Bike_Rental_Forcasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project - Bikes Rental Forcasting - Basic - Description\n",
    "\n",
    "<b>Objective</b>\n",
    "\n",
    "\n",
    "The objective of the project is - using historical usage patterns and weather data, forecast(predict) bike rental demand (number of bike users (‘cnt’)) on hourly basis.\n",
    "\n",
    "\n",
    "Use the provided “Bikes Rental” data set to predict the bike demand (bike users count - 'cnt') using various best possible models (ML algorithms). Also, report the model that performs best, fine-tune the same model using one of the model fine-tuning techniques, and report the best possible combination of hyperparameters for the selected model. Lastly, use the selected model to make final predictions and compare the predicted values with the actual values.\n",
    "\n",
    "\n",
    "Below are the details of the features list for the given Bikes data set:\n",
    "\n",
    "   1. instant: record index\n",
    "\n",
    "   2. dteday : date\n",
    "\n",
    "   3. season: season (1: springer, 2: summer, 3: fall, 4: winter)\n",
    "\n",
    "   4. yr: year (0: 2011, 1:2012)\n",
    "\n",
    "   5. mnth: month (1 to 12)\n",
    "\n",
    "   6. hr: hour (0 to 23)\n",
    "\n",
    "   7. holiday: whether the day is a holiday or not\n",
    "\n",
    "   8. weekday: day of the week\n",
    "\n",
    "   9. workingday: if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "\n",
    "   10. weathersit:\n",
    "\n",
    "       - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "\n",
    "        - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "\n",
    "        - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "\n",
    "        - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "\n",
    "   11. temp: Normalized temperature in Celsius. The values are derived via (tt_min)/(t_maxt_min), t_min=*8, t_max=+39 (only in hourly scale)\n",
    "\n",
    "   12. atemp: Normalized feeling temperature in Celsius. The values are derived via (tt_min)/(t_maxt_min), t_min=*16, t_max=+50 (only in hourly scale)\n",
    "\n",
    "   13. hum: Normalized humidity. The values are divided to 100 (max)\n",
    "\n",
    "   14. windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\n",
    "   13. casual: count of casual users\n",
    "\n",
    "   14. registered: count of registered users\n",
    "\n",
    "   15. cnt: count of total rental bikes including both casual and registered users\n",
    "\n",
    "\n",
    "The \"target\" data set ('y') should have only one 'label' i.e. 'cnt'.\n",
    "\n",
    "\n",
    "<b>We will be following the below steps to solve this problem:</b>\n",
    "\n",
    "   1. Importing the libraries\n",
    "\n",
    "   2. Loading the data\n",
    "\n",
    "   3. Using some pre-defined utility functions\n",
    "\n",
    "   4. Cleaning the data\n",
    "\n",
    "   5. Dividing the dataset into training and test dataset\n",
    "       - using train_test_split in the ratio 70:30\n",
    "       \n",
    "   6. Training several models and analyzing their performance to select a model\n",
    "\n",
    "   7. Fine-tuning the model by finding the best hyper-parameters and features\n",
    "\n",
    "   8. Evaluating selected model using test dataset\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filePath = 'dataset/bikes.csv'\n",
    "bikesData = pd.read_csv(filePath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2011-01-01</td>\n",
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       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
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       "      <td>61</td>\n",
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       "<p>17379 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "0              13   16  \n",
       "1              32   40  \n",
       "2              27   32  \n",
       "3              10   13  \n",
       "4               1    1  \n",
       "...           ...  ...  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obeservation:\n",
    "- We will have to use Standard Scaler to sandardize the values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3:  Using some pre-defined utility functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Finding the data type of a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bikesData['dteday'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  2. Finding categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0000    2180\n",
      "0.1343    1738\n",
      "0.1642    1695\n",
      "0.1940    1657\n",
      "0.1045    1617\n",
      "0.2239    1513\n",
      "0.0896    1425\n",
      "0.2537    1295\n",
      "0.2836    1048\n",
      "0.2985     808\n",
      "0.3284     602\n",
      "0.3582     460\n",
      "0.3881     381\n",
      "0.4179     296\n",
      "0.4478     176\n",
      "0.4627     146\n",
      "0.4925     112\n",
      "0.5224      84\n",
      "0.5821      44\n",
      "0.5522      39\n",
      "0.6119      23\n",
      "0.6418      14\n",
      "0.6567      11\n",
      "0.6866       5\n",
      "0.8507       2\n",
      "0.8060       2\n",
      "0.7463       2\n",
      "0.7164       2\n",
      "0.7761       1\n",
      "0.8358       1\n",
      "Name: windspeed, dtype: int64 \n",
      "\n",
      "0.88    657\n",
      "0.83    630\n",
      "0.94    560\n",
      "0.87    488\n",
      "0.70    430\n",
      "       ... \n",
      "0.10      1\n",
      "0.91      1\n",
      "0.13      1\n",
      "0.97      1\n",
      "0.12      1\n",
      "Name: hum, Length: 89, dtype: int64 \n",
      "\n",
      "0.62    726\n",
      "0.66    693\n",
      "0.64    692\n",
      "0.70    690\n",
      "0.60    675\n",
      "0.36    671\n",
      "0.34    645\n",
      "0.30    641\n",
      "0.40    614\n",
      "0.32    611\n",
      "0.56    579\n",
      "0.72    570\n",
      "0.54    569\n",
      "0.26    559\n",
      "0.46    559\n",
      "0.52    556\n",
      "0.42    548\n",
      "0.50    531\n",
      "0.24    522\n",
      "0.74    516\n",
      "0.44    507\n",
      "0.22    421\n",
      "0.76    396\n",
      "0.38    372\n",
      "0.20    354\n",
      "0.68    349\n",
      "0.80    325\n",
      "0.58    305\n",
      "0.28    301\n",
      "0.48    288\n",
      "0.16    230\n",
      "0.82    213\n",
      "0.78    169\n",
      "0.18    155\n",
      "0.14    138\n",
      "0.84    138\n",
      "0.86    131\n",
      "0.90     90\n",
      "0.12     76\n",
      "0.88     53\n",
      "0.10     51\n",
      "0.92     49\n",
      "0.94     17\n",
      "0.08     17\n",
      "0.02     17\n",
      "0.04     16\n",
      "0.96     16\n",
      "0.06     16\n",
      "0.98      1\n",
      "1.00      1\n",
      "Name: temp, dtype: int64 \n",
      "\n",
      "1    11413\n",
      "2     4544\n",
      "3     1419\n",
      "4        3\n",
      "Name: weathersit, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(bikesData['windspeed'].value_counts(), '\\n')\n",
    "print(bikesData['hum'].value_counts(), '\\n')\n",
    "print(bikesData['temp'].value_counts(), '\\n')\n",
    "print(bikesData['weathersit'].value_counts(), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation:\n",
    " - Here, 'weathersit' feature has 4 categories.\n",
    " - It donot have ordinal values, so it will not use OneHotEncoder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Finding features as per data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float columns:  Index(['temp', 'atemp', 'hum', 'windspeed'], dtype='object') \n",
      "\n",
      "integer columns:  Index(['instant', 'season', 'yr', 'mnth', 'hr', 'holiday', 'weekday',\n",
      "       'workingday', 'weathersit', 'casual', 'registered', 'cnt'],\n",
      "      dtype='object') \n",
      "\n",
      "object columns:  Index(['dteday'], dtype='object') \n",
      "\n",
      "str columns:  Index([], dtype='object') \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('float columns: ',bikesData.columns[bikesData.dtypes =='float'],'\\n')\n",
    "print('integer columns: ',bikesData.columns[bikesData.dtypes =='int'],'\\n')\n",
    "print('object columns: ',bikesData.columns[bikesData.dtypes =='object'],'\\n')\n",
    "print('str columns: ',bikesData.columns[bikesData.dtypes =='str'],'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Get table details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "bikesData.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation:\n",
    "- There is no null value\n",
    "- We donot have to use impute fill missing values\n",
    "- 17 features are present in the raw dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Find the Unique Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData['weekday'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData['yr'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData['weathersit'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData['hr'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData['season'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Finding the mean of a feature columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
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       "\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>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.0295</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>0.020000</td>\n",
       "      <td>0.000000</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>4345.5000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.5000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</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": [
       "          instant        season            yr          mnth            hr  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
       "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
       "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
       "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
       "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
       "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
       "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
       "\n",
       "            holiday       weekday    workingday    weathersit          temp  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
       "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
       "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
       "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
       "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
       "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
       "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
       "\n",
       "              atemp           hum     windspeed        casual    registered  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
       "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
       "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
       "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
       "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
       "\n",
       "                cnt  \n",
       "count  17379.000000  \n",
       "mean     189.463088  \n",
       "std      181.387599  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%      142.000000  \n",
       "75%      281.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6272288394038822"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding mean by column, by default axis = 0\n",
    "\n",
    "bikesData['hum'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.mean of 0        0.81\n",
       "1        0.80\n",
       "2        0.80\n",
       "3        0.75\n",
       "4        0.75\n",
       "         ... \n",
       "17374    0.60\n",
       "17375    0.60\n",
       "17376    0.60\n",
       "17377    0.56\n",
       "17378    0.65\n",
       "Name: hum, Length: 17379, dtype: float64>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding mean by rows\n",
    "\n",
    "bikesData['hum'].mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Cleaning the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</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>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</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": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation:\n",
    "\n",
    "As we can see, the bikesData data set contains some features which may not be needed for this problem e.g.\n",
    "\n",
    "- instant - This is just an index holder.\n",
    "\n",
    "- casual - It contains the count of casual bike riders, which are already included in the overall users count i.e. 'cnt', hence not needed.\n",
    "\n",
    "- registered - It contains the registered bike riders, which again, like the casual riders, are already included in the overall users count i.e. 'cnt', hence not needed.\n",
    "\n",
    "- atemp - It is the 'feel' temperature, which may not be needed, as we already have 'temp' feature which contains temperature of the area - duplicate feature, hence not needed.\n",
    "\n",
    "- dteday - It is the Date. Since, our prediction is not based on the Date, its based on hour of the day, hence Date is not needed.\n",
    "\n",
    "<b>Since, these features are not needed for our current problem, let us drop them from the bikesData data set.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Drop the unwanted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  yr  mnth  hr  holiday  weekday  workingday  weathersit  temp   hum  \\\n",
       "0       1   0     1   0        0        6           0           1  0.24  0.81   \n",
       "1       1   0     1   1        0        6           0           1  0.22  0.80   \n",
       "2       1   0     1   2        0        6           0           1  0.22  0.80   \n",
       "3       1   0     1   3        0        6           0           1  0.24  0.75   \n",
       "4       1   0     1   4        0        6           0           1  0.24  0.75   \n",
       "\n",
       "   windspeed  cnt  \n",
       "0        0.0   16  \n",
       "1        0.0   40  \n",
       "2        0.0   32  \n",
       "3        0.0   13  \n",
       "4        0.0    1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columnsToDrop = ['instant', 'casual', 'registered', 'atemp', 'dteday']\n",
    "\n",
    "bikesData = bikesData.drop(columnsToDrop , axis = 1)\n",
    "\n",
    "bikesData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Apply Feature Scaling using Standard Scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.947372</td>\n",
       "      <td>-1.553889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>-0.211691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>-0.211691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>-0.211691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.348473</td>\n",
       "      <td>-0.456099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>-1.230779</td>\n",
       "      <td>0.118032</td>\n",
       "      <td>-0.456099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           temp       hum  windspeed\n",
       "0     -1.334648  0.947372  -1.553889\n",
       "1     -1.438516  0.895539  -1.553889\n",
       "2     -1.438516  0.895539  -1.553889\n",
       "3     -1.334648  0.636370  -1.553889\n",
       "4     -1.334648  0.636370  -1.553889\n",
       "...         ...       ...        ...\n",
       "17374 -1.230779 -0.141137  -0.211691\n",
       "17375 -1.230779 -0.141137  -0.211691\n",
       "17376 -1.230779 -0.141137  -0.211691\n",
       "17377 -1.230779 -0.348473  -0.456099\n",
       "17378 -1.230779  0.118032  -0.456099\n",
       "\n",
       "[17379 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columnsToScale = [ 'temp', 'hum', 'windspeed'] #Creating list of columns to scale\n",
    "\n",
    "scaler = StandardScaler() #Creating an instance of StandardScaler\n",
    "\n",
    "bikesData[columnsToScale] = scaler.fit_transform(bikesData[columnsToScale])\n",
    "\n",
    "bikesData[columnsToScale]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.737900e+04</td>\n",
       "      <td>1.737900e+04</td>\n",
       "      <td>1.737900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.497212e-16</td>\n",
       "      <td>-4.195150e-16</td>\n",
       "      <td>4.035529e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000029e+00</td>\n",
       "      <td>1.000029e+00</td>\n",
       "      <td>1.000029e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.477205e+00</td>\n",
       "      <td>-3.251166e+00</td>\n",
       "      <td>-1.553889e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.153035e-01</td>\n",
       "      <td>-7.631431e-01</td>\n",
       "      <td>-6.996886e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.564696e-02</td>\n",
       "      <td>1.436398e-02</td>\n",
       "      <td>3.189880e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.465975e-01</td>\n",
       "      <td>7.918711e-01</td>\n",
       "      <td>5.198962e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.612367e+00</td>\n",
       "      <td>1.932215e+00</td>\n",
       "      <td>5.399871e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               temp           hum     windspeed\n",
       "count  1.737900e+04  1.737900e+04  1.737900e+04\n",
       "mean   3.497212e-16 -4.195150e-16  4.035529e-15\n",
       "std    1.000029e+00  1.000029e+00  1.000029e+00\n",
       "min   -2.477205e+00 -3.251166e+00 -1.553889e+00\n",
       "25%   -8.153035e-01 -7.631431e-01 -6.996886e-01\n",
       "50%    1.564696e-02  1.436398e-02  3.189880e-02\n",
       "75%    8.465975e-01  7.918711e-01  5.198962e-01\n",
       "max    2.612367e+00  1.932215e+00  5.399871e+00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the metrics of the scaled column\n",
    "\n",
    "bikesData[columnsToScale].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation:\n",
    "- After Feature Scaling std, and mean is almost same for all 3 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Feature Engineering: Add a New Column to the Table by adding a panda series i.e. column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikesData['dayCount'] = pd.Series(range(bikesData.shape[0]))/24 \n",
    "\n",
    "#bikesData.shape[0] takes the number of total number of rows which is also the number of days for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>32</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>13</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  yr  mnth  hr  holiday  weekday  workingday  weathersit      temp  \\\n",
       "0       1   0     1   0        0        6           0           1 -1.334648   \n",
       "1       1   0     1   1        0        6           0           1 -1.438516   \n",
       "2       1   0     1   2        0        6           0           1 -1.438516   \n",
       "3       1   0     1   3        0        6           0           1 -1.334648   \n",
       "4       1   0     1   4        0        6           0           1 -1.334648   \n",
       "\n",
       "        hum  windspeed  cnt  dayCount  \n",
       "0  0.947372  -1.553889   16  0.000000  \n",
       "1  0.895539  -1.553889   40  0.041667  \n",
       "2  0.895539  -1.553889   32  0.083333  \n",
       "3  0.636370  -1.553889   13  0.125000  \n",
       "4  0.636370  -1.553889    1  0.166667  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5: Dividing the Dataset into Train and Test Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Split using the Random Sampling i.e. train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(bikesData, test_size = 0.3, random_state = 42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12165, 13)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5214, 13)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Sort the train_set and test_set values by dayCount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "train_set.sort_values('dayCount', axis = 0, inplace = True)\n",
    "test_set.sort_values('dayCount', axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "       season  yr  mnth  hr  holiday  weekday  workingday  weathersit  \\\n",
       "1           1   0     1   1        0        6           0           1   \n",
       "2           1   0     1   2        0        6           0           1   \n",
       "4           1   0     1   4        0        6           0           1   \n",
       "6           1   0     1   6        0        6           0           1   \n",
       "7           1   0     1   7        0        6           0           1   \n",
       "...       ...  ..   ...  ..      ...      ...         ...         ...   \n",
       "17372       1   1    12  17        0        1           1           2   \n",
       "17373       1   1    12  18        0        1           1           2   \n",
       "17374       1   1    12  19        0        1           1           2   \n",
       "17376       1   1    12  21        0        1           1           1   \n",
       "17378       1   1    12  23        0        1           1           1   \n",
       "\n",
       "           temp       hum  windspeed  cnt    dayCount  \n",
       "1     -1.438516  0.895539  -1.553889   40    0.041667  \n",
       "2     -1.438516  0.895539  -1.553889   32    0.083333  \n",
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       "7     -1.542385  1.206542  -1.553889    3    0.291667  \n",
       "...         ...       ...        ...  ...         ...  \n",
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       "17378 -1.230779  0.118032  -0.456099   49  724.083333  \n",
       "\n",
       "[12165 rows x 13 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
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       "      <td>34</td>\n",
       "      <td>723.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17367</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>-0.348473</td>\n",
       "      <td>0.031899</td>\n",
       "      <td>224</td>\n",
       "      <td>723.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>-0.211691</td>\n",
       "      <td>89</td>\n",
       "      <td>723.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.348473</td>\n",
       "      <td>-0.456099</td>\n",
       "      <td>61</td>\n",
       "      <td>724.041667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5214 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  yr  mnth  hr  holiday  weekday  workingday  weathersit  \\\n",
       "0           1   0     1   0        0        6           0           1   \n",
       "3           1   0     1   3        0        6           0           1   \n",
       "5           1   0     1   5        0        6           0           2   \n",
       "8           1   0     1   8        0        6           0           1   \n",
       "14          1   0     1  14        0        6           0           2   \n",
       "...       ...  ..   ...  ..      ...      ...         ...         ...   \n",
       "17354       1   1    12  23        0        0           0           1   \n",
       "17355       1   1    12   0        0        1           1           1   \n",
       "17367       1   1    12  12        0        1           1           2   \n",
       "17375       1   1    12  20        0        1           1           2   \n",
       "17377       1   1    12  22        0        1           1           1   \n",
       "\n",
       "           temp       hum  windspeed  cnt    dayCount  \n",
       "0     -1.334648  0.947372  -1.553889   16    0.000000  \n",
       "3     -1.334648  0.636370  -1.553889   13    0.125000  \n",
       "5     -1.334648  0.636370  -0.821484    1    0.208333  \n",
       "8     -1.334648  0.636370  -1.553889    8    0.333333  \n",
       "14    -0.192091  0.480868   0.764304  106    0.583333  \n",
       "...         ...       ...        ...  ...         ...  \n",
       "17354 -1.542385 -0.607642   0.276306   49  723.083333  \n",
       "17355 -1.646254 -0.400306   0.031899   34  723.125000  \n",
       "17367 -1.334648 -0.348473   0.031899  224  723.625000  \n",
       "17375 -1.230779 -0.141137  -0.211691   89  723.958333  \n",
       "17377 -1.230779 -0.348473  -0.456099   61  724.041667  \n",
       "\n",
       "[5214 rows x 13 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Preparing to Train Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing cross-validation libraries\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "\n",
    "#importing regression models from libraries\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from xgboost import XGBRegressor\n",
    "#installation issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Separate Independent and Dependent Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Independent Features\n",
    "trainingCols = train_set.drop('cnt', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>dayCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>0.041667</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.542385</td>\n",
       "      <td>1.206542</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>0.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17372</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.763143</td>\n",
       "      <td>-0.821484</td>\n",
       "      <td>723.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17373</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.763143</td>\n",
       "      <td>-0.456099</td>\n",
       "      <td>723.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>-0.211691</td>\n",
       "      <td>723.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>-0.211691</td>\n",
       "      <td>724.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>0.118032</td>\n",
       "      <td>-0.456099</td>\n",
       "      <td>724.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12165 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  yr  mnth  hr  holiday  weekday  workingday  weathersit  \\\n",
       "1           1   0     1   1        0        6           0           1   \n",
       "2           1   0     1   2        0        6           0           1   \n",
       "4           1   0     1   4        0        6           0           1   \n",
       "6           1   0     1   6        0        6           0           1   \n",
       "7           1   0     1   7        0        6           0           1   \n",
       "...       ...  ..   ...  ..      ...      ...         ...         ...   \n",
       "17372       1   1    12  17        0        1           1           2   \n",
       "17373       1   1    12  18        0        1           1           2   \n",
       "17374       1   1    12  19        0        1           1           2   \n",
       "17376       1   1    12  21        0        1           1           1   \n",
       "17378       1   1    12  23        0        1           1           1   \n",
       "\n",
       "           temp       hum  windspeed    dayCount  \n",
       "1     -1.438516  0.895539  -1.553889    0.041667  \n",
       "2     -1.438516  0.895539  -1.553889    0.083333  \n",
       "4     -1.334648  0.636370  -1.553889    0.166667  \n",
       "6     -1.438516  0.895539  -1.553889    0.250000  \n",
       "7     -1.542385  1.206542  -1.553889    0.291667  \n",
       "...         ...       ...        ...         ...  \n",
       "17372 -1.230779 -0.763143  -0.821484  723.833333  \n",
       "17373 -1.230779 -0.763143  -0.456099  723.875000  \n",
       "17374 -1.230779 -0.141137  -0.211691  723.916667  \n",
       "17376 -1.230779 -0.141137  -0.211691  724.000000  \n",
       "17378 -1.230779  0.118032  -0.456099  724.083333  \n",
       "\n",
       "[12165 rows x 12 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainingCols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dependent Feature\n",
    "trainingLabels = train_set['cnt'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         40\n",
       "2         32\n",
       "4          1\n",
       "6          2\n",
       "7          3\n",
       "        ... \n",
       "17372    164\n",
       "17373    122\n",
       "17374    119\n",
       "17376     90\n",
       "17378     49\n",
       "Name: cnt, Length: 12165, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainingLabels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6: Training several models and analyzing their performance to select a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyzing performance using cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to display the scores\n",
    "\n",
    "def display_scores(scores):\n",
    "    print('Scores: ', scores)\n",
    "    print('Mean: ', scores.mean())\n",
    "    print('Standard deviation: ', scores.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. Decision Tree Regression using cross - validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [42.79622021 50.31963846 37.03615448 44.15365653 47.07723911 72.03782895\n",
      " 58.36019737 48.22615132 50.94407895 96.88898026]\n",
      "Mean:  54.78401456342171\n",
      "Standard deviation:  16.70953413912157\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "dec_reg = DecisionTreeRegressor(random_state=42)\n",
    "\n",
    "\n",
    "#mean absolute error scores\n",
    "dt_mae_scores = - cross_val_score(dec_reg,\n",
    "                              trainingCols,trainingLabels,\n",
    "                              cv = 10,\n",
    "                              scoring = 'neg_mean_absolute_error')\n",
    "display_scores(dt_mae_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [ 65.10279464  77.670527    60.59989749  73.66268699  75.76702279\n",
      " 113.26870416  96.665763    81.28550338  86.87354694 149.4085296 ]\n",
      "Mean:  88.03049759858746\n",
      "Standard deviation:  25.035783082114\n"
     ]
    }
   ],
   "source": [
    "#mean squared error scores\n",
    "dt_mae_scores = cross_val_score(dec_reg,\n",
    "                              trainingCols,trainingLabels,\n",
    "                              cv = 10,\n",
    "                              scoring = 'neg_mean_squared_error')\n",
    "dt_mse_scores = np.sqrt(-dt_mae_scores)\n",
    "display_scores(dt_mse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Linear Regression using cross - validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [ 66.96340699  80.48809095 113.84704981  93.17230086  76.11197672\n",
      "  96.5220689  133.13798218 158.02254734 158.90195479 127.15674717]\n",
      "Mean:  110.43241256942221\n",
      "Standard deviation:  31.42696570529598\n"
     ]
    }
   ],
   "source": [
    "#mean absolute error scores\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "\n",
    "lr_mae_scores = - cross_val_score (lin_reg,\n",
    "                                  trainingCols, trainingLabels,\n",
    "                                  cv = 10,\n",
    "                                  scoring = 'neg_mean_absolute_error')\n",
    "display_scores (lr_mae_scores)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [ 84.63836676 111.12038541 131.88324414 119.16350622 105.17621319\n",
      " 127.72562924 174.97188817 187.31691741 205.60028279 164.30585678]\n",
      "Mean:  141.19022901181754\n",
      "Standard deviation:  37.5556507591956\n"
     ]
    }
   ],
   "source": [
    "#mean squared error scores\n",
    "\n",
    "lr_mae_scores = cross_val_score (lin_reg,\n",
    "                                 trainingCols, trainingLabels,\n",
    "                                 cv = 10,\n",
    "                                 scoring = 'neg_mean_squared_error')\n",
    "lr_mse_scores = np.sqrt(- lr_mae_scores)\n",
    "display_scores (lr_mse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Random Forest Regression using cross - validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [33.33921665 33.5634292  28.51544782 31.74855656 36.55779239 57.78830592\n",
      " 40.91410636 40.72524123 37.58651864 84.75860197]\n",
      "Mean:  42.54972167412677\n",
      "Standard deviation:  15.998616999257116\n"
     ]
    }
   ],
   "source": [
    "#mean absolute error scores\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state = 42, n_estimators = 150)\n",
    "rf_mae_scores = - cross_val_score(forest_reg,\n",
    "                                 trainingCols, trainingLabels,\n",
    "                                 cv = 10,\n",
    "                                 scoring = 'neg_mean_absolute_error')\n",
    "display_scores(rf_mae_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [ 45.56103408  50.97710002  43.43059587  52.22561489  60.45598311\n",
      "  94.23444802  66.14917355  65.25550321  61.67555223 132.00386791]\n",
      "Mean:  67.19688728966834\n",
      "Standard deviation:  25.55764715717203\n"
     ]
    }
   ],
   "source": [
    "#mean squared error scores\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state = 42, n_estimators = 150)\n",
    "rf_mae_scores = cross_val_score(forest_reg,\n",
    "                               trainingCols, trainingLabels,\n",
    "                               cv = 10,\n",
    "                               scoring = 'neg_mean_squared_error')\n",
    "\n",
    "rf_mse_scores = np.sqrt(-rf_mae_scores)\n",
    "display_scores(rf_mse_scores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation:\n",
    "- Here, we will choose Random_Forest_Regression as it has lowest root mean square error and low variance of errors.\n",
    "- RandomForest will be used for fine tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 7: Fine Tuning the Selected Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Import GrideSearchCV from Model_Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to find the hyperparameters of Random_Forest_Regression\n",
    "help(forest_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Initiate the Parameters Grid to experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [ {'n_estimators': [120, 150],\n",
    "                'max_features': [10, 12],\n",
    "                'max_depth': [15, 28]}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Create and Instance of GridSearchCV by passing the the Selected Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV (forest_reg,\n",
    "                           param_grid,\n",
    "                           cv = 5,\n",
    "                           scoring = 'neg_mean_squared_error')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Run the Grid Search algorithm on the Training data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=150, n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_depth': [15, 28], 'max_features': [10, 12],\n",
       "                          'n_estimators': [120, 150]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(trainingCols, trainingLabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Find the best estimators/hyperparameters to be used for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=28, max_features=10, max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=150, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6. Get the relative importance of each feature (of the Training data set) by using feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00424888 0.00145493 0.00570279 0.58348648 0.00215107 0.01790669\n",
      " 0.06993018 0.01688336 0.09373438 0.03176755 0.00907719 0.16365649]\n"
     ]
    }
   ],
   "source": [
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observations:\n",
    "- The best value of n_estimators parameter suggested by Grid Search ('best parameters') : 100 to 150,since its 150\n",
    "- the best value of max_features parameter suggested by Grid Search ('best parameters') : 8 to 12, since its 10\n",
    "- the best value of max_depth parameter suggested by Grid Search ('best parameters'): 28\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 8: Evaluating the Final Model Using Test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Store the final model with the best estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Sorting as per 'Day Count'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test_set.sort_values('dayCount', axis= 0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Separate Independent and Dpendent Values of the Test Set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>dayCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.947372</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>13</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-0.821484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>8</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.192091</td>\n",
       "      <td>0.480868</td>\n",
       "      <td>0.764304</td>\n",
       "      <td>106</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17354</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.542385</td>\n",
       "      <td>-0.607642</td>\n",
       "      <td>0.276306</td>\n",
       "      <td>49</td>\n",
       "      <td>723.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17355</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.646254</td>\n",
       "      <td>-0.400306</td>\n",
       "      <td>0.031899</td>\n",
       "      <td>34</td>\n",
       "      <td>723.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17367</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>-0.348473</td>\n",
       "      <td>0.031899</td>\n",
       "      <td>224</td>\n",
       "      <td>723.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>-0.211691</td>\n",
       "      <td>89</td>\n",
       "      <td>723.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.348473</td>\n",
       "      <td>-0.456099</td>\n",
       "      <td>61</td>\n",
       "      <td>724.041667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5214 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  yr  mnth  hr  holiday  weekday  workingday  weathersit  \\\n",
       "0           1   0     1   0        0        6           0           1   \n",
       "3           1   0     1   3        0        6           0           1   \n",
       "5           1   0     1   5        0        6           0           2   \n",
       "8           1   0     1   8        0        6           0           1   \n",
       "14          1   0     1  14        0        6           0           2   \n",
       "...       ...  ..   ...  ..      ...      ...         ...         ...   \n",
       "17354       1   1    12  23        0        0           0           1   \n",
       "17355       1   1    12   0        0        1           1           1   \n",
       "17367       1   1    12  12        0        1           1           2   \n",
       "17375       1   1    12  20        0        1           1           2   \n",
       "17377       1   1    12  22        0        1           1           1   \n",
       "\n",
       "           temp       hum  windspeed  cnt    dayCount  \n",
       "0     -1.334648  0.947372  -1.553889   16    0.000000  \n",
       "3     -1.334648  0.636370  -1.553889   13    0.125000  \n",
       "5     -1.334648  0.636370  -0.821484    1    0.208333  \n",
       "8     -1.334648  0.636370  -1.553889    8    0.333333  \n",
       "14    -0.192091  0.480868   0.764304  106    0.583333  \n",
       "...         ...       ...        ...  ...         ...  \n",
       "17354 -1.542385 -0.607642   0.276306   49  723.083333  \n",
       "17355 -1.646254 -0.400306   0.031899   34  723.125000  \n",
       "17367 -1.334648 -0.348473   0.031899  224  723.625000  \n",
       "17375 -1.230779 -0.141137  -0.211691   89  723.958333  \n",
       "17377 -1.230779 -0.348473  -0.456099   61  724.041667  \n",
       "\n",
       "[5214 rows x 13 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Define the columns names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x_cols = test_set.drop('cnt', axis = 1).columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['season', 'yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday',\n",
       "       'weathersit', 'temp', 'hum', 'windspeed', 'dayCount'], dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y_cols = 'cnt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cnt'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Slice the Test Set and Create the Independent Value and Dependent Values Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_set.loc[:,test_x_cols]\n",
    "y_test = test_set.loc[:,test_y_cols]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Make the predictions using final model and store it in new column of test_set - predictedCounts_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:844: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:965: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "test_set.loc[:,'predictedCounts_test'] = final_model.predict(X_test)\n",
    "\n",
    "#here, predicted dataset is test_set.loc[:,'predictedCounts_test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Find the root mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.50507676859462\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, test_set.loc[:,'predictedCounts_test'])\n",
    "final_mse = np.sqrt(mse)\n",
    "print(final_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>dayCount</th>\n",
       "      <th>predictedCounts_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.947372</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.973333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>13</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>10.873333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-0.821484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>4.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>8</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>21.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.192091</td>\n",
       "      <td>0.480868</td>\n",
       "      <td>0.764304</td>\n",
       "      <td>106</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>120.186667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17354</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.542385</td>\n",
       "      <td>-0.607642</td>\n",
       "      <td>0.276306</td>\n",
       "      <td>49</td>\n",
       "      <td>723.083333</td>\n",
       "      <td>39.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17355</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.646254</td>\n",
       "      <td>-0.400306</td>\n",
       "      <td>0.031899</td>\n",
       "      <td>34</td>\n",
       "      <td>723.125000</td>\n",
       "      <td>40.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17367</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>-0.348473</td>\n",
       "      <td>0.031899</td>\n",
       "      <td>224</td>\n",
       "      <td>723.625000</td>\n",
       "      <td>172.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>-0.211691</td>\n",
       "      <td>89</td>\n",
       "      <td>723.958333</td>\n",
       "      <td>96.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.230779</td>\n",
       "      <td>-0.348473</td>\n",
       "      <td>-0.456099</td>\n",
       "      <td>61</td>\n",
       "      <td>724.041667</td>\n",
       "      <td>58.053333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5214 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  yr  mnth  hr  holiday  weekday  workingday  weathersit  \\\n",
       "0           1   0     1   0        0        6           0           1   \n",
       "3           1   0     1   3        0        6           0           1   \n",
       "5           1   0     1   5        0        6           0           2   \n",
       "8           1   0     1   8        0        6           0           1   \n",
       "14          1   0     1  14        0        6           0           2   \n",
       "...       ...  ..   ...  ..      ...      ...         ...         ...   \n",
       "17354       1   1    12  23        0        0           0           1   \n",
       "17355       1   1    12   0        0        1           1           1   \n",
       "17367       1   1    12  12        0        1           1           2   \n",
       "17375       1   1    12  20        0        1           1           2   \n",
       "17377       1   1    12  22        0        1           1           1   \n",
       "\n",
       "           temp       hum  windspeed  cnt    dayCount  predictedCounts_test  \n",
       "0     -1.334648  0.947372  -1.553889   16    0.000000             35.973333  \n",
       "3     -1.334648  0.636370  -1.553889   13    0.125000             10.873333  \n",
       "5     -1.334648  0.636370  -0.821484    1    0.208333              4.680000  \n",
       "8     -1.334648  0.636370  -1.553889    8    0.333333             21.013333  \n",
       "14    -0.192091  0.480868   0.764304  106    0.583333            120.186667  \n",
       "...         ...       ...        ...  ...         ...                   ...  \n",
       "17354 -1.542385 -0.607642   0.276306   49  723.083333             39.900000  \n",
       "17355 -1.646254 -0.400306   0.031899   34  723.125000             40.466667  \n",
       "17367 -1.334648 -0.348473   0.031899  224  723.625000            172.180000  \n",
       "17375 -1.230779 -0.141137  -0.211691   89  723.958333             96.440000  \n",
       "17377 -1.230779 -0.348473  -0.456099   61  724.041667             58.053333  \n",
       "\n",
       "[5214 rows x 14 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = [9,18]\n",
    "for time in times:\n",
    "    fig = plt.figure(figsize=(8, 6))\n",
    "    fig.clf()\n",
    "    ax = fig.gca()\n",
    "    test_set_freg_time = test_set[test_set.hr == time]\n",
    "    test_set_freg_time.plot(kind = 'line', x = 'dayCount', y = 'cnt', ax = ax)\n",
    "    test_set_freg_time.plot(kind = 'line', x = 'dayCount', y = 'predictedCounts_test', ax =ax)\n",
    "    plt.show()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}