{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ask Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we predict the maximum temperature tomorrow for Seattle, Washington given one year of historical data? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data we have available is the maximum temperatures in Seattle from the National Oceanic and Atmospheric Administration's (NOAA) [Climate Data Online Tool](https://www.ncdc.noaa.gov/cdo-web/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Acqusition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>day</th>\n",
       "      <th>week</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fri</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Sat</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mon</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Tues</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  week  temp_2  temp_1  average  actual  friend\n",
       "0  2016      1    1   Fri      45      45     45.6      45      29\n",
       "1  2016      1    2   Sat      44      45     45.7      44      61\n",
       "2  2016      1    3   Sun      45      44     45.8      41      56\n",
       "3  2016      1    4   Mon      44      41     45.9      40      53\n",
       "4  2016      1    5  Tues      41      40     46.0      44      41"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas is used for data manipulation\n",
    "import pandas as pd\n",
    "\n",
    "# Read in data as pandas dataframe and display first 5 rows\n",
    "features = pd.read_csv('data/temps.csv')\n",
    "features.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identify Anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of our features is: (348, 9)\n"
     ]
    }
   ],
   "source": [
    "print('The shape of our features is:', features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>day</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>348.0</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.477011</td>\n",
       "      <td>15.514368</td>\n",
       "      <td>62.511494</td>\n",
       "      <td>62.560345</td>\n",
       "      <td>59.760632</td>\n",
       "      <td>62.543103</td>\n",
       "      <td>60.034483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.498380</td>\n",
       "      <td>8.772982</td>\n",
       "      <td>11.813019</td>\n",
       "      <td>11.767406</td>\n",
       "      <td>10.527306</td>\n",
       "      <td>11.794146</td>\n",
       "      <td>15.626179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>49.975000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>47.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>58.200000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>69.025000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>77.400000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year       month         day      temp_2      temp_1     average  \\\n",
       "count   348.0  348.000000  348.000000  348.000000  348.000000  348.000000   \n",
       "mean   2016.0    6.477011   15.514368   62.511494   62.560345   59.760632   \n",
       "std       0.0    3.498380    8.772982   11.813019   11.767406   10.527306   \n",
       "min    2016.0    1.000000    1.000000   35.000000   35.000000   45.100000   \n",
       "25%    2016.0    3.000000    8.000000   54.000000   54.000000   49.975000   \n",
       "50%    2016.0    6.000000   15.000000   62.500000   62.500000   58.200000   \n",
       "75%    2016.0   10.000000   23.000000   71.000000   71.000000   69.025000   \n",
       "max    2016.0   12.000000   31.000000   92.000000   92.000000   77.400000   \n",
       "\n",
       "           actual      friend  \n",
       "count  348.000000  348.000000  \n",
       "mean    62.543103   60.034483  \n",
       "std     11.794146   15.626179  \n",
       "min     35.000000   28.000000  \n",
       "25%     54.000000   47.750000  \n",
       "50%     62.500000   60.000000  \n",
       "75%     71.000000   71.000000  \n",
       "max     92.000000   95.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics for each column\n",
    "features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use datetime for dealing with dates\n",
    "import datetime\n",
    "\n",
    "# Get years, months, and days\n",
    "years = features['year']\n",
    "months = features['month']\n",
    "days = features['day']\n",
    "\n",
    "# List and then convert to datetime object\n",
    "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
    "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import matplotlib for plotting and use magic command for Jupyter Notebooks\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Set the style\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hDxmmBBNeZWm2CPj34RK8fCwCX2WW+bQlEksRVQbYuGNV27MoMbJkDz5lTxBE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\nQwEgM5HOlRUNteVJDqoY3kK+R1BK8pPBy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KzdtXhAneS1ncti6kNAOmebfmXliwgEGuse\nf2hyKV5ZiUpigoI7UmNMpSY/kWJMTRSauNNiSqoTy1z50QGTs20TKRbTgF354HCkyiy+YRuGNBHw\nspiGefLHFFNhqBXT22+/3fV3YWGh4HY1NTWBjYgRNC4TZtDkGFPnOuFjucpF+WUx9Q1duSjPbWgK\n0NOQk6QSV0xbpF7BmA6YtbtW9JgSc5+8LKbNNg5VFBZTlcuV772OufKjAyZno4vPzzThwxON6JMW\ng/+5PgNZiZ4B4nsqmjH3QB2O1VgEj8GXZYE+qXJZAjsnKKnkDh8VONjCrHaRyvN5xZiG22LKNFNB\nqBXT9957jwXhRzkkw11ds/fT2erK95WV73+BffE6ptKPIRbnnxKjwF05ZvyrJNbndh0pAkidFtN7\nr0hEo8WOz043QW+2o8xAHkSpRJcaqY4pTYxpqytfeLLBiGyYnI0eShqtmFOoh50DNHVWLDnWgHkj\nMlzrOY7DrN21uCwy0ZU7K9/KyZORPysvBUuONaBRoltMoUDYSwIQLaaKyIoxZQhDrZj+5S9/CeY4\nGCGAZFUsJyhTUpKfxNqLkhCLoaKx7vE3EXN1d0xQ4sleFnxTHgtfOh5NBn18y2xcqVBgZv8UzOyf\nAgD428E6fHii0Wt7kqXa5/EJMaY0lgunK5+khDIhHB0wORs9fHii0UMOFZxs8lBMK412UaUUILQk\nDdSVb+cCDgcAfIcf+EKlCLzdZ6AQ65jytFXnGMOlR7PppzABFmVgRBMk5U1r9BacTospXYF96ePw\npUNyHOdXgX0xV35ci5SVWuqJRKyKvDxOYHmuxK4gCQTFlKZclLMXNMnSzWJMGQxhLhoU+P6iEbXN\nduwqb8b6YqNomJKYJ4TvOhYiUi2mSoV/7uZIUCqUpHJRvEV2zvFPhleCX/hz2p1lzfjpkjHgrlWR\nDvUb87nnnhPdRqFQYNGiRQENiBE8TIRprNYobDH1JZgDijH1sQvtTJtvqTWLKqaO/xNVCtSJiO07\neiZg/SVHJ4+cRCUqeNcoTsCUMKgjOUxgzqAUn+fj46WYWjk0UVwY524WYh3Tti3I2gpMzoaefRXN\neOC3BFg4z7jdiT0T8NVNnQT30xFkpzu0CprcnZ/kqljir4Lpr6VVTkhjUCgUiFV6TtJtnMOSag1D\nAQOpSv/83+ox/4ijUcPdfRLxf2M7BmFUkQG1Yrpu3Tqv2CebzYbq6moAQFpaGhITE5nAjGBoMrsB\nuvjRYMWYiimYTvinJYTKehCroreYLhiZgVhlHfRmO165Jg1/XO9ZD1AoK39yr0S8dLUV64uNOFHr\nqBP74tWpmNQrUfSc7vAVU6ONg5FCMXVuQlJCmcU0OpBbzg4aNAiXL1/2Wn7LLbdg1apV4DgO8+fP\nx+effw69Xo9hw4Zh0aJF6N+/f+BfJkp4eq8eFs77md5wyYTTegv6ZZAnnCRvkzu0+qF3Vn5gmqXV\nzsliMfU31DkyLKbk5TEKBSxuV8fKOYvxh37irpBoM3UqpQDw3QUjFoywoWOCgJsuyqFWTM+ePUtc\nbjAY8M9//hMrVqzA2rVrZRsYQ36oFVMfCo4Tp1XOnxjTXeXN6P9tOd4dmYE7cj2VNloFynlam53D\n8/v1+OyMwef2LospRQxpt2QVPhsnPBsVspgqFQrkD01D/tA00XP4gi9rGix0L5rLjTbc/XMVUfEP\nlcX0n6cb8c6vDchJUuKfN3YUfKkzyMgtZ7dv3w6brVWBqqiowI033ogpU6YAAJYuXYply5Zh2bJl\nUKvVWLhwIaZOnYpDhw61m0L/5+qtguv2lDcL3sNiFlPaR07urHw7F3jJKcAhz/zRTSPBYipkf4hV\nAu7zCStHH3IhN4HmONY2c+joXUmuTRDwT5KUlIS//vWvGD16NF544QU5xsQIEhSJ3QDgil/xlckd\nSIwp4Ei6eq5Q7xUrQ6tAOZWvXeXNokop0FrDTsxiSqO4BnuSmsAbg17MHOzG5tJmbCtr9loeCsVU\n32zHywfqUN1sx4laK+b/JtwfnCENf+Vs586dkZ2d7fq3efNmpKamYurUqeA4DgUFBZg9ezYmT56M\nvLw8FBQUoLGxEatXrw7it4kejtdYYOc4LDhSj9FrtXh2nx6GFsHIz1bniw7aR46/WaCPqpWTx2Lq\nb+xlJCimpBhTgNyWNFwxpu5carRiyn+q0PfrcvzXTzoUNTgmS3VmOx7bVYP7f/XWQNty8Q7Z5gpX\nX301du/eTb39oEGDkJGR4fXvnnvuAeBwZ8ybNw/9+vVDTk4OJk6ciFOnTsk13HYJKcaUhKvdqI9p\nN00cqhhaox2lBk9tWajsU/dkT23QaWV49XA91blSWrovJYsonk8N8I4HfdCtCUGMArivr3dTAjnh\nu/L1MvS8DoUrf8Mlo8d5vi8yBv+k7QypctYdjuPwxRdf4N5770ViYiKKi4uh1Woxfvx41zaJiYkY\nPXo0Dhw4INeQo5rjtRbs15ox77cGnNRb8emZJqy+QL6vsxI9X6fUrnzehoEqlTYOkEM19T8rP/zx\n7MIWU88Vra780ON+1r8drMOOsmZUmezYXWHGi/v1AID/O9WEb88bcc4QCQESoUNaurAPdu/eTexQ\nIgRzMYUe6a584W0C6fzkDl+vJVn2+mfE4J3r0jH152rXMhsnvD2JTi1mztQ44Qf8xq7x6N/B2233\n4tWpKGqw4lKjDc8PSUXnIJtM+VbdOnPggj4UFtM2PIGPGKTKWXe2b9+O4uJiTJ8+HQCg1WoBAJmZ\nmR7bZWZmory83OexNBqNpHNL3T60CE80Kxqa8fIeLYDWZ/7pvXqMUpR57ZcEq8f3rGxWABCPL7dx\nnMd+1dWxAPwPgWkyNqO21hDQMQCgSqeDzR4LqU92oCrUkDQbNBoNpuXE4t8V/n2HivJSaEihFrYE\nuI/QyilgtdoQDulVV6eHRuPIX9h0KdFjDDtKTdBoNHj7V+F7s6ioCLbE8E8C3KF9ztVqtc/11Irp\n0qVLicvr6uqwb98+HDhwAE899RTt4dC5c2ePz1988YWgiwkACgoKoFarsXr1asyYMYP6PIxWpCQ/\n2TnOp0upNSs/sDHxrQV8BeqKNBUKp2Z7LbfYgfHrKnFaLxwf5k7nBIcwSvPRt/7lq8kTnp4pMVh/\neyZxXTCQ2sKUhlBYTIXcZwx65Jaz7nz++ee45pprMGjQoECGCED8xeKORqORtH3I2VMquComNral\nQLBn56a+ffsCe8o8lqUkxkOt7uH6nNhoBQ5pKQag8Lg+GQ31wGX/w2BiYuOQnhEPlDX5fQwAyMnK\ngupyneQ0/0Bd+e/dkAN1Zhxe62LFv7+juX7eXJnbHeqseK/lCUcqAHOrQczKAQqVMiyFVzMyMqBW\nO+remnn3oEqpdNwTPu7NXr16oU+abLbFgJHzOaf+Vq+//jpxeWJiInr16oV58+Zh5syZfg2C72Iq\nKiry6WJiiql/0CqmNk5ckbG64lADe6C9yz55rncmGpFm4b9WCbf545PZopimxgrP5+WocSoHwRhH\nKCymkRBbFu0ES87qdDps3LjRI5s/Ozvbta5Hjx4e22ZlZUk+R1vZ+08+AAAgAElEQVSE47xDawCg\nmRCvz5+X0c4FvR5NGcpFhbPAfqBi4NrMOAAOg8DiURl4tlAv+RiJAn2g+ZFcNk44prdrklKwm58c\nOIdCqsKQGte+hSm1YlpRUeG1TKFQIC4uLuBBMBeTb+QaY7mOzkXUaDDitOYcfLq4dFXQaCpQXhkD\nwP974HxRMbik1gfzQqOn+8tmMUOj0bQIWv9jO611OiABsDbqIXQNKkouQVMbfteIwwotbxyrxc4F\n7V53HrdSpwIQT1wXbuRyMQWbYMnZlStXIj4+HtOmTXMty83NRXZ2NrZv345rrrkGAGAymVBYWIg3\n33wzoPO1FewA4gmRO6RJvr/1SL2SnwLUTOVKflIogBGZcdhc6p1MGSr8VY6TBCb3XjGmdmElXqgs\noNyQWr7y26eSaMuqK7Vievz4cfTp0wcdOnQgrtfr9Th//jyGDRsmeRDMxSSMnGNMrNYDpeLuHVVc\nAnJ7dwcKhScBaR06Qa1OQwdTA3CRLgGJRJfuPaHu1PrS1VeagSOtdUNT3dxjir2lfgvcAbldAdNl\n9MzuBJSSx6vunYu+6ZFR3ih+fwma7cKiJ1YJvHNdOt76tR71FDGoHBToc0VfqGQ2a7rfn8dUBuBM\nrcf6SHi+ouE5dxIMOctxHFasWIE777wTKSmtyX0KhQKzZs3C4sWLoVar0bdvXyxatAjJycm46667\nAv4ubQEhiymNYuqv1TJQa6dcBfZVCuCt69KxeW2lpP3klDD+HkuougopK19oAiFUFlAunEevI1Rd\nkWdqEb1QxynffPPN2LJli+D6bdu24eabb5Y8AKeL6c9//rNrmbuLib8tczH5D33yEyfq+nXGlgYa\nu8jfn39e91lrIBPYzi5XvvBBQjVDpiHZR37VTd3ioZ3eFTP7p0gSnjIk9/uENJJAi4W3N4IhZ3fv\n3o3z5897yFgnzzzzDGbNmoUXXngB48aNQ0VFBdasWcMSTFvgOHJoDUmWylX2KdAnxmqXqyWpAv0y\nYvGQOrhVSNzhX2l/w9aTBBRTvsU0/3Q80WLp2Na/c9Pi/G56gmGhvTfqo770Yi8Yi8UCpVL6Lynm\nYnLidDGNGDFC8jkYDqhjTO3iCmdrVn5gT5BZpI6pu3wJZALbiUIxjY+gIMlklfB1TVApXIlGUmJ8\ngx1nSrplWMcpaQRDzt5www2urk58FAoF8vPzcebMGWi1WmzcuBF5eXmSjt+W4cB51RUGgGaixZRf\nKN+/5y3Qx9TOcbJopk5x6Iz5DAV8RdRfkSxkMeUrm5dMws9SsA0VzqOTygG2d7Hp05VvMBjQ1NTq\n+m1oaPCyYgIO99LatWuRk5Mj6eTMxRRa5LSYOmucBqp48JOd+M+ou3AIRG/MSlSiFECaj3JRkZL8\nBPi2mLoLXdJsWwid0e7z+wcK6V5otnMRZYmORIItZxn+Y5diMfUzxtTrOP7t5sIqj17qslpJtVoG\nUpzDy2Lq53GExFyMhMEF21DhHArRlU/xA7blIig+FdMPPvgACxcuBOBQFp9//nk8//zzxG05jsPc\nuXMlndzpYvrkk0+81j3zzDMwGo144YUXXLN95mIKDNrOTxa7eBkopxLC79wkFe8yUJ6f3V0vgfQ0\nTooRz8qPi6C2w8kxwt/TXwX6tN6CK9KDV16ENJkx27hAyym2eYItZxn+YxOKMSW4f72TmOixc5zL\nCxJw5yfZXPmO/8Op//hbgk4hsJ9Asj6RYLvyncon0WJK8QP6Ul4v1FuhqbPi+pw4pAT7iwQBn2+p\nG264ATExMeA4Du+88w4mT56MgQMHemyjUCiQlJSEoUOHYuTIkZJO7nQxkXC6mPLz8yUdkyEMbecn\nm9131yeg1YUcqMWU7xLjH8/9mZJjApvmowxHJLnyU3y48v2tc3qmzoqJbp8PVjbj+yIjRmTFY3Iv\n8ULg7hytNuPb8wZkW1R4ui8HhUJBVExJZXUYngRbzjL8x8pxAln53sv8zcoHHMqkc2IcaOKLjeNk\nLRclVSwGIkX5+8otkmMkHDDYnh7nT0RqoEJTmUHo/tpX0Yw7f66CyQb0TYvBnslZxHCUSManYjp6\n9GiMHj0agMPdNG3aNC+ByYgeSHFRJByufN/buArsBygB+ecx88bontzjr+si261VoFDsEQDZM9YD\nwZcr32+LaW1r3dfiBitu21gFOwd8dKIJX43viIm5dMqpzmjDzet1LWEX8cjONuK+vknEe4YfQ8zw\nhsnZyMVmF6pjKl+5KMAhR+Na1LJAlUp+eJS/OK2VUqVNvxQ7SnzEbvqCL+Pllsg+Ugy8CHZWvvP+\nqPfXYiqgvD5XqHdNnM7VW/GlpgmP9PdutR3JUN89r732GhOWUY6RuvOTeFKNK/kp0BhTUVd+69/+\nTmAfy2t9KHMSI8hf7wNfrnx3i+mobPrEhKKG1jfW27/Wewi/p/fSF7FefKzBIxb4yT2OElEWwv1F\nOxliOGByNrIQ6qXeQJiF8RUFfjKUL9wPF+hczmjj0ChD1mGrxZRe8MYogCdy6Ruf8Am2xVSK8SHY\noV3On5n0DqW5BYS2OcXrhrg1jHVo/UVywNmRI0dw9OhR1NfXw273vKIKhQJPP/20bINjyIuB0pVv\nkVAuKlCLmLdi6rnePcZUCekxpr1SVXg8L9n1OSNeiQfUSfhKY/DY7tVhaZKOG2x8W0xb/35iQAoO\nVdZQddSrcvM/Hq32fHlUEwLwhfi9xnNfp+5pIYyBKab+weRs8Fl+qhHfnDf43MZqJ7vFawnPi1cd\nUwljKTfY8Ow+PUqabKgwBG7yrCD1iZeI1OSn5wanYELPRKTpi/0+p1dWvt9HIiMl3DLYFlPnfUVy\n29PMaWhfvfzuitEAtWJaX1+PBx54AHv37gXHOWLKnKVNnH8zgRnZGKXEmFKWi+K73qVi8crKF65j\nKkVOvDcqHb1TYzAyO86V+OTkw+sz8JA6CcmxSpisHGKVwNWdQ1cShQZfV7XJ7Xf8r9xEFE7NwvA1\n4kWwLzTY0GixIyVWGdCEQqi6AzH5qb3XPZEIk7Oh4XiNBc/vrxPdzsaRFQcqxVTCI/bWL/XYcMlE\nv4MIcii3Lospxbbju8bj78PSAQAa6R1EBZE781xKVj6/5qncOG8Pkiimc+XTEahXMxxQzx/eeOMN\nHDx4EB988AEOHDgAjuPwzTffYO/evbj33nsxePBgnDhxIphjZQRIE+UdauU4Cle+5//+Im4xbf1b\niiu/b1oMxndL8FJKAccLfmR2PAZ1jMXwrLiIU0oBQNcs/GXdXfIAoJbQrWrSpioAgU0ohCzv/EkG\nwCymUmFyNjTM+42uW52Nc0zU+dSQFFP+Zwm3vpxKKSCXYtoSY0ohd+XS4f4xPJ04BrmQYjElJb3J\niTPUg3Sf0Nw6tPdXsOtXBwPqn+mnn37C9OnT8eCDD6Jz584AgISEBOTl5aGgoABZWVl46623gjZQ\nRuDQWkytFBZTp+IqvytfuFyUlIzKSKpJ6g/D0oVfLGO6eCvSAzvSKae/VllwudFKXTqMhFB1B8Fy\nUQxq2qOcrTHZsL3UhEpj6Eo4HKw0U2+7ucRbaSRZTL3qmEoelXzUk+JqJCLFYiqHYjq2Szzu7evZ\nZUpuKS4lxlTK+8YfnOKS9Ao1WDlsLfU9WaG1yAu99jmOQ6G2GUeqHM9CcYMVO8pMMEgwsRY3WLG9\n1IQmmTupULvyq6urXb3sY2MdL0GDoTU+59Zbb8W8efNkHRxDPmx2jloZsXHeCmOs0lNZdd7sgbpq\nxbPyW//OiFeipInuS5AyaaOJ8Z1tKChRosrkeYE6xisxhVDa6dlBKfjLzlqv5STqzOIxxL4QSqIj\nlRhrjsLZejhpb3K2rMmGcesqoTXakZmgxOY7MtErNXi1dgHg58sm6Ez0guskL5kEAGoJJX68C+xH\n973vFKE0RkuhuqG0aKd3RZzS+zhyu/KlWEyDXWHJeXcIxYBO+7maan8xSBZ/AJi9T4/Pzzpky+Re\nCdhc0gyDlcNV6THYOUm8xNQBbTOm/lzt2uefAygHRAG1BMjMzER1teNCpaamIiUlBefOnXOtr6+v\nh8XifzYeI7gYJFqu+FaxxBgFLG7C2KnYkDKxpfDp6Sb0TFHBYOUQq1TgAs9N7T5rdfa7p8FXWaho\nICUG2D05C5tLTBjYIRYqpSNhaXzXeHRK8PYx3dknCefrrfjHbw2ixy5rsqGBYFHZdNmI63PifTYh\nAIQt78TOT6yOqSTam5x992g9tC2JOjqTHe8ebcCyP3QI6jmfLQw8CLKGMMvnx6JG+5xMSlZ+oNJW\nyMPlT/JTho9a1VLiRsNpMaXbnz6ZmU9ts92llALAD0Wt1tkzdVasumDA9CuTvfZzZ/Y+vSus60yd\nFZsqVRh4FdWQRKFWTIcNG4b9+/e7Po8fPx4ffvghevbsCbvdjoKCAlx77bXyjIohO7RufNf2PIUz\nSaVAvZvglSsrv6TJhkd8WPri/FRMo92VDwBdklQewmFIJ9+xsLf0SKBSTO/ZQp6J37elBlekqbB3\ncrbP2bJg8hNhOXPlS6O9yVn3lyMAfKUxBF0xpfW6+EJLyHqXqyVppKCQoG4Gq9MxTdeiz8d1xF92\n1Liqg/zvDcL3j5TXQqgspv46HQNJftKKhM1sLTWJKqb8slSFtfIF5VLfTo888gi6dOkCk8mhWb/5\n5ptITEzEww8/jL/85S9ISkpqUy6mtgZtqSgnfEWWb4F0WUyDHEjlLvAkWUzbgGIqlTQZWs+dr7dh\n1QXfJXSEQkKI5aKi/e0cYtqbnI3W24P0YncXhRzH4ZNTTaEbUBCQ0vkpWBnsGfG+j/vGtWmY3CsR\nmyZk4q8DU/DF+I64rYdwoxAp4wx2wxXnve9vxAfHAXsqmnHHTzr8eXu1YMIbqW242Dcj1e4VQ0q7\nV9Fj0W44ZswYjBkzxvU5NzcXhw8fxpEjR6BSqZCXl4f4+Hj5RsaQFfkVU8f/we7s4+nKp5+RtQWL\nqVRSfbiwpLC9tFl0tuyOM3uVnPwky5DaDe1dzgbL8iY3pBAV99v/lyoLvi8yhm5AQcApemmkSrDa\nsWeI3BBjuzieheFZcRieJV5dRYoVNOgWUx9Z+TQ0WTk8vL3GLQ9Bj8/HdfLajvTqF1dMpY9HziIG\nVLeT0WjEc889h/Xr13ssj4mJwbXXXouhQ4e2aWHZFpCqmDbxtk/iPaXVLQ9DsF217t03UiX0k4v2\nGFN/IMWG3t4jQZKlGRCe+eqMNpzWe8c3Wu2OmfvJWu8kESG3P8MbJmeDX9Q8mLhbvuYeEK+RGulE\nhsXUt+ySOznKnaDHmDr/99Nkuq7Y6JEc6x4n6g6p9KPYdfPnq8tpMaU6VGJiIr7++mvU1NTId2ZG\nSGmSWD6Er8jyXeOlBhtO1VqC7sp3F3hSnpVosbzICclKHKsEJuUKu7ZIkITSzrJmDF2txci13oX8\nbRxwx09VOF7jrbSyGFN6mJz1frmVNtmws6zZZ4x8UYMVeyuaw16v0T35iVTnNNpQSbCYxgXJQyVm\njJB61lIJ9V2DbttouV38FZG/6OhKnpEeHbFz+uPKj1XI9/xRv76HDBnCCjtHMUahmhEC8BVTvsUU\nAL7QNAXdle+umGYn0TsLAi1f0paQaj0mWQpm76tFo0SrO+BteWf4pr3LWXcF57cqM0au1WLyf6ow\n9sdKYv3cLSUmjFirxcSfqjBpU5XLPRoOoile9pWhqaLbOLPxabLyg+XKFzu31AL8F+u9vTpCqIJs\n3LDz/pfKMYIhgATp1S9WqtSfeYacijz1pZ83bx7WrFmDFStWwGZjgWPRhkGixZSvUCQS7PQfnWgi\ndvuRE3fL563dE3yWAnFyS/e27e6UgkJBnlT4grT5xQb/fuiLDfQvAgaTs+6u/MXHGlxlzc7WWfH5\nWe9kokd31briPQu1Zuwqbw7JOElEkV6KdAqXknMLuhjT8BgCpJ51EGUjEkBa+1J/CLRcFG0pPith\nsibW2dGfn1NOozl18tPTTz+N2NhYzJ49G/n5+ejWrRsSEhI8tlEoFNi5c6d8o2N4YLZxsHIcsc2m\nGFLrmPK7PwhZ3RqC3IjXXeAlxCjww22dsfBIg2ALv5n9k/Hy1eLWgPYCxwHJEhVTf9w4QpyqbTs1\nN0NBe5ez7pa3dcWez/jX5wx4LC/FYxnfZV6oNWNsV8/rFSqiqZ4+TfyklBjTcIVOSRVVM/ol4/Oz\nBqpJhJwxkyQ4l2Ia3BuHFG4XDFd+WLLy4+Li0K1bN3Tr1k2+szOo2VJiwl921sBg4fDm8HQ8MSBF\nfCc3pFpM+dsLWd3qCR1Q5ITvIhrSKQ5f3dQJW0pMuGuzZz3Oe/ok4t2RGUEdTzQi1ZUvpwvrXL0V\nVjsX9ESCtkJ7l7O+kp9o4jbDZbkDosuVT2PdcuomNDpKuJ5vqacd0ikO/76lE366bMLYLvF4cJtw\nPHewLaZri4y4SdMU9AkNyWIqFo/tfn9Um2z4+6F6VBhseG5IKq7PIXsk5XTlUyumW7Zske+sDMnM\nPVjnUgLnHqzD9CuTqIoPOxFqIykEP54wXHVBhYLqSctj22GJKDE6J6gku/LlvIzNNqC4wYYr0oPb\nZrKt0N7lrK9nuIaijWg4kx6jSC+lmny6Ykwpjheuagr+nHV8twSM7yZuVQ92jCkAPLVHj7wOwZWN\nxBhTkZvV/ed885d6rDznqG19WGeG5v4uxETbmHAkPzHCy9k6z1i904T+zb6QWraHn/zUSWLJIT5S\nSj05UQDon0GOCSJVEWmPmfh8XhuW5vH5rwNTJCumckdnnCKUmGIwSPiaa9Mk34XTMh9sl6yc0FgD\nVRIspvzfbWhn+lhOMR5QJwmuC+avHaqKg6Qye3JCso6Siu67466Uu3dnq7dw2HSZHEYXluQnAKir\nq8O7776LSZMmYfTo0Th8+DAAoLa2FkuXLsX58+flGxnDJ1JLo0gt68RXTGOUwOs8pUcKNF2J8oem\nYmRWHHqlqjC0cyw+uaEDuiaTM/HJpZGYxXRm/2Q80i8ZI7Li8NEfMtA7LQYJEk2gzbxJjFigvBhn\nJE6i2jttSc4arHYcrVeiktApqZbgmo9TKnC02ozTegs6Emaftc12HNA2o95MFmg0TqRgiYlocuXT\nXIPWAvs0Wfme27w7MgOZARoznLx0dSrUAh6XYGY4tJXwI9J8TmyO5yvG1GzjiNUvwuLKLykpwe23\n3w6tVgu1Wo3Tp0+jqcmRJdmhQwd88cUXKCsrw4IFC+QbHUMQkqK5o8yEZ/bqAQDvj87AODd3hVTl\ngh9jGqtUYLCEjEY+cRSVnsZ3TcBLV9MpvyTFNJqLc8tFSqwSi0Z5xtlKLenFbyMqtTkDH1JRfgaZ\ntiRnmyx2/HG9Dqf0Ccg4rcX62zMxsEWGFDdYcdtGndc+ByrNGPuj93Ino9ZqUWG0o2eKClvvyPRa\nT1NPM16pkBzaREMU6aVUtTNdWfl+WEyvzYzDsbtzoKmz4AYfvycNPVNisHdyFhotdvT5usJjncQq\niJKIth4tQrc+SfyLecV8TSmUCv+6SUmBekrz2muvwWQyYe/evfjxxx+9NOaJEye22UzRSIRviuc4\nDs/u06O40YbiRhueK9R7/EZSLaZNvDs3VhlY1h2NNVNKLTyiYipnT7Q2hFRLDr+NqK/i5jRIDTtp\nzwRDzlZUVODxxx/HFVdcgezsbIwYMQJ79uxxrec4DvPmzUO/fv2Qk5ODiRMn4tSpUwF/l1XnjTjV\n8tvrzRxePqB3rZv3Wz3KDdK1igqjY59LjTZ8eLzRaz2NMhGsrnDRZDG1UYQdSMrKJ8jjxBgF0ert\nD3EqBToSWlLTfA9/UUWZocP5/qRxkIkZqnzF1yoV3l41QF7rNfVds23bNjz22GNQq9XE4uW9evVC\naWmpjENjOCHFg/CtWhY7cMGt1uSFBptHLVKpVjO+lSxWqfC7iPKYnDgqpVZK9xBSPClz5ZO5iSLQ\n3x2+0AnUYkqTtMJwILec1ev1uPXWW8FxHFatWoUDBw5g4cKFyMxstTYuXboUy5Ytw4IFC7Bt2zZk\nZmZi6tSpaGhoCOi78HvF76lo7VTzzfnA+8h/RqhrSiNDpLbopSWaFFOasTqTn2ikqpCuT1MA/9UA\nQsQCveZKH3buaLOYOkMuaN614slPwl9eqVAQO/rJef9TP6EmkwmdOnUSXN/Y6D17ZcgDydrJt2KR\nkpvc++hKdeXzQ7hiFP6VzxjUMRZvX5dO5WaXkrzEXPn0ZMQrkU/R6cUJXzENtHsT3/rOEEZuOfvB\nBx8gJycHH3/8MYYNG4ZevXph7NixuOqqqwA4rKUFBQWYPXs2Jk+ejLy8PBQUFKCxsRGrV68O6LsQ\nDFyyQjKW0UxOg6U/RpFeSpXgGKjFFBAPA3jnunQ8M1Ba6UN3Ao3I8PXdoi3G1Gk9pnlPi1pMfRxC\nqQBIldvkvP+pVYGrrroKhYWFgus3btyIgQMHyjKotsaxajMmbXK0zCP1ExeDZO3kKwtiiilfuU2T\nmCUfq1RILp/xwfUZ2D05C0M6xVFZW6VYPEmCkGXlC/NYf3rhv62sGctONGLTZSMGrqrAH36oDOjc\nwYjna6vILWc3bNiAYcOGYcaMGejbty/+8Ic/4JNPPnGFCBQXF0Or1WL8+PGufRITEzF69GgcOHDA\n/y8CR0OMYOJvO+RgZs+HsyWqFKS48gPp/CQmkp8YkBKQyzxQV74vBSzYBfblxvk4yGEx9XVdVEKu\nfBlvferkp0cffRRPPfUUBg0ahEmTJrmWFxcXY8GCBThw4AD+9a9/yTeyNsQTe/QuhfSpPbXYMSlL\n0v6kDHx+chJZMW117fOFeGKMAvUSiu7HKhWSLabuW9PMPqW48uMJx2N1TIWRWjLqlYN1sp272eYI\nR4m2mK1wILecLSoqwj//+U888cQTmD17Nn7//Xe89NJLrnNptVoA8HDtOz+Xl5cLHlej0Yie29wU\nB/4rpnU/4RJAtJBaMpaXl0Mj0ie52ZyAYFVKPKM5B5UCMFuCdw45yDFVAEj0uU1xURGa4zmU1isB\n+A4H0lV4Xnfn7+wwiAj/1jT3kTspqkQ02lrlSEz1JWgCEFVKH9egoqwUYt87krBzHDQaDRT2RPCn\nE/zrXKpVARBu3a2vqYZGo2355Pn7VZSXIbaWA//+sRPOI4Rarfa5nlox/dOf/oTi4mK8/vrreOON\nNwAAd911F2w2GxQKBebOneshSBkOzDbOw0p6pNoCO8dRxd64jkFy5fMUURNhCuTpyvdcJzUBIEYp\nLTkJ8HSTyO3KJyU6+RsD2x4I97VpsnJIi2OKqRhyy1m73Y6hQ4fitddeAwAMGTIEFy5cwPLly/Ho\no4/6PU6xFwsAZFXWAjqDxzLXfnuCk4+QnZMDdW+yItRosUNTZ4WBq0awCg317dsXMUoF4o5pAWPw\nk/7yOsSgQ7wSe93id8WYPSgFI/rlAEcqfG53Re/e6Jqsgr7SDBzznVnfq3tXqLs7lDiNRuNxfyyw\nNWLuwTqi253mPnJneYIR/72jFk1WDnOHpuKa/oF1SFMWlgiuy+3RHTheFdDxQ4kdCqjVasT/Uu71\nwudf585cE6DRQ4jMzp2hVreEf/Ge1e5duyE7SQn86nlPcJxC8u8phKSWA/n5+bj33nvxww8/4Pz5\n87Db7ejduzemTJki24DaGiRLZrMNSJRw5UlmcxpXfrWHK5/XYlSidTFWqZAcc+O+tdyufCmKPQPE\nRJpQYrRySIsL6xCiBjnlbHZ2tiue1MmVV16JkpIS13oA0Ol06NGjh2sbnU6HrCxpnh0+pDjwYCPk\nTtQZbbhlgw4XG3xbU4N1/mCRFKPAj7d2RpXJjv/aVOXViMWdfhkxWH97Z3ROUEFHqCvLR0qMqS/5\n/lheCu65IgmP7KzB1tJm8YP54LYeiTh1bzysdo6YpS8Vn678KHzF2DmOMsbU/3MoFWRjmZxPluRe\nWH369MGcOXNkHELbhqyYcoIWy4v1Viw4Uo94lcJV05Pkypea/MS/kaRaTGOV0ltVuiuPVK78AF29\nURLe1S4JNLO/vSGXnB05ciTOnTvnsezcuXMuJTQ3NxfZ2dnYvn07rrnmGgCOBKzCwkK8+eabAZ07\nHG2Mhe6yD483Bl0pBUKvmFrtjrJG2UkqpIjkDaTEKtBZgjKn4P3vCzGjQod4JdJlSgJIkzGZwNeR\noi35CXDcf1QxpiI3qnMtKWZaqFyUnO9fyYppSUkJtmzZgkuXLgFwCLbx48d7zLYZrZAURqGgfY7j\n8Ket1a7afyVNNszv7V1XEvB+0ZsI27jPivk3otSYwxg/LKaernzx7QN1NzPVJ3Jhiqk05JKzTzzx\nBG655RYsWrQId955J44dO4ZPPvkEf//73wH8P3tnHt5Evf3/92RP05aUrqwt2NSyb2VXQFB2RBC/\nwkUEBBfwKi63Cl70ul0RRbx4haJeFy7uIsomKgp45ccuKDsUsIAsbSkEurdZfn/UhCaZJJNkMplP\nel7Pw/PQyWTmnVnOnDnn8zmnLpI+Y8YMLFy4ECaTCZmZmViwYAEMBgPGjRsX0m/QRiDk5O15+9Zh\naarGSH2V17+t/EWo6zsOQnQ61hEWMfW/khzdPJ+z8uUo2A9CHVN/00sc9xGf2bYD/OWi/O9WMIId\nU5vNhqeffhpvvfUWrFZXL0ipVOK+++7Diy++CIVCuHdx4cIFPPvss9iwYQPKysqQkZGB1157DTfc\ncAOAOkft5ZdfxrJly2A2m9GtWzcsWLAAbdq0EbyPSMP3ZsG3DACKq2xOpxQAfjxbjep0/rebcrdp\n9nxjTK/UeC+wH0zENJQxpkIMV6iTYyhi6pv2jdVBVYUQA3JMhSG2ne3atSs++ugjPP/883j11VfR\nvHlzPPXUU5g+fbpznVmzZqGyshK5ublOO7ty5UrExQkvMcZHoK1wxSDStUTrZvxL97tt9X5wIBkn\nIbYykEMp5Nkgx9FXSs77+RLyPIpVcSiTkW2z2vnLRU38sbcKjrkAACAASURBVAQZcSrM6RIHg4rD\nf4961gCuj6NyBe/Ea4sd8/Zc9VgekYjpvHnzsGTJEowZMwb33XcfMjMzAdQNdH777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FbE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\nAADAEBhMAQAAYAgMpgAAADCEn7TpnVRb6h2VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ba12e2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the plotting layout\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))\n",
    "fig.autofmt_xdate(rotation = 45)\n",
    "\n",
    "# Actual max temperature measurement\n",
    "ax1.plot(dates, features['actual'])\n",
    "ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp')\n",
    "\n",
    "# Temperature from 1 day ago\n",
    "ax2.plot(dates, features['temp_1'])\n",
    "ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp')\n",
    "\n",
    "# Temperature from 2 days ago\n",
    "ax3.plot(dates, features['temp_2'])\n",
    "ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp')\n",
    "\n",
    "# Friend Estimate\n",
    "ax4.plot(dates, features['friend'])\n",
    "ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')\n",
    "\n",
    "plt.tight_layout(pad=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One hot encoding takes this:\n",
    "\n",
    "| week |\n",
    "|------|\n",
    "| Mon  |\n",
    "| Tue  |\n",
    "| Wed  |\n",
    "| Thu  |\n",
    "| Fri  |\n",
    "\n",
    "and converts it into:\n",
    "\n",
    "| Mon | Tue | Wed | Thu | Fri |\n",
    "|-----|-----|-----|-----|-----|\n",
    "| 1   | 0   | 0   | 0   | 0   |\n",
    "| 0   | 1   | 0   | 0   | 0   |\n",
    "| 0   | 0   | 1   | 0   | 0   |\n",
    "| 0   | 0   | 0   | 1   | 0   |\n",
    "| 0   | 0   | 0   | 0   | 1   |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>day</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "      <th>week_Fri</th>\n",
       "      <th>week_Mon</th>\n",
       "      <th>week_Sat</th>\n",
       "      <th>week_Sun</th>\n",
       "      <th>week_Thurs</th>\n",
       "      <th>week_Tues</th>\n",
       "      <th>week_Wed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  temp_2  temp_1  average  actual  friend  week_Fri  \\\n",
       "0  2016      1    1      45      45     45.6      45      29         1   \n",
       "1  2016      1    2      44      45     45.7      44      61         0   \n",
       "2  2016      1    3      45      44     45.8      41      56         0   \n",
       "3  2016      1    4      44      41     45.9      40      53         0   \n",
       "4  2016      1    5      41      40     46.0      44      41         0   \n",
       "\n",
       "   week_Mon  week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n",
       "0         0         0         0           0          0         0  \n",
       "1         0         1         0           0          0         0  \n",
       "2         0         0         1           0          0         0  \n",
       "3         1         0         0           0          0         0  \n",
       "4         0         0         0           0          1         0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot encode categorical features\n",
    "features = pd.get_dummies(features)\n",
    "features.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of features after one-hot encoding: (348, 15)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of features after one-hot encoding:', features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features and Labels and Convert Data to Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use numpy to convert to arrays\n",
    "import numpy as np\n",
    "\n",
    "# Labels are the values we want to predict\n",
    "labels = np.array(features['actual'])\n",
    "\n",
    "# Remove the labels from the features\n",
    "# axis 1 refers to the columns\n",
    "features= features.drop('actual', axis = 1)\n",
    "\n",
    "# Saving feature names for later use\n",
    "feature_list = list(features.columns)\n",
    "\n",
    "# Convert to numpy array\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Testing Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Using Skicit-learn to split data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25,\n",
    "                                                                           random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (261, 14)\n",
      "Training Labels Shape: (261,)\n",
      "Testing Features Shape: (87, 14)\n",
      "Testing Labels Shape: (87,)\n"
     ]
    }
   ],
   "source": [
    "print('Training Features Shape:', train_features.shape)\n",
    "print('Training Labels Shape:', train_labels.shape)\n",
    "print('Testing Features Shape:', test_features.shape)\n",
    "print('Testing Labels Shape:', test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Establish Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average baseline error:  5.06 degrees.\n"
     ]
    }
   ],
   "source": [
    "# The baseline predictions are the historical averages\n",
    "baseline_preds = test_features[:, feature_list.index('average')]\n",
    "\n",
    "# Baseline errors, and display average baseline error\n",
    "baseline_errors = abs(baseline_preds - test_labels)\n",
    "print('Average baseline error: ', round(np.mean(baseline_errors), 2), 'degrees.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "# Import the model we are using\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Instantiate model \n",
    "rf = RandomForestRegressor(n_estimators= 1000, random_state=42)\n",
    "\n",
    "# Train the model on training data\n",
    "rf.fit(train_features, train_labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Predictions on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.83 degrees.\n"
     ]
    }
   ],
   "source": [
    "# Use the forest's predict method on the test data\n",
    "predictions = rf.predict(test_features)\n",
    "\n",
    "# Calculate the absolute errors\n",
    "errors = abs(predictions - test_labels)\n",
    "\n",
    "# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determine Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.99 %.\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean absolute percentage error (MAPE)\n",
    "mape = 100 * (errors / test_labels)\n",
    "\n",
    "# Calculate and display accuracy\n",
    "accuracy = 100 - np.mean(mape)\n",
    "print('Accuracy:', round(accuracy, 2), '%.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improve Model if Necessary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create models with different hyperparameters to try and boost performance. The only way to find the best ones\n",
    "are to try a few and evaluate them! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_new = RandomForestRegressor(n_estimators = 100, criterion = 'mse', max_depth = None, \n",
    "                               min_samples_split = 2, min_samples_leaf = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpret Model Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing a Single Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import tools needed for visualization\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "\n",
    "# Pull out one tree from the forest\n",
    "tree = rf.estimators_[5]\n",
    "\n",
    "# Export the image to a dot file\n",
    "export_graphviz(tree, out_file = 'tree.dot', feature_names = feature_list, rounded = True, precision = 1)\n",
    "\n",
    "# Use dot file to create a graph\n",
    "(graph, ) = pydot.graph_from_dot_file('tree.dot')\n",
    "\n",
    "# Write graph to a png file\n",
    "graph.write_png('tree.png'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Decision Tree](tree.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The depth of this tree is: 15\n"
     ]
    }
   ],
   "source": [
    "print('The depth of this tree is:', tree.tree_.max_depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smaller tree for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Limit depth of tree to 2 levels\n",
    "rf_small = RandomForestRegressor(n_estimators=10, max_depth = 3, random_state=42)\n",
    "rf_small.fit(train_features, train_labels)\n",
    "\n",
    "# Extract the small tree\n",
    "tree_small = rf_small.estimators_[5]\n",
    "\n",
    "# Save the tree as a png image\n",
    "export_graphviz(tree_small, out_file = 'small_tree.dot', feature_names = feature_list, rounded = True, precision = 1)\n",
    "\n",
    "(graph, ) = pydot.graph_from_dot_file('small_tree.dot')\n",
    "\n",
    "graph.write_png('small_tree.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Small Decision Tree](images/small_tree.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotated Version of Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Annotated Decision Tree](images/small_tree_annotated.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: temp_1               Importance: 0.7\n",
      "Variable: average              Importance: 0.19\n",
      "Variable: day                  Importance: 0.03\n",
      "Variable: temp_2               Importance: 0.02\n",
      "Variable: friend               Importance: 0.02\n",
      "Variable: month                Importance: 0.01\n",
      "Variable: year                 Importance: 0.0\n",
      "Variable: week_Fri             Importance: 0.0\n",
      "Variable: week_Mon             Importance: 0.0\n",
      "Variable: week_Sat             Importance: 0.0\n",
      "Variable: week_Sun             Importance: 0.0\n",
      "Variable: week_Thurs           Importance: 0.0\n",
      "Variable: week_Tues            Importance: 0.0\n",
      "Variable: week_Wed             Importance: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Get numerical feature importances\n",
    "importances = list(rf.feature_importances_)\n",
    "\n",
    "# List of tuples with variable and importance\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
    "\n",
    "# Sort the feature importances by most important first\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "\n",
    "# Print out the feature and importances \n",
    "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with Two Most Important Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.9 degrees.\n",
      "Accuracy: 93.8 %.\n"
     ]
    }
   ],
   "source": [
    "# New random forest with only the two most important variables\n",
    "rf_most_important = RandomForestRegressor(n_estimators= 1000, random_state=42)\n",
    "\n",
    "# Extract the two most important features\n",
    "important_indices = [feature_list.index('temp_1'), feature_list.index('average')]\n",
    "train_important = train_features[:, important_indices]\n",
    "test_important = test_features[:, important_indices]\n",
    "\n",
    "# Train the random forest\n",
    "rf_most_important.fit(train_important, train_labels)\n",
    "\n",
    "# Make predictions and determine the error\n",
    "predictions = rf_most_important.predict(test_important)\n",
    "\n",
    "errors = abs(predictions - test_labels)\n",
    "\n",
    "# Display the performance metrics\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n",
    "\n",
    "mape = np.mean(100 * (errors / test_labels))\n",
    "accuracy = 100 - mape\n",
    "\n",
    "print('Accuracy:', round(accuracy, 2), '%.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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93L9/H/Xq1UNqaiqmTp2Knj17okuXLmjZsiXy8/Nx9OhRPH78GHPnzpU4MpIUg0mNpDJ1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\n27cPhYWFmDJlCiIjIxESEmK6zZKfn4833nhD1O4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3N4wZMwY6nQ5qtRpnzpxBaGio6E0D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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21badc49470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of x locations for plotting\n",
    "x_values = list(range(len(importances)))\n",
    "\n",
    "# Make a bar chart\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "\n",
    "# Tick labels for x axis\n",
    "plt.xticks(x_values, feature_list, rotation='vertical')\n",
    "\n",
    "# Axis labels and title\n",
    "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Actual Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Dates of training values\n",
    "months = features[:, feature_list.index('month')]\n",
    "days = features[:, feature_list.index('day')]\n",
    "years = features[:, feature_list.index('year')]\n",
    "\n",
    "# List and then convert to datetime object\n",
    "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
    "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]\n",
    "\n",
    "# Dataframe with true values and dates\n",
    "true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})\n",
    "\n",
    "# Dates of predictions\n",
    "months = test_features[:, feature_list.index('month')]\n",
    "days = test_features[:, feature_list.index('day')]\n",
    "years = test_features[:, feature_list.index('year')]\n",
    "\n",
    "# Column of dates\n",
    "test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
    "\n",
    "# Convert to datetime objects\n",
    "test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]\n",
    "\n",
    "# Dataframe with predictions and dates\n",
    "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zZixYsIDr169bLOj69evMnz+fpk2bMmHCBFEXT09PZ+zYsbRo0YLBgwcTGhrK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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21bad524a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the actual values\n",
    "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')\n",
    "\n",
    "# Plot the predicted values\n",
    "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')\n",
    "plt.xticks(rotation = '60'); \n",
    "plt.legend()\n",
    "\n",
    "# Graph labels\n",
    "plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Targets and Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GjKFPnz4EBfgDsVqt2Gw2r4z+oaGh7Nixg9TUVDIzM3nooYfcjnXo0IFdu3ap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\nN99MzFftA++YYXDxCKgyIbPXXruK2bMfMfhEYDSVBg2S+dnPhrBXegq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SAAAg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\n6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yopqUyh/cb4eBS+vly9eLHUZ7IoL7wA69ZNQKNZydChGfz6azY3b2YiICAIEllZsG4d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ba3720eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the data accessible for plotting\n",
    "true_data['temp_1'] = features[:, feature_list.index('temp_1')]\n",
    "true_data['average'] = features[:, feature_list.index('average')]\n",
    "true_data['friend'] = features[:, feature_list.index('friend')]\n",
    "\n",
    "# Plot all the data as lines\n",
    "plt.plot(true_data['date'], true_data['actual'], 'b-', label  = 'actual', alpha = 1.0)\n",
    "plt.plot(true_data['date'], true_data['temp_1'], 'y-', label  = 'temp_1', alpha = 1.0)\n",
    "plt.plot(true_data['date'], true_data['average'], 'k-', label = 'average', alpha = 0.8)\n",
    "plt.plot(true_data['date'], true_data['friend'], 'r-', label = 'friend', alpha = 0.3)\n",
    "\n",
    "# Formatting plot\n",
    "plt.legend(); plt.xticks(rotation = '60');\n",
    "\n",
    "# Lables and title\n",
    "plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual Max Temp and Variables');"
   ]
  },
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   "source": []
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