bike-sharing-demand.ipynb

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    "Bogazici-SWE546-Spring2016 Final Project - Mustafa Atik"
   ]
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    "## Bike sharing demand competition\n",
    "I am just a beginner to kaggle competitions. After reading a few posts dealing with the famous titanic data, I felt confident to try to solve a problem on my own. Bike sharing problem is also famous but this time, I will not read the articles about it. After I am done with this problem, surely I will take a look at them. \n",
    "\n",
    "### Forecast use of a city bikeshare system\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",
    "> 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",
    "In this problem, you are asked to predict bike rental demand in Washington D.C. by using the historical data. Since you will try to predict the demand which is a continuous number, this is a regression problem, not a classification. In titanic competition, you are asked to predict whether a given person can survive or not;  obviously, this is a classification task.\n",
    "\n",
    "I assume you are familiar with python, and the popular libraries such as pandas, seaborn, pyplot, numpy or even sklearn."
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    "from IPython.core import display\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, cross_validation\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "%matplotlib inline"
   ]
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       "<div>\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>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6168</th>\n",
       "      <td>2012-02-13 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.10</td>\n",
       "      <td>5.305</td>\n",
       "      <td>54</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>4.290459</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7800</th>\n",
       "      <td>2012-06-05 09:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21.32</td>\n",
       "      <td>25.000</td>\n",
       "      <td>72</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>50</td>\n",
       "      <td>285</td>\n",
       "      <td>5.817111</td>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6767</th>\n",
       "      <td>2012-03-19 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>88</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9649</th>\n",
       "      <td>2012-10-06 10:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.42</td>\n",
       "      <td>30.305</td>\n",
       "      <td>69</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>198</td>\n",
       "      <td>341</td>\n",
       "      <td>6.291569</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4281</th>\n",
       "      <td>2011-10-10 10:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.96</td>\n",
       "      <td>26.515</td>\n",
       "      <td>73</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>107</td>\n",
       "      <td>158</td>\n",
       "      <td>5.583496</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "6168 2012-02-13 06:00:00       1        0           1        1   4.10   5.305   \n",
       "7800 2012-06-05 09:00:00       2        0           1        2  21.32  25.000   \n",
       "6767 2012-03-19 06:00:00       1        0           1        1  18.86  22.725   \n",
       "9649 2012-10-06 10:00:00       4        0           0        1  25.42  30.305   \n",
       "4281 2011-10-10 10:00:00       4        1           0        1  22.96  26.515   \n",
       "\n",
       "      humidity  windspeed  casual  registered     count  year  month  hour  \\\n",
       "6168        54    15.0013       1          71  4.290459  2012      2     6   \n",
       "7800        72    16.9979      50         285  5.817111  2012      6     9   \n",
       "6767        88     8.9981       2         118  4.795791  2012      3     6   \n",
       "9649        69    19.0012     198         341  6.291569  2012     10    10   \n",
       "4281        73    11.0014     107         158  5.583496  2011     10    10   \n",
       "\n",
       "      dayofweek  \n",
       "6168          0  \n",
       "7800          1  \n",
       "6767          0  \n",
       "9649          5  \n",
       "4281          0  "
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   "source": [
    "def readCsv(name):\n",
    "    df = pd.read_csv(\"data/bike-sharing/{}.csv\".format(name), parse_dates=[\"datetime\"])\n",
    "    df[\"year\"] = pd.DatetimeIndex(df['datetime']).year\n",
    "    df[\"month\"] = pd.DatetimeIndex(df['datetime']).month\n",
    "    df[\"hour\"] = pd.DatetimeIndex(df['datetime']).hour\n",
    "    df[\"dayofweek\"] = pd.DatetimeIndex(df['datetime']).dayofweek\n",
    "    if \"count\" in df.columns:\n",
    "        df[\"count\"] = np.log(df[\"count\"] + 1)\n",
    "    return df\n",
    "\n",
    "df = readCsv(\"train\")\n",
    "df.sample(5)"
   ]
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    "### Field descriptions \n",
    "```\n",
    "datetime - hourly date + timestamp  \n",
    "season -  1 = spring, 2 = summer, 3 = fall, 4 = winter \n",
    "holiday - whether the day is considered a holiday\n",
    "workingday - whether the day is neither a weekend nor holiday\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",
    "atemp - \"feels like\" temperature in Celsius\n",
    "humidity - relative humidity\n",
    "windspeed - wind speed\n",
    "casual - number of non-registered user rentals initiated\n",
    "registered - number of registered user rentals initiated\n",
    "count - number of total rentals\n",
    "```"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10886 entries, 0 to 10885\n",
      "Data columns (total 16 columns):\n",
      "datetime      10886 non-null datetime64[ns]\n",
      "season        10886 non-null int64\n",
      "holiday       10886 non-null int64\n",
      "workingday    10886 non-null int64\n",
      "weather       10886 non-null int64\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "casual        10886 non-null int64\n",
      "registered    10886 non-null int64\n",
      "count         10886 non-null float64\n",
      "year          10886 non-null int32\n",
      "month         10886 non-null int32\n",
      "hour          10886 non-null int32\n",
      "dayofweek     10886 non-null int32\n",
      "dtypes: datetime64[ns](1), float64(4), int32(4), int64(7)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
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   "source": [
    "def exploreUnivariate(feature):\n",
    "    print \"\\n-----------------------\\nFEATURE: {}\\n\".format(feature.name)\n",
    "    print feature.describe()\n",
    "    sns.distplot(feature)\n",
    "    plt.show()"
   ]
  },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: season\n",
      "\n",
      "count    10886.000000\n",
      "mean         2.506614\n",
      "std          1.116174\n",
      "min          1.000000\n",
      "25%          2.000000\n",
      "50%          3.000000\n",
      "75%          4.000000\n",
      "max          4.000000\n",
      "Name: season, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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lTNOSRf5Ct4chX5C1S2pTXqw7VY3VBYyHwhw/N5jVz82kI+0DGMDaxdltecbH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\nMCnyQghhYf8/QnJq4oaqnzIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e9e7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: holiday\n",
      "\n",
      "count    10886.000000\n",
      "mean         0.028569\n",
      "std          0.166599\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          1.000000\n",
      "Name: holiday, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b45210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: workingday\n",
      "\n",
      "count    10886.000000\n",
      "mean         0.680875\n",
      "std          0.466159\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          1.000000\n",
      "75%          1.000000\n",
      "max          1.000000\n",
      "Name: workingday, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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CPAKcfb/7jML7iVeO84PN+zMurFACxX6WNlaxpCHEyqZq1q+oI1CgcTYnMURf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ec3110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: weather\n",
      "\n",
      "count    10886.000000\n",
      "mean         1.418427\n",
      "std          0.633839\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          2.000000\n",
      "max          4.000000\n",
      "Name: weather, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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2fA8Qt9Zea4zZCHyH5OfIQe05YY1JTmnLVJv9KzBwjsed0JZj6xlXZ1JW7TnV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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141bcb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: temp\n",
      "\n",
      "count    10886.00000\n",
      "mean        20.23086\n",
      "std          7.79159\n",
      "min          0.82000\n",
      "25%         13.94000\n",
      "50%         20.50000\n",
      "75%         26.24000\n",
      "max         41.00000\n",
      "Name: temp, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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CnAz+22cXcduiEqrqAnz/Rc2+8kY56o+B5Yq+YRj8+s1ydp+4wrSi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\nhUjRF0IIC/l/3fCZ+hj4X94AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143a6b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: atemp\n",
      "\n",
      "count    10886.000000\n",
      "mean        23.655084\n",
      "std          8.474601\n",
      "min          0.760000\n",
      "25%         16.665000\n",
      "50%         24.240000\n",
      "75%         31.060000\n",
      "max         45.455000\n",
      "Name: atemp, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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hEIfrOyfcLhSOzgOU47SxbY6sg2smY903zzfLqlrzgST9eeR88wCvH26jtDCH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\nIpL0hRDCRCTpCyGEifz/iJbLIgdYpIcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145bb710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: humidity\n",
      "\n",
      "count    10886.000000\n",
      "mean        61.886460\n",
      "std         19.245033\n",
      "min          0.000000\n",
      "25%         47.000000\n",
      "50%         62.000000\n",
      "75%         77.000000\n",
      "max        100.000000\n",
      "Name: humidity, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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KmVLvI5lz6V0rn9t1lrjjsHVDNeFwSN0rZVYVFrhZvTjA8eYwl3v6KQ8UZTuk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\nyBsd/+9Za4cHCheQ+H+Zdvx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145f0d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: windspeed\n",
      "\n",
      "count    10886.000000\n",
      "mean        12.799395\n",
      "std          8.164537\n",
      "min          0.000000\n",
      "25%          7.001500\n",
      "50%         12.998000\n",
      "75%         16.997900\n",
      "max         56.996900\n",
      "Name: windspeed, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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bPMjh0+4ojqtN0tXTN0K+UGRFh/qBF5rjeNi0Msr167t48+gA+08M8YN95wj4\nPKztDLI1OkE45K91mSJzopb+Atm4splUZuLCSdrpHDzl3pNm82qN9KiVoN/LbaaLH71zAzdtaKNY\nLHKod4y///ZRvrW7h1PnRzQEU+qWWvoLZNOqFl7cf56DPUN0t03fdXPw1BAAm1e1LFRpMo1wyMct\nmztZHpugdyjPmcE8PX1pevrSNAUc0lm47+0hWmPBWpcqUjGF/gLZuNJtuR/uGeaOm1dMuU2xWORQ\nzxBt8SDtzaEpt5GF5/O63Tu3bOliIDnGoVNDHD2T5KmXenlm91luM53cu201162Iz/xkIjWm0F8g\nq7oiBANeDp8ennabs4kMqcwEO27orsrNOaT62uMh2t+yjBtWNxHwB/j+vgFe3H+eF/efZ/3yOHfc\n2K778cqiptBfIF7H4boVcfYdH2RkdIJo05UnBG2pa2fTanXtLHY+r8Ptb+nk/h3XceDkEM+8dIrX\nD/dzrDdJOOhly7ocm1Y1E5jmhi3FYpFUyh3NFQgUSCYv3rmpWnfkEpmKQn8BbVzZzL7jgxzuGeat\nmzquWH9osj9foV83PB4PW9a2smVtK+cSGZ78/hGe39fPbtvH64f72biymTWdV37Al0/XEI0kGEm7\ndxLVlA0y3xT6C2hT6eTsodNDU4b+wVPDRJv8rGjXGP161N0W5oN3rqEt6qUnkePAiSEOnBziwEk4\n0jvKu25bw9aNHRcuupucriESDV1xX16R+aLQX0AbVsTxeNyTuZcbGB5jIDnGLZs69NW+zgX8Djdu\naOeGdW2cPJdi//EBjpwZ4ciZfXgdD9evbWXzijDJzDihpkv7/8u7fcqpy0eqRaG/gJqCPlZ3RjnW\nm2IiV8Dvu3iZxMEede0sNY7jYd3yOF1xD2Z1K3tOpHn98AB7jyXYe8yd3M3nHWBFR5TutiZWdUbI\n6ibsMs8U+gts06oWTp4fwZ4a5Mb1F/9hqz9/aetsCfHAmm4euGMDieQYrxw4w/P7zpNI5Th5LsXJ\ncyle2g8tYS/ru0MsWxnFcdyW/XStf9A3AJk9hf4C2/GWbr75Sg//7xuH+I2HWvH7HNJjE7x+ZICg\n38ua7mitS5R51hYP8fbr2xmfGCccieFxHOyJBKfOjXC6P82rx9IcOHOUt6xvxaxuYXSK1j/oG4Bc\nG03DsMCuW9nMPbeupHcgw5PPH6dQKPLnX9vLYCrLfW9bhdfRX0mjiYYDbF7dwru2reKuG6Ks7woy\nkcvz8oHuXWDiAAALkUlEQVQ+/uG7xzjRN44/2HTJHP2ap1+ulVr6NfDBu67jtUP9PPn8Cc4PjvLm\n0QQ3bWjngZ0bal2a1Fgk5PCWNWHeduNq9h5LYE8OsvdUjkO9Wa5f68WsabnqlNvqCpKZKPRroCno\n46fvN/zR42/wg33n6G5t4hfff8OFPlyRUMDLbaaTG9a18sq+U5zsn+CNIwPsOTrA8vYIa5fF6Ihc\neeXvVLdrBHUFyUUK/RrZurGDu966glcP9fNL//ZmTdkrU2oK+jArQ2xaEWZgLMCR08Oc6U9fuCHP\ny4eS3LC+nc2rW7huRRwfF8f/i0xFoV9DP/PD1/MT91V2s3RpbD7vxSt/U5lxTpxN0XM+Sf/wGN/c\n3cM3d/cA0BL1Ew15WdYxQVdLE23xkL5ByiUU+jWmwJfZioUD3LihnQ3dAXbc0E3/iIfDp4c5cnqY\nQz1D9PSP0dPvXuHr83robGmiLeplZWeMG6MxDRZocDOGvjHGA3wG2AqMAR+21h4tW/8+4BFgAvi8\ntfaxmfYRkerweR02r26+cH3H8PAQX3+ph5Fxh/ODo5xLjNI7kKF3APaesIQCh9m8ugWzuoXNq1tY\n0x3F75t6UrhyV7tSWOpLJS39B4CgtfZ2Y8x24NHSMowxvtLj24BRYJcx5h+BndPtIyLzx+PxEG3y\n0dURY8MK96TtaDbHqd4EjuPlcG+aN44M8MaRAQAcD3S1hFje3kRrLEBzJECsyUdbc4xgwEsw4MXv\ndciOZfjBnhPEolEcxz3O5Mnhri4Ffz2pJPR3Ak8BWGtfMMZsK1u3BThkrU0CGGOeA+4C3nGVfURk\nATUFfazqbGLnTcuJx5sZTGU5eGqIvUfPs/dYgv5klrODlU74lsLjcW8pGfB52H96H90dpwg4EG3y\nEQ75CAe9tDXH8Pu8+H2Oe06hWCSdTlPE/dZQLILHA47HQywWJRT0EfJ7aQr6pp2OWqqjktCPA+Uz\nhOWMMY61tjDFuhGgGYhdZR8RqaHWWJDtN3SzZVWIZa1+msJR0mM5MmM5RrM5hlNpVnVGwfGTHc+T\nyxdIj45xpj9NAS8TuTxj43ky2QmOns1x9OzV7/s8W36vh2hTgEiTn2iTj2iTn3DIRyjgw+vJE/A5\n+H0Ofp8Hn+MQjYTxeR08jgev48HxeHA87rcRx/HgmfzZM/kzeHB/BkjnigwOprlwutvjufDz5Dae\n0jJP6T8OQOlDa3J9+faT99GZ/IArFIsUi0UKRXdZoeAun/wQnNQU9NHZ0lTV9/NylYR+EjfEJ5WH\ndxI3+CfFgMEZ9llyHIqMDJ8nM3KxtVQo5BmNxkgmp79T1rVIpZKMZtKXLBsbTeM4PjLp1IzLRzPp\nK/pmA4HClM871bbT1TCbbSutq9L9HcbJpLNTPsdU+093vOm2nWu9Mx1rsv7pjjXd3281X68DRAOl\nP17Yvil8SX99KpXkB/tyl4z/T/SfA7y0d3UzMDxKdqLARK5AOjPG8vYIXl+AiXyBYgHGJ7KcGcjg\n9/uZjNRiqa5cLo/XHySXL5LLF8lO5IE8/cMT9PQt2diY1q88eAvXr22dt+f3FGe4tZsx5keB91pr\nP2SM2QE8Yq19T2mdD9gLbAcywC7g/bjdO1PuIyIitVNJ6E+OxLm5tOgh3BO3kdJInfcAvw54gM9a\na/9sqn2stQfn4wWIiEjlZgx9ERFZOnSVhohIA1Hoi4g0EIW+iEgDUeiLiDSQRTHhmjHmA8AHrbU/\nUXq8Hfgj3Pl8nrHW/lYt65tOPc8xVHqPf9dae48x5jrgL4EC8Ka19qM1Le4qSsOEPwesAwLAJ4B9\n1E/9DvAXgMGt998DWeqkfgBjTBfwMnAvkKeOagcwxuzm4sWjx4BPUievwRjzq7jD4n3Ap3GHyf8l\ns6i95i19Y8wf4v7DLZ//9c+Af2etvQPYbozZWpPiZnZhXiLgYdw5hhY9Y8x/xw2eYGnRo8D/tNbe\nBTjGmH9Ts+Jm9pNAv7X2TuCHcX/x66n+9wFFa+1O3IkKP0kd1V/60P0z3OtyoI5qBzDGBAGste8s\n/fk56uQ1GGPuAt5Rypt7gOu4htprHvq4n1QfmXxgjIkBAWvt8dKir+O2KBajS+YlAupljqHDwAfK\nHt9mrX2u9PO/snjfb4C/ww1LAC+QA26tl/qttf8I/ELp4VrcK9jrpn7g/wB/CpzBbajVU+3gfiuP\nGGO+boz5Rukbb728hvuBN40xXwW+Vvoz69oXLPSNMR8yxuwxxrxR9v/brLVfuWzTOO40DpNSuPP5\nLEZTzktUq2IqZa19AjcsJ5V/y1rM7zfW2oy1Nl1qHHwF+Dh1VD+AtbZgjPk88MfA31An9RtjfhY4\nb619hos1l/++L9ray2SAT1lr78dtbP41dfL+Ax24F8Z+kIu1z/r9X7A+fWvt53D7Ymcy1Xw+Q/NS\n1NwtlTmGymtezO83AMaY1cA/AJ+21v6tMeb3ylYv+voBrLUPGWP+B/ASUD7D1mKu/yGgYIy5D7fF\n/EWgs2z9Yq590kHcb7pYaw8ZYwaAW8vWL+bXMADst9bmgIPGmDFgVdn6impfdK1Sa20KyBpj1pdO\nlN4PPDfDbrWyC/gRgNIcQ3tqW841e8UYc2fp53ezeN9vjDHduF1+v2Kt/UJp8at1VP9PGWMeLj0c\nwz0R+nKpvxYWcf3W2rustfdYa+8BXgN+CvjXennvSx4Cfh/AGLMCt4H5dD28/8D3cM9jTdYeAb45\n29oXxeidKfx73K+9DvC0tfalGtcznSeA+4wxu0qPH6plMXPw34C/MMb4gf3A4zWu52oeBlqAR4wx\nv4Y7WeN/Av6kTup/HPhLY8x3cP/9/TJwAHisTuq/XD397gB8FvicMea7uL87P4vbgl7077+19klj\nzB3GmBdxu6Q+AhxnlrVr7h0RkQay6Lp3RERk/ij0RUQaiEJfRKSBKPRFRBqIQl9EpIEo9EVEGohC\nX5YkY8xtxpj/O8t9UvNVz1WO2W6MObbQx5XGtVgvzhKZE2vtbi5ObFapWly04qnRcaVBKfSlbhlj\n3gB+zFprjTF/DQxZaz9amjnxW8AL1tp3GmOeBV4E7sCdtOqXrLVfN8asAf4Kd86SVyh98zXGvAv4\n37hzEg0CD5a2+QegB3dK2+PAT1prh4wx9wO/hfvv6Rjw89baQWPMNuAPcOfW6Qd+0Vp7whjzVuAx\n3MDfPb/vksil1L0j9eyfgXeVfr4Jd6prcOcg+a+XbesvzUP+MeB3Sss+DXzRWnsL8C9cnPjs47gB\n/Xbgn7g4IddbcW88cyPu1Am/YYzpAH4X+CFr7W3A08DvlS6Lfwx40Fq7DXfe88dKz/NF4FdL29s5\nvgcis6LQl3r2L8C9xpgtwF4gb4zpxA399GXbPlX6/5tAW+nne4C/BbDW/j0Xp/T+GvBVY8yfAAes\ntd8oLX/DWvt86ecv4H7gbAfWAM8aY14FPor7TWBz6f9fKy3/38A6Y0w7sLLsOT87x/dAZFbUvSP1\n7Pu4reZ3Ac8CZ3HnGvcDJy/bdqz0/yIX508v/xncGS+x1v6hMeZrwHtxW+1fwZ0AMF+2rYN7O08H\neM5a+wBcuDNTDFgBHLHW3lpa7gGWcWX/fQ6RBaSWvtSt0r0LXsCdqfLbuMH/cdxvAJV4mtLMqKV+\n+dbSz98H4tbaP8btk5/s3rnZGPOW0s8PlY7zIvAOY8ym0vJHgN/D7f5pM8ZMdjn9HPDX1toEcMwY\n877S8p+YzWsWmSuFvtS7J4GItfYg8B2gC7evv9x0o2P+I/B+Y8xrwI8D50rL/xfu9McvAz8P/Hpp\n+Xngk8aYvbg3D/mEtfYc8CHg74wxrwO3AP/VWjsO/Bjw+6Xn/+nSduDe5/fjpRt033DtL11k9jS1\nskgFjDFrgaestVtqXYvIXKilL1I5tZCk7qmlLyLSQNTSFxFpIAp9EZEGotAXEWkgCn0RkQai0BcR\naSAKfRGRBvL/Aff/0dD5/sksAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143e1f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: casual\n",
      "\n",
      "count    10886.000000\n",
      "mean        36.021955\n",
      "std         49.960477\n",
      "min          0.000000\n",
      "25%          4.000000\n",
      "50%         17.000000\n",
      "75%         49.000000\n",
      "max        367.000000\n",
      "Name: casual, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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YRs3Mvmdm7zKzFSM/CY9p/bTQvwGYy/jjCx2tVJ0FtTKW0RbCNyFmthU4BMwu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e9cb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: registered\n",
      "\n",
      "count    10886.000000\n",
      "mean       155.552177\n",
      "std        151.039033\n",
      "min          0.000000\n",
      "25%         36.000000\n",
      "50%        118.000000\n",
      "75%        222.000000\n",
      "max        886.000000\n",
      "Name: registered, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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9kSkUjkSpiSeoKVnWHs6dOJdQangnUVvfQDqbZyiTJ53J09HZRS7vEgxFvaP0\nnHfEPpjOEI9Wky+4ZHP5Yrh7u4ve/iyu6xIMgOu4OE6AfC5DwHUIhUJe0LoOARfSQwOEqkNEYzFc\n1wvX/r4U4BCOxMjlC+TyebK5AoODQ0RDLtm8y8BQjsFMjvbuAYbSudPaUZ0OBwhVezuTSHXwlCuu\nYqEA0XDQe4QCxELe82RjHQ356WrV1Cgn9BNA6aTjWWOMa63Nj1HWC9QC8ZOW54wxAby+HJYqrisi\nFRSORInVJCj9mfloMDPhTqKuoW7U8sH+4+DUTnjieuzlTSXL0qcsm+gzO9qOEYokqKltJJ3NkcsV\nyObydHZ0MJROE4nFyecL5AsF8gXoTfVQVRUmHI2RL3jfRvL5Ar19vWQzWQJVIXL5AtlcgUyuwNBQ\nmsGhPH2DWTLZ8oPc22F4O4lwdYBIKEBNNESkOlj8RuI9QlXD30RcqoJuybcPF9f1vp24joPrejPF\nuo6D43jDfa7rPR/+1rK4uabsoa9yQr8HL8SHDQf+cFnpmac40DVOnZwxJn/SusfLamUFBQIBMgNt\nDA3kRi0vZPoY6I+O3o0BgwN9uG6weNQyM8tn8jNd0vT3DVVsWyu1fKxlLulZ1caZXD6WwYH+af07\nPpnjOGTTA5AboNrBS7MgZKqHqA0HqWuIjFq/s6oX181T11A9enl7N65bRV1D/UnLW3DdIHUNjRQK\n3o4gncnT3t5OrhCgOlJDOpMnnfUeqd5+0pkcbrCawXSe/sEMPX1psnnwjoenz60blnPTlcvKWrec\n0N8C3AQ8aIy5HNhRUrYTWGmMqQP6gWuAu4tlY9V50RizwVr7JHAj8MREH5xMxis+qJZMxrnwgnMr\n3QyZtS6udANETotTmOQUf8mVOMN/3R/GO3EbK16p83PAn+Ad895vrf3SWHWstbuNMecBXwaq8HYY\nH7HWzu4BMBGROWTS0BcRkblDt+6JiPiIQl9ExEcU+iIiPqLQFxHxEU24No7J5hyaq4pTa/wTsAyo\nBj4LvMYYcyYZYz4C3IE379JnrbWPVKDJ084Y0ww8D1wP5PBpXxhjPgn8PF5u/D3e5dxfwWd9UcyG\nzYDB+3v4CGfR34WO9Md3Ys4h4C68eYP84FeBdmvtBuA9eP+4h+dM2gi4xphfMMbMA34Hb8qN9wB/\naYypqlSjp0txJ/glvPtQwKd9YYzZCFxR/PdwLbACn/YF8C68S9avBv4c+AvOor5Q6I9v1JxDwPqJ\nV58z/hUIoeVGAAAEuklEQVRv1lSAAJAF3j7GnEmXAk9ba7PFuZf2MDfvVPob4B+AI3j3ovi1L94N\nvGKM+Tbw3eLDr30xCNQWj/hr8Y7iz5q+UOiPb8w5hyrVmJlire231vYZY+LAvwH/i1PnTEpw6vxK\nw/MuzRnGmF8HWq21jzPSB6V/A77pC6AJ76bM9wMfBb6Bf/viaSAC7AL+Efg8Z9G/kTkfYm/BRHMO\nzWnGmCV4U2R81Vr7LbxxymHDcyaNNe/SrJ9L6TR9GLjBGPNjvHM7XwOSJeV+6osO4AfFo9bdFI92\nS8r91BefALZYaw0jfxelE/rM6r5Q6I9vC/BegDHmHJqziuOQPwA+Ya39anHxNmPMhuLzG4GngOeA\nq40x1caYWuB84JUZb/A0stZutNZea629FngJ+DXg+37sC7yj2/cAGGMWAjHgR8WxfvBXX9QwcgR/\nHO/E9razpS80DcM4xps/qIJNmhHGmHuBX8b76uoABeB3gS9w0pxJxpjfAP5Hcb3PWmu/XZlWTz9j\nzBPAb+L1xynzR/mhL4wxnwPeibeNdwH78a5i8VVfFCeYfABvyCsI3Au8wFnSFwp9EREf0fCOiIiP\nKPRFRHxEoS8i4iMKfRERH1Hoi4j4iEJfRMRHFPoiYzDGrDPG/N8JypcZYzZP02d/wRjzoel4bxFN\nrSwyBmvtC3hT4o5nGbB8ZlojMnUU+jJnFW+L/2u8b7QH8Ca8uhBv9tC/stb+S8nUyVfhzaRZAP4M\n7w7KT1trrzXG3Al8CG/O9GettR8F/g441xjzBWvt7xhj/hDvTmYXb46aTxpjluLN1NoODOBNY3A3\nsLHYhq9Ya/+u2Na/AW4GjuHN2vjc9PaO+JWGd2SuOw9v6oA9wPPW2nfghe4fG2OW4U2tELXWrsab\nYK10Cu2CMSYAfBJvhsn1QN4YswD4ePH9fscY8+6S8rcDi40xtxXfYxVwm7X2XXg/tlGw1q4HLgNu\nMcZcZYy5tVh/Nd7vOKycpr4QUejLnGettSm8X736TWPMNuBJvKlxL8Sb9/wbxRUPAj86qXIOb/K9\n54E/Ab5orT160mdcjzd3+gvAi3gBfmGxrNVae6hkvZ8vtmErsAi4CO9HSf7dWpu31nYBFZ+fReYu\nDe/IXDdQ/G8A+FVr7UsAxpj5eNMF/wajD34cTmKtfZ8x5jK82RN/UHIUPywA3Gutvbf43nV4QzRN\nJZ8/vN4nhifdMsY04Q05/fVJn5s9g+0UKYuO9MUvngB+C04E/jZgMfA48CvF5QuBTXjj+hSXNRpj\nXgN2WGs/DTyGN/NqFm9GxeH3/jVjTKx4juA/gF8slpWG+RPAHcaYoDGmBm/63UuH21CcgjcB3DS1\nmy4yQqEvfvGnQMQYswNvCOcPrLVv4E2T3GuMeRlvutz9lBydW2s78H4d6XljzHNAHd4PYO/E+8m8\nr1prv4cX9FuBl4Ft1tqvFd+idBrbLwG78XY4zwL3W2uftNY+DPwQb671/8Sb1lpkWmhqZfE1Y8x7\nAcda+0jxKPtFYL21tuK/cCQyHRT64mvFK3j+H96vIRWAu62136xoo0SmkUJfRMRHNKYvIuIjCn0R\nER9R6IuI+IhCX0TERxT6IiI+otAXEfGR/w+VA+Un+2W+bwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d92610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: count\n",
      "\n",
      "count    10886.000000\n",
      "mean         4.591364\n",
      "std          1.419454\n",
      "min          0.693147\n",
      "25%          3.761200\n",
      "50%          4.983607\n",
      "75%          5.652489\n",
      "max          6.885510\n",
      "Name: count, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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8yqE7t+L3y4Vt88krWlSc2Q1EHE4Pb3eFMyFf4+Tg7gD2KvmTKHZOlxu3x4fb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\nJ0jXwsX+SZ576zLhWJL79zb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147a3f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: year\n",
      "\n",
      "count    10886.000000\n",
      "mean      2011.501929\n",
      "std          0.500019\n",
      "min       2011.000000\n",
      "25%       2011.000000\n",
      "50%       2012.000000\n",
      "75%       2012.000000\n",
      "max       2012.000000\n",
      "Name: year, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147bac10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: month\n",
      "\n",
      "count    10886.000000\n",
      "mean         6.521495\n",
      "std          3.444373\n",
      "min          1.000000\n",
      "25%          4.000000\n",
      "50%          7.000000\n",
      "75%         10.000000\n",
      "max         12.000000\n",
      "Name: month, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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qyu7utxls2dA/poO4IrJBBul8/S0b+kfPNH6UtfxiiIj0yvUDdDB3S4Z+tVbj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\nPEShLyLiIQp9EREP+f9Eb2+PY5kyAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11477d9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: hour\n",
      "\n",
      "count    10886.000000\n",
      "mean        11.541613\n",
      "std          6.915838\n",
      "min          0.000000\n",
      "25%          6.000000\n",
      "50%         12.000000\n",
      "75%         18.000000\n",
      "max         23.000000\n",
      "Name: hour, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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7ZP1STRWJiEjDVA19dy8BDy65+Wig/RBwaJl1vwvcvJoCRUSkfvTlLBGRCFHo\ni4hEiEJfRCRCFPoiIhGi0BcRiRCFvohIhCj0RUQiRKEvIhIhCn0RkQhR6IuIRIhCX0QkQhT6IiIR\notAXEYkQhb6ISIQo9EVEIkShLyISIVUvomJmMeAxYDeQA+5395OB9nuAR4AC8GTlalorriMiIs0R\n5kj/XqDb3W8GHgYeXWwws0Rl+U7gDuABMxtdaR0REWmeMKF/C/AUgLu/ANwQaNsFHHP3lLsXgOeA\n26usIyIiTRIm9AeB6cDyvJnFl2mbAYaA5ArriIhIk1Sd0wdSlEN8Udzdi4G2wUBbEjhfZZ3oKS6Q\nnX6T7EyuatdsZob8XIZsJh1q07nZDPF4oiH9w/aNkyebmWuJWhrRf3F8rVBLI7a9dHzNrKVe/YN9\nVxrfWtQym82E2uZaCRP6Y8DdwAEzuwk4HGg7Auw0s2EgC9wK7Ku0LbfOcmKjo8nqvdrQj/zw+5pd\ngogIALFSqbRih8Ancd5buWkvsAfor3xS50PArwMx4HF3/+MLrePuRxsxABERCa9q6IuIyPqhN1dF\nRCJEoS8iEiEKfRGRCFHoi4hESJiPbK4ZMzsDLH7K58vu/olm1rNaUTgHkZl9lbe/iHfK3X+mmfXU\ni5ndCPyOu7/fzK4C/hdQBF52959vanF1sGR81wN/z9uPvU+5+182r7qLUzktzBPADqAL+E3g26yT\nfbfM+E5T475rmdCvPLC+6u4/3uxa6uitcxBVHmSPVm5bF8ysG8DdP9DsWurJzH4Z+EnK3zCH8n77\nNXd/zsw+ZWY/7u6fbV6Fq3OB8e0Bfs/df795VdXFR4AJd/9o5btD3wBeYv3su+D4NlAe229Q475r\npemdPcA2M/uCmf29mb272QXVwXo/B9FuoN/Mnjazf6g8sa0Hx4GfCCzvcffnKr9/jvIJBtvZ940P\n+JCZPWtm+82sv0l1rdZfUD7jL0AHMA/84Drad8HxxSmf2XgPcHct+64poW9mHzOzw2b2zcWfwFng\ntypHjb8N/Fkzaquzlc5btB5kgX3u/qPAg8Bn1sP43P0g5cBYFAv8nqZ8fqm2dYHxvQD8srvfDpwE\n/msz6lotd8+6e8bMksBfAp9gHe27C4zvvwAvAr9Uy75rygPU3Z9w9+vc/b2LP4GvAH9baR8DLm1G\nbXW23s9BdBT4DIC7HwPOsT7221LBfZYEpppVSIP8jbt/vfL7QeD6ZhazGma2HfgC8Gl3/z+ss313\ngfHVvO8SYYiOAAACJElEQVRa6ajsk8DHAcxsN+U3KNrdGPBjADWcg6id7AV+D8DMLqP8oDrb1Ioa\n42tmdlvl9w9SPoX4evKUmS1OPf4w8NVmFnOxzGwL8DTwK+7+6crNX18v+26Z8dW871rmjVzgd4A/\nM7Mfo/zS86ebW05dHATuMrOxyvLeZhbTAI8DT5jZl4AS8LF19kpm0S8Bf2JmnZRPMnigyfXU288B\n/8PM8sDrwANNrudiPQwMA4+Y2Scp/5v8ReC/r5N9d6HxfRz4g1r2nc69IyISIa00vSMiIg2m0BcR\niRCFvohIhCj0RUQiRKEvIhIhCn0RkQhR6EtkmdntZvbFZtchspYU+hJ1+qKKREorfSNXpBk2m9kh\n4CrgO8C/Af4d8BDl87Z8FfiP7p41s6K7xwHM7KeA2939Y2Z2ivJJy3YDt7r7RDMGIhKGjvQl6rYD\nD7r7D1A+WdzPUf66+63uvpvymUR/vdJ3pVcFh9x9lwJfWp1CX6LuG+7+auX3I5RPvft37r54Nsb/\nSflEVvDO0/Qu9WKD6hOpK4W+RF3wvPIl4DzvDPcYF54G7VyyPFvnukQaQqEv8k4x4J7K5fYAfpby\n+csBxs3smsq1jz/clOpEVklv5Iq8rUT5Sme/DXypciHqr1Ke54fyXP8hytcMeB4YCawn0hZ0amUR\nkQjR9I6ISIQo9EVEIkShLyISIQp9EZEIUeiLiESIQl9EJEIU+iIiEaLQFxGJkP8P328rHuuWfPIA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a3e810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------\n",
      "FEATURE: dayofweek\n",
      "\n",
      "count    10886.000000\n",
      "mean         3.013963\n",
      "std          2.004585\n",
      "min          0.000000\n",
      "25%          1.000000\n",
      "50%          3.000000\n",
      "75%          5.000000\n",
      "max          6.000000\n",
      "Name: dayofweek, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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m821vG2Hu3tLKd188R88VP+/fsTyncQIcOTuCYcCDu1bSWFdzzW/CapzrHHbe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\nJ4leCCHKnCR6IYQoc5LohRCizP3/wRSJ2Qzz1EAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a3e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in df.columns:\n",
    "    if c == \"datetime\":\n",
    "        continue\n",
    "    exploreUnivariate(df[c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WbW/ZcnYnq0cehCrAA8BIJoubzjrXdb/3770/gNpEm2EMIpGcU04XnB0IT/77mZnmK8xT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141d24d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143a3bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138df6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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RCSztTNZ1ZvYB4DRgD/Ald38sxfMeN7MnKj++FhhLe2Cj5XGufPqRuvuV9iRfLIrL6g8U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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a69a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b73790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143a6910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aPaJURH7/ezEXDqFQiFAoVJX17hensjAq7fY5DPgm4tsnBOxjjDnJWvtgkMI1\ni1hsgH7SXPrXsybv+u+mCbXx1onlaJXtB5X2ZdWqE3PdNHMHdLdt20omk8kdz20AdEZP46m02+dq\n4B2esjfGvBb4LPDqoATLR3z2p1h3z78XnI+nUmSyHb09sKK0FEcccVTR89de+4WC46uu+kLBdZ3R\n03gqVf4D+Va+tfY/jTEt3xO8c+eOnBXrbdaxbt252o2hKAHx1FP/W/T86Oj2osegM3qaRaXKP26M\n+Vtr7bcBjDFvpdC9c6CIz/4IG970NwXn193z78TT6RKh3BfD2HYGI32E3ehGfEwdkypKEPgp8d7e\nXibclpe9vb0F4XRGT3OoVPmfA3zHGPNVZKpnFjgiMKnqyGCkjyuOny1MH9yytYnSKMrixU+Jn3zy\nO3N7+J588jurirfUeICyMCrdHWMVkAT2R6Z9jgJvCEgmRVEWGatWnUg4HCYcDs8bDC43o+f2228t\naFiU+lCp8j8HONJam7DW/gY4FHhfcGIpitJulFPiJ5/8zqJWvzej56UvfVnRWUCPPPJbHnnkt7kv\nAKU+VNrt00vhit5J5jt6m4cxJgRcCxhkBdS51tpt1Qqp1I90Eu67LcuUe5u9fXIu1t9cuZT256CD\nXs7ee++TO56Lnz+fUjN6dDwgOCpV/t8C7jfGeG9iNfDtCsKdCGStta8zxhwNfBxxCqc0gVAoxLJl\nMtMpnpLx+lj/ELF+dAaUUhd27dpZUzhV6o2nUq+eFxpj3g4cDUwBV1trv1VBuG8bY+5yPw8AdtQq\nqLJwdCGXEiSbN9+Vm9GzefNdRRd6QXWKXn32B0fFXj2ttbcBt1WbgLU2Y4y5HjgJeHu14RVFaQ/m\ndtHMVf5+C7lKoSt8g6Nql861YK090xhzIfAzY8xLrbWpYveFQl0NcY3W3S3j3NM+14rJ4Reu3nhp\n1fvaypVLCu7zfheLo17XGpmWyti8a115W2l2dXUVXHv44YdzawCefvpxDj744IL4br/9dgBWr149\nL62zzjqjpBxK7QSq/I0xa4D9rLWfQDZ+n8HH9WUm0xhXDTMzpZuYWq/VmyBknJnJsH37noL7vN/F\n4qjXtUampTI279pJJ52cm8t/0kknF1y77robCo4/8pGPFsR3/fXXA3DUUcfNS2vnzmRJORShloax\n0qmetXIbcIgx5gfAZmCdtXaiTBhFUToIb6xgYmKCzZvvmndd5/kHQ6CWv+veaUm//+KzP8OF983O\nTtiRzuRCFsEuAAAgAElEQVR89iuKUh1+ff777rtfrttn3333Kwjnt8VjOb8/Su0EbfkrTSKVhDtu\nyXLHLVnuviNLqvjWqkoLMTKykXXrziUeHyMeH2NkZGOzRaobDzzwo6LH4L/F49wGRakfDRnwbUVi\nsQGiTPCpNy7Lnbvwvp10LQKr35uz73kyjfYPEdW5/G1DKNSeNtkRRxzFffdtyR3nMzMzU/QYat/i\nUVkYHav8G82eFHzpO9Ok3craSJ+cy5sgUTe8PWt1Ln97MTy8luHhtZx99prc73Yi353zXNfOe+21\nLKfg99prWcE1vy0edZ5/cKjybwD5K2vH02KN90WHGIrKngPtvvevUj82b74rt/l5sYVSrcwzzzxd\n9BggEukvegz+biF0nn9wtJTyl0HYNOu2fDN3Lp5Ktv1uXX4ra9etO5eJpO4xoAjlFkq1MvmuHap1\n8+C3qfthh72qZpmU0rSU8lcUpX3p6uoi6wy1rjn9mbFYrOixh59V/8tf/hzwdwynVE9LjSzFYgMM\n9kfZcPzbcn+D/dG2HQBTlGop5xa5lXnRiw4segyF1nsxS37btq1FXTarS+fgUK3aAoyn4IY7Z7jm\nNvkbL+r8QukEVq06kWg0SjQabTtL96mn/lj0GGat97nHHqUWculUz+DQbp86sjsFV989ScrN6Onv\nk3ND0dJh8qdfJtxg8NDQEIODQ7mpmkpn0W4Wv4fffH0/dCFXc1DlXyfyZ/TscUq8183o8Ztf703L\nhOKDwX4kk3DLN6SPta9Pfvfrpixtz/77v6DZItQdvymbfhu26FTP4FDlXyca7St/7kKu/v4h+nUh\n16KgFtfHrUBvb2/O4u/t7S24VuuUzYMOejnRaLTqcEp5VPm3KbqQa3HSzl0g++77fB57zOaO51LK\ncvez7rdt25pb99Bu+dHq6ICvorQQ7TzA+bvfPVb02OOgg15eVHn7beDezvnR6qjlrygtxNjYaNHj\ndiCbtxgzW+XCTO3Pbzxq+StKC7GQVbLNJt9nz1z/PeUo9VXQzuseWh21/BWlheju7i563A4897nP\nc76q5LgeqG+f4Ggb5R9PpVh3z7+TmJRJ9LG+PuKpgNxiKkqTWL36lNxWiO1m6R522Ktyg9X19MfT\nbvnQLrSF8pc59MsBmEinAYhFYwxGYzlLoxiZTIZ4epIPbpldFh5PT+puXUrLsmrVibmBzXZb4Tt3\nFW+95FeLPxjaQvmX84pJcvFu7JxIws13Zphwq4anpyGiC7kWNcWmSS4GPN88qsxbg7ZQ/rUSCoUY\nDPdwxfGzhe2DW7ZCm1j9+Qu2kqlC1w/K4qXYNMl2oNxq3HZdvLZYWdTKv93xc/2gLE6uv/7LZDKZ\n3PGZZ57TZIkq58knf19wnK/k23nx2mJFp3rWwO4UrL9zgvV3TnD5PRPsVi+cSp24//57ix63A34L\nssot1irl0lkJDrX8q2SuT50l0SEGyzhvUxTFH+0Sajyq/KtEfeooQXLIIYfxq1/9PHfcTkSjsZwf\nnmi0cLcuv2mg2iXUHAJT/saYHuA64ACgD/iYtfauINKKpyb4wJYfkpgUj4Kxvl7iqQldA6C0HalU\nsuhxqzAyspEHH/xp7st3ZGQjw8NrARgdnd2LOv8Y/KeB+rl0VoIjyD7/04BRa+3rgVXA54JIZHBw\niMGhFRBdwkQmy0QmC9ElDA6t0O0fFSUgwuEw4XC42WIoCyBI7Xgr8E956VS+tU8VXHrpx9iw4Ro2\nbLhGGoLBodxvb2GYorQLre7LZnh4ratrKxgcXJGz+gEOPNAUPQb/5yr3zDoYHAyBdftYa5MAxpgl\nwDeAS4JKq1Z2pDNceN9OElMytW5yBgZjZQIpSoDkd3m0cvfH3D16AS677OOceurbcsf5+G3KUs5/\njw4GB0OgA77GmOcDtwOfs9beUu7+UKiLTJHz3d0hVq5ckjsGcr/n3pd/rbs7xEyJ+FasWJG7f3K7\n9E+u3HslK1asYHR0tK5yBHmtVeRQGetz7bOf/Wzu+Otfv4H3ve99LSfj2WefnTu++OLzufbaa8vK\n/vDDD+cGg59++nEOPvjggjjPOuuMomk9/PDDucHgYuGU2glywHcfYAvwHmvt9yoJk8kU9wE+M5Nh\n+/Y9uWMg93vuffnXvN/F7rv44o/mfnszd6688gu53zvTWS68WybwR3u72JnOMhirTY4gr7WKHCpj\nfa7dfffdBcd/93dntJyMTz75ZMGxd91P9uuuu6Hg+CMfma1/AM973guLplUunCIUa7zLEaTl/2Fg\nGfBPxph/BrLAKmvtRIBp1oW5c/kHYkMMxnQuv6Ioi4cg+/zPA84LKv4g0bn8SrM49tjjuO++Lbnj\nVmTffZ+f6/PPd0LnJ3s5vz+lnL6VC6fUjs6FVJQWIt+XT6v69bn88s8UPfaT3RvwjUajJQd1i7l9\n8NvfV1kYqvwVpYXYvPmuosetxAUXnFf0+LLLLi56DGLZJ5NJksnkvGmb3grfRx75bdEpnatXn6JW\nfwCo8leUABgZ2Ug8PkY8PsbIyMaKw5VygJYf37p151YVZ73Jn+aZf/zYY7boMSzM6Vup/X2VhaG+\nfRQlIOq9AlZX1Cr1RC1/RQmA4eG1dHWF6OoKFayCLUep1a7Dw2s58sjXc+SRr2fDhmuqirPe5A/y\n5h8HtcJXCQZV/ooSEMlkgmQyUVWYVatOJBQKEQqF5u2Be//997aEj/9SA775q3qLrfD1nqvYCl+/\nwWAlGFT5K0oAnH326UWPy7Ft21YymQyZTKZg8NPb4SuTyXD99V+uq6zVcsUVnyx6DGLxz7X6QQav\nPfnnDmT7DQYrwaHKX6mIkZGNrFt3btWDmH4Dla00iFlv8i3+/ONy+Vhq8LOVdvjy9huYewxi8c+1\n+mFhA75KMOiAr1IVtQw6+oXp1EHMTn1upXVQ5a9UxPDw2poGGf3C1RpnOyC7WiVyxx7lnrnUitZW\nWvl76KGvyln8hx76qjJ3C6tXn8KmTTfkjude01W8jUe7fRQlAK699mtFj8tRakXrmWeekxswbfbK\n3w9+8KKix36sWnViblB37kC2ruJtDove8o+nJ/nglq0kpqYBmJjJqM9+pSHM3ce2UkpZv822+POp\n1OLPx8+qV4u/8Sxq5Z/vhXPCeegcHFqp3jkXgLecP3+KXznOOutUAK677qZ517zZIpVakO3E6tUn\n1xSulPX7nOc8dyHi1JVVq95c9Lw3E6nY18lciz8ftfgbz6JW/p53TlAPnfWi2A5O5ZiYSJe8Nne2\nyGLCm7nip/SaGd9CKLW7ljcTqdldU0p5tM9fqZhSDr388Kz+ucfgP1+83dm8+a7c3PV6OGird3wL\noZQjtlZai6CUp62Uf63OspT6UMqhl9/c9Xyrf+4XgN988UYSxFqEes9drzW+WurMYliLoJSn7bp9\ndH5069LO72Yxr0WoVf52f27Fn7ZS/ot5Xng7UGoHJ7/3Eg5HchZ/OBwpuFbLfPEgCGItgt+89lqo\nNb5a5F8MaxGU8rRVt4/SXEo59PIjf4bP3Nk+tcwXbxf85rW3QnwLoR3WIijlaSvLX2k++RZ/pcy1\n+PNppsUfNPWeu95Kc+HbYS2C4o8qf6Uqqpnf71Fsfr/HYrP486m3hd5siz+fUvPy1eJvH7TbR6mK\ns88+vaSL4gsuOK/oFFC/MJs339X0qYsg0xdLuROuVcYrrvhkySmsl1128bx9bsuld/31X26ZKZSl\n8ssvH5XWQi1/pSr8NicptQDML0yrLFwqtWgp/1q1MvpNX527x20l6bXSAqpS+eWXj0proZa/UjF+\nG5SUWgDmF6ZVFi6VWrQEtcvot4At3+Kfa/2XSq+VFlCVyi+/fFRaj8CVvzHmNcaY7wWdji4Aqw9+\n+VhqgxIovQDML4zfwqVGvs9aNxrxk9FvAVu+1T/3C6CWBVS1LkSrNY9LyaibsrQXgXb7GGP+EVgD\njAeZjocuSqkPrZKPrSKHH60ioy7kUqol6D7//wFOAm4MOB1dAFYn/PKx1AYlUHoBmF8Yv4VLjXyf\nfpuJ1Cqj3wK2Aw80OYt/7n63pdLzW0AVxEY7fpTKL92Upb0ItNvHWnsHMB1kGkrj8NugpNQCML8w\nrbJwyW8zkVpl9FvAlr/H7dz9bkul10oLqErll27K0l601GyfUKiLTJHz3d0hVq5csqC4u7ulnSsW\nT6lrtYRp9LUg0vJjYGCgZLj999+/6DW/MKeffnpNctT7uc8664y6y3j44YeXDHfQQQdVnd4JJ5xQ\nkxxBUCq//PJRaS0apfy7Krkpk8kWPT8zk2H79j0LEmBmRpqVYvGUulZLmEZfCyItP770pY0lw338\n41cWveYX5qijjqtJjno/9/Oe98K6y/je936oZLhLLvmXqtP7u787oyY5gqBUfvnloxIctTS2jVL+\nxbV6EeKpJO+662YAYn19xFNJBqPRwARTFEXpRAJX/tbaJ4EjKrnX214x7rZcjEWjDEajuu2ioihK\nnWmpPn9v20XdclFRFCVYdIWvoihKB6LKX5mHn0MyPydnpZyV1eo0rZSjuEbjJ3+jr9UiYxDhlPan\npbp9lNbAzyGZn5OzUs7KanWaVspRXKOpRP5GXatFxiDCKe2PWv5KAX4OyfycnJVyVlar07RSjuIa\njZ/8jb5Wi4xBhFMWBy2n/Gt1UuUX37p15xZ1YFXuWik5Kr02V/ZS11pJRj+HZH6Ou0o5K6vVaVop\nR3F+8teaj37UKn8Q12qRMYhwyuKgJbt9gnA25RdnqWu1hGn0tSDSagc69bkVpV50ZbMVr78KnO3b\n97SOMB3KFVd8ssAhWb5fms2b78o5HTvttDMK+ukvu+ziAmdlns+abdu25px9XXLJ+oK+Zb/4Lrjg\nvAJHcbVsH1kP/ORv9LVaZAwinNJ6rFy5pCIvCvm0XLeP0lz8HJL5OTkr5aysVqdppRzFNRo/+Rt9\nrRYZgwinLA5asttHaS5zXRDn4+eqd6574krC+F3Ldw3dTGqVP4hr9QyzkHBK+6PdPoqiKG2Odvso\niqIoFaHKX1EUpQNR5a8oitKBqPJXFEXpQFT5K4qidCCq/BVFUToQVf6KoigdiCp/RVGUDkSVv6Io\nSgeiyl9RFKUDUeWvKIrSgajyVxRF6UAC9eppjOkCvgAcDKSBd1lrHw8yTUVRFKU8QVv+bwXC1toj\ngA8DVwacnqIoilIBQSv/1wH3AFhrHwReGXB6iqIoSgUErfyXArvyfk8bY3ScQVEUpckErYh3A0vy\n07PWZgJOU1EURSlD0Ns4/gR4M3CbMea1wH/53VzLbjSKoihK9QSt/O8AjjPG/MT9PjPg9BRFUZQK\naKk9fBVFUZTGoIOviqIoHYgqf0VRlA5Elb+iKEoHospfURSlAwl6tk9Ryvn8McZcAHwEeAS43lp7\njTEmDPwOeI677RHgUGvtlFs4diPwNmRtwbestee4uC4CzgKeBzwF/AdwGfAL4EHguUgjeADwBuB/\nge+6MPcAK4EZ4L+tta81xvQAI8CJQC8wCWSBD1hrv2yMWQtc6uJLAFHgMGvtQ06WM4D9gAyw3aX3\n/4BvA/u489cCRwFTwG+BA4FXu2tXWWsvM8YcAdwOjLm0ulxc9wJfAz4JvMM95xuBr7o8GAM+B1jg\nCmDUyfOXTtYfARcDG1y6KZdPrwIeByIurSSw0+XfHmvtMcaYQ4DPAi9H1nf8Fpiw1r7aXbsf6HNx\n/A54PnAC8C9OjjcCYRf/duBnLk/+0oXZDvyPe66HgKddHBn3XBlr7SuMMX/r8iCC8AiQstYebow5\nGPgicJB73v8E3o24HzkQMO6dDrlyMenkPh/4CvAC994tcKh7jgngL5BFjb1Imb4Qmep8HfAioN/d\nB3Cqe45rXZqenMcA57lz/8ed88pYDHgMuAC41b2XPuAJYAApaymkLI4DLwX2cs/+T8A/AIe79xIC\npoFPuLT2cXn+WkQn/BjYAaxwefQXwHJ3Leme67PATS7vAZ4F9gb+28l1sLs3hJTjNHCsu3a1y7uw\nk/lR4HRr7TYAY8y/A69wz/u4eyfdLr4jXbgZ4GHg/cyW1V6k/oPU9XHgbGA9ohsm3PnVLn+/5vIt\n5uR8CFiLlLOvAEe7vP0NojP+FngZ8GKX/nKXbymkPOwGDkPedTcQBx5w+fdCpL6sdPkRcvIei5TH\nl7s4x4F7rLWnGmP2Rurv00h5fcjlxwPITMprgWVOhtOBi6jCj1qzLP+SPn+MMf8I/CtSaF8HfNAY\nsxfweWAQeAmS+QchFRHkpRyBZO4O5KVhjDkaKSxdiJK4GXlh33LnoojCn0Fe8suAHyAvagWwL6IY\nlwNpp8BOc2l+F3mRu4E/Iy8CpEL0AE8iCv0cp/iPRipfF9Kw/AEpBJ8B7gS6rLVLgQ8hiuZoa+1R\nSGF6rZPlcOAfjTGXAbcgFS+KVMbdLu5XuWth4BqkwN8K/F/gU4iCOc3J+xJgk3s+r5LeC9zg8vEU\n4I/AXwP91trXIRUw4WT+K6TQht2zfwap/An3Lp7v3hPuOa5DlO3PXDrfQSruS4CvO/nHEGXwTuDL\nwO9dfo0iius57rkGkMpwE/CMu/YSl9aXkQb6JuBPSHnxrl2LKLe0e29bESUw6mS/xuXHs8A/O/le\nD1zvnrPH5fsViAJf5p73PqQheI97tk+7/Mi44yfd840jje/HXF780IUfRRpzT473OBm/DfzK5cn+\nSMN8PdJIPAlsdM8QRRrb37lzk+4Z3uXS60cMn/9Cyt7XgYutte+01h7rZO4Gfo0YAK+w1r4eWaG/\nnNnG6jdIXbsJMdDWumf8PlIP/+Ty5EqXv0+6e94BfBypx0MuL54ANiPK8+PGmBXGmB8jRsBK966z\nrtztYFZBngVchTRwN7hrVyHGz5+dDH9EjIoR4G/cPc8BfurkWO/e7d8De9zfZ9y1y51Mu9y7+BTS\nGGQRvXSvk//PSP15GKlnPS6OW5D6+AtEGVvgg0iD+wzS+N/n4trg8s+6dO8D/uSMzGuQMvcCZhvV\ntyNcDmyy1r4BMTbPpko/as1S/n4+f/4HeYk9SIEFyfQvI0rjfKTgYq191F0/mtmK9hBSaQCORwrr\nY8BtiMI6DFH+2xEF8mnEsksiLfFbkYw+CqlAtyIv+0lECdzqjrciL34lcJ611psz+2kn71LgLe66\nJ8tW5IVfyKxVFEVe8C73RbQDUcKeQt0H+F9r7W7PMkIsujchlXgSKWAJd+2HSAN2AGJRbEcUwi+R\nL4w7gS3ASYjC3M/F96DLtx6k8P4RseS6EaXyjIv/qy6+JxAr5A/Mci5wHPLVMY1UvD5jzFJEYZzh\n0jJIRb45T45XI++7H7Hi1rn3kgLOQSydnzuZvogo0ccQq3YKqaDeO/gPxNq6BykT/4G8a9z5o1zY\nPYgF+riL50gnZ8jl+xtdXu1B3uOdiALc7uKJuvMhpHwezmy5nUYU3puRivgjRIlngSlr7WpESb7Q\n5WcKeef/5uTY18U/4PLqLGbL1esQpRVDyvN/I8bKavc+LkPK2u+YbdTf4fLxQaR87HZ5gzFmCCmT\n33Ryf5LZr5G/dOnu5Z7vT4ji60MaqjsRxXUQ0jiFkC/Mb7i40kjjcyxStt+PlIPLkfrc7+Lf4Z4Z\npL79yZ07x5071N33eyeHdXl9B1JWH8973qV58RlEX/Q4WX/vrv3U5d1nESW+x92/w+XvwU6GFYix\nsNvJcqS71oOU20eA1yDl4Amkkd7hjp/D7LtNu3dpkbr5l4hu2g9p0LuBVUhDtRzRI5vc+bPc/3tc\n/Dg59jPG3AsMO1mq8qPWLOVf0uePtfYOpCK9ArFSvmOt3Y1k9qsQhWGAPxpj3uC6WZJIxoUQhftb\nF+8K5AW/BVF830O6Bi536e+HtMLvQT4HH7LWPoUU7t8jCvU04ANI5Rmw1iaRivBqF/7nSEHGGHMG\nYm15VsJ7gCONMSfkyfJ2xAJ+JaLINiBW1HORSvxV96wxY8z7EOXe7eJ/LVJZ/oAovwxSsfZ2efod\n4G7kC2bKWnsvYpFMIxXySy4fznbnwkilvRJpHPpdflyFNHI3INZVD9I4Xe/k/S5iuU67PMK9v0+4\nZ3+Lk+c/kQY1BhyCWKEnu3Sece/ak2O7e54HEKXyeuQz9kZECf8V0igk3HM9hSisn7t3fRqzDdG/\nufuvRpT4vwBZY0yfk+fz7v9LEKVylbU2gVTKg5Hyk0Aq6ncQhf/fiLX9bvc8v0Ss0DPdc13nwn4M\naQA3WGuftdb+2T37MUh5WQJc4vLrNU6GJUiZ8wyFA9zz/D2iiH6CKIM/Ig3qvoj1jHt/a9077XWy\nXOPCHODe1yVOjpDLy9cg7/kLTo6vIeV1M/IFmkTq5F5IuXwWKYcvRxqzp1x+fMHl1Qvc39WI4tqN\n1Ml/QyzeDPKVMGKt/aW19ufIV+FbESPmUPdcl7ln9hQc1tqMK3f7IF8lP0Xqzhfd896AlNXrka/4\n5yAN8xsRPfIgomgfRb5GL0Xq2/eRRvA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ab3a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GOEX9k2hfxKX/9XHO/PHXSU75O6oxTCIr/KOEEqR1TmqS6Rtug5lZdQq9oigm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\nysiqL5OSQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114a3c050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YXjfdqk07ahSUqiF/d5PPCDS3FdXdpFQ/nU0d/GjP7xSUOfmh06dJm5lDjYJSNZRTd1NR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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114afc610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#days_in_month\", \"dayofweek\n",
    "cols = [\n",
    "    \"hour\", \"workingday\", \"holiday\", \"month\", \n",
    "    \"weather\", \"season\", \"temp\", \"humidity\", \"windspeed\"]\n",
    "for i in cols:\n",
    "    sns.boxplot(x=i, y=\"count\", data=df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Inferences\n",
    "1. count is high between 6am and 8pm, especially at 7-8 and 17-18\n",
    "2. count is low on weekends\n",
    "3. count is high from March to November\n",
    "4. count is high when the season is 2 and 3\n",
    "5. count is high when temp is higher than 10 and lower than 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Constructing an estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "First, data is split into two sets as `training set` and `test set`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('traing set: ', (8708, 9), (8708,))\n",
      "('test set: ', (2178, 9), (2178,))\n"
     ]
    }
   ],
   "source": [
    "X = df[cols]\n",
    "y = df[\"count\"]\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(\n",
    "    X,y, test_size=0.2, random_state=0)\n",
    "print(\"traing set: \", X_train.shape, y_train.shape)\n",
    "print(\"test set: \", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Choosing the right regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def calcError(clf, x_test):\n",
    "    y_hat = clf.predict(X_test)\n",
    "    return np.sum(np.sqrt(np.power(np.mat(y_test) - np.mat(y_hat), 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Evaluation \n",
    "To evaluate proposed regressors, R² score, the coefficient of determination method is used. http://scikit-learn.org/stable/modules/model_evaluation.html#r2-score-the-coefficient-of-determination\n",
    "$$\n",
    "R^2(y, {\\hat{y}}) = 1 - \\frac{\n",
    "\\sum_{i=1}^{n=samples} (y_i - \\hat{y_i})^2\n",
    "}{\n",
    "\\sum_{i=1}^{n=samples} (y_i - \\bar{y})^2\n",
    "}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Support Vector Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79381930861656336"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVR(kernel=\"rbf\", C=6, gamma=.02)\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Random Forest Regressor\n",
    "ID3 Algorithm -- Michael Crawford, \n",
    "http://www.saedsayad.com/decision_tree.htm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90984494885776079"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[cols]\n",
    "y = df[\"count\"]\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(\n",
    "    X,y, test_size=0.2, random_state=0)\n",
    "\n",
    "clf = RandomForestRegressor()\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We can compare random forest regressor(rfr) and svm regressor by looking at their scores. The random forest regressor looks like far better than the svm regressor. So, I choose rfr."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Finding the optimum parameters for randomforest regressor\n",
    "To fine tune our randomforest regressor, we find the optimum parameters through grid searching. For each combination of parameters, a new instance of randomforest regressor will be created with that parameter set and its score will be calculated automatically. After grid searching, it is easy to find the best parameter combination to pass to our regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {\"n_estimators\": [2, 5, 10, 15, 20, 100], \"bootstrap\": [False, True]}\n",
    "]\n",
    "clf = GridSearchCV(\n",
    "    RandomForestRegressor(), param_grid=param_grid, cv=5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.85461, std: 0.00366, params: {'n_estimators': 2, 'bootstrap': False},\n",
       " mean: 0.85688, std: 0.00528, params: {'n_estimators': 5, 'bootstrap': False},\n",
       " mean: 0.85758, std: 0.00487, params: {'n_estimators': 10, 'bootstrap': False},\n",
       " mean: 0.85732, std: 0.00487, params: {'n_estimators': 15, 'bootstrap': False},\n",
       " mean: 0.85818, std: 0.00452, params: {'n_estimators': 20, 'bootstrap': False},\n",
       " mean: 0.85827, std: 0.00494, params: {'n_estimators': 100, 'bootstrap': False},\n",
       " mean: 0.87826, std: 0.00511, params: {'n_estimators': 2, 'bootstrap': True},\n",
       " mean: 0.90554, std: 0.00250, params: {'n_estimators': 5, 'bootstrap': True},\n",
       " mean: 0.91342, std: 0.00208, params: {'n_estimators': 10, 'bootstrap': True},\n",
       " mean: 0.91523, std: 0.00280, params: {'n_estimators': 15, 'bootstrap': True},\n",
       " mean: 0.91605, std: 0.00250, params: {'n_estimators': 20, 'bootstrap': True},\n",
       " mean: 0.91991, std: 0.00270, params: {'n_estimators': 100, 'bootstrap': True}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.grid_scores_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As shown above, the optimum paramet set is `{n_estimators:100, bootstrap:True}`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Preparing the final regressor and testing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.98712586999\n",
      "actual:6 , predicted:[ 6.19752345]\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestRegressor(n_estimators=100, bootstrap=True, max_depth=20)\n",
    "clf.fit(X, y)\n",
    "print \"score: \", clf.score(X_test, y_test)\n",
    "\n",
    "i = 22\n",
    "y_hat = clf.predict(X_train[i:i+1])\n",
    "print(\"actual:{} , predicted:{}\".format(int(y_train[i:i+1]), y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114297110>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HC7lOW+reaWdyNp12HM71FB5GmWmN2jBgIpYqGfKQrWvtFG37G48maA4X7qVW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\nj/j/bhYlkJvvVlwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139fd810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=y_test, y=clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Making prediction on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>datetime</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>40.271258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>15.807444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>6.116351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>7.137744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>8.547666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-20 05:00:00</td>\n",
       "      <td>34.937701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-01-20 06:00:00</td>\n",
       "      <td>118.945244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-01-20 07:00:00</td>\n",
       "      <td>389.747271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011-01-20 08:00:00</td>\n",
       "      <td>651.526031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011-01-20 09:00:00</td>\n",
       "      <td>293.013254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime       count\n",
       "0 2011-01-20 00:00:00   40.271258\n",
       "1 2011-01-20 01:00:00   15.807444\n",
       "2 2011-01-20 02:00:00    6.116351\n",
       "3 2011-01-20 03:00:00    7.137744\n",
       "4 2011-01-20 04:00:00    8.547666\n",
       "5 2011-01-20 05:00:00   34.937701\n",
       "6 2011-01-20 06:00:00  118.945244\n",
       "7 2011-01-20 07:00:00  389.747271\n",
       "8 2011-01-20 08:00:00  651.526031\n",
       "9 2011-01-20 09:00:00  293.013254"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = readCsv(\"test\")\n",
    "result = pd.concat([\n",
    "        test_df[\"datetime\"],\n",
    "        pd.DataFrame(np.exp(clf.predict(test_df[cols])))\n",
    "    ], axis=1, join='inner')\n",
    "result.columns = [\"datetime\", \"count\"]\n",
    "result.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## A better approach: predicting month by month (not completed yet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month  2011 1\n",
      "month  2011 2\n",
      "month  2011 3\n",
      "month  2011 4\n",
      "month  2011 5\n",
      "month  2011 6\n",
      "month  2011 7\n",
      "month  2011 8\n",
      "month  2011 9\n",
      "month  2011 10\n",
      "month  2011 11\n",
      "month  2011 12\n",
      "month  2012 1\n",
      "month  2012 2\n",
      "month  2012 3\n",
      "month  2012 4\n",
      "month  2012 5\n",
      "month  2012 6\n",
      "month  2012 7\n",
      "month  2012 8\n",
      "month  2012 9\n",
      "month  2012 10\n",
      "month  2012 11\n",
      "month  2012 12\n"
     ]
    }
   ],
   "source": [
    "uniqueMonths = test_df[\"month\"].unique()\n",
    "uniqueYears = test_df[\"year\"].unique()\n",
    "result = []\n",
    "for year in uniqueYears:\n",
    "    for m in uniqueMonths:\n",
    "        print \"month \", year, m\n",
    "        \n",
    "        date_condition = (df[\"month\"] == m) & (df[\"year\"] == year )\n",
    "        X = df[date_condition][cols]\n",
    "        y = df[date_condition][\"count\"]\n",
    "        \n",
    "        date_condition = (test_df[\"month\"] == m) & (test_df[\"year\"] == year )\n",
    "        q = test_df[date_condition][cols]\n",
    "\n",
    "        clf = RandomForestRegressor(n_estimators=200)\n",
    "        clf.fit(X, y)\n",
    "        result.append(clf.predict(q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "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>datetime</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>6.991726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>6.257386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>5.872800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>3.270695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>2.840367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-20 05:00:00</td>\n",
       "      <td>3.559689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-01-20 06:00:00</td>\n",
       "      <td>26.462941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-01-20 07:00:00</td>\n",
       "      <td>81.220191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011-01-20 08:00:00</td>\n",
       "      <td>163.667520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011-01-20 09:00:00</td>\n",
       "      <td>106.090600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime       count\n",
       "0 2011-01-20 00:00:00    6.991726\n",
       "1 2011-01-20 01:00:00    6.257386\n",
       "2 2011-01-20 02:00:00    5.872800\n",
       "3 2011-01-20 03:00:00    3.270695\n",
       "4 2011-01-20 04:00:00    2.840367\n",
       "5 2011-01-20 05:00:00    3.559689\n",
       "6 2011-01-20 06:00:00   26.462941\n",
       "7 2011-01-20 07:00:00   81.220191\n",
       "8 2011-01-20 08:00:00  163.667520\n",
       "9 2011-01-20 09:00:00  106.090600"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.concat([\n",
    "        test_df[\"datetime\"],\n",
    "        pd.DataFrame(result)\n",
    "    ], axis=1, join='inner')\n",
    "result.columns = [\"datetime\", \"count\"]\n",
    "result.head(10)"
   ]
  }
 ],
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