BikeSharing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "common pandas methods on dataframe object:\n",
    "- .head()\n",
    "- .dtypes\n",
    "- .shape\n",
    "- .describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "station = pd.read_csv('station.csv', parse_dates=[6])\n",
    "# parse_dates parameter will keep the specified column as data type = date\n",
    "# dataframe shape: (70,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "status = pd.read_csv('status.csv', parse_dates=[3])\n",
    "# dataframe shape: (71984434, 4)\n",
    "# this is the biggest dataset, takes about 2-3 minutes to load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "trip = pd.read_csv('trip.csv', parse_dates=[2])\n",
    "# dataframe shape: (669959, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather = pd.read_csv('weather.csv', parse_dates=[0])\n",
    "# dataframe shape: (3665, 24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View data sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>max_dew_point_f</th>\n",
       "      <th>mean_dew_point_f</th>\n",
       "      <th>min_dew_point_f</th>\n",
       "      <th>max_humidity</th>\n",
       "      <th>mean_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_visibility_miles</th>\n",
       "      <th>min_visibility_miles</th>\n",
       "      <th>max_wind_Speed_mph</th>\n",
       "      <th>mean_wind_speed_mph</th>\n",
       "      <th>max_gust_speed_mph</th>\n",
       "      <th>precipitation_inches</th>\n",
       "      <th>cloud_cover</th>\n",
       "      <th>events</th>\n",
       "      <th>wind_dir_degrees</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>78.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>291.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-31</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>74.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-09-02</td>\n",
       "      <td>75.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  max_temperature_f  mean_temperature_f  min_temperature_f  \\\n",
       "0 2013-08-29               74.0                68.0               61.0   \n",
       "1 2013-08-30               78.0                69.0               60.0   \n",
       "2 2013-08-31               71.0                64.0               57.0   \n",
       "3 2013-09-01               74.0                66.0               58.0   \n",
       "4 2013-09-02               75.0                69.0               62.0   \n",
       "\n",
       "   max_dew_point_f  mean_dew_point_f  min_dew_point_f  max_humidity  \\\n",
       "0             61.0              58.0             56.0          93.0   \n",
       "1             61.0              58.0             56.0          90.0   \n",
       "2             57.0              56.0             54.0          93.0   \n",
       "3             60.0              56.0             53.0          87.0   \n",
       "4             61.0              60.0             58.0          93.0   \n",
       "\n",
       "   mean_humidity  min_humidity   ...     mean_visibility_miles  \\\n",
       "0           75.0          57.0   ...                      10.0   \n",
       "1           70.0          50.0   ...                      10.0   \n",
       "2           75.0          57.0   ...                      10.0   \n",
       "3           68.0          49.0   ...                      10.0   \n",
       "4           77.0          61.0   ...                      10.0   \n",
       "\n",
       "   min_visibility_miles  max_wind_Speed_mph  mean_wind_speed_mph  \\\n",
       "0                  10.0                23.0                 11.0   \n",
       "1                   7.0                29.0                 13.0   \n",
       "2                  10.0                26.0                 15.0   \n",
       "3                  10.0                25.0                 13.0   \n",
       "4                   6.0                23.0                 12.0   \n",
       "\n",
       "   max_gust_speed_mph  precipitation_inches  cloud_cover  events  \\\n",
       "0                28.0                     0          4.0     NaN   \n",
       "1                35.0                     0          2.0     NaN   \n",
       "2                31.0                     0          4.0     NaN   \n",
       "3                29.0                     0          4.0     NaN   \n",
       "4                30.0                     0          6.0     NaN   \n",
       "\n",
       "   wind_dir_degrees zip_code  \n",
       "0             286.0    94107  \n",
       "1             291.0    94107  \n",
       "2             284.0    94107  \n",
       "3             284.0    94107  \n",
       "4             277.0    94107  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>dock_count</th>\n",
       "      <th>city</th>\n",
       "      <th>installation_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>2013-08-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>San Jose Civic Center</td>\n",
       "      <td>37.330698</td>\n",
       "      <td>-121.888979</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>2013-08-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Santa Clara at Almaden</td>\n",
       "      <td>37.333988</td>\n",
       "      <td>-121.894902</td>\n",
       "      <td>11</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>2013-08-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Adobe on Almaden</td>\n",
       "      <td>37.331415</td>\n",
       "      <td>-121.893200</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>2013-08-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>San Pedro Square</td>\n",
       "      <td>37.336721</td>\n",
       "      <td>-121.894074</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>2013-08-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                               name        lat        long  dock_count  \\\n",
       "0   2  San Jose Diridon Caltrain Station  37.329732 -121.901782          27   \n",
       "1   3              San Jose Civic Center  37.330698 -121.888979          15   \n",
       "2   4             Santa Clara at Almaden  37.333988 -121.894902          11   \n",
       "3   5                   Adobe on Almaden  37.331415 -121.893200          19   \n",
       "4   6                   San Pedro Square  37.336721 -121.894074          15   \n",
       "\n",
       "       city installation_date  \n",
       "0  San Jose        2013-08-06  \n",
       "1  San Jose        2013-08-05  \n",
       "2  San Jose        2013-08-06  \n",
       "3  San Jose        2013-08-05  \n",
       "4  San Jose        2013-08-07  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>bikes_available</th>\n",
       "      <th>docks_available</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-08-29 12:06:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-08-29 12:07:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-08-29 12:08:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-08-29 12:09:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-08-29 12:10:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station_id  bikes_available  docks_available                time\n",
       "0           2                2               25 2013-08-29 12:06:01\n",
       "1           2                2               25 2013-08-29 12:07:01\n",
       "2           2                2               25 2013-08-29 12:08:01\n",
       "3           2                2               25 2013-08-29 12:09:01\n",
       "4           2                2               25 2013-08-29 12:10:01"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4576</td>\n",
       "      <td>63</td>\n",
       "      <td>2013-08-29 14:13:00</td>\n",
       "      <td>South Van Ness at Market</td>\n",
       "      <td>66</td>\n",
       "      <td>8/29/2013 14:14</td>\n",
       "      <td>South Van Ness at Market</td>\n",
       "      <td>66</td>\n",
       "      <td>520</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>94127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4607</td>\n",
       "      <td>70</td>\n",
       "      <td>2013-08-29 14:42:00</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>10</td>\n",
       "      <td>8/29/2013 14:43</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>10</td>\n",
       "      <td>661</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>95138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4130</td>\n",
       "      <td>71</td>\n",
       "      <td>2013-08-29 10:16:00</td>\n",
       "      <td>Mountain View City Hall</td>\n",
       "      <td>27</td>\n",
       "      <td>8/29/2013 10:17</td>\n",
       "      <td>Mountain View City Hall</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>97214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4251</td>\n",
       "      <td>77</td>\n",
       "      <td>2013-08-29 11:29:00</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>10</td>\n",
       "      <td>8/29/2013 11:30</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>95060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4299</td>\n",
       "      <td>83</td>\n",
       "      <td>2013-08-29 12:02:00</td>\n",
       "      <td>South Van Ness at Market</td>\n",
       "      <td>66</td>\n",
       "      <td>8/29/2013 12:04</td>\n",
       "      <td>Market at 10th</td>\n",
       "      <td>67</td>\n",
       "      <td>319</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>94103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  duration          start_date        start_station_name  \\\n",
       "0  4576        63 2013-08-29 14:13:00  South Van Ness at Market   \n",
       "1  4607        70 2013-08-29 14:42:00        San Jose City Hall   \n",
       "2  4130        71 2013-08-29 10:16:00   Mountain View City Hall   \n",
       "3  4251        77 2013-08-29 11:29:00        San Jose City Hall   \n",
       "4  4299        83 2013-08-29 12:02:00  South Van Ness at Market   \n",
       "\n",
       "   start_station_id         end_date          end_station_name  \\\n",
       "0                66  8/29/2013 14:14  South Van Ness at Market   \n",
       "1                10  8/29/2013 14:43        San Jose City Hall   \n",
       "2                27  8/29/2013 10:17   Mountain View City Hall   \n",
       "3                10  8/29/2013 11:30        San Jose City Hall   \n",
       "4                66  8/29/2013 12:04            Market at 10th   \n",
       "\n",
       "   end_station_id  bike_id subscription_type zip_code  \n",
       "0              66      520        Subscriber    94127  \n",
       "1              10      661        Subscriber    95138  \n",
       "2              27       48        Subscriber    97214  \n",
       "3              10       26        Subscriber    95060  \n",
       "4              67      319        Subscriber    94103  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>max_dew_point_f</th>\n",
       "      <th>mean_dew_point_f</th>\n",
       "      <th>min_dew_point_f</th>\n",
       "      <th>max_humidity</th>\n",
       "      <th>mean_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_visibility_miles</th>\n",
       "      <th>min_visibility_miles</th>\n",
       "      <th>max_wind_Speed_mph</th>\n",
       "      <th>mean_wind_speed_mph</th>\n",
       "      <th>max_gust_speed_mph</th>\n",
       "      <th>precipitation_inches</th>\n",
       "      <th>cloud_cover</th>\n",
       "      <th>events</th>\n",
       "      <th>wind_dir_degrees</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>78.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>291.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-31</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>74.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-09-02</td>\n",
       "      <td>75.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  max_temperature_f  mean_temperature_f  min_temperature_f  \\\n",
       "0 2013-08-29               74.0                68.0               61.0   \n",
       "1 2013-08-30               78.0                69.0               60.0   \n",
       "2 2013-08-31               71.0                64.0               57.0   \n",
       "3 2013-09-01               74.0                66.0               58.0   \n",
       "4 2013-09-02               75.0                69.0               62.0   \n",
       "\n",
       "   max_dew_point_f  mean_dew_point_f  min_dew_point_f  max_humidity  \\\n",
       "0             61.0              58.0             56.0          93.0   \n",
       "1             61.0              58.0             56.0          90.0   \n",
       "2             57.0              56.0             54.0          93.0   \n",
       "3             60.0              56.0             53.0          87.0   \n",
       "4             61.0              60.0             58.0          93.0   \n",
       "\n",
       "   mean_humidity  min_humidity   ...     mean_visibility_miles  \\\n",
       "0           75.0          57.0   ...                      10.0   \n",
       "1           70.0          50.0   ...                      10.0   \n",
       "2           75.0          57.0   ...                      10.0   \n",
       "3           68.0          49.0   ...                      10.0   \n",
       "4           77.0          61.0   ...                      10.0   \n",
       "\n",
       "   min_visibility_miles  max_wind_Speed_mph  mean_wind_speed_mph  \\\n",
       "0                  10.0                23.0                 11.0   \n",
       "1                   7.0                29.0                 13.0   \n",
       "2                  10.0                26.0                 15.0   \n",
       "3                  10.0                25.0                 13.0   \n",
       "4                   6.0                23.0                 12.0   \n",
       "\n",
       "   max_gust_speed_mph  precipitation_inches  cloud_cover  events  \\\n",
       "0                28.0                     0          4.0     NaN   \n",
       "1                35.0                     0          2.0     NaN   \n",
       "2                31.0                     0          4.0     NaN   \n",
       "3                29.0                     0          4.0     NaN   \n",
       "4                30.0                     0          6.0     NaN   \n",
       "\n",
       "   wind_dir_degrees zip_code  \n",
       "0             286.0    94107  \n",
       "1             291.0    94107  \n",
       "2             284.0    94107  \n",
       "3             284.0    94107  \n",
       "4             277.0    94107  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further data exploration & cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 1: zip_code is null in 6619 rows in trip dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       0\n",
       "duration                 0\n",
       "start_date               0\n",
       "start_station_name       0\n",
       "start_station_id         0\n",
       "end_date                 0\n",
       "end_station_name         0\n",
       "end_station_id           0\n",
       "bike_id                  0\n",
       "subscription_type        0\n",
       "zip_code              6619\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check trip data missing value\n",
    "trip.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# change duration to minutes\n",
    "trip.duration /= 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             int64\n",
       "duration                     float64\n",
       "start_date            datetime64[ns]\n",
       "start_station_name            object\n",
       "start_station_id               int64\n",
       "end_date                      object\n",
       "end_station_name              object\n",
       "end_station_id                 int64\n",
       "bike_id                        int64\n",
       "subscription_type             object\n",
       "zip_code                      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                              datetime64[ns]\n",
       "max_temperature_f                        float64\n",
       "mean_temperature_f                       float64\n",
       "min_temperature_f                        float64\n",
       "max_dew_point_f                          float64\n",
       "mean_dew_point_f                         float64\n",
       "min_dew_point_f                          float64\n",
       "max_humidity                             float64\n",
       "mean_humidity                            float64\n",
       "min_humidity                             float64\n",
       "max_sea_level_pressure_inches            float64\n",
       "mean_sea_level_pressure_inches           float64\n",
       "min_sea_level_pressure_inches            float64\n",
       "max_visibility_miles                     float64\n",
       "mean_visibility_miles                    float64\n",
       "min_visibility_miles                     float64\n",
       "max_wind_Speed_mph                       float64\n",
       "mean_wind_speed_mph                      float64\n",
       "max_gust_speed_mph                       float64\n",
       "precipitation_inches                      object\n",
       "cloud_cover                              float64\n",
       "events                                    object\n",
       "wind_dir_degrees                         float64\n",
       "zip_code                                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 2: trip has 7440 unique zip_code, weather has only 5 unique zip_code\n",
    "\n",
    "possible solution:\n",
    "- count trip by day only (not by zip_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7440"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trip.zip_code.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(weather.zip_code.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['94127', '95138', '97214', ..., '2400105', '1004004', '29609'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip.zip_code.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['94107', '94063', '94301', '94041', '95113'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.zip_code.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 3: non-numeric values in weather.precipitation_inches [resolved]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '0.23', 'T', '0.01', '0.28', '0.63', '0.29', '0.06', '0.85',\n",
       "       '0.09', '0.64', '0.42', '0.35', '0.43', '0.22', '0.74', '0.03',\n",
       "       '0.12', '0.16', '0.49', '0.17', '0.08', '0.04', '0.53', '0.07',\n",
       "       '0.02', '0.83', '1.06', '1.71', '0.37', '0.27', '0.45', '0.78',\n",
       "       '0.88', '0.66', '0.47', '0.1', '0.61', '0.14', '0.05', '0.68',\n",
       "       '0.97', '0.26', '0.15', '0.87', '0.57', '0.69', '0.32', '0.21',\n",
       "       '0.24', '0.52', '0.36', '0.33', '0.25', '0.11', '0.2', '1.18',\n",
       "       '1.43', '3.12', '0.48', '0.19', '1.09', '0.65', '0.13', '0.91',\n",
       "       '0.99', '0.18', '0.4', '1.07', nan, '0.41', '0.34', '1.25', '1.85',\n",
       "       '3.36', '0.71', '1.3', '0.72', '0.6', '0.51', '1.2', '1.28', '3.23',\n",
       "       '0.55', '1.26', '0.39'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.precipitation_inches.unique()\n",
    "# precipitation_inches column includes 'T', nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace 'T', nan with 0\n",
    "weather.loc[weather.precipitation_inches == \"T\", 'precipitation_inches'] = 0\n",
    "weather.loc[weather.precipitation_inches.isnull(), 'precipitation_inches'] = 0\n",
    "weather.precipitation_inches = pd.to_numeric(weather.precipitation_inches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.23,  0.01,  0.28,  0.63,  0.29,  0.06,  0.85,  0.09,\n",
       "        0.64,  0.42,  0.35,  0.43,  0.22,  0.74,  0.03,  0.12,  0.16,\n",
       "        0.49,  0.17,  0.08,  0.04,  0.53,  0.07,  0.02,  0.83,  1.06,\n",
       "        1.71,  0.37,  0.27,  0.45,  0.78,  0.88,  0.66,  0.47,  0.1 ,\n",
       "        0.61,  0.14,  0.05,  0.68,  0.97,  0.26,  0.15,  0.87,  0.57,\n",
       "        0.69,  0.32,  0.21,  0.24,  0.52,  0.36,  0.33,  0.25,  0.11,\n",
       "        0.2 ,  1.18,  1.43,  3.12,  0.48,  0.19,  1.09,  0.65,  0.13,\n",
       "        0.91,  0.99,  0.18,  0.4 ,  1.07,  0.41,  0.34,  1.25,  1.85,\n",
       "        3.36,  0.71,  1.3 ,  0.72,  0.6 ,  0.51,  1.2 ,  1.28,  3.23,\n",
       "        0.55,  1.26,  0.39])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.precipitation_inches.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 4: missing values in weather dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                 0\n",
       "max_temperature_f                    4\n",
       "mean_temperature_f                   4\n",
       "min_temperature_f                    4\n",
       "max_dew_point_f                     54\n",
       "mean_dew_point_f                    54\n",
       "min_dew_point_f                     54\n",
       "max_humidity                        54\n",
       "mean_humidity                       54\n",
       "min_humidity                        54\n",
       "max_sea_level_pressure_inches        1\n",
       "mean_sea_level_pressure_inches       1\n",
       "min_sea_level_pressure_inches        1\n",
       "max_visibility_miles                13\n",
       "mean_visibility_miles               13\n",
       "min_visibility_miles                13\n",
       "max_wind_Speed_mph                   1\n",
       "mean_wind_speed_mph                  1\n",
       "max_gust_speed_mph                 899\n",
       "precipitation_inches                 0\n",
       "cloud_cover                          1\n",
       "events                            3143\n",
       "wind_dir_degrees                     1\n",
       "zip_code                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check weather data missing value\n",
    "weather.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. weather.events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Fog', 'Rain', 'Fog-Rain', 'rain', 'Rain-Thunderstorm'], dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.events.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather.events = weather.events.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather.loc[weather.events.isnull(), 'events'] = \"normal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['normal', 'fog', 'rain', 'fog-rain', 'rain-thunderstorm'], dtype=object)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.events.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. weather.temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# impute missing value with median\n",
    "weather.loc[weather.max_temperature_f.isnull(), 'max_temperature_f'] = weather[weather.max_temperature_f.notnull()].max_temperature_f.median()\n",
    "weather.loc[weather.mean_temperature_f.isnull(), 'mean_temperature_f'] = weather[weather.mean_temperature_f.notnull()].mean_temperature_f.median()\n",
    "weather.loc[weather.min_temperature_f.isnull(), 'min_temperature_f'] = weather[weather.min_temperature_f.notnull()].min_temperature_f.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# impute missing value with median for mean_temperature_f\n",
    "weather.loc[weather.mean_temperature_f.isnull(), 'mean_temperature_f'] = weather[weather.mean_temperature_f.notnull()].mean_temperature_f.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. impute for other fields if we need to use them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trip data: group by Day & Zip\n",
    "\n",
    "- **`id`**: count of bike trips\n",
    "- **`duration`**: total duration of bike trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "trip_count_by_day = trip.groupby([trip.start_date.dt.date,'zip_code']).agg({\"id\": \"count\", \"duration\":\"sum\"}).reset_index()\n",
    "# https://www.shanelynn.ie/summarising-aggregation-and-grouping-data-in-python-pandas/\n",
    "\n",
    "# set data type to \"date\" again\n",
    "trip_count_by_day['start_date'] = trip_count_by_day['start_date'].astype('datetime64[s]')\n",
    "\n",
    "# rename columns\n",
    "trip_count_by_day.rename(columns = {'start_date':'date', 'zip_code':'zip', 'id':'count', }, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>zip</th>\n",
       "      <th>count</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>10003</td>\n",
       "      <td>4</td>\n",
       "      <td>75.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>10009</td>\n",
       "      <td>3</td>\n",
       "      <td>47.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>10010</td>\n",
       "      <td>3</td>\n",
       "      <td>164.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>10012</td>\n",
       "      <td>1</td>\n",
       "      <td>15.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>10014</td>\n",
       "      <td>1</td>\n",
       "      <td>12.116667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    zip  count    duration\n",
       "0 2013-08-29  10003      4   75.150000\n",
       "1 2013-08-29  10009      3   47.916667\n",
       "2 2013-08-29  10010      3  164.383333\n",
       "3 2013-08-29  10012      1   15.200000\n",
       "4 2013-08-29  10014      1   12.116667"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip_count_by_day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        datetime64[ns]\n",
       "zip                 object\n",
       "count                int64\n",
       "duration           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types\n",
    "trip_count_by_day.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weather data: feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# change zip_code to string\n",
    "weather['zip_code'] = weather['zip_code'].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>max_dew_point_f</th>\n",
       "      <th>mean_dew_point_f</th>\n",
       "      <th>min_dew_point_f</th>\n",
       "      <th>max_humidity</th>\n",
       "      <th>mean_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_visibility_miles</th>\n",
       "      <th>min_visibility_miles</th>\n",
       "      <th>max_wind_Speed_mph</th>\n",
       "      <th>mean_wind_speed_mph</th>\n",
       "      <th>max_gust_speed_mph</th>\n",
       "      <th>precipitation_inches</th>\n",
       "      <th>cloud_cover</th>\n",
       "      <th>events</th>\n",
       "      <th>wind_dir_degrees</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>286.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>78.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>291.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-31</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>284.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>74.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>284.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-09-02</td>\n",
       "      <td>75.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>277.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  max_temperature_f  mean_temperature_f  min_temperature_f  \\\n",
       "0 2013-08-29               74.0                68.0               61.0   \n",
       "1 2013-08-30               78.0                69.0               60.0   \n",
       "2 2013-08-31               71.0                64.0               57.0   \n",
       "3 2013-09-01               74.0                66.0               58.0   \n",
       "4 2013-09-02               75.0                69.0               62.0   \n",
       "\n",
       "   max_dew_point_f  mean_dew_point_f  min_dew_point_f  max_humidity  \\\n",
       "0             61.0              58.0             56.0          93.0   \n",
       "1             61.0              58.0             56.0          90.0   \n",
       "2             57.0              56.0             54.0          93.0   \n",
       "3             60.0              56.0             53.0          87.0   \n",
       "4             61.0              60.0             58.0          93.0   \n",
       "\n",
       "   mean_humidity  min_humidity    ...     mean_visibility_miles  \\\n",
       "0           75.0          57.0    ...                      10.0   \n",
       "1           70.0          50.0    ...                      10.0   \n",
       "2           75.0          57.0    ...                      10.0   \n",
       "3           68.0          49.0    ...                      10.0   \n",
       "4           77.0          61.0    ...                      10.0   \n",
       "\n",
       "   min_visibility_miles  max_wind_Speed_mph  mean_wind_speed_mph  \\\n",
       "0                  10.0                23.0                 11.0   \n",
       "1                   7.0                29.0                 13.0   \n",
       "2                  10.0                26.0                 15.0   \n",
       "3                  10.0                25.0                 13.0   \n",
       "4                   6.0                23.0                 12.0   \n",
       "\n",
       "   max_gust_speed_mph  precipitation_inches  cloud_cover  events  \\\n",
       "0                28.0                   0.0          4.0  normal   \n",
       "1                35.0                   0.0          2.0  normal   \n",
       "2                31.0                   0.0          4.0  normal   \n",
       "3                29.0                   0.0          4.0  normal   \n",
       "4                30.0                   0.0          6.0  normal   \n",
       "\n",
       "   wind_dir_degrees  zip_code  \n",
       "0             286.0     94107  \n",
       "1             291.0     94107  \n",
       "2             284.0     94107  \n",
       "3             284.0     94107  \n",
       "4             277.0     94107  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                              datetime64[ns]\n",
       "max_temperature_f                        float64\n",
       "mean_temperature_f                       float64\n",
       "min_temperature_f                        float64\n",
       "max_dew_point_f                          float64\n",
       "mean_dew_point_f                         float64\n",
       "min_dew_point_f                          float64\n",
       "max_humidity                             float64\n",
       "mean_humidity                            float64\n",
       "min_humidity                             float64\n",
       "max_sea_level_pressure_inches            float64\n",
       "mean_sea_level_pressure_inches           float64\n",
       "min_sea_level_pressure_inches            float64\n",
       "max_visibility_miles                     float64\n",
       "mean_visibility_miles                    float64\n",
       "min_visibility_miles                     float64\n",
       "max_wind_Speed_mph                       float64\n",
       "mean_wind_speed_mph                      float64\n",
       "max_gust_speed_mph                       float64\n",
       "precipitation_inches                     float64\n",
       "cloud_cover                              float64\n",
       "events                                    object\n",
       "wind_dir_degrees                         float64\n",
       "zip_code                                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge trip_count_by_day and weather dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "data = pd.merge(trip_count_by_day, weather, left_on=['date','zip'], right_on=['date','zip_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>zip</th>\n",
       "      <th>count</th>\n",
       "      <th>duration</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>max_dew_point_f</th>\n",
       "      <th>mean_dew_point_f</th>\n",
       "      <th>min_dew_point_f</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_visibility_miles</th>\n",
       "      <th>min_visibility_miles</th>\n",
       "      <th>max_wind_Speed_mph</th>\n",
       "      <th>mean_wind_speed_mph</th>\n",
       "      <th>max_gust_speed_mph</th>\n",
       "      <th>precipitation_inches</th>\n",
       "      <th>cloud_cover</th>\n",
       "      <th>events</th>\n",
       "      <th>wind_dir_degrees</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>94041</td>\n",
       "      <td>5</td>\n",
       "      <td>44.766667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>355.0</td>\n",
       "      <td>94041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>94063</td>\n",
       "      <td>2</td>\n",
       "      <td>62.650000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>313.0</td>\n",
       "      <td>94063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>94107</td>\n",
       "      <td>37</td>\n",
       "      <td>423.366667</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>286.0</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>94301</td>\n",
       "      <td>4</td>\n",
       "      <td>944.566667</td>\n",
       "      <td>78.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>355.0</td>\n",
       "      <td>94301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>95113</td>\n",
       "      <td>2</td>\n",
       "      <td>49.266667</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>320.0</td>\n",
       "      <td>95113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    zip  count    duration  max_temperature_f  mean_temperature_f  \\\n",
       "0 2013-08-29  94041      5   44.766667               80.0                70.0   \n",
       "1 2013-08-29  94063      2   62.650000               80.0                71.0   \n",
       "2 2013-08-29  94107     37  423.366667               74.0                68.0   \n",
       "3 2013-08-29  94301      4  944.566667               78.0                71.0   \n",
       "4 2013-08-29  95113      2   49.266667               81.0                72.0   \n",
       "\n",
       "   min_temperature_f  max_dew_point_f  mean_dew_point_f  min_dew_point_f  \\\n",
       "0               64.0             65.0              61.0             58.0   \n",
       "1               62.0             63.0              59.0             57.0   \n",
       "2               61.0             61.0              58.0             56.0   \n",
       "3               64.0             62.0              61.0             60.0   \n",
       "4               63.0             62.0              61.0             59.0   \n",
       "\n",
       "     ...     mean_visibility_miles  min_visibility_miles  max_wind_Speed_mph  \\\n",
       "0    ...                      10.0                  10.0                16.0   \n",
       "1    ...                      10.0                  10.0                14.0   \n",
       "2    ...                      10.0                  10.0                23.0   \n",
       "3    ...                      10.0                  10.0                20.0   \n",
       "4    ...                      10.0                  10.0                16.0   \n",
       "\n",
       "   mean_wind_speed_mph  max_gust_speed_mph  precipitation_inches  cloud_cover  \\\n",
       "0                  5.0                16.0                   0.0          4.0   \n",
       "1                  6.0                17.0                   0.0          5.0   \n",
       "2                 11.0                28.0                   0.0          4.0   \n",
       "3                  8.0                23.0                   0.0          4.0   \n",
       "4                  7.0                24.0                   0.0          4.0   \n",
       "\n",
       "   events  wind_dir_degrees  zip_code  \n",
       "0  normal             355.0     94041  \n",
       "1  normal             313.0     94063  \n",
       "2  normal             286.0     94107  \n",
       "3  normal             355.0     94301  \n",
       "4  normal             320.0     95113  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select features for our prediction model\n",
    "X = data[['mean_temperature_f', 'precipitation_inches', 'cloud_cover', 'events']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean_temperature_f      float64\n",
       "precipitation_inches    float64\n",
       "cloud_cover             float64\n",
       "events                   object\n",
       "dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert events from object to one-hot representation\n",
    "# since model.fit() doest not accept str\n",
    "X = pd.get_dummies(X, columns=['events'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>precipitation_inches</th>\n",
       "      <th>cloud_cover</th>\n",
       "      <th>events_fog</th>\n",
       "      <th>events_fog-rain</th>\n",
       "      <th>events_normal</th>\n",
       "      <th>events_rain</th>\n",
       "      <th>events_rain-thunderstorm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_temperature_f  precipitation_inches  cloud_cover  events_fog  \\\n",
       "0                70.0                   0.0          4.0           0   \n",
       "1                71.0                   0.0          5.0           0   \n",
       "2                68.0                   0.0          4.0           0   \n",
       "3                71.0                   0.0          4.0           0   \n",
       "4                72.0                   0.0          4.0           0   \n",
       "\n",
       "   events_fog-rain  events_normal  events_rain  events_rain-thunderstorm  \n",
       "0                0              1            0                         0  \n",
       "1                0              1            0                         0  \n",
       "2                0              1            0                         0  \n",
       "3                0              1            0                         0  \n",
       "4                0              1            0                         0  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data[['count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count\n",
       "0      5\n",
       "1      2\n",
       "2     37\n",
       "3      4\n",
       "4      2"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3287, 1)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 10-fold cv\n",
    "def scoring(model):\n",
    "    scores = cross_val_score(model, X, Y, cv=10, n_jobs=1, scoring = 'neg_median_absolute_error')\n",
    "    print (np.median(scores) * -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm = svm.SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.89280588962\n"
     ]
    }
   ],
   "source": [
    "scoring(svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.fit(X,Y)\n",
    "# fit() does not accept str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xinyi/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "Y_svm = model.fit(X, Y).predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztvX9wXFd25/c9jSYAkoBBEPw1K1FLOpz1lKKhOGhiJMaM\nPbEnG2dlk+Y/W+sqp4YbJ2K2NpbXlZAjaUYCONJKI3HzwyxXJVBihXatM47jmCPE2nV2dmt3plRF\nyWBjKEoeze7AS8kgPSQgEKQJkgDY6JM/Xt/G6/fuve++H/0T51OlEtH9+r1777vvvHvPT2JmCIIg\nCO1PrtkNEARBELJBBLogCEKHIAJdEAShQxCBLgiC0CGIQBcEQegQRKALgiB0CCLQBUEQOgQR6IIg\nCB2CCHRBEIQOId/Ii23bto337NnTyEsKgiC0PcVi8VNm3h51XEMF+p49e3Dx4sVGXlIQBKHtIaJP\nXI4TlYsgCEKHIAJdEAShQxCBLgiC0CGIQBcEQegQRKALgiB0CCLQBUEQOgQR6IIgCB1CpEAnojeJ\naJaIPvR9doCI3iWiS0R0kYi+WN9mCoIgCFG4BBadA/DbAH7P99nrAE4z8z8nor9T+ftLmbcuJpMT\n49g9dQY7eA6ztB0zwycxcuRE4uMajWrXTp7DKnLIodxS7WsmWd+zVp0DgpCGSIHOzN8joj3BjwH8\nROXfAwD+KttmxWdyYhyPFb+OjbQCELALcxgofh2TQM2D6npcs9ufRxlA67SvmWR9z1p1DghCWpLq\n0P8RgDNENAPgnwB4LrsmJWP31BnvAfWxkVawe+pMouMaja5dilZoXzPJ+p616hwQhLQkFej/AMBv\nMvNuAL8J4HdMBxLR0xU9+8W5ubmEl4tmB+vPvYM/TXRcozG1a+375ravmWR9z1p1DghCWpIK9K8A\n+OPKv/9vAEajKDO/wcwHmfng9u2RycISM0v6c8/StkTHNRpTu9a+b277mknW96xV54AgpCWpQP8r\nAD9b+ffPAfhRNs1JzszwSdzn7prP7nM3ZoZPJjqu0ejapWiF9jWTrO9Zq84BQUhLpFGUiL4Fz4Nl\nGxFdBTAK4L8G8FtElAewBODpejbShZEjJzAJVDwXPsUsbcNMIey54Hpco/G3K+Tl0gLtayZZ37NW\nnQOCkBZi5oZd7ODBgyz50AVBEOJBREVmPhh1nESKCoIgdAgi0AVBEDoEEeiCIAgdggh0QRCEDkEE\nuiAIQocgAl0QBKFDEIEuCILQIYhAFwRB6BBc8qELGSO5uDsDuY9CqyECvcFILu7OQO6j0IqIyqXB\nSC7uzkDuo9CKiEBvMJKLuzOQ+yi0IiLQG4zk4u4M5D4KrYgI9AbT6FzckxPjuD62D+XRAVwf24fJ\nifG6XGe9ITnVhVZEjKINppG5uMVwVz8kp7rQikg+9A7m+tg+7EJY13sd27FrbLoJLRIEIQmu+dDb\ndoU+OTGOfVMvYQvfqX52i/rxw61fxufn/xk2YxkAUAbhz4Z+GYeeOZf6elE+x7pjAGDf1DewhRdr\n2rj35jux/Jfj+DxfOHscI/NvYSfKAIW/b6bhrpG+28E5cov6MD38YuyxvrL1cOz7ZW5P7VyYHn7B\nW+0b5o767Db1YwOvVOe1a1+E9UVbrtAnJ8bxheJXkadw25kBovBn7w4dSyzUa1QXFe5zNz4svFx9\noHTHrHAehFVsCLQz2MbguZJcX3Hh7HE8OX8+NAZ+mrVCj9OPLK71ePF5dFOp5vNl7sLlwqvWl+ET\n8+eR841f3Ptlas/+4nPoodWaz1c4j+LQL+HA/NuhucPg0PFx+iJ0DplVLCKiN4lolog+DHz+60T0\nQyL6cyJ6PU1j47Jv6htaYe61S//ZyPxbia/n4nOsO6abSiFhrmtjlP9yHJ/nkfm3rMK8mYa7Rvpu\n7546ExLmANBDq8brTU6Mh4Q5EP9+mdqjE87dVMLI/FvauWMT5oC9L8L6xEXlcg7AbwP4PfUBEf0n\nAI4CeJyZl4loR32ap2cLL2pVCTa6UE58vR08F6m6MB3jfg2zGsTl+gpTP5mBG00uOB2nH/W6lu16\nu6fOhIS5+fzx2mxrT7q5KX7vwhqRK3Rm/h6Am4GP/wGAbzLzcuWY2Tq0LVNWU3houvgcm45xv4bZ\nfzmOz7Opn6vIYdfYdFO354303bbdD9P1TMFCcc6RpD3p5qb4vQtrJJ1JfwvAf0xE7xHRd4loJMtG\nRXGL+o3f6UwCzMDk0NHE13PxOdYds8J5PODwsizYxig1SByf58mho6Hzp+1/VjTSd3tm+CRWOLwB\nXeYu4/VMQjfu/TK1Z5m7Qp+vcB6TQ0e1c0d3vB9bX4T1SVKBngewFcCTAE4C+EMiveaWiJ4mootE\ndHFuzn0FZGN6+AWUONx0ZuCD7gNY5B4we3+vMqUyiAKez/GHhZdxHdtRZsJ1bA8ZxXTHvF94BZcK\nr2EBfdX2LKAf7w4ds54ryfUVh545h3eHjqHEOTADJc6l7n9WxOlHFtd6v/AKFtDvG/s+qxFR98Ip\nV+ZU2jaPHDmBy4VXQ3Ph/cIrOPTMOe3cuVx4tfrZAvpr5nVUX4T1iZOXCxHtAfAnzPxY5e8/BfAa\nM//ryt9/AeBJZvueNUs/9KQuaYJgY819sBIsJClxhRbA1cslqUD/bwD8DWZ+kYj+FoB/BeARjjiZ\nBBYJgiDEJ7PAIiL6FoAvAdhGRFcBjAJ4E8CbFVfGFQBfiRLmgiAIQn2JFOjM/CuGr34147YIQksg\nlYiEdqVtQ/8Bcyg1gIaGl9tCtkUgtDbB+ze74SEUVi55/uiVhGaDxeewMPUSBviO3E/BiWYtCtoy\n9B8wh1KXmFBGV02UYD3Dy4Oh7MvcBQI15PpCOnT3T5c6IojcT8FGPVJcZBb636qYQqnzxKGQ73qG\nlwdDtntotWHXF9Khu39RwhyQ+ynYaWZ5wrZVucQNtW90eHkjri+kI026hrT3U/T0nUsjU1wEadsV\netxQ+0aHlzfi+kI60qRrSHM/1ZZ8F+aQq+jpHyt+XapJdQjNLE/YtgLdFEpdYgqFfNczvDwYWbjM\nXQ27vpAO3f0LmpTKlWhbP2nvZzO35EL9aWZ5wrZVuagSYCEvl4Lfy6W+pcFMZcgadX0hTBxVhu7+\nXRlSxSzqdz+buSUX6k8zyxO2rZeLIARpZAGNNEhpQCEuHV+CDjCvxvyfl5FDF8peLnBHw1M9DVa2\nc4uhLB1WVUbM6kJR8ypOaTrd7wYCFYruczdmCiexK3n3BaF9V+im1diloadC5bz830et1uq5yrOd\nG0BbrC5bmfLogLZARZkJudO3nM4RZ165lKazna9GtSMvb8FCpsm5siJLgW7atpY4hzyZK8BEbWvr\nuR22nRuAbMNTksW9SzqvTNcS9YqQBR0fWGSqLhNVzivK8GQ6bxYGK9u563nd9UIW3gVJ59Xa7z8N\n/C33VWgcbSvQTb6eUeW8onxB6+lDajt3M31XO4UsCmgknVdrv98W+Fvuq9A42lagm1ZjunJe/u+j\nVmv19CG1nbuZvqudxMiRE9g1No3c6VuJaqjGmVcupenkvgqNpG29XIK+nrepDyDGE/PncZv6sYRu\nDPBirZeLgy9oPX1Io87dLN9VYQ3TPTpU4+Vi9lkP3q9m+iQL64+2Moqa3MZ2VvSUfo+DFc7j/cIr\nAMKpbHWfNfoBq5eLorg+pkfGUGg1Os7LJUmq07vcixzKLZfetl6uke0SWNPKyBgKrUjHebkkSXW6\nCUstmd62Xrk8JEdIemQMhXambQS6yf0ru/M3zo2sXq5s4iKXHhlDoZ2JFOhE9CYRzVYKQge/+++I\niInq74OVJNWpm+ewOn/j3Mjq5comLnLpkTEU2hmXFfo5AL8Q/JCIdgP42wD+MuM2adG5f9lY5i78\n2dCxlkxvWy9XNnGRS4+ModDORAp0Zv4egJuar/4nAKcANMSqGgwaCeaoVjB7YdWXC6/i0DPnQoEm\nlwuv4v3CK6mCT7LuS1ZtqNd51xMyhkI74+TlQkR7APwJMz9W+fsogJ9j5t8goo8BHGTWKxmJ6GkA\nTwPAI488Uvjkk08yaXhUoitxOxMEoVOoW/pcItoE4Hl46pZImPkNAG8Antti3OuZsBWXqAr6Snmv\ngeLXMVn5jSCIn7nQqcReoRPR5wH8KwD3Kl8/DOCvAHyRma/bztOIAheS3U6wIX7mQjtSNz90Zv6A\nmXcw8x5m3gPgKoDhKGHeKMTtTLAhfuZCJ+PitvgtABcA/BQRXSWiX6t/s+IzOTGOhbGHdaUaAXSO\n29nkxDiuj+1DeXQA18f2retK8UnGQl74QicTqUNn5l+J+H5PZq1JyOTEOB4vPu9FgGok+jJ3dUR5\nrxp1wTq3DyQdi1narlXJzdK2tp8fgtA2kaI2dk+dCYXz+yHjur29EHXBGknHQvzMhU6mIwR6VFqA\nbip1hNATdcEaScdC/MyFTqZt86H7MW2j/XSC0BN1wRppxmLkyAmgIsB3Vf4ThE6gI1boM8MnQ+H8\nQTrBKCrqgjVkLAQhTEcI9JEjJ/B+4RWssl5XXmZ0xIMu6oI1ZCwEIUzbFLhwQRc0UmbgvaFjOPTM\nubpdVxAEoZ7ULfS/lVHpAPZNvYQtfAcAcA89+NzNf4ny6EBtfVEJ9xYEocPoKIEOAKWPL2CA71Sr\nGfVhGcAyQECukiHd77MMSCIvQRA6g44S6JMT43hi/jxyDm7nG2kF+6ZeQi8vS6COIAgdQdsKdF3G\nvN1TZ5yEuWKLbyWvqAaniEAXBKHNaEuBbgr77sGKNvQ/Lp3gsy4IwvqjLd0WTWHf5Rjduc/duEV9\n2u86wWddEIT1R1sK9J2GsO8ulEPBJszAIvegzECJc9USdR8WXsb08IsSnCIIQsfQliqXVeSQr3is\nBD//sPByqIqRMnCqt5c/3FtX9UgMooIgtCNtGVhUHh3QGj/LDORO3059fkEQhFaibhWLWoFZ2h7r\nc0FoZ6SoieBKW6lcLpw9jifmz2MnAAZqXA6ZgZ2YA48OhCJB26UocLCdV7Yext6b72Te7lYfD1P7\nbO2enBiviRC+RX2YHn4x1C+Xvmc5PnHPpZsDB+bfllgJwYm2UblcOHscT86fD/mNmwh2y/+7Fc7j\n/cIrLfVA6PLQMNe2O4tixrrrrHAed6kXA7zYdAFvKuJ8aeipNcHm+/zDwssAsFaxyscyd+Fy4dUa\noW86t3px3qZ+bOJ76KHV0HXijonL9YIvLF0uIp16MUnR81Z/kQtmXFUukQKdiN4E8IsAZpn5scpn\nZwD8EoAVAH8B4O8z862oi6UR6KXRQeQpbAhNygL6MTh2NbPzKZI+NNfH9kXmdAdqH+Qk13K5ThYv\njqSY2lfinPb+X4enZjP1yT9epnObhKbpPK64Xk+N9+6pM05zwDsHIXc6/Mj554Q/d1HNSj9wXRHq\nrU+WOvRzAH4h8Nl3ADzGzPsB/DsAz8VuYUy6NF4taVBb8yxRK6xdmEOusj1+rPj1VMWLw8d9mvha\nkxPjRpdPP80oa6f0xDaXVB07+FPr2PmDxEzHuUQXJwk2c72eGm/XOQCEYyVUkfSDxVPVOZGnMqgy\nN56YPy/lC9cBkQKdmb8H4Gbgs3/BzGp/+y6Ah+vQthpW62C/jWtoijJOpan56WrQVQ/yvqlvxLqW\negG4qqwaGS3rfzmZ2me6/7O0zTp2fsGXxmjuGmzmnyNxAt08t1l9+4Kb6GCshBq/QYRTWShML612\njooWY3GYLKTkfwngn2dwHiuTQ0dDEzstcVe3USviNDU/dRV4TA/y5MQ4tvCi9jw7eU47wXUvGxuN\njJaNatt97sbk0FFjEJipYtUyd9UIPt0Ylx3mFDsWSAnOkTyVQ/fQdL1Z2oaZ4ZPa74k8lZOpkEfc\ne+vnNm1O9Ltmk2Y33Mmk8nIhoq8BKAH4fcsxTwN4GgAeeeSRxNfK7zkEzJ9P/PsgcZNyWVffld+k\nrXMZDHK6MqS8XGqDnq6P7TOuxIi8dDbBFMEHeU6b54YZWEFXyAg4UzjZsFqbOyxtW0UOl4aewqFn\nzuHC2eMYmX8LXSivfa4MngAeLb6ITVgCAJQBTA0dqX4PmMc4qFtOim6OKGGcAxuvp8Z75MgJcPGU\n9tw5MHKnb2lroJrGzw33H8ax2aQ51sW7y+V5XI8kFuhEdByesfTn2WJZZeY3ALwBeEbRpNfbPXXG\nWV2QFNtK2vTQ+H8zM3wSAxqvBlfhaCte7P/b9QH2pwg2jd0NX6bKZkXLml6EREAeZRyYfxsXzh7H\ngfm3q4ZR9fnkxLgvErhc7WcXEPoe0I+xEihKfx8cKyI4CQrTfQkK4zUBFh7vGwkWBS5F0oMeU4oB\nR1uSKSGezn0y7bE7lTeb5bcuz+N6JJHKhYh+AcApAEeY+V62TdITx2CUFJuawRzMtPabRtW5jKML\n3sJ3jKtPpbIYOXICu8amPaEzNt1wrwedKsTPRlrByPxbVptBGvuF6j9ZooxdBIXLHPFfTzfeSYpf\nX9l6OKTaYfbUOyp3kcmlwFXPH2d80x5r3D37cB3r9Ubk3SSibwG4AOCniOgqEf0agN8G0A/gO0R0\niYj+1zq3M/Mo0ChDU5CoB00ZaAqVLXOx8Foq4Wgz+CTVBfthRsNc1vx9WRh7CAtjD9f0a+TICVwa\neqqaPE2HyctFrarT2C/83EghKJII4yBJFgV7b76j3VXM0nbQ6dvey8Pw2y6UnQyKpvHdyXOheRrn\nXsT17lJkMdadSKTKhZl/RfPx79ShLVZmNzyEnStmL4i4MIBb6K8E00SrGXT6V/WbOFtMF6LO56oL\nvs/dWKJuDCJsQL1B2xsmzP19qbZFba+Lp7BYfAEFrFrjDEz3nQFcnBjH7hT2Cz9p1Ga2ORIHm+pN\nh4v6waTKIcf5alLrMCoxAL7z3KZ+DCKsytHdCxd1ke63WY11p9E2of+PrlzOVIeeI2AZvcidvur0\n0ADhB22msvI8yOEXTRoDjYvBR/fQ64yG+T2HtNGKjTJ6RnlgEKm6r8nIVfTbae0XfpaoG73snecW\n9WO68IJW9+tPNXAXPXhA3SjwHczSdhQLr2HkyInqHPGiUPsAELbwHawihxzKsYLPTIZGF2O8bnz8\nRM1X3e91AVkbaQVL6MZ97na6F7rz6iKk7fexcdHurU7bJOfKOrAISGdAcfGdTnr+JOqDyYnxqtGQ\nKi5zB+bfBoCG6PVNpLF9MIdVY/prfJqJ/WLNn3vR8xYioJfDL5vJiXE8Xny+6vdNBPTRMgZxp8aF\n7sLZ4zWudYNYrP4mT+VY7nY2Nz0X9YN/fExjaptfuvE1McB3ne+F7rzvDh2L/K24Leppm1wuPDqQ\nuZdLknDu6m8dQuiTnt90btv5kvymEbimNND+FtuxjecjUz5k1UfXMXTtkyldgQ5mhJLKxWlbyHPG\nsurPaq40c8616nyvFx2dPjcLXINFTEStPNMYaJIYfLIyCmZNlAeLCdXfXMTOLEtDmOsYuu464uwq\nKWKVGdW2OJ5KWRkUm2mYbNX53mzaRqCbvA/SsHvqTOItmi1MO61aI4n6oBFuXElCrYN9WUAf7nJv\naNu/zF1YQH+ov2nGOW57XcfQ1eMqSboKk3tflvc3K/faRrnp6hC3RT1tYxSdGT6JHcWvIkfZqIhc\nrfu29uiMcGpC6ww4yTIxuvU3S6OgjjSePH4D7qDvfCYPBb+R+srWw2uBJhWYgXeHjuHQM+eMfUvS\nXtcxnBk+ia2adL1+TCl/XdCtMrO+v3E9aep9nrjUe763K22jQweAhbGHtC54aUmqd4ujtzTlxrYZ\nfPzHLnMX7tEmDFS8KOyFGaLbE5dm6SyTXjfp71zH0OTlUnWD9eU49x9XBiEHxmolta3OLlTiHL5f\n+GboukEvpsmhozj0zDljXzqdes73ViOzfOhZklag18MwCphzS2dJHAHTijnLzXVc6zt2Sa9rmivM\nAJ2+3bBiD7YXOYDQd4pgEZY4C4Is2y4FMVqDjjSKlpNnILISR++WNGVn1tFzjc5l3Sydpee7rfvc\nniXQpL9eRa6hLm+2mAKlg17l8LzuphL2Tb3kdJ560ElugespzW5bCfRcHQII4ljl00zyOALR1ei2\nk+caNjmb59FgeonbX+4m75gcyg0Vji7eKaZ57S/C0mivjka/QOpFJ72YXGgrgZ4lzMAC+mJtWdNM\n8jgC0dXVjwgNm5zN8mgwZQMcMOSDV5hfoNsbKhyz2tnEPU/aVWmnuAV2yovJlbYS6LeoP5PzMAMX\nC69jcOxaLIGUZpLHEYg6Vz9dAQcgm8np+vA3IytjUoFoe4FmJWRdxs3lRX7LoFZSn09OjKMH950q\nF10f2wceHUDBV4puF+ZQKJ7ChbPHnfsWNUZeybuHwKMD4NEBLIw93JKr3k55MbnSVkZRFXJtcxeL\nghn4oPsAdjy4llkh50ZEp01OjONg8ZTW0JfGMNloY1tcQ1ua9pm8ILLos8s5/HnWbblbJifGsb/4\nXE2RkWXuwuXCqwDChlNm4B56sUJ5bOHF6rkBe33UMgPvDR3TFo/QFZkwFZUGEGovEDbkZk0SI+3C\n2MPaRGHtFlHakV4uQK3rVlKPF13yH1cB0WhPAz/1eKE08iWVdPzq4Z6W9pwuofhx+mpqj+k6usRY\nLgR/Z/KXV5/XVMyKaJO//1mTZO7oXpRA/V889aAjBbrupmaF60TMQrgkdQerxwulke6InZR/I2rc\nsuqr6TpZYso5Y2qrrU31cmPNMr/RAvowOHYt8zbWE1eB3jaRokC6YrhRuOrU0kbGpY241NYdnTqD\ncvFUIl/hNHVQ49KosmFJXphxfxM1bln11TVfeBpMOWdMbbW1qR7zxmtL/PE0/WaA72bYstairQR6\numK4duo1EYOkLW7rf6HMTIzjQMrCGlmGUEcV++2hPm2kb9Kx1wlhALFfmPVIE5D0Rakbw4GAOiSp\nusVUV3QVOeQ1Qt3U1pnhkxjUqDJKnKtb6H2S8WzkYqVVaCsvl5ImACMLmL2cIY0gS6t7Fi5ZWbkj\n6vx9n5w/X/P3Zl7CMnfV/C6pL7vJv/jR4ouxxyTJOEaNWxK/fV2fDsy/jUtDT9Vc572hY5FurUFN\narniDKBr0+TQ0VhtHTlyAlNDR0JlD1frtdpCsvFcj2XqIlfoRPQmgF8EMMvMj1U+2wrg/wKwB8DH\nAP4uMy/Ur5kVL4+MEnMFIQKenD+P62Pv1D28OctVQ1bb+iwSLLkU++2mEhbQjwX0pi4bZhLCrsUb\n/CvhnYDTOAZXzxg+WdXfBsctSYk0U5/23nwndB2/Bw0QHmsib8WcA4fKJQbbdMjweVQd0+AuoYdW\nE1fpiiLJeK7HMnWRRlEi+hkAiwB+zyfQXwdwk5m/SUTPAhhk5q9GXSyNUTRNoYQ4NCI/RlaGTdOY\nmJI71RNX411WRrO4xkL/mLga1/0Gt0Z4OCU1UNfbsK1TbRWKp5qS22e9kplRlJm/R0R7Ah8fBfCl\nyr9/F8C/ARAp0NNQT/25n4204uXQSPGQ2gxsaVYNLvpVwCtvViiewoWPLzQsG1/SYr86XLIKxjUW\n5qmM/cXnqmMfJcyZgR66j8mJcYwcOWFcPX+h+Gykft5mbPV/X/b5k9f21T5mrrs+L/PjN7ClEmV7\ni/oxPRyulxpsv86+EKcQdJwxUHVXbVlFBTNObosVgf4nvhX6LWbeUvk3AVhQf9tIs0JvhPuWQkWS\nJvGMAMKBIElWci6BHsvchRI2YBOWjJkFXfqRBSafXz8u43Dh7HE8acl/7r9ecJxdjIUL6MMALzrP\nJWagDM/YZIp7iJMG2X+s7vu4MRJ+IW37XVKfbNMucJF7sAnLNeOodOrBgKW4Y+Cn0VlFW5VM/dBt\nAr3y9wIzDxp++zSApwHgkUceKXzyySdOHQhSr9S5JlzyZusm6hL1pI5MSyqsdCygH4NjV+P/MAGm\nqLyoepl+SqODWp/oEueQP11rpgnqfXUvPVNb4qrvTB4iijhpkNWxNpVZVfcdM8c+s37lnTQYyLSQ\nCo6H6UW0e+pMojFwbd96od5+6DeI6DPM/GMi+gyAWdOBzPwGgDcAb4We8HoNJ8qwaNqC9/JKakOl\n7txJdydbDMmt6sEA39H2nUHYNTbtZGw1+UTrPtcZc/3GQpMA1rkcRkFkF+rGNMjauTCH62P7PIOm\n5vsc2MuXA7uB2mSIXkZv6CVgU1na5qZJnaMzwvpRXkJRhnsXVWqn5l2pB0ndFicAfKXy768AeCub\n5rQOpjzcCtdCwYpg4qcLZ4+jNDoIHh1AaXSwJnFS3HO3ClkkvbLlMXdBJRAzJXK7h17snjqDXqyg\nxDmjV4yJEuvbQeBQgi5bGuRdML9wdOOlSwQWxwXW1hbb9XbwXMg90XXMvJ2T/rplECYnxp1SRa/3\nOqFxiHxKiOhbAC4A+CkiukpEvwbgmwD+UyL6EYAvV/6uK41f2tuvaJqIt6gv5Pu6wnn04H71Ybz8\nj38WT86fR568fDR5KuPJ+fNVoW4rjBwXUya/eqDz+w32fXJi3JqlcHLoaKifzN7ncZgefiGUobLE\nOeTxoCpM81TGKsh5XFfhecrofMBVjVp/OmPdeESpzu5zN65sPVwzPhfOHtf63N9Dj/YcuuIfM8Mn\nQzEAgHd/gn7ZQX/4HHntVoW5XbOeKpWRbrzyVMZjxa/jytbDVp/6Tvcbz5pIgc7Mv8LMn2HmDcz8\nMDP/DjPPM/PPM/NnmfnLzHyz3g1toPocQHR4sCloYXr4xUDq234wGINYrD6Mn1+5pN2yjsy/ZTx3\ncLuvHjAby9yF6eEX7QdVyKKqSzjtb7jv+4vP4fHi88aCA4eeOYd3h45VV88lzlUNorZdja4t7xde\nqQnIuUObQkbBPHHoXrBmbNVLxd9H3fj7A5J0wUemeayEpUqU5R+fJ+bPa9V7G2FSGa1dRd3XQvEU\n7tEmLHJPtX8L6Edx6Jewe+pMzX03qfxukKfLnh5+QTs/g3+rYL0l6jaO1d6b74RSRS+gvyZYC8C6\nqTiUlrZJztVqRlHALVFXHP95Ve8yeO4ySGsodPU00Le5/p45PVjSGkl1RI23q/eLjTieUne5Fz1Y\nsbpOxvU12pUUAAAgAElEQVT/npwYxxeKz1oTYcWeL5brJ/Ewuc/d6MGK0RDKQCCtg3l+LqAPvbxi\ntVVE+a03O8Npq9BxybluGfxes0BnoXfJSeESYRnHf97LqRE+N0YHtMdvwgqKhdedcm4DZp/iJepO\nlV/GdG5mOPc9yvA1Mv+WdVfjklwrju/6RixXBU0ewCHfdxfOHscX579t3N6afLEfK35dK/j88y2L\neAt1/ai8QabvgzpzBZHXtF2Yw47585VrmStABV0pbW3VYXoBxp2b64m2yeUys+E/SKRDdiGoyrg0\n9FQmb3/b1jCOnthmbBw5cgIzwyexhG7kqWytm2h6gLcYyrml9cyJs6OKMnzZvF9c60a6lvaztUft\nFLo0qhrArPM1BTOVOFez2jQaETXzJer6UUZT0/cut03p1m0vyKj7b9OP216AgHi+mGgbgf7oyuWG\nqFxyBHzu5ncyOdfuqTOGLbmXKEmnJ9YRlWTINblUWs8cG67nXuaukLHSxfBl835x7b/Saa9GLAx0\nhsLqOTQ7BUVQOPsxjU8OXHP8la2HQ8L7PnfjvaFjVT1ziXPaNri+HNR9NX0f9zlTRlNXlL3ApjaJ\niuYVzxc9baNyMa3Q6oFpxRoX0/aZAOz/2nerf39/YtzLaT46oC0LVqiERK8yYTOWAQBLtObh4Jqk\ny6RyuIdebOSlUCWboNrJptawqTM83StV87d/7uZ3sMEffl7Qh5/7r7eEHmzm5ZAOfXLoKJ6YPx/L\n99+0iqkG5RjaA9jnYVA4+3EJz5+cGMeB+bdDNpFLQ0/VvuwNKrgcyp6Bs5IbX5cawn9fZ4ZPYqeh\nrGFcCNHBV+r6SpDbVJo21VPS9M7rgbZZoTcak7okjjeIzbXRfz6duiDoqjaIRWyGJ9CIgEHcqaoV\nXP2/Z4ZPGl35dEIkmG/DptaYGT5pXKXdoO3Inb6FmeGTODD/NgaxWO1HLy9rfxO8Xh8tYxWEVabQ\nriaO//vuqTNGoXODtmNw7KpV3Wbzh7etGl1SuZq8S/befCdwHbt/uy317qWhp6peLbunzuAeeo3n\nisMt6jM6+pY4Fzs1s8klc5URmQphPXvEiEDXQAQcLJ6qusdxhD+wadJc2XpYq/fv57vV35jUBU9q\nXNV00XhfKD6LnZrgD5MagwOPXQ7lkCufTohEqTVGjpzAe0PHrO2Ik3dcd2yeGHO0DXT6NvKnF6qr\nVp2P9TJ3aftvUn0we+eJEgg6P3kAeMBUvZ7uHC55501t21mJLI3r3+5Pvet/ofrnbx4PQi95nX3H\n9rfXFjKqF79f+KYX+To27ZyETu1Eg/w19UemQnB9PjsRcVuMgSkoJJg/wyX8XLncBV3x0qBupSlv\nShyXuKA7mauLnpcs6qVqyoEyvJ2z8oYwjd+Nyvcq294WvmNMOHbD527pzx7oP77EnqthsMK9KbfI\nArz7F0xgVWbgCu3G3+RrVRfGT+gh7OGryFVejnfRg48KLzklozJhc2nUnSeYkdJUNN1/f0z3f5Wp\n2pcygC6Dfl7ll/G7LCp3XVM6XeXqqL4yuYD6MeUE8voD5CquveHfPaStiOUv3J2klm8r0HFui62A\nyYdZqUC+UPwqLnx8YS1BlEVQq6IaMTz7IlH5Rq5sPYxDJsOc48WC7mRxCnP0+nTdat28C3PW2Ntd\n8NpWfSAtY70LcxgsPoculJEn1h6rom+JUOOi+aPuR7FzZS6ki//h0JfxualvaHcrP8kz1ePzKOMn\neabGiN0HYKRyfJISg1EeHcHzTAI4MP929fg8yijrh6Hm/pjuf5evcEzOcJNM+WXU39cNL0rl6qjI\nQ0VFwyjUtxhyAgGopBgOMzkxjoO8aLSlpKnl206IyiVD8sT44vy3nZM+BSd7FqgXhW6b6eoSp1PX\nuJbzsnknmHYiSXYoPbTqCXMLOhXVf7jyvtaffe/Nd4zGcN3xX5z/tvbYJCUG4xQ/38GfGnXtUffR\nJW+K6V5E5TaK4xK6ViEsvo7bZJS22UZmaVsm5RrbARHoMYnSUOViZp2phxqJCFrdoU6nzwx82H0g\nsqaoa+3ReiQWy1IraLo/cf2aTeexGWhN+vk4YzZL26z+47b7E0foBtnE96zCNzg/ou6ZLveNwpZ/\n6IZhfG22kR7cd37RtrtRVVQuLYiL+1cUum3+3pvvaFebOx5cM9bG9GNLV1sN969DRG8ZhK6M0rNF\nrUDTtv3K1sPYqUlRMNv9kHHLv9sxgrXMwEzhJGBQb6hcK4D+PgarZZlC9nW41Av1zw9Xe41unk4P\nv4gDxa9iQ2AHtsJ5o7uiLc3vIBaNzqZBt9F2V8vICl2DbXURJWjvose4CrKdlxnVldWy4T0b5W0Q\nJLj6sHlRJFmJ6LwKNvG9kNeEYoXz2ox/NlY4r30YS0wosflmPGCK9N6ohTE9/ILzbuCuwa3O9NJ8\ndOWycctvSsbmp8zAe0PHqpHBSavZq/TCudO3jJkjTcTZxcTZDQTPO3LkBC4VXsMC+moSidkqK0Vd\nz0Ul1QlqmXUr0NVEYQZWyrX+zUlZ4Tw+KrxUk43P88H1tsIPLBpz5au9a2wa3SgZj/Nvqd8dOlbN\nnqcj6BdtiwxM4t6lewB6aBV3qVfb//cLr+By4dXIMfa/3O5Sb2ilBgB3aDO+H3joV5mq17pUeC2U\nbdG2lR/guxg5csIp3fADJnxUeEn7nemladL97uBPteqsd32RodexHcXC61Ujoqv6S4dfpbB76kyN\nn3rUfYkTnVnbRn0GS9t5R46cwODYNdDp26DTtyPjA6KyYALRKqkk9o9WY92qXNQqihlVgXGDtmN2\nw0P4/Mol7W9MqpCqK52/2HPl/2qAZybGMVh8DkC45uYyd9VsJU3bxzKoxuVKebKYXOWC21NbpZ4k\nCY9MXhMDfBe509dq1DEKte231ZG8RX0YHLuGXfDcJU3XqElgBr9HjU/d4HMj3cKLnleR5nxq6z09\n/GKobcvchXu0CQO8GFnU23TvvMRr5gLQtkRvJvVJVGK4IBfOHscT8+c9by2lUph/Gx8WXsauIydQ\ntNT3DM5RF4L3x3WeJkVdz6TuiVJJxfHkalXaZoX+72l3XZJzqYhFZaTR5SoHPKH9QfcB7Vb3YuH1\nyKCJ3VNntAWUVxm4XHg1ZLzSqS26iGvyii+MPaxdaUUZNk3jGHclEmUANAV5rOVUMe1YyHeudFWQ\n/O1Q91kXGKO23rrV7+XCqxgcu+oUHGNSh0wOHdUGA+0IBA3Vi8mJ8TVh7kMFp/nnkdr1+HGxYUQZ\nFNPsLOKQVCWVRpXVKrRNYNHkxDgOZpR3IgkqQEIFvlRXa47BCdZiu5pAicXRHegjfbScDlPwSjAA\nRQXbmIoTf7/gFZ8yBWD4V9330YuNCOeAcSkObBsTfzCMS05v1Z61e3MnMpBIBcp4v+HK/TQHm5iC\nUkz55XV58v33AtAXVa6X8c3VSHmfu1FGDptpKfSdreB4q+Utd6lVkOXv6o1rYFHbCPQHo1uxQbPC\nbQZqogJmwRfE9ECVGSgWXg/9LklkbPCBMxWF+KD7AD678gPt1nqF82BwzW7C31+bqkQZ7g49c85J\nWJvGJFjswvSQ6YSIn6hiDe8OHVsLAgv0VZfHJnicqi7k/1wF+ASjdScnxkNRqFH9zpI4xT1sqkXd\n4gNwv5dCMhoi0InoNwH8V/AWrx8A+PvMHH61V2j30H8/C+hHLy87r0gmJ8aN4dEL6MP08Is1Lwdb\n2gATSkipcHeC/sFUK/GoUHM/1+GpPqJWeQvowzI2Gtvvf8BdV3X+VfA99GAjVqp+4FFjtMpUEwnp\nx5TKYQGeDr/aZoOwinIv9ffFFJZe2x5CsfCa0yLBNYzdJQ2Fa79sAj1u9SZdG+OG5LdzKH9c6i7Q\nieghAO8AeJSZ7xPRHwL4Z8x8zvSbThLopklvW5HY1C4PkEc3lWo+S9Jfl9+pBzPuqg1wPLfhGLuw\n1m9xo1bhLu0Ojq3Lby76dk1xxinIdXiCxkVdqFsklCuBXzseXKsKritbDyfeWQT7aRTcjgI96oUR\ntUJPk/umlVQ89aZRAv1dAI8D+GsA3wZwlpn/hek360Ggl3ktEZVfp6qSSMXpQxYBRjpKnEP+9EKs\nZF1AdMV6E/6EWnEftrhtNF0fiDeW/twojGT9jnPt+9yNJerWruKD88A0L4IqN9vO4oblxeCqQ3dR\neUUJWJuqRtlAdCvwZqh4mrkjcBXoib1cmPkagH8C4C8B/BjAbZsw7yTuc7fVX9nv2fF48XnsLz5X\nzQEel6xNHP5Sdzqrvi34J6oyjamtDIr0DskiLN6E8m7Rts2UjMrn/ZRUmEddW6Hyzw/EyCWjYwvf\nqfEssY3dzPBJHHrmnNbr5AeFb4S8rFY4j+nhF2o+s+WgcS3laAt2s6XCbbTPeLuk5k0s0IloEMBR\nAHsB/A0Am4noVzXHPU1EF4no4txc9nk+Gom/dNb08ItOOam7qWQ1hDWaEgj5PV7JY50bmQr+sQVn\nLKAfZSYsoB8L6PMF7vQbfsNW1zzbw+KSUCopDfQHsKLyz6ftKxFqohpNCbX8x/kjR9VLd+TIiVBQ\nli5K0/bC0OXU12Hq8ypy1qhNU9/qVZquXaJI0wQWfRnAFWbvrhLRHwP4jwD8U/9BzPwGgDcAT+WS\n4noNJbitXeauqr94NfcDat3TdqZUDQSph7plA3FNAJEpQMWWDrWXl1EsvFbzgKu8LrotuPLxN+XF\nsD0stmAoHXHUVK2kwtvBn6JYeA07DIbzOOcBvHvxOC8Z03nu5LlQyUM/LoFLtrKD/rbY0N3fFc5j\ngyFaWqXC1fUtGPyUpYrEtcxjs0kTWPSXAJ4kok1ERAB+HsBH2TSr+YS2uSCUPr5QoxYAUFMNxlae\nTEfaFWLc3C4K1wfNlBtjI61guPhVLIw9ZKzKo2uLaUVj2z5HBUOp9AJq95RVSbVGM0vbqlWf0syL\nMgjl0QF8ofis1RCsVEmuqgOdSiwqf4pqi393FjwPgJpd4gL6wWDjy7YMwsHiKW3f7tGmGjfRLFUk\naQPcGkUaHfp7AP4IwBQ8l8UcKivxTqSbSnhi/rx2grgWKAiSdoWooh695EV9+KD7gJMwuE2bI48Z\nOXICl4aeMp6vixiDWNRGgdoi63TpStO08xb1I396ARcLrwMANsHoNZsZUUnW4uKPRjz0zDnjfWQG\n7nKv0cbB7BX2yBFizcUo1YFJOAKeMF5Av3ZxodpiqpPrP49aGAEwqijVOU3Pjd8GYdv1JUmR2y5R\npKlyuTDzKIDRjNrS8ujCptWDoFMJJPFSietJos7fyyvY/eAvHK8XfZCqQO/afhVCzsVTKMDc72Be\njN1TZyz9peox5nZwKO2pjah7YnPZY5BVtaaCth5buVTTJ2bgHnqRx4MaYVUtXVh4oUYVsP9r38WF\ns8fxxflvG8vcKVfBVeSQQxll5GIvKPz4VTVBNYVNOO4am8YkgMeLz9ckldMVFxmZfyvURn8Vpmrp\nwhiulH78c2unQUWyk+fWVDwRqkA/wdTDUTl9msW6Tc6VFd6DwEZB4irUq65kQ4cxPD8R25C6kVbQ\ny9ECDYDRm8JPnCo6CvWwmpqgS8RkK4un2mk7ZgsvOrfVZfVs9IYBqiXYbMmf9n/tu2Hf+kIwTUDt\n5yOhM3krdb9wXaSfqH7n129XH+DRgejOWZilbZgx5APvhX5eqZfA7qkzTn7+poyTQSGbhODcMiVD\nA8KLL9fEdEkSojUaEeiOmFbOSodmMiC6ojLBKeOiqfhxFtymPgwGPvMLjzJy2Ily4odLBzO0Psk2\nw5pacdmOWUXOKPD9ArwMrxBy0rH02uChM+T5BYrpwY8jEOIWWyhXVupJYPYKZ5hW4iW2Z4qMU6vW\nhO2F7O1k+ow++qFMp0DssWg142ZS2ibbYjPw5+V+b+iYVod2Zeth9GAplSErqIvz54K+WHjduSjE\nXZhzo9dSe1BQR2rTUyblBm3XCqKZ4ZPa/pWY0IMllEcH0IP7Zl0+ykZZcov6cIO2g+GpS5L2aZm7\ncGXrYWMe8aT5yG3627hucqbVrysjR04YjdM5lK36Yxd3S1v8QhS3qE/rJmzLdGpzh9TRasbNpIhA\nj6BYeA27xqa1QRgqOdMg7tTkV9ehjJfKI2OV1z5bIn31G8B70O7Rpsh2MgMbseIktPwql8mJcXyh\n+Gzi0Hp1bRs249HIkRO4XHi1plDFIvegjC4M4g5y5JUQM13CFLjD7Klj/C+ppHRjFU8GDOKF+f8X\nM8Mnq/7bAIyC2i/EC8VTTp4XcQNnTLU2VeEWVQBEewxyKI8OoGwUdtutaW91BsNSoK5ojoLLCDfu\nczemh1+MnXr3ytbD2gpFujTGrWjcTErbZFtsVui/LWw9bli6MoL9cOuXQyHXJSYQvOLDZRDuoxub\nsGxN1JUkpB2oqI9O306dJ0WxgH708yLygSRYzLXGPABag55rWHc9UiF4uV5y2ID4u5JVJhAYt6kf\nm/heyOB5Fz0gEDZhyXruBfRjGb0hQ2Sc0HbdvVzhPFbQhc3w0jDfRQ+6sWrNGRT8Oyp8P6iq60IZ\nN2g7enBfqyIJqi5NqkylZlHC3HRNnX+5bixU+mvVRtPcM9HsRGCuof+iQ4/AFhRjsqSbBI9XsPaO\nttCAXxh2gdFXeQh3Yc64sllN6NlAWJugaYV5iXP44dCX8eT8+fB1CNjEazndg3phpZcNjm8WOllX\nPNdPqmapjJOVUGVxHETYM4MI1XsYxRa+A6I7NbryS0NPYUCTZ8VU3SfohXGb+tCPRfT5hHcflvGg\n4us9wIvaItFEa7niozw5gvdTqWZUMjLdPSR4LyVlGN5h8hgCMDh2LWQwdrEt6OY1ESoLJq+/eV9b\nTZk9/bmY2qV4tKzQYxBcHZVGB7UCNenKOQ4qmi7pNbyHai5VVCLg1dfMAcYUtepau8amsTD2sCf8\nIo7LIiFXXNS1W2Geqfb4k1P5V78uq0PbGKq+pkl5a7vGddh3lf6MjXGTbLkcHyc7pks65yXq0c7b\nRuZ6r3tyrvVIUH9psqRTQn2hCyq4xBZN59ebmvBWSOnzpGwgtgpzda3JiXHPxzjiOECv/6z3umNn\npRTcXZjtGfXAVg5QBWkto7tqqA4GtJn09rY8K2qc00Y/ZpEgK27Ajss148xr/+9MhmjTvG1FzxgR\n6DEITnTbxMlRfYQQEbARS0Y/9RXOo4RcZJY/lXvc1fsgTV9maVtEcJCHChUPqqTKFX10PVHCshcP\nUnlkxMHWLzXXTEJm39RL1tB229xU59YJ0zJ7L1Qb6kViup2ztM3oTRL8PK6x09Sv29RXfbn14L62\nJq+prYq4mT1b0TNGBLojulVDVC6Lem3dTdtJZmAFXZFBSTrdoY00xkh1raiHxR8qHuxfjoAH1G0d\n66xQbfC3q167A1O//HPNNG5b+I7VrXFm+CRKHH682SewVXoH/wssR8CB+bedMmOaVCo9uI8fdO/X\npgNQqZv9qJ3ILG3DDp6rhue75o9Z4Tw28b3qy83ziuJqSgLTCzr4TJteFreor208Y0SgG1APspcn\npV+7alCrC92DYztvvSBC1aNBd93gCmhyYty4yvILsiSl8ILjZnpYlCtn1DUG+A6WqGfN1ZPzicfS\nf2+jSPtSjrrWAC/i0tBTVXfWEudq8ojHVYspNcDIkRO4o3F3JapNa7v35jvWlBZBTIZ0/1wZxCI+\nu/IDfNB9oKZf71bqzQbR5YrZX3wOjxefN+aP8a/o71JvaBHTQ6tYRi/o9G0UC69Xjq9N5BZ8pk3q\nn+nhF2PtIpqJeLkY8D/IvWz2VlDeBVHuf/7Q/qDLYpIVcNzf+CNRZyorn4MWjw6Xc68yYRVdobDv\ntfwya+Nmiq78sPAyCsVTTn3we5Mw57R5U1xolNHT5R7dps04MP921bieR7m6QlYrV924maob3abN\nWB7bhx08hy2AIWR/DgtjD2ML38FO4zF6/bDJA0mXu2XHg2vIn16o9As4pD2j/iWh22X688f4I27L\nowPWPvgjdNXSSxepG5mvpcXD/gFZoTuhdJYmotLGAmsCVReg5F/JuKwabTree+i1bg+jtsyulBmY\nKrxWLYQQlS7XpiuNWoXqfJU3kpeMrFh4PdYOKSlxjLSuu45yJfjJpjoxjZsucnKZu7CZl6qrWuPL\nGqgGw9mSqCn8qg9T8JEOV6NhHN217pxZprbVFfxoJ2SF7ogq72W6wWoVYHJ98vsP+1cMKiGSiz+5\nEiK2FWkeDzA1dAR7b76jXWVk4Xuu8K9cyqMDWvWN/wE05TLRrUJVTc8btN2Y3VB5H3y/8E0UUhSG\nUJkUb1MfBvhO6DzKh1m1iRGdETMqlwgbAmoULuMWXE320JLVLRRwy+bpn686X/PgzsOW58hlJRtV\nKCPqnFG5ddYTItAdqZbtcsjIFnzQrgwdxt6pMygXT4WizPZNfcNZwLqsqHtoFXtvvlP1jw1uD6OC\ndlxVOf5kVWt/hx9Kl4fattXdBRj914mAg8VTuEHbU8UhqajBZfSCLAIxVwm6iXr53iIv8tMmpOKk\ngjURFPQm1YNaCNyifmyx9M+f6AqAUS0XDD7SqRF1AtUUbakTyMvcBQLVqPNMQrpdUts2AhHoMXDd\nQgZX4AcMUWYAcJAXM4+KtLXTJHhtOv4gugcr7SrJuAqdGMd+vmccI+VuaNKAuOQ/VwJ6F+ZQhv12\nRCXBWuE8pgsvoPTxBeycP59IrZV0dWm6t36bxi3qN67ilVowmEZXRxfKoNO3q/fKlBpYERXhqRPI\n0HwWtUMGWlvHXW8kUjQGwcgwl/wOtsg2QJ92N+t2+jGphPxW+8mJcQwXv6oNGCpxDt8vfFP7YF04\nexwj829Vc2VMDh3VejWY2uUfyytbD8cOxdexyD1YpJ+o6mmDLom6c8ctMqLO5Y/itEVq2vKXuEaC\n6nDJzbOAPmzmpZAh218z1yVSt8S5qsHThbgRoUItEimaMUG/U9eahbbINtN3cd6xOl/fLbyAhbGH\ntRGEroEcJc3UWOG8UZirCkcqojFPZas/c/C3wbFU2Q3TvsQ3YaVi5PK7r1H1haqDAG1ZNRO6NK62\ne2tKxWxKBeuKi3F+gO/i/cIr1f557qV9VWFua7uf+PnGTc9BY1M8dDqyQo+6rmHV5LriMB1X4hwI\n+rD5Mq9lhQM8IxwhvJp01XdHZcwL9cHQ5gX0YXDsWqzfuKzA6pm7JU2b/WoEMqRaYAYuFl533pmp\n9oRUFMPBqkbpsvrV+37EXVmb8h7FXemvVxqyQieiLUT0R0T0QyL6iIhMrqZtC4O0qybXPBamaNI8\nlav1IoMQgPzpBdDp26DTt5GrFLoIrqpcX8VRRYDDfdD3bYDvxv6Ni90h7iptmbucQ7s385JxlxCV\nR8TvwmbKNx63cIdqj849LstK9WmKGkdFQCeJkjTZHtIW5hBqSaty+S0Af8rMnwPwOICP0jeptTD5\nsrr6vtqiSU2ra53w8KoYXa0K+cGxa4mTEEWRxK83jS9wnH4wA5cLr+L9witO/ufdVKp5mfl9quNU\nHoorIE2FSYLt8RO3SpGNuDlSbL9dQB8W0J8qStL2QhSyI7GXCxENAPgZAMcBgJlXAGTj4NwiMAM7\nMIfy6EBNbmRVoKGMWuOWza2qbIiG1BUUuDJ0GKhE+5m23ZMT49hXKX3nona5h270OfQZ8HJ97Agk\nyIryvEjj5TIzfBKDxedCBSJ0/fKviF0idIG1TIo7eA4FVO6Z8rSYfxsfFl7GroqLpKmtI0dO4MLH\nF6pG3zI8tViheArXp85o79GAoYJ93ChM0w4mSj2TxvPD/1t//Vl/pHEctVA7+4o3u7hFHNK4Le4F\nMAfg/yCixwEUAfwGc+2+nIieBvA0ADzyyCMpLtd4VEAJ4D38g8Xn1nxjfcEmgLfKtLlVmVzKblEf\nlrGxxme96jaoce8Cwi5gQLQ+faPju1YZN4PZDi8NPYVDlkmc1heYHHw3l7krFKDlv6auYAPg2SB2\nQS8sbRXf/Q/yberHMN+rnr8LwGYsATAXPLhtKGxcBgG+RYI/b4tJdx0Maot0A6yTEIpbvFphmx+t\nLDCT9rdZJDaKEtFBAO8C+Glmfo+IfgvAXzPzC6bftKNR1AUXA5GLuyDgZsxKYkQMFhYw0Qz3Mtf+\nLKAfg2NXjd9PToxjf2Cl74KuoEOS8nzBYgmPF58PuQfaSrxNTowbI15dje2qMIbLXEtC1vPD9blo\nFq3ibtkIo+hVAFeZ+b3K338EYDjF+doWF/20q07T5t6ldL87E7g7mvJT666j/7x+yfxdjaL+4tY6\nTHrrKHR6/iQpEoLFEoLCHNAnsfLnbTGtWYLjbxqznTynLfqdVBcfbke28yNLu0E9aMbzkIbEAp2Z\nrwOYIaKfqnz08wB+kEmr2gzXJEAuiX9sBsKopEu3qM+rZuSYh1pHlHHTViUnKa5GUZdxHoioihTE\nnx/cTxL/6KTFEvzCwWQkdC2uomIAoq6TlCwTYQGtLzCz7m+9Sevl8usAfp+ILgM4AOCV9E1qXXTu\nclknujdVkXFJqDQ9/CI2n76Bd4eOOeWhdr3+fe7Gla2HsTD2EA4WT2XiVhd1zSBlhtM436b+WNcO\n5gdXxM1D7losQYdfOLh607iMme06SUnjDqlvU2sLzKz7W29SCXRmvsTMB5l5PzP/MjN3VIQAM7Ba\nqXhyHdur7nL1THSvU83YilDo2nHomXNVP/b86QVnYW66/qWhp3Bg/m0MYtGqMkhKMMIxmKq2XImu\ndBtnvd5p1ZKaWLcaNFXGWUCfkyuf6fdB3/SgcHBVzblEhdquk5Q07pA6Wl1gZt3feiORohWUEFGX\niJuLJC5xLPvNNsxEGS1dq8S7YoqidMFU8Z3ZU0npvE5M0aRp2mH6PYBU59Rhi0ZWGREb4TmS1Fsl\n7Tg3oo3NxtUoKtkW4a0I1CpUGWiClWOA7CZDXFeoZvvwRqXcdc17HZ/4iw1bxsHNvIQHIGwIpFvw\nRzD0TtwAAAhsSURBVG/6SZvBz/j7jLMC2qpBqRTE9Z4nadz7GpUpsd1cEJOwrpNz+WsL7r35jtXa\nnmVYdlzLfrO3fTZ9cNbb47TjbNMtd1MJec1LoptK1opUrU6z5wfQ+t4qQHu0MS3rWuXCDDwA4S71\nYQvfMaRS9dQJWao9TGqBrFUXaVE7kp08F6rS46kw+jE9/EKmgiOLcZ6cGMfB4qlY88WUZEtwox3m\ndDu00YSkz3WACOgmrtZX1KGs7Vm6V7W6ZR8I1x7NUSULZGVXc7HwOgbHrmYuALMY55EjJ2LnCKlW\npBIS0Q5zuh3amJZ1LdCj8KsTsvTPbnXLPqDfnuaooqOuY/HcrB66JG59reL73I60w5xuhzamRQS6\nBr9uXQku22SIq/dtpM4zaSBQswI+snro1Bj7Uw5H0UkrtUbTCnr8KNqhjWlZ1zp0EyZ9rcm9qtlu\nhSbS5MloZp+ycmOLk4+llfKHCEKQjnNb/KD7AD6/cqnuQt3mDmhyrzKnPW3uFt5q1W/hdKdZubG5\n5GOpGncL2Rp3BaEZtI3KZf/XvpvJeUwbEp2axZVWNbakUZt0wvbUVtdT9alexl1BaAZts0K//I9/\nFp9PeY773I0fdT8aWukzezuA/V/7bqLVYLMDf0yYgmxcA4EaFfBRL0z9v1Ex7LZjnwTBRtus0NOq\nW0qcw4eFl7HjwbXQeYiAx1YuJU4y1aqr2fVg1bex3vsvrD/WjVFUBQ+YgguA5hsx60Ej82S0Imn6\n3655P4TOw9Uoum4EukrAZEs01Q4RY0JjaPVKOsL6ouMiRT/oPuDkS2w+xnsbzAyfDKVnVTTbiCm0\nDush74fQebSNUXT/174LHh1I/PstfAel0UEUUMZ99GIjL8Wqai+0HvVUibSqK6og2GibFTpQqZae\nEFWaK0fAZlpCCWvFClrFiCm4k2X2Sx2t6ooqCDbaSqCvOqr7SxzdrW4qYRkbrfU91wP1qBHaCOqt\nEhEPGaEdSS3QiaiLiL5PRH+SRYNMXDh7PFSYwEQX9EVyg/i3z+0q2NJQ71VuPal3rplWdUUVBBtZ\n6NB/A8BHAH4ig3MZGZl/y9nLxfU4FWCzHiqZ6EiTGqDZpA2acqHdA6uE9UeqFToRPQzgKQD/ezbN\nMeO66nbFv33OevveLqv9ZmVUzIJWVIm0y30XOpe0Kpf/GcApIGNpqyGNQRTw3BlLnKsWaPBvn7MU\nbO2kxmhnw1+rqUTa6b4LnUtilQsR/SKAWWYuEtGXLMc9DeBpAHjkkUeSXg730Y0+LDsdy4xQrpZ3\nh47h0DPnAIS3z1lu39tJjdGqOWhcaSWVSDvdd6FzSbNC/2kAR4joYwB/AODniOifBg9i5jeY+SAz\nH9y+PV5ZMD+bHIU54AnvEueqq3K/MNeR5fa9ndQYrbbKbWfa6b4LnUviFTozPwfgOQCorND/e2b+\n1YzaFcK0ig5yg7bXCO88gEMRvxk5cgKTQG3Oj0KyIJVGGOuypJVWue1Mu913oTNpGz/0meGTWOYu\n6zFpjGIjR05g19h0ar/0VjTWCfVH7rvQCmQi0Jn53zDzL2ZxLhMjR07gcuFVLKCvWiNykXuwgP6W\nUheIGmN9IvddaAXaJtuiIAjCeqXjsi0KgiAIdkSgC4IgdAgi0AVBEDoEEeiCIAgdggh0QRCEDkEE\nuiAIQocgAl0QBKFDEIEuCILQITQ0sIiI5gB8kvI02wCs54xH0n/pv/R//fE3mTkyu2FDBXoWENFF\nl4ipTkX6L/2X/q/f/kchKhdBEIQOQQS6IAhCh9COAv2NZjegyUj/1zfSf8FI2+nQBUEQBD3tuEIX\nBEEQNLSNQCeiXyCif0tE00T0bLPb0wiI6GMi+oCILhHRxcpnW4noO0T0o8r/B5vdzqwgojeJaJaI\nPvR9ZuwvET1XmQ//loj+s+a0OlsMYzBGRNcq8+ASEf0d33cdMwZEtJuI/jUR/YCI/pyIfqPy+bqa\nA6lg5pb/D0AXgL8A8JMAugG8D+DRZrerAf3+GMC2wGevA3i28u9nAbzW7HZm2N+fATAM4MOo/gJ4\ntDIPegDsrcyPrmb3oU5jMAavZm/w2I4aAwCfATBc+Xc/gH9X6eO6mgNp/muXFfoXAUwz879n5hUA\nfwDgaJPb1CyOAvjdyr9/F8AvN7EtmcLM3wNwM/Cxqb9HAfwBMy8z8xUA0/DmSVtjGAMTHTUGzPxj\nZp6q/PsOgI8APIR1NgfS0C4C/SEAM76/r1Y+63QYwL8koiIRPV35bCcz/7jy7+sAdjanaQ3D1N/1\nNid+nYguV1QySuXQsWNARHsAfAHAe5A54Ey7CPT1ymFmPgDgPwfwD4noZ/xfsrfvXDduSuutvz7+\nF3jqxgMAfgzgf2huc+oLEfUB+H8A/CNm/mv/d+t4DjjRLgL9GoDdvr8frnzW0TDztcr/ZwGch7ed\nvEFEnwGAyv9nm9fChmDq77qZE8x8g5lXmbkM4H/Dmlqh48aAiDbAE+a/z8x/XPl43c8BV9pFoE8C\n+CwR7SWibgB/D8BEk9tUV4hoMxH1q38D+NsAPoTX769UDvsKgLea08KGYervBIC/R0Q9RLQXwGcB\n/FkT2ld3lDCrcAzePAA6bAyIiAD8DoCPmPl/9H217ueAK/lmN8AFZi4R0X8L4P+D5/HyJjP/eZOb\nVW92AjjvzXHkAfyfzPynRDQJ4A+J6NfgZa78u01sY6YQ0bcAfAnANiK6CmAUwDeh6S8z/zkR/SGA\nHwAoAfiHzLzalIZniGEMvkREB+CpGj4GcALoyDH4aQD/BYAPiOhS5bPnsc7mQBokUlQQBKFDaBeV\niyAIghCBCHRBEIQOQQS6IAhChyACXRAEoUMQgS4IgtAhiEAXBEHoEESgC4IgdAgi0AVBEDqE/x/p\nz1ksxDyS4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaa72860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y, Y_svm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}