bike-sharing-features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "import pandas as pd\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engeneering for Bike Sharing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# loading of original dataset\n",
    "train = pd.read_csv('data/kaggle/train.csv', error_bad_lines=False)\n",
    "test = pd.read_csv('data/kaggle/test.csv', error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def to_date(str_date):\n",
    "    return dt.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "def to_day_of_year(str_date):\n",
    "    date = dt.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')\n",
    "    return date.toordinal() - dt.datetime(date.year, 1, 1).toordinal() + 1\n",
    "\n",
    "def to_month_of_year(str_date):\n",
    "    date = dt.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')\n",
    "    return date.month\n",
    "\n",
    "def to_day_of_week(str_date):\n",
    "    date = dt.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')\n",
    "    return date.weekday()\n",
    "\n",
    "def to_year(str_date):\n",
    "    date = dt.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')\n",
    "    return date.year\n",
    "\n",
    "def to_hour(str_date):\n",
    "    date = dt.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')\n",
    "    return date.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  holiday  workingday  weather  temp   atemp  humidity  windspeed  \\\n",
       "0       1        0           0        1  9.84  14.395        81        0.0   \n",
       "1       1        0           0        1  9.02  13.635        80        0.0   \n",
       "2       1        0           0        1  9.02  13.635        80        0.0   \n",
       "3       1        0           0        1  9.84  14.395        75        0.0   \n",
       "4       1        0           0        1  9.84  14.395        75        0.0   \n",
       "\n",
       "   casual  registered  count  year  month  weekday  day  hour  \n",
       "0       3          13     16  2011      1        5    1     0  \n",
       "1       8          32     40  2011      1        5    1     1  \n",
       "2       5          27     32  2011      1        5    1     2  \n",
       "3       3          10     13  2011      1        5    1     3  \n",
       "4       0           1      1  2011      1        5    1     4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add date/time features\n",
    "train.loc[:,'year'] = train['datetime'].map(to_year)\n",
    "train.loc[:,'month'] = train['datetime'].map(to_month_of_year)\n",
    "train.loc[:,'weekday'] = train['datetime'].map(to_day_of_week)\n",
    "train.loc[:,'day'] = train['datetime'].map(to_day_of_year)\n",
    "train.loc[:,'hour'] = train['datetime'].map(to_hour)\n",
    "del(train['datetime'])\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "1  2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2  2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "3  2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "4  2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "   humidity  windspeed  year  month  weekday  day  hour  \n",
       "0        56    26.0027  2011      1        3   20     0  \n",
       "1        56     0.0000  2011      1        3   20     1  \n",
       "2        56     0.0000  2011      1        3   20     2  \n",
       "3        56    11.0014  2011      1        3   20     3  \n",
       "4        56    11.0014  2011      1        3   20     4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.loc[:,'year'] = test['datetime'].map(to_year)\n",
    "test.loc[:,'month'] = test['datetime'].map(to_month_of_year)\n",
    "test.loc[:,'weekday'] = test['datetime'].map(to_day_of_week)\n",
    "test.loc[:,'day'] = test['datetime'].map(to_day_of_year)\n",
    "test.loc[:,'hour'] = test['datetime'].map(to_hour)\n",
    "#del(test['datetime'])\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier\n",
    "replace zero-values using Random Forest Classifier as per the article [here](https://medium.com/@viveksrinivasan/how-to-finish-top-10-percentile-in-bike-sharing-demand-competition-in-kaggle-part-2-29e854aaab7d)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "wCol= [\"season\",\"weather\",\"humidity\",\"month\",\"temp\",\"year\",\"atemp\"]\n",
    "\n",
    "dataWind0 = train[train[\"windspeed\"]==0]\n",
    "dataWindNot0 = train[train[\"windspeed\"]!=0]\n",
    "dataWindNot0[\"windspeed\"] = dataWindNot0[\"windspeed\"].astype(\"str\")\n",
    "\n",
    "rfModel_wind = RandomForestClassifier()\n",
    "rfModel_wind.fit(dataWindNot0[wCol], dataWindNot0[\"windspeed\"])\n",
    "wind0Values = rfModel_wind.predict(X= dataWind0[wCol])\n",
    "dataWind0[\"windspeed\"] = wind0Values\n",
    "train = dataWindNot0.append(dataWind0)\n",
    "train[\"windspeed\"] = train[\"windspeed\"].astype(\"float\")\n",
    "train.reset_index(inplace=True)\n",
    "train.drop('index',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Features from Past"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.84</td>\n",
       "      <td>12.880</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>76</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.76</td>\n",
       "      <td>16.665</td>\n",
       "      <td>81</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>77</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>29</td>\n",
       "      <td>55</td>\n",
       "      <td>84</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>72</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>94</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  holiday  workingday  weather   temp   atemp  humidity  windspeed  \\\n",
       "0       1        0           0        2   9.84  12.880        75     6.0032   \n",
       "1       1        0           0        1  15.58  19.695        76    16.9979   \n",
       "2       1        0           0        1  14.76  16.665        81    19.0012   \n",
       "3       1        0           0        1  17.22  21.210        77    19.0012   \n",
       "4       1        0           0        2  18.86  22.725        72    19.9995   \n",
       "\n",
       "   casual  registered  count  year  month  weekday  day  hour  \n",
       "0       0           1      1  2011      1        5    1     5  \n",
       "1      12          24     36  2011      1        5    1    10  \n",
       "2      26          30     56  2011      1        5    1    11  \n",
       "3      29          55     84  2011      1        5    1    12  \n",
       "4      47          47     94  2011      1        5    1    13  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "1  2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2  2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "3  2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "4  2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "   humidity  windspeed  year  month  weekday  day  hour  \n",
       "0        56    26.0027  2011      1        3   20     0  \n",
       "1        56     0.0000  2011      1        3   20     1  \n",
       "2        56     0.0000  2011      1        3   20     2  \n",
       "3        56    11.0014  2011      1        3   20     3  \n",
       "4        56    11.0014  2011      1        3   20     4  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Regressor\n",
    "Predicting *count* with teh decision tree regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "# Import the necessary modules and libraries\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X = train.drop(['casual','registered','count'], axis=1)\n",
    "y = train['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=15, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit regression model\n",
    "regr_1 = DecisionTreeRegressor(max_depth=2)\n",
    "regr_2 = DecisionTreeRegressor(max_depth=15)\n",
    "regr_1.fit(X, y)\n",
    "regr_2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predict\n",
    "X_test = X\n",
    "y_1 = regr_1.predict(X_test)\n",
    "y_2 = regr_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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arYX0O3ro1Z+itPkDWrv3ceWGfjUY29vXicFgIH5uBlW9U8i4aj4+/r1Drqc3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\n5JueXFdelDWWXA5chRtWeYEUMEvW69n2Sy++NE6VcakiIkrDWbfXczxpsYa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x241b02b4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "plt.figure()\n",
    "plt.scatter(X.index, y, s=5, edgecolor=\"black\", c=\"darkorange\", label=\"data\",alpha=0.5)\n",
    "plt.plot(X_test.index, y_1, color=\"cornflowerblue\", label=\"max_depth=2\", linewidth=2,alpha=0.5)\n",
    "plt.plot(X_test.index, y_2, color=\"yellowgreen\", label=\"max_depth=15\", linewidth=2,alpha=0.5)\n",
    "plt.xlabel(\"data\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.title(\"Decision Tree Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 98.90591782670423\n",
      "Mean Squared Error: 19539.342465176986\n",
      "Root Mean Squared Error: 139.7831980789429\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_1))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y, y_1))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y, y_1))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 7.937913950476256\n",
      "Mean Squared Error: 303.7215485293481\n",
      "Root Mean Squared Error: 17.427608801248326\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_2))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y, y_2))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y, y_2))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rmsle(y, y_):\n",
    "    log1 = np.nan_to_num(np.array([np.log(v + 1) for v in y]))\n",
    "    log2 = np.nan_to_num(np.array([np.log(v + 1) for v in y_]))\n",
    "    calc = (log1 - log2) ** 2\n",
    "    return np.sqrt(np.mean(calc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9228020915189369"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmsle(y, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10735703241435382"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmsle(y, y_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  holiday  workingday  weather   temp   atemp  humidity  windspeed  \\\n",
       "0       1        0           1        1  10.66  11.365        56    26.0027   \n",
       "1       1        0           1        1  10.66  13.635        56     0.0000   \n",
       "2       1        0           1        1  10.66  13.635        56     0.0000   \n",
       "3       1        0           1        1  10.66  12.880        56    11.0014   \n",
       "4       1        0           1        1  10.66  12.880        56    11.0014   \n",
       "\n",
       "   year  month  weekday  day  hour  \n",
       "0  2011      1        3   20     0  \n",
       "1  2011      1        3   20     1  \n",
       "2  2011      1        3   20     2  \n",
       "3  2011      1        3   20     3  \n",
       "4  2011      1        3   20     4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp',\n",
    "      'humidity', 'windspeed', 'year', 'month', 'weekday', 'day', 'hour']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = regr_2.predict(test[['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp',\n",
    "                               'humidity', 'windspeed', 'year', 'month', 'weekday', 'day', 'hour']])\n",
    "result_df = test.copy()\n",
    "result_df[\"count\"] = results\n",
    "result_df[\"count\"] = result_df[\"count\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\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>datetime</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  count\n",
       "0  2011-01-20 00:00:00      9\n",
       "1  2011-01-20 01:00:00      3\n",
       "2  2011-01-20 02:00:00     16\n",
       "3  2011-01-20 03:00:00      4\n",
       "4  2011-01-20 04:00:00      1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df[['datetime','count']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result_df[['datetime','count']].to_csv('bsd_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "        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>TeamId</th>\n",
       "      <th>TeamName</th>\n",
       "      <th>SubmissionDate</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97125</td>\n",
       "      <td>Mania Yang</td>\n",
       "      <td>2014-05-29 03:39:08</td>\n",
       "      <td>4.76188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97148</td>\n",
       "      <td>Francis</td>\n",
       "      <td>2014-05-29 07:26:46</td>\n",
       "      <td>0.62413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97154</td>\n",
       "      <td>Deep Learner (Rahul Mohan)</td>\n",
       "      <td>2014-05-29 07:56:23</td>\n",
       "      <td>4.76188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97154</td>\n",
       "      <td>Deep Learner (Rahul Mohan)</td>\n",
       "      <td>2014-05-29 08:43:26</td>\n",
       "      <td>1.84122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97154</td>\n",
       "      <td>Deep Learner (Rahul Mohan)</td>\n",
       "      <td>2014-05-29 08:46:51</td>\n",
       "      <td>1.84099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TeamId                    TeamName       SubmissionDate    Score\n",
       "0   97125                  Mania Yang  2014-05-29 03:39:08  4.76188\n",
       "1   97148                     Francis  2014-05-29 07:26:46  0.62413\n",
       "2   97154  Deep Learner (Rahul Mohan)  2014-05-29 07:56:23  4.76188\n",
       "3   97154  Deep Learner (Rahul Mohan)  2014-05-29 08:43:26  1.84122\n",
       "4   97154  Deep Learner (Rahul Mohan)  2014-05-29 08:46:51  1.84099"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb = pd.read_csv('publicleaderboarddata/bike-sharing-demand-publicleaderboard.csv')\n",
    "lb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max 0.99925\n",
      "min 0.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TeamId</th>\n",
       "      <th>TeamName</th>\n",
       "      <th>SubmissionDate</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97148</td>\n",
       "      <td>Francis</td>\n",
       "      <td>2014-05-29 07:26:46</td>\n",
       "      <td>0.62413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97225</td>\n",
       "      <td>Stephen Helms</td>\n",
       "      <td>2014-05-29 16:19:59</td>\n",
       "      <td>0.55705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97314</td>\n",
       "      <td>razgon</td>\n",
       "      <td>2014-05-30 06:55:43</td>\n",
       "      <td>0.55609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97314</td>\n",
       "      <td>razgon</td>\n",
       "      <td>2014-05-30 07:05:30</td>\n",
       "      <td>0.55239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97148</td>\n",
       "      <td>Francis</td>\n",
       "      <td>2014-05-30 09:58:30</td>\n",
       "      <td>0.62197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>97225</td>\n",
       "      <td>Stephen Helms</td>\n",
       "      <td>2014-05-30 11:11:11</td>\n",
       "      <td>0.53531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>97314</td>\n",
       "      <td>razgon</td>\n",
       "      <td>2014-05-30 11:21:49</td>\n",
       "      <td>0.45255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>97308</td>\n",
       "      <td>Vivek Aditya</td>\n",
       "      <td>2014-05-30 12:55:04</td>\n",
       "      <td>0.75507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>97306</td>\n",
       "      <td>Hong</td>\n",
       "      <td>2014-05-30 17:38:10</td>\n",
       "      <td>0.66761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>97306</td>\n",
       "      <td>Hong</td>\n",
       "      <td>2014-05-31 00:12:02</td>\n",
       "      <td>0.50965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>97425</td>\n",
       "      <td>Chris Hammond</td>\n",
       "      <td>2014-05-31 03:24:48</td>\n",
       "      <td>0.53221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>97408</td>\n",
       "      <td>hedgehog</td>\n",
       "      <td>2014-05-31 09:53:22</td>\n",
       "      <td>0.51198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>97408</td>\n",
       "      <td>hedgehog</td>\n",
       "      <td>2014-05-31 10:01:48</td>\n",
       "      <td>0.49821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>97408</td>\n",
       "      <td>hedgehog</td>\n",
       "      <td>2014-05-31 12:57:41</td>\n",
       "      <td>0.48623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>97408</td>\n",
       "      <td>hedgehog</td>\n",
       "      <td>2014-05-31 13:51:37</td>\n",
       "      <td>0.47964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>97477</td>\n",
       "      <td>levibecker</td>\n",
       "      <td>2014-05-31 15:42:43</td>\n",
       "      <td>0.60077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>97408</td>\n",
       "      <td>hedgehog</td>\n",
       "      <td>2014-05-31 16:32:53</td>\n",
       "      <td>0.46232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>97314</td>\n",
       "      <td>razgon</td>\n",
       "      <td>2014-05-31 18:49:22</td>\n",
       "      <td>0.45103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>97425</td>\n",
       "      <td>Chris Hammond</td>\n",
       "      <td>2014-05-31 20:17:39</td>\n",
       "      <td>0.48866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>97306</td>\n",
       "      <td>Hong</td>\n",
       "      <td>2014-05-31 20:26:06</td>\n",
       "      <td>0.50900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>97445</td>\n",
       "      <td>Blue Ocean</td>\n",
       "      <td>2014-06-01 03:27:26</td>\n",
       "      <td>0.57728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>97556</td>\n",
       "      <td>Jason Karpeles</td>\n",
       "      <td>2014-06-01 04:05:15</td>\n",
       "      <td>0.64803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>97556</td>\n",
       "      <td>Jason Karpeles</td>\n",
       "      <td>2014-06-01 04:36:59</td>\n",
       "      <td>0.54726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>97445</td>\n",
       "      <td>Blue Ocean</td>\n",
       "      <td>2014-06-01 05:06:08</td>\n",
       "      <td>0.48693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>97445</td>\n",
       "      <td>Blue Ocean</td>\n",
       "      <td>2014-06-01 05:48:51</td>\n",
       "      <td>0.47836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>97445</td>\n",
       "      <td>Blue Ocean</td>\n",
       "      <td>2014-06-01 06:17:39</td>\n",
       "      <td>0.47714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>97372</td>\n",
       "      <td>hassiktir</td>\n",
       "      <td>2014-06-01 06:45:50</td>\n",
       "      <td>0.74165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>97372</td>\n",
       "      <td>hassiktir</td>\n",
       "      <td>2014-06-01 06:47:57</td>\n",
       "      <td>0.73522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>98529</td>\n",
       "      <td>oncemore</td>\n",
       "      <td>2014-06-01 10:46:46</td>\n",
       "      <td>0.59314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>97314</td>\n",
       "      <td>razgon</td>\n",
       "      <td>2014-06-01 11:54:43</td>\n",
       "      <td>0.43282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10326</th>\n",
       "      <td>102807</td>\n",
       "      <td>jsolis</td>\n",
       "      <td>2017-01-22 09:04:51</td>\n",
       "      <td>0.41609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10327</th>\n",
       "      <td>139965</td>\n",
       "      <td>octacube</td>\n",
       "      <td>2017-02-11 01:25:47</td>\n",
       "      <td>0.46719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10328</th>\n",
       "      <td>139965</td>\n",
       "      <td>octacube</td>\n",
       "      <td>2017-02-11 01:59:01</td>\n",
       "      <td>0.46571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10329</th>\n",
       "      <td>139965</td>\n",
       "      <td>octacube</td>\n",
       "      <td>2017-02-11 02:58:36</td>\n",
       "      <td>0.46321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10330</th>\n",
       "      <td>139965</td>\n",
       "      <td>octacube</td>\n",
       "      <td>2017-02-13 18:09:21</td>\n",
       "      <td>0.45615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10331</th>\n",
       "      <td>139965</td>\n",
       "      <td>octacube</td>\n",
       "      <td>2017-03-03 04:22:27</td>\n",
       "      <td>0.43787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10332</th>\n",
       "      <td>119894</td>\n",
       "      <td>COlsen</td>\n",
       "      <td>2017-03-09 00:46:22</td>\n",
       "      <td>0.59522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10333</th>\n",
       "      <td>139965</td>\n",
       "      <td>octacube</td>\n",
       "      <td>2017-04-21 17:52:10</td>\n",
       "      <td>0.42343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334</th>\n",
       "      <td>139965</td>\n",
       "      <td>octacube</td>\n",
       "      <td>2017-04-21 18:42:18</td>\n",
       "      <td>0.41648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10335</th>\n",
       "      <td>142307</td>\n",
       "      <td>Fan Peng</td>\n",
       "      <td>2017-05-02 14:55:16</td>\n",
       "      <td>0.37068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10336</th>\n",
       "      <td>158100</td>\n",
       "      <td>Jasiek</td>\n",
       "      <td>2017-05-17 11:47:23</td>\n",
       "      <td>0.36143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10337</th>\n",
       "      <td>130419</td>\n",
       "      <td>Team KMF</td>\n",
       "      <td>2017-06-18 13:38:40</td>\n",
       "      <td>0.48179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10338</th>\n",
       "      <td>107180</td>\n",
       "      <td>Steve Bezek</td>\n",
       "      <td>2017-07-20 06:00:38</td>\n",
       "      <td>0.46787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10339</th>\n",
       "      <td>179996</td>\n",
       "      <td>shivrajp</td>\n",
       "      <td>2017-10-21 14:27:07</td>\n",
       "      <td>0.41414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10340</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-21 02:43:43</td>\n",
       "      <td>0.47800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10341</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-21 02:45:45</td>\n",
       "      <td>0.46905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10342</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-22 02:45:28</td>\n",
       "      <td>0.45456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10343</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-24 14:05:54</td>\n",
       "      <td>0.44321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10344</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-24 14:06:13</td>\n",
       "      <td>0.44107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10345</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-24 14:08:13</td>\n",
       "      <td>0.44050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10346</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-24 14:36:12</td>\n",
       "      <td>0.43501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10347</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-31 03:59:40</td>\n",
       "      <td>0.43181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10348</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-31 04:10:27</td>\n",
       "      <td>0.43178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10349</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-31 04:11:51</td>\n",
       "      <td>0.43167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10350</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-31 22:32:17</td>\n",
       "      <td>0.42849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10351</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-31 22:32:54</td>\n",
       "      <td>0.42656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10352</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-31 22:33:23</td>\n",
       "      <td>0.42621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10353</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-31 22:35:07</td>\n",
       "      <td>0.42519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10354</th>\n",
       "      <td>104601</td>\n",
       "      <td>Victor Rebli</td>\n",
       "      <td>2018-03-31 22:38:12</td>\n",
       "      <td>0.42494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10355</th>\n",
       "      <td>115919</td>\n",
       "      <td>alphajuliet</td>\n",
       "      <td>2018-08-24 02:20:41</td>\n",
       "      <td>0.41289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10356 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TeamId        TeamName       SubmissionDate    Score\n",
       "index                                                      \n",
       "0       97148         Francis  2014-05-29 07:26:46  0.62413\n",
       "1       97225   Stephen Helms  2014-05-29 16:19:59  0.55705\n",
       "2       97314          razgon  2014-05-30 06:55:43  0.55609\n",
       "3       97314          razgon  2014-05-30 07:05:30  0.55239\n",
       "4       97148         Francis  2014-05-30 09:58:30  0.62197\n",
       "5       97225   Stephen Helms  2014-05-30 11:11:11  0.53531\n",
       "6       97314          razgon  2014-05-30 11:21:49  0.45255\n",
       "7       97308    Vivek Aditya  2014-05-30 12:55:04  0.75507\n",
       "8       97306            Hong  2014-05-30 17:38:10  0.66761\n",
       "9       97306            Hong  2014-05-31 00:12:02  0.50965\n",
       "10      97425   Chris Hammond  2014-05-31 03:24:48  0.53221\n",
       "11      97408        hedgehog  2014-05-31 09:53:22  0.51198\n",
       "12      97408        hedgehog  2014-05-31 10:01:48  0.49821\n",
       "13      97408        hedgehog  2014-05-31 12:57:41  0.48623\n",
       "14      97408        hedgehog  2014-05-31 13:51:37  0.47964\n",
       "15      97477      levibecker  2014-05-31 15:42:43  0.60077\n",
       "16      97408        hedgehog  2014-05-31 16:32:53  0.46232\n",
       "17      97314          razgon  2014-05-31 18:49:22  0.45103\n",
       "18      97425   Chris Hammond  2014-05-31 20:17:39  0.48866\n",
       "19      97306            Hong  2014-05-31 20:26:06  0.50900\n",
       "20      97445      Blue Ocean  2014-06-01 03:27:26  0.57728\n",
       "21      97556  Jason Karpeles  2014-06-01 04:05:15  0.64803\n",
       "22      97556  Jason Karpeles  2014-06-01 04:36:59  0.54726\n",
       "23      97445      Blue Ocean  2014-06-01 05:06:08  0.48693\n",
       "24      97445      Blue Ocean  2014-06-01 05:48:51  0.47836\n",
       "25      97445      Blue Ocean  2014-06-01 06:17:39  0.47714\n",
       "26      97372       hassiktir  2014-06-01 06:45:50  0.74165\n",
       "27      97372       hassiktir  2014-06-01 06:47:57  0.73522\n",
       "28      98529        oncemore  2014-06-01 10:46:46  0.59314\n",
       "29      97314          razgon  2014-06-01 11:54:43  0.43282\n",
       "...       ...             ...                  ...      ...\n",
       "10326  102807          jsolis  2017-01-22 09:04:51  0.41609\n",
       "10327  139965        octacube  2017-02-11 01:25:47  0.46719\n",
       "10328  139965        octacube  2017-02-11 01:59:01  0.46571\n",
       "10329  139965        octacube  2017-02-11 02:58:36  0.46321\n",
       "10330  139965        octacube  2017-02-13 18:09:21  0.45615\n",
       "10331  139965        octacube  2017-03-03 04:22:27  0.43787\n",
       "10332  119894          COlsen  2017-03-09 00:46:22  0.59522\n",
       "10333  139965        octacube  2017-04-21 17:52:10  0.42343\n",
       "10334  139965        octacube  2017-04-21 18:42:18  0.41648\n",
       "10335  142307        Fan Peng  2017-05-02 14:55:16  0.37068\n",
       "10336  158100          Jasiek  2017-05-17 11:47:23  0.36143\n",
       "10337  130419        Team KMF  2017-06-18 13:38:40  0.48179\n",
       "10338  107180     Steve Bezek  2017-07-20 06:00:38  0.46787\n",
       "10339  179996        shivrajp  2017-10-21 14:27:07  0.41414\n",
       "10340  104601    Victor Rebli  2018-03-21 02:43:43  0.47800\n",
       "10341  104601    Victor Rebli  2018-03-21 02:45:45  0.46905\n",
       "10342  104601    Victor Rebli  2018-03-22 02:45:28  0.45456\n",
       "10343  104601    Victor Rebli  2018-03-24 14:05:54  0.44321\n",
       "10344  104601    Victor Rebli  2018-03-24 14:06:13  0.44107\n",
       "10345  104601    Victor Rebli  2018-03-24 14:08:13  0.44050\n",
       "10346  104601    Victor Rebli  2018-03-24 14:36:12  0.43501\n",
       "10347  104601    Victor Rebli  2018-03-31 03:59:40  0.43181\n",
       "10348  104601    Victor Rebli  2018-03-31 04:10:27  0.43178\n",
       "10349  104601    Victor Rebli  2018-03-31 04:11:51  0.43167\n",
       "10350  104601    Victor Rebli  2018-03-31 22:32:17  0.42849\n",
       "10351  104601    Victor Rebli  2018-03-31 22:32:54  0.42656\n",
       "10352  104601    Victor Rebli  2018-03-31 22:33:23  0.42621\n",
       "10353  104601    Victor Rebli  2018-03-31 22:35:07  0.42519\n",
       "10354  104601    Victor Rebli  2018-03-31 22:38:12  0.42494\n",
       "10355  115919     alphajuliet  2018-08-24 02:20:41  0.41289\n",
       "\n",
       "[10356 rows x 4 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "in1scores = lb[lb['Score']<1]\n",
    "\n",
    "print(\"max\",in1scores['Score'].max())\n",
    "print(\"min\",in1scores['Score'].min())\n",
    "\n",
    "d = in1scores.reset_index().reset_index()\n",
    "d['index'] = d['level_0']\n",
    "del(d['level_0'])\n",
    "d.set_index('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i4GLgDh/3JyIio/Cthe+cGzCzTwB/AfKBnzrnnvNrfyIiMjpfx7Q55+4C7vJz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\n9QS+I/gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x241b57afbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d['Score'].plot.kde()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mkadcLAqS0CPw4GAYEXuK43HWohDG85xfPB1hk5Cyrim14WqWEwbbbAUedTdw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\nvRjnizet1sShn6MOswgs/iJUd8S7J6xhF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zBNQ3pjKFof6z6ZpnSdYZwETNOQOFJM84iq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1Qu5ZbFJUFcstn/ettlnsRPzVC5sMigJRwTAtcURFN45436GSZy/02RnniMpEJug2AjylONSN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\nrrnGSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x241b5330208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d[[\"index\",\"Score\"]].plot.scatter(x=\"index\",y=\"Score\", alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5% 0.39126\n",
      "10% 0.40929\n",
      "15% 0.422\n",
      "20% 0.43304\n",
      "25% 0.44590250000000003\n",
      "30% 0.460175\n",
      "35% 0.46939\n",
      "40% 0.47916000000000003\n",
      "45% 0.48681749999999996\n",
      "50% 0.49522\n",
      "55% 0.5029625000000001\n",
      "60% 0.5170600000000001\n",
      "65% 0.5374325\n",
      "70% 0.565985\n",
      "75% 0.59888\n",
      "80% 0.6438\n",
      "85% 0.6939975\n",
      "90% 0.751115\n",
      "95% 0.85376\n"
     ]
    }
   ],
   "source": [
    "print(\"5%\",np.percentile(d['Score'], 5))\n",
    "print(\"10%\",np.percentile(d['Score'], 10))\n",
    "print(\"15%\",np.percentile(d['Score'], 15))\n",
    "print(\"20%\",np.percentile(d['Score'], 20))\n",
    "print(\"25%\",np.percentile(d['Score'], 25))\n",
    "print(\"30%\",np.percentile(d['Score'], 30))\n",
    "print(\"35%\",np.percentile(d['Score'], 35))\n",
    "print(\"40%\",np.percentile(d['Score'], 40))\n",
    "print(\"45%\",np.percentile(d['Score'], 45))\n",
    "print(\"50%\",np.percentile(d['Score'], 50))\n",
    "print(\"55%\",np.percentile(d['Score'], 55))\n",
    "print(\"60%\",np.percentile(d['Score'], 60))\n",
    "print(\"65%\",np.percentile(d['Score'], 65))\n",
    "print(\"70%\",np.percentile(d['Score'], 70))\n",
    "print(\"75%\",np.percentile(d['Score'], 75))\n",
    "print(\"80%\",np.percentile(d['Score'], 80))\n",
    "print(\"85%\",np.percentile(d['Score'], 85))\n",
    "print(\"90%\",np.percentile(d['Score'], 90))\n",
    "print(\"95%\",np.percentile(d['Score'], 95))"
   ]
  },
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