geo-ml-workshop-1-vector-data.ipynb

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    "_uuid": "6ded7eab7bcfa45c6dc3dd6c5bedbdfaa17e19af"
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   "source": [
    "# Machine Learning for Geospatial Data Workshop #1\n",
    "*Geospatial Club -\n",
    "Winter 2019*\n",
    "\n",
    "## [Slides](http://bit.ly/geoml-1) and [Kernel](https://www.kaggle.com/jrmistry/uwgeo-club-ml-for-vector-geodata-workshop-1) for the first workshop on Machine Learning for [Vector data](https://www.kaggle.com/jrmistry/killed-or-seriously-injured-ksi-toronto-clean).\n",
    "\n",
    "## [Slides](http://bit.ly/geoml-2) and [Kernel](https://www.kaggle.com/jcarrillo/machine-learning-for-geospatial-data-workshop-2a) for the second workshop on Machine Learning for [Raster data](https://www.kaggle.com/c/planet-understanding-the-amazon-from-space).\n",
    "\n",
    "\n",
    "\n",
    "The purpose of the workshop is to give the [UWaterloo](https://uwaterloo.ca/) students and the members of the [Geospatial Club](http://bit.ly/uwgeospatial) an introduction to the most recent Machine Learning tools for analysis of Geospatial data. The workshop is held in collaboration with the [Geospatial Centre at the University of Waterloo](https://uwaterloo.ca/library/geospatial/).\n",
    "\n",
    "All of the software tools and libraries are open source. The dataset we use is a table titled \"Killed or Seriously Injured\" (KSI) from the [City of Toronto's Police Open Data portal](http://bit.ly/open-data-ksi-toronto). It contains registries of all traffic collisions between 2007 and 2017 where a person was injured or property damage was reported.\n",
    "\n",
    "We hope that you will learn something new from the content of this workshop. If you have questions after the workshop, then please feel free to message the Geospatial Club or the workshop presenters.\n",
    "\n",
    "* [Jaydeep Mistry](https://www.linkedin.com/in/jaydeep-mistry/) at jaydeep.mistry@uwaterloo.ca\n",
    "\n",
    "* [Juan Carrillo](https://www.linkedin.com/in/jmcarrillog/) at jmcarril@uwaterloo.ca\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9b5206c6782f411bc09a0b7f64e39ce8ec35f6f6"
   },
   "source": [
    "### **Press CTRL+Enter to run each cell**\n",
    "### Press SHIFT+Enter to run a cell and move onto the next cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3ec53ed6830d1fe7cbb91bbcb4e1c8341df7e870"
   },
   "source": [
    "# Working with Python Libraries\n",
    "\n",
    "https://www.python.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "af7dd5ccbb92d9269105f846cc1de97278092e4d"
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c64b30380ba17ac0d45ec886487374ad26d31c1f"
   },
   "source": [
    "# Numpy Overview\n",
    "\n",
    "Numpy is a Python library for linear algebra (matrices) calculations.\n",
    "\n",
    "http://www.numpy.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "d2f90138a769268f2a627b56d5ca4a40839284ef"
   },
   "outputs": [],
   "source": [
    "data = np.array([['a','b','c'], [4,5,6], [True,False,None]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "2e7afa90409c256bd2d5b48b59b9c8f0029321ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['a', 'b', 'c'],\n",
       "       [4, 5, 6],\n",
       "       [True, False, None]], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "434ce70e23ff1817be2fd7d80f42e231a8b12766"
   },
   "source": [
    "# Pandas Overview\n",
    "\n",
    "Pandas is a Python library for data preparation and analysis.\n",
    "\n",
    "https://pandas.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "c669ba4ebc359617035f449a0957a7a55a5fda0f"
   },
   "outputs": [],
   "source": [
    "df_data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "cf572e07b69c7cd26bcb377c97ec9e683c0dbb2a"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>c</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>6</td>\n",
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       "      <th>2</th>\n",
       "      <td>True</td>\n",
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      "text/plain": [
       "      0      1     2\n",
       "0     a      b     c\n",
       "1     4      5     6\n",
       "2  True  False  None"
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "dc8deab075add8ab9c2c39de2e54f27b75400a50"
   },
   "source": [
    "# Matplotlib Overview\n",
    "\n",
    "Matplotlib is a Python library for data visualization.\n",
    "\n",
    "https://matplotlib.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "17206dfa6410aa17727578459f4d48285b8f899d"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "c60b4bdb3913b0293aa6cc609657aecca5a3e94e"
   },
   "outputs": [],
   "source": [
    "df_emissions = pd.DataFrame(data = [[25, 30], [45, 25], [30, 45]],\n",
    "                            index = [2011, 2012, 2013],\n",
    "                            columns = ['Canada', 'USA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "dc48e1d1b8e897e35c0fc42bf26a6204a2ea2b6a"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Canada</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      Canada  USA\n",
       "2011      25   30\n",
       "2012      45   25\n",
       "2013      30   45"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "c21224ef70c5ab1e4d82ac641cc9470ac7372b18",
    "scrolled": true
   },
   "outputs": [
    {
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17Vmz0Llz5/Dx8QFsa4DWq1fvj/eWLVtG586dCQoKIjg42KqISjm9qPNX6D15C5eTUpk/IucKtRuKenkyMciXD3s24vcTF+kwIYQNB87laAal8pzwWbZCrfqD0G+xUxdqoMWapZ577jlq165N9+7dmTx58p8WWV+4cCF9+/alb9++LFy40MKUSjmvw+cS6T15C8mp6SwY0YJGFa25BCIi9G5WiVVjWlOmSH6GztrOO6v3kZyqc7Iple22TYVV46DmI7YetXzOv7pI3roMuu5ftgVbs1O5htDhg5u+bVuvPvPtb775Jv379+eHH35gwYIFLFy4kF9++YWzZ88SGRlJmzZtEBE8PT3Zs2fP38a+KaVu7uCZy/SfFgoIC0cEULtcEasjcV+ZInz7dGveX7uf6RuPsfVYHJP6NqVa6UJWR1PKNWz+HH54DWp3hMdngkd+qxNlC+1Zc7BSpUpx4cKFP22Lj4+ndGnb4OYaNWowevRofvrpJ3bu3ElcXByLFy/mwoULVKtWjapVqxIVFaW9a0rdgb2nEgiasgU3EYJH5o5C7QYvT3f+3bUBUwb6EXPhGp0mhrBsR4zVsZRyfiGf2Aq1el2h92yXKdQgr/Ws3aIHzFEKFy6Mj48PP//8M+3atSM+Pp7vvvuOcePGsWbNGgIDAxERIiMjcXd3p3jx4ixcuJDvvvuOli1bAnDs2DEefvhh3nvvvRzPr5Sz2RVzkYHTt1EwnzsLRgTk2l6rR+uXo0GFYjy7KILnF+9kY+R53u7WgML589bXslL3zBj49UP45T/Q8HHo9jW4u9a/I+1ZywFz5szhnXfewdfXl3bt2vHWW29Ro0YN5s6dS+3atfH19WXgwIHMnz+f6Ohojh8//qcpO6pVq0axYsXYunWrhZ9Cqdwv/PgF+k/dShEvDxaPaplrC7UbyhcvwMIRATz7cE2+jThJ50kb2R2TYHUspZyHMfDzO7ZCzbc/dJ/scoUagLjScij+/v4mLCzsT9v2799P3bp1LUrkPPTnpJzdtmPxDJ25jdJF8rNgRAAVihewOtId2Xo0jnHBEcRdSeblx+owrE21m455VUphK9R+eB22fA5NB0Onz8DNufqgRCTcGON/u/2c61MppVQmNh8+z+AZ2yhbzIvFo1o6XaEG0KJ6KdaNa8uDtcvw7pr9PDFrO3GJyVbHUip3MgbWvWwr1JqNcMpC7U647idTSuUJvx6KZeis7VQqWYBFI1s69YSzJQrlY8pAP97uWp9NR+LoMCGEzYfPWx1LqdwlPR1WPwfbJkPLZyDwI5cu1ECLNaWUE/tp/1lGzA6jundhFo4IwLuI89/9JSIMalmVb59qTWEvD/pP38pH3x8gNS3d6mhKWS89DVaOgfCZ0OZ5ePRdyAPDBfJEseZK4/IcQX8+yhl9t+cMT84Lp3a5Iiwc0YJShZ2/UMuoXvmirB7Thsf9KvLFhiP0nryF6PirVsdSyjppqbD8SYiYBw++Ag+9mScKNcgDxZqXlxdxcXFakNyEMYa4uDi8vJz30pHKe1bvOsXTC3bQoEIx5g1vQfGC+ayO5BAF83nwYa/GTOzbhENnEwmcGMLa3aetjqVUzktLgaXDYPdiaPcGPPivPFOoQR64GzQlJYWYmJg/LeWk/szLy4uKFSvi6elpdRSlbmv57zG8sHgnflVKMHNo8zwzL9mJuKuMCf6dndEX6deiMm92qoeXp7vVsZRyvNTrsGQoHFhtu+zZaozVibJNVu8GdXixJiLuQBhw0hjTSUTmA/5ACrANGGWMScmkXRpwY22oE8aYLrc7V2bFmlLKdSzeHs3Ly3YRUK0U04f4UzBf3ijUbkhJS+fjHw4y+dej1CpbmEl9m+aq1RmUynYpSbB4EER+Dx0+hBajrE6UrXLT1B3jgP0ZXs8H6gANgQLA8Ju0u2aM8bU/bluoKaVc2/ytx3lp6S7a3FeaGUOa5blCDcDT3Y1XOtRlzhPNib9ynS6fb2T+1uM6zEO5putXIbivrVDr9JnLFWp3wqHFmohUBDoC025sM8asNXbYetYqOjKDUsr5zdp0jNeW76FdnTJMHeRPgXx5+/Lf/bW8WTfufppXK8lry/fw1PwdJFz92wUKpZzX9SuwoDcc2QBdvwD/oVYnspSje9Y+A14C/nbPuYh4AgOB727S1ktEwkQkVES6OTCjUioXm/LbEcav2sej9cry9QA/Hadl510kP7OHNueVDnX4cd9ZAieGEBYVb3Uspe5d0iWY1xOOb7ItH9VkgNWJLOewYk1EOgHnjDHhN9nlS+A3Y0zITd6vYr+O2w/4TERq3OQ8I+1FXVhsbOy9B1dK5Rqf/xzJf9YeoGNDH77o35R8Hi5/A/sdcXMTRj1QgyWjW+HuJvSZEsrnP0eSlq6XRZWTunYR5vWA6G3Qczo07mN1olzBkd98rYEuIhIFBAPtRGQegIi8BXgDz9+ssTHmpP3Po8AvQJOb7DfFGONvjPH39vbO1g+glLKGMYZPfjzExz8coptveSYE+eLproXazfhWKs7qsW3o2NCHj384xIBpWzl7Se+AV07majzM6QqnIqD3HGjQw+pEuYbDvv2MMa8YYyoaY6oCQcDPxpgBIjIcaA/0NcZkOiW3iJQQkfz256WxFX77HJVVKZV7GGP48PuDTPwpkl5+Fflfb188tFC7raJenkwI8uXDXo2IiL5Ihwkh/HzgrNWxlMqaK+dhdhc4tx+C5kPdTlYnylWs+Ab8GigLbBGRCBF5E0BE/EXkxo0IdYEwEdkJbAA+MMZosaaUizPG8O6a/Xz1yxH6tajMhz0b4e6Wdya+vFciQm//Sqwa04ayRb14YlYYb6/aR3JqmtXRlLq5y2dhVieIi4S+C6FWe6sT5TouPymuUso5pKcbxq/ay5wtxxnSqipvda6H5KEZyrNbUkoaH6w7wKzNUTSoUJSJQU2o7l3Y6lhK/dmlU7YetUsnoW8wVH/A6kQ5KjfNs6aUUreUnm54dflu5mw5zoi21bRQywZenu6M71KfKQP9iLlwjU6TNrI0PMbqWEr9v4vRMDMQLp+GAUvzXKF2J7RYU0pZKi3d8OKSXQRvj+bpf9Tg1cC6Wqhlo0frl2PduLY0qFCMF77ZyXOLIkhMTrU6lsrrLkTBrEDbTQUDv4UqraxOlKtpsaaUskxqWjrPL45g6Y4Ynnu4Fv98tLYWag7gU6wAC0cE8NzDtVgRcZJOE0PYHZNgdSyVV8UdgZkdbfOpDfoWKjWzOlGup8WaUsoSKWnpjA3+nRURp3jpsdqMe7imFmoO5O4mjHu4JgtHBJCcmk6PrzYxLeQo6Tonm8pJsYdslz5Tr8HgVVChqdWJnIIWa0qpHJecmsboeTtYu/sMr3esy1MP3md1pDyjRfVSrB3blgdrl+HdNfsZNns75xOTrY6l8oKz+2yXPk06DF4NPo2sTuQ0tFhTSuWopJQ0Rs0NZ/3+s/y7S32Gt61udaQ8p0ShfEwZ6MfbXeuz6UgcHSaEsOnweatjKVd2ZjfM7gTiDkPWQNl6VidyKlqsKaVyzLXraQyfHcavh2L5T/eGDG5V1epIeZaIMKhlVb59qjVFvTwYMH0rH31/gJS0TOcqV+rundxhm0fNowAMXQvetaxO5HS0WFNK5YgryakMnbWNTUfO82HPRvRrUdnqSAqoV74oq8a0obdfJb7YcIQ+k7cQHX/V6ljKVURvty0h5VUUhq6BUpku861uQ4s1pZTDXU5KYfCMbWyPusBnfXx53L+S1ZFUBgXzefDfXo2Y1LcJkWcTCZwYwppdp62OpZzd8c0wtxsULAVD1kKJqlYnclparCmlHCrhagoDpm8jIvoiE4Oa0NW3gtWR1E10blyeNWPbUsO7ME8v2MEry3Zz7bouVaXuwrHfYF5PKOIDQ9dBcf0F7V5osaaUcpgLV67Tf3oo+04l8GX/pnRs5GN1JHUblUsV5JsnW/LkAzVYuO0EXT7fyMEzl62OpZzJ4Z9g/uNQvIptjFpR/Xd/r7RYU0o5xPnEZPpODeXQ2USmDPTn0frlrI6kssjT3Y1/dajD3GHNuXA1hS6fb2Re6HFcaS1p5SCHvoeFQVCqJgxZDYXLWJ3IJWixppTKducuJdF3SihRcVeYPtiff9TRL2xn1LamN+vGtaVF9VK8/u0enpq/g4SrKVbHUrnV/lUQ3B/K1IPBK6FQaasTuQwt1pRS2epMQhJBU0I5efEaM4c0p21Nb6sjqXvgXSQ/s4Y049XAOvy47yyBE0MIi4q3OpbKbfYsg8WDobwvDFoBBUtancilaLGmlMo2MReu0nvyFs5dTmb2E81pWaOU1ZFUNnBzE0beX4Olo1vh7ib0mRLKpJ8iSdOlqhTAzkWwdBhUag4Dl0OB4lYncjlarCmlssWJuKv0mRzKhavXmTusOc2q6m/WrqZxpeKsGduGTo18+N+Ph+g/LZQzCUlWx1JW+n0eLB8FVVrDgKWQv4jViVySFmtKqXt27PwV+kzZwpXrqSwYHkCTyiWsjqQcpIiXJ5/18eWjXo3YGZ1Ahwm/8dP+s1bHUlYImwErnoYa/4B+iyFfIasTuSwt1pRS9+Twucv0nryF5NR0FgwPoGHFYlZHUg4mIjzuX4lVY9pQrlgBhs0O49+r9pKcqnOy5RlbJ8Pq56BmewhaCPkKWp3IpWmxppS6awfOXKLP5FCMgeCRAdQrX9TqSCoH3VemMMufasWQVlWZuSmKHl9u5mhsotWxlKNtngTrXoI6naDPPPD0sjqRy9NiTSl1V/acTKDvlFA83IVFowKoVVbHquRFXp7ujO9Sn6mD/Dl58RqdJm1kaXiM1bGUo/z2MfzwOtTvDo/PAo98VifKE7RYU0rdsZ3RF+k3NZQCnu4sGtmSGt6FrY6kLPZIvbKsG9eWhhWK8cI3O3luUQSJyalWx1LZxRjY8D78/A406gM9poG7p9Wp8gwt1pRSdyT8eDwDpm2lWEFPFo1qSdXSOqhY2fgUK8CCEQE893AtVkScpOPEEHbFXLQ6lrpXxsBP/4ZfPwDfAdDtK3D3sDpVnuLwYk1E3EXkdxFZbX9dTUS2ishhEVkkIpn2oYrIK/Z9DopIe0fnVErd3tajcQycvo3SRfKzaGRLKpXUQcXqz9zdhHEP1yR4ZEtSUtPp+dVmpv52lHSdk805GQPfvwYbPwW/odBlEri5W50qz8mJnrVxwP4Mr/8LfGqMuQ+4AAz7awMRqQcEAfWBx4AvRUT/dihloU2HzzN45jZ8inkRPDKA8sULWB1J5WLNq5Vk7bi2/KN2Gd5bu5+hs7ZzPjHZ6ljqTqSnw9oXIfQLaD4KOn0KbnpBzgoO/amLSEWgIzDN/lqAdsAS+y6zgW6ZNO0KBBtjko0xx4DDQHNHZlVK3dwvB8/xxKztVClZiOCRLSlbVO/+UrdXvGA+Jg/0452u9dlyNI4OE0LYdPi81bFUVqSnw+pnYftUaDUGOvwXRKxOlWc5ukT+DHgJSLe/LgVcNMbcGHUaA1TIpF0FIDrD65vtp5RysPX7zjJyTjg1vAuzcGQA3kXyWx1JORERYWDLqqx4ujXFCngyYPpWPvzuAClp6bdvrKyRnmab7HbHbGj7AjzyjhZqFnNYsSYinYBzxphwR53Dfp6RIhImImGxsbGOPJVSec53e07z5Lxw6vgUYcGIFpQspLfpq7tT16coK59pTR//Snz5yxF6T95CdPxVq2Opv0pLtS0ftXMBPPgqtHtDC7VcwJE9a62BLiISBQRju/w5ASguIjduI6kInMyk7UmgUobXN9sPY8wUY4y/Mcbf29s7u7Irleet3HmKpxf8TqOKxZg3vAXFC2qhpu5NwXwefNCzEZP6NuHw2UQCJ4awZtdpq2OpG9JSYOkTsPsbeOgtePBlLdRyCYcVa8aYV4wxFY0xVbHdLPCzMaY/sAHoZd9tMLAik+YrgSARyS8i1YCawDZHZVVK/dmyHTE8G/w7fpVLMGdYC4p66XxKKvt0blyetePaUsO7ME8v2MEry3Zx7bouVWWp1GRYPBj2rYD2/4G2z1udSGVgxW0dLwPPi8hhbGPYpgOISBcReRvAGLMXWAzsA74DnjbG6L9kpXLA4u3RvPDNTgKql2LWE80onF/nU1LZr1Junyg1AAAgAElEQVTJgnzzZEtGP1iD4O3RdPl8IwfOXLI6Vt6UkgSLBsDBNRD4MbR82upE6i/EGNeZ+8bf39+EhYVZHUMppzU39DhvfLuH+2t5M2WgH16eOmOOcryQyFieX7yTS9dSeL1TPQa0qIzo5beccf0qBPeFo79C58/Ab4jVifIUEQk3xvjfbj+dMEUpBcCMjcd449s9PFSnjBZqKke1renNunFtCaheije+3cOT88K5ePW61bFcX3IiLOhtK9S6fqGFWi6mxZpSiq9/PcLbq/fRvn5ZvhqghZrKeaUL52fmkGa8FliXn/afI3BCCNuj4q2O5bqSLsG8nnB8M/SYCk36W51I3YIWa0rlcZN+iuSDdQfo1MiHz/s1JZ+Hfi0oa7i5CSPur87S0a3w9HCjz+QtTPwpkjRdqip7XbsIc7vDyTDoNQMaPW51InUb+q2sVB5ljOGTHw7yvx8P0aNJBT7r44unu34lKOs1rlSc1WPa0LlxeT758RD9p4VyJiHJ6liu4Wo8zOkCp3dC7zlQP7NFhFRuo9/MSuVBxhg++O4AE38+TG//inz0eGM8tFBTuUgRL08+6+PLR70asTM6gQ4TfmP9vrNWx3JuibEwuzOcOwBBC6BOR6sTqSzSb2el8hhjDG+v3sfkX4/Sv0VlPujRCHc3vfNO5T4iwuP+lVg9tg0+xQowfE4Y41fuJTlVZ3K6Y5fPwOxOEHcE+gVDrUetTqTugBZrSuUh6emGN1bsYeamKIa0qsq73RrgpoWayuVqeBdm+dOtGNKqKrM2R9H9i80ciU20OpbzuHQKZnWEi9HQ/xuo0c7qROoOabGmVB6Rnm54Zdlu5oWeYNT91Xmrcz2dy0o5jfwe7ozvUp9pg/w5nXCNzpM28k1YNK40V6hDXIyGmYFw+SwMXAbV2lqdSN0FLdaUygPS0g3/XLKTRWHRjGl3H//qUEcLNeWUHq5XlnXj7qdhhWK8uGQXzy6K4HJSitWxcqf4Y7ZC7Wo8DPoWKgdYnUjdJS3WlHJxKWnpPLsogmU7TvL8I7V44dHaWqgpp1aumBcLRgTw/CO1WLXzFJ0mbWRXzEWrY+UucUdslz6vX4bBK6DibSfJV7mYFmtKubDrqemMWfA7q3ae4uXH6jD2oZpWR1IqW7i7CWMfqsmiUS1JSU2n51ebmfrbUdJ1TjaIPWjrUUtNgsGroHwTqxOpe6TFmlIuKjk1jafmh/Pd3jO83rEuox+sYXUkpbJds6olWTuuLe3qlOG9tfsZOms75xOTrY5lnbP7bD1qJh2GrIFyDa1OpLKBFmtKuaCklDRGzgln/f5zvNO1PsPbVrc6klIOU7xgPr4e4Mc73Rqw5WgcHSaEsDHyvNWxct7pnbZCzc0Dhq6FMnWtTqSyiRZrSrmYq9dTGTZ7O79FxvJBj4YMbFnV6khKOZyIMDCgCiufaU2xAp4MnLGV/353gJS0dKuj5YyT4bYJbz0L2nrUSuuQB1eixZpSLiQxOZUhM7ez5UgcH/dqTFDzylZHUipH1SlXlFXPtCGoWSW++uUIvSdvITr+qtWxHOvEVpjTDbyK23rUSumQB1ejxZpSLuJSUgqDpm8l/PgFPu3jS0+/ilZHUsoSBfK5836PRnzerwmHzyUSOCGE1btOWR3LMaI2wbweUMjbVqiVqGJ1IuUAWqwp5QISrqYwcNpWdsUk8HnfJnT1rWB1JKUs16lRedaObct9ZQvzzILf+dfSXVy77kJLVR39Beb3gqLlbYVaMf0FzVVpsaaUk4u/cp1+00LZf/oyXw3wo0NDH6sjKZVrVCpZkMWjWvLUgzVYFBZN5883cuDMJatj3bvD62FBHyhR1TZGrUg5qxMpB9JiTSkndj4xmX5TQ4k8l8iUQX48Uq+s1ZGUynU83d146bE6zH2iBQnXUujy+Sbmboly3qWqDq6DhX1tNxEMXg2Fy1idSDmYFmtKOalzl5IImhJKVNwVZgxuxoO19QtbqVtpU7M068a1pVWNUryxYi9Pzgvn4tXrVse6M/tWwqIBULY+DFoJhUpZnUjlAC3WlHJCpxOu0WdKKKcuXmPW0Oa0qVna6khKOYXShfMzY3AzXgusy88HzhE4IYRtx+KtjpU1e5bCN0OgfFMYtAIKlrQ6kcohWqwp5WRiLlylz+RQYi8nM+eJ5gRU19+slboTbm7CiPurs3R0Kzw93AiasoUJ6yNJy81LVe0MhqXDoVILGLgMvIpZnUjlIIcVayLiJSLbRGSniOwVkX/bt4eISIT9cUpEvr1J+7QM+610VE6lnMnxuCv0mRzKxavXmTe8Bf5V9Tdrpe5Wo4rFWT2mDV0al+fT9YfoNzWU0wnXrI71dzvmwvInoWobGLAE8hexOpHKYR4OPHYy0M4YkyginsBGEVlnjGl7YwcRWQqsuEn7a8YYXwfmU8qpHIlNpP/UrSSlprFgRAANKuhv1krdqyJennzax5c2Nb15c8UeOkwI4aNejXPPzTrbp8GaF6DGQxA0HzwLWJ1IWcBhPWvGJtH+0tP++KOPWUSKAu2ATHvWlFL/L/LsZfpMDiUlLZ2FWqgpla1EhF5+FVk9pg0VihdgxJwwxq/cS1KKxXOyhX5lK9RqPQZBC7RQy8McOmZNRNxFJAI4B/xojNma4e1uwE/GmJtNeOMlImEiEioi3RyZU6ncbP/pSwRNCUUEgkcGUNenqNWRlHJJ1b0Ls+ypVgxtXZVZm6Po8eVmjsQm3r6hI2yaAN/9C+p2ht5zwdPLmhwqV3BosWaMSbNfyqwINBeRBhne7gssvEXzKsYYf6Af8JmIZLrYmYiMtBd1YbGxsdmWXancYM/JBPpODcXT3Y1FIwOoWVbHqijlSPk93Hmrc32mD/bndMI1Ok3cyOKw6Jydk+3Xj+DHN6F+D+g1Ezzy5dy5Va6UI3eDGmMuAhuAxwBEpDTQHFhzizYn7X8eBX4BmtxkvynGGH9jjL+3t3c2J1fKOhHRF+k3NZRC+TxYNCqA6t6FrY6kVJ7xUN2yrBt3P40rFeOlJbt4dlEEl5NSHHtSY+Dn92DDu9AoCHpMBXdPx55TOQVH3g3qLSLF7c8LAI8AB+xv9wJWG2OSbtK2hIjktz8vDbQG9jkqq1K5TVhUPAOmbaVYQU8WjQqgSqlCVkdSKs8pV8yL+cMDeOGRWqzaeYqOEzeyM/qiY05mDKx/C377EJoMgG5fgrsj7wFUzsSRPWs+wAYR2QVsxzZmbbX9vSD+cglURPxFZJr9ZV0gTER2YuuR+8AYo8WayhNCj8YxaMY2vIvkZ/GollQsUdDqSErlWe5uwpiHarJ4VEvS0g09v9rMlN+OkJ6dc7IZA9+/ahun5j8MOk8CN/fsO75yeuK0a6Nlwt/f34SFhVkdQ6m7tjHyPMPnbKdiiYIsGN6CMkV1ULFSuUXC1RReXrqL7/ae4f5a3vzv8cZ4F8l/bwdNT4d1L9qm6GgxGh57H0SyJ7DK9UQk3D4+/5Z0BQOlcokNB8/xxOztVC1ViOCRAVqoKZXLFCvoyVcDmvJutwZsPRpHhwkhhETew41t6emwepytUGs1Vgs1dVNarCmVC/y47yyj5oRTs0xhFo4IoHThe/xtXSnlECLCgIAqrHymDSUKejJw+jY+WHeAlLT0OztQehqseAp2zIH7X4RH3tZCTd2UFmtKWWzt7tOMnhdOXZ8iLBgeQIlCepu+Urld7XJFWPlMG/o2r8zXvx7h8a+3EB1/NWuN01Jg2QjYuRD+8Rq0e10LNXVLd1ys2e/UbOSIMErlNSsiTjJm4e80rlScucNbUKyg3qavlLMokM+d93s05It+TTkSm0jghBBW7Tx160ap12HJE7BnKTz8b3jgpZwJq5xaloo1EflFRIqKSElgBzBVRD5xbDSlXNuS8BieWxSBX5USzH6iOUW9tFBTyhl1bOTD2rFtqVm2MGMW/s7LS3Zx9Xrq33dMTYbFg2D/Smj/PrR5NufDKqeU1Z61YvZloXoAc4wxLYCHHRdLKdcWvO0ELy7ZScsapZg1tBmF8+t8Sko5s0olC7JoVEueerAGi8Oj6TxpI/tPZ1hNMeUaBPeDQ+sg8GNo+ZR1YZXTyWqx5iEiPkBvYPXtdlZK3dycLVH8a9lu7q/pzfTBzSiYTws1pVyBp7sbLz1Wh3nDWnApKZWuX2xizpYoTHIiLOgDh3+CzhOh+Qiroyonk9Vi7W3ge+CwMWa7iFQHIh0XSynXNC3kKG+u2MvDdcswZZAfXp468aVSrqb1faVZN64trWqU4r8rwoj8tAMmKgS6fQV+g62Op5xQloo1Y8w3xphGxpin7K+PGmN6OjaaUq7lq1+O8O6a/XRoUI4v+/uR30MLNaVcVenC+ZnRpxY/l51I9Wt7eNP9WbYVa291LOWksnT9RUS8gRFA1YxtjDFPOCaWUq5l4k+RfPLjITo3Ls+nvRvj4a6z5ijl0q5dwG1eD8pe3svxh78kJLQc86dsYexDNRnTribubjpVh8q6rA6WWQGEAOuBNMfFUcq1GGP43w+H+HzDYXo0rcBHvRrrl7RSru5KHMztBrEHoPdcqtQJZHXzVN74dg+frY9k85E4JgT54lOsgNVJlZPI0tqgIhJhjPHNgTz3RNcGVbmJMYb31x1gym9HCWpWif90b4ibFmpKubbEWJjTFeIOQ9ACqPnniROWhsfwxoo95PNw46NejXmkXlmLgqrcILvXBl0tIoH3mEmpPMMYw79X7WPKb0cZEFBZCzWl8oLLZ2BWR4g/Cv0X/61QA+jpV5HVY9pQoXgBRswJY/zKvSSl6AUrdWtZLdbGYSvYkkTksv1x6batlMqD0tMNr3+7h1mbo3iidTXe6dpACzWlXF3CSZgZCAkxMGAJVH/wprtW9y7Msqda8UTraszaHEX3Lzdz+FxijkVVzierd4MWMca4GWO87M+LGGOKOjqcUs4mLd3wr2W7mL/1BE8+UIM3OtVFdM0/pVzbxRMwKxASz8HAZVC1zW2b5Pdw583O9Zg+2J+zl5LoPGkji8OiycrQJJX3ZPmWNBHpIiIf2x+dHBlKKWeUmpbOP7/ZyeKwGMa2u4+XH6uthZpSri7+qK1H7doFGLQCKgfcUfOH6pZl3bi2+FYqzktLdjEuOIJLSSkOCqucVVbXBv0A26XQffbHOBF535HBlHImKWnpjFsUwfLfT/LCI7V4/lEt1JRyeecjYWZHuJ4Ig1ZCRb+7OkzZol7MG96Cfz5aizW7T9NxYggR0RezOaxyZlntWQsEHjHGzDDGzAAeAzo6LpZSzuN6ajrPLNjBml2neaVDHcY8VNPqSEopRzt3wHYzQdp1GLIGyt/bhAnubsIz7WqyeFQA6enQ66vNTP71COnpellU3cFlUKB4hufFsjuIUs4oKSWN0fPC+X7vWd7sVI9RD9SwOpJSytHO7LEVamAr1MrWz7ZD+1UpydqxbXmkXlneX3eAIbO2E3s5OduOr5xTVou194HfRWSWiMwGwoH3HBdLqdwvKSWNEXPC+OnAOd7t1oAn2lSzOpJSytFORcDsTuCeD4ashTJ1sv0UxQp68mX/przXvQFbj8bRYUIIvx2KzfbzKOeRpUlxAUTEB2hmf7nNGHPGYanukk6Kq3LK1eupDJsVRuixOP7boxG9m1WyOpJSytFiwmFed8hfFAavhJLVHX7Kg2cu88yCHUSeS2TUA9X556O18dTl6lxGtkyKKyJ17H82BXyAGPujvH2bUnlOYnIqQ2ZsZ+uxOP73eGMt1JTKC06E2lYmKFAChq7NkUINoHa5Iqx8pg39WlRm8q9H6fX1Fk7EXc2Rc6vc45Y9ayIyxRgzUkQ2ZPK2Mca0u0VbL+A3ID+2NUiXGGPeEpFZwANAgn3XIcaYiEzaDwZet7981xgz+3YfRnvWlKNdSkphyIxt7IxJ4LM+vnRuXN7qSEopR4vaCPN7Q5FyMHgVFKtgSYy1u0/z8tJdYOC9Hg3pot8/Ti+rPWtZvgx6FwEEKGSMSRQRT2Ajtuk/ngRWG2OW3KJtSSAM8AcMtjFyfsaYC7c6pxZrypEuXr3OoBnb2H/6EpP6NuGxBj5WR1JKOdqRDbCwLxSvbLv0WaScpXGi468yLvh3dpy4SG//iozvUp+C+TwszaTuXrauDSoij4tIEfvz10VkmYg0uVUbY3Nj/QxP+yOrlWF74EdjTLy9QPsR23QhSlki/sp1+k3dyoHTl/mqv58WakrlBZE/woI+tkueQ9ZYXqgBVCpZkEWjWvL0P2rwTXgMnSdtZN8pXf3R1WV1lOIbxpjLItIGeBiYDnx9u0Yi4i4iEcA5bMXXVvtb74nILhH5VETyZ9K0AhCd4XWMfZtSOS72cjJBU7ZwJDaRKYP8eLheWasjKaUc7cBaCO4H3rVhyGoo7G11oj94urvxYvs6zB/WgstJqXT7chOzN0fpUlUuLKvFWpr9z47AFGPMGiDf7RoZY9KMMb5ARaC5iDQAXgHqYLuztCTw8h2nzkBERopImIiExcbqrc0qe529lETQlC2ciL/KjCHNeLB2GasjKaUcbd8KWDwQyjW0XfosWNLqRJlqdV9p1o1rS+sapXhr5V5Gzg3nwpXrVsdSDpDVYu2kiEwG+gBr7b1hWb532BhzEdgAPGaMOW2/RJoMzASaZ3Y+IOMtdhXt2zI79hRjjL8xxt/bO/f85qOc36mL1+gzeQtnEpKYPbQ5re8rbXUkpZSj7V4C3wyFCn4wcLnt7s9crFTh/Ewf3IzXO9bll4PnCJwYwtajcVbHUtksqwVXb+B7oL298CoJvHirBiLiLSLF7c8LAI8AB+zztd24AaEbsCeT5t8Dj4pICREpATxq36ZUjoiOv0qfKVuIS7zOnGEtaFG9lNWRlFKOFrEAlo2wLcY+YCl4OcdiPW5uwvC21Vk2ujX5PdzoOzWUz9YfIk2XqnIZWS3WfIA1xphIEXkQeBzYloU2G0RkF7Ad25i11cB8EdkN7AZKA+8CiIi/iEwDMMbEA+/Y220H3rZvU8rhos5foc/kLSRcTWHe8Bb4Vcndv1krpbJB+Gz49imo2hb6fwP5i1id6I41rFiM1WPb0s23Ap+tj6Tv1FBOXbxmdSyVDbI0dYf9JgF/oCqwFlgB1DfGBDo03R3SqTvUvToSm0i/qaFcT01n7rAWNKjgHL9ZK6XuwbapsPafcN/D0GceeBawOtE9W7Yjhte/3UM+Dzc+7NmIR+tbfyer+rtsnboDSDfGpAI9gEnGmBex9Zwp5TIOnb1Mn8mhpKUbFo4M0EJNqbxgyxe2Qq1WBwha4BKFGkCPphVZM7YtFUsUYOTccN5asYeklLTbN1S5UlaLtRQR6QsMAlbbt3k6JpJSOW/fqUsETQnFTSB4ZAB1yhW1OpJSytE2fgrfvwp1u0DvOeCR2UxSzqta6UIsHd2KYW2qMXvLcbp9sYnD5y5bHUvdhawWa0OBlsB7xphjIlINmOu4WErlnN0xCfSdGkp+DzcWjWrJfWWcb6yKUuoO/fohrB8PDXpCr5ngcdvZqJxSfg933uhUj5lDmnHucjKdJ21i8fZonZPNyThsuSkr6Jg1dad2nLjA4BnbKOrlycIRAVQuVdDqSEopRzIGfn4XQj6Gxn2h6xfg5m51qhxx9lISzwZHsOVoHJ0bl+e97g0o6qUXyayULWPWRGSx/c/d9hUHbjx22+/yVMppbY+KZ9D0bZQomI9Fo7RQU8rlGQM/vmkr1JoOgq5f5plCDaBsUS/mDW/Bi+1rs3b3aTpODCEi+qLVsVQW3LJnTUR8jDGnRaRKZu8bY447LNld0J41lVVbjsQxbPZ2yhX1YsGIAMoV87I6klLKkYyB716BrV9Bs+HQ4SNwy/Lc7i4n/Hg8YxdGcPZSEv9sX5uRbavj5iZWx8pzsqVnzRhz2v7ncXthdgG4nOGhlNMJiYxl6KxtVChegOBRWqgp5fLS02HN87ZCLeApCPw4TxdqAH5VSrJ2bFseqVeWD9YdYPDMbZy7nGR1LHUTWfrbKiKjROQMsAsItz+0C0s5nQ0HzjFsdhhVSxUieGQAZYpooaaUS0tPg1VjIGwGtH4W2v8HRHuQAIoV9OTL/k35T/eGbDsWT+CEEH47pGts50ZZ/dXin0ADY0xVY0w1+6O6I4Mpld1+2HuGkXPDqFW2MAtHBFCqsGvdpq+U+ou0VPh2NPw+Dx54GR4er4XaX4gI/VpUZuUzbShZKB+DZmzj/bX7uZ6abnU0lUFWi7UjwFVHBlHKkdbsOs1T83dQr3wx5g8PoEQh17xNXylll5ZiW+dz1yJo9zr841Ut1G6hdrkirHi6Df1aVGbyb0d5/OvNnIjT//Zzi6wuN9UEmAlsBZJvbDfGjHVctDunNxiozKyIOMlziyJoWrkEM4c2o4jeqq6Ua0u9DkuGwoHV8Mjb0Hqc1Ymcyrrdp3l56S7SDbzXvQFdfStYHcllZfUGA48sHm8y8DO2xde1b1Q5jW/Conlp6S5aVCvJ9MHNKJQ/q3/llVJOKSUJvhkMh76Dxz6AgNFWJ3I6HRr60LBiMcYFRzAuOIKNkef5d9f6FMyn359WyepP3tMY87xDkyiVzRZsPcGry3fT5r7STB3kT4F8eWc+JaXypJRrENwPjvwMHT+BZsOsTuS0KpYoyKKRAXy2PpIvfjlM+IkLTOrbhPrldc1kK2R1zNo6ERkpIj4iUvLGw6HJlLoHszdH8ery3TxY25tpg7VQU8rlXb8CC3rDkQ3Q5XMt1LKBh7sb/2xfm/nDWpCYlEr3LzYza9MxXarKAlkds3Ysk80mt90RqmPWFMC0kKO8u2Y/j9Qry+f9mpDfQws1pVxa8mWY3xuiQ6Hb19C4j9WJXE5cYjIvLtnFzwfO8XDdsnzUq5HeqJUNsjpmTdcGVS7liw2H+ej7gwQ2LMeEoCZ4uuftiS+VcnlJCTCvF5wMh55TbQuzK4cwxjBjUxQfrNtPqUL5+SzIl4DqpayO5dSya23QlzI8f/wv7/3n7uMplb2MMXy2/hAffX+Qrr7lmaiFmlKu72o8zOkKp36Hx2dpoeZgIsKwNtVY/lRrCuRzp9/UUD798RCpaXrfoaPd7n+zoAzPX/nLe49lcxal7ooxho++P8hn6yPp2bQin/T2xUMLNaVc25U4mNMFzu6FPvOgXherE+UZDSoUY9WYNnRrUoEJP0XSb+pWTl28ZnUsl3a7/9HkJs8ze61UjjPG8J+1+/nylyP0bV6Jj3o1wl0XI1bKtSWeg9md4Hwk9F0ItbXvIKcVzu/BJ719+bRPY/aeSqDDhBB+2HvG6lgu63bFmrnJ88xeK5WjjDH8e9U+poYcY1DLKrzXrSFuWqgp5dounYZZHeFCFPRbDPc9bHWiPK17k4qsHtuWSiULMHJuOG+u2ENSSprVsVzO7eZZaywil7D1ohWwP8f+WlfAVpZJTze89u0eFm47wfA21XitY11El5JRyrUlxMDszraetf5LoGprqxMpoFrpQiwd3YqPvjvItI3H2HYsns/7NeG+MkWsjuYybtmzZoxxN8YUNcYUMcZ42J/feK1r9ihLpKUbXlq6i4XbTjD6wRpaqCmVF1w4DjMD4cp5GLhcC7VcJr+HO693qsfMIc04dzmZzpM2sWj7CZ2TLZs4bBS2iHiJyDYR2Skie0Xk3/bt80XkoIjsEZEZIpJp0SciaSISYX+sdFRO5VxS09J5fnEES8JjGPdQTV5qX1sLNaVcXdwRW6GWdBEGfQuVmludSN3EP+qUYd24tjSpXJyXl+5mzMLfuZSUYnUsp+fIW+aSgXbGmMaAL/CYiAQA84E6QEOgADD8Ju2vGWN87Q+9zUeRkpbOuOAIVkSc4sX2tXnukVpaqCnl6s5H2saopVyFwauggp/VidRtlC3qxdxhLXixfW3W7TlDx4kh/H7igtWxnJrDijVjk2h/6Wl/GGPMWvt7BtgGVHRUBuU6klPTeHr+DtbsPs1rgXV5+h/3WR1JKeVo5/bbetTSU2HIavBpbHUilUXubsLT/7iPxaMCSE+Hx7/ewle/HCE9XS+L3g2HTkYlIu4iEgGcA340xmzN8J4nMBD47ibNvUQkTERCRaSbI3Oq3C0pJY0n54bzw76zjO9cjxH356pVzpRSjnBmt61HTdxgyBooW9/qROou+FUpydpxbXm0fln++90BBs/cxrnLSVbHcjoOLdaMMWnGGF9svWfNRaRBhre/BH4zxoTcpHkV+xIM/YDPRKRGZjvZF5gPE5Gw2NjYbM2vrHftehoj5oSx4WAs73VvwJDW1ayOpJRytFO/w6xO4OEFQ9eCd+3/a+++46Oq8j6Of04aCSEQaqhJ6EiTEgjVXXV17VgQ6UUBd627uu1xn1133ea6llXXXaUKSlVREcvqWqmB0Js0IfQaICSQkGTO88cZngSXJmRyJzPf9+t1X8zce+fOb156J7855Xe8jkguQbW4aF4a2Ik/39aOxVuzueH5uXy5UX+vv4tyKfNurT0CfI5/1QNjzONAbeCRc7xml//fb4AvgI5nOW+MtTbNWptWu3btMo5cvJRXUMSIVxczb/NBnurbnkHpKV6HJCKBtjMTJvWBSlVdolbzjL/TpYIxxjAwPZn3HuxFjfgYhk1YzJ8/WM/JIi1VdSECORu0tjEm0f84DrgG+NoYMxL4ITDAWnvG/0rGmOrGmEr+x7WAnsC6QMUqwedYfiHDJy5m8dZsnu13Of3SGnkdkogEWtZCmHwrVK7uErXqqV5HJGWsRVICsx/oxaD0ZMZ89Q13vryArEN5XocV9ALZslYP+NwYswpYghuzNgd4GUgCFvrLcvwWwBiTZowZ53/tZUCmMWYlrkXuSWutkrUwcfREIUPGL2bZ9iO8MKAjt3XUHBSRkLd1Lrx+ByQkwYgPIVE/0EJVbHQkf7qtHf8a1ImtB/O48YV5vLtil9dhBTUTSgXr0tLSbGZmptdhyCU4cvwkQ8Yv5uu9OaFEQQcAACAASURBVLw4oBPXta3rdUgiEmhbPoNpA6F6Cgyd7RI2CQs7Dx/nJ9NXkJl1mL6dG/L7W9oQX+l8iyuFDmPMUv/4/HMqlzFrIhfiUG4BA8ZmsGHvMV4e3FmJmkg42PgxTO3vxqYNf1+JWphpWL0y00d348GrmvHWsp3c/I95rN191Ouwgo6SNQkK+4/lM2DsIr45kMu4YWlcfZm+sEVC3tfvw/SBUKeVK3gbX8vriMQDUZERPHptS6aMTCevoIjbXlrAxPlbtVRVKUrWxHN7j+bTf8widmSfYOLwLlzRQrN6RULe2rdh5lCo1951fVau4XVE4rEeTWvx4cNX0Lt5LX7/3jpGTc4kO++k12EFBSVr4qldR05w15iF7Duaz6S7u9KjmX5Zi4S8VW/Am3dDgzQY8g7EJXodkQSJGvExjBuWxm9vas1XGw9yw/NzWbjlkNdheU7JmnhmR/Zx7nplIdm5J5l8TzpdG+uXtUjIWz4FZo2ClJ4w+C2Irep1RBJkjDHc3asxs+7rQVxMJAPHLeLZjzdQVBy+NdmUrIknth3Mo98rCzmWX8SUUel0TqnudUgiEmiZE+Hd+6DJ92HgTKhUxeuIJIi1bVCN9x7sxe0dG/LCZ5sZMHYRu4+c8DosTyhZk3K3eX8u/V5ZSEGRj6mj0mnfUF0gIiEvYwzM+Qk0vxYGTIeYyl5HJBVAlUpRPNPvcp6763LW7c7h+ufn8tGavV6HVe6UrEm52rD3GP3HLMRnYdqobrSpX83rkEQk0Bb8Az78ObS8Ee56HaJjvY5IKpjbOjbk/Yd6k1yjMj96fSm/eWcN+YXFXodVbpSsSblZu/so/ccsJMIYpo/uRsu6CV6HJCKBNvcZ+PjX0LoP9JsEUZW8jkgqqNRa8bz14x6M6t2Y1xZlcetL89m8/5jXYZULJWtSLlbtPMLAsRnERkcy497uNKujsSoiIc1a+OJJ+PQJaHcn3DEBIqO9jkoquJioCH59Y2smjujCgWMF3PTiPKYv3h7yNdmUrEnALdt+mEFjM0iIjWLmvd1pXCve65BEJJCshc/+AF/8BS4fCLe9ApHhs4SQBN6VLevw4cO96ZxSnV/NWs0D05aTk1/odVgBo2RNAmrx1myGjMugRpUYZtzbnUY1NKhYJKRZCx//r+v+7DQM+rwEEZFeRyUhqE7VWF67O52f/7AlH63Zyw3Pz2XZ9sNehxUQStYkYBZsPsiwCYtJqhbLzHu70yAxzuuQRCSQrIUPfwkL/wFdRsFNf4cI/ZmRwImIMNx/ZTNm3tsda+HOlxfyzy824/OFVreo7iIJiC83HmDEq0toVCOOGaO7k1RVs79EQprP50pzLH4Fuj8AN/xNiZqUm84p1fng4d5c16YuT320gaETFrP/WL7XYZUZ3UlS5j5dv49RkzJpUrsK00Z1o3aCZn+JhDRfMcx+AJa+Cr0egWv/CMZ4HZWEmWpx0fxjYEf+cns7MrOyuf7vc/liw36vwyoTStakTH20Zi8/en0pLesmMG1UOjWrKFETCWnFRfD2vbBiCnzvV3D1b5WoiWeMMQzomsx7D/SiVpVKDJ+4hD+9v46TRRV7qSola1Jm5qzazf1Tl9G2QTVeH5lOYuUYr0MSkUAqLoS37oHVb8BVv4Er/0eJmgSF5kkJvPtATwZ3S2bs3K30fXkB2w7meR3WRVOyJmXi7eU7eWjacjolJzL57q5Ui1M9JZGQVlQAbwyHde+4bs8rfuZ1RCKniY2O5I+3tuPlwZ3YdjCPG1+YyzvLd3kd1kVRsiaXbGbmDh6ZuZL0xjV5dURXEmKVqImEtMJ8mDEYvp4D1z8FPR70OiKRs7qubT0+/MkVXFavKj+ZsYJHZ64kr6DI67C+EyVrckmmZGTxizdX0atZLSYM70J8JRW+FAlpJ4/DtP6w6WNXmiP9Xq8jEjmvBolxTB/djYeuasas5Tu5+cV5rNl11OuwLpiSNblor87fyq/fXsNVreowdmgacTEqfCkS0gpyYWo/+OYLV+w2bYTXEYlcsKjICB65tiVTRqaTd7KI2/+5gAnztlaIpaqUrMlFGfPVFn733jqubZ3Ey4M7ExutRE0kpOXnwJS+kDXfLR/VcbDXEYlclB5Na/Hhw1fQu3ktnpizjlGTM8nOO+l1WOcUsGTNGBNrjFlsjFlpjFlrjPm9f39jY0yGMWazMWaGMeaMUwaNMf/jP2eDMeaHgYpTvrt/fLaJP3/wNTe2q8dLgzoRE6WcXySknTgCr90GOxbDHePh8ru8jkjkktSIj2HcsDQev7k1X208yPXPf8XCLYe8DuusAvlXtgC4ylp7OdABuM4Y0w34K/CctbYZcBi459svNMa0BvoDbYDrgH8aY9R04zFrLc9+spGnP97IrR3q83z/DkRHKlETCWnHs2FyH9izEvpNhra3ex2RSJkwxjCiZ2Nm3deD+JgoBo5bxLMfb6CoOPhqsgXsL611cv1Po/2bBa4C3vTvnwTceoaX9wGmW2sLrLVbgc1A10DFesFydrsCkGHIWstT/97AC59uom/nhjzTrwNRStREQlveQZh0C+xfB/2nwGU3eR2RSJlr26Aa7z3Yizs6NeSFzzbTf8widh054XVYpwnoX1tjTKQxZgWwH/gE2AIcsdaeynh2Ag3O8NIGwI5Sz892XvmaNgD+muK6A756GrIWulpDIc5ayx/fX8+/vtjCwPRknrqjPZERKnwpEtKO7YNXb4JDm2DAdGih0SgSuuIrRfH0nZfz97s6sH5PDje+MJcjx4NnHFtA6yxYa4uBDsaYROBtoFVZv4cxZjQwGiA5ObmsL3+63o/A1rlugO1nf3D7omKhYRdI6eG2hl0hpnJg4yhHPp/ld++tZfLCLIb3SOXxm1tjVKFcJLTl7HYtajm7YOBMaPI9ryMSKRe3dmxAh0aJzN18MKhW4SmXoljW2iPGmM+B7kCiMSbK37rWEDhTOeFdQKNSz892HtbaMcAYgLS0tMDOv23dx23gxnFsXwjb5rvk7au/gfVBRBTU7+QSt9Re0CgdYqsGNKxA8fksj729mulLdjCqd2Meu+EyJWoioe7IDph0M+QdgMFvue8ykTCSWiue1FrxXodxGhOo+iLGmNpAoT9RiwM+xk0uGAa8Za2dbox5GVhlrf3nt17bBpiKG6dWH/gUaO5vqTurtLQ0m5mZGYBPcwHyc2BHhkvcshbArmXgKwQTAXXbQUqvkta3yjW8ifE7KPZZfvHmKt5atpP7r2zKz65tqURNJNQd3uYStRNHXaLWqIvXEYmENGPMUmtt2vnOC2TLWj1gkn8WZwQw01o7xxizDphujPkjsBwY7w/4FiDNWvtba+1aY8xMYB1QBNx/vkTNc7FVofk1bgNX5XvnkpLkLXM8LHrJHavTuiRxS+kJCXW9i/sMiop9PPrGSt5dsZuf/qAFD13dTImaSKg7tMUlaifzYOg70KCT1xGJiF/AWta84GnL2vkUFbjWtlPJ244MOOmfLFujaUm3aUoPSAzw2LtzKCz28fD05Xywei+/uK4l932/mWexiEg5ObDRJWq+QhjyDtRr73VEImEhGFrWpLSoSpDS3W3gSoDsXekSt23zYf1sWP6aO1atkWtxO5XA1WgC5dCyVVBUzP1TlvOf9fv43xsvY2TvJgF/TxHx2L51MPkWwMCwOZDU2uuIRORb1LIWLHw+V8soa35J61veAXesSlJJl2lKT6jdCiLKtupKfmExP3p9KV9sOMDvb2nDsB6pZXp9EQlCe1a5greRMTDsPajdwuuIRMKKWtYqmogIqNvWben3grVwcFNJ8rZtPqx9250bV6PUmLceULc9RFz8Ag8nThYzanIm87cc5M+3tWNgunfdsCJSTnYtczUjY6rAsNlQs6nXEYnIWShZC1bGuF+5tVtA2giXvB3J8pcKWeASuK/nuHMrVXUlQk51m9brAFEXVh8mr6CIeyYtIWNrNk/d0Z470xqd/0UiUrHtWAyv3wFxia5FrXqq1xGJyDkoWasojHFfqNVToeMgt+/oLlfr7VTL2+ZP3P6oOGjUtWTcW8M0iI77r0seyy9kxMQlLNt+mL/f1YE+HbxfJEJEAixrAUy5E+Jru0QtUT/QRIKdkrWKrFoDaNfXbQC5B0qSt6z58MVfAOvGozToXDLurVFXjhbHMnTiYtbuOsqLAzpxY/t6nn4UESkH33wJ0/pD1QYuUauq+16kItAEg1B24jBszyhJ3navAFuMNZFsimjCVydb0vmKG+nY63qIq+51tCISSJv/A9MHQfXGboxalTpeRyQS9i50goGStXBSkMvRTfP54L03aZa/is6RW4jwFQIGktqeXqi3Sm2voxWRsrLhI5g5BGq1dAVv42t5HZGIoNmgcgb7C6IY9HEsO47fztihfyYiNQF2ZZZMWFj+Gix+xZ1cq4U/cfMX6q2m8WwiFdL69+CNEZDUBoa8XSGWuxOR0ylZCxN7j+YzcOwi9ubkM3F4V7o3rekOpPZyG0DRSdizsqTbdM0sWPqqO5aYUrLCQkoP15WiJahEgtuaWfDWSLd01KA33exPEalw1A0aBnYePs7AsRlk551k4ogudEm9wF/WvmLYt8ZfLsRfMuREtjuWUN9fKsRfqLdWCyVvIsFk5Qx450eurM+gN6BSgtcRici3qBtUANh+6DgDxi4iJ7+Q1+7pSsfk7zCRICIS6l3utu73uVUWDm4oKRWybR6sedOdW7lWqVUWergul0so1Csil2DZazD7QdcaPnAGxMR7HZGIXAIlayFs68E8Bo5dxInCYqaO7Ea7htUu7YIREVDnMrd1GekK9WZ/U9LqluVf4xQgthokdy8Z91avPURGX/qHEpFzy5wAc34KTa6E/lMhprLXEYnIJVKyFqI27z/GgLEZFPssU0d2o3X9qmX/Jsa4JWpqNoVOQ92+IztKEres+bDxI7c/Ot4V6j3Vbdqgs1vcXkTKTsYr8OEvoPkPod9kiI71OiIRKQNK1kLQ13tzGDQ2A2MM00d3o0VSOY5VSWwEiXfB5Xe558f2wfYFJctkffZHtz+yEjTsUjJhoVFXddWIXIr5L8Anv4FWN0HfiRe85JyIBD9NMAgxa3YdZcj4DGKiIpg6qhtNa1fxOqTTHc/2r7KwwI1527sKrA8ioqB+x5Ju0+R015UqIuf31d/cD6E2t8HtYzXkQKSCUFHcMLRyxxGGjM+gSqUopo7qRmqtCtBSlZ/jFpXOmucSuF3LwFcIJsIV6j1VLiS5B8TX9DpakeBirVtW7su/Qrt+cOu/IFIdJiIVhZK1MLM0K5vhE5aQGB/N1JHdaFSjgg4qPnkcdi4pGfe2cwkU5btjtS87vVxIQl1vYxXxkrXw6e9h3nPQYRDc8qJmYItUMCrdEUYyvjnEiFeXUCehElNHdaN+YpzXIV28mMrQ5HtuAygqgN3LS8qFrJoBmePdsRpN/KVC/OVCqqd4F7dIebIW/v1rWPQSdB4BNz7rZmuLSEhSy1oFN3/zQe6ZtIQGiXFMHdWNpKohPvuruMiNc/v/ciELIP+IO1atUan1TXu5Waoq1CuhxudzMz6XjIWu98L1f9X/5yIVlLpBw8CXGw8wenImqTXjeX1kOrUTwrAUhs8H+9edXi4k74A7ViXp9EK9tS9T64NUbD4fzHkYlk2GHg/CNX9QoiZSgakbNMT9Z90+7puyjGZ1qvD6yHRqxIfpNP2ICKjb1m3po1330KHNJd2mWfNh7dvu3LjqbqLCqXFvSe00GFsqDl8xvPsArJwKvR+Fq36jRE0kTATsL5UxphEwGUgCLDDGWvu8MWYG0NJ/WiJwxFrb4Qyv3wYcA4qBogvJPMPFR2v28MDU5bSuX5XJd3clsXKYJmpnYgzUau62zsNd8nYkq6Tlbdt82PC+OzcmwZUIOTXurX5H1aaS4FRc5Nb5XP0GfP8x+N4vlKiJhJFANisUAY9aa5cZYxKApcaYT6y1d506wRjzDHD0HNe40lp7MIAxVjizV+7mpzNWcHnDarx6d1eqxqqe0jkZA9VT3dZhoNuXs7tUt+kCN6MOICoOGnUp6TZt2AWiK/BkDQkNxYXw1j2w7l24+nHo/YjXEYlIOQtYsmat3QPs8T8+ZoxZDzQA1gEYYwzQD7gqUDGEmlnLdvKzN1aSllKDCSO6UKWSuvAuStX60K6v2wDyDpZMVsiaD188CViIiHbLYp3qNm2UDpXKcTUIkaICeGM4bPgAfvhn6H6/1xGJiAfKZYKBMSYV+Apoa63N8e+7Anj2bN2bxpitwGFcF+or1tox53ufUJ5gMHPJDn45axXdm9Rk3LA0KscoUQuYE0dgR4ZbYSFrgSsdYovBRLoF6U91myZ3g8o1vI5WQlXhCZgxBDZ/Ajc8DV1HeR2RiJSxoJkNaoypAnwJ/MlaO6vU/n8Bm621z5zldQ2stbuMMXWAT4AHrbVfneG80cBogOTk5M5ZWVmB+Bieem1RFr95Zw1XtKjNmCGdiY1W4ctyVZALOxeXtL7tzITiAsBAUpvTZ5xWqeN1tBIKTh6H6QPgmy/hpucgbYTXEYlIAARFsmaMiQbmAP+21j5ban8UsAvobK3deQHX+R2Qa619+lznhWLL2oR5W3lizjqublWHlwZ1UqIWDArzYddSf/I2zy2XVXjcHavZvGSFhZQeUK2ht7FKxVOQC9P6u5bdPi9Bx0FeRyQiAeJ56Q7/mLTxwPrSiZrfD4Cvz5aoGWPigQj/WLd44FrgiUDFGqxe/nILT374NT9sk8SLAzoRE6UaYUEhOtYlZKk9gZ+7AeB7VpZ0m66ZBUtfdecmppQkbqk9oXpjzeKTs8vPgSl3umXWbh8L7e/0OiIRCQKBHPjUExgCrDbGrPDve8xa+wHQH5hW+mRjTH1gnLX2Bly5j7ddvkcUMNVa+1EAYw06L366iWc+2chN7evx3F0diI5Uoha0IqOhYZrbev3E1cPat8YlbtvmwcaPXG0sgIR6pbpNe0LtlkrexDlxGF6/wyX+fSdAm1u9jkhEgoRWMAgy1lqe+2QjL3y2mds7NuCpvu2JUqJWsfl8cHCj6zLNWuBqveXudccq1zx9zFtSWy3GHY6OZ8Nrt8K+ddBvErS60euIRKQceN4NKt+dtZYnP/qaV778hn5pDfnL7e2JjFCrS4UXEQF1Wrmty0hXqPfwVv8KC/5xb+vfc+dWquZmmab0gNReUO9y13InoSv3gEvUDm6C/lOhxbVeRyQiQUbJWpCw1vLEnHVMnL+NQenJ/KFPWyKUqIUmY6BGE7d1GuL2HdkB2xeWjHvb9G+3P7oyNOrqFqZP6eHqvkXHehe7lK1je2FyHzicBQOnQ1OVnRSR/6ZkLQj4fJbfzl7D64u2M7xHKo/f3BqjcUzhJbGR29r3c89z95essJC1AD7/o9sfWcmNjTvVddqoK8TEexe3XLyc3TDpZsjZA4PegMa9vY5IRIKUxqx5zOezPPb2aqYv2cG9VzThV9e3UqIm/+14Nmxf5E/g5rtB6NYHEVFQr0NJuZBG6RCX6HW0cj5HtrtELe8QDH7TdX2LSNgJijpr5a2iJWvFPsvP31zJrGW7ePCqZjxyTQslanJh8nNcfbdTrW+7loKvEDBQt13JhIWUnhBf0+topbTsrTDpFsg/CkNmuZZSEQlLStaCXGGxj0dmruS9lbt55JoWPHR1c69Dkors5HHYlVlSLmTnEijKd8dqtzo9eataz9tYw9mhLa5FrfA4DHkb6nf0OiIR8ZBmgwaxk0U+Hpq2nI/W7uWX17Xix99v6nVIUtHFVIbGV7gNoOikW9P0VLmQVTMgc7w7VqPJ6eVCElNU6608HNjgEjVfEQx7z7WAiohcALWslbOComLun7KM/6zfz//eeBkjezfxOiQJB8VFsHeVf8KCv+s0/4g7VrWhP3nzlwup2UzJW1nbt9Z1fZoIGDYb6lzmdUQiEgTUDRqE8guLufe1pXy58QB/6NOGId1TvQ5JwpXPBwfW+2u9+ZO3vP3uWHyd01ve6rR2teLk4uxZCZNvhahKrkWtloY8iIijbtAgc/xkEaMmZ7JgyyGevL0d/bsmex2ShLOICEhq47b00a5Q76HNJYnbtvmw7h13bmxiSctbSk+o2x4i9dVxQXYthddug5gE16JWU0MeROS70zduOcgtKOLuV5eQuS2bp/tezh2dG3odksjpjHEtPrWaQ+fhLnk7sr1khYWsBbDhA3duTBVXIuRUuZD6HV2rkZxuewZM6Qtx1V2LWvUUryMSkQpKyVqA5eQXMnzCYlbuPMpzd3WgT4cGXockcn7GuOSiegp0GOD25ew5vVDvp0+4/VGx0LCLS9xSe0KDNDfhIZxtmw9T7oSEuq5FrZp+oInIxdOYtQA6eryQoRMyWLs7hxcHdOT6diqZICEk76BbIutUuZC9qwELEdHQoJN/zJt/lYXYql5HW36++QKm9ncrUgydrVIpInJWmmDgsey8kwwZn8Gmfbm8NKgT17RO8jokkcA6ccRfqNffbbp7uStTYSLcgvSnJiwkd4fKNbyONjA2/QdmDHLlUYa+C1XqeB2RiAQxJWseOphbwOBxGXxzMI9XhnTmypb6wpYwdDLPn7z5y4XszITiAnesTht/qZCekNwDEkLgx8yGD2HmUKjdEoa8q5UjROS8NBvUI/tz8hk4LoOdh48zYVgXejWv5XVIIt6IiYemV7oNoDAfdi8rKReyYiosGeuO1WxW0m2a0sN1IVYk62bDmyNcodvBs0K35VBEPKGWtTK05+gJBo7NYF9OPhOGd6FbE/2yFjmr4kJXg+z/Jy0shIKj7lhi8ulLZNVoEryFele/CbNGQ4POblH22GpeRyQiFYS6QcvZzsPHGTg2g+y8k7w6ogtpqfplLfKd+Ipdpf+sUoV6jx9yx6rULek2Tenp1jsNhuRtxTR49z5o1A0GzYRKCV5HJCIViLpBy1HWoTwGjs3gWH4hr49Mp0OjRK9DEql4IiKhXnu3dfuxq/V2YEOplrf5sHaWO7dyTTdR4VS5kKS27vXladlkmP0QNO4NA6a7bl8RkQBQsnaJthzIZdDYDPKLipk6qhttG6gLRKRMGAN1Wrmtyz0ueTu8tWSFhaz58PUcd26lqpDcrWTcW/0OEBkduNiWjIP3H4WmV0P/KRAdF7j3EpGwp2TtEmzad4yB4zLw+SzTRnXjsnphVEtKpLwZ48au1WgCHQe7fUd3nr44/aaP3f7oyq6+26lxbw3SIDq2bOJY9C/46FfQ4jq4c1LZXVdE5Cw0Zu0ird+Tw+BxGUREGKaOTKd5ksaqiHgud//pydu+tYCFyBiXsKX6k7eGXaFSle9+/Xl/h/88Dq1ugr4TISqmzD+CiIQPzycYGGMaAZOBJMACY6y1zxtjfgeMAg74T33MWvvBGV5/HfA8EAmMs9Y+eb73LK9kbc2uowwen0FsVCRTR6XTpPZFfOmLSOAdz4bti0qStz0rwRZDRBTU61Ay2zS5G8SdZ6zpl0/B53+CNrfD7WMC280qImEhGJK1ekA9a+0yY0wCsBS4FegH5Fprnz7HayOBjcA1wE5gCTDAWrvuXO9ZHsnaih1HGDo+g4TYaKaOSielpgYVi1QYBcdgR0bJuLddS8FXCBio2/b0ciHx/hqJ1rok7au/Qfv+0OcliNQIEhG5dJ7PBrXW7gH2+B8fM8asBy50FfOuwGZr7TcAxpjpQB/gnMlaoGVuy2b4xCVUj49m2qhuNKwe5otVi1Q0lRKg2Q/cBlB4wq2scKpcyNJJkPGyO1arpes29RW5mZ8dB8PNL5T/rFMRCXvl8vPQGJMKdAQygJ7AA8aYoUAm8Ki19vC3XtIA2FHq+U4g/SzXHg2MBkhOTi7TuEsrKvbx6BsrqZ1Qiamj0qlXTbO/RCq86DhXeqNxb/e86KRb0/RUt+mqN+DkMUi7G254BiIivI1XRMJSwCcYGGOqAF8Cf7LWzjLGJAEHcePY/oDrKr37W6/pC1xnrR3pfz4ESLfWPnCu9wp0N+imfceoFhdNnaqa/SUSFoqLIHcfVK0fHEV4RSSkeN4N6g8iGngLmGKtnQVgrd1X6vhYYM4ZXroLKL04YEP/Pk9pxqdImImMgmoXOnpDRCQwAtamb4wxwHhgvbX22VL765U67TZgzRlevgRoboxpbIyJAfoDswMVq4iIiEiwCmTLWk9gCLDaGLPCv+8xYIAxpgOuG3QbcC+AMaY+rkTHDdbaImPMA8C/caU7Jlhr1wYwVhEREZGgFMjZoPOAMw3y+K+aav7zdwM3lHr+wdnOFREREQkXmtokIiIiEsSUrImIiIgEMSVrIiIiIkFMyZqIiIhIEFOyJiIiIhLElKyJiIiIBDElayIiIiJBLOBrg5YnY8wBICvAb1MLt7apiIQP3fci4aW87vkUa23t850UUslaeTDGZF7IoqsiEjp034uEl2C759UNKiIiIhLElKyJiIiIBDEla9/dGK8DEJFyp/teJLwE1T2vMWsiIiIiQUwtayIiIiJBLOyTNWNMI2PM58aYdcaYtcaYh/37axhjPjHGbPL/W92/v5UxZqExpsAY87NvXWuCMWa/MWaNF59FRC5MWd33Z7uOiASXMrznY40xi40xK/3X+X25xB/u3aDGmHpAPWvtMmNMArAUuBUYDmRba580xvwKqG6t/aUxpg6Q4j/nsLX26VLXugLIBSZba9uW92cRkQtTVvf92a5jrV3nwccSkbMow3veAPHW2lxjTDQwD3jYWrsokPGHfcuatXaPtXaZ//ExYD3QAOgDTPKfNgn3Hwxr7X5r7RKg8AzX+grILo+4ReTildV9f47riEgQKcN73lprc/1Po/1bwFu9wj5ZK80Ykwp0BDKAJGvtHv+hvUCSR2GJSACV1X3/reuISJC61HveGBNpjFkB7Ac+sdYG/J5XsuZnjKkCvAX8xFqbU/qYdX3F4d1fLBKCyuq+XhailQAAAmFJREFUP9d1RCR4lMU9b60tttZ2ABoCXY0xAR/2pGQN8Pc7vwVMsdbO8u/e5+/jPtXXvd+r+ESk7JXVfX+W64hIkCnrv/XW2iPA58B1ZR3rt4V9suYfLDgeWG+tfbbUodnAMP/jYcC75R2biARGWd3357iOiASRMrznaxtjEv2P44BrgK/LPuJvva9mg5pewFxgNeDz734M15c9E0gGsoB+1tpsY0xdIBOo6j8/F2htrc0xxkwDvg/UAvYBj1trx5fjxxGRC1BW9z3Q/kzXsdZ+UE4fRUQuQBne86m4iQiRuAavmdbaJwIef7gnayIiIiLBLOy7QUVERESCmZI1ERERkSCmZE1EREQkiClZExEREQliStZEREREgliU1wGIiJQnY0xN4FP/07pAMXDA//y4tbaHJ4GJiJyFSneISNgyxvwOyLXWPu11LCIiZ6NuUBERP2NMrv/f7xtjvjTGvGuM+cYY86QxZpAxZrExZrUxpqn/vNrGmLeMMUv8W09vP4GIhCIlayIiZ3Y58CPgMmAI0MJa2xUYBzzoP+d54DlrbRfgDv8xEZEypTFrIiJntsRauwfAGLMF+Ni/fzVwpf/xD4DWbtlBAKoaY6pYa3PLNVIRCWlK1kREzqyg1GNfqec+Sr47I4Bu1tr88gxMRMKLukFFRC7ex5R0iWKM6eBhLCISopSsiYhcvIeANGPMKmPMOtwYNxGRMqXSHSIiIiJBTC1rIiIiIkFMyZqIiIhIEFOyJiIiIhLElKyJiIiIBDElayIiIiJBTMmaiIiISBBTsiYiIiISxJSsiYiIiASx/wNjLUmVdFqWcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(df_emissions)\n",
    "plt.xticks(df_emissions.index)\n",
    "plt.legend(df_emissions.columns)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Emissions')\n",
    "plt.title('Emissions From Cars')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "dbbc8ba4e688f2bb72e0451a6a48312124f2d95e"
   },
   "source": [
    "# Folium Overview\n",
    "\n",
    "Folium is a Python library for visualizing geospatial data, in fact it, uses Leaflet under the hood.\n",
    "\n",
    "* http://python-visualization.github.io/folium/\n",
    "\n",
    "* https://leafletjs.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "c60ed14428c072bc3cdb7ffdfa1f0cfa9c12f4e3"
   },
   "outputs": [],
   "source": [
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "e349815c88465fab23dbdc4d8e8d2314b4c8340b"
   },
   "outputs": [],
   "source": [
    "data = (\n",
    "    # Matrix of size 70 by 3, filled with numbers \n",
    "    # between -1 and 1 following a normal distribution\n",
    "    np.random.normal(size=(70, 3)) *\n",
    "    # Matrix of size 3 by 1, to scale values in the previous matrix\n",
    "    np.array([[0.01, 0.01, 1]]) +\n",
    "    # Matrix of size 3 by 1, with coordiates of University of Waterloo\n",
    "    np.array([[43.471257, -80.543021, 1]])\n",
    ").tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "d607df8bb0ae82b0d1e515707e3e23701852f355",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[43.477700681787226, -80.55914223371971, 0.9482984308451783],\n",
       " [43.47503230015702, -80.54282361968076, 0.6101789612810646],\n",
       " [43.47433523790503, -80.53753146097546, -0.09431079216106242]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# format of data: [latitude, longitude, weight]\n",
    "data[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "186d5bfea917786fabce8ec98e8b207c387b09c5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
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       "<folium.folium.Map at 0x7fedec8c0198>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from folium.plugins import HeatMap\n",
    "\n",
    "map_demo = folium.Map([43.471257, -80.543021],\n",
    "                      #tiles='Stamen Toner',\n",
    "                      zoom_start=14)\n",
    "HeatMap(data).add_to(map_demo)\n",
    "\n",
    "map_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8588a095309a9acc2d6ef64d2020b8dd21bb634e"
   },
   "source": [
    "# Loading Data\n",
    " [Killed or Seriously Injured (KSI) from 2007 – 2017](http://bit.ly/open-data-ksi-toronto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "bf3d5051ea0bcb4d9b04a943a57da5ad6040db42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['KSI_CLEAN.csv']\n"
     ]
    }
   ],
   "source": [
    "# Importing OS is typically used for reading and writing from files\n",
    "import os\n",
    "# Print contents in the input folder\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "# Pandas for data importing\n",
    "df_ksi = pd.read_csv('../input/KSI_CLEAN.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8c3d48bdc5f2a92e1101173d8032a5ac195d6023"
   },
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "ccd7719f90a6b6f42549758494d2e82becc54515"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12557 entries, 0 to 12556\n",
      "Data columns (total 56 columns):\n",
      "ACCNUM           12557 non-null int64\n",
      "YEAR             12557 non-null int64\n",
      "MONTH            12557 non-null int64\n",
      "DAY              12557 non-null int64\n",
      "HOUR             12557 non-null int64\n",
      "MINUTES          12557 non-null int64\n",
      "WEEKDAY          12557 non-null int64\n",
      "LATITUDE         12557 non-null float64\n",
      "LONGITUDE        12557 non-null float64\n",
      "Ward_Name        12557 non-null object\n",
      "Ward_ID          12557 non-null int64\n",
      "Hood_Name        12557 non-null object\n",
      "Hood_ID          12557 non-null int64\n",
      "Division         12557 non-null object\n",
      "District         12557 non-null object\n",
      "STREET1          12557 non-null object\n",
      "STREET2          12557 non-null object\n",
      "OFFSET           12557 non-null object\n",
      "ROAD_CLASS       12557 non-null object\n",
      "LOCCOORD         12557 non-null object\n",
      "ACCLOC           12557 non-null object\n",
      "TRAFFCTL         12557 non-null object\n",
      "VISIBILITY       12557 non-null object\n",
      "LIGHT            12557 non-null object\n",
      "RDSFCOND         12557 non-null object\n",
      "ACCLASS          12557 non-null object\n",
      "IMPACTYPE        12557 non-null object\n",
      "INVTYPE          12557 non-null object\n",
      "INVAGE           12557 non-null object\n",
      "INJURY           12557 non-null object\n",
      "FATAL_NO         12557 non-null int64\n",
      "INITDIR          12557 non-null object\n",
      "VEHTYPE          12557 non-null object\n",
      "MANOEUVER        12557 non-null object\n",
      "DRIVACT          12557 non-null object\n",
      "DRIVCOND         12557 non-null object\n",
      "PEDTYPE          12557 non-null object\n",
      "PEDACT           12557 non-null object\n",
      "PEDCOND          12557 non-null object\n",
      "CYCLISTYPE       12557 non-null object\n",
      "CYCACT           12557 non-null object\n",
      "CYCCOND          12557 non-null object\n",
      "PEDESTRIAN       12557 non-null int64\n",
      "CYCLIST          12557 non-null int64\n",
      "AUTOMOBILE       12557 non-null int64\n",
      "MOTORCYCLE       12557 non-null int64\n",
      "TRUCK            12557 non-null int64\n",
      "TRSN_CITY_VEH    12557 non-null int64\n",
      "EMERG_VEH        12557 non-null int64\n",
      "PASSENGER        12557 non-null int64\n",
      "SPEEDING         12557 non-null int64\n",
      "AG_DRIV          12557 non-null int64\n",
      "REDLIGHT         12557 non-null int64\n",
      "ALCOHOL          12557 non-null int64\n",
      "DISABILITY       12557 non-null int64\n",
      "FATAL            12557 non-null int64\n",
      "dtypes: float64(2), int64(24), object(30)\n",
      "memory usage: 5.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# the info() function writes down descriptive information about the dataset such as:\n",
    "#  - Number of rows\n",
    "#  - Attribute names, here each attribute is represented as a column of data\n",
    "#  - Data types of attributes\n",
    "#  - Order of attributes\n",
    "\n",
    "df_ksi.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fb4c77d593045b262ea8d643c8b441649a5cd578"
   },
   "source": [
    "## Attribute names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "4fa95389d8af95ca8e8d76f9b25a89891e80e796"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ACCNUM', 'YEAR', 'MONTH', 'DAY', 'HOUR', 'MINUTES', 'WEEKDAY',\n",
      "       'LATITUDE', 'LONGITUDE', 'Ward_Name', 'Ward_ID', 'Hood_Name', 'Hood_ID',\n",
      "       'Division', 'District', 'STREET1', 'STREET2', 'OFFSET', 'ROAD_CLASS',\n",
      "       'LOCCOORD', 'ACCLOC', 'TRAFFCTL', 'VISIBILITY', 'LIGHT', 'RDSFCOND',\n",
      "       'ACCLASS', 'IMPACTYPE', 'INVTYPE', 'INVAGE', 'INJURY', 'FATAL_NO',\n",
      "       'INITDIR', 'VEHTYPE', 'MANOEUVER', 'DRIVACT', 'DRIVCOND', 'PEDTYPE',\n",
      "       'PEDACT', 'PEDCOND', 'CYCLISTYPE', 'CYCACT', 'CYCCOND', 'PEDESTRIAN',\n",
      "       'CYCLIST', 'AUTOMOBILE', 'MOTORCYCLE', 'TRUCK', 'TRSN_CITY_VEH',\n",
      "       'EMERG_VEH', 'PASSENGER', 'SPEEDING', 'AG_DRIV', 'REDLIGHT', 'ALCOHOL',\n",
      "       'DISABILITY', 'FATAL'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# write down just the column names of dataset\n",
    "print(df_ksi.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "3b06310417b67eb62fcec18d41533479b5533e1d"
   },
   "outputs": [],
   "source": [
    "# Shortlist of the numerical attributes\n",
    "ksi_numerical = ['YEAR', 'MONTH', 'DAY', 'HOUR', 'MINUTES', 'WEEKDAY', \n",
    "                 'LATITUDE', 'LONGITUDE', 'PEDESTRIAN', 'CYCLIST', 'AUTOMOBILE', \n",
    "                 'MOTORCYCLE', 'TRUCK', 'TRSN_CITY_VEH', 'EMERG_VEH', 'PASSENGER', \n",
    "                 'SPEEDING', 'AG_DRIV', 'REDLIGHT', 'ALCOHOL', 'DISABILITY', 'FATAL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "c7a91fad159bb312a98363f27cef4b48c2626ed9"
   },
   "outputs": [],
   "source": [
    "# Shortlist of the categorical attributes\n",
    "ksi_categorical = ['Ward_Name', 'Ward_ID', 'Hood_Name', 'Hood_ID', 'Division', \n",
    "                   'District', 'STREET1', 'STREET2', 'OFFSET', 'ROAD_CLASS', \n",
    "                   'LOCCOORD', 'ACCLOC', 'TRAFFCTL', 'VISIBILITY', 'LIGHT', \n",
    "                   'RDSFCOND', 'ACCLASS', 'IMPACTYPE', 'INVTYPE', 'INVAGE', \n",
    "                   'INJURY', 'INITDIR', 'VEHTYPE', 'MANOEUVER', 'DRIVACT', \n",
    "                   'DRIVCOND', 'PEDTYPE', 'PEDACT', 'PEDCOND', 'CYCLISTYPE', \n",
    "                   'CYCACT', 'CYCCOND']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "8a000c739c6e3bdc8d44cf583b21b8c51ebce146"
   },
   "outputs": [],
   "source": [
    "# Shortlist of the geographic attributes\n",
    "ksi_geographical = ['LATITUDE', 'LONGITUDE', 'Ward_Name', 'Hood_Name', 'District',\n",
    "                   'Division', 'STREET1', 'STREET2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "9e96d9224eb767e7de8a12ce82e8e97dc74a71b6"
   },
   "outputs": [],
   "source": [
    "# Shortlist of the Boolean attributes\n",
    "ksi_boolean = ['PEDESTRIAN', 'CYCLIST', 'AUTOMOBILE', 'MOTORCYCLE', 'TRUCK', \n",
    "               'TRSN_CITY_VEH','EMERG_VEH', 'PASSENGER', 'SPEEDING', 'AG_DRIV', \n",
    "               'REDLIGHT', 'ALCOHOL', 'DISABILITY', 'FATAL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "a6b471db43eeb0e2b37b3a6a9543677d0204e237"
   },
   "outputs": [
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       "      <td>Toronto Centre-Rosedale (27)</td>\n",
       "      <td>27</td>\n",
       "      <td>Bay Street Corridor (76)</td>\n",
       "      <td>76</td>\n",
       "      <td>D52</td>\n",
       "      <td>Toronto East York</td>\n",
       "      <td>QUEEN ST W</td>\n",
       "      <td>YORK ST</td>\n",
       "      <td></td>\n",
       "      <td>Major Arterial</td>\n",
       "      <td>Mid-Block</td>\n",
       "      <td></td>\n",
       "      <td>No Control</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Dark</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Non-Fatal Injury</td>\n",
       "      <td>Pedestrian Collisions</td>\n",
       "      <td>Driver</td>\n",
       "      <td>50 to 54</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>West</td>\n",
       "      <td>Municipal Transit Bus (TTC)</td>\n",
       "      <td>Going Ahead</td>\n",
       "      <td>Driving Properly</td>\n",
       "      <td>Normal</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1311542</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>43.780445</td>\n",
       "      <td>-79.300490</td>\n",
       "      <td>Scarborough-Agincourt (40)</td>\n",
       "      <td>40</td>\n",
       "      <td>Tam O'Shanter-Sullivan (118)</td>\n",
       "      <td>118</td>\n",
       "      <td>D42</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>SHEPPARD AVE E</td>\n",
       "      <td>AMETHYST RD</td>\n",
       "      <td></td>\n",
       "      <td>Major Arterial</td>\n",
       "      <td>Intersection</td>\n",
       "      <td></td>\n",
       "      <td>No Control</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Dark, artificial</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Non-Fatal Injury</td>\n",
       "      <td>Pedestrian Collisions</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>25 to 29</td>\n",
       "      <td>Minimal</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Other</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5002235651</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>43.682342</td>\n",
       "      <td>-79.328266</td>\n",
       "      <td>Toronto-Danforth (30)</td>\n",
       "      <td>30</td>\n",
       "      <td>Greenwood-Coxwell (65)</td>\n",
       "      <td>65</td>\n",
       "      <td>D55</td>\n",
       "      <td>Toronto East York</td>\n",
       "      <td>DANFORTH AVE</td>\n",
       "      <td>GILLARD AVE</td>\n",
       "      <td></td>\n",
       "      <td>Major Arterial</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>At Intersection</td>\n",
       "      <td>No Control</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Dark, artificial</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Fatal</td>\n",
       "      <td>Pedestrian Collisions</td>\n",
       "      <td>Pedestrian</td>\n",
       "      <td>60 to 64</td>\n",
       "      <td>Fatal</td>\n",
       "      <td>64</td>\n",
       "      <td>South</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Vehicle is going straight thru inter.while ped...</td>\n",
       "      <td>Crossing, no Traffic Control</td>\n",
       "      <td>Normal</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ACCNUM  YEAR  MONTH  DAY  ...    REDLIGHT  ALCOHOL  DISABILITY  FATAL\n",
       "0     1249781  2011      8    4  ...           0        0           0      0\n",
       "1     1311542  2012      8   19  ...           0        0           0      0\n",
       "2  5002235651  2015     12   30  ...           0        0           0      1\n",
       "\n",
       "[3 rows x 56 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ksi.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "b047a779418810002919ad34faa3e9dc586f791a"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACCNUM</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>MINUTES</th>\n",
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       "      <th>LATITUDE</th>\n",
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       "      <th>Hood_Name</th>\n",
       "      <th>Hood_ID</th>\n",
       "      <th>Division</th>\n",
       "      <th>District</th>\n",
       "      <th>STREET1</th>\n",
       "      <th>STREET2</th>\n",
       "      <th>OFFSET</th>\n",
       "      <th>ROAD_CLASS</th>\n",
       "      <th>LOCCOORD</th>\n",
       "      <th>ACCLOC</th>\n",
       "      <th>TRAFFCTL</th>\n",
       "      <th>VISIBILITY</th>\n",
       "      <th>LIGHT</th>\n",
       "      <th>RDSFCOND</th>\n",
       "      <th>ACCLASS</th>\n",
       "      <th>IMPACTYPE</th>\n",
       "      <th>INVTYPE</th>\n",
       "      <th>INVAGE</th>\n",
       "      <th>INJURY</th>\n",
       "      <th>FATAL_NO</th>\n",
       "      <th>INITDIR</th>\n",
       "      <th>VEHTYPE</th>\n",
       "      <th>MANOEUVER</th>\n",
       "      <th>DRIVACT</th>\n",
       "      <th>DRIVCOND</th>\n",
       "      <th>PEDTYPE</th>\n",
       "      <th>PEDACT</th>\n",
       "      <th>PEDCOND</th>\n",
       "      <th>CYCLISTYPE</th>\n",
       "      <th>CYCACT</th>\n",
       "      <th>CYCCOND</th>\n",
       "      <th>PEDESTRIAN</th>\n",
       "      <th>CYCLIST</th>\n",
       "      <th>AUTOMOBILE</th>\n",
       "      <th>MOTORCYCLE</th>\n",
       "      <th>TRUCK</th>\n",
       "      <th>TRSN_CITY_VEH</th>\n",
       "      <th>EMERG_VEH</th>\n",
       "      <th>PASSENGER</th>\n",
       "      <th>SPEEDING</th>\n",
       "      <th>AG_DRIV</th>\n",
       "      <th>REDLIGHT</th>\n",
       "      <th>ALCOHOL</th>\n",
       "      <th>DISABILITY</th>\n",
       "      <th>FATAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12554</th>\n",
       "      <td>1291534</td>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>43.663245</td>\n",
       "      <td>-79.36759</td>\n",
       "      <td>Toronto Centre-Rosedale (28)</td>\n",
       "      <td>28</td>\n",
       "      <td>Moss Park (73)</td>\n",
       "      <td>73</td>\n",
       "      <td>D51</td>\n",
       "      <td>Toronto East York</td>\n",
       "      <td>PARLIAMENT ST</td>\n",
       "      <td>SPRUCE ST</td>\n",
       "      <td></td>\n",
       "      <td>Minor Arterial</td>\n",
       "      <td>Intersection</td>\n",
       "      <td></td>\n",
       "      <td>No Control</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Non-Fatal Injury</td>\n",
       "      <td>Pedestrian Collisions</td>\n",
       "      <td>Pedestrian</td>\n",
       "      <td>30 to 34</td>\n",
       "      <td>Minimal</td>\n",
       "      <td>0</td>\n",
       "      <td>East</td>\n",
       "      <td>Other</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Pedestrian hit at mid-block</td>\n",
       "      <td>Walking on Roadway with Traffic</td>\n",
       "      <td>Normal</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12555</th>\n",
       "      <td>6001157650</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>43.645874</td>\n",
       "      <td>-79.36343</td>\n",
       "      <td>Toronto Centre-Rosedale (28)</td>\n",
       "      <td>28</td>\n",
       "      <td>Waterfront Communities-The Island (77)</td>\n",
       "      <td>77</td>\n",
       "      <td>D51</td>\n",
       "      <td>Toronto East York</td>\n",
       "      <td>QUEENS QUAY E</td>\n",
       "      <td>BONNYCASTLE ST</td>\n",
       "      <td>65.6 M E of</td>\n",
       "      <td>Minor Arterial</td>\n",
       "      <td>Mid-Block</td>\n",
       "      <td>Non Intersection</td>\n",
       "      <td>No Control</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Fatal</td>\n",
       "      <td>SMV Other</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>35 to 39</td>\n",
       "      <td>Fatal</td>\n",
       "      <td>45</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",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>6001157650</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>43.645874</td>\n",
       "      <td>-79.36343</td>\n",
       "      <td>Toronto Centre-Rosedale (28)</td>\n",
       "      <td>28</td>\n",
       "      <td>Waterfront Communities-The Island (77)</td>\n",
       "      <td>77</td>\n",
       "      <td>D51</td>\n",
       "      <td>Toronto East York</td>\n",
       "      <td>QUEENS QUAY E</td>\n",
       "      <td>BONNYCASTLE ST</td>\n",
       "      <td>65.6 M E of</td>\n",
       "      <td>Minor Arterial</td>\n",
       "      <td>Mid-Block</td>\n",
       "      <td>Non Intersection</td>\n",
       "      <td>No Control</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Fatal</td>\n",
       "      <td>SMV Other</td>\n",
       "      <td>Driver</td>\n",
       "      <td>30 to 34</td>\n",
       "      <td>Major</td>\n",
       "      <td>0</td>\n",
       "      <td>East</td>\n",
       "      <td>Automobile, Station Wagon</td>\n",
       "      <td>Going Ahead</td>\n",
       "      <td>Exceeding Speed Limit</td>\n",
       "      <td>Inattentive</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ACCNUM  YEAR  MONTH  DAY  ...    REDLIGHT  ALCOHOL  DISABILITY  FATAL\n",
       "12554     1291534  2012      3    2  ...           0        0           0      0\n",
       "12555  6001157650  2016      7    2  ...           0        0           0      1\n",
       "12556  6001157650  2016      7    2  ...           0        0           0      1\n",
       "\n",
       "[3 rows x 56 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ksi.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8a47df53f630c63cad49a5e7fb9f40867c232216"
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "6121930c014e2fea2b7af2ea1ed40dc212d4484f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSI number of rows:  12557\n",
      "KSI number of rows:  12545\n"
     ]
    }
   ],
   "source": [
    "# Remove incidents which are listed as property damage\n",
    "# in order to keep only Fatal and Non-Fatal injuries\n",
    "\n",
    "print(\"KSI number of rows: \", len(df_ksi))\n",
    "\n",
    "df_ksi = df_ksi[df_ksi['ACCLASS'] != 'Property Damage Only']\n",
    "\n",
    "print(\"KSI number of rows: \", len(df_ksi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "634c22a9af7f35624571540b618e33da1935b188",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>PEDESTRIAN</th>\n",
       "      <th>CYCLIST</th>\n",
       "      <th>AUTOMOBILE</th>\n",
       "      <th>MOTORCYCLE</th>\n",
       "      <th>TRUCK</th>\n",
       "      <th>TRSN_CITY_VEH</th>\n",
       "      <th>EMERG_VEH</th>\n",
       "      <th>PASSENGER</th>\n",
       "      <th>SPEEDING</th>\n",
       "      <th>AG_DRIV</th>\n",
       "      <th>REDLIGHT</th>\n",
       "      <th>ALCOHOL</th>\n",
       "      <th>DISABILITY</th>\n",
       "      <th>FATAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4983</th>\n",
       "      <td>1300266</td>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>43.657045</td>\n",
       "      <td>-79.37459</td>\n",
       "      <td>Toronto Centre-Rosedale (27)</td>\n",
       "      <td>27</td>\n",
       "      <td>Church-Yonge Corridor (75)</td>\n",
       "      <td>75</td>\n",
       "      <td>D51</td>\n",
       "      <td>Toronto East York</td>\n",
       "      <td>JARVIS ST</td>\n",
       "      <td>DUNDAS ST E</td>\n",
       "      <td></td>\n",
       "      <td>Major Arterial</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>At Intersection</td>\n",
       "      <td>Traffic Signal</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Non-Fatal Injury</td>\n",
       "      <td>Pedestrian Collisions</td>\n",
       "      <td>Driver</td>\n",
       "      <td>35 to 39</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>South</td>\n",
       "      <td>Automobile, Station Wagon</td>\n",
       "      <td>Going Ahead</td>\n",
       "      <td>Driving Properly</td>\n",
       "      <td>Normal</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2244</th>\n",
       "      <td>1260139</td>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>43.772145</td>\n",
       "      <td>-79.25149</td>\n",
       "      <td>Scarborough Centre (38)</td>\n",
       "      <td>38</td>\n",
       "      <td>Woburn (137)</td>\n",
       "      <td>137</td>\n",
       "      <td>D43</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>ELLESMERE RD</td>\n",
       "      <td>MCCOWAN RD</td>\n",
       "      <td></td>\n",
       "      <td>Major Arterial</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>At Intersection</td>\n",
       "      <td>Traffic Signal</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Non-Fatal Injury</td>\n",
       "      <td>Angle</td>\n",
       "      <td>Driver</td>\n",
       "      <td>60 to 64</td>\n",
       "      <td>Minimal</td>\n",
       "      <td>0</td>\n",
       "      <td>East</td>\n",
       "      <td>Automobile, Station Wagon</td>\n",
       "      <td>Going Ahead</td>\n",
       "      <td>Driving Properly</td>\n",
       "      <td>Normal</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>1190299</td>\n",
       "      <td>2010</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>43.789145</td>\n",
       "      <td>-79.37329</td>\n",
       "      <td>Don Valley East (33)</td>\n",
       "      <td>33</td>\n",
       "      <td>Bayview Village (52)</td>\n",
       "      <td>52</td>\n",
       "      <td>D33</td>\n",
       "      <td>North York</td>\n",
       "      <td>PINEWAY BLVD</td>\n",
       "      <td>FINCH AVE E</td>\n",
       "      <td></td>\n",
       "      <td>Major Arterial</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>At Intersection</td>\n",
       "      <td>Traffic Signal</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Non-Fatal Injury</td>\n",
       "      <td>Pedestrian Collisions</td>\n",
       "      <td>Driver</td>\n",
       "      <td>30 to 34</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>South</td>\n",
       "      <td>Automobile, Station Wagon</td>\n",
       "      <td>Turning Left</td>\n",
       "      <td>Failed to Yield Right of Way</td>\n",
       "      <td>Normal</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ACCNUM  YEAR  MONTH  DAY  ...    REDLIGHT  ALCOHOL  DISABILITY  FATAL\n",
       "4983  1300266  2012      4   28  ...           0        0           0      0\n",
       "2244  1260139  2011      8   23  ...           1        0           1      0\n",
       "1637  1190299  2010      9   20  ...           0        0           0      0\n",
       "\n",
       "[3 rows x 56 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ksi.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5bf94c405f0a499edc166c3e1cb44a45bee71b7e"
   },
   "source": [
    "## Plotting Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "d54848f75e779733665e607914abdbcf05e822da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Attributes: \n",
      " ['YEAR', 'MONTH', 'DAY', 'HOUR', 'MINUTES', 'WEEKDAY', 'LATITUDE', 'LONGITUDE', 'PEDESTRIAN', 'CYCLIST', 'AUTOMOBILE', 'MOTORCYCLE', 'TRUCK', 'TRSN_CITY_VEH', 'EMERG_VEH', 'PASSENGER', 'SPEEDING', 'AG_DRIV', 'REDLIGHT', 'ALCOHOL', 'DISABILITY', 'FATAL']\n"
     ]
    }
   ],
   "source": [
    "print(\"Numerical Attributes: \\n\", str(ksi_numerical))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "af231dcf4b1653f856186261088e776fc12d3851"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Learning Exercise\n",
    "# Try changing the attribute with another numerical attribute listed above:\n",
    "attribute = 'YEAR'\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "data = df_ksi[attribute].value_counts().sort_index()\n",
    "data.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "354e83f28ce6b1310e15ec2667bd78a00e1e718b"
   },
   "source": [
    "## Plotting Categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "4332b78edeeafcade4320df566e1da158f191084"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Attributes: \n",
      " ['Ward_Name', 'Ward_ID', 'Hood_Name', 'Hood_ID', 'Division', 'District', 'STREET1', 'STREET2', 'OFFSET', 'ROAD_CLASS', 'LOCCOORD', 'ACCLOC', 'TRAFFCTL', 'VISIBILITY', 'LIGHT', 'RDSFCOND', 'ACCLASS', 'IMPACTYPE', 'INVTYPE', 'INVAGE', 'INJURY', 'INITDIR', 'VEHTYPE', 'MANOEUVER', 'DRIVACT', 'DRIVCOND', 'PEDTYPE', 'PEDACT', 'PEDCOND', 'CYCLISTYPE', 'CYCACT', 'CYCCOND']\n"
     ]
    }
   ],
   "source": [
    "print(\"Categorical Attributes: \\n\", str(ksi_categorical))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "5df1dee9966e4e294ad7467367951097ab975b26"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Learning Exercise\n",
    "# Try changing the attribute with another categorical attribute listed above:\n",
    "# INVAGE is \"Age of Involved Party\"\n",
    "attribute = 'INVAGE'\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "data = df_ksi[attribute].value_counts()\n",
    "data.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f01ca337018a3c1c39319cd54cf2bf82ce775e9b"
   },
   "source": [
    "## Working with Aggregrate Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "ab546098d1ddbf87324537cd8af99c676eaf207a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PEDESTRIAN', 'CYCLIST', 'AUTOMOBILE', 'MOTORCYCLE', 'TRUCK', 'TRSN_CITY_VEH', 'EMERG_VEH', 'PASSENGER', 'SPEEDING', 'AG_DRIV', 'REDLIGHT', 'ALCOHOL', 'DISABILITY', 'FATAL']\n"
     ]
    }
   ],
   "source": [
    "print(ksi_boolean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "6b20f662b480c6e696e540a3885311618b2b0bbd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of incidents that involve AUTOMOBILE = 0.9055400557991231\n"
     ]
    }
   ],
   "source": [
    "# Learning Exercise\n",
    "# Change the attribute below to calculate what percentage of incidents\n",
    "# reported th involvemnt of possible items listed above\n",
    "attribute = 'AUTOMOBILE'\n",
    "\n",
    "print(\"Percentage of incidents that involve\", attribute, \"=\", df_ksi[attribute].sum() / df_ksi[attribute].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "29dbe0b43139bcc6eb9243d482ac136b8f38a1a5"
   },
   "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>ACCNUM</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>MINUTES</th>\n",
       "      <th>WEEKDAY</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>Ward_ID</th>\n",
       "      <th>Hood_ID</th>\n",
       "      <th>FATAL_NO</th>\n",
       "      <th>PEDESTRIAN</th>\n",
       "      <th>CYCLIST</th>\n",
       "      <th>AUTOMOBILE</th>\n",
       "      <th>MOTORCYCLE</th>\n",
       "      <th>TRUCK</th>\n",
       "      <th>TRSN_CITY_VEH</th>\n",
       "      <th>EMERG_VEH</th>\n",
       "      <th>PASSENGER</th>\n",
       "      <th>SPEEDING</th>\n",
       "      <th>AG_DRIV</th>\n",
       "      <th>REDLIGHT</th>\n",
       "      <th>ALCOHOL</th>\n",
       "      <th>DISABILITY</th>\n",
       "      <th>FATAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.254500e+04</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "      <td>12545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.571897e+09</td>\n",
       "      <td>2011.542925</td>\n",
       "      <td>6.764368</td>\n",
       "      <td>15.650379</td>\n",
       "      <td>13.176644</td>\n",
       "      <td>28.239697</td>\n",
       "      <td>2.985492</td>\n",
       "      <td>43.710713</td>\n",
       "      <td>-79.396035</td>\n",
       "      <td>22.562535</td>\n",
       "      <td>73.591152</td>\n",
       "      <td>1.253567</td>\n",
       "      <td>0.398565</td>\n",
       "      <td>0.107214</td>\n",
       "      <td>0.905540</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.063452</td>\n",
       "      <td>0.064966</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>0.368912</td>\n",
       "      <td>0.174173</td>\n",
       "      <td>0.510881</td>\n",
       "      <td>0.084018</td>\n",
       "      <td>0.043204</td>\n",
       "      <td>0.028378</td>\n",
       "      <td>0.136788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.537308e+09</td>\n",
       "      <td>3.101559</td>\n",
       "      <td>3.278275</td>\n",
       "      <td>8.861860</td>\n",
       "      <td>6.236238</td>\n",
       "      <td>17.518918</td>\n",
       "      <td>1.964151</td>\n",
       "      <td>0.056015</td>\n",
       "      <td>0.104202</td>\n",
       "      <td>12.531908</td>\n",
       "      <td>41.331108</td>\n",
       "      <td>6.963029</td>\n",
       "      <td>0.489622</td>\n",
       "      <td>0.309397</td>\n",
       "      <td>0.292479</td>\n",
       "      <td>0.268369</td>\n",
       "      <td>0.243783</td>\n",
       "      <td>0.246476</td>\n",
       "      <td>0.034560</td>\n",
       "      <td>0.482529</td>\n",
       "      <td>0.379273</td>\n",
       "      <td>0.499902</td>\n",
       "      <td>0.277425</td>\n",
       "      <td>0.203325</td>\n",
       "      <td>0.166056</td>\n",
       "      <td>0.343637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.284070e+05</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.592048</td>\n",
       "      <td>-79.638390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.108205e+06</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>43.662645</td>\n",
       "      <td>-79.467434</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.275736e+06</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>43.702345</td>\n",
       "      <td>-79.396450</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.002339e+09</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>43.755992</td>\n",
       "      <td>-79.318090</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.003292e+09</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>43.855445</td>\n",
       "      <td>-79.125897</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ACCNUM          YEAR      ...         DISABILITY         FATAL\n",
       "count  1.254500e+04  12545.000000      ...       12545.000000  12545.000000\n",
       "mean   1.571897e+09   2011.542925      ...           0.028378      0.136788\n",
       "std    2.537308e+09      3.101559      ...           0.166056      0.343637\n",
       "min    1.284070e+05   2007.000000      ...           0.000000      0.000000\n",
       "25%    1.108205e+06   2009.000000      ...           0.000000      0.000000\n",
       "50%    1.275736e+06   2011.000000      ...           0.000000      0.000000\n",
       "75%    4.002339e+09   2014.000000      ...           0.000000      0.000000\n",
       "max    7.003292e+09   2017.000000      ...           1.000000      1.000000\n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics, for simplicity\n",
    "# we run it over all attributes,\n",
    "# but they only make sense for numeric\n",
    "df_ksi.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "b640a0f03bd0278f988be4129de0b654a6d7b7ed",
    "scrolled": true
   },
   "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>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>ACCNUM</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>MINUTES</th>\n",
       "      <th>WEEKDAY</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>Ward_ID</th>\n",
       "      <th>Hood_ID</th>\n",
       "      <th>FATAL_NO</th>\n",
       "      <th>PEDESTRIAN</th>\n",
       "      <th>CYCLIST</th>\n",
       "      <th>AUTOMOBILE</th>\n",
       "      <th>MOTORCYCLE</th>\n",
       "      <th>TRUCK</th>\n",
       "      <th>TRSN_CITY_VEH</th>\n",
       "      <th>EMERG_VEH</th>\n",
       "      <th>PASSENGER</th>\n",
       "      <th>SPEEDING</th>\n",
       "      <th>AG_DRIV</th>\n",
       "      <th>REDLIGHT</th>\n",
       "      <th>ALCOHOL</th>\n",
       "      <th>DISABILITY</th>\n",
       "      <th>FATAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>385026646063</td>\n",
       "      <td>994</td>\n",
       "      <td>582</td>\n",
       "      <td>1681</td>\n",
       "      <td>181</td>\n",
       "      <td>2405.881462</td>\n",
       "      <td>-4364.655111</td>\n",
       "      <td>1514</td>\n",
       "      <td>5136</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>441041185600</td>\n",
       "      <td>850</td>\n",
       "      <td>841</td>\n",
       "      <td>1592</td>\n",
       "      <td>185</td>\n",
       "      <td>2754.119269</td>\n",
       "      <td>-5004.149088</td>\n",
       "      <td>1084</td>\n",
       "      <td>3691</td>\n",
       "      <td>105</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>700087331974</td>\n",
       "      <td>1743</td>\n",
       "      <td>1458</td>\n",
       "      <td>3237</td>\n",
       "      <td>341</td>\n",
       "      <td>4372.383926</td>\n",
       "      <td>-7934.930436</td>\n",
       "      <td>2757</td>\n",
       "      <td>9061</td>\n",
       "      <td>115</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>92</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>14</td>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>581088900536</td>\n",
       "      <td>1292</td>\n",
       "      <td>1201</td>\n",
       "      <td>1509</td>\n",
       "      <td>219</td>\n",
       "      <td>3627.606029</td>\n",
       "      <td>-6589.651996</td>\n",
       "      <td>1923</td>\n",
       "      <td>6053</td>\n",
       "      <td>81</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>74</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>504092414138</td>\n",
       "      <td>1230</td>\n",
       "      <td>1068</td>\n",
       "      <td>2176</td>\n",
       "      <td>216</td>\n",
       "      <td>3147.952434</td>\n",
       "      <td>-5717.389648</td>\n",
       "      <td>1571</td>\n",
       "      <td>4791</td>\n",
       "      <td>155</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>623132610617</td>\n",
       "      <td>1558</td>\n",
       "      <td>1224</td>\n",
       "      <td>2344</td>\n",
       "      <td>214</td>\n",
       "      <td>3891.465471</td>\n",
       "      <td>-7067.014574</td>\n",
       "      <td>1905</td>\n",
       "      <td>5926</td>\n",
       "      <td>219</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>87</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>455110488083</td>\n",
       "      <td>1217</td>\n",
       "      <td>976</td>\n",
       "      <td>1817</td>\n",
       "      <td>194</td>\n",
       "      <td>2841.074606</td>\n",
       "      <td>-5161.158662</td>\n",
       "      <td>1434</td>\n",
       "      <td>5269</td>\n",
       "      <td>301</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>581153482079</td>\n",
       "      <td>991</td>\n",
       "      <td>1238</td>\n",
       "      <td>1872</td>\n",
       "      <td>216</td>\n",
       "      <td>3628.687772</td>\n",
       "      <td>-6588.683656</td>\n",
       "      <td>1988</td>\n",
       "      <td>6562</td>\n",
       "      <td>245</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>72</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>427337858381</td>\n",
       "      <td>1073</td>\n",
       "      <td>820</td>\n",
       "      <td>1650</td>\n",
       "      <td>183</td>\n",
       "      <td>2711.253705</td>\n",
       "      <td>-4922.590912</td>\n",
       "      <td>1410</td>\n",
       "      <td>4575</td>\n",
       "      <td>271</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>329150524385</td>\n",
       "      <td>722</td>\n",
       "      <td>633</td>\n",
       "      <td>1579</td>\n",
       "      <td>107</td>\n",
       "      <td>2054.480385</td>\n",
       "      <td>-3730.934695</td>\n",
       "      <td>1218</td>\n",
       "      <td>3579</td>\n",
       "      <td>412</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     YEAR  MONTH        ACCNUM  ...    ALCOHOL  DISABILITY  FATAL\n",
       "122  2017      3  385026646063  ...          2           4     14\n",
       "123  2017      4  441041185600  ...          0           0     12\n",
       "124  2017      5  700087331974  ...          4           4     12\n",
       "125  2017      6  581088900536  ...          0           4      8\n",
       "126  2017      7  504092414138  ...          2           0     14\n",
       "127  2017      8  623132610617  ...          4           1     11\n",
       "128  2017      9  455110488083  ...          4           7     18\n",
       "129  2017     10  581153482079  ...          0           0     13\n",
       "130  2017     11  427337858381  ...          0           0      8\n",
       "131  2017     12  329150524385  ...          2           0     13\n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics aggregated by month\n",
    "df_ksi_monthly = df_ksi.groupby(by=['YEAR', 'MONTH'],as_index=False).sum()\n",
    "\n",
    "# Show only the last 10 rows\n",
    "df_ksi_monthly.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f21b1f51e36f1cabf9d4471137676f7e730e4ee9"
   },
   "source": [
    "## Seaborn\n",
    "Seaborn is another visualization library similar to matplotlib. https://seaborn.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "e9ce313bd72e694b3fb03a445f17fe09e930d649"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "a472115b5898358e6bb5b719128b926c6578335a",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Learning Exercise\n",
    "\n",
    "# Try changing the independent (x) and dependent (y) \n",
    "# variables to visualize the trends in the data\n",
    "# (optional: uncomment the Hue and see how that changes the plot)\n",
    "\n",
    "plt.figure(figsize=(18,6))\n",
    "\n",
    "sns.barplot(data = df_ksi_monthly,\n",
    "            #hue = 'MONTH',\n",
    "            x = 'YEAR',\n",
    "            # Number of events involving automobiles\n",
    "            y = 'AUTOMOBILE',\n",
    "            capsize = 0.2)\n",
    "\n",
    "# Legend Placement\n",
    "plt.legend(loc='best')\n",
    "#plt.legend(loc='upper right')\n",
    "#plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "c8b152e955d4f437798db2516148062115df6234"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Learning Exercise\n",
    "# Try changing the independent (x) and dependent (y) \n",
    "# variables to visualize the trends in the data\n",
    "\n",
    "sns.jointplot(data = df_ksi_monthly,\n",
    "              kind = 'reg',\n",
    "              x = 'AG_DRIV', # Aggressive and Distracted Driving Collision\n",
    "              y = 'SPEEDING') # Speeding Related Collision\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "0dfa2e3323941a312da8fb5c9364c55437ede166",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# More correlations between attributes accumulated by month\n",
    "\n",
    "# PASSENGER Passenger Involved in Collision\n",
    "# SPEEDING Speeding Related Collision\n",
    "# AG_DRIV Aggressive and Distracted Driving Collision\n",
    "# ALCOHOL Alcohol Related Collision\n",
    "# FATAL Fatal event\n",
    "\n",
    "sns.pairplot(df_ksi_monthly[['PASSENGER','SPEEDING','AG_DRIV','ALCOHOL','FATAL']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9ccc5afd63b5a9eba3e9f107b14ebe37f36fbe83"
   },
   "source": [
    "## Plotting Time-Series Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "50f70baf63e8fe66518a3c7e60ea00a2d75febf8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>YEAR</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "YEAR   2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017\n",
       "MONTH                                                                  \n",
       "1         3    15     4    28    11    14    10     6     5    22    12\n",
       "2        14    16     5    16     3     3     6     8     8    12     6\n",
       "3        19     6    13    11     6     2    15     1    16    12    14\n",
       "4        13     7    10     4     2    12    18     2     9    16    12\n",
       "5         2    30     8    10     0    12     9    12    16    17    12\n",
       "6        25    16    11     9     8     4     4     3    16    16     8\n",
       "7        23    28     8    28    16    15    35    14     9    21    14\n",
       "8        16    10     6     3    37    11    39    24    14    15    11\n",
       "9        23    13    11    17    12    13    21    10    27    14    18\n",
       "10       13    25    25     2    10    12    21    11     9    17    13\n",
       "11       11    10    12     8    10    15    25    25    17    17     8\n",
       "12        7     3    20    21    11    12     9    18     9     6    13"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_ksi_monthly.pivot('MONTH','YEAR','FATAL')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "d4e2e7e03f71f40cdc7b23aec6511596d13e7f41"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TueG2JEnSCJtujuIbG4pDkiRpVujQWpZpK4pExDPoLWL5b8Wp64EPZ+Z1dQYmSZI0iro09DxnqosRcQxwNnAxvUUtbypen1VckyRJ0lbIjMpHU6arKP418KLMvHXCudUR8V3gnOKQJEnSCJouUdxuUpIIQGbeWmydI0mSpK0w3nYAW2HKoWdgU0Q8cfLJiNiP3rY5kiRJ2gpJVD6aMl1F8WTgOxHxN8DVxbllwEnA++oMTJIkaRSNd2jZ83Tb43w9In4EvAt4W3H6BuDVmXlt3cFJkiSNmvEGK4JVTbs9TpEQvmFrGo2ItwNnZ+bt2xqYJEmS2jXds57Pnep6Zr68z6X/A5wUEbcAZwBfzcx7ty1ESZKk0dHkHMOqpqsoPhe4nV6ydwXM+M5+CDwb+B3gWGBFRFxdtHNWZv6i7EMRsRxYDnDK/k/ldUv2mmF3kiRJ3dClVc/TJYpPAF4EHAccD/wzcEZmXj/N5zIzx4ELgAuKrXSOLtr5MLBHnw+NAWMAdx52RIemekqSJM1MlyqKU26Pk5mbM/NbmXkCcCjwA+CiiPjTadp9zP+BzNyYmedm5nHAfpUiliRJUiNm8qznBcDL6FUDlwJ/R++xflM5tt+FzFy/FfFJkiSNlJEZeo6IzwHPAL4BrMjMNTNpNDNvHkBskiRJI2dkEkXgdcBDwDuAt0f8ckQ56M1D3KnG2CRJkkZOl+YoTrfh9nSP+JMkSdJWGO9Onjjts54lSZI0S027mEWSJEmDM1KP8JMkSdLgdGmjaBNFSZKkBo3SqmdJkiQN0Hh0Z+jZxSySJEkjJiJOj4h7ImLNpPNvi4ibIuL6iPjQdO0MbUXx4lv3ajuESv71zkfbDqGy+zZ3/yE6Tz/ttrZDqOz7l32i7RAqee9Rn2w7hMrmPu25bYdQ2et/Y6ztEKq5cd+2I6js39bv0HYIld263aa2QxgJDc1R/CfgE8DntpyIiCOAY4BnZuajEfH46RoZ2kRRUveTREnSr2pijmJmXhIRSyed/mPgbzPz0eI990zXjkPPkiRJDRqP6sc2eirw2xFxRURcHBHPme4DJoqSJEkdExHLI2LlhGP5DD62HbAbcCjwHuArEVOvrHHoWZIkqUGD2HA7M8eArZ18fAdwVmYmcGVEjAO7A/f2+4AVRUmSpAblAI5t9HXgCICIeCowH7hvqg9YUZQkSWpQhTmGMxYRZwCHA7tHxB3AycDpwOnFljkbgBOK6mJfJoqSJEkNamjV83F9Lr1ua9px6FmSJEmlrChKkiQ1qKENtwfCRFGSJKlBTcxRHBQTRUmSpAY1MUdxUGpJFCNiPvAa4M7M/E5EHA8cBtwIjGXmxjr6lSRJGnazPlEEPlO0vSgiTgAWA2cBRwKHACfU1K8kSZIGpK5E8YDMPDAitgPWAntl5uaI+Dxwbb8PFY+fWQ5w4s6HcOSiJ9cUniRJUjuyQ3MU69oeZ04x/LwjsAjYuTi/AJjX70OZOZaZyzJzmUmiJEkaReMDOJpSV0Xx08BNwFzg/cBXI+KH9B5C/aWa+pQkSRp6s36OYmZ+NCK+XLy+MyI+B/wO8I+ZeWUdfUqSJGmwatseJzPvnPD6fuDMuvqSJEnqCjfcliRJUik33JYkSVKpWT9HUZIkSeW6lCjWtT2OJEmSOs6KoiRJUoNczCJJkqRSLmaRJElSqS7NUTRRlCRJalCXhp5dzCJJkqRSQ1tRfMHSO6d/0xD74dq92g6hsh3mPb7tECp77+YntB1CJX911Glth1DZ43Nu2yFUdsthf9p2CJV9eMMebYdQyUdfcX/bIVT2qa/v0nYIlb384S7VwobXeIdqikObKEqSJI0i5yhKkiSpVHfqic5RlCRJUh9WFCVJkhrk0LMkSZJKueG2JEmSSrnqWZIkSaW6kya6mEWSJEl9WFGUJElqkItZJEmSVMo5ikBE7A/8AbAvsBm4GfhiZj5YV5+SJEnDrjtpYk1zFCPi7cD/BRYCzwEW0EsYvxcRh9fRpyRJUheMD+BoSl0VxbcAB2Xm5og4FfhGZh4eEacB5wDPqqlfSZIkDUidq563JKELgMUAmfljYF6/D0TE8ohYGRErP/+TO2sMTZIkqR3jZOWjKXVVFD8FXBURVwC/DZwCEBF7AD/r96HMHAPGAO487IguDeFLkiTNSJcSnFoSxcz8eER8B/gN4COZeVNx/l7g+XX0KUmS1AVujwNk5vXA9XW1L0mSpHq5j6IkSVKDskODzyaKkiRJDXLoWZIkSaV8MoskSZJKdSdNrHcfRUmSJHWYFUVJkqQGOfQsSZKkUi5mkSRJUim3x5EkSVIpK4oD8Jm1e7UdQiX/45C1bYcg4KkX3NV2CJUcs/CAtkOoLua3HYGAHYb3x/2MvPWceW2HUNmxG7qUHpT73sJu/zkCOLLtADqm+19xSZKkDnHoWZIkSaW6VFs2UZQkSWrQeHanouiG25IkSSplRVGSJKlB3aknmihKkiQ1yiezSJIkqZSrniVJklSqS6ueXcwiSZKkUlYUJUmSGtSlOYpWFCVJkhqUA/hvOhHxZxFxfUSsiYgzImLhtsRqoihJktSg8QEcU4mIvYG3A8sy8xnAXOA12xKrQ8+SJEkNymaezLIdsH1EbAQWAXduSyO1VBQjYueI+NuIuCkifhYRP42IG4tzu0zxueURsTIiVl617gd1hCZJkjTSMnMt8GHgx8BdwAOZecG2tFXX0PNXgJ8Dh2fmbpn5OOCI4txX+n0oM8cyc1lmLnvO4ifXFJokSVJ7xsnKx8TiWnEs39J+ROwKHAM8CdgL2CEiXrctsdY19Lw0M0+ZeCIz7wZOiYg31dSnJEnS0BvEPoqZOQaM9bn8O8CPMvNegIg4CzgM+PzW9lNXRfG2iHhvRCzZciIilkTE+4Dba+pTkiRp6DWw6vnHwKERsSgiAjgSuHFbYq0rUTwWeBxwcTFH8WfARcBuwKtq6lOSJGnWy8wrgDOBa4Dr6OV7/aqPU6pl6Dkzfw68rzgeIyL+CPhMHf1KkiQNuyY23M7Mk4GTq7bTxj6KK1roU5IkaShkZuWjKbVUFCNidb9LwJI+1yRJkkbeIBazNKWuVc9LgJfQ2w5nogAuq6lPSZKkoTeTR/ANi7oSxfOBxZm5avKFiLiopj4lSZI0QHUtZjlximvH19GnJElSFzSxmGVQfNazJElSg5pcjFKViaIkSVKDrCgOwD2xue0QKnnPVbu3HUJlezC/7RAqO2znxW2HMOvtv6mNXbgGa8lz1rcdQmV/Rbfv4ZJ/26vtEARcmQ+0HYIaNrSJoiRJ0ihy1bMkSZJKjTtHUZIkSWW6kyaaKEqSJDWqS4tZuj/LXJIkSbWwoihJktSgLlUUTRQlSZIa5IbbkiRJKmVFUZIkSaW6tI+ii1kkSZJUyoqiJElSg5yjKEmSpFLOUZQkSVKpLlUUG5+jGBHfnOLa8ohYGREr1/zilibDkiRJ0iS1VBQj4uB+l4CD+n0uM8eAMYB3LH1Nd9JtSZKkGXLoGa4CLqaXGE62S019SpIkDb0ubY9TV6J4I/DWzPz+5AsRcXtNfUqSJA298Q7NUawrUfwr+s9/fFtNfUqSJA29WV9RzMwzp7i8ax19SpIkabDaeDLLihb6lCRJGgrjmZWPptS16nl1v0vAkjr6lCRJ6oJZP/RMLxl8CfDzSecDuKymPiVJkoaei1ngfGBxZq6afCEiLqqpT0mSpKE36yuKmXniFNeOr6NPSZIkDZbPepYkSWqQQ8+SJEkqNeuHngfh+s2T18F0y80P3dl2CJWdu+OT2g6hsoULx9sOoaL1fHLDTm0HUckPt+v61wD+5Mrub/964iPz2w6hki8vfLjtECq7bdMDbYdQ2Utjz7ZDGAmZ3fm52MY+ipJmqOtJoiSp24a2oihJkjSKxh16liRJUpl0MYskSZLKWFGUJElSqS5VFF3MIkmSpFJWFCVJkhrkhtuSJEkq5YbbkiRJKtWlOYomipIkSQ3q0qpnF7NIkiSplBVFSZKkBjn0LEmSpFJdWvVcy9BzROwUER+MiP8XEcdPuvYPU3xueUSsjIiVa9fdUUdokiRJrcrMykdT6pqj+BkggK8Br4mIr0XEguLaof0+lJljmbksM5ftvXifmkKTJEnSTNQ19PxrmfmHxeuvR8T7ge9GxMtr6k+SJKkTurTqua5EcUFEzMnMcYDM/EBErAUuARbX1KckSdLQ69JilrqGns8DXjjxRGb+E/AuYENNfUqSJA298czKR1NqqShm5nv7nP9WRPxNHX1KkiR1QZce4dfGhtsrWuhTkiRJW6mWimJErO53CVhSR5+SJEld0KV9FOtazLIEeAnw80nnA7ispj4lSZKGXpcWs9SVKJ4PLM7MVZMvRMRFNfUpSZI09Gb9HMXMPDEzL+1z7fiy85IkSbNBE09miYijIuI/I+IHEXHStsbaxmIWSZIk1SQi5gJ/DxwNPB04LiKevi1t1TX0LEmSpBINzFE8BPhBZv4QICK+BBwD3LC1DVlRlCRJalAO4JjG3sDtE35/R3Fuqw1tRfE7t/9L1N1HRCzPzLG6+6lL1+MH72E6H6+j0Un8GgyHrt9D3fEfWVfDE3T9awDeQ1ds2rC2co4TEcuB5RNOjdXx/222VxSXT/+Wodb1+MF7GAZdjx+8h2HQ9fjBexgWo3APtcvMscxcNuGYmCSuBfad8Pt9inNbbbYnipIkSaPmKuApEfGkiJgPvAY4d1saGtqhZ0mSJG29zNwUEX8K/AswFzg9M6/flrZme6LY9TkQXY8fvIdh0PX4wXsYBl2PH7yHYTEK99C6zPwG8I2q7USXHiMjSZKk5jhHUZIkSaVGKlGMiH0j4sKIuCEiro+IdxTnd4uIb0fE94tfdy3OR0T8XfF4m9URcXBx/oiIWDXheCQiXtGV+ItrHyrauLF4T+3bDdVwD6dExJriOLaJ+LfxHn49Ii6PiEcj4t2T2hrII5RajP/0iLgnItY0Efug76FfOx27h4URcWVEXFu0s6Jr9zChvbkR8R8RcX7X4o+IWyPiuuj9nbCyifhruIddIuLMiLgpen83PLdL9xART4vH/t38YES8s4l7mNUG8bzBYTmAPYGDi9c7AjfTe3TNh4CTivMnAacUr18KfBMI4FDgipI2dwN+BizqSvzAYcC/05vAOhe4HDi8S18D4GXAt+nNo92B3gqunYb0Hh4PPAf4APDuCe3MBW4B9gfmA9cCT+9K/MW15wMHA2ua+H9fw9egtJ2O3UMAi4vX84ArgEO7dA8T2vtz4IvA+V2LH7gV2L3J74Ma7uGzwJuL1/OBXbp2DxPanAvcDezX9Ndkth0jVVHMzLsy85ri9S+AG+ntRH4MvW8Qil+3VAePAT6XPd8DdomIPSc1+0rgm5m5vkPxJ7CQ3g+CBfT+cvlJ3fEP+B6eDlySmZsy8yFgNXDUMN5DZt6TmVcBGyc19ctHKGXmBmDLI5S6Ej+ZeQm9fyg1alD3MEU7XbqHzMx1xW/nFUcjk8sH+WcpIvah9w/ATzUQOkU8A4u/LYO6h4jYmd4//D5dvG9DZt7fpXuY5Ejglsy8rbbABYzY0PNEEbEUeBa9f30vycy7ikt3A0uK1zN5xM1rgDNqC7SPKvFn5uXAhcBdxfEvmXljA2E/RsWvwbXAURGxKCJ2B47gsZuHNmKG99DPwB6htK0qxj8UBnUPk9ppVNV7KIZsVwH3AN/OzM7dA/Ax4L3AeB3xTWcA8SdwQURcHb0nYjSu4j08CbgX+Ewx/P+piNihrlj7GeDPpFb+bp6NRjJRjIjFwNeAd2bmgxOvZeYMH5MIRWXrAHr7EDWmavwR8WTgN+jtxL438MKI+O2awu0XQ6V7yMwL6C3rv4zeD4PLgc31RFtuUH+O2tL1+GGg38t926nbIO4hMzdn5kH0vqcPiYhn1BJsHwP4mfS7wD2ZeXV9UU7Z/yD+HD0vMw8Gjgb+JCKeP/hI+xvAPWxHbxrJJzPzWcBD9IZ7GzPA7+f5wMuBrw48SP2KkUsUI2IevT+IX8jMs4rTP9kypFz8ek9xfrpH3LwaODszGxuGGFD8vw98LzPXFUNW3wQambRcxDiQr0FmfiAzD8rMF9Gbp3VzE/EXMW7NPfQzsEcoba0SN9ZNAAADiUlEQVQBxd+qQd1Dn3YaMeivQzFUeCENTcOAgd3DbwEvj4hb6U3BeGFEfL6mkB9jUF+DzNzyc+ke4Gx6U0saMaB7uAO4Y0I1+kx6iWMjBvy9cDRwTWY2MqVqthupRDEigt78ixsz89QJl84FTihenwCcM+H8G6LnUOCBCWVwgONosLQ9wPh/DLwgIrYrvjlfQG9OSGfuoRhqe1zR5oHAgcAFQ3oP/QzsEUpbY4Dxt2ZQ9zBFO7Ub4D3sERG7FK+3B14E3DT4iEv7Hsg9ZOb/zMx9MnMpve+D72bm62oI+TEG+DXYISJ23PIaeDHQyE4AA/wa3A3cHhFPK04dCdww4HBL1fAzqdG/m2e9HIIVNYM6gOfRK12vBlYVx0uBxwH/Cnwf+A6wW/H+AP6e3srU64BlE9paSq/6M6dr8dNbDXYaveTwBuDUDt7DwiL2G4DvAQcN8T08gd6/1h8E7i9e71Rceym9SugtwPs7GP8Z9Oa5bizOn9ile+jXTsfu4UDgP4p21gB/2cXvhQltHk5zq54H9TXYn9686WuB65v6Xh701wA4CFhZtPV1YNcO3sMOwE+BnZv6Gsz2wyezSJIkqdRIDT1LkiRpcEwUJUmSVMpEUZIkSaVMFCVJklTKRFGSJEmlTBQlDZViT81LI+LoCedeFRHfiojNEbFqwnHShPfsHhEbI+K/T2rv1oi4LiJWR8TFEbFfk/cjSV3m9jiShk7xiLqv0nsm7Hb09hE8Crg2Mxf3+cwfA8cD45n5ggnnb6W3P+d9EbEC2Csz31LzLUjSSLCiKGnoZOYa4DzgfcBfAp/LzFum+dhxwLuAvSNinz7vuZze888lSTOwXdsBSFIfK4BrgA3AsuLc9hGxasJ7PpiZX46IfYE9M/PKiPgKcCzwkZI2j6L3RApJ0gyYKEoaSpn5UER8GViXmY8Wpx/OzINK3n4s8JXi9ZeA03lsonhhROwGrAP+oq6YJWnUOPQsaZiNF8d0jgPeWMxHPBc4MCKeMuH6EcB+9J4xu2LQQUrSqDJRlNRpEfFUYHFm7p2ZSzNzKfBBesnjL2XmJuCdwBuK6qIkaRomipK6ZPtJ2+P8Lb2E8OxJ7/sakxJFgMy8CzgD+JP6Q5Wk7nN7HEmSJJWyoihJkqRSJoqSJEkqZaIoSZKkUiaKkiRJKmWiKEmSpFImipIkSSploihJkqRSJoqSJEkq9f8BfaazQSxnPa8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a46b7b1a59b20359bc06f363ad702ef20ae9c42e"
   },
   "source": [
    "## Plotting Geographic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "280fa7f8aa03f3307928b034069deec888f5ac4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geographic Attributes: \n",
      " ['LATITUDE', 'LONGITUDE', 'Ward_Name', 'Hood_Name', 'District', 'Division', 'STREET1', 'STREET2']\n"
     ]
    }
   ],
   "source": [
    "print(\"Geographic Attributes: \\n\", str(ksi_geographical))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "15c49e35b4e7b3a0ad344b4f456a65ec8a2b5f5d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Learning Exercise\n",
    "# Try changing the geographic attribute with another attribute listed above:\n",
    "attribute = 'District'\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "data = df_ksi[attribute].value_counts().head(12)\n",
    "data.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "143fbf38fafbea560a92657609f86038856879fa"
   },
   "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>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FATAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10364</th>\n",
       "      <td>43.680725</td>\n",
       "      <td>-79.382404</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7197</th>\n",
       "      <td>43.727844</td>\n",
       "      <td>-79.417889</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>43.667644</td>\n",
       "      <td>-79.342793</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LATITUDE  LONGITUDE  FATAL\n",
       "10364  43.680725 -79.382404      1\n",
       "7197   43.727844 -79.417889      1\n",
       "1628   43.667644 -79.342793      1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ksi_geo = df_ksi[df_ksi['FATAL'] == 1]\n",
    "df_ksi_geo = df_ksi_geo[['LATITUDE', 'LONGITUDE', 'FATAL']].sample(1000)\n",
    "df_ksi_geo.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "2165121ab9d33a49d38bf7e5c9a30116c677eb09"
   },
   "outputs": [],
   "source": [
    "lat_Toronto = df_ksi.describe().at['mean','LATITUDE']\n",
    "lng_Toronto = df_ksi.describe().at['mean','LONGITUDE']"
   ]
  },
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-79.31207667, 1.0], [43.74024538, -79.32799029, 1.0], [43.72004538, -79.59679029, 1.0], [43.76434668, -79.57489405, 1.0], [43.63264538, -79.53159029, 1.0], [43.75484538, -79.51709029, 1.0], [43.7646633, -79.57332239, 1.0], [43.63644538, -79.44399029, 1.0]],
                {
                    minOpacity: 0.4,
                    maxZoom: 18,
                    max: 1.0,
                    radius: 25,
                    blur: 15,
                    gradient: null
                    })
                .addTo(map_a2b01cef81494b50a231e8872cb05259);
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fedd8190710>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Heatmap of fatal events\n",
    "map_ksi_fatal = folium.Map(location = [lat_Toronto, lng_Toronto],\n",
    "                           #tiles = 'Stamen Toner', # uncomment this code to change basemap\n",
    "                           zoom_start = 11)\n",
    "\n",
    "HeatMap(df_ksi_geo.values, min_opacity =0.4).add_to(map_ksi_fatal)\n",
    "map_ksi_fatal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "827346f2b327ad0637218016bf8b81bd2970fbb0"
   },
   "source": [
    "## Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "9d87ee3426f1f988821f19456e100685df5e1e53"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below we are visualizing the correlation of the attributes.\n",
    "#  - If the correlation for two attributes is closer to positive one, \n",
    "#    they are positively correlated, meaning that if the value for one increases, \n",
    "#    the other is expected to increase as well.\n",
    "#  - If the correlation for two attributes is close to negative one, \n",
    "#    they are negatively correlated, meaning that if the value for one increases, \n",
    "#    the other is expected to decrease as well.\n",
    "#  - If the correlation is close to zero, there is likely no correlation.\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "sns.heatmap(df_ksi_monthly[ksi_boolean].corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fae8f71a23bb7e62b04b4bc5f67e05fa0649046e"
   },
   "source": [
    "# Machine Learning for predicting fatal events\n",
    "Scikit-learn is one of the most popular Machine Learning libraries written in Python.\n",
    "\n",
    "https://scikit-learn.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b9434eac2574e334364e3ad7ac11afc5e8bf0943"
   },
   "source": [
    "## Train/Test Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "f2faef55d34da5c7e197da59c2dd9563036da606"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "d555e07f9cda7753acddabf7efb2a4595e846578"
   },
   "outputs": [],
   "source": [
    "# the function below takes in the dataset, and a list of input columns names \n",
    "# as model input and a column name to predict for.\n",
    "def split_train_test(data, X, y):\n",
    "    X_all = data[X]\n",
    "    y_all = data[y]\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_all,\n",
    "                                                        y_all,\n",
    "                                                        test_size=0.4,\n",
    "                                                        random_state=42)\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "c332f1726173d5dfdf7cbeb8b0586ca3440e454d"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5ec521abba39507f52599e5409c9fb3b9b3036f9"
   },
   "source": [
    "## Descision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "e5821aabc6ba8187a16295bc8a9850d6d82c0213"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "d496ae96d88deef0b8aa25894b2a69d07dcdbd82"
   },
   "outputs": [],
   "source": [
    "# Learning Exercise:\n",
    "# Try changing the variables of the model and see how the model accuracy changes!\n",
    "model_input = ['YEAR','MONTH','WEEKDAY','Hood_ID']\n",
    "model_output = 'FATAL'\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_train_test(data = df_ksi,\n",
    "                                                    X = model_input,\n",
    "                                                    y = model_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "79d951ccb087472548410f92875ecc7c368d2b02"
   },
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "3e4ebf58c29cc503b44df6371d6ce3ce91df0695"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "d286b4b8c6c79de4ca83c38165abd8d1d614fa5b"
   },
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "bea55fcf343b0e1592e34d8f2420f3e22f8b8e6e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4191  156]\n",
      " [ 136  535]]\n"
     ]
    }
   ],
   "source": [
    "# The confusion matrix shows the proportion of incidents predicted correctly, and incorrectly\n",
    "\n",
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "10a22de92c03c60b0f03c99fe52717e417467933"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.96      0.97      4347\n",
      "           1       0.77      0.80      0.79       671\n",
      "\n",
      "   micro avg       0.94      0.94      0.94      5018\n",
      "   macro avg       0.87      0.88      0.88      5018\n",
      "weighted avg       0.94      0.94      0.94      5018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "8221566d7ee3053ca5ce153e6e80edf5f23ac758"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9418094858509366"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "42b1acd8e208a135f1be4b378937054c17340370"
   },
   "source": [
    "## Visualizing the Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "107680b3ba49dadc4cc724c11bcedbf17a052568"
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "from sklearn.externals.six import StringIO\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "\n",
    "dot_data = StringIO()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "784f76fc2a20a27ca58852f1dd7c965dfc5277bb"
   },
   "outputs": [],
   "source": [
    "def draw_decision_tree(tree):\n",
    "    export_graphviz(tree,\n",
    "                    out_file=dot_data,\n",
    "                    feature_names = X_train.columns,\n",
    "                    filled = True,\n",
    "                    rounded = True)\n",
    "    \n",
    "    graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "    \n",
    "    return Image(graph[0].create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "f44694988aaa9c5b5adf50399d50cae756c94644"
   },
   "outputs": [],
   "source": [
    "# NOTE: Depending on your device performance, you may not want to\n",
    "#       run the command below because it computationally intensive\n",
    "\n",
    "# Running the command below will draw the entire descision tree!\n",
    "# You can copy the image and paste it in Microsoft Paint or another image viewer and see the decision paths.\n",
    "\n",
    "#draw_decision_tree(dtree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5216072ca16989f99cf0cc7dc35affb03584156b"
   },
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "da8200d3b9cedfdd2b492f107b1366bd8fd251f3"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_uuid": "2b7725550cd156352b666d0fd05382f4160e90e0"
   },
   "outputs": [],
   "source": [
    "# Learning Exercise:\n",
    "# Try changing the variables of the model and see how the model accuracy changes!\n",
    "model_input = ['YEAR','MONTH','WEEKDAY','Hood_ID']\n",
    "model_output = 'FATAL'\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_train_test(data = df_ksi,\n",
    "                                                    X = model_input,\n",
    "                                                    y = model_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_uuid": "d46659912fe44a25f2911dca047cf53dbef586ed"
   },
   "outputs": [],
   "source": [
    "model_input = ['YEAR','MONTH','WEEKDAY','LATITUDE','LONGITUDE']\n",
    "model_output = 'FATAL'\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_train_test(data = df_ksi,\n",
    "                                                    X = model_input,\n",
    "                                                    y = model_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "873e8d0023b6fd3ebc1c22b7f3145730397527b6"
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "6458ca36d64785cc0383f9543dfcac42573e5d9a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_uuid": "52153f95ab0b1be062e5e27c269e128217ed008c"
   },
   "outputs": [],
   "source": [
    "predictions = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_uuid": "cfe923002ec7df3395bdd1b7cbb4d624f8bba32d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4331   16]\n",
      " [ 133  538]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_uuid": "ddaaad6bc1e08a96c005dbd8e66c4af14dad0b8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98      4347\n",
      "           1       0.97      0.80      0.88       671\n",
      "\n",
      "   micro avg       0.97      0.97      0.97      5018\n",
      "   macro avg       0.97      0.90      0.93      5018\n",
      "weighted avg       0.97      0.97      0.97      5018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_uuid": "d94aff80755652e6304737de4733f350686e1321"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9703068951773615"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_uuid": "4585be02672d81d6b9f49949e49c5e041f1fa6c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Descision Trees  our Random Forest Model contains:  200\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Descision Trees  our Random Forest Model contains: \", len(rfc.estimators_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_uuid": "5b15f1c11c91dcdd046ef2842529cbe2811ca486",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# NOTE: Depending on your device performance, you may want to\n",
    "#       run the command below because it is resource intensive\n",
    "\n",
    "# Running the command below will draw the entire descision tree!\n",
    "# You can copy the image and paste it in Microsoft Paint or another image viewer and see the decision paths.\n",
    "\n",
    "#draw_decision_tree(rfc.estimators_[5])"
   ]
  }
 ],
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