1-biketrain-data_preparation.ipynb

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    "# Bike Sharing Demand\n",
    "\n",
    "## Forecast use of a city bikeshare system\n",
    "\n",
    "Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.\n",
    "\n",
    "\n",
    "The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.\n",
    "\n",
    "\n",
    "By harnessing the power of regression algorithms, this project aims to predict the total count of bikes rented during each hour covered by the test set, based on only information available before the rental period. By evaluating the performance of different regression models using the Root Mean Squared Logarithmic Error (RMSLE), we will select the most accurate model that best captures the underlying relationships within the data.\n",
    "\n",
    "Ultimately, the goal of this project is to not only develop a reliable prediction model but also through a comprehensive analysis of the dataset, this project seeks to unearth meaningful insights about the factors influencing bike rentals. These insights could potentially aid stakeholders in making informed decisions to optimize bike availability, marketing strategies, and resource allocation."
   ]
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   "source": [
    "For this competition, the training set is comprised of the first 19 days of each month, while the test set is the 20th to the end of the month.\n",
    "\n",
    "You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0102da",
   "metadata": {},
   "source": [
    "### Understanding the Variables\n",
    "\n",
    "datetime: hourly date + timestamp\n",
    "Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "\n",
    "season:\n",
    "\n",
    "    1 = spring   \n",
    "    2 = summer\n",
    "    3 = fall\n",
    "    4 = winter\n",
    "holiday: whether the day is considered a holiday,\n",
    "      1 = holiday \n",
    "      0 = not holiday\n",
    "workingday: whether the day is neither a weekend nor holiday\n",
    "         1 = working day \n",
    "         0 = not working day\n",
    "weather:\n",
    "     1 = Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "     2 = Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "     3 = Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "     4 = Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \n",
    "temp: temperature in Celsius\n",
    "\n",
    "atemp: \"feels like\" temperature in Celsius\n",
    "\n",
    "humidity: relative humidity\n",
    "\n",
    "windspeed: wind speed\n",
    "\n",
    "casual: number of non-registered user rentals initiated\n",
    "\n",
    "registered: number of registered user rentals initiated\n",
    "\n",
    "count: number of total rentals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d7c7181",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a064d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv', parse_dates = ['datetime'])\n",
    "df_test = pd.read_csv('test.csv', parse_dates = ['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d2e02f5",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
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       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>2011-01-01 01:00:00</td>\n",
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       "      <td>80</td>\n",
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       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
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       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
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       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
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       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
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       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-01 05:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.84</td>\n",
       "      <td>12.880</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-01-01 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-01-01 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.20</td>\n",
       "      <td>12.880</td>\n",
       "      <td>86</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011-01-01 08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011-01-01 09:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.12</td>\n",
       "      <td>17.425</td>\n",
       "      <td>76</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011-01-01 10:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>76</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011-01-01 11:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.76</td>\n",
       "      <td>16.665</td>\n",
       "      <td>81</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2011-01-01 12:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>77</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>29</td>\n",
       "      <td>55</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2011-01-01 13:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>72</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011-01-01 14:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>72</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2011-01-01 15:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.04</td>\n",
       "      <td>21.970</td>\n",
       "      <td>77</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2011-01-01 16:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>82</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2011-01-01 17:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.04</td>\n",
       "      <td>21.970</td>\n",
       "      <td>82</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>15</td>\n",
       "      <td>52</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011-01-01 18:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>88</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2011-01-01 19:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>88</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011-01-01 20:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.40</td>\n",
       "      <td>20.455</td>\n",
       "      <td>87</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2011-01-01 21:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.40</td>\n",
       "      <td>20.455</td>\n",
       "      <td>87</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2011-01-01 22:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.40</td>\n",
       "      <td>20.455</td>\n",
       "      <td>94</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2011-01-01 23:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>88</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2011-01-02 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>88</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "5  2011-01-01 05:00:00       1        0           0        2   9.84  12.880   \n",
       "6  2011-01-01 06:00:00       1        0           0        1   9.02  13.635   \n",
       "7  2011-01-01 07:00:00       1        0           0        1   8.20  12.880   \n",
       "8  2011-01-01 08:00:00       1        0           0        1   9.84  14.395   \n",
       "9  2011-01-01 09:00:00       1        0           0        1  13.12  17.425   \n",
       "10 2011-01-01 10:00:00       1        0           0        1  15.58  19.695   \n",
       "11 2011-01-01 11:00:00       1        0           0        1  14.76  16.665   \n",
       "12 2011-01-01 12:00:00       1        0           0        1  17.22  21.210   \n",
       "13 2011-01-01 13:00:00       1        0           0        2  18.86  22.725   \n",
       "14 2011-01-01 14:00:00       1        0           0        2  18.86  22.725   \n",
       "15 2011-01-01 15:00:00       1        0           0        2  18.04  21.970   \n",
       "16 2011-01-01 16:00:00       1        0           0        2  17.22  21.210   \n",
       "17 2011-01-01 17:00:00       1        0           0        2  18.04  21.970   \n",
       "18 2011-01-01 18:00:00       1        0           0        3  17.22  21.210   \n",
       "19 2011-01-01 19:00:00       1        0           0        3  17.22  21.210   \n",
       "20 2011-01-01 20:00:00       1        0           0        2  16.40  20.455   \n",
       "21 2011-01-01 21:00:00       1        0           0        2  16.40  20.455   \n",
       "22 2011-01-01 22:00:00       1        0           0        2  16.40  20.455   \n",
       "23 2011-01-01 23:00:00       1        0           0        2  18.86  22.725   \n",
       "24 2011-01-02 00:00:00       1        0           0        2  18.86  22.725   \n",
       "\n",
       "    humidity  windspeed  casual  registered  count  \n",
       "0         81     0.0000       3          13     16  \n",
       "1         80     0.0000       8          32     40  \n",
       "2         80     0.0000       5          27     32  \n",
       "3         75     0.0000       3          10     13  \n",
       "4         75     0.0000       0           1      1  \n",
       "5         75     6.0032       0           1      1  \n",
       "6         80     0.0000       2           0      2  \n",
       "7         86     0.0000       1           2      3  \n",
       "8         75     0.0000       1           7      8  \n",
       "9         76     0.0000       8           6     14  \n",
       "10        76    16.9979      12          24     36  \n",
       "11        81    19.0012      26          30     56  \n",
       "12        77    19.0012      29          55     84  \n",
       "13        72    19.9995      47          47     94  \n",
       "14        72    19.0012      35          71    106  \n",
       "15        77    19.9995      40          70    110  \n",
       "16        82    19.9995      41          52     93  \n",
       "17        82    19.0012      15          52     67  \n",
       "18        88    16.9979       9          26     35  \n",
       "19        88    16.9979       6          31     37  \n",
       "20        87    16.9979      11          25     36  \n",
       "21        87    12.9980       3          31     34  \n",
       "22        94    15.0013      11          17     28  \n",
       "23        88    19.9995      15          24     39  \n",
       "24        88    19.9995       4          13     17  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b6306d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_features(df):\n",
    "    df['year'] = df['datetime'].dt.year\n",
    "    df['month'] = df['datetime'].dt.month\n",
    "    df['day'] = df['datetime'].dt.day\n",
    "    df['dayofweek'] = df['datetime'].dt.dayofweek\n",
    "    df['hour'] = df['datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c1d52fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "add_features(df)\n",
    "add_features(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36e40ff3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      datetime64[ns]\n",
       "season                 int64\n",
       "holiday                int64\n",
       "workingday             int64\n",
       "weather                int64\n",
       "temp                 float64\n",
       "atemp                float64\n",
       "humidity               int64\n",
       "windspeed            float64\n",
       "casual                 int64\n",
       "registered             int64\n",
       "count                  int64\n",
       "year                   int32\n",
       "month                  int32\n",
       "day                    int32\n",
       "dayofweek              int32\n",
       "hour                   int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22eba815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'count'}>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "141320e9",
   "metadata": {},
   "source": [
    "We can see that the target variable Count is skewed so we will apply log transformation to the count variable into log(count + 1) since distribution of counts is skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd010e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"count\"] = df[\"count\"].map(np.log1p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "127e0c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2.639057</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered     count  year  month  day  \\\n",
       "0        81        0.0       3          13  2.833213  2011      1    1   \n",
       "1        80        0.0       8          32  3.713572  2011      1    1   \n",
       "2        80        0.0       5          27  3.496508  2011      1    1   \n",
       "3        75        0.0       3          10  2.639057  2011      1    1   \n",
       "4        75        0.0       0           1  0.693147  2011      1    1   \n",
       "\n",
       "   dayofweek  hour  \n",
       "0          5     0  \n",
       "1          5     1  \n",
       "2          5     2  \n",
       "3          5     3  \n",
       "4          5     4  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c027ce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'count'}>]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb4b973a",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_hour = df.groupby(['hour'])\n",
    "avg_by_hour = group_hour['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6bd1451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rental count average by hour')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(avg_by_hour.index, avg_by_hour)\n",
    "plt.xlabel('hour')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(np.arange(24))\n",
    "plt.grid(True)\n",
    "plt.title('Rental count average by hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9b31157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      0.237444\n",
       "season        0.160452\n",
       "holiday      -0.001231\n",
       "workingday   -0.015326\n",
       "weather      -0.108005\n",
       "temp          0.376587\n",
       "atemp         0.372167\n",
       "humidity     -0.333158\n",
       "windspeed     0.122754\n",
       "casual        0.579034\n",
       "registered    0.792129\n",
       "count         1.000000\n",
       "year          0.178236\n",
       "month         0.163731\n",
       "day           0.019008\n",
       "dayofweek     0.027265\n",
       "hour          0.570638\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['count']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b651fb3f",
   "metadata": {},
   "source": [
    "We will exclude the casual and registered columns from the final dataset since they have high correlation with count, our target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f5857ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_yr_hour = df.groupby(['year', 'hour'])\n",
    "avg_by_yr_hour = group_yr_hour['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cda9671f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year  hour\n",
      "2011  0       3.502060\n",
      "      1       2.891321\n",
      "      2       2.473136\n",
      "      3       2.007282\n",
      "      4       1.686262\n",
      "      5       2.486522\n",
      "      6       3.662822\n",
      "      7       4.618962\n",
      "      8       5.276212\n",
      "      9       4.999673\n",
      "      10      4.719866\n",
      "      11      4.862747\n",
      "      12      5.082646\n",
      "      13      5.086519\n",
      "      14      5.020945\n",
      "      15      5.056894\n",
      "      16      5.326948\n",
      "      17      5.729295\n",
      "      18      5.619503\n",
      "      19      5.306056\n",
      "      20      4.992485\n",
      "      21      4.729681\n",
      "      22      4.476463\n",
      "      23      4.061601\n",
      "2012  0       3.971435\n",
      "      1       3.316618\n",
      "      2       2.755444\n",
      "      3       2.256099\n",
      "      4       1.980326\n",
      "      5       3.066929\n",
      "      6       4.213505\n",
      "      7       5.215524\n",
      "      8       5.872845\n",
      "      9       5.568828\n",
      "      10      5.276638\n",
      "      11      5.461930\n",
      "      12      5.667054\n",
      "      13      5.668790\n",
      "      14      5.588674\n",
      "      15      5.662563\n",
      "      16      5.907332\n",
      "      17      6.272040\n",
      "      18      6.176945\n",
      "      19      5.876938\n",
      "      20      5.546438\n",
      "      21      5.264216\n",
      "      22      4.995701\n",
      "      23      4.568418\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(avg_by_yr_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e772edef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rental Count avg by Year, Hour')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for year in avg_by_yr_hour.index.levels[0]:\n",
    "    #print(avg_by_yr_hour[year])\n",
    "    plt.plot(avg_by_yr_hour[year].index, avg_by_yr_hour[year], label=year)\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel('hour')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(np.arange(24))\n",
    "plt.grid(True)\n",
    "plt.title('Rental Count avg by Year, Hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b58186e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_workingday_hour = df.groupby(['workingday', 'hour'])\n",
    "avg_by_workingday_hour = group_workingday_hour['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "068524e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rental Count avg by Workingday, Hour')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for workingday in avg_by_workingday_hour.index.levels[0]:\n",
    "    #print(avg_by_yr_hour[year])\n",
    "    plt.plot(avg_by_workingday_hour[workingday].index, avg_by_workingday_hour[workingday], label=workingday)\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel('hour')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(np.arange(24))\n",
    "plt.grid(True)\n",
    "plt.title('Rental Count avg by Workingday, Hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d3c46e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sagemaker built-in algorithms require datasets where the first feature is the label or target (y) \n",
    "# and the rest are independent variables (X).\n",
    "# We need to concatenate the y and X to create the dataset in the expected format and order and save it locally as a CSV file.\n",
    "\n",
    "columns = ['count','season','holiday','workingday','weather','temp','atemp','humidity','windspeed','year','month','day'\n",
    "           ,'dayofweek','hour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4ec432e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('bike_all.csv', index= False, columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc43eb6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training = 70% of train data\n",
    "# validation = 30% of train data\n",
    "\n",
    "np.random.seed(5)\n",
    "l = list(df.index)\n",
    "np.random.shuffle(l)\n",
    "df = df.iloc[l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8b45d9b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = df.shape[0]\n",
    "train = int(.7 * rows)\n",
    "test = int(.3 * rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2f03cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 7620, 3265)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows, train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9dbca4f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['count','season','holiday','workingday','weather','temp','atemp','humidity','windspeed','year','month','day'\n",
    "           ,'dayofweek','hour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "55fd8bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[:train].to_csv('bike_train.csv', index = False, header= False, columns = columns)\n",
    "df[train:].to_csv('bike_val.csv', index = False, header= False, columns = columns)\n",
    "df_test.to_csv('bike_test.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "268a96fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'count,season,holiday,workingday,weather,temp,atemp,humidity,windspeed,year,month,day,dayofweek,hour'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "','.join(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6f399abd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write Columns List\n",
    "with open('bike_train_column_list.txt', 'w') as f:\n",
    "    f.write(','.join(columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2ea07dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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