bike-random_forest-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# 노트북 안에 그래프를 그리기 위해\n",
    "%matplotlib inline\n",
    "\n",
    "# 그래프에서 격자로 숫자 범위가 눈에 잘 띄도록 ggplot 스타일을 사용\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# 그래프에서 마이너스 폰트 깨지는 문제에 대한 대처\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 12)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train.csv\", parse_dates=['datetime'])\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 9)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"test.csv\", parse_dates=['datetime'])\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</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",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</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",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</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  year  month  hour  \\\n",
       "0        81        0.0       3          13     16  2011      1     0   \n",
       "1        80        0.0       8          32     40  2011      1     1   \n",
       "2        80        0.0       5          27     32  2011      1     2   \n",
       "3        75        0.0       3          10     13  2011      1     3   \n",
       "4        75        0.0       0           1      1  2011      1     4   \n",
       "\n",
       "   dayofweek  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          5  \n",
       "4          5  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['year'] = train['datetime'].dt.year\n",
    "train['month'] = train['datetime'].dt.month\n",
    "train['hour'] = train['datetime'].dt.hour\n",
    "train['dayofweek'] = train['datetime'].dt.dayofweek\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0 2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "1 2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2 2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "3 2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "4 2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "   humidity  windspeed  year  month  hour  dayofweek  \n",
       "0        56    26.0027  2011      1     0          3  \n",
       "1        56     0.0000  2011      1     1          3  \n",
       "2        56     0.0000  2011      1     2          3  \n",
       "3        56    11.0014  2011      1     3          3  \n",
       "4        56    11.0014  2011      1     4          3  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['year'] = test['datetime'].dt.year\n",
    "test['month'] = test['datetime'].dt.month\n",
    "test['hour'] = test['datetime'].dt.hour\n",
    "test['dayofweek'] = test['datetime'].dt.dayofweek\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b7a4b25978>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7a4c980b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2)\n",
    "fig.set_size_inches(18,10)\n",
    "\n",
    "sns.countplot(data=train, x='windspeed', ax=axes[0])\n",
    "\n",
    "sns.countplot(data=test, x='windspeed', ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 9)\n",
      "(10886,)\n",
      "(10886,)\n"
     ]
    }
   ],
   "source": [
    "rf_model_wind = RandomForestRegressor()\n",
    "f_columns = [\"season\", \"weather\", \"humidity\", \"month\", \"temp\", \"year\", \"hour\", \"dayofweek\", \"atemp\"]\n",
    "x_train = train[f_columns]\n",
    "y_r_label = train['registered']\n",
    "y_c_label = train['casual']\n",
    "\n",
    "print(x_train.shape)\n",
    "print(y_r_label.shape)\n",
    "print(y_c_label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 9)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test =  test[f_columns]\n",
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor()\n",
    "rf_model.fit(x_train, y_r_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_registered_value = rf_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"registered\"] = test_registered_value\n",
    "test['registered'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1313, 16)\n",
      "(9573, 16)\n"
     ]
    }
   ],
   "source": [
    "### windspeed == 0 seems a bit strange\n",
    "### predict windspeed by regression\n",
    "\n",
    "train_wind_0 = train.loc[train['windspeed'] == 0]\n",
    "train_wind_not_0 = train.loc[train['windspeed'] != 0]\n",
    "\n",
    "print(train_wind_0.shape)\n",
    "print(train_wind_not_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 16 columns):\n",
      "datetime      10886 non-null datetime64[ns]\n",
      "season        10886 non-null int64\n",
      "holiday       10886 non-null int64\n",
      "workingday    10886 non-null int64\n",
      "weather       10886 non-null int64\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "casual        10886 non-null int64\n",
      "registered    10886 non-null int64\n",
      "count         10886 non-null int64\n",
      "year          10886 non-null int64\n",
      "month         10886 non-null int64\n",
      "hour          10886 non-null int64\n",
      "dayofweek     10886 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(3), int64(12)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\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",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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",
       "      <td>2011.501929</td>\n",
       "      <td>6.521495</td>\n",
       "      <td>11.541613</td>\n",
       "      <td>3.013963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\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",
       "      <td>0.500019</td>\n",
       "      <td>3.444373</td>\n",
       "      <td>6.915838</td>\n",
       "      <td>2.004585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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",
       "      <td>2011.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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",
       "      <td>2011.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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",
       "      <td>2012.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\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",
       "      <td>2012.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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",
       "      <td>2012.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</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          year         month          hour     dayofweek  \n",
       "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.000000  \n",
       "mean     191.574132   2011.501929      6.521495     11.541613      3.013963  \n",
       "std      181.144454      0.500019      3.444373      6.915838      2.004585  \n",
       "min        1.000000   2011.000000      1.000000      0.000000      0.000000  \n",
       "25%       42.000000   2011.000000      4.000000      6.000000      1.000000  \n",
       "50%      145.000000   2012.000000      7.000000     12.000000      3.000000  \n",
       "75%      284.000000   2012.000000     10.000000     18.000000      5.000000  \n",
       "max      977.000000   2012.000000     12.000000     23.000000      6.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_windspeed(data):\n",
    "    \n",
    "    data_wind_0 = data.loc[data['windspeed'] ==0 ]\n",
    "    data_wind_not_0 = data.loc[data['windspeed'] !=0 ]\n",
    "    \n",
    "    feature_col = ['season', 'weather', 'humidity', 'month', 'temp', 'year', 'atemp']\n",
    "    \n",
    "    rf_model_wind = RandomForestRegressor()\n",
    "    rf_model_wind.fit(data_wind_not_0[feature_col], data_wind_not_0['windspeed'])\n",
    "    \n",
    "    wind_0_val = rf_model_wind.predict(X = data_wind_0[feature_col])\n",
    "    \n",
    "    data_wind_0['windspeed'] = wind_0_val\n",
    "    \n",
    "    results = data_wind_not_0.append(data_wind_0)\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b7ab8e3048>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7ac65eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_new = predict_windspeed(train)\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "fig.set_size_inches(18,6)\n",
    "\n",
    "sns.countplot(data=train_new, x='windspeed', ax=ax1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10886 entries, 5 to 10862\n",
      "Data columns (total 16 columns):\n",
      "datetime      10886 non-null datetime64[ns]\n",
      "season        10886 non-null category\n",
      "holiday       10886 non-null category\n",
      "workingday    10886 non-null category\n",
      "weather       10886 non-null category\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "casual        10886 non-null int64\n",
      "registered    10886 non-null int64\n",
      "count         10886 non-null int64\n",
      "year          10886 non-null category\n",
      "month         10886 non-null category\n",
      "hour          10886 non-null category\n",
      "dayofweek     10886 non-null category\n",
      "dtypes: category(8), datetime64[ns](1), float64(3), int64(4)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "train_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numerical variables to categoryical variables\n",
    "\n",
    "categorical_feature_names = [\"season\",\"holiday\",\"workingday\",\"weather\",\n",
    "                             \"dayofweek\",\"month\",\"year\",\"hour\"]\n",
    "\n",
    "for var in categorical_feature_names:\n",
    "    train_new[var] = train_new[var].astype(\"category\")\n",
    "    test[var] = test[var].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9.84</td>\n",
       "      <td>12.880</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>76</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.76</td>\n",
       "      <td>16.665</td>\n",
       "      <td>81</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>77</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>72</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>2011</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season weather   temp   atemp  humidity  windspeed  year hour dayofweek  \\\n",
       "5       1       2   9.84  12.880        75     6.0032  2011    5         5   \n",
       "10      1       1  15.58  19.695        76    16.9979  2011   10         5   \n",
       "11      1       1  14.76  16.665        81    19.0012  2011   11         5   \n",
       "12      1       1  17.22  21.210        77    19.0012  2011   12         5   \n",
       "13      1       2  18.86  22.725        72    19.9995  2011   13         5   \n",
       "\n",
       "   holiday workingday  \n",
       "5        0          0  \n",
       "10       0          0  \n",
       "11       0          0  \n",
       "12       0          0  \n",
       "13       0          0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = [\"season\", \"weather\", \"temp\", \"atemp\", \"humidity\", \"windspeed\",\n",
    "                 \"year\", \"hour\", \"dayofweek\", \"holiday\", \"workingday\"]\n",
    "\n",
    "\n",
    "X_train = train_new[feature_names]\n",
    "print(X_train.shape)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10886 entries, 5 to 10862\n",
      "Data columns (total 11 columns):\n",
      "season        10886 non-null category\n",
      "weather       10886 non-null category\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "year          10886 non-null category\n",
      "hour          10886 non-null category\n",
      "dayofweek     10886 non-null category\n",
      "holiday       10886 non-null category\n",
      "workingday    10886 non-null category\n",
      "dtypes: category(7), float64(3), int64(1)\n",
      "memory usage: 821.5 KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6493, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>season</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <td>1</td>\n",
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       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  season weather   temp   atemp  humidity  windspeed  year hour dayofweek  \\\n",
       "0      1       1  10.66  11.365        56    26.0027  2011    0         3   \n",
       "1      1       1  10.66  13.635        56     0.0000  2011    1         3   \n",
       "2      1       1  10.66  13.635        56     0.0000  2011    2         3   \n",
       "3      1       1  10.66  12.880        56    11.0014  2011    3         3   \n",
       "4      1       1  10.66  12.880        56    11.0014  2011    4         3   \n",
       "\n",
       "  holiday workingday  \n",
       "0       0          1  \n",
       "1       0          1  \n",
       "2       0          1  \n",
       "3       0          1  \n",
       "4       0          1  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = test[feature_names]\n",
    "\n",
    "print(X_test.shape)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6493 entries, 0 to 6492\n",
      "Data columns (total 11 columns):\n",
      "season        6493 non-null category\n",
      "weather       6493 non-null category\n",
      "temp          6493 non-null float64\n",
      "atemp         6493 non-null float64\n",
      "humidity      6493 non-null int64\n",
      "windspeed     6493 non-null float64\n",
      "year          6493 non-null category\n",
      "hour          6493 non-null category\n",
      "dayofweek     6493 non-null category\n",
      "holiday       6493 non-null category\n",
      "workingday    6493 non-null category\n",
      "dtypes: category(7), float64(3), int64(1)\n",
      "memory usage: 249.2 KB\n"
     ]
    }
   ],
   "source": [
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RMSLE & K-fold validataion\n",
    "\n",
    "from sklearn.metrics import mean_squared_log_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_depth_list = []\n",
    "\n",
    "model = RandomForestRegressor(n_estimators=100,\n",
    "                              n_jobs=-1,\n",
    "                              random_state=0)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886,)\n"
     ]
    }
   ],
   "source": [
    "Y_train = train_new['count']\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 12.3 s\n",
      "Score= 0.94529\n"
     ]
    }
   ],
   "source": [
    "%time score = cross_val_score(model, X_train, Y_train, cv=k_fold)\n",
    "score = score.mean()\n",
    "# 0에 근접할수록 좋은 데이터\n",
    "print(\"Score= {0:.5f}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6493,)\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "\n",
    "print(predictions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'test')]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7aefb9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)= plt.subplots(ncols=2)\n",
    "fig.set_size_inches(12,5)\n",
    "sns.distplot(Y_train,ax=ax1,bins=50)\n",
    "ax1.set(title=\"train\")\n",
    "sns.distplot(predictions,ax=ax2,bins=50)\n",
    "ax2.set(title=\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 12.42,   5.44,   3.86, ..., 104.48, 102.06,  46.84])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.42,  59.06,  67.35, ..., 663.71, 130.78,  42.58])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "tmp_predictions = model.predict(X_train)\n",
    "\n",
    "tmp_predictions\n",
    "#mean_squared_error(X_train[\"count\"], Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "251.347897898693"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(tmp_predictions, Y_train)"
   ]
  },
  {
   "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
}