Bike Rental Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Bike Rentals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AIM:\n",
    "\n",
    "In this project, the aim is to predict the total number of bikes people rented in a given hour.\n",
    "The target feature is `cnt`, i.e. total number of bike rentals (casual and registered)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Many American cities have communal bike sharing stations where you can rent bicycles by the hour or day. Washington, D.C. is one of these cities. The District collects detailed data on the number of bicycles people rent by the hour and day.\n",
    "\n",
    "Hadi Fanaee-T at the University of Porto compiled this data into a CSV file, in which each row represents the number of bike rentals for a single hour of a single day. \n",
    "\n",
    "The descriptions for the columns in the dataframe are:\n",
    "\n",
    "- instant - A unique sequential ID number for each row\n",
    "- dteday - The date of the rentals\n",
    "- season - The season in which the rentals occurred\n",
    "- yr - The year the rentals occurred\n",
    "- mnth - The month the rentals occurred\n",
    "- hr - The hour the rentals occurred\n",
    "- holiday - Whether or not the day was a holiday\n",
    "- weekday - The day of the week (as a number, 0 to 7)\n",
    "- workingday - Whether or not the day was a working day\n",
    "- weathersit - The weather (as a categorical variable)\n",
    "- temp - The temperature, on a 0-1 scale\n",
    "- atemp - The adjusted temperature\n",
    "- hum - The humidity, on a 0-1 scale\n",
    "- windspeed - The wind speed, on a 0-1 scale\n",
    "- casual - The number of casual riders (people who hadn't previously signed up with the bike sharing program)\n",
    "- registered - The number of registered riders (people who had already signed up)\n",
    "- cnt - The total number of bike rentals (casual + registered)\n",
    "                                      \n",
    "                                      \n",
    "To accomplish this,  will create a few different machine learning models and evaluate their performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Studying the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
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       "      <td>0.0000</td>\n",
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      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "5        6  2011-01-01       1   0     1   5        0        6           0   \n",
       "6        7  2011-01-01       1   0     1   6        0        6           0   \n",
       "7        8  2011-01-01       1   0     1   7        0        6           0   \n",
       "8        9  2011-01-01       1   0     1   8        0        6           0   \n",
       "9       10  2011-01-01       1   0     1   9        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81     0.0000       3          13   16  \n",
       "1           1  0.22  0.2727  0.80     0.0000       8          32   40  \n",
       "2           1  0.22  0.2727  0.80     0.0000       5          27   32  \n",
       "3           1  0.24  0.2879  0.75     0.0000       3          10   13  \n",
       "4           1  0.24  0.2879  0.75     0.0000       0           1    1  \n",
       "5           2  0.24  0.2576  0.75     0.0896       0           1    1  \n",
       "6           1  0.22  0.2727  0.80     0.0000       2           0    2  \n",
       "7           1  0.20  0.2576  0.86     0.0000       1           2    3  \n",
       "8           1  0.24  0.2879  0.75     0.0000       1           7    8  \n",
       "9           1  0.32  0.3485  0.76     0.0000       8           6   14  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading in the data\n",
    "\n",
    "bike_rentals= pd.read_csv('bike_rental_hour.csv')\n",
    "bike_rentals.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of rows are: 17379\n"
     ]
    }
   ],
   "source": [
    "# Printing out the dataframe size\n",
    "print('Total number of rows are:', bike_rentals.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bike_rentals.cnt);\n",
    "plt.title('Distribution of Total Rentals');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=(bike_rentals.casual, bike_rentals.registered), log=True, label=['Casual', 'Registered'])\n",
    "plt.legend()\n",
    "plt.title('Distribution of Casual and Registered Rentals');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total number of pre-registered rentals are much greater than casual. The tapering end seen in the total count histogram is enlarged using a log scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i1GvPwRkukHavnJi6PGOQn5A0AHwTWCNpJ7CtvWW1lqhcJlur3XpC8hkeV+/lzC9zPaE0ObY01JtWq4nptpzhApHAfeT6Ju0gR8Rbspsfk3QmcCjwL80cNPsH8gXgNVT+b/8gsBn4KjAfeAR4Z0TskiTgM8DZwC+A90fE96ZyvFl94vmxA1Mwq89d5F5Qhgyb9VKOPVNLMbT65FIvZTgF7hw3NunnJJK+PH47Im6LiFXAlU0e9zPAtyLiVcBJwP3AMuC2iDgeuC3bBngTcHz2dSHwuakerFbnuFG7lUsZMmzWKzken6lleGSUYN9MLZ1eZtZa/4lVr2S43YY2DrNoxVqOXbaaRSvWdruc0sozkOhXqzckzaBy5em0ZFO8/BZwFUBEPB8RI8C5wNXZblcDS7Lb5wLXRMU6YEDS0dM9vvUkZ9jKoCdy3GimFkteT2S4nWq9gbT2qNtBlvRRSbuAX5W0M/vaBfwUuKWJYx4L7AC+JGmjpC9IejFwVEQ8ke3zE+Co7PYgsLXq57dlbWYNOcNWBr2W48fr/Idfr92Kr9cy3E6eu3h/E8+mt/KTpkZnkD8FzAauyL7PBo6MiMMjYmkTx5wJnAJ8LiJOBp5h38cfAEREMMVPcSRdKGmDpA07duxoojwrkaQyDM6x1ZRUjpvNcL3lbr0MbtJ6KsPt5DeK+7R7OFajpaYDeAFYKumXqayVPlPZvHlNrHyzDdgWEeuz7ZuoBPpJSUdHxBPZRx7bs/uHgeplKudmbRPrvZJsLNPChQs9uNiSy3BWk3Ns+0ktx81m+PWvms216x6r2W5p6rUMt9Oh/bMYGd3d7TK6ZtGKtXsv3n3muRfaunBWnpX0Pgm8F3gAGK8kqFwFOmUR8RNJWyUtiIjNwJnAD7Ov84EV2fdvZD9yM/Cnkr4CnA48XfXRidmknGErgzLkOM/sFLc/UPuMXb12S0cZMtxtu8emPcVeKYyPuW409rpVZ9nzzIP8duCVEfFsS45Y8WfAdZIOAh4CPkBluMcN2VKUjwLvzPa9hco/ni1UpmX5QAvrsN7gDFsZJJ3j8Y9Dx8/4jH8cCuzXSfZqkaWWdIaL4JnnPf54Mq0ajpWng/ww0NeSo2Ui4h4qy01OdGaNfQP4UCuPbz3HGbYySDrHjWan8BzHPSPpDKdu4ic4ZdQ/q4+li1uzOGOeDvLPge9J+jbw3HhjRFzckgrM2s8ZtjJIOseencJIPMMpq/UJThlIMOfQ/rYsKpSng/yt7MssVc6wlUHSOZ4z0F/zP+WynsmympLOcMrKOj1cBNy57A1teew8S01flY3tOSYitrSlCrM2coatDFLPsWensNQznLKyflIz0D+rbY+dZ6npc4BNwK3Z9mslfb1tFZm1mDNsZZB6jld/v/ZkAfXarXxSz3DKyvpJTTZTYFvkWWr641SmQxmBvQPiX9G+ksxazhm2Mkg6x7t+UXvu1nrtVkpJZzhlSxcvoH9WS6+PLISJrx+tXFkvTwd5d7a2ebVCTZxtNgln2MrAObbUOcNdsuTkQeYednC3y2irVq+sl6eDfL+kdwIzJB0r6Qpg3bSOZtYdzrCVgXNsqXOGu+Q9n/9PHtz+TLfLaKtGU0lOR54O8p8CpwJ7gFXA88BF0zqaWXc4w1YGzrGlzhnukjt/vLPbJbRdq6eSzDOLxTPAR7MvACQNUlmFxqzwnGErA+fYUucMWzu1eirJhmeQJf2apCWSjsy2Xy3pGmD9tI5m1mHOsJWBc2ypc4at3WpdiNjMynp1O8iSLgeuA94DfEvSZcDtwL3AK6d1NLMOcoatDJxjS50zbO00PmvFyjWbedupgwwO9CNgcKCfy9964rRX1ms0xOJc4KSIGJV0OLAVODEiHprWkcw6zxm2MnCOLXXOsLXN+LCK4ZFRvnb3cFOd4mqNhlg8GxGjABGxE/iRw2yJcYatDJxjS50zbB3RzKwVEzU6g/wrklZltwUcW7VNRLy1JRWYtY8zbGXgHFvqnGHrmFYtq92og/y2Cdt/35IjmnWOM2xl4Bxb6pzhLlm0Ym3LOoypaNWy2nU7yBFxW0uOYNYlzrCVgXNsqXOGu6fWtGdl1sysFRPlWSikLST1Sdoo6V+y7WMlrZe0RdJXJR2Utb8o296S3T+/WzWbVXOGrQycY0udM9zbWjVrxURd6yADHwHur9r+G+CKiHgFsAu4IGu/ANiVtV+R7WdWBM6wlYFzbKlzhnvY8MgokX2/ccNjLXvc3B1kSS9q1UElzQXOAb6QbQt4A3BTtsvVwJLs9rnZNtn9Z2b7m02JM2xl4Bxb6pxha5c7f7yT93z+P1vyWJN2kCWdJmkT8GC2fZKk/9vkcT8N/AWV9dgBjgBGIuKFbHsbMH6OfJDKnIlk9z+d7W+WizNsZeAcW+qcYeuEO3+8syWPk+cM8meBNwNPAUTEvcDrp3tASW8GtkfE3dN9jDqPe6GkDZI27Nixo5UPbelLIsPZYzvHVk8SOXaGrQFn2JKRp4M8IyIendA21sQxFwFvkfQI8BUqH4V8BhiQND6rxlxgOLs9DMwDyO4/lOwfV7WIuDIiFkbEwtmzZzdRnpVQEhkG59gaSiLHzrA14AxbMvJ0kLdKOg2I7ErRi4AfTfeAEbE8IuZGxHzg3cDaiHgPlXXZ357tdj7wjez2zdk22f1rIyKme3zrSc6wlYFzbKlzhi0ZeTrIfwxcDBwDPAmckbW12keBiyVtoTIm6Kqs/SrgiKz9YmBZG45t5eYMWxk4x5Y6Z9iS0WglvXHPR8S7qxskvRz4abMHj4g7gDuy2w8Bp9XY51ngHc0ey3qaM2xl4Bxb6pxhS0aeM8jflPSS8Q1JC4DV7SvJrOWcYSsD59hS5wxbRxy7bDWLVqxlaOPw5DvXkaeDvJJKqA+R9FpgFfC+aR/RrPOcYSsD59hS5wxbR4wvHLL0pnun3UmedIhFRNwsaRbwr1Su+HxbRDwwraOZdYEzbGXgHFvqnGHrtN1jwf/+5n3TWn66bgdZ0hVUOuEAAgaALcCFkoiIi6dTrFmnOMNWBs6xpc4Ztm7a9Yvd0/q5RmeQfzDJtlnROcNWBs6xpc4ZtuTU7SBHxFX17jNLgTNsZeAcW+qcYUtRoyEW10fEeZI2su+jkb0i4pS2VmbWJGfYysA5ttQ5w5aiRkMslmbf395gH7Mic4atDJxjS11pMjy0cZiVazbz+Mgocwb6Wbp4wbQuALPiazTEYlv2/cfjbZIGImKkE4WZNcsZtjJwji11Zcnw0MZhlq/axOjuMaAyjdjyVZsA3EkuobrzIEs6TdK3Jd0g6SRJ3we2SHpS0hs7WKPZtDjDVgbOsaWuLBleuWbz3s7xuNHdY6xcs7lLFVk7NRpi8Q/ApVTmKrwd+N2IuFPSq4F/ojKPoVmROcNWBs6xpa4UGX58ZHRK7VYci1asnfKwmEYr6c2MiFsi4nrgyYi4EyAi7qMyj6FZ0TnDVgbOsaWuFBmeM9A/pXYrjuGR0X2r692Yb3W9Rh3k6itNJ749OuAqVLMCcoatDJxjS10pMvz6V82eUrsV0+49wWU33zfpfo2GWJwkaSeVd3cvzW6Tbb+k+RLN2s4ZtjJwji11pcjw7Q/smFK7FdfI6OSr6zXqIB/UulLMusIZtjJwji11pciwxyD3lkbTvI3Vu88sBc6wlYFzbKkrS4bnDPQzXKMz3M4xyBPnXbbOaTQGuS0kzZN0u6QfSrpP0key9sMl3Srpwez7YVm7JH1W0hZJ35fkFXesq5xhKwPn2FLX6QwvXbyAvhn7X1PYN0MsXbygZc+p2tDGYS766j37XWBmndPxDjLwAvDnEXECcAbwIUknAMuA2yLieOC2bBvgTcDx2deFwOc6X7LZfpxhKwPn2FLX0QxveHQnY3v2v6ZwbE+w4dGddX6iOUtvvKctj2v5dLyDHBFPRMT3sts/B+4HBoFzgauz3a4GlmS3zwWuiYp1wICkoztcttlezrCVgXNsqet0hq9fv3VK7c3avactD2s51R2DLGkXtadfERARcXizB5c0HzgZWA8cFRFPZHf9BDgquz0IVKdvW9b2BGYNOMNWBs6xpa4sGR6L2jPS1Wu3tDWaxeLIdh5Y0kuArwEXRcTPpH3jeiIiJE0pcZIupPKRCcccc0wrS7V0JZXh7DGdY5soqRw7w1aDM2zJyT2LhaTDgYOrmh6f7kElzaIS5usiYlXW/KSkoyPiiewjj+1Z+zAwr+rH52ZtE+u9ErgSYOHChX47Z8llOKvZObb9pJZjZ9gm6oUMH7f8FsYi6JM47/R5fGLJidN8RlYUk45BlnSOpB9R+ShiffZ97XQPqMpbu6uA+yPi76ruuhk4P7t9PvCNqvb3ZVefngE8XfXRidmknGErA+fYUlfmDI8PsxiL4Np1j3HJ0KZWPKx1UZ6L9D4JLAI2R8Q8YDHwnSaOuQh4L/AGSfdkX2cDK4DfkfQg8NvZNsAtwEPAFuDzwJ80cWzrTc6wlYFzbKnrmQxft/6xdj20dUijMcjjXoiIHZJmSFJE3Crpb6d7wIj4dyoD82s5s8b+AXxousczwxm2cnCOLXU9k2Fft5e+PB3kp7MB8P8OXCNpO+DZqi0lzrCVgXNsqeupDB+7bDVzBvpZungBS04e7HY5NkV5hlgsoRLgi4A7qAxof3MbazJrNWfYysA5ttT1VIbHV79bvmoTQxtrXpdtBZang7w8IsYiYndEXJUNhL+43YWZtZAzbGXgHFvqejLDo7vHWLlmc7fLsCnK00E+q0bbOa0uxKyNnGErA+fYUtezGR4eKe1IktJqtJLeHwJ/BLxS0veq7nopcHe7CzNrljNsZeAcW+qcYUtRo4v0bgBuAy4HllW1/zwittf+EbNCcYatDJxjS50zbMlptJLeLmAX8A5JrwZ+M7vrO+xblcassJxhKwPn2FLnDFuK8qyk9yHgRuCY7OsGSZ4g3pLhDFsZOMeWul7P8KIVazl22WoWrVjrWS0SkGce5D8ETouI/wKQ9NfAfwD/2M7CzFrIGbYycI4tdT2d4fEL9canfgM8P3KB5ekgC3i+ans39VeuMSsiZ9jKwDm21DnDmfGp3yZ2kIc2DrNyzWYe96wXXddoFouZEfEC8E/Aeklfy+7678DVnSjOrBnOsJWBc2ypc4Zrmzj129DGYS766j1dqsYmanQG+S7glIj4lKQ7gN/I2v8oIr7b9srMmucMWxk4x5Y6ZzgHd46LpVEHee/HHhFxF5WAm6XEGbYycI4tdc6wJadRB3m2pLpLQGZLRJoVmTNsZeAcW+qc4ToWrVjL4yOjzBno73YpNkGjDnIf8BJ6dAC9lYIzbGXgHFvqnOE6qme2sGJp1EF+IiI+3rFKzFrPGbYycI4tdc6wJafRQiGFeqcn6SxJmyVtkbRs8p8wc4atFAqTY2fYpqkwGQbn2PJp1EE+s2NVTEJSH/APwJuAE4DzJJ3Q3aosAc6wlUEhcuwMWxMKkWFwji2/uh3kiNjZyUImcRqwJSIeiojnga8A53a5Jis4Z9jKoEA5doZtWgqUYXCOLadGZ5CLZBDYWrW9LWszS4UzbKlzhq0MnGPLJZUO8qQkXShpg6QNO3bs6HY5ZtPiHFvqnGFLnTPceeOD1PukwnRMi1LHZIaBeVXbc7O2vSLiyohYGBELZ8+e3dHizHKYNMPgHFuhOcNWBj3Vn+hTZ66PPP6XXlyzfcaEw8+aIRYdd/jeuvok/scZx/DwinN4ZMU5/Pjys/m7d722Zud0Vl9nr/VsNM1bkXwXOF7SsVSC/G7g97pbktmUOMOWOmc4JwFRp926bto5ftmL+vjZc2PtrK1lZgq2XH7Ofm2XDG3i+vVbGYugT+K80+dx630/4cmfP793n5e9qI/R3XvYvWdfgmfNEO86bR63P7Bj76ImSxcvYMnJ+49MGdo4zMo1m/fbBzigbeLPTTR+f57HunHDY9z5431D3A/uE8+O7av9qJcetN/zG/fpd722YQ0Aiqj1z7h4JJ0NfJrKhONfjIhP1tt34cKFsWHDhv3a5i9bfcB+j6w454C2TilSPUWqBfLVI+nuiFjYqZpaYSoZhto5huL9vWx/ef8+vZjhvL+bPPu18rHasd+xy1bv10kW8HCB6mvFfsBAPHIAACAASURBVClmGJrrT/zqpd/ar5PcjU7zTMELsf/2xM5wM2p1dCfr1BbZZM+nXo6T6SBPRb2OhZVHqi/MU+Ecl5szbKlzhq0M6uU4lTHIZmZmZmYdUcozyJJ2AI/WuftI4KcdLGcyRaqnSLVA43peHhFpXz0xiUlyDMX7ezWr156PM1xMZclhJ55HL2c45ZykXDu0vv6aOS5lB7kRSRuK9JFQkeopUi1QvHqKpmy/Hz8fK4Ky/N3K8jyKKuXfb8q1Q+fq9xALMzMzM7Mq7iCbmZmZmVXpxQ7yld0uYIIi1VOkWqB49RRN2X4/fj5WBGX5u5XleRRVyr/flGuHDtXfc2OQzczMzMwa6cUzyGZmZmZmdfVMB1nSFyVtl/SDAtQyT9Ltkn4o6T5JH+lyPQdLukvSvVk9/7ub9YyT1Cdpo6R/6XYt3SLpLEmbJW2RtKzG/S+S9NXs/vWS5ne+yqnJ8ZzeL2mHpHuyr9/vRp15TPa6oorPZs/1+5JO6XSNlp+kRyRtynKXzOoQtXIo6XBJt0p6MPt+WDdrLIvJXr+KJuVs1Osrdar+nukgA18Gzup2EZkXgD+PiBOAM4APSTqhi/U8B7whIk4CXgucJemMLtYz7iPA/d0uolsk9QH/ALwJOAE4r0ZOLgB2RcQrgCuAv+lslVOT8zkBfDUiXpt9faGjRU7Nl2n8uvIm4Pjs60Lgcx2oyZrz+ix3KU2D9WUOzOEy4LaIOB64Ldu2Jkzh9atIvky62ajXV+pI/T3TQY6IfwN2drsOgIh4IiK+l93+OZVOYNcWOo+K/8o2Z2VfXR2cLmkucA5Q5M5Ru50GbImIhyLieeArwLkT9jkXuDq7fRNwpiR1sMapyvOckpHjdeVc4Jrs39g6YEDS0Z2pznpFnRxWvzZcDSzpaFHllNzrV8rZaNBX6kj9PdNBLqrsI/GTgfVdrqNP0j3AduDWiOhqPcCngb8A9nS5jm4aBLZWbW/jwDdSe/eJiBeAp4EjOlLd9OR5TgBvy4Yk3CRpXmdKa4u8z9eKIYB/lXS3pAu7XUyTjoqIJ7LbPwGO6mYxJVGWf8/JZWNCX6kj9buD3EWSXgJ8DbgoIn7WzVoiYiwiXgvMBU6T9Jpu1SLpzcD2iLi7WzVYV30TmB8Rvwrcyr4zBWbt9hsRcQqVj9A/JOm3ul1QK0RluipPWWUHSCEbjfpK7azfHeQukTSLyh/8uohY1e16xkXECHA73R2vvQh4i6RHqHyE9QZJ13axnm4ZBqrPns7N2mruI2kmcCjwVEeqm55Jn1NEPBURz2WbXwBO7VBt7ZDnb2gFERHD2fftwNepfKSeqifHh/Nk37d3uZ4yKMu/52SyUaev1JH63UHugmyM6FXA/RHxdwWoZ7akgex2P/A7wAPdqicilkfE3IiYD7wbWBsR/6Nb9XTRd4HjJR0r6SAqv4ubJ+xzM3B+dvvtVH5XRT4bMOlzmjBG9y2kfaHmzcD7stkszgCervpo0ApE0oslvXT8NvBGoOuzHjWh+rXhfOAbXaylLPK8JqcgiWw06Ct1pP6Z7XjQIpJ0PfA64EhJ24BLI+KqLpWzCHgvsCkb9wvwlxFxS5fqORq4OrtCdwZwQ0T07NRqRRERL0j6U2AN0Ad8MSLuk/RxYENE3EzlxeOfJG2hciHGu7tX8eRyPqcPS3oLlSuYdwLv71rBk6j1ukLlIlci4v8BbgHOBrYAvwA+0J1KLYejgK9n17jOBP45Ir7V3ZLyqZPDFcANki4AHgXe2b0Ky6He61eXy2oo8WzU7CvRofq9kp6ZmZmZWRUPsTAzMzMzq+IOspmZmZlZFXeQzczMzMyquINsZmZmZlbFHWQzMzMzsyruIJuZmbWYpAFJf1K1/TpJnj7TCkXSfEkpz/fdNu4gm1nhZKsCmqVsAPiTSfcyK7hefT12B7kAshWcVku6V9IPJL1L0qmS/j9Jd0taU7Ws4h9I+m6279ckHZK1vyP72Xsl/VvWdrCkL0naJGmjpNdn7e+XtErStyQ9KOlT3Xv21oskfVzSRVXbn5T0EUnfkXQz8MMulmcG7D279oCkL0v6kaTrJP22pDuz187TJF0m6YuS7pD0kKQPZz++AjhO0j2SVmZtL5F0U/aY12UrhZl1W5+kz0u6T9K/SurP8vxpSRuAj3S7wG7wQiEFIOltwFkR8QfZ9qHA/wucGxE7JL0LWBwRH5R0REQ8le33CeDJiPi/kjZljzEsaSAiRiT9OfDq7OdeBfwr8Eoqq639FXAy8BywGfiNiNja4aduPUrSfGBVRJwiaQbwIPAXwDXAayLi4S6WZwbszekWKq+V91FZavhe4AIqy6B/ALiHyrLUrwdeSuX19JeBQeBfIuI12WO9jsqSuK8GHgfuBJZGxL936vmYTVSV8YURcY+kG6gs5fz7wA8jomc/BfEZ5GLYBPyOpL+R9JvAPOA1wK3Z8oqXAHOzfV+TnWXbBLyHyostVF5svyzpD6gsgQnwG8C1ABHxAJUlGV+Z3XdbRDwdEc9SOVv38rY+Q7MqEfEI8JSkk6l0LjYCTwF3uXNsBfNwRGyKiD1UOsm3ReXM0iZgfrbP6oh4LiJ+Cmynsmx1LXdFxLbsse6p+nmzbno4IsaXcr6bfbn8anfKKYaeHFdSNBHxI0mnAGcDnwDWAvdFxK/X2P3LwJKIuFfS+6mssU5E/JGk04FzgLslnTrJYZ+ruj2Gs2Cd9wXg/VTOtn0xa3uma9WY1Vb9WrmnansP+143876e+nXXimhiLvuz2z39euwzyAUgaQ7wi4i4FlgJnA7MlvTr2f2zJI2fKX4p8ISkWVTOII8/xnERsT4i/grYQeUs9HfG95H0SuAYKh//mRXB14GzgF8D1nS5FrNW+zmV12szS5DfvRbDicBKSXuA3cAfAy8An83GI88EPk3l472PAeupdILXs+8FeKWk4wEBt1EZJ/cA8LlsOMYLwPsj4jlfF2JFEBHPS7odGImIMefSyiQinsou5vsBlWtKVne7JjPLzxfpmVlXZBfnfQ94R0Q82O16zMzMxnmIhZl1nKQTqFw5fZs7x2ZmVjQ+g2xmZmZmVsVnkM3MzMzMqriDbGZmZmZWxR1kMzMzM7Mq7iCbmZmZmVVxB9nMzMzMrIo7yGZmZmZmVdxBNjMzMzOr4g6ymZmZmVkVd5DNzMzMzKq4g2xmZmZmVqVtHWRJX5S0XdIPqtoOl3SrpAez74dl7ZL0WUlbJH1f0ilVP3N+tv+Dks5vV71mtTjHljpn2FLnDFs3KCLa88DSbwH/BVwTEa/J2j4F7IyIFZKWAYdFxEclnQ38GXA2cDrwmYg4XdLhwAZgIRDA3cCpEbGr0bGPPPLImD9/fluelxXD3Xff/dOImN3u4zjH1i7OsKXOGbYyqJfjme06YET8m6T5E5rPBV6X3b4auAP4aNZ+TVR66+skDUg6Otv31ojYCSDpVuAs4PpGx54/fz4bNmxoyfOwYpL0aCeO4xxbuzjDljpn2MqgXo47PQb5qIh4Irv9E+Co7PYgsLVqv21ZW712s25yji11zrClzhm2tmrbGeTJRERIatn4DkkXAhcCHHPMMTX3uWRoE9ev38pYBH0S550+j08sObFVJVgP6kaOy2Zo4zAr12zm8ZFR5gz0s3TxApacPPX/t1r1OL3GGbaJUvu/stMZTu33Y9PT6TPIT2YfdZB93561DwPzqvabm7XVaz9ARFwZEQsjYuHs2QcOibpkaBPXrnuMsWzM9VgE1657jEuGNjX5lKwHdS3HrTC0cZhFK9Zy7LLVLFqxlqGNNUuZ1CVDmzhu+S3MX7aa45bfMq1/S0Mbh1m+ahPDI6MEMDwyyvJVm6ZcU6sep4cknWFrn4T+r+xKhhP6/ViTOt1BvhkYv3L0fOAbVe3vy64+PQN4OvvoZA3wRkmHZVeovjFrm7Jr1z02pXazBrqW42a1qiPZqv8kVq7ZzOjusf3aRnePsXLN5q48DrTuDUTBJZtha6+E/q/sSoaL9vvpkderrmjbEAtJ11MZFH+kpG3ApcAK4AZJFwCPAu/Mdr+FyhWnW4BfAB8AiIidkv4P8N1sv4+PD7A364Sy5bhRR3IqwxGuX7+1bvtUPmocHhmdUns9j9fZv157PeNvIMZ/R+NvIIBkh2uULcPWe5zh2sr4elUk7ZzF4rw6d51ZY98APlTncb4IfLGFpZnlVrYct6ojOVZnesh67fVIUOtHpCk9DHMG+mt2qucM9E/pcVr1BqJIypZh6z3OcG1lfL0qEq+kZ9ZD6nUYp9qRrNd/nWK/tmbnuFF7Pa9/Ve2xrvXa62nVGwgzs3bz61V7uYNs1kOWLl5A/6y+/dr6Z/WxdPGCKT3OrL7aXeF67e12+wM7ptReT6veQJiZtZtfr9pr0g6ypAOGYdRqMysqZ3ifJScP8rZTB+nLxjD0Sbzt1MEpfxz3/FjtU7z12tutVWdSWvUGoh2cY0udM9xaRX69KoM8Z5DvytlmVlTOcGZo4zBfu3t4v9knvnb3cNeufG7VUI1WnUlZcvIgl7/1RAYH+hEwONDP5W89sSjj+ZxjS50z3EIFf71KXt13bpJ+CTga6Jd0Ivv+z3oZcEgHajNrijN8oFZd1HHYIbPY9YvdNdunot755qmeh166eAEXf/Ue9lS1zcjap2rJyVM/o95OzrGlzhm2FDX6aOMc4INUJtP+x6r2nwMfa2dRZi3iDE/QqqEIl/7uq/nzG+9lbM++rmzfDHHp7766qfqma8OjO/frHAPsydqn2tkt4CpZzrGlzhlug6GNwyy98V52Z6/DwyOjLL3xXsDTvLVC3Q5yRHwJ+JKkd0bEDR2syawlnOEDHXJQH888P1azfapmAGMTtrul0eT9U+ncji+AMm58ARSga51k59hS5wy3x2U337e3czxu957gspvv61oHeWjjMCvXbObxkVHmDPSzdPGCZDvreQbHD0l6JzC/ev+I+Ot2FWXWYs5w5hc1OseN2utZuWZzzRfm1OffbNUCKG3iHFvqnOEWGhk9cJhbo/Z2K9vCJXlO+nwdeBeVMI9VfZmlwhnOtGrMb6tWwCuaVi2A0ibOsaXOGS6xRte4pCjPGeSXR8Rr2l6JWfs4wxlRuzPcndmLi6dPqtkZ7pvq0n7t4Rxb6pzhEivbwiV5ziCvk3RC2ysxax9nONOqM8hldd7p86bU3mHOsaXOGW6hF82s3YWr195uZVu4JM8Z5NOBjZK2AM+RnYSKiFPaWplZ6zjDlssnlpzIwzv+izt/vHNv26LjDi/C+GNwji19znALPffCxLl7Gre32+tfNbvmBdOvf9XsLlTTvDwd5CVtr8KsvZxhy2Vo4zB3Pbxrv7a7Ht7F0MbhIlxk4hxb6pzhErv9gR1Tai+6PB3kNAePmO3jDFsuRZw2qYpzbKlzhkusbGOQ83SQb6MyRFHAwcA84MeAF/u2VDjDlkvRpk2awDm21DnDJTZnoL/mTEalHYMcEf+telvSacDvt60isxZzhq0MnGNLnTNcbksXL2DpTfeye2zfp3Cz+sTSxWm+/5nypY4RcRdwRhtqMesIZ9jKwDm21DnD5TM2Fg23UzLpGWRJH67anAGcCjzZzEEl/U8q7xoD2AR8ADga+ApwBHA38N6IeF7Si4BrsuM+BbwrIh5p5vjWW5xhKwPn2FLnDJfbZTffx8T5M/Zk7QW4hmPK8pxBnl31dSjwbeDc6R5Q0iDwYWBhNmF4H/Bu4G+AKyLiFcAu4ILsRy4AdmXtV2T7mU2FM2xl4Bxb6pzhEiv4NRxTlmcM8scAJPVn2624HHEm0C9pN3AI8ATwBuD3svuvBi4DPkflH89lWftNwN9LUkQx1n614nOGrRuGNg6zcs1mHh8ZZc5AP0sXL2jqLEqZctzq342loUwZtvKb9AyypBMkfRd4EHhQ0vpmVsKJiGHgb4HHqAT5aSofgYxExAvZbtuA8VfLQWBr9rMvZPsfUaPOCyVtkLRhx44059yz9kglw1mtdXM8tHGYRSvWcuyy1SxasZahjcPTfQrWZkMbh1m+ahPDI6MEMDwyyvJVm5r6m6WS48lei4c2DrP0xnv3+90svfFe57kHlCXDVttBfZpSe9HlGWJxJfCXETE3IuYC/ytrmxZJh1F5F3csMAd4MXDWdB9vXERcGRELI2Lh7NlprtpibZNEhqF+jtvR4bL2WblmM6O7x/ZrG909xso1m5t52CRyPNlrcaO5pq30SpFhq23iv+vJ2osuTwf5pRFx6/hGRHwbeGkTx/xt4OGI2BERu4FVwCJgQNL4kI+5wPj//MNU5koku/9QKoPrzfJKPsNt6nBZm7RpwvzkcwzlG6doU1KKDFtt9QaqpDqAJU8H+RFJyyXNzb6WAY80cczHgDMkHSJJwJnAD4Hbgbdn+5wPfCO7fXO2TXb/Wo8XsilKPsO1Jl9v1G7ddWj/rCm155R8jq3nOcMl1qfaQynqtRddng7yB6m847oFWE3l3dgHp3vAiFhPZXD896hMyTKDykcsHwUulrSFypigq7IfuQo4Imu/GFg23WNbz0o+w2V74Sm7nz9b+2xovfacks+x9TxnuMTO+JXDptRedHlmsXgK+JNWHjQiLgUundD8EHBajX2fBd7RyuNbbylDhsfqnOSo127dVW9u/GbmzC9Djq23OcPl9shTtT/RrNdedHlmsfiWpIGq7cMkrW5vWWatU4YMD9ZZy75eu5VPGXJsvc0ZLrc2XXvRNXmGWBwVESPjGxGxi8rVomapSD7Dr39V7Sup67VbKSWfY+t5znCJzalzwqZee9Hl6SDvkTR3fEPSMW2sx3pcm+b6TT7DX7t725TarZSSz7H1PGe4xJYuXsCsGftfFzNrhli6eEGXKmrOpGOQgb8C7pS0FhDwOuCP21mU9abxuX7HpzMbn+sXaHaVreQzPLp74gr3jdutlJLPsfU8Z7jsJl43nvB15Hku0lst6TTg17Omv4iI7e0ty3pRo7l+m1yi1xm25DnHljpnuNxWrtnM7glXIu8ei6b/D++WPGeQiYgngaE212I9rp0D/J1hKwPn2FLnDJdXL16kZ9YRZRvgb2Zm1isOnlW7S1mvvejSrNpKqWwD/M3MzHrFs3WuianXXnR1h1hIelmjH4yIn7W+HOt5LRzg7wxbGTjHljpnuDfUWwcp1eWsGo1Bvo/K86rVRQnA07NYS7VhgL8zbGXgHFvqnGFLTt0OckTM62QhZq0e4O8MWxk4x5Y6Z7g39M+aUXPq0f5ExyDnmsVC0qHAccDB420R8R/tKsp605yBfoZrdIZbcZGeM2xl4Bxb6pzh8nrbqXO5dt1jNdtTNGkHWdIFwMXAILAJ+DVgHZUJvs1aZuniBfstFALQP6uv6Yv0nGErA+fYUucMl9vtD+yYUnvR5TnvfRGwEHgkIn4TOBV4qq1VWU9acvIgl7/1RAYH+hEwONDP5W89sRUTjDvDVgbOsaXOGS6xss2DnGeIxbMRMSoJSQdFxH2SPO+WtcWSkwfbseKOM2xl4Bxb6pzhEjvkoD6eeX6sZnuK8nSQn5A0AHwTWCNpJ7CtvWWZtZQzbGXgHFvqnOESq9U5btRedJMOsYiIt0TESER8DPgEcB3wlmYOKmlA0k2SHpB0v6Rfl3S4pFslPZh9PyzbV5I+K2mLpO9LOqWZY1vvcYatDJxjS50zbCmZtIMs6cvjtyPitohYBVzZ5HE/A3wrIl4FnATcDywDbouI44Hbsm2ANwHHZ18XAp9r8thWYJcMbeK45bcwf9lqjlt+C5cMbWr6MZ1hKwPn2FLnDFtK8lyk96vVG5JmULnydFqyKV5+C7gKICKej4gR4Fzg6my3q4El2e1zgWuiYh0wIOno6R7fiuuSoU1cu+4xxqKyWMhYBNeue6wVnWRn2MrAObbUOcOWjLodZEkflbQL+FVJO7OvXcBPgVuaOOaxwA7gS5I2SvqCpBcDR0XEE9k+PwGOym4PAlurfn5b1jax3gslbZC0YceONKcU6XXXrT9w/sRG7ZNJLcNZzc6x7Se1HDvDNpEzbClqdAb5U8Bs4Irs+2zgyIg4PCKWNnHMmcApwOci4mTgGfZ9/AFARARTXL47Iq6MiIURsXD27NlNlGfdEnX+4vXac0gqw9nPOcc2UVI5doatBmfYktNoqekAXgCWSvplKmulz5Q0fv90V77ZBmyLiPXZ9k1UAv2kpKMj4onsI4/t2f3DQPUylXOzNrOGnGErA+fYUucMW4ryrKT3SeC9wAPA+FwdAZw9nQNGxE8kbZW0ICI2A2cCP8y+zgdWZN+/kf3IzcCfSvoKcDrwdNVHJ2aTcoatDJxjS50zbCnJMw/y24FXRsSzLTzunwHXSToIeAj4AJXhHjeoshTlo8A7s31vofKPZwvwi2xfs6lwhq0MnGNLnTNsycjTQX4YaOkyKBFxD5XlJic6s8a+AXyolce3nuMMWxk4x5Y6Z9iSkaeD/HPge5K+DTw33hgRF7etKrPWcoatDJxjS50zbMnI00H+VvZllipn2MrAObbUOcOWjEk7yBFxVTa255iI2NKBmsxayhm2MnCOLXXOsKUkz1LT5wCbgFuz7ddK+nq7CzNrFWfYysA5ttQ5w5aSPEtNf5zKdCgjsHdA/CvaWZRZiznDVgbOsaXOGbZk5Okg787WNq82/bXNzDrPGbYycI4tdc6wJSPPRXr3S3onMEPSscCHgXXtLcuspZxhKwPn2FLnDFsy8pxB/lPgVGAPsAp4HrionUWZtZgzbGXgHFvqnGFLRp5ZLJ4BPpp9ASBpkMoqNGaF5wxbGTjHljpn2FLS8AyypF+TtETSkdn2qyVdA6zvSHVmTXKGrQycY0udM2ypqdtBlnQ5cB3wHuBbki4DbgfuBV7ZkerMmuAMWxk4x5Y6Z9hS1GiIxbnASRExKulwYCtwYkQ81JnSzJrmDFsZOMeWOmfYktNoiMWzETEKEBE7gR85zJYYZ9jKwDm21DnDlpxGZ5B/RdKq7LaAY6u2iYi3trUys+Y5w1YGzrGlzhm25DTqIL9twvbft7MQszZwhq0MnGNLnTNsyanbQY6I2zpZiFmrOcNWBs6xpc4ZthTlWSikLST1Sdoo6V+y7WMlrZe0RdJXJR2Utb8o296S3T+/WzWbVXOGrQycY0udM2zt0LUOMvAR4P6q7b8BroiIVwC7gAuy9guAXVn7Fdl+ZkXgDFsZOMeWOmfYWi53B1nSi1p1UElzgXOAL2TbAt4A3JTtcjWwJLt9brZNdv+Z2f5mU+IMWxk4x5Y6Z9hSMGkHWdJpkjYBD2bbJ0n6v00e99PAX1BZjx3gCGAkIl7ItrcBg9ntQSpzJpLd/3S2/8Q6L5S0QdKGHTt2NFmelUkqGc5qc46tplRy7AxbPc6wpSTPGeTPAm8GngKIiHuB10/3gJLeDGyPiLun+xi1RMSVEbEwIhbOnj27lQ9t6Usiw+AcW0NJ5NgZtgacYUtGo2nexs2IiEcnfAox1sQxFwFvkXQ2cDDwMuAzwICkmdm7urnAcLb/MDAP2CZpJnAo2T8us5ycYSsD59hS5wxbMvKcQd4q6TQgsitFLwJ+NN0DRsTyiJgbEfOBdwNrI+I9VNZlf3u22/nAN7LbN2fbZPevjYiY7vGtJznDVgbOsaXOGbZk5Okg/zFwMXAM8CRwRtbWah8FLpa0hcqYoKuy9quAI7L2i4FlbTi2lZszbGXgHFvqnGFLRp4hFs9HxLurGyS9HPhpswePiDuAO7LbDwGn1djnWeAdzR7LepozbGXgHFvqnGFLRp4zyN+U9JLxDUkLgNXtK8ms5ZxhKwPn2FLnDFsy8nSQV1IJ9SGSXgusAt7X3rLMWsoZtjJwji11zrAlY9IhFhFxs6RZwL9SueLzbRHxQNsrM2sRZ9jKwDm21DnDlpK6HWRJVwDjV3cKGAC2ABdKIiIu7kB9ZtPmDFsZOMeWOmfYUtToDPIPJtk2Kzpn2MrAObbUOcOWnLod5Ii4qt59Zilwhq0MnGNLnTNsKWo0xOL6iDhP0kb2fTSyV0Sc0tbKzJrkDFsZOMeWOmfYUtRoiMXS7PvbG+xjVmTOsJWBc2ypc4YtOY2GWGzLvv94vE3SQESMdKIws2Y5w1YGzrGlzhm2FNWdB1nSaZK+LekGSSdJ+j6wRdKTkt7YwRrNpsUZtjJwji11zrClqNEQi38ALqUyV+HtwO9GxJ2SXg38E5V5DM2KzBm2MnCOLXXOsCWn0Up6MyPiloi4HngyIu4EiIj7qMxjaFZ0zrCVgXNsqXOGLTmNOsjVV5qONrjPrKicYSsD59hS5wxbchoNsThJ0k4q7+5emt0m235J2ysza54zbGXgHFvqnGFLTqMO8kEdq8KsPZxhKwPn2FLnDFtyGk3zNtbJQsxazRm2MnCOLXXOsKWo0RjktpA0T9Ltkn4o6T5JH8naD5d0q6QHs++HZe2S9FlJWyR9X5JX3LGucoatDJxjS50zbO3U8Q4y8ALw5xFxAnAG8CFJJwDLgNsi4njgtmwb4E3A8dnXhcDnOl+y2X6cYSsD59hS5wxb23S8gxwRT0TE97LbPwfuBwaBc4Grs92uBpZkt88FromKdcCApKM7XLbZXs6wlYFzbKlzhq2d6o5BlrSL2tOvCIiIOLzZg0uaD5wMrAeOiognsrt+AhyV3R4Etlb92Las7YmqNiRdSOUdIcccc0yzpVkJpJbh7PGcY9tPajl2hm0iZ9hS1GgWiyPbeWBJLwG+BlwUET+T9s0VHhEhaUpzI0bElcCVAAsXLvS8igaJZTj7OefYJkoqx86w1eAMW3Jyz2Ih6XDg4Kqmx6d7UEmzqIT5uohYlTU/KenoiHgi+8hje9Y+DMyr+vG5WZtZQ86wlYFzbKlzhq1bhjYOs3LNZh4fGWXOQD9LFy9gycmDuX520jHIks6R9CMqH0Wsz76vnW6xqry1uwq4PyL+ruqum4Hzs9vnA9+owDgOeAAAIABJREFUan9fdvXpGcDTVR+dmE3KGbYycI4tdc6wddLQxmGWr9rE8MgoAQyPjLJ81SaGNuZ7T5TnIr1PAouAzRExD1gMfGfaFVce673AGyTdk32dDawAfkfSg8BvZ9sAtwAPAVuAzwN/0sSxrTc5w1YGzrGlzhm2jlm5ZjOju/efgnt09xgr12zO9fONxiCPeyEidkiaIUkRcaukv51GrQBExL9TGZhfy5k19g/gQ9M9nhnOsJWDc2ypc4atYx4fGZ1S+0R5OshPZwPg/x24RtJ2IN+jmxWDM2xl4Bxb6pxh65g5A/0M1+gMzxnoz/XzeYZYLKES4IuAO6gMaH9z7grNus8ZtjJwji11zrB1zNLFC+if1bdfW/+sPpYuXpDr5/N0kJdHxFhE7I6Iq7KB8BdPvVSzrnGGrQycY0udM2wds+TkQS5/64kMDvQjYHCgn8vfemLrZrEAzqrRds5UijTrMmfYysA5ttQ5w9ZRS04e5M5lb+DhFedw57I35O4cQ+OV9P4Q+CPglZK+V3XXS4G7p12tWYc4w1YGzrGlzhm2FDW6SO8G4DbgcmBZVfvPI2J77R8xKxRn2MrAObbUOcOWnEYr6e0CdgHvkPRq4Dezu77DvlVpzArLGbYycI4tdc6wpSjPSnofAm4Ejsm+bpDkybUtGc6wlYFzbKlzhi0leeZB/kPgtIj4LwBJfw38B/CP7SzMrIWcYSsD59hS5wxbMvLMYiHg+art3dRfucasiJxhKwPn2FLnDFsyGs1iMTMiXgD+CVgv6WvZXf8duLoTxZk1wxm2MnCOLXXOsKWo0RCLu4BTIuJTku4AfiNr/6OI+G7bKzNrnjNsZeAcW+qcYUtOow7y3o89IuIuKgE3S4kzbGXgHFvqnGHriqGNw6xcs5nHR0aZM9DP0sULci8W0qiDPFtS3SUgsyUizYrMGbYycI4tdc6wddzQxmGW3ngvu/cEAMMjoyy98V6AXJ3kRh3kPuAleAC9pcsZtjJwji11zrB13GU337e3czxu957gspvva7qD/EREfLzJ+sy6yRm2MnCOLXXOsHXcyOjuKbVP1Giat0K905N0lqTNkrZIWjb5T5g5w1YKhcmxM2zTVJgMg3Ns+TTqIJ/ZsSomIakP+AfgTcAJwHmSTuhuVZYAZ9jKoBA5doatCYXIMDjHveSwQ2ZNqX2iuh3kiNg5vZLa4jRgS0Q8FBHPA18Bzu1yTVZwzrCVQYFy7AzbtBQow+Ac94xLf/fVzOrb/8OLWX3i0t99da6fz7OSXhEMAlurtrdlbXtJulDSBkkbduzY0dHizHKYNMPgHFuhOcNWBu5P9IglJw+y8u0nMTjQj4DBgX5Wvv2klkzzlpSIuBK4EmDhwoUxye5mheQcW+qcYUudM1weS04ezN0hniiVM8jDwLyq7blZm1kqnGFLnTNsZeAcWy6pdJC/Cxwv6VhJBwHvBm6eygM8suKcKbVb55X8b9RUhlv1u/HjpPU4BdP06zCU9ndTGj3w9ynEa3ErFKmWRsdNNTuKSOPTA0lnA5+mMuH4FyPik/X2XbhwYWzYsKFjtVnnSbo7IhZ2u46pmEqGwTkuO2fYUpdihsH9CdtfvRwnMwY5Im4Bbul2HWbT5Qxb6pxhKwPn2PJI5gzyVEjaATzaYJcjgZ92qJxOKdtzmuz5vDwiZneqmG5okOOi/a1dT2P16unlDI8r2t+qWb32fHo9w0X6exepFkirnpo5LmUHeTKSNqT4sVAjZXtOZXs+rVS0343raaxo9RRJ2X43fj69pUi/nyLVAuWoJ5WL9MzMzMzMOsIdZDMz+//Ze/s4u6rq/v+9ZjLABCwTJPqDgRhUGgpGMpACNtWvoBIFhSmgiE+gttTWqoDf1FD5lmCxxKYVtVpbilRQSsNTxyDUiASrRhNImIQQIfL8MKYQTYYCGWAys35/nH0mZ+6ce++5957nu96v103O2ffcc/a587n7rL332msZhmEYAdrVQL4i6wokQNnuqWz3Eyd5+26sPrXJW33yRNm+G7uf9iJP30+e6gIlqE9b+iAbhmEYhmEYRjXadQTZMAzDMAzDMEIxA9kwDMMwDMMwApTaQBaRd4rIFhF5SEQWh7y/p4gsd++vFZHZ6dcyOhHu5xwR2SYiG9zrj7OoZ1RE5CoReUZE7qvyvojI19z93isiR6VdxzxR7++fcl0OFpE7ReSXIrJZRD6TZX18RKRTRAZF5PtZ1wVARHpE5EYReUBE7heRN2Vdpyywtji/bbG1w41T7ztLuS65aotFZC8RuUtENrr6XJJlfXyaeTaU1kAWkU7gG8C7gMOBs0Tk8IrDPg7sUNXXA5cDX0q3ltGJeD8Ay1V1nntdmWolG+fbwDtrvP8u4FD3Ohf4Zgp1yiUN/P3TYhfwWVU9HDgO+GTG9fH5DHB/1pUI8FXgB6p6GHAk+apbKlhbnPu2+NtYO9wo36b2d5YmeWuLXwJOUNUjgXnAO0XkuAzr49Pws6G0BjJwDPCQqj6iqi8D/wGcWnHMqcDVbvtG4G0iIinWsRGi3E+hUNWfANtrHHIqcI16rAF6ROSAdGqXO3L191fVrap6j9t+Dq/h6c2qPgAichBwMpALY0RE9gXeAnwLQFVfVtXhbGuVCdYW5xhrhxsnwneWGnlri51Onne7Xe6VaTSIZp8NZTaQe4EnA/tPMVU0E8eo6i7gWeCVqdSucaLcD8DpbhrsRhE5OJ2qJUbUe24HcvtduOnwPmBttjXhK8BfAuMZ18PnEGAb8G9uau9KEdk760plgLXFxW6Lc9v2GJPJS1vs3Bk2AM8At6tqIZ8NZTaQ25FbgNmq+kbgdnaPyBhGIojIPsBNwHmq+r8Z1uPdwDOquj6rOoQwDTgK+Kaq9gEvAJn6jhupYW2xkSp5aYsBVHVMVecBBwHHiMgbsqpLK8+GMhvIQ0Cw136QKws9RkSmAfsCv02ldo1T935U9beq+pLbvRI4OqW6JUWUv2G7kLvvQkS68Brka1X15izrAiwAThGRx/CmvE8Qke9mWyWeAp4KjJ7ciGcwtxvWFhe7Lc5d22NMJmdt8QTOpexOsvXXbvrZUGYD+W7gUBE5RET2AN4PrKg4ZgVwtts+A1il+c2cUvd+KvzCTqH4C4JWAB9xq6iPA55V1a1ZVyojoug5NZx/6LeA+1X1y1nVw0dVL1TVg1R1Nt53s0pVP5Rxnf4HeFJE5riitwG/zLBKWWFtcbHbYmuHc0ze2mIRmSkiPW67G3gH8EBW9Wnl2TAt0ZpliKruEpG/AFYCncBVqrpZRL4ArFPVFXii+o6IPITncP/+7Gpcm4j382kROQVvVet24JzMKhwBEbkOeCuwv4g8BVyM59CPqv4zcBtwEvAQsBP4aDY1zZ5qf/8Mq7QA+DCwyfmaAfyVqt6WYZ3yyKeAa50h9QhtqGFri/PdFls73Dhh35mqfiuj6uStLT4AuNpFe+kArlfVXITdbBRLNW0YhmEYhmEYAcrsYmEYhmEYhmEYDWMGsmEYhmEYhmEEMAPZMAzDMAzDMAKYgWwYhmEYhmEYAcxANgzDMAzDMIwAZiBnjIjMFpH7Gjj+2yJyhtu+UkQODznmHBH5epz1NIykEJEfi8j8OseYpo3MEJHHRGT/kPKfJ30Nw0gC16YeGNhPRH8icpuI9LjXn8d9/iQxA7nAqOofq2o7Jh4wDMNIBRfPNRRV/YM062IYMXIOcGC9g6Lgsl+GoqonuYx6PYAZyEbDdIrIv4rIZhH5oYh0i8g8EVkjIveKyH+KyIzKDwVH3kTkoyLyKxG5Cy9wuH/Me0RkrYgMisiPROTVItIhIg+KyEx3TIeIPOTvG0YtRGSRiHzabV8uIqvc9gkicq2InCgivxCRe0TkBhHZx71/tIj8t4isF5GVFdnGfB1+W0QudfumaaMlImj1LBHZJCL3iciXAp97XkT+QUQ2Am8KlHeLyH+JyJ/4x7n/3+ra4xtF5AF3bnHvneTK1ovI10Tk+678la693ywiVwISuM6AO36ziJzryj4mIl8JHPMnInJ5ct+ekSdaaHf/WkTudhq/QjzOAObjJTHaIF7GO4BPuc9vEpHD3Of3FpGrROQu1+ae6srPEZEVrh53iMgBIvITd777ROTN7jh/ZHop8Dr3/rI0v7tmMQM5HxwKfENVjwCGgdOBa4DPqeobgU142Y1CcYbGJXhGxB8CQbeLnwHHqWofXh7yv1TVceC7wAfdMW8HNqrqtljvyigrPwXe7LbnA/uISJcruxe4CHi7qh4FrAMucO//I3CGqh4NXAV8MXDOacC1wIOqepFp2oiJWlr9FfAl4ARgHvD7ItLvjt0bWKuqR6rqz1zZPsAtwHWq+q8h1+oDzsPT6muBBSKyF/AvwLuc7oMdtouBn7l2/z+BWYH3PuaOn4+Xle+VwPXAe1z9wctod1XD34hRVBpud92xX1fV31fVNwDdwLtV9UZ3zAdVdZ6qjrhjf+M+/03g/7qyz+OlZz4GOB5YJiJ7u/eOwmvT/w/wAWClqs4DjgT8rH4+i4GH3fUWxfKNJIwZyPngUVX1xbQeeB3Qo6r/7cquBt5S4/PHAj9W1W2q+jKwPPDeQcBKEdkELAKOcOVXAR9x2x8D/q312zDahPXA0SLyO8BLwC/wGuw3AyN4BsJq8dKeng28BpgDvAG43ZVfhKdNn38B7lNV32g2TRtxUEurw+zW2C68Dprfzo4BN1Wc63vAv6nqNVWudZeqPuU6axuA2cBhwCOq+qg75rrA8W/B69ShqrcCOwLvfdqNXq8BDgYOVdXngVXAu93oXpeqbor+VRgFp5l2F+B4N+O2Ca8zeMSUM+/m5sC1ZrvtE4HF7rw/BvZid2fudlXd7rbvBj4qIkuAuar6XPO3mg/MQM4HLwW2x/B8deLiH/F6kHOBP8UTN6r6JPC0iJwAHAP8V4zXNEqMqo4Cj+L5sP0cb2TjeOD1rvx2N0owT1UPV9WP400fbw6Uz1XVEwOn/TleQ75XhCqYpo1I1NHqYzU++qKqjlWUrQbe6btOhFDZjlf1y6yFiLwVbwbkTap6JDCI0zhwJd69fBTrALYVzbS7rj39J7xR3rnAv7JbS2H4Gg7qV4DTA+eepar3u/deCNTvJ3idviHg2yLiD1YUFjOQ88mzwA7fhwf4MPDfNY5fC/wf59PWBbw38N6+eIIFr1cZ5Eq8EYwbQh4GhlGLn+JNwf3EbX8C70G+Bm9q+fUw4b/2u8AWYKaIvMmVd4lIcCTjW8BtwPXiLfgwTRtxUU2rd+FpbH/xFuKdRe129q/xRnm/0cC1twCvFZHZbv/MwHs/wZuWRkTeBfjrTPYFdqjqTjdSfJz/AVVdizei/AEmj0Yb7UGj7a5vDP/G+SSfETjXc8ArIlxzJZ5vsu9T3xd2kIi8BnjauR9died+ESTq9XKDGcj55Ww8X5978fzjvlDtQFXdCizBm3JZDdwfeHsJcIOIrAd+U/HRFXh+dTYSYTTKT4EDgF+o6tPAi8BPnc/vOcB1Tru/AA5zbhJnAF9yU8cbgEkRAFT1y3iN/XeApzFNG/FQTatb8fwi7wQ2AutV9Xt1zvUZoFtE/i7KhZ1v558DP3B6fQ5vAAQ8H/u3iMhm4DTgCVf+A2CaiNyPt7BpTcVprwdWq+oOjHaj0XZ3GG/U+D48Q/fuwLm+DfxzxSK9MP4G6ALudVr9myrHvRXYKCKDeB3BrwbfVNXf4rmA3FeURXqiqlnXwcgI8SJgXK6qb657sGEUANO0kTdEZB9Vfd6NwH0DbyFq09EnxIuCcbmq3hFbJQ3DmIKNILcpIrIYbxHKhVnXxTDiwDRt5JQ/cQucNuO5T/xLMycRL9HCr4ARM44NI3lsBNkwDMMwDMMwAtgIsmEYhmEYhmEEMAPZMAzDMAzDMAKYgWwYhmEYhmEYAcxANgzDMAzDMIwAZiAbhmEYhmEYRgAzkA3DMAzDMAwjgBnIhmEYhmEYhhHADGTDMAzDMAzDCGAGsmEYhmEYhmEEMAPZMAzDMAzDMAJMS+rEInIV8G7gGVV9gyvbD1gOzAYeA96nqjtERICvAicBO4FzVPUe95mzgYvcaS9V1avrXXv//ffX2bNnx3o/Rr5Yv379b1R1ZtLXMR0bSWEaNoqOadgoA1V1rKqJvIC3AEcB9wXK/g5Y7LYXA19y2ycB/wUIcByw1pXvBzzi/p/htmfUu/bRRx+tRrkB1mlC2g2+TMdGUpiGjaJjGjbKQDUdJ+Zioao/AbZXFJ8K+D22q4H+QPk1rq5rgB4ROQBYCNyuqttVdQdwO/DOpOpsGJWYjo2iYxo2io5p2MiCxFwsqvBqVd3qtv8HeLXb7gWeDBz3lCurVj4FETkXOBdg1qxZMVa5fAwMDrFs5RaGhkfoFGFMld6ebhYtnEN/X+jXa0zGdJwhFw1s4rq1TzKmSqcIZx17MJf2z826WkXDNNwEftv56+ERDgy0mdXKjURpKw1Xauz4w2Zy5wPbTHMJkraBPIGqqohojOe7ArgCYP78+bGdt0wMDA6xZMVmhkdGJ8rGvKknhoZHuPDmTax7fLv96BrAdJwuFw1s4rtrnpjYH1Plu2ue4Kb1T3HZaW80rTZBu2i4lhEbxcAdGBziwps3MTI6BkxuM29aPzSlHDA9pkRWGk6rYxSmvWA7aM/vZEjbQH5aRA5Q1a1uyuMZVz4EHBw47iBXNgS8taL8xynUs3RU/sDCGBkd49o1T+C3BtbQV8V0nBH/vvaJ0PKR0XHTamO0lYarGbc+1QzfoLHxwku7prSfI6NjkwyVYPmylVtMi8mSqYYHBodYdMNGRsd3DzItumEjEH8btGzllprPbpiqRXt+t07aYd5WAGe77bOB7wXKPyIexwHPuqmTlcCJIjJDRGYAJ7oyo0Gi/MAAKrvKI6NjnLd8A7MX38qCpasYGBxKpoLFwnScEeM1xoh8rfZ94Yem0/q0lYbD2j/fiK323rVrnmBoeATFMzaCM29R+PXwSKvVNmqTqYaXrNg8YRz7jI4rS1ZsbvaUDAwOsWDpKg6peN42qyVf40ZzJBnm7Tq83tr+IvIUcDGwFLheRD4OPA68zx1+G97K04fwwrJ8FEBVt4vI3wB3u+O+oKqVjvpGFYLTP3HMPbVjj9R0nA2tTF3u2DnKohuTGckpIqbh6gZGLcOj1TbzwJ7uFs9g+ORRw9U6TI12pHxqzXIc2NPNUJNGcrOfMxI0kFX1rCpvvS3kWAU+WeU8VwFXxVi10hFmTAB1XSqaod2mDk3HyRO2+KSaT2dURse0rXRaC9NwdQPDN2LjNiK6uzon2mGjddpBw9VmMs5bvoG99+hs+rydIq1WrW3JbJGeEQ/Vep17TuuI3Tj2sR6pERdh+g36wfv4HbMOqe1mEcR0avgsWjhnyoBB0IiNezDhstPmWues5Oy9RycvvDxVM80as7VmM8KuA0RqD/2F+Ebj1PVBFpEpRnRYmZEN1XqdzU7zRKFoPVLTcH4J02+15vzXwyN84NjoIZeKptN6mI6bp7+vl8tOm0tvTzcC9PZ0c9lpXmhAX4O+Xnp7ulsesTPjOJwyabirM9x8qlYO1X2MoTmXnHGFr5w5j+6u6nrtNVefpomySO+uiGVGBrSyEKRDoBkbYky15g89h5iGc0oj+j2wp5v5r9mPjoiaHVMtgjYbwXTcAv19vaxefAKPLj2ZRQvnsGTFZs5bvmFipmFMdWJUeWeVEbsoNDNiV7D2tBVKo+FnqwxCVSv3Z8uCCz8vvHnTxN960cI5NQ3davidv+ld4ebc8Yclngm8tFTtuYnIq4ADgG4RmYuXthHgd4DpKdTNiEA137oZ07t4cXS85rThuHqjHWMNLkeZMb2r6mKCPI2cmIbzTzX9CpNHkn3DZdnKLZFdLCC/2mwE03G81Ap56bvyRFkUValRn0ZH7MLcjBbduJElKzbz7MhoKeLZllHD06u4WEyvMvtQK5JKf1/vxN/XT+IVhZ7uLsBr25at3MLOkM/d+cC2SOcyplJrauNk4GN4sQL/KVD+HPD/kqyUEZ1qvnUXv+cI1j2+fSLjWDUaHe3o7upEldAf+mevz13kANNwzqmm36Nm7cuaR3ZMZMs7/WjvAXLe8g0NX8PX5vnLNxTV2DAdx0Awg2gthoZH+MqZ8+r6JX/wuFmTFpOCZ/UNDY+wYOmqCf/metFYwgyn0TGdcJNLMr5uipROw9VmGaqVNxNJpR4icMjiW2t26KIa25YNciqidQwkEXmfql6fUn1iYf78+bpu3bqsq5EaYamjZ0zvYsfOeP2QoywI6O7qTGWBioisV9X5EY8tnIahfXRcmeExbPFLh8CX3zeP85dvaDn8VloarUcjGnbHF07HedHwRQObQhd/hiHA5WfOA+D86zcQ9ogMHlPN6O7sEHRcGa8o7+nuYskpR9T8bBg93V1suPjESMemRTtrePbiW6t+5rGlJ08pW7B0VbhORBhXZd/uLl54eRejY/EvqhOoafSGzarkpZ1Mg2o6juIcPyAi7wNmB49X1b+Nr3pGq+x8eRewe0Q4buMYokUPyGkYONNwjln3+PZJfnth05bjSizGMeRWo1EwHTfBwOBQZOMYPLcJP9lDtfEjxTNua4VyG6vSYA6PjHLe8g10wBTjuRZJLrxOkbbV8PGHzQzNuug/s5P8+wZ9nmHqTEQ99492JYqB/J/Ai8B6IJm4YUbTREkhnTY5zCBlGs4pjRgvcY6r5FCjUTAdN8GylVsa1k4UY8X3FW52xK8R47hEtK2Gb713a9ZVqGr0JuH+UQaiGMivUdU3JF4ToymippBOkxxmkDIN55RmjJe48H33CuRrZzpugiQf8klMh1cjavSWnFMaDfdW8futtkgziVndZvDrHPQ57nCumZXk8FmeKlHCvK0RkcMTr4nRFHns4eUwrIxpOKdkpV+F0FBLOcd03AQ907uyrkIsNBK9JceURsOLFs6Z0mnpEAqRQbHvCz9k0Q0bJ0LOhRnHYdkg2ygcIRDNQD4WGBSRzSJyj4gMisg9SVfMiEYee3jf35j9VFIFpuGckgf9+tOOBcB03ARlSSQmUAaDpDQaXvf49imdlnH1ysPwQ7LlgR07RxkN6XF1ikxKpBOcWasXx7mMRHGx6E+8FkbThIXJypocLiYxDeeURQvnRPLj7OyQqoue4iCPMzEhmI4bwJ9CzmF71BT+wsCCuANVozQa/ve1Uxfc+eWX9s+dUr7klCNYdMPGUMM0L4yr8mhIBA5oz4V8UQzkQjw5ykpY7M5OEV47czqPbNvJmCpC9aD1BmAazpwwHTei2X9475FccsvmxPz4CjINbzqOyMDgUEsL6PLK0PDIlPBivc6PHurHXM4BpdFwNTu3lv27957Tct1h89vBsNCx1SjI4EJTRDGQ78B7jgmwF3Aw8DCQf0ebglMtQsWYKg8+88LEfrkeAYlgGs6QajqOqtsO2Z0pKikDuSDT8KbjiFxyy+bSGcfVmEgkIrsXDeY4g2RbanhgcCj3o8cAL46OTWmv6yUTy4ObXFLUNZBV9feC+yJyDPDHidWozYmystRoDNNwNkTNXFYP/5mS5EjFszke1fExHUcnLxED0iLM8Mrj9He7anjJis25N44BRkbHG4qMFbaQr0xEGUGehKreJSJXJlGZdqfRnlueWbB0VW6n+kzDyRN3fO6+L/wwlvNUo4ijIKZjox5DwyMTi6jy6H7RLhrOs1tFJVEGNOpl5isLdQ1kEfl0YLcDOBp4upWLisj5eL1GBTYBHwUOAP4DeCVeEPEPq+rLIrIncI277m+BM1X1sVaun1fyGNO4WfwfWR6m+kzD6RO3lpMeESzCKIjpODo93V2FMkqS5LM3bKSD3aPMWbbJpuHi09vTzerFJ2RdjVSIEuZtZuC1L/Aj4NRmLygivcCngfkuYHgn8H7gS8Dlqvp6YAfwcfeRjwM7XPnl7rjSMTA41PJUdF7JQRgt03DKFE3L5y/fUIS4nqbjiCw55Qi6SpJZo1XGxnXK9H6GbXJbanhGMRYB16XsLhWVRPFB/n8AItLt9uN48k0DukVkFJgObAVOAD7g3r8aWAJ8E+/Hs8SV3wh8XUREtcD+BxX409HVqLeKtAhkudLVNNw4QV/4RqfSBgaHChdVJRjXE3K3sAkwHTeC//f77PUbC992JkUWbXK7avjkNx7Ad9eEh4UrCr1t4FJRSd0RZBE5XETuBh4EHhSRta1kwlHVIeDvgSfwhPws3hTIsKrucoc9Bfh/hV7gSffZXe74V4bU81wRWSci67Zt29Zs9TKh1nR0d1cnZx17MN1dnSnXKl6y9PEsioZdXTPXcasB4Wulj877qF4OZjuqUhQd50XDy1ZuMeO4Blm0ye2q4VvvzV3yrIbwR47byTiGaC4WVwB/paoHqepBwOddWVOIyAy8XtwhwIHA3sA7mz2fj6peoarzVXX+zJm5S3Vck1o9+ctOm8udD2wrvG/y9hdeYvbiW3ndhbcxO/00lYXQMORDx7UCwkehlp6LsJI7uLApZxRCx1lrONjBaycE6IryRCfTqfK21HDRo6qEtf/tkHY6ys/pFap6u7+jqj8CXtHCNd8OPKqq21R1FLgZWAD0iIjv8nEQ4H/bQ3ixEnHv74vnXF8aqvXke3u66e/rLUUg7pHRcWB3ZI6U01Sahhugmt6i6rCIESEqyWkKVdNxBMq02LkRFNhjWvhM4957dNLb0101jXCKmIYLSrD9b5e001EM5MdE5EIROci9FgOPtXDNJ4DjRGS6iAjwNuCXwJ3AGe6Ys4Hvue0Vbh/3/qo8+ry1wqKFc6a4UPg9/IHBIW9ooISkOJ1tGm6AagZuVMM3TM9dHVIoGefU1cJ0XEHYKFYZBhSa5YWXwzsGO18eY/XiE3h06cmsXnxCllPlpuEc0NPdRVdnYy1ysP2vN8tYltHlKAbyx/B6XLcBt+L1xj7W7AVVdS3yoT21AAAgAElEQVSec/w9eCFZOvCmWD4HXCAiD+H5BH3LfeRbwCtd+QXA4mavnVf6+3q57LS5k3r4px/dy5IVmzlv+YaiZPlqipQeZqbhBqjVYYtCpZ67uzoYHddCLdqDXKZQNR0HCBvFWnTDRqRIPbGUyNGsjmk4B2y4+ETO/P2D6Yz4Y+nqlEntf61ZxjKNLksZO0/z58/XdevWZV2Npok7yUKeaTamooisV9X5CVQpN2Sp41aiWFSe5/zlGwpnHIM3yrL3ntMSS65gGm6NBUtXtZ2fcTN0d3Vy2WlzgfiThbSzhmcvvrXqZx5benLkY7PgK2fOayj1dVeHsOy9R07ope8LPwz1q54xvYvpe0wL/V3mOX5yNR1HSRTyA+D9qjrs9mcA31XVk2t/sn1oNSRW5WfbxYcurYUipuHG6e/rpb+vd0Kf5y/fwLKVW+pqu1LPL7y0q5DGcVeH8MLLuyaSTeQhBJzpeDI5HOHPBV0d8Krf6Z70TFn3+HauXfPExG/RH22/5JbNDO8cTS0rmmk4HzSa+np0XCelLa82rqra+hqWPBEl1fSrfTEDqOoOETkwwToVisrR3igPUt+IGBoemRQvdmh4hEU3bmR0rIgmRWN0iqS5UMQ03ARh2q71UA07voh0irDPXtOmjJD4PnYZ+m+ajgMc2NNdWI0lyeg4U36XYTF4R8d1QuMpdgBNwxnT2+TvJmjgPlslS+WzI6NVf5c5cvOJTBQf5HEROcjfEZFZCdancDQaEqsyBFGlKdwOxjHAuGqahoZpuAnCtO0/VMN8y8oy8zGuynCVsEwZj4KYjgOE+cobHuct3zARVvMvb9wY6TMpLUw1DWdMs53KoIFbayF3q2tY8kSUEeS/BlaLyCq8eApvBf4syUoViUamEwYGhyyzkyPl3qRpuAmiGIMjo2Oc59wvyjKa52szh6MgpuMAfge7TNqLmzFVxhros6bQATQNF5BgVK1av7fjD5s56XeZ1PqNtIiSavpWETkGeJMr+ktVfSbZahWHqNMJ/sixGcfp9yZNw83RyBR2pbtQUQlqs3KhbNajIKbjqfi+8vMu+eGEv7jRPD3TuxI9v2m4ePgppmFqm1jJ9zdu5dL+uRO/y6ITZQQZVX0aGEi4LoVk0cI5oaL59bMjzF58K50ijKlO/G94o46fvX4j6x7fzqX9c1O5pmm4PpUL7I4/bCb/vvYJoq7lUCi8kTwyOsaSFZsR8baD97NX1DRlCWI6DsdCu8XD8y/uYmBwKFHjxjRcLPzIEwuWrqrrQle2Tmr2LX7B8WO+zqjoefu2sG8Um3E8mTFVvrvmCS4a2JR1VQzCY8o2Yhz7KNBRcGNleGR0YvFS8PZ37BwtbDzPslPNZ9xoDD9agWH4+Ik+2tGNyQzkFvCzxZy/fAP/O7Ir6+pkTjOG0XVrn4y/IkbDhC2wa9Q4bvVzRSCnGfbaniKukM8r7WgIGdXxF2NHeb53CIXPnhekqouFiPxOrQ+q6v/GX53iUBnSykaImzOMkvzeTMPRKWKMyqxI+7syHdenmqub0ThRs6s1gmm42ET9Xfk2QB7ixsdBLR/kzex2K6xEgbYOz1KWkFZZk0RjHMA0HBGLKRudDEYrTcd18B/CFiWodRL6/kzDbUYO4sa3TFUDWVUPTrMiRcNG3GoTdVHiWccmJzPTcHSOP2xmaDIBYzJZRLIwHUfDfxDbSHJrTE9gMappuBx0d3U29Nsqup0UKYqFiOwLvA7Yyy9T1Z8nVakiYCNutfn/9t2LXw+P0FHDUP7QcbNSi2JhGq7NnQ9sy7oKuaSnuwsRUk3HWwvT8WQqI68sWjiHo2bty+qHt2ddtcIyMjqe6PlNw8XlqFn78thvRyLbPkVfG1DXQBaRjwMXAL3AJuD3gTV4Ab7blkUL57Doho0N5TNvJ/wfUJhx3N3VmWaaadNwBIre04+btDUaBdPxZMJSm5+3fEPGtSo+ST7RTMPFZvXD2/nQcbMizTZmHTc+DqLMpZwHzAceU9U3A0cDv020VkWhjvushQjZTacIghd0PAPDwzRch6L39OOkp7srd8axw3QcwNaBNE9XdrEYTcMF57q1T04Ja+vTIWT5nI+dKC4WL6rqiIggInuo6mYRKXa3IAaWrdzC6FjtvnayE1XFYkyVr5w5L6sfjGm4DosWzuH85RsKneQjLjZcfGLWVaiG6TiAzXo0z+i40kH4M6o72YQ4puGCM6bKxe85gkU3bpxkA3V1CsvOOLLwRnGQKL+ErSLSA9wCrBSRm4Cnkq1W/rHGuXHOW76BeZf8MIv4iKbhOvT39VY1jgue96MhegMj6X6c8xzF9TQdB0g6LXLZCTOOO4DLTntjkpc1DZeA85dvYO89pjFjetfEiHHZjGOIMIKsqqe4zf8nIm8D9gW+38pF3Q/kSuANeC5PHwO2AMuB2cBjwPtUdYeICPBV4CRgJ3COqt7TyvXjwBbpNcfwyCiLbtwIpBcf0TQcjd4qmu4QYa+uDl54ufzT2bNf2c2CpasYGh6ZlGY6D3E9Tce7GRgc4vkXLTlTq8yY3sX0PaZNWuSYcJpp03AJULxneXdXJ5dnNzOcOHVHkEXk2/62qt6hqjcDV7R43a8CP1DVw4AjgfuBxcAdqnoocIfbB3gXcKh7nQt8s8Vrx8KihXPo7urMuhqFZHRMueSWzaldzzQcjWqaHlPl5V3t4TC0+uHtE52EyhH1rLPomY53s2zlFlsgHQM7Uk7RbRouF622iTmcpZtEFB/kSfMtItKBt/K0KVyIl7cA5wCo6svAyyJyKrtXsl4N/Bj4HHAqcI2qKrBGRHpE5ABV3dpsHeLA7zEtW7mFoeGRyHF/DY+UG2bTcAT6+3pZ9/j20BXKZox4ZOxaZTp2mItbPAi7Iw4FZ0mAKeHzYholbEsN771HZ2ln4PzfYljIxVqaCYtCk/UsXSVVR5BF5HMisgN4o4hsd68dwG+A21q45iHANuDfRGRQRK4Ukb2BVwdE+j/Aq912L/Bk4PNPubLK+p4rIutEZN22benEdO3v62XRwjl0dZpx3AxJ9xaLpmFX59R17HPRwCautWQhNcki2kfRdJyGhi3qSjyEzZIsWbGZC2/exNDwCMpuw6WV9rrdNXxgz171DyooB/Z0Txi7jWgmLApN1rN0ldRysfg7YCZwuft/JrC/qu6nqotauOY04Cjgm6raB7zA7ukPAFzvriGLU1WvUNX5qjp/5syZLVSvMS65ZXPdaBZGOK02uhEolIbd5zLR8cDgENeuecKiWNQgw7iehdJxGho2F7fkGB4ZTcJwaWsNP/jMCy2fI4/4MxCfvX5jw5qpNguUp9mhqgayeuxy4p2JF6/wWBH5AxH5gxau+RTwlKqudfs34gn8aRE5AMD9/4x7fwgIpqk8yJXlgrR9uMpE0r1F03B0lq3cYsZxCH4EjyzjepqOp9Lf18tlp82lt6e7raKsZEkrhotpuDx0iveLCy5irjaDPjQ8UtW3uNosUJ5mh6Jk0vsi8GHgAcDvIijeKtCGUdX/EZEnRWSOqm4B3gb80r3OBpa6/7/nPrIC+AsR+Q/gWODZvPluGs2TRm/RNFyfPPXa80SeVmibjifT39dLf18vA4NDlkEvJro6hH32mhY68BOH4WIaLj7jqlUjHoVRzbd40cI5k3yQIX/Z96Is0jsD+F1VfTHG634KuFZE9gAeAT6KN5p9vXipKB8H3ueOvQ3vx/MQXliWj8ZYj5bp6e5ieMRGkZslpd6iabgOFrZwKr093bkxjh2m4xDy5LNYeAROfuMB3LR+KCnDxTRccA7s6W54QMWfLQ62p8FAB2mFGWyUKAbyo0Cszl6qugEv3WQlbws5VoFPxnn9OFlyyhEsumGjrfJvAr/RbXT1axOYhusQ1ptvd44/LNz3MAW9VsN0HILNfsTH6Jhy5wPbuOy0uUlp3DRccI4/bCZ3PrAtdEClVjSvsN+pPwuUV6IYyM8B94jIj4CX/EJVvSCxWhWIynBvRnQuO20uQBqhXkzDdTAdT+XOB6auXs84NJHpOASb/YiXXw+PJGm4mIYLzp0PbKvqHuF3rMJ+j3nyLY5KlFTTP8BbgXoPsDnwMhx+uDdbVd0Y/X29aYV6MQ1HoL+vl9WLT2CGpfAFwheYZByayHQcQp58FstAz/SuJBM4mIYLjt+BOv3o3okFe50inH50b1VbKG++xVGJkmr6W863Z5aqPpRCnQpJ2IPTqE8aoV5Mw9EZGBxi2CKzTDA0PDIpNXqWoYlMx0YavDQ6ltgsiWm4+OzV1cHA4BA3rR+acKcYU+Wm9UPMf81+hfAtjkqUVNMnA5uA293+PBH5z6QrVjRsiq9xBgaHUgn1YhqOxsDgEItu2Gjh3ioIpkbPMjSR6XgqvsuLER87R8cTmyUxDRefF0fH686k+bORjy49mdWLTyikcQzRfJC/gBcO5U7wHOJF5PWJ1iqHhC3Mgd29JKNxLrx5E6cf3Zvkimkf03AVgrpGwBJChuOHvco4NJHpuAKbuUuPmJ5zpuGCoxQjyUccRDGQR1V1WGRSOPa2eoyGLcxZdONGxsaU8YzrVmRGRseSXjHt0/YaDqNS1/aN1Cfj6UPTcQU2c5ceMc2SmIZLQLU/2L7d5Vq/EsVAvl9E3gd0iMghwKeBNclWK1+EjVJYeul4SHjFtE/bazgMG31rjgxDE5mOKwhm8zLio7urM6lZEtNwiXl5V7meJ1GiWPwFXlrIceBm4GXgvCQrlTfKNm2QJ1IK/dL2Gg7DdB2dnnyMjJiOAwwMDplxnAAdwqQ03jGnWTcNl5ido+WaU48SxeIF4HPuBYCI9OJloWkLLM5mMnR1CDtf3sUhi29NdKraNByO6To6Ip5BluViE9PxZCyDXjKMa3KzJKZho0jUHEEWkd8XkX4R2d/tHyEi1wBrU6ldTrAYx/Ej7p8dO0dRdocSijHepncd03BVihiXMit27BxNRJ9RMR1PxWZAkmFGQnGQTcPlRyCJ2NmZUdVAFpHLgGuBDwI/EJEleCtPNwK/m0rtckJ/Xy+XnTZ3Iii20TrKVD/uuBMumIZrU9TQO1mRYkKQSZiOwyliZq4i4MdBHhoeiW3wwjTcHqh7JTXglTa1XCxOBY5U1RER2Q94Epirqo+kU7X8US3HuBEfMY8KmYaNWMlo1NJ0HMKihXNYdMNGRsetXY6TMD9Sv3PYQqfaNNxmxKCZzKnlYvGiqo4AqOp24FftKmYLRp8eMY8KmYZrMDA4hM2JNEZGo5am4xD6+3pZ9t4j6e6KstbcaJUWO4em4Tak6G5QtUaQXysiN7ttAQ4J7KOqpyVasxxh4bDSIYGEC6bhGixbucWiADRAiglBKjEd12C/vfe0xaYx0t3VwUjIKHKLnUPTcBtSdDeoWgby6RX7X0+yInnBzyw2NDxCp4i5VaREpwinHx37yum21HAlwWx5PdO7UIVnR0bNOG6QBPQZ+dIV+22p40r81OjmYhEve3V1AhJ3HGTTcJsheL7IC5auSjOZUqxUNZBV9Y40K5IHKjOLmXGcHmOq3LR+iPmv2S+2H1I7ariSSk37KZONxll+95N8f+NWnh0ZTTWDnuk4nAtvvteM4wTYsXOUBa/bj9UPb58oO2rWvi1p3TTcXgQT+PgL9qB4C8Mzc94SkU4RGRSR77v9Q0RkrYg8JCLLRWQPV76n23/IvT87qTqZK0W2ZBUloFnyqOFKTNPxMTqmDI8kG5YwC4qg40oGBodC3QCMeAgax/7+RQP5XYdTRA2Xld6e7imzk0V7tvtkubrhM8D9gf0vAZer6uuBHcDHXfnHgR2u/HJ3XCJEcSj3MwvtvYfFRU6Cgjn1507DlRTs+ywURW30Q8i9jispyfdeKK5b+2TWVahF4TRcRjpFqj5zivgsimwgi8iecV1URA4CTgaudPsCnADc6A65Guh326e6fdz7b3PHx049h/JgHOSy5RzPC0k69beDhisp+iKJvJNFo9+OOq6kiA/bohOny6FpuJycdezBVZ85RXwW1TWQReQYEdkEPOj2jxSRf2zxul8B/hIvHzvAK4FhVd3l9p8CfGeVXryYibj3n3XHV9bzXBFZJyLrtm3b1lSl6mXMG1OdmF612b34SSpKQFE07OrWso6DWBbIZEmz0S+KjuPWcBhFfNgWnTgSZZmGy0sHMP81+3H8YTND369WnmeijCB/DXg38FsAVd0IHN/sBUXk3cAzqrq+2XOEoapXqOp8VZ0/c2Zzfwg/Y16va3z9BsEy6CVPb083l502Nykn/kJoGOLRcZCgpgUvjWxPdxeC6ToOUg77Vggdx63hMKzjlz5nHXtwHKcxDZeUcTzXpzsfCO9QhJUnkdI8TmqFefPpUNXHK2YhWvEvWACcIiInAXsBvwN8FegRkWmuV3cQ4H9TQ8DBwFMiMg3YF/fjSoL+vqmhnA5ZfGtSlzMcL7y0q/5BzdNWGq4kTNNguo6DlFdlt7WOg/jfux++0GJZxEtlFIsFr9uPS/vnxnFq03CJqfVbHBoe4ZDFt05EAAImRVjKY7SLKCPIT4rIMYC6laLnAb9q9oKqeqGqHqSqs4H3A6tU9YN4ednPcIedDXzPba9w+7j3V6mmG3/NpvOSZ3hkNMmoAG2vYZjaW9+3uyvtKhitYToO0N/Xy+rFJ/Do0pOzqkJpueeJZ6fsx9Q2m4ZLzL7dXTVnJoMRgC65ZfOUCEtxLnyOY3Q6ioH8Z8AFwCzgaeA4VxY3nwMuEJGH8HyCvuXKvwW80pVfACxO4No1KaLvTBFJMCpA22vYj4c85Hr4Q8MjvPDyLro6zM2iWTL45tpex9WYMd06e3GSoOFiGi4xItEWc46MjlWNyR/HAtyw510zA3BRXCxeVtX3BwtE5DXAbxq6Ugiq+mPgx277EeCYkGNeBN7b6rVaoZpPjRE/CaWMbXsNh8VDHh1TZkzvYvoe0yxVbxOIeA1xitOBba/jIMGsp0byxBQ5xDRcYoZ3jracgTiOGfuw553fyWukvY4ygnyLiOzj74jIHKCtnBctpFB6JLRuzDRcRcPDO0dZvfiElGtTDsY19Xi8ba9jn+AIkZEOMbkamoZLzIE93S0Zx3FFsoorFnMUA3kZnqini8g84GbgIw1dpeBUaxhscjp+EvIGMw2XKDZlnki589z2OvaxDJHpE5OroWm4xBx/2MyJKGCN0ikSWySruJ53dQ1kVV0BfB34IfAd4HRVvaehq+SEZp22w0IKdXd18sHjZpmRXADKpOFa1NJ3NQ37vfUeW7DXFGl2MNpFx1GwkeP0icPV0DRcbm69d2vos6arUybCi1ZjXDU2d7V6z7uoVPVBFpHLYSJihwA9wEPAuSKCql7Q0JUyxp+SayakSGVIoWCYklvv3VrV2dxonOld8WU/L5uGa1FP39U07JcvOeUIzlu+IZvKF5g0FvC2k46j0qqfo9E4rcyWmIbbgx07R+s+axYsXRXawY1zsKFeHaJSa5HefXX2C0WrTtuVsWQHBodYdONGRseskY6TmL/OUmm4FlH0XS0estE8KS3gbRsdR8WM4/TZq7XBC9NwG1HrWXP8YTP57ponppTPfmU3C5auasmgjVqHqFQ1kFX1W9XeKyJxOW37XHLLZjOOE+ClXeOx/UjKpuFatKrvlBeblYY0fJDbScdR6e3pNjeLlHlp13j9g6pgGm4PfFc9P8JM2HO82qDCzx/ePjHFkJekIVW7hCJynft/UETuqXylV8V4iHuRkrlVJEersQt9yqbhWrSi74HBITM2mqRDJPH0qO2k46iknOLbwIva0iym4fLTgeeq58+uB5/ji27cONFOVhtUqJTXyOgYn71+Y6ZpqGu5WCxy/59R45jCsGjhnEk+mtCY03Yw5matTDFGvDQTuzBAqTRci2b1fdHAJq4Nme4yojGmmsZIR9voOCr9fb2cf/2GpKLeGPFjGi47ziwKm10fHVMuuWUz/X29HNjA7I/vSpXViHItF4un3P8P+2Ui0qOqw2lULG5acdquXABl/m/p0uw0dtk0XItm9D0wOMS1a56Y0nM3GqPFTlxd2knHjWDNcHEwDZefcaXmQu8dO0c5ZPGt9EzvoqtDGA1MSQhTR5ArSbqdDaNWFItjgL8FtgNfxAvJcqCIjAEfVtUfplPF+GjWadtibmZLs24wZdRwLRrV97KVW8w4jokkfZHbTcdRMT/k4mAaNsAzgnfsHJ0I+zY8sjvzXhQjOe2kbbWWpX4D+DLwn8CdwJ+p6v7ACcDSFOqWGyyTXna0mFnHNFwD03Vj1HKsSjgesuk4hEUL59DVae5uBcE0bEwwOqaIeM93f0Ze2d3GVnNjTTuxVS0DeZqq3qaq1wFPq+pqAFXdTBslkRsYHKLDfI5TY8b0Lnp7uhG8EaIWM+u0vYZrJQ+xLHqNsVdXBx86blYsAegbpO11HEZ/Xy/LzjiSGdMtyU0BMA0bk9ixc3TKzLziPff/4X1HZtHOTqHWIr3gaHflUFNbzMwODA7x2Rs2ms9xSnR2CBe/54g4fYzaWsNhyUM+e8NGzl++ofw3nwAjo+NT4nf2xhCvMwJtreNaBH3vzd0i15iGjUj8engktkQfrVLLQD5SRLbj9e5e4bZx+/skXrMc8Pn/3MRYK7FtjIYYG1fWPb49zh9B22p4YHCIz14/tXNneo6XlBrtttVxPSxhU2EwDRuR8Gc285DYqpaBvEdqtcgRwQDX1uSmz7VrnuDS/rlxna5tNXzhzZts5iMFlqzYnEYj3pY6joIlbCoMpmGjLlm4UdSiVpi3tgvbUDklbaRPnI+6dtQwWNSVNBkeGWVgcChRI7lddRwFS9hUDEzDRhRaXHMUOy0lV28GETlYRO4UkV+KyGYR+Ywr309EbheRB93/M1y5iMjXROQhEblXRI5Kqm5mWBhRyLOGwaJTpE0r2R6zJO86Nox6mIbLQ29Pd66MY8jAQAZ2AZ9V1cOB44BPisjhwGLgDlU9FLjD7QO8CzjUvc4FvplUxcywyJ699+isf1D25FbDYNEp0sYPYF9Acq3jWvjRWYy2p7AaNiaz/YWXcjfQkLqBrKpbVfUet/0ccD/QC5wKXO0Ouxrod9unAteoxxqgR0QOSKJu3V1Z9BcMn84O4Yt/FJv/cWLkWcPgLRyrDJFjJEsRO9d513E1fFc4i1phFFXDxlRGRsc5f/kGZoeEJK0VrjRJamXS20G4S6gAqqr7tXpxEZkN9AFrgVer6lb31v8Ar3bbvcCTgY895cq2BsoQkXPxeoTMmjWr7rUvGtjEdWufZEyVThHOOvZgRnaNN38zRsv8w3uPjHWKpWgadudrSMdB/AWmQ8MjWOjudEly1L5oOm5Fw1EwV7jiYRo2ouALZGh4hAtv3jRRXhmu1H+vlr0QDLjQbJi4WlEs9m/oTA0iIvsANwHnqer/SuCJrqoqIg2t11LVK4ArAObPn1/zsxcNbJoUz3RMdUp8U6MUFErD7nORdRykcoGpBbBIjxRWXhdKx81qOCpFHK03TMNGYwRd1yo7xP571QzesBwAUYzqSqr6FKjqWPAF7IvXC/NfTSMiXXhivlZVb3bFT/tTHe7/Z1z5EHBw4OMHubKmuW7tk/UPMlIn7sVOZdZwJTaqli493V1xZXusSzvpOArmY188TMNGM/x6eKSqK1UtF6uw52Eza0XqOt2KyMki8iu8qYi17v+mV0eI17X7FnC/qn458NYK4Gy3fTbwvUD5R9zq0+OAZwNTJw3h+7FYfNh8ktRipzJpOIyBwSHzx0yRPad1sOHiE3l06cmsXnxCaiuvy67jqCxaOMdyExcU07DRCAf2dNNZxV8wWF7po1ztedjo7FOUVWlfBBYAW1T1YGAh8NOGrjKZBcCHgRNEZIN7nQQsBd4hIg8Cb3f7ALcBjwAPAf8K/HkzF7WFHcUgoenTUmg4DF/XRnq8tGs8q9XWpdVxI/T39VoSp+JiGjYi4buuVRvQ9MuDtp3ijSxX60A3OvtUywfZZ5eqbhORDhERVb1dRP6+oasEUNWfQdX6vy3keAU+2ez1fKJMQS943X7c88SzNlWdIT3Tu5I4bSk0HIa5VmRDM/5sMVBaHTdKp4jNBBYT07BRl97Aojp/4XnYMRD+DFTc6s9AWTNrRaIYyM86B/ifAdeIyDNA4YZha41M+lEsLu2fy8DgEOct35BizYwgCT3zSqHhMGzBUjaMjI5x3vINLFu5ZaIhj2PVdB1Kq+NGMeO4sJiGjZr0dHexevEJE/uLFs6ZkuE4aOxWewYqnhGdVBQLn348AZ8HfATPuf7dDV0lBxzY0121FxL8Y/T39ZqBnCHDI4mkji2FhsOopmsjHfzV0ese387yu59kdEwnyhfduBGIdZS5tDpulF7TfVExDRs1ebbCBvDbz2qDD1Ftu2aI4oN8oVt5Oqqq33KO8Be0dNUMCEuekEJ4JqNBqjnkt0gpNByGJQXJnpHRMb675okJ49hndEy55JbNcV6qtDpulOMPm5l1FYzmMA0bNQnzE+7v62X14hNCF0YnadtFMZDfGVJ2cstXTpn+vl4uO20uvT3dqYVnMhonoanTUmg4jEpdJ9TBMJpkx85YZ0RKo+OombGqHff9jRZ4oKCURsNG/HR1SsOGbZK2Xa1Men8KfAL4XRG5J/DWK4D1LV85A/r7eqd8aZUZ9Y577YyMamfAbsf7OCijhsMI6npgcIgLrt/AuLlo5oaBwaGWGuuy6ThqEP9axyXkimUkRNk0bCREk8+tMNsuDmr5IF8P3AFcBiwOlD+nqs+Ef6RYhGXUW/3w9gxrZMQ8dVp6DYdhxnG+iCHiRal0XCuIf/A7iivYv5ELSqVhIxlGx7Vmhry0qWogq+oOYAfwXhE5Anize+un7M5KUygqV5lvfdYWeeSNOx/YFtu5yqjhepjxkD/qpUWtR9l0XG3VeWV5teNscV7xKJuGjeQYGh5hwdJVSUQBapgomfQ+CffCKH0AACAASURBVNwAzHKv60WkcMG1w4JJ20hb/kgibFlZNBwFC/uWT+L4u5RFx/t2h8c6D5YPDA7RESGDllEsyqJhI1l8d6qMEjJNECXM258Cx6jq8wAi8rfAz4F/SrJicWMJFYpBo5luIlIKDUfBwr6lS093FyIwvHOUA3u62fnyrtCFeTHpuhQ6rmbf+uX+YEbYgt3urk5rx4tNKTRsJE+rM29xEMVAFuDlwP4o1TPX5BYbWcsH4l7jIe8lGHavFBquRtB1qGd6Fx1ifshJ0yHw5ffNm9J4Vy4sg1h1XQodD1eJ7OGXVxvM6BThstPmVs2sZRSCUmjYSIes7bZaUSymqeou4DvAWhG5yb31R8DVaVQuTvbt7rKVzxkyY3oXF7/niEkZx4aGRyZSxvYmkHmsbBoOo9IgizmsmBFCLa3WC2rfDGXTcbVZDn+UvdpDcVx14nus7IQY+aZsGjbSIerMW1JZTGuNIN8FHKWqfyciPwb+0JV/QlXvbvnKKWNua9nxlTMnj7QlFZIlhFJpOAxzHUqXKNmZEtB3qXR8/GEzJ0UPCpZDfQO6v6+XdY9v59o1TzQbFcpIn1Jp2EieqDNvUcNGNkMtA3nCpFTVu/AEXliqTesZydLb052lD1GpNByGTTWnR4aZN0ul41vvDU/yceu9W7m0fy6LFs6p66Zy5wPbzDguFqXSsJEsjcwoRw0b2Qy1DOSZIlI1BaRLEVkYbPFS+uQglXepNOwTdFEx0iEJF6AGKJWOq7kB+eVhbirHHzaTZSu3cP7yDeYuV0xKpWEjWerN0gWJGjayGWoZyJ3APpTEgb7atJ6RDBkbFD6l0jCELwIzkqeRBjsBSqfjelRmhwxq3ozjQtJ2GjbSoZ5LVivUMpC3quoXWr5CTvj+xvBpPSN+ovhppkSpNAzmc5wFcaY/b5LS6bgRTPOloK01bDRGI4lCwlyyhHgSjtRKFJKrnp6IvFNEtojIQyKyuP4nJmOjDumQA7eKIKXSMJjPcdrkRM+50XEcGm6UrEM9GbGQGw1DNjo2ojM0PMIF12+g7ws/5JDFt7Jg6apJSUMGBodYsHQVhyy+lWUrt3D60b0TAxkCE+sTWk04UstAfltTZ0wAEekEvgG8CzgcOEtEDs+2VkYYl502N2u3iiCl07BlEUueThEEb+Q4J3rOhY6zaocTSh5kpEsuNAxmTxSFcfXWJfiZj31DNywr8k3rh1i0cA69Pd1TFu/6C/aaoaqLhapub+qMyXAM8JCqPgIgIv8BnAr8MtNaGZMQaT2sSpyUUcNh2cWMeBlX5dGlJ2ddjQlypONM2uGwKdRaBEeQjHyQIw2D2ROFJGjoVotaEfeCvVojyHmiF3gysP+UKzNyxAePnZV1FfJMLBrOgT9s6bERy6pk0g739/Vy2Wlz6enuinS8GcdGHcyeKCi/Hh6paQRXa7ubbdOLYiDXRUTOFZF1IrJu27ZtWVen7fjQcbO4tH9u1tUoPPV0nAN/2FIj2HfcKkm0xf19vWy4+ERmTK9vJPf2dFtH0mgJsyfyyYE93TWN4EUL59Dd1TmpvJV1JEUxkIeAgwP7B7myCVT1ClWdr6rzZ86cOeUE5rmZHIe+am8zjutTV8NQX8f9fb28+hV7JFfLNkaADx43K1duQjkjFg0f+qq9Q09erTzIxe85ouZDy38Yhj0oDcPRsj1hxEtXp9Q1Rmv9tv33/Nmm3p7uWNaRFMVAvhs4VEQOEZE9gPcDKxo5waNLTzYjOQEOfdXe3H7BW7OuRhFoWcM+az//jkjGhFGbDoHuro6JhvTyM+dZR682sWj49gveOkW/UduR/r5evnzmPLq7pj66gg/D4IPSaIzHqvjfVysvILHouJHvqUTfXVWmCROGaU93FzOmd020rR86btYko7Vyf9kZR/LlM+fVPCbstx1mBPf39bJ68Qk8uvRkVi8+oaUBD9GCLPoRkZOAr+AFHL9KVb9Y7dj58+frunXrUqubkT4isl5V52ddj0ZoRMNgOi47pmGj6BRRw2D2hDGZajqulSgkV6jqbcBtWdfDMJrFNGwUHdOwUQZMx0YUiuJiYRiGYRiGYRipUBgXi0YQkW3A4yFv7Q/8JuXqJE0Z7wnq39drVLXUqydq6NinbH/7drsf03D7/c2Lhmm4voYhu797u103qWuH6riUBnI1RGRdEf2lalHGe4Ly3leclO07svtpP8r2Hdn9tCdZfU/tdt20r20uFoZhGIZhGIYRwAxkwzAMwzAMwwjQbgbyFVlXIAHKeE9Q3vuKk7J9R3Y/7UfZviO7n/Ykq++p3a6b6rXbygfZMAzDMAzDMOrRbiPIhmEYhmEYhlGTUhrIIvJOEdkiIg+JyOKQ9/cUkeXu/bUiMjv9WjZGhHs6R0S2icgG9/rjLOrZCCJylYg8IyL3VXlfRORr7p7vFZGj0q5j1piW861l03A0yqZj03B70IpuReRCV75FRBYmcO0LROSX7m9yh4i8JvDeWEB7DaXRbkXbInK2iDzoXmfHfN3LA9f8lYgMx3G/NVHVUr3wUkc+DLwW2APYCBxeccyfA//stt8PLM+63jHc0znA17Oua4P39RbgKOC+Ku+fBPwXIMBxwNqs65zDv7tpOdv7MQ3H8zcvjI5Nw+2h4VZ0Cxzujt8TOMSdpzPmax8PTHfbfxb8zQDPJ3jPodoG9gMecf/PcNsz4rpuxfGfwksR3tL91nuVcQT5GOAhVX1EVV8G/gM4teKYU4Gr3faNwNtERFKsY6NEuafCoao/AbbXOORU4Br1WAP0iMgB6dQuF5iWc45pOBJl07FpuD003IpuTwX+Q1VfUtVHgYfc+WK7tqreqao73e4a4KAGzt/0dWuwELhdVber6g7gduCdCV33LOC6iOdumjIayL3Ak4H9p1xZ6DGqugt4FnhlKrVrjij3BHC6m265UUQOTqdqiRL1vsuKabn4Wm53DUP5dGwabg8Nt6LbVr+zRj//cbxRfp+9RGSdiKwRkf4Erhum7VbuOfJnnSvJIcCqQHGz91uTMhrI7cotwGxVfSNez+3qOscbRl4xLRtFxzRspIKIfAiYDywLFL9GvWxzHwC+IiKvi/GSWWv7/cCNqjoWKEvkfstoIA8Bwd76Qa4s9BgRmQbsC/w2ldo1R917UtXfqupLbvdK4OiU6pYkUf6WZca0XHwtt7uGoXw6Ng23h4Zb0W2r31mkz4vI24HPA6cE9IaqDrn/HwF+DPTFdd0a2m7lnhv57PupcK9o4X5rUkYD+W7gUBE5RET2wPsyK1c1rgD8FZZnAKvUeXrnlLr3VOETdgpwf4r1S4oVwEfcKurjgGdVdWvWlUoR03LxtdzuGoby6dg03B4abkW3K4D3uygXhwCHAnfFeW0R6QP+Bc84fiZQPkNE9nTb+wMLgF/GeN1q2l4JnOiuPwM40ZXFcl137cPwFgD+IlDWyv3WJomVf1m/8Fbd/gpvVeTnXdkX8IQEsBdwA57j/F3Aa7Oucwz3dBmwGW/1553AYVnXOcI9XQdsBUbxfI4+DnwC+IR7X4BvuHveBMzPus45/LublrO9F9NwPH/zQunYNJx9vXPyd66qW7yR3YeBLcC7Erj2j4CngQ3utcKV/4H7O210/3885utW1TbwMfddPAR8NM7ruv0lwNKKz7V0v7VelknPMAzDMAzDMAKU0cXCMAzDMAzDMJrGDGTDMAzDMAzDCGAGsmEYhmEYhmEEMAPZMAzDMAzDMAKYgWwYhmEYhmEYAcxAziEi0iMif551PQwjDkTkr7Kug2G0gojMFpH7sq6HYVRDRG4TkZ4Gjs9M0yLyfBbXbRQzkPNJD2AGslEWzEA2DMNIEFU9SVWHs65HmTADOZ8sBV4nIhtEZJmILBKRu0XkXhG5BCZ6fw+IyLdF5Fcicq2IvF1EVovIgyJyjDtuiYh8R0R+4cr/JNM7M0qNiAyIyHoR2Swi54rIUqDbaflad8yHROQuV/YvItLpyp93et8sIj8SkWNE5Mci8oiInOKOOUdEvufKHxSRizO8XaO96BSRf3X6/KGIdDsdzgcvi5eIPOa2z3G/hdtF5DER+QsRuUBEBkVkjYjsl+mdGIXD2QGfdtuXi8gqt32Ce/4/5jQ4W0Tur9SqO/ZoEdkoIhuBTwbOfUSgTb5XRA4N2BjXuvPdKCLTA+f5b9fWr/Sz64nI60TkB678py7zHS5D3i9EZJOIXJryV9c0ZiDnk8XAw6o6D7gdL03lMcA84GgReYs77vXAPwCHudcHgD8E/i+TR+3eCJwAvAn4axE5MI2bMNqSj6nq0cB84NPAMmBEVeep6gdF5PeAM4EFTt9jwAfdZ/fGS9V6BPAccCnwDuCP8DIq+RwDnI6n6/f6BophJMyhwDecPofxNFiLNwCnAb8PfBHYqap9eGlyP5JkRY1S8lPgzW57PrCPiHS5sp9UHFtNq/8GfEpVj6w4/hPAV12bPB8voyLAHOCfVPX3gP8F/txd8x+BM1xbfxWevgGucOc/Gs8O+SdX/lXgm6o6Fy9rYyGYlnUFjLqc6F6Dbn8fPPE/ATyqqpsARGQzcIeqqohsAmYHzvE9VR0BRkTkTjwDYyCl+hvtxadF5I/c9sF4Wg3yNuBo4G4RAegGnnHvvQz8wG1vAl5S1dEQPd+uqr8FEJGb8TqF62K+D8Oo5FFV3eC21zNZk2HcqarPAc+JyLPALa58E17nzjAaYT3eANnvAC8B9+AZs2/GG4y4MHDsFK06/+QeVfWN6e8A73LbvwA+LyIHATer6oOufX5SVVe7Y77rrvMDvM7f7e6YTmCriOyDl/b5BlcOsKf7fwG7jfTvAF9q5YtICzOQ848Al6nqv0wqFJmN9yPxGQ/sjzP5b1uZT9zyixuxIyJvBd4OvElVd4rIj4G9Kg8DrlbVC5nKqKr62pzQs6qOi4jp2ciaYHs7hte528XumdhKrUdtnw2jLm6w4FHgHODnwL3A8XgzyfdXHB6m1Vrn/ncRWQucDNwmIn8KPEJ4WyvAZlV9U/ANZ7gPu1Ho0MvUqkMeMReLfPIc8Aq3vRL4mOudISK9IvKqBs93qojsJSKvBN4K3B1bTQ1jN/sCO5xxfBhwnCsfddNyAHcAZ/gaFpH9ROQ1DV7nHe5z3UA/sLreBwwjIR7DmxEBOCPDehjtwU/xXBd+4rY/AQwGBhaq4hbwDYvIH7oi37UNEXkt8Iiqfg34HrtnOGaJiG8IfwD4GbAFmOmXi0iXiByhqv8LPCoi73XlIiK+K8dq4P2V1807ZiDnEDd9vFq8ECzvAP4d+IWbar6R3cZzVO4F7gTWAH+jqr+Os76G4fgBME1E7sdbaLrGlV8B3Csi16rqL4GLgB+KyL14PvYHNHidu4Cb8HR9k6qae4WRFX8P/JmIDAL7Z10Zo/T8FK+9/IWqPg286Mqi8lHgGyKyAW8k2Od9wH2u/A3ANa58C/BJ16bPwPMjfhmvM/glt9hvA55rBXjG78dd+WbgVFf+GXeeTUBvIzecJRKh42EUGBFZAjyvqn+fdV0Mo1VE5Bxgvqr+RdZ1MQzDKCvOjfP7qvqGjKuSGTaCbBiGYRiGYRgBbATZMAzDMAzDMALYCLJhGIZhGIZhBDAD2TAMwzAMwzACmIFsGIZhGIZhGAHMQDYMwzAMwzCMAGYgG4ZhGIZhGEYAM5ANwzAMwzAMI4AZyIZhGIZhGIYRwAxkwzAMwzAMwwhgBrJhGIZhGIZhBDAD2TAMwzAMwzACTEvqxCJyFfBu4BlVfYMr2w9YDswGHgPep6o7RESArwInATuBc1T1HveZs4GL3GkvVdWr6117//3319mzZ8d6P0a+WL9+/W9UdWbS1zEdG0mRloYNwzCMxhFVTebEIm8BngeuCRgWfwdsV9WlIrIYmKGqnxORk4BP4RkWxwJfVdVjnSGyDpgPKLAeOFpVd9S69vz583XdunWJ3JeRD0RkvarOT+E6pmMjEdLSsGEYhtE4iblYqOpPgO0VxacC/sjZ1UB/oPwa9VgD9IjIAcBC4HZV3e6MiduBdyZVZ8OoxHRsGIZhGO1H2j7Ir1bVrW77f4BXu+1e4MnAcU+5smrlhpElpmPDMAzDKDGJ+SDXQ1VVRGLz7xCRc4FzAWbNmhXXaRvmooFNXLf2ScZU6RThrGMP5tL+ubGce2BwiGUrt/Dr4REO7Olm0cI59PeZnZUlZdWxUZ2ov8OBwSEuuWUzO3aOAtDT3cWSU46w36xhGEYBSNtAflpEDlDVrW7q+RlXPgQcHDjuIFc2BLy1ovzHYSdW1SuAK8Dz3Yy32tH44L/+gtUP756NH1Plu2ue4NFtz/PLrc+19KAcGBziwps3MTI6BsDQ8AgX3ryJ/7+9+4+Tq67vPf5672YCG0A2qZEHLEQoRSxcJLG5EJu2V6GCgkJEfsgVpdYr7a22RdrUUFCiFUNNVWpt+ygttqhcBBTWeOEaEfDe21yJAhtII0aj8mtAQMOiwgKb5HP/mDPJZDMzOzszZ2bOmffz8djHzpw5c853sp/JfOZ7Pt/vF/AHbuflOo6ttuneh5VfkKcan5hk+Y337dzXzMx6V6dLLNYA5ye3zwe+UrH9nSpZAjyTXMJeC5wkaa6kucBJybaeMzpW3C05rrTuh1t3Jsew64NydKzY8PFXr92880O5bGJyO6vXbm6uwQ0aHSuy9Io7OGzFLSy94o4ZtTnHchvH/Wam8V3vfXjp6Ea+cNfDVZPjsskdkfp71szMWpfmNG/XUeo1e6mkR4HLgCuAGyS9G3gIODvZ/VZKI/+3UJoe610AEbFV0l8B30n2+0hEVM9Cu6R8ubU4PjGj55U/KBvtSXqsxvFrbW8H91r3Txz3o2biu9778Lr1j1R9rNFjmJlZ70gtQY6Ic2s8dGKVfQN4b43jfBb4bBub1rLKpFiU5u1qxmPjEw3XMx40PFQ1CT9oeKjJs0+vXm9ZvyTIeY7jftdMfNd6Hw4VBnhuckdD503zPWtmZu3hlfRmqNzrVP6QbKVIdHhOYeexgl09WNUu8y4/+UiGCoO7bRsqDLL85CNbaEF93ei1NuuUZuJ7+clHUhjUHtsbTY4LA0r1PWtmZu0xbYIsaY9e5mrb+kW1XqdmPT+5veG64mWLRlh1xjGMDA8hYGR4iFVnHJNqT26tnq6s9YA5hq2aZuJ72aIR9pndfOisPuvYvrn6YmaWZY38T/9t4NUNbOsL7ew9najR61TrHMsWjXT0w3X5yUfuVqMJ6fdap8QxbHtoNr7HJybrPl6Pk2Mzs2yomSBLehlwIDAk6RigfF3xJcCcDrStJ9WqQWz3OXpB+cM8q3MvO4atnpnGd3m8QLPmzik0/VwzM+usej3IpwK/T2nO1n+o2P4L4INpNqqXVet1qmdwQGzfMbNK5de9cv7O291eHKTTvdZt5hi2umrF99T33eteOZ/rv/0IkzN8L5cVBsVlbz661eaamVmH1EyQI+JfgX+VdHZE3NDBNvW0yl6nRmaxmGlyDHDd+kdY/PJ5AH0/zVorHMPWjGrTv33hrodnfJxBiR0RmbvyYmZmjdUgj0o6Gzi0cv+I+FhajcqSNJY62x7BxTdtZO/CQN9Ps9YmjmFrWDsG4gr4xNkekGdmllWNJMg3A88D9wDtmb4hw6b2LqVlosoMF2WeZm3GHMPWsHa8v96+ZIGTYzOzDGskQX55RPyn1FuSEe2c5q1ZvTKIL0Mcw9aw4TmF3ZaGn/Hzhwp8dNkxbWyRmZl1WiMLhdwl6ajUW5IRac9gUWl4qNDxxUFyyjFsDRkdK7aUHAOsPM2D8czMsq6RHuTjgTFJW4AXKJXXRUT03Ryy1Va4S4vY9UGb1WnWeohj2KY1Olbkz264r6VjDA8V/P40M8uBRhLkZam3IgNGx4pcdMOGjp0v2DVThT9wW+YYtrrKYwu2R/PDbgfk3mMzs7xoJEHuixFhl45u5Nq7Ht5tVorzlizgo8uO2fnh2eQUqE0ZHvKiAm3UFzFsMzc6VmTlmk0trY4HMCj4xNkL/WXWzCwnGkmQb6fUoSlgb+AQ4IdAbgphLx3dWHWe0/K2O7/3VMcH5j374jZGx4qpfuB2exGSDsp9DNvM43l0rMjyG+9revGPStsD7n5oa17fP2ZmfWfaBDkifr3yvqTjgP+WWou64Lr1j9R9bEcLl12bNbk9dtZDpvGhW20xhLwuQtIPMdzvZhrP5XrjVkoqprpu/SOevcLMLCcamcViNxHxbWBJCm3pmnofktsjSv2OXbA9gvdfv4FDV9zC0ivuaOsgwWrT1ZUXIcm7PMZwv2sknkfHiiy94g4OXXEL779+Q1uTY6j//4iZmWXLtD3Ikv6k4u4A8BvAE62cVNL7KfXgBbAReBdwIPBF4FcoLejwjoh4UdJewOeS8/4MOCciHmzl/Hu0h/or4nXzc6986nb38NZaDCGPi5D0Qwz3u+nieWoPcxpv6UF16Zu0mZm1XSM9yPMrfvYHvgGc3uwJJY0AfwIsThZvGATeBvw18KmI+DXgaeDdyVPeDTydbP9Usl9bjI4V+dUVt6TyYZmGdvbw1lpsJKeLkOQ2hq1kunjuxAI/5x5/SKrHNzOzzpk2QY6ID0bEB4GPAR+PiGsi4rkWzzsLGJI0C5gDPA6cAHwpefwadk3NdXpyn+TxE6XWu2pGx4pcdP0GdrR6oA5rVw/v8pOP7JtFSPIaw7bLdPGc9gI/5RlvzMwsHxopsTiK0of7gcn9IvCuiPhuMyeMiKKkvwEepjT91tcpXY4ej4htyW6PAuU6ghHgkeS52yQ9Q+kS9k+bOX/Z6rWbM5ccQ/t6eMtlGr0wi0Xas2nkNYbzoF1/+1rxDHDUB/9XW9tcScCPrzg1teObmVl3NDLN21XAX0bEbQCSfjfZ9lvNnFDSXEo9aocB48CNwBuaOdaU414AXACwYMGCaffPYq1tYVBt7eFdtmhkt2SkPIipkwlzh2bTyEQMJ8eeURxnWTMzT9RLpqvF8/Iv3cfk9vSKqHJakmRm1vcaqUHer5xYAETEN4D9Wjjn7wI/joinImISuAlYCgwnl6sBDgbKUzYUKc1bS/L4/pQGOu0mIq6KiMURsXj+/PnTNiKLH2z7zJ6VWsJaTlaK4xMEu5KVtJfX7tBsGpmI4aRtM4rjLJvJ376Z+Fy9dnOqyTGQy5IkMzNrLEF+UNLFkg5OflYAD7ZwzoeBJZLmJHWYJwLfBe4Ezkz2OR/4SnJ7TXKf5PE7IlqfVyKLH2zPtLjaVz0f/uqmtiWq5Z7owxqYnq5Ds2nkMoazrtbfuDg+sVvMlOcsnml8pn2V6IiX7ZO7OcPNzKykkRKL3wf+CriV0uxI/zfZ1pSIWC/pS8C9wDZgjNLl7luAL0r6aLLt6uQpVwOfl7QF2EpptoC2KAwq9R6mRkw3zVxZWr3eo2NFnn6uevI9kyRjdKzIX950P89N7qruLvf03f3QVu783lN7XB4/aHio6gCqNr/W3MZwlu0/VKi5xHO51KJ8u9Ycw/Xis97xW7X08Hlc+57XpHJsMzPrvkZW0vsZ8EftPGlEXAZcNmXzj4Djquz7PHBWO88Pnbn82qgA5s4p8PzkjppTUaU5w0S9XrhGE9V69Z4Tk9u59q6Hq87pvPzkI3erQwUoDIjnXtzGYStuaUstdF5jOCuq1Q5DaTn1Wip7h+tNz1YvPp99of3JcWFQrD7zWPccm5nl3LQlFpK+Jmm44v5cSbek26z0pT3t00yNPzfJqjOOYWR4CAHDQwXmzikgYGR4iFVnHJPah3K9XrhGk/LpvnBMfaQyAdq7sCsMCwMwuSN4+rnJttVC5zWGs+DS0Y28//oNu9UOv//6DVxy88Zpv6A+Nj5RNzbrfWkcHSsy2eZpaubOKTg5NjPrE42UWBwQEePlOxHxtKSDUmxT6tIeeNaMgWRa3HUrTkh12rOpx37dK+czIFW9hD08VNh53tGxIivXbNp5yXrunAKXvfloli0aYXSs2NQXjnLyW9lDWC2pKSfTLfwb5C6Gs2B0rLjblYOyAJ59cfpFO+bMHqy536C080tjZUwPzykQQdtLKwYlxj50UluPaWZmvauRBHmHpIMj4lEASZmfe2rlmk3dbsIetkfsrNX98j3FVKY9qzat1hfuerjqvkOFQVaedvTO501dVOXp5yZZ/qX7dra3GYNSw6ubtTjgKncxnAWr125uaZXKWsnxUGFwt+S4MqZr1dK3yqvkmZn1l0YS5A8B6yTdQWk82WuB/55mo9KW1sCdVk1Mbue69Y/s0Zvbhh5UoPHldkWp7OH9129g9drNbH32haqLqkxuj5oJdqW9B8XzVS6n1xp4VU2Lg/ZyF8NZkNYsEpXlRp1YQtqr5JmZ9Z9GBundIuk4oDxk+y8i4sl0m5WeXiyvqNTMaP1GNXqMYFdPXDtqtaslxzMhWpuWL28xnBW1ZihpVeUXxbSncrvynIWuOTYz60ON9CATEU8Aoym3JXXl+VR72WCNeuBWpz0bHSvWrDXuZQLevmRBy0lKXmK411XWA89qZJb1GRpMavXL0krCy5wcm5n1p4YS5Dwo1yr2coI4VBjkrb8xslsNcnn7dD2otQb2TR1clyUDgh0Bd37vKUbHik5WelQ59orjE7vN6d3uWSRgz1rgQ38lvQT5vCUuVTcz61d9kyB3olaxFZWj8he/fN6MZrGoNvhu+Y33ccnNGxuaLaBX7UgyrXYOVLT2mhp7aX79HBwQi18+b+f9S0c3su6HW1M519LD57nu2Mysj9VMkCW9pN4TI+Ln7W9OetKuVWzVjoidyd+yRSMzSgSrJf+TO4LJDCfHUzUzUDFvMdyLOvnFc/uO2BkDl45ubGiAaDO8Sp6ZmdXrQd5EqUNIVR4LIFPXH9OuVWzV8JxC08/t9eS/nvKMGRMNXI9v4nXmKoZ7UaffU4+NTzA6VkwtOfaMFWZmBnUS5IjI1cSfy08+kguv39DtZtT0QkUvqzLSSwAAHUlJREFUXL2FQqo9NjynkNr8r2kaHirsnGu5kb/NTAcq5i2Ge1GtQaVpkRqLlWZ4xgozMytrqAZZ0v7A4cDe5W0R8f/SalQali0a4f03bKBXx+g9N7lj5xR0U+uJy/W31R5bfuN9PT3wsJZyclxOSKZLehoZqFhPHmK4l5S/qHU69nakdLqhwoCTYzMz22naBFnSu4GLgBFgI/CfgbsoLbaQKb2eR65cs4l99pq1R03nxOT2mo9NppUxpGx8YnK3gXf71FlWeKTF5bbzFMO9YOrAvDxYdcarut0EMzPrIY3MVHohsBh4MCJ+G/gN4GeptioFo2PFqoWovWR8YrJmne34xGRP11A3ozzwDqAwWD0Uh4cKrFtxQqu9e7mI4V7R6zPCzJRLK8zMbKpGEuTnI2ICQNLsiNgENH+tu0tWr92c6hRU7VKvznbqIgl5UP5C8EyNeZprbZ+hXMRwr8jyoNCpnBybmVk1jdQgPy5pGPgqsFbSVuDRdJvVflnofZ1TGKg7mHB7BEOFwUz03hUGxWQDS0yXvxDsP1SouphJqysIJnIRw91UOTg0iysyTjVrQPzNWcc6OTYzs6qm7UGOiNMiYjwiPgh8FLgWOK2Vk0oalvQlSd+T9ICk10iaJ+k2ST9Ifs9N9pWkT0vaIul+Sa9u5pxZ6H0t1xPPrTHl28jwEKvOOIaR4SGU3O9Fe80aYPWZx07bvvLAu9GxIs++uG2PxwsDamlgXlleYrhbyjXHxfEJAjKfHM+dU2DLx05xcmxmZjVNmyBL+rfy7Yi4PSJuAq5q8bx/C3wtIl4JHAs8AKwAbo+II4Dbk/sAbwSOSH4uAP6xmRNm4UN9cntpIYTL3nw0Q4XB3R6rnMXh2Re2EfRur/hQYZBli0ZYt+KEmkly5cqBq9durtrbvO/es9qSxOQlhrslTzXHA4LL3nx0t5thZmY9rpEa5N2Gd0saoDQLQFOS6bZ+B7gaICJejIhx4HTgmmS3a4Blye3Tgc9FyV3AsKQDZ3reXu1tneqx8QmWLRrZo6d41RmlxQuW33hf1VKEXlJZN/y6V87fY3DkUGGQT5y96/J2zYGJ7ZvbORcx3C31ao6nfpFrVdrXeT55tmuOzcxsejUTZEkfkPQ08CpJW5Ofp4GfAre2cM7DgKeAf5U0JulfJO0DHBARjyf7/AQ4ILk9AjxS8fxHk20z0o5L9Z0QwOEX38qNd++5UtjqtZszMa3b/kOlEpHRsSJfvqe4x+BITdlSq8641frjvMVwt9Rb5bHdPctpRffggDwgz8zMGlavB/njwHzgU8nv+cBLI2JeRCxv4ZyzgFcD/xgRi4Bn2XUpGoCICGb4WSnpAkl3S7r7qaeeaqF53bc9gnU/3Lqz5rO8WEivllRMNT4xycIPf52VazZVTaCem9zBxTdt3LkwyvKTj6xbUtKCTMUw9F4cj44V+eXze9aHZ8msAfEJD8gzM7MZqJkgJ5eDtyWJxHxKc8ceL+k3Jf1mC+d8FHg0ItYn979EKdl4onzZOfn9ZPJ4EahcMvjgZNvU9l4VEYsjYvH8+fP3OOmHv7qphSZ3X9ZqQMcnJuuWglTOgVyrpKTVhCZrMZy0uW4cd1pWrlrUsvTweR6QZ2ZmM9bISnqXA+8AvgeUs7QATmnmhBHxE0mPSDoyIjYDJwLfTX7OB65Ifn8lecoa4H2SvggcDzxTcRm7YU+3r57V2qSytnXZopHUkpi8xHA3ZH3O42vf85puN8HMzDKokXmQzwReERHPt/G8fwxcK2k28CPgXZR6s29IlgV+CDg72fdWSonMFuC5ZF/LgTbNcdwIx3ATRseKmZ7z+MpzFna7CWZmllGNJMg/Bto6VD0iNlBa+neqE6vsG8B7Wz3ncI2FKKw72lRj3KhcxHAnlec+zmpyvPTweS6rMDOzpjWSIP8CuFfSN4AXyhsj4qLUWpWClacdXXOFum7o54R9ZHiI5Scf2ckEJhcx3ElZnvv4iJft49IKMzNrSSMJ8teSH2ujicntzCkM8Nzkjm43paMErFtxQqdP6xieoazWHi89fJ6TYzMza9m0CXJEXJ3UWS6IiC0daFMqLrl5Y7ebsJsXtuU3MR6sU7fawbrjnfISw5100PBQZqYVLHNybGZm7dLIUtOnAhuB25L7CyXdnHbD2ml0rMizL2bzcnHWCPjhqlO48pyFac1tPPM25SCGOy0rC+uUuazCzMzaqZGlpj9CaWqqcdg5OOnX0mxUu2V9DuQsKfcQpzW3cZMyH8PdkPayz+2y9PB53HbRa7vdDDMzy5FGapAnI2Jc2u3jMlND2z0HcmdM7SFOc27jGcp8DHfS6FiR5Tfel4l/oKHCgHuOzcys7RpJkB+QdDYwIOkw4E+Au9JtlmXF8FCBZyYmOajzM1PMhGN4Blau2ZSZ1fNWnfGqbjfBzMxyqJEE+X3Ah4AdwE3A14FL0mxUu/XzlGpp23DZSd1uQiMyH8OdlJX3ynlLFvTqFzIzM8u4aWuQI+LZiPhARCxKfj4AzO1A29pm5WlHd7sJuTQ8VOh2ExqShxi23Z23ZAEfXXZMt5thZmY5VTdBlvSfJS2T9NLk/tGSPges70jr2sS9TOnIwhePvMSwlQxQWkLaybGZmaWpZoIsaRVwLfB24GuSVgJ3AvcBr+hI69pkdKzY7SbkisjG5e08xXCnXDraW/OFVzriZfvwoytO7fm4MzOz7KtXg3w6cGxETEiaBzwCHBMRP+pM09rH07y1TxeWiW5FbmK4E0bHinzhroe73YyqDthvtqdyMzOzjqmXID8fERMAEbFV0vezmFiMjhU9zVsbZLTmMxcx3AmXjm7s2eQYYP0lr+92E8zMrI/US5B/VdJNyW0Bh1XcJyLOSLVlbbJ67eZuNyHzhocKWUyOIScxnLZeT46vPGdht5tgZmZ9pl6C/NYp9z+TZkPS8tj4RLebkGkiG4PxashFDKdldKzIxTfdz8Tkjm43paYs1LqbmVn+1EyQI+L2TjYkLQcND1F0kty03zx8XmYTlLzEcBpGx4pcdP0Gejc1zmxZj5mZ5cC08yCnRdKgpDFJ/zO5f5ik9ZK2SLpe0uxk+17J/S3J44fO5DyVSx/bzD34M3+5qKVTMZyG1Ws3Ozk2MzOroWsJMvCnwAMV9/8a+FRE/BrwNPDuZPu7gaeT7Z9K9mtYVns/e4VLVOrqSAynoZevqnieYzMz67aGE2RJe7XrpJIOBk4F/iW5L+AE4EvJLtcAy5Lbpyf3SR4/MdnfOuCg4aFuN6FtHMO7DPboW+hBz3NsZmY9YNoEWdJxkjYCP0juHyvp71o875XAX8DOq7y/AoxHxLbk/qNA+VNyhNL8tSSPP5Ps37A5hW52lGdXYUC5KFHJQwy32/aIbp6+Ks9WYWZmvaKRzPHTwJuAnwFExH3A65o9oaQ3AU9GxD3NHqPGcS+QdLeku5966qndHpvc3svVlr1peKjA6rOOzUtvXiZiODl2zThul15cLe+Il+2Tl1gzM7McqDfNW9lARDw05Yrw9hbOuRQ4TdIpwN7AS4C/BYYlzUp62A4GyutDF4FDgEclzQL2J0l0KkXEVcBVAIsXL96te6yHZ7HqKYVBsfrM3CTFlTIRw1A/jtvh7f/8Ldb9cGu7D9uSl+w16FXyzMyspzTSg/yIpOOASEbtXwh8v9kTRsTFEXFwRBwKvA24IyLeDtwJnJnsdj7wleT2muQ+yeN3RDR+fXh0rDj9TsbI8FBek2PIeAy3y+hYseeS4wP2m839H35Dt5thZma2m0Z6kP87pUvUC4AngG8k29rtA8AXJX0UGAOuTrZfDXxe0hZgK6WEpGFeSa8xj41P7Py3ymGSnOkYbsXoWJHVazfz2PgEAz02MO+A/WZ7CWkzM+tJjSTIL0bEbh/okl4O/LTVk0fEN4FvJrd/BBxXZZ/ngbOaPYenKWtMUJr66+KbSvWpOUuSMx3DzSqtlLeRiclSNUkvDcybJZwcm5lZz2qkxOKrkvYt35F0JHBLek1qrzxNU9YJE5Pb89jrnukYbtbqtZt3Jse9ZO9BsWXVqd1uhpmZWU2NJMirKSUYcyQtBG4C3plus9onD9OUdVoOe90zHcPN6sW/40v2GuR7l5/S7WaYmZnVNW2CHBFrgM8AXwc+D7w1Iu5Nu2HtkrNSgaYVBsXwUAFRGpB35TkLGanRu563Xvesx3CzevHv6AF5ZmaWBTVrkCV9ilJpKoCAYWALcIEkIuKiDrTP2mBkeIjlJx9Z9ctCZY0qwFBhMDe97v0ew8tPPnKPv283PXiFyyrMzCwb6g3S+49p7lsGjAwPsW7FCVUfKyfM5VkODqqTSGdUX8dw5d+32MVyi70H5bIKMzPLlJoJckRcXeuxLOnneZAb6Q1etmgkTwnxbvISw61YtmiEG+9+uGsJ8tLD53Hte17TlXObmZk1q16JxXURca6kMXZdpt4pIl6dasvaJIczMtS09PB5PPizibz2Bs9YXmK4Fd1cHOS8JQv46LJjunJuMzOzVtQrsVie/D6zzj49rxdH8qfFPXV7yEUMt2Llmk1dOe8B+812cmxmZplVr8Ti0eT3D8vbJA1HxHgnGtYuexcGmJjc0e1mWBfkJYabNTpWZHxisuPn3XtQXgTEzMwyreY0b5KOk/QNSTdIOlbS/cAWSU9IOqmDbWxJvyTH+8we7HYTek5eYrhZ3Sgv8oA8MzPLg3olFn8PXAbsD9wJvDki1kk6mtJcsl/vQPusAYMD4vK3+HJ2FX0dw50uLxI4OTYzs1yot1DIrIi4NSKuA56IiHUAEbGJ0meh9YCR4SE+cdaxfT0Yr46+juE5HbyqIODHnufYzMxyol4PcuWo/6ldUXvMCGCdNVQYZNUZxzgxrq9vY/jt//wtnn2xcwuEODk2M7M8qZcgHytpK6XOof2S2yT39029Zbab85Ys4M7vPeUp3GamL2O401O7eYU8MzPLm3oJ8uyOtcKm5SmzmtKXMdzJqd2uPGdhx85lZmbWKfWmeevc9Vmra2R4qNtNyKR+jeFOTe125TkLfRXDzMxyqd4gvVRIOkTSnZK+K2mTpD9Nts+TdJukHyS/5ybbJenTkrZIul9S7lc/qySYdrlo6yzHsJNjMzPLt44nyMA24M8i4ihgCfBeSUcBK4DbI+II4PbkPsAbgSOSnwuAf+x8k7tDwNuXLHAi0nv6OobPc0yamVnO1atBTkVEPA48ntz+haQHgBHgdOC1yW7XAN8EPpBs/1xEBHCXpGFJBybHyR1Rml5hxAPxelYvx/DoWLHdh9yNe47NzKwf1EyQJT1N9amwBEREzGv15JIOBRYB64EDKhKGnwAHJLdHgEcqnvZosi13CfLcOQUue/PRTkDapB9j+OKb7m/3IXdyz7GZmfWLej3IL03zxJL2Bb4MXBgRP5d2rdsQESFpRvPUSrqA0uVrFixY0M6mpkrgadvSk6kYTo7ZdBxfOroxtaXVlx4+zzOpmJlZ32h4FgtJ84C9KzY91uxJJRUoJRbXRsRNyeYnypedJR0IPJlsLwKHVDz94GTb1PZeBVwFsHjx4kwsAjEyPMS6FSd0uxm5lbUYTtrcdBxft/6R6XdqwgH7zeba97wmlWObmZn1omkH6Uk6VdL3KV0WXp/8vqPZE6rUzXY18EBEfLLioTXA+cnt84GvVGx/ZzITwBLgmTzUHw8VBj07RYf0Swxvj/Z/L3zJXoOsv+T1bT+umZlZL2tkkN7lwFLg6xGxSNLrgbNbOOdS4B3ARkkbkm1/CVwB3CDp3cBDFee4FTgF2AI8B7yrhXP3BA/A67i+iGEJ2pkjLz18nnuOzcysLzWSIG+LiKckDUhSRNwm6W+aPWFE/Dul0ttqTqyyfwDvbfZ8vea8JQtcy9l5uY/h0bFiW5Pjl+w16OTYzMz6ViMJ8jPJYKR/Bz4n6UlgIt1m5Ue5V29Q4tzjD3Fy3B25j+FLbt7YtmPtPSju//Ab2nY8MzOzrGkkQV5GKZm4EHgnsD/wpjQblQe+PN1Tch3Do2NFnn2xPatqv2SvQSfHZmbW9xpZSe/iiNgeEZMRcXUyKOmitBuWZcNDBSfHvSW3MTw6VuTC6zdMv2MDZgknx2ZmZjSWIFf7xDy13Q3Jk5WnHd3tJtjuchnDl45u5P1tSo4BtqzK/D+JmZlZW9RbSe8PgD8EXiHp3oqH9gPuSbthWTV3TsGzU/SIPMfw6FiRL9z1cNuO9+AVTo7NzMzK6tUg3wDcDqwCVlRs/0VEPFn9KTb+3GS3m2C75DaGP/zVTW07lpNjMzOz3dVbSe9p4GngLElHA7+dPPR/2bVCmE1x0PBQt5tgiTzH8NNt+iLm5NjMzGxPjayk917gRmBB8nODpD9Ku2FZ5NXxepNjuDonx2ZmZtU1Ms3bHwDHRcQvASR9DPh/wD+k2bCs8ep4Pc0xPIWTYzMzs9oaSZAFvFhxf5Laq4j1ncKAWH3WsU6Me5tjuIKTYzMzs/rqzWIxKyK2AZ8H1kv6cvLQW4BrOtG4XrTP7EGG58zmsfEJDnKvcU/Lawy/6rKvNf1cJ8dmZmbTq9eD/G3g1RHxcUnfBH4r2f6HEfGd1FvWgwYEl7/lGCfE2ZG7GD7+8tv4+QvNrZrn5NjMzKwx9RLknZegI+LblJKNvrXXrAH++q2vcnKcLbmK4dGxIk/84sXpd6zCybGZmVnj6iXI8yXVXI43Wa63L1x5zkInxtmUqxhudklpJ8dmZmYzUy9BHgT2pY8HM4GT44zLTQy//pPfbOp5To7NzMxmrl6C/HhEfKRjLelB+8wedHKcbbmJ4R88+eyMn+Pk2MzMrDn1FgrpqV43SW+QtFnSFkkrpn9GawqD4vK3HJP2aSxduYjh4y+/bcbncnJsZmbWvHoJ8okda8U0JA0Cfw+8ETgKOFfSUWmec/WZnts4B3IRwzMdmOfk2MzMrDU1E+SI2NrJhkzjOGBLRPwoIl4EvgicntbJzluywMlxDvRjDDs5NjMza129HuReMgI8UnH/0WRb2523ZAEfXebSCmu71GPYybGZmVl7NLLUdCZIugC4AGDBggUzfv4B+81m/SWvb3ezzGak2Th2cmxmZtY+WelBLgKHVNw/ONm2U0RcFRGLI2Lx/PnzZ3TwK89Z6OTY0jZtDENzcezk2MzMrL2y0oP8HeAISYdRSireBvzXVg86S7BllZML64hUYtjJsZmZWftlogc5IrYB7wPWAg8AN0TEpkaeWyuBePCKU50cW8ekFcNmZmbWflnpQSYibgVubea5TiSsFziGzczMsiETPchmZmZmZp2iiOh2G9pO0lPAQ1Ueeinw0w43p1vy/lpfHhEzG42ZMTXiuJt/V5+7vXIfw2ZmWZXLBLkWSXdHxOJut6MT+um19pNu/l19bjMz6xcusTAzMzMzq+AE2czMzMysQr8lyFd1uwEd1E+vtZ908+/qc5uZWV/oqxpkMzMzM7Pp9FsPspmZmZlZXX2RIEt6g6TNkrZIWtHt9rSDpEMk3Snpu5I2SfrTZPs8SbdJ+kHye26yXZI+nfwb3C/p1d19BTZTacZxnXhaKakoaUPyc0rFcy5O2rJZ0sktnv9BSRuTc9ydbEs9liUdWfHaNkj6uaQLO/W6zcysN+W+xELSIPB94PXAo8B3gHMj4rtdbViLJB0IHBgR90raD7gHWAb8HrA1Iq5Ikqi5EfGB5AP+j4FTgOOBv42I47vUfJuhtOO4TjydDfwyIv5myv5HAdcBxwEHAd8AXhER25s8/4PA4oj4acW2j9PBWE7+jYvJMd9FB163mZn1pn7oQT4O2BIRP4qIF4EvAqd3uU0ti4jHI+Le5PYvgAeAEUqv7Zpkt2soJTkk2z8XJXcBw0lSZNmQahzXiadaTge+GBEvRMSPgS1JG9up07F8IvDDiKi2yFBlm9J+3WZm1mX9kCCPAI9U3H+U+h/8mSPpUGARsB44ICIeTx76CXBAcjv3/w4517G/35R4AnhfUsrw2XKZQwrtCeDrku6RdEGyrdOx/DZKvcNlnXjdZmbWg/ohQc41SfsCXwYujIifVz4WpfqZfNfQWFtViad/BA4HFgKPA59I6dS/FRGvBt4IvFfS71Q+mHYsS5oNnAbcmGzq1Os2M7Me1A8JchE4pOL+wcm2zJNUoJTMXBsRNyWbnyhfbk5+P5lsz+2/Q59I/e9XLZ4i4omI2B4RO4B/Zlc5QVvbExHF5PeTwM3JeToZy28E7o2IJ5J2dOR1m5lZb+qHBPk7wBGSDkt6id4GrOlym1omScDVwAMR8cmKh9YA5ye3zwe+UrH9nckMAEuAZyouX1vvSzWOa8XTlNretwD/kdxeA7xN0l6SDgOOAL7d5Ln3SQYGImkf4KTkPJ2M5XOpKK/oxOs2M7PeNavbDUhbRGyT9D5gLTAIfDYiNnW5We2wFHgHsFHShmTbXwJXADdIejfwEKVZCABupTTqfwvwHKVR+pYRHYjjWvF0rqSFlMobHgT+IGnPJkk3AN8FtgHvbWEmhwOAm0s5OrOA/xERX5P0HToQy0lS/nqS15b4eAdet5mZ9ajcT/NmZmZmZjYT/VBiYWZmZmbWMCfIZmZmZmYVnCCbmZmZmVVwgmxmZmZmVsEJspmZmZlZBSfIfUzSv0k6s9vtsPyTdJqkFXUeXyjplBTPv1LSn6d1fDMzyxcnyGY2I8kCHTP6vyMi1kTEFXV2WUhpbuOZtCP387ibmVl3OEHuIZLeKel+SfdJ+rykN0taL2lM0jckHZDs918kbUh+xiTtJ+m1kv5nxbE+I+n3ktsfkvQdSf8h6apk1TSzhkk6VNJmSZ+jtKrcOyR9S9K9km6UtG+y3ymSvifpHkmfLsekpN+T9Jnk9llJLN4n6f8kKwN+BDgnielzktX1Pivp20mMn15xnDWS7gBuT7YtT+L7fkkfrmjzJZK+L+nfgSM7+e9lZmbZ5h6YHiHpaOBS4Dcj4qeS5lFaxWtJRISk/wb8BfBnwJ9TWsFrXZKYPD/N4T8TER9JzvN54E3AV9N6LZZbR1Ba8nkLcBPwuxHxrKQPABdJ+jjwT8DvRMSPJV1X4zgfAk6OiKKk4Yh4UdKHgMUR8T4ASR8D7oiI35c0DHxb0jeS578aeFVEbJV0UtKu4wABayT9DvAspeW4F1L6f+5e4J52/4OYmVk+OUHuHScAN0bETwGSD/9jgOslHQjMBn6c7LsO+KSka4GbIuLRaTqFXyfpL4A5wDxgE06QbeYeioi7JL0JOApYl8TdbOBbwCuBH0VEOU6vAy6ocpx1wL8lSzbfVONcJwGnVdQN7w0sSG7fFhFbK/Y7CRhL7u9LKWHeD7g5Ip4DkLRmpi/WzMz6lxPk3vZ3wCcjYo2k1wIrASLiCkm3UKrZXCfpZGAbu5fM7A0gaW/gHyj1zj0iaWX5MbMZejb5LUpJ6rmVD0pa2MhBIuIPJR0PnArcI+k3quwm4K0RsXnKOY6vaEd5v1UR8U9T9ruwkbaYmZlV4xrk3nEHcJakXwFISiz2B4rJ4+eXd5R0eERsjIi/Br5DqefuIeAoSXsll6RPTHYvJ8M/TcoxPGuFteouYKmkXwNI6oVfAWwGflXSocl+51R7chK/6yPiQ8BTwCHALyj1+patBf64XC8vaVGNtqwFfr+iBnpE0suA/wMskzQkaT/gzU2/WjMz6zvuQe4REbFJ0uXA/5a0ndIl45XAjZKeppRAH5bsfqGk1wE7KJVL/K+IeCG5ZP0flEoxxpLjjkv652T7Tygl1GZNi4inkgGg10naK9l8aUR8X9IfAV+T9Cy1Y221pCMo9f7eDtwHPAyskLQBWAX8FXAlcL9KM2b8mFLt/NS2fF3SrwPfSnLpXwLnRcS9kq5Pjv1knbaYmZntQRHR7TaYWU5I2jcifpn0/P498IOI+FS322VmZjYTLrEws3Z6T9ILvIlSidA/TbO/mZlZz3EPspmZmZlZBfcgm5mZmZlVcIJsZmZmZlbBCbKZmZmZWQUnyGZmZmZmFZwgm5mZmZlVcIJsZmZmZlbh/wOXUMyTb34w2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "column= bike_rentals.columns.drop(['cnt', 'instant', 'dteday'])\n",
    "fig=plt.figure(figsize=(10,10))\n",
    "\n",
    "for i in range(len(column)):\n",
    "    ax=fig.add_subplot(4,4,i+1)\n",
    "    ax.scatter( bike_rentals[column[i]], bike_rentals.cnt)\n",
    "    ax.set_ylabel('Total Bike Rental count')\n",
    "    ax.set_xlabel(column[i])\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(bike_rentals.season);\n",
    "plt.title('Distibution across seasons');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>instant</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>5017.029500</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4345.5000</td>\n",
       "      <td>8690.0000</td>\n",
       "      <td>13034.5000</td>\n",
       "      <td>17379.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>12.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>23.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6600</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.4848</td>\n",
       "      <td>0.6212</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.6300</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>0.8507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>48.0000</td>\n",
       "      <td>367.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.0000</td>\n",
       "      <td>115.0000</td>\n",
       "      <td>220.0000</td>\n",
       "      <td>886.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>189.463088</td>\n",
       "      <td>181.387599</td>\n",
       "      <td>1.00</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>142.0000</td>\n",
       "      <td>281.0000</td>\n",
       "      <td>977.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_label</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>2.492548</td>\n",
       "      <td>1.115284</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean          std   min        25%        50%  \\\n",
       "instant     17379.0  8690.000000  5017.029500  1.00  4345.5000  8690.0000   \n",
       "season      17379.0     2.501640     1.106918  1.00     2.0000     3.0000   \n",
       "yr          17379.0     0.502561     0.500008  0.00     0.0000     1.0000   \n",
       "mnth        17379.0     6.537775     3.438776  1.00     4.0000     7.0000   \n",
       "hr          17379.0    11.546752     6.914405  0.00     6.0000    12.0000   \n",
       "holiday     17379.0     0.028770     0.167165  0.00     0.0000     0.0000   \n",
       "weekday     17379.0     3.003683     2.005771  0.00     1.0000     3.0000   \n",
       "workingday  17379.0     0.682721     0.465431  0.00     0.0000     1.0000   \n",
       "weathersit  17379.0     1.425283     0.639357  1.00     1.0000     1.0000   \n",
       "temp        17379.0     0.496987     0.192556  0.02     0.3400     0.5000   \n",
       "atemp       17379.0     0.475775     0.171850  0.00     0.3333     0.4848   \n",
       "hum         17379.0     0.627229     0.192930  0.00     0.4800     0.6300   \n",
       "windspeed   17379.0     0.190098     0.122340  0.00     0.1045     0.1940   \n",
       "casual      17379.0    35.676218    49.305030  0.00     4.0000    17.0000   \n",
       "registered  17379.0   153.786869   151.357286  0.00    34.0000   115.0000   \n",
       "cnt         17379.0   189.463088   181.387599  1.00    40.0000   142.0000   \n",
       "time_label  17379.0     2.492548     1.115284  1.00     1.0000     2.0000   \n",
       "\n",
       "                   75%         max  \n",
       "instant     13034.5000  17379.0000  \n",
       "season          3.0000      4.0000  \n",
       "yr              1.0000      1.0000  \n",
       "mnth           10.0000     12.0000  \n",
       "hr             18.0000     23.0000  \n",
       "holiday         0.0000      1.0000  \n",
       "weekday         5.0000      6.0000  \n",
       "workingday      1.0000      1.0000  \n",
       "weathersit      2.0000      4.0000  \n",
       "temp            0.6600      1.0000  \n",
       "atemp           0.6212      1.0000  \n",
       "hum             0.7800      1.0000  \n",
       "windspeed       0.2537      0.8507  \n",
       "casual         48.0000    367.0000  \n",
       "registered    220.0000    886.0000  \n",
       "cnt           281.0000    977.0000  \n",
       "time_label      3.0000      4.0000  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_rentals.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122679d50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,16))\n",
    "sns.heatmap(bike_rentals.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hum          -0.322911\n",
       "weathersit   -0.142426\n",
       "holiday      -0.030927\n",
       "weekday       0.026900\n",
       "workingday    0.030284\n",
       "windspeed     0.093234\n",
       "mnth          0.120638\n",
       "season        0.178056\n",
       "yr            0.250495\n",
       "instant       0.278379\n",
       "hr            0.394071\n",
       "atemp         0.400929\n",
       "temp          0.404772\n",
       "casual        0.694564\n",
       "registered    0.972151\n",
       "cnt           1.000000\n",
       "Name: cnt, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Studying the correlation of each feature with Total Count column\n",
    "bike_rentals.corr()['cnt'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Casual` and `Registered`, even though show a strong correlation, are used to find the total count of rentals i.e. our target feature, so we will ignore it from further analysis.  \n",
    "\n",
    "`Humidity` has a strong negative correlation with the total count, which is valid. No one wants to cycle on a humid day. \n",
    "\n",
    "`Holiday` also is negatively correlated, which may indicate that bike rental services are used more for daily commue (see positive realtion with working day) rather than for an excursion. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(bike_rentals.hr, order=range(0,24));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Apart from a small dip at 3 and 4 am, the distribution is almost the same. \n",
    "\n",
    "A machine learning model would treat each hour as unique, but we can segregate them as morning, afternon, evening and night.\n",
    "\n",
    "\n",
    "For example, the hr column in bike_rentals contains the hours during which bikes are rented, from 1 to 24. We will introduce some order into the process by creating a new column with labels for morning, afternoon, evening, and night. This will bundle similar times together, enabling the model to make better decisions.\n",
    "\n",
    "Extracting features from our existing dataset can help enrich the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_labels(hr):\n",
    "    if hr in range(6,12):         # Morning slot\n",
    "        return 1\n",
    "    elif hr in range (12, 18):    # Afternoon slot\n",
    "        return 2 \n",
    "    elif hr in range (18, 24):    # Evening slot\n",
    "        return 3\n",
    "    else:                         # Night slot\n",
    "        return 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_rentals['time_label']= bike_rentals.hr.apply(assign_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    4375\n",
       "3    4368\n",
       "1    4360\n",
       "4    4276\n",
       "Name: time_label, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_rentals['time_label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## APPLYING MACHINE LEARNING ALGORITHMS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can start, we split the dataset into train and test data.\n",
    "\n",
    "First, we need to ignore the casual and registered columns because cnt is derived from them. Since we are trying to predict the number of people who rent bikes in a given hour (cnt), it doesn't make sense that we already know casual or registered rentals, because those numbers are added together to get cnt. This is considered as data leakage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X= bike_rentals.drop(columns=['cnt', 'casual', 'registered'])\n",
    "y= bike_rentals.cnt\n",
    "\n",
    "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors= X_train.columns.drop(['dteday', 'instant'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CASE 1: LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's start off with Linear Regression. Linear regression will probably work fairly well on this data, given that many of the columns are highly correlated with cnt.\n",
    "\n",
    "Linear regression works best when predictors are linearly correlated to the target and also independent -- in other words, they don't change meaning when we combine them with each other. \n",
    "\n",
    "The good thing about linear regression is that it's fairly resistant to overfitting because it's straightforward. It also can be prone to underfitting the data, however, and not building a powerful enough model. This means that linear regression usually isn't the most accurate option.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>time_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>336</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7035</th>\n",
       "      <td>7036</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8051</th>\n",
       "      <td>8052</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2133</th>\n",
       "      <td>2134</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8485</th>\n",
       "      <td>8486</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11284</th>\n",
       "      <td>11285</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11964</th>\n",
       "      <td>11965</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>5391</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>861</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.4179</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>15796</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13903 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "335        336       1   0     1        0        6           0           1   \n",
       "7035      7036       4   0    10        0        2           1           1   \n",
       "8051      8052       4   0    12        0        3           1           3   \n",
       "2133      2134       2   0     4        0        0           0           2   \n",
       "8485      8486       1   0    12        0        0           0           1   \n",
       "...        ...     ...  ..   ...      ...      ...         ...         ...   \n",
       "11284    11285       2   1     4        0        5           1           1   \n",
       "11964    11965       2   1     5        0        5           1           1   \n",
       "5390      5391       3   0     8        0        3           1           1   \n",
       "860        861       1   0     2        0        2           1           1   \n",
       "15795    15796       4   1    10        0        4           1           2   \n",
       "\n",
       "       temp   hum  windspeed  time_label  \n",
       "335    0.20  0.55     0.2239           1  \n",
       "7035   0.52  0.42     0.1045           3  \n",
       "8051   0.46  1.00     0.2239           4  \n",
       "2133   0.46  0.31     0.0000           3  \n",
       "8485   0.20  0.75     0.1045           1  \n",
       "...     ...   ...        ...         ...  \n",
       "11284  0.46  0.88     0.0896           1  \n",
       "11964  0.66  0.34     0.1343           2  \n",
       "5390   0.80  0.43     0.2836           2  \n",
       "860    0.24  0.65     0.4179           1  \n",
       "15795  0.52  0.83     0.1642           1  \n",
       "\n",
       "[13903 rows x 12 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[predictors]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Accuracy with Linear Regression is: 130.88\n"
     ]
    }
   ],
   "source": [
    "lin_model=LinearRegression()\n",
    "lin_model.fit(X_train[predictors], y_train)\n",
    "y_pred= lin_model.predict(X_test[predictors])\n",
    "lin_acc=round(np.sqrt(mean_squared_error(y_test, y_pred)), 2)\n",
    "print('RMSE Accuracy with Linear Regression is:',lin_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CASE 2: DECISION TREES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Decision trees tend to predict outcomes much more reliably than linear regression models. Because a decision tree is a fairly complex model, it also tends to overfit, particularly when we don't tweak parameters like maximum depth and minimum number of samples per leaf. \n",
    "\n",
    "Decision trees are also prone to instability -- small changes in the input data can result in a very different output model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Accuracy with Decision Tree is: 58.62\n"
     ]
    }
   ],
   "source": [
    "model= DecisionTreeRegressor()\n",
    "model.fit(X_train[predictors], y_train)\n",
    "y_pred= model.predict(X_test[predictors])\n",
    "dt_acc=round(np.sqrt(mean_squared_error(y_test, y_pred)), 2)\n",
    "print('RMSE Accuracy with Decision Tree is:',dt_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'mse',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'presort': 'deprecated',\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### USING GRID SEARCH TO OPTIMIZE THE PARAMETERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={'ccp_alpha': [0.0, 1, 5, 10],\n",
       "                         'max_depth': [100, 200, 300, 500],\n",
       "                         'min_samples_leaf': [1, 2, 5]})"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid={'ccp_alpha': [0.0, 1, 5, 10],\n",
    "            'max_depth': [100, 200,300, 500],\n",
    "            'min_samples_leaf': [1, 2,5]\n",
    "            \n",
    "           }\n",
    "\n",
    "gv=GridSearchCV(model, param_grid, cv=5)\n",
    "gv.fit(X[predictors], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters are: {'ccp_alpha': 1, 'max_depth': 100, 'min_samples_leaf': 5}\n",
      "Best Score is: 0.6863495161222366\n"
     ]
    }
   ],
   "source": [
    "print('Best Parameters are:', gv.best_params_)\n",
    "print('Best Score is:', gv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Accuracy with Decision Tree is: 53.73\n"
     ]
    }
   ],
   "source": [
    "model_refined= DecisionTreeRegressor(ccp_alpha= 1, max_depth= 100, min_samples_leaf=5)\n",
    "model_refined.fit(X_train[predictors], y_train)\n",
    "y_pred= model_refined.predict(X_test[predictors])\n",
    "dt_refined_acc=round(np.sqrt(mean_squared_error(y_test, y_pred)), 2)\n",
    "print('RMSE Accuracy with Decision Tree is:',dt_refined_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CASE 3: RANDOM FOREST REGRESSOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we will apply the random forest algorithm, which improves on the decision tree algorithm. Random forests tend to be much more accurate than simple models like linear regression. \n",
    "\n",
    "Due to the way random forests are constructed, they tend to overfit much less than decision trees. Random forests can still be prone to overfitting, though, so it's important to tune parameters like maximum depth and minimum samples per leaf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Accuracy with Random Forest Regressor is: 41.9\n"
     ]
    }
   ],
   "source": [
    "model_rf= RandomForestRegressor()\n",
    "model_rf.fit(X_train[predictors], y_train)\n",
    "y_pred= model_rf.predict(X_test[predictors])\n",
    "rf_acc=round(np.sqrt(mean_squared_error(y_test, y_pred)), 2)\n",
    "print('RMSE Accuracy with Random Forest Regressor is:',rf_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### USING GRID SEARCH TO OPTIMIZE THE PARAMETERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': [100, 200, 500],\n",
       "                         'min_samples_leaf': [1, 2, 3, 5]})"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "param_grid={\n",
    "            'max_depth': [100, 200, 500],\n",
    "            'min_samples_leaf': [1, 2, 3, 5]\n",
    "            }\n",
    "\n",
    "gv=GridSearchCV(model_rf, param_grid, cv=3)\n",
    "gv.fit(X[predictors], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters are: {'max_depth': 200, 'min_samples_leaf': 1}\n",
      "Best Score is: 0.7436093926700821\n"
     ]
    }
   ],
   "source": [
    "print('Best Parameters are:', gv.best_params_)\n",
    "print('Best Score is:', gv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Accuracy with Random Forest Regressor is: 41.71\n"
     ]
    }
   ],
   "source": [
    "model_refined= RandomForestRegressor(max_depth= 200, min_samples_leaf=1)\n",
    "model_refined.fit(X_train[predictors], y_train)\n",
    "y_pred= model_refined.predict(X_test[predictors])\n",
    "rf_refined_acc= round(np.sqrt(mean_squared_error(y_test, y_pred)), 2)\n",
    "print('RMSE Accuracy with Random Forest Regressor is:',rf_refined_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "output= pd.Series([lin_acc, dt_acc, dt_refined_acc, rf_acc, rf_refined_acc], index=['Linear Regression', 'Decision Tree', 'Optimized Decision Tree', 'Random Forest', 'Optimized RF'], name= 'RMSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear Regression          130.88\n",
       "Decision Tree               58.62\n",
       "Optimized Decision Tree     53.73\n",
       "Random Forest               41.90\n",
       "Optimized RF                41.71\n",
       "Name: RMSE, dtype: float64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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