Your_first_neural_network.ipynb

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    "# Your first neural network\n",
    "\n",
    "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
    "\n"
   ]
  },
  {
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   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Load and prepare the data\n",
    "\n",
    "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [],
   "source": [
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>29</td>\n",
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       "      <td>84</td>\n",
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       "      <td>0.72</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>94</td>\n",
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       "    <tr>\n",
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       "      <td>15</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.4545</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>106</td>\n",
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       "    <tr>\n",
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       "      <td>16</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.4394</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>110</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.4242</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "      <td>93</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.4394</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>15</td>\n",
       "      <td>52</td>\n",
       "      <td>67</td>\n",
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       "      <td>19</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.4242</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
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       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.4242</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.4091</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.4091</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.4091</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.4545</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>2011-01-02</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>2</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.4545</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.4394</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.4242</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.4545</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.4545</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.4242</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.4091</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.4091</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.3939</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.3485</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>7</td>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.3485</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.3485</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>7</td>\n",
       "      <td>58</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.3284</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.4478</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.3284</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.1970</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>2011-01-03</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.1667</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.4179</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.1364</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.3881</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0         1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1         2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2         3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3         4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4         5  2011-01-01       1   0     1   4        0        6           0   \n",
       "5         6  2011-01-01       1   0     1   5        0        6           0   \n",
       "6         7  2011-01-01       1   0     1   6        0        6           0   \n",
       "7         8  2011-01-01       1   0     1   7        0        6           0   \n",
       "8         9  2011-01-01       1   0     1   8        0        6           0   \n",
       "9        10  2011-01-01       1   0     1   9        0        6           0   \n",
       "10       11  2011-01-01       1   0     1  10        0        6           0   \n",
       "11       12  2011-01-01       1   0     1  11        0        6           0   \n",
       "12       13  2011-01-01       1   0     1  12        0        6           0   \n",
       "13       14  2011-01-01       1   0     1  13        0        6           0   \n",
       "14       15  2011-01-01       1   0     1  14        0        6           0   \n",
       "15       16  2011-01-01       1   0     1  15        0        6           0   \n",
       "16       17  2011-01-01       1   0     1  16        0        6           0   \n",
       "17       18  2011-01-01       1   0     1  17        0        6           0   \n",
       "18       19  2011-01-01       1   0     1  18        0        6           0   \n",
       "19       20  2011-01-01       1   0     1  19        0        6           0   \n",
       "20       21  2011-01-01       1   0     1  20        0        6           0   \n",
       "21       22  2011-01-01       1   0     1  21        0        6           0   \n",
       "22       23  2011-01-01       1   0     1  22        0        6           0   \n",
       "23       24  2011-01-01       1   0     1  23        0        6           0   \n",
       "24       25  2011-01-02       1   0     1   0        0        0           0   \n",
       "25       26  2011-01-02       1   0     1   1        0        0           0   \n",
       "26       27  2011-01-02       1   0     1   2        0        0           0   \n",
       "27       28  2011-01-02       1   0     1   3        0        0           0   \n",
       "28       29  2011-01-02       1   0     1   4        0        0           0   \n",
       "29       30  2011-01-02       1   0     1   6        0        0           0   \n",
       "30       31  2011-01-02       1   0     1   7        0        0           0   \n",
       "31       32  2011-01-02       1   0     1   8        0        0           0   \n",
       "32       33  2011-01-02       1   0     1   9        0        0           0   \n",
       "33       34  2011-01-02       1   0     1  10        0        0           0   \n",
       "34       35  2011-01-02       1   0     1  11        0        0           0   \n",
       "35       36  2011-01-02       1   0     1  12        0        0           0   \n",
       "36       37  2011-01-02       1   0     1  13        0        0           0   \n",
       "37       38  2011-01-02       1   0     1  14        0        0           0   \n",
       "38       39  2011-01-02       1   0     1  15        0        0           0   \n",
       "39       40  2011-01-02       1   0     1  16        0        0           0   \n",
       "40       41  2011-01-02       1   0     1  17        0        0           0   \n",
       "41       42  2011-01-02       1   0     1  18        0        0           0   \n",
       "42       43  2011-01-02       1   0     1  19        0        0           0   \n",
       "43       44  2011-01-02       1   0     1  20        0        0           0   \n",
       "44       45  2011-01-02       1   0     1  21        0        0           0   \n",
       "45       46  2011-01-02       1   0     1  22        0        0           0   \n",
       "46       47  2011-01-02       1   0     1  23        0        0           0   \n",
       "47       48  2011-01-03       1   0     1   0        0        1           1   \n",
       "48       49  2011-01-03       1   0     1   1        0        1           1   \n",
       "49       50  2011-01-03       1   0     1   4        0        1           1   \n",
       "\n",
       "    weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0            1  0.24  0.2879  0.81     0.0000       3          13   16  \n",
       "1            1  0.22  0.2727  0.80     0.0000       8          32   40  \n",
       "2            1  0.22  0.2727  0.80     0.0000       5          27   32  \n",
       "3            1  0.24  0.2879  0.75     0.0000       3          10   13  \n",
       "4            1  0.24  0.2879  0.75     0.0000       0           1    1  \n",
       "5            2  0.24  0.2576  0.75     0.0896       0           1    1  \n",
       "6            1  0.22  0.2727  0.80     0.0000       2           0    2  \n",
       "7            1  0.20  0.2576  0.86     0.0000       1           2    3  \n",
       "8            1  0.24  0.2879  0.75     0.0000       1           7    8  \n",
       "9            1  0.32  0.3485  0.76     0.0000       8           6   14  \n",
       "10           1  0.38  0.3939  0.76     0.2537      12          24   36  \n",
       "11           1  0.36  0.3333  0.81     0.2836      26          30   56  \n",
       "12           1  0.42  0.4242  0.77     0.2836      29          55   84  \n",
       "13           2  0.46  0.4545  0.72     0.2985      47          47   94  \n",
       "14           2  0.46  0.4545  0.72     0.2836      35          71  106  \n",
       "15           2  0.44  0.4394  0.77     0.2985      40          70  110  \n",
       "16           2  0.42  0.4242  0.82     0.2985      41          52   93  \n",
       "17           2  0.44  0.4394  0.82     0.2836      15          52   67  \n",
       "18           3  0.42  0.4242  0.88     0.2537       9          26   35  \n",
       "19           3  0.42  0.4242  0.88     0.2537       6          31   37  \n",
       "20           2  0.40  0.4091  0.87     0.2537      11          25   36  \n",
       "21           2  0.40  0.4091  0.87     0.1940       3          31   34  \n",
       "22           2  0.40  0.4091  0.94     0.2239      11          17   28  \n",
       "23           2  0.46  0.4545  0.88     0.2985      15          24   39  \n",
       "24           2  0.46  0.4545  0.88     0.2985       4          13   17  \n",
       "25           2  0.44  0.4394  0.94     0.2537       1          16   17  \n",
       "26           2  0.42  0.4242  1.00     0.2836       1           8    9  \n",
       "27           2  0.46  0.4545  0.94     0.1940       2           4    6  \n",
       "28           2  0.46  0.4545  0.94     0.1940       2           1    3  \n",
       "29           3  0.42  0.4242  0.77     0.2985       0           2    2  \n",
       "30           2  0.40  0.4091  0.76     0.1940       0           1    1  \n",
       "31           3  0.40  0.4091  0.71     0.2239       0           8    8  \n",
       "32           2  0.38  0.3939  0.76     0.2239       1          19   20  \n",
       "33           2  0.36  0.3485  0.81     0.2239       7          46   53  \n",
       "34           2  0.36  0.3333  0.71     0.2537      16          54   70  \n",
       "35           2  0.36  0.3333  0.66     0.2985      20          73   93  \n",
       "36           2  0.36  0.3485  0.66     0.1343      11          64   75  \n",
       "37           3  0.36  0.3485  0.76     0.1940       4          55   59  \n",
       "38           3  0.34  0.3333  0.81     0.1642      19          55   74  \n",
       "39           3  0.34  0.3333  0.71     0.1642       9          67   76  \n",
       "40           1  0.34  0.3333  0.57     0.1940       7          58   65  \n",
       "41           2  0.36  0.3333  0.46     0.3284      10          43   53  \n",
       "42           1  0.32  0.2879  0.42     0.4478       1          29   30  \n",
       "43           1  0.30  0.2727  0.39     0.3582       5          17   22  \n",
       "44           1  0.26  0.2273  0.44     0.3284      11          20   31  \n",
       "45           1  0.24  0.2121  0.44     0.2985       0           9    9  \n",
       "46           1  0.22  0.2273  0.47     0.1642       0           8    8  \n",
       "47           1  0.22  0.1970  0.44     0.3582       0           5    5  \n",
       "48           1  0.20  0.1667  0.44     0.4179       0           2    2  \n",
       "49           1  0.16  0.1364  0.47     0.3881       0           1    1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Checking out the data\n",
    "\n",
    "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
    "\n",
    "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10554d9e8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SeMPpm/CGv17KB1hozJ5aKvjL2WhMCCnT2JhMQgiJge0gmYtN5y8fPj/ZvrtOnJvx24sH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\nC3xCSBRiDLpS77J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zwCckEqZSG9qD3zEU/JTWBMDVZOuxuDVy1gu6ms85pUVn+th6Bvpy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\nMzOzCnGBb2ZmZmZWIf8HXKqiIyqMTswAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10554d208>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Dummy variables\n",
    "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
       "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
       "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
       "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
       "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
       "\n",
       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0         0    ...          0      0      0          0          0          0   \n",
       "1         0    ...          0      0      0          0          0          0   \n",
       "2         0    ...          0      0      0          0          0          0   \n",
       "3         0    ...          0      0      0          0          0          0   \n",
       "4         0    ...          0      0      0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          1  \n",
       "1          0          0          0          1  \n",
       "2          0          0          0          1  \n",
       "3          0          0          0          1  \n",
       "4          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "for each in dummy_fields:\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides = pd.concat([rides, dummies], axis=1)\n",
    "\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "data = rides.drop(fields_to_drop, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Scaling target variables\n",
    "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
    "\n",
    "The scaling factors are saved so we can go backwards when we use the network for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Store scalings in a dictionary so we can convert back later\n",
    "scaled_features = {}\n",
    "for each in quant_features:\n",
    "    mean, std = data[each].mean(), data[each].std()\n",
    "    scaled_features[each] = [mean, std]\n",
    "    data.loc[:, each] = (data[each] - mean)/std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Splitting the data into training, testing, and validation sets\n",
    "\n",
    "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Save data for approximately the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "\n",
    "# Now remove the test data from the data set \n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separate the data into features and targets\n",
    "target_fields = ['cnt', 'casual', 'registered']\n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Hold out the last 60 days or so of the remaining data as a validation set\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Time to build the network\n",
    "\n",
    "Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
    "\n",
    "<img src=\"assets/neural_network.png\" width=300px>\n",
    "\n",
    "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
    "\n",
    "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
    "\n",
    "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
    "\n",
    "Below, you have these tasks:\n",
    "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
    "2. Implement the forward pass in the `train` method.\n",
    "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
    "4. Implement the forward pass in the `run` method.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class NeuralNetwork(object):\n",
    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        # Set number of nodes in input, hidden and output layers.\n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "\n",
    "        # Initialize weights\n",
    "        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n",
    "                                       (self.input_nodes, self.hidden_nodes))\n",
    "\n",
    "        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
    "                                       (self.hidden_nodes, self.output_nodes))\n",
    "        self.lr = learning_rate\n",
    "        \n",
    "        #### TODO: Set self.activation_function to your implemented sigmoid function ####\n",
    "        #\n",
    "        # Note: in Python, you can define a function with a lambda expression,\n",
    "        # as shown below.\n",
    "        self.activation_function = lambda x : 1.0/(1+np.exp(-x))  # Replace 0 with your sigmoid calculation.\n",
    "        \n",
    "        ### If the lambda code above is not something you're familiar with,\n",
    "        # You can uncomment out the following three lines and put your \n",
    "        # implementation there instead.\n",
    "        #\n",
    "#         def sigmoid(x):\n",
    "#            return 1.0/(1+np.exp(-x))  # Replace 0 with your sigmoid calculation here\n",
    "#         self.activation_function = sigmoid\n",
    "                    \n",
    "    \n",
    "    def train(self, features, targets):\n",
    "        ''' Train the network on batch of features and targets. \n",
    "        \n",
    "            Arguments\n",
    "            ---------\n",
    "            \n",
    "            features: 2D array, each row is one data record, each column is a feature\n",
    "            targets: 1D array of target values\n",
    "        \n",
    "        '''\n",
    "        n_records = features.shape[0]\n",
    "        \n",
    "        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n",
    "        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n",
    "        for X, y in zip(features, targets):\n",
    "            #### Implement the forward pass here ####\n",
    "            ### Forward pass ###\n",
    "            # TODO: Hidden layer - Replace these values with your calculations.\n",
    "            hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer\n",
    "            hidden_outputs =  self.activation_function(hidden_inputs) # signals from hidden layer\n",
    "\n",
    "            # TODO: Output layer - Replace these values with your calculations.\n",
    "            final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n",
    "            final_outputs = final_inputs # signals from final output layer\n",
    "            \n",
    "            #### Implement the backward pass here ####\n",
    "            ### Backward pass ###\n",
    "\n",
    "            # TODO: Output error - Replace this value with your calculations.\n",
    "            error = y - final_outputs # Output layer error is the difference between desired target and actual output.\n",
    "            \n",
    "            # TODO: Calculate the hidden layer's contribution to the error\n",
    "            output_error_term = error * final_outputs * (1 - final_outputs)\n",
    "            hidden_error = np.dot(self.weights_hidden_to_output, error)\n",
    "            \n",
    "            # TODO: Backpropagated error terms - Replace these values with your calculations.\n",
    "            output_error_term = error\n",
    "            hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs) \n",
    "\n",
    "            # Weight step (input to hidden)\n",
    "            delta_weights_i_h += np.dot(X[:, None], hidden_error_term[:, None].T)\n",
    "            # Weight step (hidden to output)\n",
    "            delta_weights_h_o += np.dot(hidden_outputs[:, None], output_error_term[:, None])\n",
    "            \n",
    "        # TODO: Update the weights - Replace these values with your calculations.\n",
    "        self.weights_hidden_to_output +=  delta_weights_h_o * self.lr / n_records # update hidden-to-output weights with gradient descent step\n",
    "        self.weights_input_to_hidden +=   delta_weights_i_h * self.lr / n_records # update input-to-hidden weights with gradient descent step\n",
    "    def run(self, features):\n",
    "        ''' Run a forward pass through the network with input features \n",
    "        \n",
    "            Arguments\n",
    "            ---------\n",
    "            features: 1D array of feature values\n",
    "        '''\n",
    "        \n",
    "        #### Implement the forward pass here ####\n",
    "        # TODO: Hidden layer - replace these values with the appropriate calculations.\n",
    "        hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer\n",
    "        hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
    "        \n",
    "        # TODO: Output layer - Replace these values with the appropriate calculations.\n",
    "        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n",
    "        final_outputs = self.activation_function(final_inputs) # signals from final output layer # signals from final output layer \n",
    "        return final_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Unit tests\n",
    "\n",
    "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.014s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
    "targets = np.array([[0.4]])\n",
    "test_w_i_h = np.array([[0.1, -0.2],\n",
    "                       [0.4, 0.5],\n",
    "                       [-0.3, 0.2]])\n",
    "test_w_h_o = np.array([[0.3],\n",
    "                       [-0.1]])\n",
    "\n",
    "class TestMethods(unittest.TestCase):\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for data loading\n",
    "    ##########\n",
    "    \n",
    "    def test_data_path(self):\n",
    "        # Test that file path to dataset has been unaltered\n",
    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
    "        \n",
    "    def test_data_loaded(self):\n",
    "        # Test that data frame loaded\n",
    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for network functionality\n",
    "    ##########\n",
    "\n",
    "    def test_activation(self):\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        # Test that the activation function is a sigmoid\n",
    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
    "\n",
    "    def test_train(self):\n",
    "        # Test that weights are updated correctly on training\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "        \n",
    "        network.train(inputs, targets)\n",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
    "                                    np.array([[ 0.37275328], \n",
    "                                              [-0.03172939]])))\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
    "                                    np.array([[ 0.10562014, -0.20185996], \n",
    "                                              [0.39775194, 0.50074398], \n",
    "                                              [-0.29887597, 0.19962801]])))\n",
    "\n",
    "    def test_run(self):\n",
    "        # Test correctness of run method\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "\n",
    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Training the network\n",
    "\n",
    "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
    "\n",
    "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
    "\n",
    "### Choose the number of iterations\n",
    "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.\n",
    "\n",
    "### Choose the learning rate\n",
    "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
    "\n",
    "### Choose the number of hidden nodes\n",
    "The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.0% ... Training loss: 0.052 ... Validation loss: 0.171"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "### Set the hyperparameters here ###\n",
    "iterations = 10000\n",
    "learning_rate = 0.8\n",
    "hidden_nodes = 8\n",
    "output_nodes = 1\n",
    "\n",
    "N_i = train_features.shape[1]\n",
    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
    "\n",
    "losses = {'train':[], 'validation':[]}\n",
    "for ii in range(iterations):\n",
    "    # Go through a random batch of 128 records from the training data set\n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "                             \n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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ApEwVJUmSJGVTxsN6CKEh0B1YF2OcBZB+mPTsCs6/mlRYf6iya9brHh8wlSRJ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\ndUEQBEEQBEEIU0RYFwRBEARBEIQw5f8BQQJD/AZfMbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102bbfd30>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 373
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Check out your predictions\n",
    "\n",
    "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ly5drHz/ppJNw0kknaZ8rl8s4//zzcf7554eemz9/PubPn9/sUEnGYU17AQn3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kp5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d+1MKXYs4MvrdZtn2eWb6dHYq+XZVHSPfdzn3fEoR9IFfqbFVvKQGoyjmkv2buOEqWfmXWl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\nBEEQhIgiTrsgCIIgCIIgCIIgRJT/DzIk4h9g7xeIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105bc6d68>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 502
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features).T*std + mean\n",
    "ax.plot(predictions[0], label='Prediction')\n",
    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
    "ax.set_xlim(right=len(predictions))\n",
    "ax.legend()\n",
    "\n",
    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n",
    " \n",
    "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
    "\n",
    "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
    "\n",
    "#### Your answer below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "The model seems to fail around Christmas and overestimate the need for that time of year. I guessing during that time of year there are alot of people of work, out of school, or ou of town. Other than that it seem to be suprisingly accurate. "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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