8r7/+QuvWrRWrIUppkdYuXbqkuEjrkydPsiQGJkAGpHv37ggNDcWVK1fg6uqKKlWqoEqVKqhatSoKFy6sdHh6ycrKCuHh4Xr1/hgZGWmVJbfxK92mT+nTpk0bhIaGon379ikm2P369VMosrTT9ezs+kpfh+dbWFggKipKaxHs69evw9vbG2/evFEkLmdnZ/UirV27dk2xqffJkycoVapUliVpbAIzIAsWLAAAxMbG4tChQzh06BBmzpyJb7/9FtmzZ0dMTIzCEepPX5tk3t7eePjwoSLHTo0+t+lT+vzxxx/YuXOnejj3l0iJ39CXL1/GoUOHUrxWjBo1SufxAPo7PN/d3R3Hjh3TSoCOHTum6DxA06dPR4sWLT5Zo+/g4JCl1zsmQAbIxsYGDg4OcHBwgL29PUxMTODm5qZoTPrY1wYAJk+ejO+//x4TJ06Ej4+P1siEDyew0xX2/fnvcHBwUGSdr7SKj4/HoUOHcO3aNQQEBMDGxgZ3796Fra2tuqO2rkdlLlq0CD179oSzszPc3Nw0as1UKpViCdCmTZuwceNGVK1aVV1Wr149WFpaomXLloolQF26dEH//v3x/v17VK9eHQCwf/9+fP/99xg0aJAiMQFJEzU2btxYKwHS5SKtbAIzIEOGDEFoaCjOnDmDYsWKoXLlyqhSpQoqV64Me3t7RWPTx742wP+amz6OR9eJ2fbt21G3bl2Ymppi+/btn9z2m2++0UlM9O+tXr0a27Ztw4oVKxRrIknNrVu3UKdOHURHR+Pt27fqpXP69++PN2/eYP78+YrE5eHhgV69eulF590P6evwfBHB0KFDMWvWLPWEmhYWFhgyZIhiySKgH4u0MgEyIEZGRnBxccGAAQPQqFEjrQ+qkvSxrw2Az04PkDzbalb7eF6P1LAP0JfF19cX165dg4jA09NTq4bx9OnTCkUGNG7cGDY2NliyZAmcnJzUHZ1DQ0PRpUsXXLlyRZG49LXPUY0aNeDk5KQ1PL9Dhw54/Pgx9u3bp2h8L168QFRUFCwtLVGgQAHFphTRp0Va2QRmQCIiIhAaGopDhw7hl19+gbGxsboTdNWqVRVNiPSxrw2guwTncz7s5/Bxnwf6cjVu3FjpEFJ19OhRHDt2DGZmZhrlHh4euHPnjkJRAS1atMDevXvRo0cPxWJIib4Oz09mbW2NMmXKKB2GXi3SyhogA3bmzBnMmDEDq1evRmJioqI1BwcOHMCIESP0oq/N2bNnUaxYMRgZGeHs2bOf3FZXQ84dHR1x+fJlODs7o1OnTpg5c6Z6qChRVnB0dMTRo0fh7e2tMdT96NGjaNasGe7du6ezWGbNmqX+/8uXLzFt2jTUr18/xWtF3759dRbXx/R5eL6+CA0N1ZtFWpkAGZiIiAj1CLAjR44gLi4OJUuWRLVq1fDzzz8rFpe+9LVJjkXfppG3trbG2bNnkTdvXhgbGyM2Nlbx+UXov61Vq1aws7PDwoUL1UvnuLi4oFGjRsiTJ49OJ/77eARTalQq1RcxdxIl9TFTepFWJkAGxMHBAS9evECJEiXUzV6VK1dWZCTTx/Slrw2g+cG8devWJ7fV1YismjVr4t69e/Dz88OKFSvQqlWrVH9V6mL0BGXch7V5Dg4On/wCePz4sQ4j03T37l1Uq1YNxsbGuHLlCkqXLo0rV67A2dkZhw8f1uq8qoTkry99WelcH4fn6xN9q11nAmRAfv/9d71JeL4Uhw8fRsWKFWFiotldLj4+HsePH0flypV1Ese9e/cwffp0XLt2DZs3b0bt2rVT7cS4ZcsWncREGbNixQq0bt0a5ubmWLFixSe37dChg46iStnr16+xbt06hIeHIzExEaVKldKLJp0lS5Zg+vTp6o7YBQoUQP/+/dGlSxfFYvrc8HwlO7TrC32rXWcCRIrRt18DKUltqOajR4+QPXt2RfpNeXl54dSpU3ByctL5sSlzvH///rOrXZ87dw7FihXTUUTa9CX5/9jIkSMxffp09OnTR734aVhYGObMmYN+/fph/PjxisSlr8Pz9Ym+1a4zASLF6NuvgZQYGRnh3r17Wv1tLl++jNKlSyMuLk6RuNLCx8cHu3btgru7u9Kh0EeaN2+ODRs2pNp0c+7cOdSoUUOnHY0/po/JP5C0hMLs2bPRpk0bjfLg4GD06dNHsdGk+jo8X19NmjRJ8UVaOQyeFHPjxg11YqFvyzs0bdoUQFLyFRQUpNHclJCQgLNnz2otLqhvbt68iffv3ysdBqXgr7/+Qvfu3bFw4UKtx86fP48aNWooVsOSLHkAwscePXqktQCvLiUkJKB06dJa5X5+fjqZPC81+jo8X18tWLAAa9eu1SovWrQoWrduzQSI/ts+rOK8devWJ6vbdb38g52dHYCkLwEbGxuNPg9mZmYoX748unbtqtOY6L9j7969qFy5MhwdHfHTTz+py6OiolCjRg34+/tj3bp1isSm78l/u3btMG/ePEybNk2jfOHChWjbtq1OY/lweH7+/PkxcuRI/Pnnn3o3PF8fxcbGIkeOHFrlLi4uOluXkgkQ6YVq1aqlWN3+7NkzVKtWTefV7clDfD09PfHdd98p+ouX/nuKFCmCXbt2qWcPHjx4MC5evIjq1aujXLly2LBhA4yNjRWJ7UtI/pcsWYK9e/eifPnyAIA///wTt2/fRmBgIAYOHKje7uMkKbNNnz5d4761tTVCQ0O1RrWqVComQB/Rh0VamQCRXtDX6vbRo0crdmz6bytTpgy2bt2KBg0a4OXLl1i0aBFKly6NjRs3Kpb8APqf/J87dw6lSpUCAFy7dg1AUq2Bi4sLzp07p95OF0PjU2u617fh+fpIHxZpZSdoUlRydfu2bdtQp06dFKvbCxUqhN27dysVIjZu3IjffvsN0dHR6sUEk+nz0NYPZ+8l/bV161a0aNECtWrVwtb/a+/eg6Iq3ziAf3dJQCBWUHC1iBUwybgJpmSpiSnZGCLmFQxRSrRi8jJhJSCZOpK3yMoLukymqEGalte4VNqFLAosUZMVHQUVTVGJZJfz+4Nhf66LigrnnJXvZ2Zn2HcPex52dHh43+d9n61bb7s7jORLjtvz5UoOTVo5A0SSkvt0e1paGt555x1ER0fjyy+/RExMDI4dO4ZffvkFr776qmRxkWVr7ADE77//Hh07djQZk/IgRMByk38p3Gx7/rRp03D8+HHJtufLlUKhwMKFC5GYmChZk1bOAJEspKSkyHK63dvbG8nJyRg7dqzJjEpSUhIuXLiA5cuXSx3iTW3YsAHDhg2T3WdKuO0BiA2kPAjx+uR/9erVZsn/vHnzJItNjuS6PZ9ujgkQ0S3Y2dnh0KFDcHd3h6urK/bu3Qt/f38cPXoUwcHBOH/+vCRx5eTkICcnp9Ej99kK4/6TmZmJsLAwUZNZS07+peDk5ISCggJ07drVZPzIkSPo1asXLl68KE1gdFNKqQMgapCVlYVRo0YhODgYgYGBJg+pqNVqY5Lj7u6On376CUB98aNUfzukpKRg8ODByMnJQWVlJf755x+TB91/Jk+eLPqhiCdOnDBud2/bti0uX74MABg/fjwyMzNFjcUSNGzPv5EU2/OpaVgDRLIg11qbkJAQbN++HYGBgZg0aRKmTZuGrKwsHDhwwFjALbYVK1YgIyMD48ePl+T+JD4pku2G5N/d3d2Y/Pv7+0ua/MudXLbnU9NwCYxkQa7T7XV1dairqzMe0Lh582bs27cPXl5eiIuLg7W1tegxtW/fHgUFBfD09BT93iQNKXb0xcbGws3NDcnJyVixYgWmT5+Op556ypj8r1mzRrRYLMGAAQOadJ1CoUBubm4LR0NNwQSIZEGOtTZ6vR7z5s3DxIkTZdVPKyEhAQ4ODkhMTJQ6FBKJFAmQHJN/oubEJTCSBTlOtz/wwAN4//33Jd2J05iamhqsWrUK33zzDfz8/MzOjeH0OjUHpVIJpfL/ZaKjRo3CqFGjJIyIqHkxASJZkGOtDQA8++yzyM/Px4QJEySL4UZFRUUICAgAAJOTbwGePEvNq6amBkVFRY3uNgwLC5MoKqLmwSUwkgW5TrevXLkSc+bMQWRkJIKCgsy2IfOXAInBx8cHO3fuFHUpdteuXXjppZcaPb9GoVCI3p+PqLkxASLJybXWBoDJEsCN+EuA7tWECRMwceJE9OvXT+pQzHh5eSE0NBRJSUlmJ1QT3Q+YAJEsODg44ODBg9BoNFKHIksRERHIyMiAo6PjbZcEv/jiC5Gions1YsQIfP3113Bzc0NMTAyio6Px0EMPSR0WAMDR0RGFhYXcbUj3LR6ESLLQUGtjqXx9fXHy5MkWe3+VSmWs71GpVLd8kOXIzs7GqVOn8Nprr+Hzzz+HRqPBkCFDkJWVhdraWklje/HFFy36/yTR7XAGiGTB0mtt5Nh5ff/+/ejZs6eozQXp3hQWFmLt2rVIT0+Hg4MDoqKiMHXqVLP2CmKorq7GyJEj4eLiAl9fX7PdhvHx8aLHRNScmACRLFh6rY0cEyBHR0f8/vvvsoqJbq68vByffvop1q5di1OnTmHEiBEoLy9HXl4eUlNTMW3aNFHjSU9PR1xcHNq2bYv27dub7DBUKBQoLS0VNR6i5sYEiKgZyDEBkmNMZKq2thbbtm2DVqvFnj174Ofnh9jYWERGRuLBBx8EAGzcuBFTpkwRvc+bWq1GfHw8Zs2adcs/UIgsFc8BIovi6+uLHTt2yG63GNHd6NSpE+rq6jB27FgUFBQYz3e6XmhoKNq1ayd6bNeuXcPo0aOZ/NB9i/+yyaIcP35c8uJQouaydOlSnD59Gh999FGjyQ8AODk5QafTiRsYgOjoaGzatEn0+xKJhTNAREQSycvLQ3h4OGxtbU3Gr169itdffx1r166VKDLAYDAgNTUVu3fvZssVui+xBogsilzrWjZs2IBhw4aZ7V6TEoug5c/Kygrl5eVwdXU1Ga+srIRarYZer5coslt3N2dHc7ofcAaI6BbS0tIaHVcoFLC1tYWXlxf69euHcePGiRzZ7fFvG/mqqqqCIAgQBAGXL182mQEyGAzYsWOHWVIktry8PEnvT9TSmAAR3cLSpUtx7tw5VFdXw8nJCYIg4OLFi7Czs4ODgwPOnj0LDw8P5OXliVaY/e+//0IQBNjZ2QEAysrKsGXLFnTv3h2DBw82Xnf58mVR4qE7165dOygUCigUCjz66KNmrysUCqSkpEgQGVHrwSUwsihiL4FlZmZi1apVSE9PN7YE+PvvvzF58mS88soreOqppzBmzBio1WpkZWWJEtPgwYMRERGBuLg4XLx4Ed7e3mjTpg0qKyuxZMkSTJkyRZQ46O59++23EAQBISEhyM7OhrOzs/E1a2truLu7o3PnzqLHxZYr1JpwBogsysqVK0VtzDh79mxkZ2eb9EPy8vLCokWLMGLECJSWliI1NRUjRowQLabffvsNS5cuBQBkZWWhY8eOKCwsRHZ2NpKSkpgAWYD+/fsDAHQ6HR555BGTQwaldH3LFUdHR9nERdQSmACRLMi11qa8vLzRQlS9Xo+KigoAQOfOnUVdbqqurjYekrdnzx5ERERAqVQiODgYZWVlosVBd6eoqAg+Pj5QKpW4dOkSiouLb3qtn5+fiJEBWq3W+HVGRoao9yYSGxMgkgU51toA9TthJk+ejPT0dPTo0QNAfb+mKVOmICQkBABQXFyMLl26iBaTl5cXtm7diuHDh2P37t3GFglnz56Fo6OjaHHQ3QkICEBFRQVcXV0REBAAhULRaMG61C1gQkJC8MUXX5gdwlhVVYXw8HDuAiOLx4MQSRbmz5+PJ554AkePHsX58+dx4cIFHDlyBL1798YHH3yAEydOQK1Wi94Pac2aNXB2dkZQUBBsbGxgY2ODnj17wtnZGWvWrAEAODg4YPHixaLFlJSUhJkzZ0Kj0aBXr1548sknAdTPBjUkaSRfOp0OLi4uxq9LS0uh0+nMHlL32srPz8e1a9fMxmtqavD9999LEBFR82IRNMmCp6cnsrOzzU7DLSwsNNba/PDDD8YGkWIrKSnBkSNHIAgCvL290a1bN9FjuF5FRQXKy8vh7+9vbFVQUFAAR0dHeHt7SxobWbaioiIA9TNVubm5JgXaBoMBu3btwsqVK3H8+HGJIiRqHlwCI1mQY60NUL9bp3///vD29pZVYqFWq6FWq3Hy5EkoFAo8/PDD6NWrl9Rh0R1asGABOnbsiIkTJ5qMr127FufOnUNCQoLoMTUsyykUCuMy7/Xatm2LDz/8UPS4iJobl8BIFhpqbQoLC41jUtfaAMCgQYPwyCOPYNasWTh48KCo974ZvV6PxMREqFQqaDQauLu7Q6VSYfbs2eyTZmFWrlzZaGL9+OOPY8WKFRJEVL8sd+zYMQiCgIKCApNluVOnTqGqqsosYSOyRJwBIllYs2YNxo8fj6CgIGPPIb1ej4EDB0pWawMAp0+fxsaNG5GZmYnU1FT4+PggKioK48aNw8MPPyxqLA1ee+01bNmyBampqcb6nx9//BFz5sxBZWWlZL846c5VVFSgU6dOZuMuLi6SLPUCgLu7OwCgrq5OkvsTiYU1QCQrcqu1uZ5Op8OGDRuQmZmJkpIS9OvXT5KdMCqVChs3bsSQIUNMxnfu3IkxY8bg0qVLosdEd6dr165ITk5GVFSUyfi6deuQnJwseiH0tm3bmnxtWFhYC0ZC1PI4A0SyINdam+t16dIFs2bNgr+/PxITE/Htt99KEoetrS00Go3ZuEajgbW1tfgB0V2LjY3FG2+8gdraWuNSb05ODt58803MmDFD9HjCw8ObdJ3UW/SJmgNngEgWrK2toVarMW7cOERFRcHHx0fqkEzs378f69evR1ZWFmpqahAWFobIyEizWRgxvPvuuygpKYFWq4WNjQ0A4L///sOkSZOMMwpkGQRBwKxZs5CWlmbccm5ra4uEhAQkJSVJHB3R/Y0JEMlCZWWlsdbmxx9/lEWtDQC8/fbbyMzMxKlTpzBo0CBERkYiPDzc2IhUCsOHD0dOTg5sbGzg7+8PAPjjjz9w7do1DBw40ORa9muyDFeuXMGhQ4fQtm1bdO3a1ZjYSundd9+96WsKhQKJiYkiRkPU/JgAkezIpdYGAPr06YPIyEiMHj0aHTp0kCSGG8XExDT52utbGxDdiRsP1aytrYVOp8MDDzwAT09P/PbbbxJFRtQ8mACRLBkMBuzcuROJiYkoKiqSvN7gr7/+wokTJ8xOxmUhKLUmVVVVmDBhAoYPH47x48dLHQ7RPWECRLIip1oboH42avjw4SgqKjLp2dTQJVvqxIxIbAcPHsTQoUN5EjRZPO4CI1m4sdZm2bJlktfaAEB8fDw0Gg327t0LDw8PFBQU4Pz585gxYwYWLVokWhyBgYHIycmBk5MTevToYUzAGsOlCWpJFy9e5FELdF9gAkSykJ+fj5kzZ8qq1gaoP2AwNzcXLi4uUCqVUCqVePrpp7FgwQLEx8ebnFzdkoYNG2YsjG3qVmWie5GWlmbyXBAElJeXY926dXjuueckioqo+XAJjGRFbrU2Tk5O+PXXX+Hh4QFPT0+kp6djwIABOHbsGHx9fVFdXS1qPAaDAfv27YOfnx+cnJxEvTe1Lje2nVEqlXBxcUFISAjeeustPPjggxJFRtQ8OANEsiDXWhsfHx8UFRXBw8MDvXv3RmpqKqytrbFq1Sp4eHiIHo+VlRVCQ0Nx6NAhJkDUonQ6ndQhELUoNkMlWWiotTlz5gzs7Ozw559/4rvvvkPPnj2Rn58vWVyzZ8829kR67733UFZWhr59+2LHjh1mSwRi8fX1Fb1FAhHR/YZLYCQLHTp0QG5uLvz8/KBSqVBQUIBu3bohNzcXM2bMEK3WpikuXLgAJyenWxYit6Q9e/YgISEBc+fORVBQEOzt7U1ed3R0lCQuIiJLwiUwkgWDwQAHBwcA9cnQ6dOn0a1bN7i7u+Pw4cMSR2fK2dlZ0vs3FKCGhYWZJGGCILBHExFREzEBIlmQW62NnGm1Wri5ucHKyspkvK6uDidOnJAoKiIiy8IlMJKF3bt34+rVq4iIiEBpaSmGDh2KkpIStG/fHps2bTJ2yqb6Qujy8nK4urqajJ8/fx6urq6cASIiagImQCRbUtfayJVSqcSZM2fg4uJiMl5WVobu3bvj6tWrEkVGRGQ5uARGsiV1rY3cTJ8+HcD/O3Fff0q2wWDAzz//jICAAImiIyKyLEyAiCxEw044QRBQXFwMa2tr42vW1tbw9/fHzJkzpQqPiMiicAmMyMLExMTggw8+4HZ3IqJ7wASIiIiIWh2eBE1EREStDhMgIiIianWYABEREVGrwwSIiIiIWh0mQERERNTqMAEiIlmZMGECFAqF2ePvv/++5/fOyMhAu3bt7j1IIrJ4PAiRiGTnueeeg1arNRm7sfWH1Gpra9GmTRupwyCiu8QZICKSHRsbG6jVapOHlZUVtm/fjqCgINja2sLDwwMpKSnQ6/XG71uyZAl8fX1hb28PNzc3TJ06FVeuXAEA5OfnIyYmBpcuXTLOKs2ZMwdAfXuRrVu3msTQrl07ZGRkAACOHz8OhUKBzZs345lnnoGtrS0+++wzAIBWq8Vjjz0GW1tbeHt74+OPP27xz4eI7h1ngIjIIuzevRtRUVFIS0tD3759cezYMbzyyisAgOTkZAD1jWLT0tKg0Wig0+kwdepUvPnmm/j444/Rp08fLFu2DElJSTh8+DAAwMHB4Y5iSEhIwOLFi6HVamFjY4PVq1cjOTkZy5cvR48ePVBYWIiXX34Z9vb2iI6Obt4PgIial0BEJCPR0dGClZWVYG9vb3y8+OKLQt++fYX58+ebXLtu3TqhU6dON32vzZs3C+3btzc+12q1gkqlMrsOgLBlyxaTMZVKJWi1WkEQBEGn0wkAhGXLlplc4+bmJmzYsMFkbO7cucKTTz7ZhJ+UiKTEGSAikp0BAwbgk08+MT63t7eHl5cXfvnlF8ybN884bjAYUFNTg+rqatjZ2SEvLw/z58/HX3/9haqqKuj1etTU1ODq1auwt7e/57h69uxp/PrcuXM4efIkJk2ahJdfftk4rtfroVKp7vleRNSymAARkew0JDzXq6urQ0pKCiIiIsyut7W1RVlZGZ5//nnExcVh7ty5cHZ2xr59+zBp0iTU1tbe8n4KhQLCDW0RG/ue65Oouro6AMDq1avRu3dvk+usrKxu/QMSkeSYABGRRQgMDMThw4fNEqMGBw4cgF6vx+LFi6FU1u/v2Lx5s8k11tbWMBgMZt/r4uKC8vJy4/OjR4+iurr6lvF07NgRDz30EEpLSxEZGXmnPw4RSYwJEBFZhKSkJAwdOhRubm4YOXIklEolioqKUFxcjPfeew+enp7Q6/X48MMP8cILL2D//v1YsWKFyXtoNBpcuXIFOTk58Pf3h52dHezs7BASEoLly5cjODgYdXV1SEhIaNIW9zlz5iA+Ph6Ojo4YMmQI/vvvPxw4cAD//PMPpk+f3lIfBRE1A26DJyKLEBoaiq+++gp79+7FE088geDgYCxZsgTu7u4AgICAACxZsgQLFy6Ej48P1q9fjwULFpi8R58+fRAXF4fRo0fDxcUFqampAIDFixfDzc0N/fr1w7hx4zBz5kzY2dndNqbY2Fikp6cjIyMDvr6+6N+/PzIyMtClS5fm/wCIqFkphBsXvomIiIjuc5wBIiIiolaHCRARERG1OkyAiIiIqNVhAkREREStDhMgIiIianWYABEREVGrwwSIiIiIWh0mQERERNTqMAEiIiKiVocJEBEREbU6TICIiIio1fkfQKaIR4w7KHAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(df_fi, x='Feature', y='Importance');\n",
    "plt.xticks(rotation=90);\n",
    "ax.set_title('Relative Importance of Features in Prediction Retention');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "id": "d2382c4a-8d13-45bc-bb27-cc7ae587fa23",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        avg_dist  weekday_pct  avg_rating_by_driver\n",
      "Active                                             \n",
      "0       6.188478    60.181597              4.786830\n",
      "1       5.118977    62.214604              4.763149\n"
     ]
    }
   ],
   "source": [
    "# Examine aggregate means for top 3 features\n",
    "print(df.groupby('Active')[['avg_dist', 'weekday_pct', 'avg_rating_by_driver']].mean());"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2be5cef-e8ed-4d45-80a4-700f4585db15",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "\n",
    "With only a little over 1/3 of users who signed up in January staying on the app in June, Ultimate Inc. has room to reduce its customer churn. In order to predict customer retention, I built a Random Forest Classifier with 95% accuracy on correctly predicting the test set. Morevover, precision, recall, and f1 score were all fairly high. The model had slightly higher performance for negative predictions (not retained) than positives (retained), which could be due to the imbalance of negatives / positives in the true labels, with about 2/3 of actual values being negative cases (the 2/3 of users who didn't retain). The model thus predicted slightly more false negatvies than false positives but overall was reasonably balanced. Future work could include hyperparameter tuning and other models, but the basic Random Forest Classifier worked well to get a snapshot of predicted retention and the importance of features in predicting this outcome. I chose a Random Forest Classifier as it combines various decision trees to improve accuracy and prevent overfitting in trying out feature combinations.\n",
    "\n",
    "The most important feature in determining retention was by far the average distance per trip in the first 30 days after signup. Interstingly, retained users traveled about a mile less on average per trip in the first 30 days than users who churned. Perhaps shorter trips make return usage more attractive. Ultimate can leverage this information to promote quick inner-city travel over longer-distance treks. The second most important feature was the percent of the user’s trips occurring during a weekday. Active users have about 2% more trips per weekday. This aligns with the shorter trip preference, as perhaps shorter trips on weekdays include commutes to work, lunch, etc. Again Ultimate can build on this knowledge to frame their service as being a simple travel supplement to a daily routine, rather than a more extravagant long-distance weekend voyage. The third most important feature was average rating by driver, with an unexpected indication that riders who retained had a rating of about 0.03 less than riders who didn't retain. Does this mean that the friendlist riders are less likely to retain? Perhaps this feature needs further consideration, but understanding a general picture of what retained users look like will help Ultimate increase retention by prioritizing these aspects, such as in emphasizing shorter weekday trips over longer weekend trips."
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