bike_sharing_demand_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bike Sharing Demand Prediction\n",
    "\n",
    "We used the dataset from [Kaggle](https://www.kaggle.com/c/bike-sharing-demand/overview) to develop a machine learning model to predict a bike sharing demand. \n",
    "\n",
    "Note that there are two types of data:\n",
    "1. Categorical: Nominal or ordinal. To use this type of data, we need to encode it (e.g. one-hot encoding) for developing machine learning models.\n",
    "2. Quantitative: discrete or continuous.\n",
    "\n",
    "We use [pandas](https://pandas.pydata.org/) and [scikit-learn](https://scikit-learn.org/stable/) in this implementation for bike sharing demand prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import important modules\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error, mean_squared_log_error, mean_absolute_error, r2_score\n",
    "\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81        0.0       3          13     16  \n",
       "1        80        0.0       8          32     40  \n",
       "2        80        0.0       5          27     32  \n",
       "3        75        0.0       3          10     13  \n",
       "4        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load training dataset\n",
    "train = pd.read_csv('train.csv')    # Pandas DataFrame.\n",
    "# print(train)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
       "       'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore columns - it has 12 columns\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description i.e. summary statistics.\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 12)\n"
     ]
    }
   ],
   "source": [
    "# The given train dataset has 10886 rows and 12 columns. Season, holiday, workingday and weather columns are \n",
    "# categorical. Rest all, except ‘datetime’ are numerical columns. We engineer two more features,‘hour’ and \n",
    "# ‘month’, using ‘datetime’ column. And then drop unnecessary column(s) (See 'pre_processing' function below \n",
    "#for more detail).\n",
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
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       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>10.66</td>\n",
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       "      <td>26.0027</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "1  2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2  2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "3  2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "4  2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "   humidity  windspeed  \n",
       "0        56    26.0027  \n",
       "1        56     0.0000  \n",
       "2        56     0.0000  \n",
       "3        56    11.0014  \n",
       "4        56    11.0014  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load test data\n",
    "test = pd.read_csv('test.csv')   # Pandas DataFrame.\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
       "       'atemp', 'humidity', 'windspeed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore columns - it has 9 columns\n",
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6493.000000</td>\n",
       "      <td>6493.000000</td>\n",
       "      <td>6493.000000</td>\n",
       "      <td>6493.000000</td>\n",
       "      <td>6493.000000</td>\n",
       "      <td>6493.000000</td>\n",
       "      <td>6493.000000</td>\n",
       "      <td>6493.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.493300</td>\n",
       "      <td>0.029108</td>\n",
       "      <td>0.685815</td>\n",
       "      <td>1.436778</td>\n",
       "      <td>20.620607</td>\n",
       "      <td>24.012865</td>\n",
       "      <td>64.125212</td>\n",
       "      <td>12.631157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.091258</td>\n",
       "      <td>0.168123</td>\n",
       "      <td>0.464226</td>\n",
       "      <td>0.648390</td>\n",
       "      <td>8.059583</td>\n",
       "      <td>8.782741</td>\n",
       "      <td>19.293391</td>\n",
       "      <td>8.250151</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>0.820000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</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>13.940000</td>\n",
       "      <td>16.665000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>7.001500</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>21.320000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>11.001400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.060000</td>\n",
       "      <td>31.060000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>16.997900</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>40.180000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>55.998600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            season      holiday   workingday      weather         temp  \\\n",
       "count  6493.000000  6493.000000  6493.000000  6493.000000  6493.000000   \n",
       "mean      2.493300     0.029108     0.685815     1.436778    20.620607   \n",
       "std       1.091258     0.168123     0.464226     0.648390     8.059583   \n",
       "min       1.000000     0.000000     0.000000     1.000000     0.820000   \n",
       "25%       2.000000     0.000000     0.000000     1.000000    13.940000   \n",
       "50%       3.000000     0.000000     1.000000     1.000000    21.320000   \n",
       "75%       3.000000     0.000000     1.000000     2.000000    27.060000   \n",
       "max       4.000000     1.000000     1.000000     4.000000    40.180000   \n",
       "\n",
       "             atemp     humidity    windspeed  \n",
       "count  6493.000000  6493.000000  6493.000000  \n",
       "mean     24.012865    64.125212    12.631157  \n",
       "std       8.782741    19.293391     8.250151  \n",
       "min       0.000000    16.000000     0.000000  \n",
       "25%      16.665000    49.000000     7.001500  \n",
       "50%      25.000000    65.000000    11.001400  \n",
       "75%      31.060000    81.000000    16.997900  \n",
       "max      50.000000   100.000000    55.998600  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description i.e. summary statistics.\n",
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The given test dataset has 6493 rows and 9 columns\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_processing(df):\n",
    "    \n",
    "    \"\"\"\n",
    "    df is input Pandas DataFrame. DataFrame is a 2-dimensional data structure, like a 2-dimensional array, or \n",
    "    a table with rows and columns.\n",
    "    \"\"\"\n",
    "        \n",
    "    # Pre-processing train data\n",
    "    #  Adding hour and month to the dataframe using datatime column\n",
    "    hour=[]\n",
    "    month=[]\n",
    "    for i in df['datetime']:\n",
    "            dt_object2 = datetime.strptime(i, \"%Y-%m-%d %H:%M:%S\")\n",
    "            hour.append(dt_object2.hour)\n",
    "            month.append(dt_object2.month)\n",
    "    df['hour'] = pd.DataFrame(hour)\n",
    "    df['hour'] = df['hour'].astype(float)\n",
    "    df['month'] = pd.DataFrame(month)\n",
    "    df['month'] = df['month'].astype(float)\n",
    "\n",
    "    # Dropping datetime column\n",
    "    df.drop('datetime',axis=1, inplace=True) \n",
    "\n",
    "\n",
    "    # One hot encoding on categorical columns. pd.get_dummies converts categorical variable into dummy/indicator \n",
    "    # variables. More details about one-hot encoding is given in [3]. # drop_first=True for Dummy Variable \n",
    "    # Encoding.\n",
    "    # df = pd.get_dummies(df, columns=['season','weather'], drop_first=True)  \n",
    "    df = pd.get_dummies(df, columns=['season','weather', 'holiday', 'workingday'])\n",
    "\n",
    "\n",
    "    # Median imputation if any\n",
    "    for i in df.columns:\n",
    "        df[i].fillna(value = df[i].median(), inplace=True) # Fill NA/NaN values using the specified method, here \n",
    "                                                           # using median().\n",
    "    \n",
    "    # Remove duplicated samples\n",
    "    # df = df[df.duplicated() == False]  \n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(df):\n",
    "        \n",
    "    \"\"\"\n",
    "    df is input Pandas DataFrame. DataFrame is a 2-dimensional data structure, like a 2-dimensional array, or \n",
    "    a table with rows and columns.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Adjust samples and labels to predict count\n",
    "    y = df['count']  # df['count'] == df.count\n",
    "    x = df.drop(['casual','registered','count'], axis = 1)  # drop 'casual' and 'registered' columns in addition \n",
    "    # to 'count' column (label) since they are similar to count.\n",
    "    \n",
    "    # Split the data into train and test\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.30 ,random_state = 2)\n",
    "     \n",
    "\n",
    "    # Finding best parameters for RandomForestRegressor on X_train and y_train.\n",
    "    rf = RandomForestRegressor(random_state=0)\n",
    "    rf_params = {'n_estimators':np.arange(25,150,25),\n",
    "                 'max_depth':np.arange(1,11,2),\n",
    "                 'min_samples_leaf':np.arange(2,15,3)}\n",
    "   \n",
    "    gs_rf = GridSearchCV(rf, rf_params, cv=3)\n",
    "    gs_rf.fit(x_train, y_train)\n",
    "    \n",
    "    best_params = gs_rf.best_params_\n",
    "    \n",
    "    # Fitting the model with best params on the whole data (x & y)\n",
    "    RF = RandomForestRegressor(n_estimators=best_params['n_estimators'],\n",
    "                               max_depth=best_params['max_depth'],\n",
    "                               min_samples_leaf=best_params['min_samples_leaf'],\n",
    "                               random_state=0)\n",
    "    \n",
    "    model = RF.fit(x,y)  # Fit on whole data!\n",
    "    \n",
    "    return model, x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>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>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>season_1</th>\n",
       "      <th>...</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weather_1</th>\n",
       "      <th>weather_2</th>\n",
       "      <th>weather_3</th>\n",
       "      <th>weather_4</th>\n",
       "      <th>holiday_0</th>\n",
       "      <th>holiday_1</th>\n",
       "      <th>workingday_0</th>\n",
       "      <th>workingday_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp   atemp  humidity  windspeed  casual  registered  count  hour  month  \\\n",
       "0  9.84  14.395        81        0.0       3          13     16   0.0    1.0   \n",
       "1  9.02  13.635        80        0.0       8          32     40   1.0    1.0   \n",
       "2  9.02  13.635        80        0.0       5          27     32   2.0    1.0   \n",
       "3  9.84  14.395        75        0.0       3          10     13   3.0    1.0   \n",
       "4  9.84  14.395        75        0.0       0           1      1   4.0    1.0   \n",
       "\n",
       "   season_1  ...  season_3  season_4  weather_1  weather_2  weather_3  \\\n",
       "0         1  ...         0         0          1          0          0   \n",
       "1         1  ...         0         0          1          0          0   \n",
       "2         1  ...         0         0          1          0          0   \n",
       "3         1  ...         0         0          1          0          0   \n",
       "4         1  ...         0         0          1          0          0   \n",
       "\n",
       "   weather_4  holiday_0  holiday_1  workingday_0  workingday_1  \n",
       "0          0          1          0             1             0  \n",
       "1          0          1          0             1             0  \n",
       "2          0          1          0             1             0  \n",
       "3          0          1          0             1             0  \n",
       "4          0          1          0             1             0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After pre-processing, the training dataset looks like\n",
    "train_p = pre_processing(train)\n",
    "train_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 21)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After pre-processing, the training dataset has 21 columns, however, 3 columns will be dropped in the \n",
    "# train_model() function. \n",
    "train_p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model\n",
    "model, x, y = train_model(train_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the trained model\n",
    "count_pred_train = model.predict(x) # Make prediction on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 67.98521729019076\n",
      "RMLSE: 0.3917830842592559\n",
      "RMAE: 6.760605930215257\n",
      "RR2: 0.9075613691307509\n"
     ]
    }
   ],
   "source": [
    "# Compute Errors\n",
    "mse = mean_squared_error(count_pred_train, y) # The lower the better.\n",
    "rmse = np.sqrt(mse)\n",
    "print('MSE:', rmse)\n",
    "\n",
    "msle = mean_squared_log_error(count_pred_train, y) # The lower the better.\n",
    "rmsle = np.sqrt(msle)\n",
    "print('RMLSE:', rmsle) \n",
    "\n",
    "mae = mean_absolute_error(count_pred_train, y) # The lower the better.\n",
    "rmae = np.sqrt(mae)\n",
    "print('RMAE:', rmae) \n",
    "\n",
    "r2 = r2_score(count_pred_train, y) # r2_score, pronounced 'R-squared', computes the coefficient of determination, \n",
    "                                   # which is the proportion of the variation in the dependent variable that is \n",
    "                                   # predictable from the independent variable(s). The higher the better.\n",
    "rr2 = np.sqrt(r2)\n",
    "print('RR2:', rr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting important features\n",
    "importances = model.feature_importances_\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.title('Count Feature Importance')\n",
    "plt.barh(range(len(importances)), importances, color='g', align='center')\n",
    "plt.yticks(range(len(importances)), x.columns)\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weather_1</th>\n",
       "      <th>weather_2</th>\n",
       "      <th>weather_3</th>\n",
       "      <th>weather_4</th>\n",
       "      <th>holiday_0</th>\n",
       "      <th>holiday_1</th>\n",
       "      <th>workingday_0</th>\n",
       "      <th>workingday_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp   atemp  humidity  windspeed  hour  month  season_1  season_2  \\\n",
       "0  10.66  11.365        56    26.0027   0.0    1.0         1         0   \n",
       "1  10.66  13.635        56     0.0000   1.0    1.0         1         0   \n",
       "2  10.66  13.635        56     0.0000   2.0    1.0         1         0   \n",
       "3  10.66  12.880        56    11.0014   3.0    1.0         1         0   \n",
       "4  10.66  12.880        56    11.0014   4.0    1.0         1         0   \n",
       "\n",
       "   season_3  season_4  weather_1  weather_2  weather_3  weather_4  holiday_0  \\\n",
       "0         0         0          1          0          0          0          1   \n",
       "1         0         0          1          0          0          0          1   \n",
       "2         0         0          1          0          0          0          1   \n",
       "3         0         0          1          0          0          0          1   \n",
       "4         0         0          1          0          0          0          1   \n",
       "\n",
       "   holiday_1  workingday_0  workingday_1  \n",
       "0          0             0             1  \n",
       "1          0             0             1  \n",
       "2          0             0             1  \n",
       "3          0             0             1  \n",
       "4          0             0             1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After pre-processing, the test dataset looks like\n",
    "test_p = pre_processing(test)\n",
    "test_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 18)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After pre-processing, the training dataset has 18 columns\n",
    "test_p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make prediction on test data\n",
    "count_pred_test = model.predict(test_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.55510518,   6.20972905,   4.25987126, ..., 130.08224872,\n",
       "        98.53955649,  61.79182167])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  count\n",
       "0  2011-01-20 00:00:00      0\n",
       "1  2011-01-20 01:00:00      0\n",
       "2  2011-01-20 02:00:00      0\n",
       "3  2011-01-20 03:00:00      0\n",
       "4  2011-01-20 04:00:00      0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss = pd.read_csv('sampleSubmission.csv')\n",
    "ss.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss['count'] = count_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>count</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>14.555105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>6.209729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>4.259871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>2.763255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>2.626996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime      count\n",
       "0  2011-01-20 00:00:00  14.555105\n",
       "1  2011-01-20 01:00:00   6.209729\n",
       "2  2011-01-20 02:00:00   4.259871\n",
       "3  2011-01-20 03:00:00   2.763255\n",
       "4  2011-01-20 04:00:00   2.626996"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss.to_csv('finalSubmission.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[1]. https://towardsdatascience.com/end-to-end-case-study-bike-sharing-demand-dataset-53201926c8db\n",
    "\n",
    "[2]. https://anindya-saha.github.io/blog/machine-learning-with-python/kaggle-bike-sharing-demand/kaggle-bike-sharing-demand.html\n",
    "\n",
    "[3]. https://towardsdatascience.com/what-is-one-hot-encoding-and-how-to-use-pandas-get-dummies-function-922eb9bd4970"
   ]
  }
 ],
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