8.7. Project_linear_regression_with_bicycle_numbers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) 데이터 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <td>0</td>\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>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10881</td>\n",
       "      <td>2012-12-19 19:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>50</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>7</td>\n",
       "      <td>329</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10882</td>\n",
       "      <td>2012-12-19 20:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.76</td>\n",
       "      <td>17.425</td>\n",
       "      <td>57</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>10</td>\n",
       "      <td>231</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10883</td>\n",
       "      <td>2012-12-19 21:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>15.910</td>\n",
       "      <td>61</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10884</td>\n",
       "      <td>2012-12-19 22:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>17.425</td>\n",
       "      <td>61</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12</td>\n",
       "      <td>117</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10885</td>\n",
       "      <td>2012-12-19 23:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.12</td>\n",
       "      <td>16.665</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>4</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10886 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  season  holiday  workingday  weather   temp  \\\n",
       "0      2011-01-01 00:00:00       1        0           0        1   9.84   \n",
       "1      2011-01-01 01:00:00       1        0           0        1   9.02   \n",
       "2      2011-01-01 02:00:00       1        0           0        1   9.02   \n",
       "3      2011-01-01 03:00:00       1        0           0        1   9.84   \n",
       "4      2011-01-01 04:00:00       1        0           0        1   9.84   \n",
       "...                    ...     ...      ...         ...      ...    ...   \n",
       "10881  2012-12-19 19:00:00       4        0           1        1  15.58   \n",
       "10882  2012-12-19 20:00:00       4        0           1        1  14.76   \n",
       "10883  2012-12-19 21:00:00       4        0           1        1  13.94   \n",
       "10884  2012-12-19 22:00:00       4        0           1        1  13.94   \n",
       "10885  2012-12-19 23:00:00       4        0           1        1  13.12   \n",
       "\n",
       "        atemp  humidity  windspeed  casual  registered  count  \n",
       "0      14.395        81     0.0000       3          13     16  \n",
       "1      13.635        80     0.0000       8          32     40  \n",
       "2      13.635        80     0.0000       5          27     32  \n",
       "3      14.395        75     0.0000       3          10     13  \n",
       "4      14.395        75     0.0000       0           1      1  \n",
       "...       ...       ...        ...     ...         ...    ...  \n",
       "10881  19.695        50    26.0027       7         329    336  \n",
       "10882  17.425        57    15.0013      10         231    241  \n",
       "10883  15.910        61    15.0013       4         164    168  \n",
       "10884  17.425        61     6.0032      12         117    129  \n",
       "10885  16.665        66     8.9981       4          84     88  \n",
       "\n",
       "[10886 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data preparation\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "file_path = \"bike-sharing-demand/train.csv\"\n",
    "train = pd.read_csv(file_path)\n",
    "\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime       object\n",
       "season          int64\n",
       "holiday         int64\n",
       "workingday      int64\n",
       "weather         int64\n",
       "temp          float64\n",
       "atemp         float64\n",
       "humidity        int64\n",
       "windspeed     float64\n",
       "casual          int64\n",
       "registered      int64\n",
       "count           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.00000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2.506614</td>\n",
       "      <td>0.028569</td>\n",
       "      <td>0.680875</td>\n",
       "      <td>1.418427</td>\n",
       "      <td>20.23086</td>\n",
       "      <td>23.655084</td>\n",
       "      <td>61.886460</td>\n",
       "      <td>12.799395</td>\n",
       "      <td>36.021955</td>\n",
       "      <td>155.552177</td>\n",
       "      <td>191.574132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.116174</td>\n",
       "      <td>0.166599</td>\n",
       "      <td>0.466159</td>\n",
       "      <td>0.633839</td>\n",
       "      <td>7.79159</td>\n",
       "      <td>8.474601</td>\n",
       "      <td>19.245033</td>\n",
       "      <td>8.164537</td>\n",
       "      <td>49.960477</td>\n",
       "      <td>151.039033</td>\n",
       "      <td>181.144454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.82000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.94000</td>\n",
       "      <td>16.665000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>7.001500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.50000</td>\n",
       "      <td>24.240000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>12.998000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.24000</td>\n",
       "      <td>31.060000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>16.997900</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>284.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>45.455000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>56.996900</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             season       holiday    workingday       weather         temp  \\\n",
       "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.00000   \n",
       "mean       2.506614      0.028569      0.680875      1.418427     20.23086   \n",
       "std        1.116174      0.166599      0.466159      0.633839      7.79159   \n",
       "min        1.000000      0.000000      0.000000      1.000000      0.82000   \n",
       "25%        2.000000      0.000000      0.000000      1.000000     13.94000   \n",
       "50%        3.000000      0.000000      1.000000      1.000000     20.50000   \n",
       "75%        4.000000      0.000000      1.000000      2.000000     26.24000   \n",
       "max        4.000000      1.000000      1.000000      4.000000     41.00000   \n",
       "\n",
       "              atemp      humidity     windspeed        casual    registered  \\\n",
       "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.000000   \n",
       "mean      23.655084     61.886460     12.799395     36.021955    155.552177   \n",
       "std        8.474601     19.245033      8.164537     49.960477    151.039033   \n",
       "min        0.760000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       16.665000     47.000000      7.001500      4.000000     36.000000   \n",
       "50%       24.240000     62.000000     12.998000     17.000000    118.000000   \n",
       "75%       31.060000     77.000000     16.997900     49.000000    222.000000   \n",
       "max       45.455000    100.000000     56.996900    367.000000    886.000000   \n",
       "\n",
       "              count  \n",
       "count  10886.000000  \n",
       "mean     191.574132  \n",
       "std      181.144454  \n",
       "min        1.000000  \n",
       "25%       42.000000  \n",
       "50%      145.000000  \n",
       "75%      284.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      0\n",
       "season        0\n",
       "holiday       0\n",
       "workingday    0\n",
       "weather       0\n",
       "temp          0\n",
       "atemp         0\n",
       "humidity      0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "count         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) datetime 컬럼을 datetime 자료형으로 변환하고 연, 월, 일, 시, 분, 초까지 6가지 컬럼 생성하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datetime 컬럼 형식 바꾸기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10881</td>\n",
       "      <td>2012-12-19 19:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>50</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>7</td>\n",
       "      <td>329</td>\n",
       "      <td>336</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10882</td>\n",
       "      <td>2012-12-19 20:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.76</td>\n",
       "      <td>17.425</td>\n",
       "      <td>57</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>10</td>\n",
       "      <td>231</td>\n",
       "      <td>241</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10883</td>\n",
       "      <td>2012-12-19 21:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>15.910</td>\n",
       "      <td>61</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>168</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10884</td>\n",
       "      <td>2012-12-19 22:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>17.425</td>\n",
       "      <td>61</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12</td>\n",
       "      <td>117</td>\n",
       "      <td>129</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10885</td>\n",
       "      <td>2012-12-19 23:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.12</td>\n",
       "      <td>16.665</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>4</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime  season  holiday  workingday  weather   temp  \\\n",
       "10881 2012-12-19 19:00:00       4        0           1        1  15.58   \n",
       "10882 2012-12-19 20:00:00       4        0           1        1  14.76   \n",
       "10883 2012-12-19 21:00:00       4        0           1        1  13.94   \n",
       "10884 2012-12-19 22:00:00       4        0           1        1  13.94   \n",
       "10885 2012-12-19 23:00:00       4        0           1        1  13.12   \n",
       "\n",
       "        atemp  humidity  windspeed  casual  registered  count  year  month  \\\n",
       "10881  19.695        50    26.0027       7         329    336  2012     12   \n",
       "10882  17.425        57    15.0013      10         231    241  2012     12   \n",
       "10883  15.910        61    15.0013       4         164    168  2012     12   \n",
       "10884  17.425        61     6.0032      12         117    129  2012     12   \n",
       "10885  16.665        66     8.9981       4          84     88  2012     12   \n",
       "\n",
       "       day  hour  minute  second  \n",
       "10881   19    19       0       0  \n",
       "10882   19    20       0       0  \n",
       "10883   19    21       0       0  \n",
       "10884   19    22       0       0  \n",
       "10885   19    23       0       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['datetime'] = pd.to_datetime(train['datetime'])\n",
    "\n",
    "train['year'] = pd.DatetimeIndex(train['datetime']).year\n",
    "train['month'] = pd.DatetimeIndex(train['datetime']).month\n",
    "train['day'] = pd.DatetimeIndex(train['datetime']).day\n",
    "train['hour'] = pd.DatetimeIndex(train['datetime']).hour\n",
    "train['minute'] = pd.DatetimeIndex(train['datetime']).minute\n",
    "train['second'] = pd.DatetimeIndex(train['datetime']).second\n",
    "\n",
    "train.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) year, month, day, hour, minute, second 데이터 개수 시각화하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Jinmi/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:22: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn 불러오기\n",
    "import seaborn as sns\n",
    "#matplotlib 불러오기\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#plt.subplots함수로 \"도화지(figure)\" 및 \"축(axis)\" 그리기 \n",
    "fig, axes = plt.subplots(2,3, figsize=(12,8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "# 그래프 제목 정하기 \n",
    "fig.suptitle('time count visualization')\n",
    "\n",
    "#sns.countplot함수를 이용하여 그래프 그리기\n",
    "sns.countplot(train['year'], ax=axes[0])\n",
    "sns.countplot(train['month'], ax=axes[1])\n",
    "sns.countplot(train['day'], ax=axes[2])\n",
    "sns.countplot(train['hour'], ax=axes[3])\n",
    "sns.countplot(train['month'], ax=axes[4])\n",
    "sns.countplot(train['second'], ax=axes[5])\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (4) X, y 컬럼 선택 및 train/test 데이터 분리\n",
    "\n",
    "- X에는 문자열 데이터, 쓸모없는 데이터를 제외한 컬럼의 데이터 넣기\n",
    "- count 값을 맞추고자 하므로, y 변수에 count 컬럼의 데이터 넣기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      datetime64[ns]\n",
       "season                 int64\n",
       "holiday                int64\n",
       "workingday             int64\n",
       "weather                int64\n",
       "temp                 float64\n",
       "atemp                float64\n",
       "humidity               int64\n",
       "windspeed            float64\n",
       "casual                 int64\n",
       "registered             int64\n",
       "count                  int64\n",
       "year                   int64\n",
       "month                  int64\n",
       "day                    int64\n",
       "hour                   int64\n",
       "minute                 int64\n",
       "second                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop(columns=['datetime','count'], axis=1)\n",
    "\n",
    "y = train['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>50</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>7</td>\n",
       "      <td>329</td>\n",
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       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>10882</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>164</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>13.12</td>\n",
       "      <td>16.665</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>4</td>\n",
       "      <td>84</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10886 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  holiday  workingday  weather   temp   atemp  humidity  \\\n",
       "0           1        0           0        1   9.84  14.395        81   \n",
       "1           1        0           0        1   9.02  13.635        80   \n",
       "2           1        0           0        1   9.02  13.635        80   \n",
       "3           1        0           0        1   9.84  14.395        75   \n",
       "4           1        0           0        1   9.84  14.395        75   \n",
       "...       ...      ...         ...      ...    ...     ...       ...   \n",
       "10881       4        0           1        1  15.58  19.695        50   \n",
       "10882       4        0           1        1  14.76  17.425        57   \n",
       "10883       4        0           1        1  13.94  15.910        61   \n",
       "10884       4        0           1        1  13.94  17.425        61   \n",
       "10885       4        0           1        1  13.12  16.665        66   \n",
       "\n",
       "       windspeed  casual  registered  year  month  day  hour  minute  second  \n",
       "0         0.0000       3          13  2011      1    1     0       0       0  \n",
       "1         0.0000       8          32  2011      1    1     1       0       0  \n",
       "2         0.0000       5          27  2011      1    1     2       0       0  \n",
       "3         0.0000       3          10  2011      1    1     3       0       0  \n",
       "4         0.0000       0           1  2011      1    1     4       0       0  \n",
       "...          ...     ...         ...   ...    ...  ...   ...     ...     ...  \n",
       "10881    26.0027       7         329  2012     12   19    19       0       0  \n",
       "10882    15.0013      10         231  2012     12   19    20       0       0  \n",
       "10883    15.0013       4         164  2012     12   19    21       0       0  \n",
       "10884     6.0032      12         117  2012     12   19    22       0       0  \n",
       "10885     8.9981       4          84  2012     12   19    23       0       0  \n",
       "\n",
       "[10886 rows x 16 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train number : 8708\n",
      "X_test number : 2178\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#sklearn.model_selection의 train_test_split함수를 사용하여 8:2 데이터셋 분할\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=7)\n",
    "\n",
    "print(\"X_train number :\", len(X_train))\n",
    "print(\"X_test number :\", len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (5) LinearRegression 모델 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "#선형회귀 모델 생성\n",
    "reg = LinearRegression()\n",
    "\n",
    "#테스트 데이터로 학습\n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (6) 학습된 모델로 X_test에 대한 예측값 출력 및 손실함수값 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[285. 138. 215. ...  58.  76.   4.]\n",
      "1.126297183935458e-24\n",
      "1.061271493980432e-12\n"
     ]
    }
   ],
   "source": [
    "#reg모델로 X_test 예측\n",
    "y_pred = reg.predict(X_test)\n",
    "print(y_pred)\n",
    "\n",
    "#sklearn.metrics의 mean_squared_error 함수로 MSE와 RSME 계산\n",
    "from sklearn.metrics import mean_squared_error\n",
    "MSE = mean_squared_error(y_test, y_pred) # MSE\n",
    "RMSE = MSE **0.5 # RMSE\n",
    "\n",
    "print(MSE)\n",
    "print(RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (7) x축은 temp 또는 humidity로, y축은 count로 예측 결과 시각화하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
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       "      <td>34</td>\n",
       "      <td>150</td>\n",
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       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>16.40</td>\n",
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       "      <td>32.9975</td>\n",
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       "      <td>22</td>\n",
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       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8764</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>32.80</td>\n",
       "      <td>37.120</td>\n",
       "      <td>52</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>68</td>\n",
       "      <td>185</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3760</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.42</td>\n",
       "      <td>27.275</td>\n",
       "      <td>94</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>2011</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13.94</td>\n",
       "      <td>16.665</td>\n",
       "      <td>71</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9116</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.06</td>\n",
       "      <td>29.545</td>\n",
       "      <td>89</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2178 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season  holiday  workingday  weather   temp   atemp  humidity  \\\n",
       "3502       3        0           1        1  27.06  31.060        74   \n",
       "6462       1        0           1        1  10.66  13.635        41   \n",
       "3275       3        0           0        1  30.34  34.850        70   \n",
       "3503       3        0           1        1  27.06  31.060        65   \n",
       "6813       2        0           1        1  16.40  20.455        76   \n",
       "...      ...      ...         ...      ...    ...     ...       ...   \n",
       "8674       3        0           1        2  32.80  39.395        63   \n",
       "8764       3        0           1        2  32.80  37.120        52   \n",
       "3760       3        0           1        3  25.42  27.275        94   \n",
       "39         1        0           0        3  13.94  16.665        71   \n",
       "9116       3        1           0        1  27.06  29.545        89   \n",
       "\n",
       "      windspeed  casual  registered  year  month  day  hour  minute  second  \n",
       "3502     8.9981      45         240  2011      8   15    20       0       0  \n",
       "6462     7.0015       9         129  2012      3    6    12       0       0  \n",
       "3275    15.0013      60         155  2011      8    6     9       0       0  \n",
       "3503    12.9980      34         150  2011      8   15    21       0       0  \n",
       "6813    32.9975       2          22  2012      4    2     5       0       0  \n",
       "...         ...     ...         ...   ...    ...  ...   ...     ...     ...  \n",
       "8674    15.0013      81         388  2012      8    3    19       0       0  \n",
       "8764    12.9980      68         185  2012      8    7    13       0       0  \n",
       "3760     7.0015       3          55  2011      9    7    15       0       0  \n",
       "39      11.0014       9          67  2011      1    2    16       0       0  \n",
       "9116     0.0000       1           3  2012      9    3     5       0       0  \n",
       "\n",
       "[2178 rows x 16 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fdec95810d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplolib의 scatter plot 으로 시각화 \n",
    "#x축에 X 데이터 중 `temp` 데이터를, y축에는 count 데이터를 넣어서 시각화하기\n",
    "plt.scatter(x=X_test['temp'], y=y_pred, color='red', s = 2)\n",
    "plt.scatter(x=X_test['temp'], y=y_test, color='blue', s = 1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fdeca5413d0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rd5IYJNdCJ64ey2Bx7vU6payeRBXUvdGu+lp1qX+MLvpOIv3oWtCwCqBlILAOgturhqdOglYnUsdzuoWLvpNEvw8Id5J/WVME6+AaqiKfbjc2qX75Orgk69Qwuug7tSXVOur2D0u7Xs6UwjqQOsUxdapsLpZ3JEvqdn97TTvR9zAMTk9JDSdc1ZJ5LRyAdr3UclcRNlmSGs7gzAumsXhkA2deMK20EAZl1vHa5dtZPLKBa5dvN6Wv+v4OFFprUIePW/pOK1VZ+lXMF+8mVSxmsljxdXJpOHvB3TtOtyjL1131tMZ2eXVzYVCvfPSpg9KdPA9vEHqHi77TNVKnO+ZMj6zKj1uF1Swpyw8u87EMfOY0DJ3MXBr2qaO9xEXf6Rqplntq1MzUNJ2QKnY5Qp9TB5mPZeAzR4Qt7p26WPo+qOui7yjUYWpmt3+gqY1PWeVL9aGn1iXV0i/LHdSuXr1quN3Sd9F3FLo5OFfFtTo5VyuHZilrQlm1D71qyrpWO1dVL110w06log/sC9wPrCv+Pgq4G9gCfBnYvzg+pfh7rPj/nMnydtGvlm5a3zkDuTnltFqf68/7dJzLlrj+vE+recnrVTFgW7WFWlYDZsl/or8t5zjlULXoXwZ8SYj+TcD5xf5ngP9R7L8f+Eyxfz7w5cnydtHvHlULgoZlIDenl9BOnFMHVHPuS9XxjHLuRSeNZxX0YwNQ1zJXJvrAEcB64K3AOiAAPwVGiv+/Gbi92L8deHOxP1KkC+3yd9HvHjmCkIPFx57jA2+XRtatrHC82vVkPKNuus+0clqOp+bfCakNr3Zut92E49TVPVWl6N8CvAlYVIj+q4Ex8f8jgY3F/kbgCPG/HwCvniDPpcAoMDp79uxu3B8n5glCFeT4g61lzhEcibTiL5n+hQgxXjL9C5VcS8uzm66hMmk3EJxybs49zTl3qCx94AzgU8X+uOjPmED0Hy72N00g+oe2u4Zb+r2hV1/kVBErc0wip87Sil+4z50RYly4z51Z01Et5AhmXSjLQu+VpV9XqhL9a4BtwA+Bp4DngC+6e6f/6ZWPtywXhcU1VCbatVODhqUir1VXN0O3GEThzqHyKZvjln6xf3PLQO77i/1LWgZyb5osXxf9zqnKDz5Ormul7OtqXf1eimHVr+3LsfRzypZazm4w7I1eK90W/bnAPcXUzJuBKcXxA4q/x4r/z50sXxf9zql6YFbrWmsvYLE0QpbuuiV9N33dnVCWoOfUraxeSF3E1t07zVQu+lV9XPQ7p5sDs/KHr01N1MRBNhJy39KT6JUbKhdN0LX9KgYay3KBlXl/6zxpoN9w0XdKIdX6lmjuhOaZL39fzHz5e1NYhE7cQVU0hqnnaoJuaehSezEWQeuG6JVV1iqoo2GQi4u+Uwo5lrXF7ZNq6XdyPMftpV0jp6yp+7KRLMsFlNNoWam6V1aWcA9KA+Ci75RCjrVmWZEqQyGkDjRarcQccUwdr8i5F1oPQFvklWoll9U7sVL1OEtZvYRBcfW46DtdQ/MVW37ocnAx9cfXyZz41AYgNZRCaq9Hq4/MR6tnWY2kZVzBKtqpjaHlXC3NoM1GysVF36kUi/tBYhGu1B9fJ26b1EHnHEFIrVtqfcqy9LUeRpkuHUtjmzoWUQcLvU4Nhou+UzraD/T9RRiC90//gvojqGLRUidum1R3TQ6pdc4ZuyjLN557H3Ia7lRLP6fcZfUS6tDwjOOi7+yhLEHTfqAWF0hZPzJJJy6Hbgprap0t+WvhoHMs9LKE1EoV10gV37KMELf0XfS7QllukLKum3q8LDG01sviT9dEM9WdkCMCFtfIXLZEiHEuW1T/tqW+2nUtrrpcUssn6aR3NxFVGCG9xkV/gCnLl1s1mpBKK8ti9Wn5WOtl6YlYBjAtg4ip4ZRT6y8tfW26a87gc075reS8Z6CsHk1ZuKXvot8V6vRFa4cmpFrQMIulq+WjpZ/o78nKahFizT0gBU0TNyncljprZZAL23JcJjnunU56AznfX0vjbClrr9xKVeKi70xKNxuPVJ+5RdA14bX+EDUBseRlKZ/mxtBcNJbySCxukhzhLtOdVYUfPzXiaOr9tVAnA8xF35mUqq2UnDEAy0CbNqhp/SFaLL8cF4d2/OaFK+N0fhpvXrgyuTypZbM84zLfAWBxUeVY3JrQVzFo3m+46DuTkmN9WX5kFgtSs76koOdYw7l1sJBajtTeg0Y3GwZrOS1TYnPeCaCNB/XKzVKnxsNF30ki58enof3QLX5Z+QrCVCuxE0s/h9TG02INW0gdoM5JI9GEN8bmcQaNsiz9nDqURc6gdNm46DtJpK4eTbVKtUZCu5Z8BaFFVDux+ix1SBWTnIYk9VqWmTZVWMDtLPWccYYqGqiq85ELE3uNi74zIak+V8u5qY1EFek78e9arFJpyWmNTxVx6lMF0OIy6sYCuZxrWBqoshqxsix0t/Rd9GuPRdA1wZEuF0ueGjmLh8q09I/jgQiNrcaSqTdGaGy1efEWV5elYbA0vBqWPMsKC9EuTY4IVm3py3Pls6z6FZfdysdF35mQ1C+XHFCVLpecPFN90XK/LD92jDHO5LEIja3GW4o6v2WfO1Wr3yIaMh/LYGfueMU4OZZ+ajjoGPNW21aNZjwsDP/a+F6Hf61F2TrFRd+ZkFTRkHPKy5pdkmr1ahZarnX0B9P+IUJjq5W7LNeN9P1qjYeFVHGwBMPT6MQ/X5YlXnV6uW9x81WNW/pOZaSKhjYXPvVamvvF4ovW/PW5DYB2ThXWqiY4qc8jteE9KdwVobEty2VU5jk59yLHOra4DPsNF31nQqr4glsseu1Hpu1rPlctzEGZ1prm1rC8LMbilkkdsJWkCp0ch6nCZ96aPrV30MkA/Dh18MXXCRd9p2PKEqLUwTKZjxbtUcvzZL4TIcaT+U523bQ02lu+tH1N3CzunbLelpXzLC3ntop86jhAqqWf0zMYdNqJ/j44Thuuu3gTK8aWcN3Fm0zpz7xgGotHNnDmBdOaju96ft8929tu3MX63Yu47cZdpnwuXX0cV81by6Wrj2s6/p6LprJ4ZAPvuWgqX1r9HOt3L+JLq5/jTdPHAPZsJU+tG+Xqo9fy1LpRVi5t1G3l0k1NxyUzz1jAFVuWMPOMBU1pPnj2I8xljA+e/QgXrZzPVfPWctHK+er+mmUbWTG2hDXLNjYdj6FxnRhoSiO57JoZrN+9iMuumdF0XN5HWTatLhbkufJeW74HITZv5XPTriHvhbzX2vdIIp+fJb1WBsvxgUJrDerwcUu/Wjqx3iZDs7hSByy1fDRLWvPpt17L0oOwDChbxh8sA78W945l5akl9r3FGrb0qjSsPQlLOVLLmrowKuce9QO4e8eZiNQutAVNoFIHgVPfo6ulb3UxaOdYYsBYRFy6fdqFKBgnx6cvsQxkW45bG89ULOMYqdeTaeQ02NTyWI73Gy76zoRU8QXXBFMLpZCaT+rxdhaqpaegpbdY5TKCpqV8OY1wzrO0DKCmDlC33guL7z51DEEyiG+/ysFF3+ka2o9VC5qmnZsqbp28REX2PrRBR61MmkBp1qdW7rLcXpZ7pKXJEed2ZbNEwbTc36oZFOte4qLv9ByLKybnh65NR2zn07e8qtHSGGg9Bouf2SI4lpW0qSGKLRa2RZzb1SU1CmaOOynHNdTNBqZbuOg7SeRYPpZzU61qC9rCo1bh1RofTTRTBzDlGoHUyJ1anaVrTBMoy/hB6nVzp4SmPsMc947FTWZp0FKpay/BRd9JIsfysViHZfnPm8V2YkvfOsCX+oJ2rRypPnrLrBtZN8tAdmrvQatL6vegNb2lcS/r2hY3Wep3KvW6dcJF30kix3rRLGa5b3FFaEIq0RZnWVxJrddInS2kCZSWXnv7l9YAWnohlnuqPUutMUx16Wj3M0Y9vnwV4ttJ+cbRVnlbKDP+U5lUIvrAkcC3gM3AJuCPi+OHAHcAW4rt9OJ4AD4JjAEPASdOdg0X/fpiEUBLGsu7bbUfojyuWdjtXA6WH7iMuqgJrsVClcHqLKuTNZeO1qjIcloaVU2QNeHqRNy08NuWBr0K0dfSy15e6rqU1O9Tt6hK9GeNCzfwSuD7wOuBjwGXF8cvBz5a7L8T+OdC/E8B7p7sGi76vcfSVU71j2o/OEnqvHOte98qevJvy0Dr+VO+EqGxtfQMtLLK6ZtaYyVZMrUR9XPJ1H8wCYuMEmppeFNFUnuXQDssIqhFN81x71iwNKSpDN2bs4CvAm8HvgfMinsbhu8V+/8XuECk35NO+7jol0POF7kKAZFYF1VNhqXr3ppOs0QlMo6PpUejIeujuRMsZdME1xI22SKk2nPV3iXQ7hlY7ovsoeR8pyzPsl1Zy2DgLf2mTGAO8BjwKmBny/92FNt1wEJxfD2wYIK8lgKjwOjs2bMrvznDQKrVJLG4LnJI9elq1rZV6Cy9A4kU01SXjkyTmk+q20Na4jl+csuYgeaS6+T7kTrV1PKcuklZxk/ZVCr6wCuA+4Czi7810f9/E4j+m9rl7ZZ+OeR8Gav+UqdazDK9dA1J0dPK3+56mqVoGcjV0shrWRoYS09KO1da4mU18tq1Ul9sb6WTRnycXln6VRhCZVCZ6AP7AbcDl4lj7t6pGWVZfmVdV0tjaQA0sT2cH0WI8XB+1LZMmjtJzvOXaPXXBo4t7rDUe2TpAWizg1KxnFtmyAN5vdS59pIqVjanlr9OVDWQG4DPA9e1HP94y0Dux4r932sZyL1nsmu46JeD9gXP6U7nXFfLP8eVdN7+jQHX8/b/Stt0mhvEMtPE4hpKDSxnETftuhLLi2PKEsPUXki781N7QxqpbqIqYvWU1QCUkU9Vor8QiMX0yweKzzuBQwvXzZZie0jc20hcD/wAeHgif37rx0W/HFKtzByrVEufKu6pP0rr4OJstkZobC09i9T576mLvCyuIUv6k4oB55PavDjGkqflvmtC3W5Wj1Y3bawg9buWuhI61a1ooazeQxn5dGX2ThWfYRf9XnUdc3oGGjmx6yW54imFJTVUskVMtMialnuauqpUiqzF0tfqmzpPXd5DOYDargzaM5fXeyP3RYjxjdyX3AOSjV5qbyXne91JTyclz05x0e9TyrIcLKT6n1O/mKlx2rXjmhtGE9h25bQ0RFLgNAtVE1DNgtbqnx7nJ+2dt9o9Sl2RKqdZtrP0Lc9Z1kGOy1gaPbmvrWuw3Mccke3mbzQFF/0+pZuWfuqXN9UST3XvaOe+kdHCGhw1uY/a3UMpUpq4a42B9o5cKaBaOOlUK1arQ05sG0sjpCEt+nbnWp6tFGvZS9LQ6mDp6UjKEmutPL3GRd+ZlFSLO9UXLdFE0hJyWXbjLQuSrH5mbRGWJSaPGhpBeXFMjlUuscyXtzS2qQ2v9btiqae8R6mhHqzjCROROo5hQZvK2gtc9J2O0QTE4iqwWK7SGtYGQbUfqGVuunWVpOxBpJ4v05QVWtky3nBCUeYTWsqsPSdZztRBU0vvxGo9a89TCyWhofXULMj8pbsqFVl/S0+lW7joOx2j/agtfmmLNSktPa27rllxqQ1Ma30k2uKu1N6EJsSWXkyq9T2TxyM0thbrW5bTYpVa7qksp1V4tcZdCxpnKZ/sqVmQ52rxfyzI+qe+p7dK2on+PjhOG2aesYArtixh5hkLuGjlfK6at5aLVs5vOi73dz0/AsCu50ea0st9yfV/8SSLRzZw/V88yWEnHcWiOT/ksJOO4sGdcwB4cOccQmykHd9OxpplG1kxtoQ1yzY2lQ1g5dJNrBhbwsqlm5rO+Y1XvbBn+9S6Ua4+ei1PrRslEBrXJjTlK1m26jiumreWZauOgyL93m2Dy66Zwfrdi7jsmhlNx7V7dOqJzzOXMU498fmm9LJsp0/7NwBOn/Zvar1k/d9z0VQWj2zgPRdN5YNnP8Jcxvjg2Y+Y7qN2/MwLprF4ZANnXjCNj9x6LFuZx0duPVbNE2i6tszr+iueaHwXrniCL655jvW7F/HFNc+p9ZfsYmrTNoUZr3q+aZuCfGZvOOiHAHu2naLVsTS01qAOH7f064XFDZAaA8ViNWrXlRaj5rpoRXPXaDNYUv3MmtWYarla4t1bZt1YYvFr6UHQuXUAABNASURBVGV5tB6PVp52WEJLa/XR6qCNyWhovdZUUsdqLJQxyIxb+k4ZaJaf5JVTft20TclTWk2333kgW5nH7Xce+DJrfZxrl29n8cgGrl2+nVgY1jHAh29+A1uZx4dvfsPLrnfp6oZVfunq45qOS8tS5tVsxU/Ogfu/1LQd5/grz+UbLy3i+CvPVc+V1rq0GqXlJ+/Rl1Y3yvyl1c/x+xc3rPjfv7jZ0pU9DGmVaz0vmV7e94efnQOwZzuOzNOK7A3eduMu1u9exG037mqqp/actJ7RR857gLmM8ZHzHlB7Pdp9zMHym0ilrLKpaK1BHT5u6U9OWdaFhVRrNee4nMqnpbdM37Pen9SFURqapZ86G8USftkSq8aSj0TrJVl6DNYBUUuvIfXeyXO16Zta/qkzf7SylWGhlwU+kDu45HxhU8n5UqeGCpYDvFoZ5OCrZZZRu+tZpvBZGj0tHILFnaAJq7x3mnCnut60Z2kRXm3fOiBqWV9gcSHJ/VWv+0Q8gF1x1es+kbwQ8L/t+9UIjW0OZRlgZeTTTvTdvdPnpA5y5pDalW8aEFXKqXWP/+qcjcxljL86Z6PaLf/mcycD8M3nTlbL1tpVll1/ma9071x89WzW717ExVfPbspLulO0wbbfmv6DPVut3Fdf+AgrxpZw9YWP8NCHbuZt+23goQ/d3OTqkOmli0m6nlKRrjfNhaDdR/mcnr7nUTb8cA5P3/NoU5p2A6IW1410J8nnJMt6ydWvYf3uRVxy9Wv4o/81i/W7F/FH/2sWf7r5D3ieqfzp5j9oul/tBvXH+favF+7ZVj6IaqAKl1ETWmtQh49b+pPTTfdOqqVvsW4trhTtupZVtK1ooRfkcWmty/JpK2w1N5NWDhnGOTW2j+beSX29ZKoLS6bXBtBby69Z65ZyWF4LKXt6WpRVy734+OF/E/flhfjxw/8m+TuuPY8cylgshrt3+pOqBd2Sf87shLJmReQscrLmaxFuiy9ac0XI9FKsLFE8NT+7FEatMbOsD7DcE4lMo7me2l1DK7f2nCRyrCd1IZxWf23ldGo+Z418LUKMZ418zXSuJc9OcdHvU6oeGLLkn2P5lEVV98Gy2tYyEGrxRWu9Cu1cmWYuWyLEOJctTWWziLXWk7CU03Lf21m62ndBNp6pU3zlYHEnL2sfRzakqcHaJPKeTmNnhBinsdN0riXPTnHR71OqEFCLJWcpg8U90M2BrU56JBah0OpjEcScyJpSDOUgZWo5ZRm0QWZtTUTq82u3PqLZTbbXQk99+Yu8hnSTpb6UXK6clvc6NR95349hU4QYj2GT6VxLnp3iou/sIXX5vYbFR5vTAKT6pTUL1erq0dCsLkv5LD9eTWTkccszs7iJTiji1Z/AfU3py/pOtC4os8xGsszq0p6t5WXwGjKMc07gNhlv591TGuMK757S/u1tk1GGseSi7+zB0nVM7dZr6VMF0FIGi7hrVmUn5JTb0quyuImkdatZ5dqAqiyPHAS1DCBrPRKL2yZG21vEUnuMWp1TkWIt91NdK9L1Znl7Wbdw0XeSKMsqryJPi2DI460LhnLE11I+i3WroTUwmn9f5qnNwdeEO7Xx72TwNef1hzm9MEuesm7aPbUgXW/nF5b++ZmWfhm0E32fpz+g5Mw31uYz56S3pJFz1lOvK+urBfECWxAxLY28xnUXN+aRX3fxpqZyr/rbF1m/exGr/vbFpjnvlvUUWpA1OTd/9XnfYC5jrD7vG015ysBwWmA8GXBNBrfT6ijzkWEefvHCvgB7thM9D4B//+4BbGUe//7dA5ruqfbdlMct3xe5rsHy3ZHPTD4b7Z5a+ML3TuZ5pvKF753M1hdeA7BnW1u01qAOH7f008hxp+Rcq6wBW+1tVJLU6Y7WcqeOFWhhirV3tWorSeW1tCBrlt5Gak9Cu1+W49ZVwVqdtWdrqYNllpKGFrZCe2+yBfl9zAnRrNWxU3D3znBgEb4cgbaIYQ4Wd8BvsjVCY6v5jNthER+tzhafu6VR0l5Enjp9UStnal0sQm8ZS2i9n9p3xJKXhvaym9S1HLIMOe6dnEFgDS0ESQou+kOC5YefI9CWwbWccmr5yx+0NqhpvZZl3npqvpoVrFn6Mv92K1rLxjIOoaXXeiHtXjJj6dFo90hDzrqx1E1Dfg+0OfuW+5g6BdqCpQyT4aLv7KEsSz8HreHRhFSz6C2WYeu1LNEiZxe9idlsNZXPMh/fYvXmNMg5Db4l+qbWC2l3rvZ8ZF5a/bVyaDNtUmdpyXxkGSxWttbIW9ZTWCijx+Ci7/Qci5tES68JtfaKQy0fa5m0WRgWN4iGxerNcclpZbCMJaSGaNbK1iryWi9A88Vr4R20esp1B5awztp1D2NbhBgPY5vpPbcyH1nHKeyKEOMUdqnnWpCxgDrFRd/pmKqte4vVrx2XKzLLRAZWSy2ThlZPix/fEtNFK4MUN9lIaha61kho5ZRpWhvhdv7+ic6X1rqlfDMLsZ7JtuQpqNK6P5CfR4jxQH5uGhCWyDKfFr4eIcbTwtdN52rsXzQe+2c0Hi76TsfkuBwkqYJpuW4Zvs+J0CI2SlLHNFJ7OhJpQaY2wppIWixp+QrC+dwfIcb53K+u5m1thFNnxchBWotrzNLT0+6FzOcYNkaI8Rg2mix9SU4cIY3X8kiExrZThlL0qx4UGxbKsvSruG5VL5A5jCeK7v4TpmunNoyp6aWv2OL31pDCndqTkg2GFHopkq2ipw3YSuT583mwKN+DpnESzS9vMSRk+vOm3NJo5Kfcog5Ya8geidZDTMUHcjvE+uq2YaZXgl4WqXFx2p0jOZQnIzS2Wl5aZMbUMYrUqYadrE3YW+Y0y1g2NucV4xznTfmKaum3lqHdzJ5xXskzERrb44pG6TjuN7lZ5EweOe3S4jKU4i6vm2qty8ajrDdwldF4DKXolxX8aJApy3WjkeOKsGDxk7fmaRGTNxQW5xt4UBVWbZaHFupAc1Fo8Vq0c7X3/2rllPty8ZBlqqGcxaT1qto9M0sDOJvvF9f4fpOlr7lZpMjK55QaP0c2gAexPUKMB7E9+TsovwdlhVb22TsdUsYCh0Gnaks/tVFJHUTTyi/dEq2iapmDr0Vv1NwDEi06psxHCw+s3QspRLIHqwm3FidH+oplPtqgqdZIWJ+H1vjI60lrXYY71hpD2RDJ3of2VjONRayP0NjuywsRYtyXF5K/s3KQ/RgeLsYGHjadq+Ercjukzj79fnerWEmtp0WQLdeSAtAqqpauvyWgmOa60BZhSeQAqXShaOfKXqs8rjWS2uDifvwqQoz78aumHoMlPIHWw9DuSYzNbgp5vvauANnIaI3hafsUM2T2+XqTW0amt/z29+HFCI3tfsVA+X7sStaNQ4vxn0N5orQwDDnRQ8dpJ/pdD7gWQnhHCOF7IYSxEMLlVV1HCyhVByp/8XFNSA3cJgN0aWjBuuQ9veLzx3LVvLVc8fljWX3FYywe2cDqKx4DXv6i9HFkMC7txd2S7+x4/Z6tfIm3DAL2L98+kK3M41++fWBTuV9kPwBeZD/+evNZPM9U/nrzWVx2zQzW717EZdfMaLoX615YDMC6FxbzlTtewVbm8ZU7XsEfnvIA0/kZf3jKA01lC2IrA7EdTyPd8TzA2W//BXMZ4+y3/6LpReIy6Nuf3PRbbGUef3LTb3HvjtcCcO+O16rB6UZ3Hg2wZ/vvO163Z/vgzjkAPLhzTlP5PrX5d3ieqXxq8+80vVj9mnPvZy5jXHPu/U337sH/bDyTB//zOHZyCAA7OYSD4g4ADoo7mp6BxnS279mO0Hhh/Ai/5otrnmP97kV8cc1z6rmS53jlnu22XQcB7Nl2yqM7Xtm0LZuuin4IYV/geuB04PXABSGE11dxrToLa6vw5ETEHCRk5EONqy98hBVjS7j6wkeajst7+vQ9j7Lhh3N4+p5HzY2/jGQp0SJrvlj8dF5kn6bIjLIOMs9Lz3mMFWNLuPScxzh1+mYATp2+mY8uXMd0fsZHF67j2uXbWTyygWuXb2/K50i2AXAk2/hOIaTf2fE6PnPXCezgUD5z1wl8892f4bVhjG+++zO8YkpDxBrbOF5Dvs8xAHyfY/jATaewlXl84KZTmhrnj9x6LFuZx0duPZZtzAJgG7OYNfJTAGaN/JRjZ+5kOj/j2Jk7mxqJOfv/GGDPdj9e2rO98Ni7OIDnuPDYu5DN0o+La/yYWZz2ll8xlzFOe8uvuLVo3G694xUsf/cYK8aWsPzdY0zjlwBM45dct/BWpvMzrlt4K3dyKgB3cqr6PZKROJ9hBgDPMIMTisbwBB5g89PTAfZsJ+NQnt6z/QaLAPZsO+XfXljQtC0drQtQxQd4M3C7+Hs5sFxLn+Pe6ScXSs40vH4hdQGTll57ibXm9mi3StTiE28eRJ3YbaQFXJPH5SCfNuNDlkHODpJTCo8ofNpHsLXJNSJ93bJecs2B3D+AX0aI8QB+qd7HGYXrYgZPxJk8HiHGmTze5IeX90EOrMYY1bzkfZzPA8Xg7QPqnH95ven8JEKM0/lJ08rpqcUCq6n8XHXhyfxhd4QYYXeTe+fA4jkdaByM3bc4t7Hdm2cOlnUik0FdfPrAOcAa8fd/B/5WSz8si7O0wcJBwlIvy33QZrXIKXvtlvPLqXqaT1x7S5Q2M0fmI8VXHtfETS68kvWR+chB132KQcd9eKGpzrKxkfnIqKSyLgfybCFuz6ozi6SIyZAH2oBrazA0mdc0dhSN3o6m+ygbgObVuXuPy7rJxvMgfhYhxoP4WdPsHc1gkPnLVa/w66Kevzat0ZDIc+V+DlX79Lst+udOIPr/pyXNUmAUGJ09e3bHle5XhtnST02vWajt8rHMkbdMNZRoA7DateS+tNa1+sj0Mi6L5YXm2n3RRFLuL5v2qQi747JpnzLVpfVa2kKq1HUNWj6pq2e1+st6pk4mWDrlhgi/jkun3NC0n0MZGtBO9EPj/90hhPBm4C9jjKcVfy8HiDFeM1H6BQsWxNHR4fZzO47jpBJCuC/GOOGgQLdn79wLHB1COCqEsD9wPnBbl8vgOI4ztIx082Ixxt0hhD8Cbgf2BT4bY9zUzTI4juMMM10VfYAY4z8B/9Tt6zqO4zjdd+84juM4PcRF33EcZ4hw0XccxxkiXPQdx3GGCBd9x3GcIaKri7NSCSFsB36UkcWrgZ+WVJx+YdjqPGz1Ba/zMJBb39+MMc6Y6B+1Fv1cQgij2qq0QWXY6jxs9QWv8zBQZX3dveM4jjNEuOg7juMMEYMu+qt6XYAeMGx1Hrb6gtd5GKisvgPt03ccx3GaGXRL33EcxxG46DuO4wwRAyv6IYR3hBC+F0IYCyFc3uvylE0I4cgQwrdCCJtDCJtCCH9cHD8khHBHCGFLsbW94bmPCCHsG0K4P4Swrvj7qBDC3UWdv1y8q2EgCCEcHEK4JYTwSPGs3zzozziEsKz4Tm8MIdwYQjhg0J5xCOGzIYSnQwgbxbEJn2to8MlCyx4KIZyYc+2BFP0Qwr7A9cDpwOuBC0IIr+9tqUpnN/A/Y4yvA04BLinqeDmwPsZ4NLC++HvQ+GNgs/j7o8DKos47gPf1pFTV8L+Br8cYjwX+C416D+wzDiEcDnwAWBBjnE/jvRvnM3jPeC3wjpZj2nM9HTi6+CwFPp1z4YEUfeAkYCzGuDXG+CLwD8BZPS5TqcQYn4wxfrfY/wUNMTicRj0/VyT7HPCu3pSwGkIIRwC/B6wp/g7AW4FbiiQDU+cQwquA3wZuAIgxvhhj3MmAP2Ma7/k4MIQwAkwFnmTAnnGM8dvAMy2Hted6FvD54vW3dwEHhxBmdXrtQRX9w4HHxd/bimMDSQhhDvBG4G7gN2KMT0KjYQAO613JKuE64M+A/yz+PhTYGWPcXfw9SM96LrAd+LvCnbUmhDCNAX7GMcYfA58AHqMh9s8C9zG4z1iiPddS9WxQRT9McGwg56aGEF4BfAW4NMb4816Xp0pCCGcAT8cY75OHJ0g6KM96BDgR+HSM8Y3ALgbIlTMRhR/7LOAo4DXANBrujVYG5RlbKPU7Pqiivw04Uvx9BPBEj8pSGSGE/WgI/hdjjLcWh38y3vUrtk/3qnwV8F+BM0MIP6ThsnsrDcv/4MIVAIP1rLcB22KMdxd/30KjERjkZ/w24NEY4/YY40vArcCpDO4zlmjPtVQ9G1TRvxc4uhjx35/GQNBtPS5TqRS+7BuAzTHGa8W/bgPeW+y/F/hqt8tWFTHG5THGI2KMc2g802/GGN8DfAs4p0g2MHWOMT4FPB5COKY4tBj4Dwb4GdNw65wSQphafMfH6zyQz7gF7bneBlxYzOI5BXh23A3UETHGgfwA7wS+D/wA+Itel6eC+i2k0cV7CHig+LyTho97PbCl2B7S67JWVP9FwLpify5wDzAG3AxM6XX5SqznCcBo8Zz/EZg+6M8Y+CvgEWAj8AVgyqA9Y+BGGmMWL9Gw5N+nPVca7p3rCy17mMbMpo6v7WEYHMdxhohBde84juM4E+Ci7ziOM0S46DuO4wwRLvqO4zhDhIu+4zjOEOGi7ziOM0S46DuO4wwR/x+SsftWqlg0JwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x축에 X 데이터 중 `humidity` 데이터를, y축에는 count 데이터를 넣어서 시각화하기\n",
    "plt.scatter(X_test['humidity'], y_pred,  color='red', s = 2)\n",
    "plt.scatter(X_test['humidity'], y_test,  color='blue', s = 1.1)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}