kag_bike_rent.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set(context='talk', style='whitegrid', palette='tab20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "submission = pd.read_csv(\"sampleSubmission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81        0.0       3          13     16  \n",
       "1        80        0.0       8          32     40  \n",
       "2        80        0.0       5          27     32  \n",
       "3        75        0.0       3          10     13  \n",
       "4        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
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       "    <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",
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       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
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       "    <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",
       "    </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",
       "    </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  \n",
       "0        56    26.0027  \n",
       "1        56     0.0000  \n",
       "2        56     0.0000  \n",
       "3        56    11.0014  \n",
       "4        56    11.0014  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "              datetime  count\n",
       "0  2011-01-20 00:00:00      0\n",
       "1  2011-01-20 01:00:00      0\n",
       "2  2011-01-20 02:00:00      0\n",
       "3  2011-01-20 03:00:00      0\n",
       "4  2011-01-20 04:00:00      0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First look at data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkit import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>n non-null</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>10883</th>\n",
       "      <th>10884</th>\n",
       "      <th>10885</th>\n",
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       "      <th>datetime</th>\n",
       "      <td>object</td>\n",
       "      <td>10886</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>2012-12-19 21:00:00</td>\n",
       "      <td>2012-12-19 22:00:00</td>\n",
       "      <td>2012-12-19 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>int64</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>int64</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>int64</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather</th>\n",
       "      <td>int64</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>float64</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>9.84</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.94</td>\n",
       "      <td>13.94</td>\n",
       "      <td>13.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>float64</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>14.395</td>\n",
       "      <td>13.635</td>\n",
       "      <td>13.635</td>\n",
       "      <td>15.91</td>\n",
       "      <td>17.425</td>\n",
       "      <td>16.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>int64</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>81</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>float64</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>8.9981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>int64</td>\n",
       "      <td>309</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>int64</td>\n",
       "      <td>731</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>27</td>\n",
       "      <td>164</td>\n",
       "      <td>117</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>int64</td>\n",
       "      <td>822</td>\n",
       "      <td>0</td>\n",
       "      <td>10886</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>168</td>\n",
       "      <td>129</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               type  n unique  n null  n non-null                    0  \\\n",
       "datetime     object     10886       0       10886  2011-01-01 00:00:00   \n",
       "season        int64         4       0       10886                    1   \n",
       "holiday       int64         2       0       10886                    0   \n",
       "workingday    int64         2       0       10886                    0   \n",
       "weather       int64         4       0       10886                    1   \n",
       "temp        float64        49       0       10886                 9.84   \n",
       "atemp       float64        60       0       10886               14.395   \n",
       "humidity      int64        89       0       10886                   81   \n",
       "windspeed   float64        28       0       10886                    0   \n",
       "casual        int64       309       0       10886                    3   \n",
       "registered    int64       731       0       10886                   13   \n",
       "count         int64       822       0       10886                   16   \n",
       "\n",
       "                              1                    2                10883  \\\n",
       "datetime    2011-01-01 01:00:00  2011-01-01 02:00:00  2012-12-19 21:00:00   \n",
       "season                        1                    1                    4   \n",
       "holiday                       0                    0                    0   \n",
       "workingday                    0                    0                    1   \n",
       "weather                       1                    1                    1   \n",
       "temp                       9.02                 9.02                13.94   \n",
       "atemp                    13.635               13.635                15.91   \n",
       "humidity                     80                   80                   61   \n",
       "windspeed                     0                    0              15.0013   \n",
       "casual                        8                    5                    4   \n",
       "registered                   32                   27                  164   \n",
       "count                        40                   32                  168   \n",
       "\n",
       "                          10884                10885  \n",
       "datetime    2012-12-19 22:00:00  2012-12-19 23:00:00  \n",
       "season                        4                    4  \n",
       "holiday                       0                    0  \n",
       "workingday                    1                    1  \n",
       "weather                       1                    1  \n",
       "temp                      13.94                13.12  \n",
       "atemp                    17.425               16.665  \n",
       "humidity                     61                   66  \n",
       "windspeed                6.0032               8.9981  \n",
       "casual                       12                    4  \n",
       "registered                  117                   84  \n",
       "count                       129                   88  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combaning together\n",
    "train['source'] = 'train'\n",
    "test['source'] = 'test'\n",
    "\n",
    "data = pd.concat([train, test], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>train</td>\n",
       "      <td>9.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    atemp  casual  count             datetime  holiday  humidity  registered  \\\n",
       "0  14.395     3.0   16.0  2011-01-01 00:00:00        0        81        13.0   \n",
       "1  13.635     8.0   40.0  2011-01-01 01:00:00        0        80        32.0   \n",
       "2  13.635     5.0   32.0  2011-01-01 02:00:00        0        80        27.0   \n",
       "3  14.395     3.0   13.0  2011-01-01 03:00:00        0        75        10.0   \n",
       "4  14.395     0.0    1.0  2011-01-01 04:00:00        0        75         1.0   \n",
       "\n",
       "   season source  temp  weather  windspeed  workingday  \n",
       "0       1  train  9.84        1        0.0           0  \n",
       "1       1  train  9.02        1        0.0           0  \n",
       "2       1  train  9.02        1        0.0           0  \n",
       "3       1  train  9.84        1        0.0           0  \n",
       "4       1  train  9.84        1        0.0           0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>casual</th>\n",
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       "      <th>holiday</th>\n",
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       "      <td>12.880</td>\n",
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       "      <td>12.880</td>\n",
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       "      <td>10.66</td>\n",
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       "      <td>11.0014</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6491</th>\n",
       "      <td>13.635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-12-31 22:00:00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>13.635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-12-31 23:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
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       "      <td>1</td>\n",
       "      <td>test</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       atemp  casual  count             datetime  holiday  humidity  \\\n",
       "6488  12.880     NaN    NaN  2012-12-31 19:00:00        0        60   \n",
       "6489  12.880     NaN    NaN  2012-12-31 20:00:00        0        60   \n",
       "6490  12.880     NaN    NaN  2012-12-31 21:00:00        0        60   \n",
       "6491  13.635     NaN    NaN  2012-12-31 22:00:00        0        56   \n",
       "6492  13.635     NaN    NaN  2012-12-31 23:00:00        0        65   \n",
       "\n",
       "      registered  season source   temp  weather  windspeed  workingday  \n",
       "6488         NaN       1   test  10.66        2    11.0014           1  \n",
       "6489         NaN       1   test  10.66        2    11.0014           1  \n",
       "6490         NaN       1   test  10.66        1    11.0014           1  \n",
       "6491         NaN       1   test  10.66        1     8.9981           1  \n",
       "6492         NaN       1   test  10.66        1     8.9981           1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting datetime\n",
    "data['datetime'] = pd.to_datetime(data['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating year, month, weekday, hour features\n",
    "\n",
    "data['yr']  = data['datetime'].dt.year\n",
    "data['mnth']  = data['datetime'].dt.month\n",
    "data['hr']  = data['datetime'].dt.hour\n",
    "data['weekday'] = data['datetime'].dt.weekday\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating 'time of day' feature\n",
    "\n",
    "def assign_label(hour):\n",
    "    if hour in np.arange(6,12):\n",
    "        return 1\n",
    "    elif hour in np.arange(12,18):\n",
    "        return 2\n",
    "    elif hour in np.arange(18,24):\n",
    "        return 3\n",
    "    elif hour in np.arange(0,6):\n",
    "        return 4\n",
    "    \n",
    "data['time_of_day'] = data['hr'].apply(assign_label)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.395</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
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       "      <td>train</td>\n",
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       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.395</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    atemp  casual  count            datetime  holiday  humidity  registered  \\\n",
       "0  14.395     3.0   16.0 2011-01-01 00:00:00        0        81        13.0   \n",
       "1  13.635     8.0   40.0 2011-01-01 01:00:00        0        80        32.0   \n",
       "2  13.635     5.0   32.0 2011-01-01 02:00:00        0        80        27.0   \n",
       "3  14.395     3.0   13.0 2011-01-01 03:00:00        0        75        10.0   \n",
       "4  14.395     0.0    1.0 2011-01-01 04:00:00        0        75         1.0   \n",
       "\n",
       "   season source  temp  weather  windspeed  workingday    yr  mnth  hr  \\\n",
       "0       1  train  9.84        1        0.0           0  2011     1   0   \n",
       "1       1  train  9.02        1        0.0           0  2011     1   1   \n",
       "2       1  train  9.02        1        0.0           0  2011     1   2   \n",
       "3       1  train  9.84        1        0.0           0  2011     1   3   \n",
       "4       1  train  9.84        1        0.0           0  2011     1   4   \n",
       "\n",
       "   weekday  time_of_day  \n",
       "0        5            4  \n",
       "1        5            4  \n",
       "2        5            4  \n",
       "3        5            4  \n",
       "4        5            4  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting back to train and test\n",
    "\n",
    "train = data[data.source == 'train']\n",
    "test = data[data.source == 'test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>atemp</th>\n",
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       "      <th>datetime</th>\n",
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       "      <th>workingday</th>\n",
       "      <th>yr</th>\n",
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       "      <th>hr</th>\n",
       "      <th>weekday</th>\n",
       "      <th>time_of_day</th>\n",
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       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "    atemp  casual  count            datetime  holiday  humidity  registered  \\\n",
       "0  14.395     3.0   16.0 2011-01-01 00:00:00        0        81        13.0   \n",
       "1  13.635     8.0   40.0 2011-01-01 01:00:00        0        80        32.0   \n",
       "2  13.635     5.0   32.0 2011-01-01 02:00:00        0        80        27.0   \n",
       "3  14.395     3.0   13.0 2011-01-01 03:00:00        0        75        10.0   \n",
       "4  14.395     0.0    1.0 2011-01-01 04:00:00        0        75         1.0   \n",
       "\n",
       "   season source  temp  weather  windspeed  workingday    yr  mnth  hr  \\\n",
       "0       1  train  9.84        1        0.0           0  2011     1   0   \n",
       "1       1  train  9.02        1        0.0           0  2011     1   1   \n",
       "2       1  train  9.02        1        0.0           0  2011     1   2   \n",
       "3       1  train  9.84        1        0.0           0  2011     1   3   \n",
       "4       1  train  9.84        1        0.0           0  2011     1   4   \n",
       "\n",
       "   weekday  time_of_day  \n",
       "0        5            4  \n",
       "1        5            4  \n",
       "2        5            4  \n",
       "3        5            4  \n",
       "4        5            4  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>13.635</td>\n",
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       "      <td>0</td>\n",
       "      <td>56</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>12.880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    atemp  casual  count            datetime  holiday  humidity  registered  \\\n",
       "0  11.365     NaN    NaN 2011-01-20 00:00:00        0        56         NaN   \n",
       "1  13.635     NaN    NaN 2011-01-20 01:00:00        0        56         NaN   \n",
       "2  13.635     NaN    NaN 2011-01-20 02:00:00        0        56         NaN   \n",
       "3  12.880     NaN    NaN 2011-01-20 03:00:00        0        56         NaN   \n",
       "4  12.880     NaN    NaN 2011-01-20 04:00:00        0        56         NaN   \n",
       "\n",
       "   season source   temp  weather  windspeed  workingday    yr  mnth  hr  \\\n",
       "0       1   test  10.66        1    26.0027           1  2011     1   0   \n",
       "1       1   test  10.66        1     0.0000           1  2011     1   1   \n",
       "2       1   test  10.66        1     0.0000           1  2011     1   2   \n",
       "3       1   test  10.66        1    11.0014           1  2011     1   3   \n",
       "4       1   test  10.66        1    11.0014           1  2011     1   4   \n",
       "\n",
       "   weekday  time_of_day  \n",
       "0        3            4  \n",
       "1        3            4  \n",
       "2        3            4  \n",
       "3        3            4  \n",
       "4        3            4  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to choose a model we'll split train on sub_train and sub_test\n",
    "\n",
    "sub_train = train.sample(frac=0.7, random_state=1)\n",
    "sub_test = train.loc[~train.index.isin(sub_train.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <th>9848</th>\n",
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       "      <td>33.335</td>\n",
       "      <td>47.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>2011-04-04 15:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
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       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "        atemp  casual  count            datetime  holiday  humidity  \\\n",
       "3709   33.335   101.0  308.0 2011-09-05 11:00:00        1        74   \n",
       "6741   21.210     6.0   14.0 2012-03-18 04:00:00        0        94   \n",
       "9848   31.060   193.0  539.0 2012-10-14 17:00:00        0        44   \n",
       "1410   33.335    47.0  143.0 2011-04-04 15:00:00        0        23   \n",
       "10672  20.455     0.0    1.0 2012-12-11 02:00:00        0        66   \n",
       "\n",
       "       registered  season source   temp  weather  windspeed  workingday    yr  \\\n",
       "3709        207.0       3  train  28.70        2    11.0014           0  2011   \n",
       "6741          8.0       1  train  17.22        2    11.0014           0  2012   \n",
       "9848        346.0       4  train  26.24        1    12.9980           0  2012   \n",
       "1410         96.0       2  train  31.16        1    36.9974           1  2011   \n",
       "10672         1.0       4  train  16.40        2    22.0028           1  2012   \n",
       "\n",
       "       mnth  hr  weekday  time_of_day  \n",
       "3709      9  11        0            1  \n",
       "6741      3   4        6            4  \n",
       "9848     10  17        6            2  \n",
       "1410      4  15        0            2  \n",
       "10672    12   2        1            4  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atemp</th>\n",
       "      <th>casual</th>\n",
       "      <th>count</th>\n",
       "      <th>datetime</th>\n",
       "      <th>holiday</th>\n",
       "      <th>humidity</th>\n",
       "      <th>registered</th>\n",
       "      <th>season</th>\n",
       "      <th>source</th>\n",
       "      <th>temp</th>\n",
       "      <th>weather</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>workingday</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>weekday</th>\n",
       "      <th>time_of_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.395</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>9.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.635</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>9.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.425</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011-01-01 09:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>13.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>22.725</td>\n",
       "      <td>35.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2011-01-01 14:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>18.86</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21.970</td>\n",
       "      <td>40.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2011-01-01 15:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>18.04</td>\n",
       "      <td>2</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     atemp  casual  count            datetime  holiday  humidity  registered  \\\n",
       "0   14.395     3.0   16.0 2011-01-01 00:00:00        0        81        13.0   \n",
       "2   13.635     5.0   32.0 2011-01-01 02:00:00        0        80        27.0   \n",
       "9   17.425     8.0   14.0 2011-01-01 09:00:00        0        76         6.0   \n",
       "14  22.725    35.0  106.0 2011-01-01 14:00:00        0        72        71.0   \n",
       "15  21.970    40.0  110.0 2011-01-01 15:00:00        0        77        70.0   \n",
       "\n",
       "    season source   temp  weather  windspeed  workingday    yr  mnth  hr  \\\n",
       "0        1  train   9.84        1     0.0000           0  2011     1   0   \n",
       "2        1  train   9.02        1     0.0000           0  2011     1   2   \n",
       "9        1  train  13.12        1     0.0000           0  2011     1   9   \n",
       "14       1  train  18.86        2    19.0012           0  2011     1  14   \n",
       "15       1  train  18.04        2    19.9995           0  2011     1  15   \n",
       "\n",
       "    weekday  time_of_day  \n",
       "0         5            4  \n",
       "2         5            4  \n",
       "9         5            1  \n",
       "14        5            2  \n",
       "15        5            2  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first set of features\n",
    "features_1 = ['atemp', 'holiday', 'humidity', 'season', 'temp', 'weather', 'windspeed',\n",
    "            'workingday', 'yr', 'mnth', 'hr', 'weekday', 'time_of_day']\n",
    "\n",
    "target = 'count'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_log_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_model(model, train, test):\n",
    "    model.fit(train[features], train[target])\n",
    "\n",
    "    train_pred = model.predict(train[features])\n",
    "    test_pred = model.predict(test[features])\n",
    "    # getting rid of negative predictions\n",
    "    train_pred[train_pred < 0] = 0\n",
    "    test_pred[test_pred < 0] = 0\n",
    "    \n",
    "    train_rmsle = np.sqrt(mean_squared_log_error(train[target], train_pred))\n",
    "    test_rmsle = np.sqrt(mean_squared_log_error(test[target], test_pred))\n",
    "\n",
    "    print('train error is: {} \\n test error is: {}'.format(train_rmsle, test_rmsle))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train error is: 0.17127799920270412 \n",
      " test error is: 0.35452489580453245\n"
     ]
    }
   ],
   "source": [
    "features = features_1\n",
    "\n",
    "model1 = RandomForestRegressor()\n",
    "run_model(model1, sub_train, sub_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train error is: 0.20267815953977794 \n",
      " test error is: 0.3444124866465869\n"
     ]
    }
   ],
   "source": [
    "# tweaking models\n",
    "\n",
    "model2 = RandomForestRegressor(min_samples_leaf=2)\n",
    "run_model(model2, sub_train, sub_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train error is: 0.16398653665821872 \n",
      " test error is: 0.34719788638356497\n"
     ]
    }
   ],
   "source": [
    "model3 = RandomForestRegressor(max_depth=25)\n",
    "run_model(model3, sub_train, sub_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train error is: 0.20517927519674295 \n",
      " test error is: 0.34289014610195373\n"
     ]
    }
   ],
   "source": [
    "model4 = RandomForestRegressor(min_samples_leaf=2, max_depth=16)\n",
    "run_model(model4, sub_train, sub_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model4 looks ok. Let's increase number of trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train error is: 0.1970399460954467 \n",
      " test error is: 0.3367681595419403\n"
     ]
    }
   ],
   "source": [
    "features = features_1\n",
    "model4_1 = RandomForestRegressor(n_estimators=200, min_samples_leaf=2, max_depth=16)\n",
    "run_model(model4_1, sub_train, sub_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train error is: 0.19604384549295764 \n",
      " test error is: 0.3366186177648372\n"
     ]
    }
   ],
   "source": [
    "# more trees and tweaking max_depth a little...\n",
    "\n",
    "model4_2 = RandomForestRegressor(n_estimators=1000, min_samples_leaf=2, max_depth=16)\n",
    "run_model(model4_2, sub_train, sub_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, let's stay with these parameters for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying chosen model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=16,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=2, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training a model with chosen parameters of global train dataset\n",
    "\n",
    "prod_model = RandomForestRegressor(n_estimators=1000, min_samples_leaf=2, max_depth=16)\n",
    "prod_model.fit(train[features], train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10.9315131 ,   5.47005815,   3.61384523, ..., 149.25671429,\n",
       "       112.06224444,  62.60039643])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting on test\n",
    "\n",
    "test_pred = prod_model.predict(test[features])\n",
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196b6b83be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(test_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>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>10.931513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>5.470058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>3.613845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>3.526936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>3.022810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime      count\n",
       "0 2011-01-20 00:00:00  10.931513\n",
       "1 2011-01-20 01:00:00   5.470058\n",
       "2 2011-01-20 02:00:00   3.613845\n",
       "3 2011-01-20 03:00:00   3.526936\n",
       "4 2011-01-20 04:00:00   3.022810"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating submission\n",
    "submission_4_2 = pd.concat([test['datetime'], pd.Series(test_pred, name='count')], axis=1)\n",
    "submission_4_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  count\n",
       "0  2011-01-20 00:00:00      0\n",
       "1  2011-01-20 01:00:00      0\n",
       "2  2011-01-20 02:00:00      0\n",
       "3  2011-01-20 03:00:00      0\n",
       "4  2011-01-20 04:00:00      0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's compare to sample submission. Looks ok.\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to csv\n",
    "\n",
    "submission_4_2.to_csv('submission_4_2_(16).csv', index=False)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the final score is...\n",
    "\n",
    "![image.png](attachment:image.png)  \n",
    "\n",
    "Well not as good, but something."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}