6. Time Series Analysis.ipynb

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    " # Predict the traffic on a new mode of transport.\n",
    " \n",
    "#### Problem Statement\n",
    "Unicorn Investors wants to make an investment in a new form of transportation - JetRail. JetRail uses Jet propulsion technology to run rails and move people at a high speed! The investment would only make sense, if they can get more than 1 Million monthly users with in next 18 months. In order to help Unicorn Ventures in their decision, you need to forecast the traffic on JetRail for the next 7 months. You are provided with traffic data of JetRail since inception in the test file.\n",
    " \n",
    "\n",
    "Dataset - https://datahack.analyticsvidhya.com/contest/practice-problem-time-series-2/\n",
    "\n",
    "Tutorial - https://trainings.analyticsvidhya.com/courses/course-v1:AnalyticsVidhya+TS_101+TS_term1/info\n",
    "\n",
    "\n",
    "\n",
    "#### Lets divide the work in 3 parts\n",
    "1. Understanding Time Series\n",
    "2. Data Understanding and Exploration\n",
    "3. Time Series Forecasting using different methods\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Understanding Time Series\n",
    "\n",
    "Time Series is generally data which is collected over time and is dependent on it.\n",
    "\n",
    "Time series data can use in forcasting sales number for the next year, website traffic, count of traffic, number of calls received, etc. \n",
    "\n",
    "\n",
    "### Components of a Time Series\n",
    "#### 1. Trend\n",
    "    Trend is a general direction in which something is developing or changing. \n",
    "    So we see an increasing trend in this time series. We can see that the passenger count is increasing with the number of years.\n",
    "\n",
    "\n",
    "#### 2. Seasonality\n",
    "    The pattern is repeating at regular time interval. \n",
    "    \n",
    "    \n",
    "### Difference between a time series and regression problem\n",
    "1. The main difference is that a time series is time dependent. So the basic assumption of a linear regression model that the observations (Xs) are independent doesn’t hold in this case.\n",
    "\n",
    "2. Along with an increasing or decreasing trend, most Time Series have some form of seasonality trends,i.e. variations specific to a particular time frame.\n",
    "\n",
    "\n",
    "So, predicting a time series using regression techniques is not a good approach."
   ]
  },
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   "metadata": {},
   "source": [
    "Table of Contents\n",
    "\n",
    "a) Understanding Data:\n",
    "1) Hypothesis Generation\n",
    "2) Getting the system ready and loading the data\n",
    "3) Dataset Structure and Content\n",
    "4) Feature Extraction\n",
    "5) Exploratory Analysis\n",
    "\n",
    "b) Forecasting using Multiple Modeling Techniques:\n",
    "1) Splitting the data into training and validation part\n",
    "2) Modeling techniques\n",
    "3) Holt’s Linear Trend Model on daily time series\n",
    "4) Holt Winter’s Model on daily time series\n",
    "5) Introduction to ARIMA model\n",
    "6) Parameter tuning for ARIMA model\n",
    "7) SARIMAX model on daily time series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Understanding Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd          \n",
    "import numpy as np          # For mathematical calculations\n",
    "import matplotlib.pyplot as plt  # For plotting graphs\n",
    "from datetime import datetime    # To access datetime\n",
    "from pandas import Series        # To work on series\n",
    "%matplotlib inline\n",
    "import warnings                   # To ignore the warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"Data/Train.csv\")\n",
    "test=pd.read_csv(\"Data/Test.csv\")\n",
    "\n",
    "train_original=train.copy()\n",
    "test_original=test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape   : (18288, 3) (5112, 2)\n",
      "Columns : Index(['ID', 'Datetime', 'Count'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print('Shape   :',train.shape, test.shape)\n",
    "print('Columns :',train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>25-08-2012 00:00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>25-08-2012 01:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>25-08-2012 02:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>25-08-2012 03:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>25-08-2012 04:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID          Datetime  Count\n",
       "0   0  25-08-2012 00:00      8\n",
       "1   1  25-08-2012 01:00      2\n",
       "2   2  25-08-2012 02:00      6\n",
       "3   3  25-08-2012 03:00      2\n",
       "4   4  25-08-2012 04:00      2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID           int64\n",
       "Datetime    object\n",
       "Count        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Datetime'] = pd.to_datetime(train.Datetime,format='%d-%m-%Y %H:%M') \n",
    "test['Datetime'] = pd.to_datetime(test.Datetime,format='%d-%m-%Y %H:%M') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                   int64\n",
       "Datetime    datetime64[ns]\n",
       "Count                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Date Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in (train, test):\n",
    "    i['year']=i.Datetime.dt.year \n",
    "    i['month']=i.Datetime.dt.month \n",
    "    i['day']=i.Datetime.dt.day\n",
    "    i['Hour']=i.Datetime.dt.hour "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2012-08-25 00:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-08-25 01:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-08-25 02:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2012-08-25 03:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2012-08-25 04:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID            Datetime  Count  year  month  day  Hour\n",
       "0   0 2012-08-25 00:00:00      8  2012      8   25     0\n",
       "1   1 2012-08-25 01:00:00      2  2012      8   25     1\n",
       "2   2 2012-08-25 02:00:00      6  2012      8   25     2\n",
       "3   3 2012-08-25 03:00:00      2  2012      8   25     3\n",
       "4   4 2012-08-25 04:00:00      2  2012      8   25     4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['day_of_week']=train['Datetime'].dt.dayofweek\n",
    "test['day_of_week']=train['Datetime'].dt.dayofweek \n",
    "# weekend yes no"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EDA \n",
    "By plotting each column against 'Count' we will idenfity if any Trend/ seasonality present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118557710>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('year')['Count'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation from above plot : Traffic will increase as the years pass by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118c02940>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('month')['Count'].mean().plot.bar()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation from above plot : Traffic will be high from May to October"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11924c8d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EedqiWvNzzN2PZRr4CvBq4BeYu2PenwC3A1+tqlckORn4e+ZvE/s04MXM3Vr45iTvBc4GnvnAXfiSnAQ8BzgeuAP4IvBC4Avr8ZeTVuIIXa35RlXdMLxj3o3A52ru3NwbgC3MlfsHAarqCuAxSR548Mknq+ruqroT2MfyVwd+uar2Dvexa/i50sRZ6GrN3Qu+v3/B6/uZ+x/pUtMjD1yMsfC997H8/2DXup20rix0HWyuBF4DP5k+ubOqfrDC9j9k+HQnaaNzZKGDzXnABcMbMf2IucfOLauqvjf8pepu4NPAJ/d/RGk8XvovSY1wykWSGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUiP8D5o6dMCLvR0AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[train.year==2012].groupby('month')['Count'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1192fada0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAENCAYAAAABh67pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFGdJREFUeJzt3X20XXV95/H3R1JQBA2YCwIhBm1QwaIyV0qLuhBQQRygM7IGdGpqmcnqyIhTO0uj/gH/MI19GKedts5KBQyzGBTxAUasBUFkdAZogEACgZICE648JFRFR7qwge/8cXZmbtOb3OScfUjyy/u1VtY9+7cfvt97k3zuPr+zzz6pKiRJ7XrRzm5AkjReBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDVuzs5uAGDevHm1cOHCnd2GJO1W7rjjjqeqamK27XaJoF+4cCErV67c2W1I0m4lyf/enu2cupGkxs0a9EkuTbIhyZotxj+S5IEk9yb5vWnjn0yyrlv37nE0LUnaftszdfMF4E+AyzcPJHkHcCZwTFU9m+Sgbvwo4BzgaOBQ4NtJjqyq5/puXJK0fWY9o6+qW4AfbjH8b4BlVfVst82GbvxM4ItV9WxVPQysA47rsV9J0g4ado7+SOBtSW5L8t0kb+nGDwMenbbdVDcmSdpJhr3qZg5wAHA88BbgqiSvBjLDtjN+hFWSJcASgAULFgzZhiRpNsOe0U8BX62B24HngXnd+OHTtpsPPDbTAapqeVVNVtXkxMSsl4FKkoY0bNB/HTgJIMmRwN7AU8C1wDlJ9klyBLAIuL2PRiVJw5l16ibJlcCJwLwkU8CFwKXApd0llz8HFtfgU8bvTXIVcB+wCTjfK24kCRYuvW6o/R5ZdvrItWcN+qo6dyur/uVWtr8YuHiUpiRJ/fGdsZLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4WYM+yaVJNnQfG7jlun+fpJLM65aT5I+TrEtyT5Jjx9G0JGn7bc8Z/ReAU7ccTHI48E5g/bTh0xh8IPgiYAnwudFblCSNYtagr6pbgB/OsOqzwMeBmjZ2JnB5DdwKzE1ySC+dSpKGMuuHg88kyRnAD6rq7iTTVx0GPDpteaobe3zoDiVpDBYuvW6o/R5ZdnrPnYzfDgd9kn2BTwPvmmn1DGM1wxhJljCY3mHBggU72oakxrzyO6uG2u+Jd7yp507aM8xVN68BjgDuTvIIMB+4M8krGZzBHz5t2/nAYzMdpKqWV9VkVU1OTEwM0YYkaXvscNBX1eqqOqiqFlbVQgbhfmxVPQFcC3ywu/rmeODpqnLaRpJ2ou25vPJK4H8Br00yleS8bWz+TeAhYB3w58CHe+lSkjS0Wefoq+rcWdYvnPa4gPNHb0uS1BffGStJjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN256PErw0yYYka6aN/X6S+5Pck+RrSeZOW/fJJOuSPJDk3eNqXJK0fbbnjP4LwKlbjN0AvKGqjgH+GvgkQJKjgHOAo7t9/izJXr11K0naYbMGfVXdAvxwi7Hrq2pTt3grML97fCbwxap6tqoeZvAh4cf12K8kaQfN+uHg2+E3gS91jw9jEPybTXVjknYzN970mqH2O/mkv+m5E41qpBdjk3wa2ARcsXlohs1qK/suSbIyycqNGzeO0oYkaRuGDvoki4H3Ah+oqs1hPgUcPm2z+cBjM+1fVcurarKqJicmJoZtQ5I0i6GCPsmpwCeAM6rqmWmrrgXOSbJPkiOARcDto7cpSRrWrHP0Sa4ETgTmJZkCLmRwlc0+wA1JAG6tqt+qqnuTXAXcx2BK5/yqem5czUuSZjdr0FfVuTMMX7KN7S8GLh6lKUlSf3xnrCQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxs0a9EkuTbIhyZppYwcmuSHJg93XA7rxJPnjJOuS3JPk2HE2L0ma3fac0X8BOHWLsaXAjVW1CLixWwY4DVjU/VkCfK6fNiVJw5o16KvqFuCHWwyfCazoHq8Azpo2fnkN3ArMTXJIX81KknbcnCH3O7iqHgeoqseTHNSNHwY8Om27qW7s8S0PkGQJg7N+FixYMGQb0p7joosuekH3Uzv6fjE2M4zVTBtW1fKqmqyqyYmJiZ7bkCRtNmzQP7l5Sqb7uqEbnwIOn7bdfOCx4duTJI1q2KC/FljcPV4MXDNt/IPd1TfHA09vnuKRJO0cs87RJ7kSOBGYl2QKuBBYBlyV5DxgPXB2t/k3gfcA64BngA+NoWdJ0g6YNeir6tytrDp5hm0LOH/UpiRJ/fGdsZLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4kYI+yW8nuTfJmiRXJnlxkiOS3JbkwSRfSrJ3X81Kknbc0EGf5DDgAmCyqt4A7AWcA3wG+GxVLQJ+BJzXR6OSpOGMOnUzB3hJkjnAvsDjwEnA1d36FcBZI9aQJI1g6KCvqh8AfwCsZxDwTwN3AD+uqk3dZlPAYaM2KUka3ihTNwcAZwJHAIcCLwVOm2HT2sr+S5KsTLJy48aNw7YhSZrFKFM3pwAPV9XGqvp74KvArwJzu6kcgPnAYzPtXFXLq2qyqiYnJiZGaEOStC2jBP164Pgk+yYJcDJwH/Ad4H3dNouBa0ZrUZI0ilHm6G9j8KLrncDq7ljLgU8AH0uyDngFcEkPfUqShjRn9k22rqouBC7cYvgh4LhRjitJ6o/vjJWkxhn0ktS4kaZupD3d1NL/MdR+85e9redOpK3zjF6SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNW6koE8yN8nVSe5PsjbJryQ5MMkNSR7svh7QV7OSpB036hn9HwHfqqrXAW8E1gJLgRurahFwY7csSdpJhg76JC8D3k734d9V9fOq+jFwJrCi22wFcNaoTUqShjfKGf2rgY3AZUnuSvL5JC8FDq6qxwG6rwf10KckaUijBP0c4Fjgc1X1ZuBn7MA0TZIlSVYmWblx48YR2pAkbcsoQT8FTFXVbd3y1QyC/8kkhwB0XzfMtHNVLa+qyaqanJiYGKENSdK2DB30VfUE8GiS13ZDJwP3AdcCi7uxxcA1I3UoSRrJnBH3/whwRZK9gYeADzH45XFVkvOA9cDZI9aQJI1gpKCvqlXA5AyrTh7luNKw/vBfvHeo/X7nS9/ouRNp1+E7YyWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS40a9H720TX/6WzcNtd/5/+WknjuR9lye0UtS4wx6SWqcQS9JjRs56JPsleSuJN/olo9IcluSB5N8qfs8WUnSTtLHGf1HgbXTlj8DfLaqFgE/As7roYYkaUgjBX2S+cDpwOe75QAnAVd3m6wAzhqlhiRpNKOe0f8n4OPA893yK4AfV9WmbnkKOGymHZMsSbIyycqNGzeO2IYkaWuGDvok7wU2VNUd04dn2LRm2r+qllfVZFVNTkxMDNuGJGkWo7xh6gTgjCTvAV4MvIzBGf7cJHO6s/r5wGOjtylJGtbQZ/RV9cmqml9VC4FzgJuq6gPAd4D3dZstBq4ZuUtJ0tDGcR39J4CPJVnHYM7+kjHUkCRtp17udVNVNwM3d48fAo7r47iSpNH5zlhJapxBL0mN8zbFe5i1r3v9UPu9/v61s28kaZfkGb0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjvHvlTvZLK35pqP1WL17dcyeSWjX0GX2Sw5N8J8naJPcm+Wg3fmCSG5I82H09oL92JUk7apQz+k3A71TVnUn2B+5IcgPwG8CNVbUsyVJgKYPPkd09XPTyIfd7ut8+JKknQ5/RV9XjVXVn9/inwFrgMOBMYEW32QrgrFGblCQNr5cXY5MsBN4M3AYcXFWPw+CXAXBQHzUkScMZOeiT7Ad8Bfh3VfWTHdhvSZKVSVZu3Lhx1DYkSVsxUtAn+QUGIX9FVX21G34yySHd+kOADTPtW1XLq2qyqiYnJiZGaUOStA1DvxibJMAlwNqq+o/TVl0LLAaWdV+vGaXBhUuvG2q/R5adPkpZSWrGKFfdnAD8OrA6yapu7FMMAv6qJOcB64GzR2tRkjSKoYO+qr4HZCurTx72uJKkfnkLBElqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrc2II+yalJHkiyLsnScdWRJG3bWII+yV7AnwKnAUcB5yY5ahy1JEnbNq4z+uOAdVX1UFX9HPgicOaYakmStmFcQX8Y8Oi05aluTJL0AktV9X/Q5Gzg3VX1r7rlXweOq6qPTNtmCbCkW3wt8MAQpeYBT43YrvWs10K9lr83623dq6pqYraN5gxx4O0xBRw+bXk+8Nj0DapqObB8lCJJVlbV5CjHsJ71WqjX8vdmvdGNa+rmr4BFSY5IsjdwDnDtmGpJkrZhLGf0VbUpyb8F/hLYC7i0qu4dRy1J0raNa+qGqvom8M1xHb8z0tSP9azXUL2WvzfrjWgsL8ZKknYd3gJBkhpn0EtS4wz6XUiS45K8pXt8VJKPJXnPC1T78heijkaXZO8kH0xySrf8/iR/kuT8JL+ws/vTrsc5+m1I8joG7+i9rar+z7TxU6vqWz3XupDBvYHmADcAvwzcDJwC/GVVXdxjrS0vdQ3wDuAmgKo6o69a2+jhrQxulbGmqq7v+di/DKytqp8keQmwFDgWuA/4D1X1dM/1LgC+VlWPzrpxP/WuYPDvZF/gx8B+wFeBkxn8n148hpqvAX6NwftjNgEPAlf2/bPUeDQR9Ek+VFWX9XzMC4DzgbXAm4CPVtU13bo7q+rYnuut7ursAzwBzJ8WVLdV1TE91rqTQeh9HigGQX8lg/c7UFXf7avWtJq3V9Vx3eN/zeBn+zXgXcB/r6plPda6F3hjd5nvcuAZ4GoGQfjGqvpnfdXq6j0N/Az4GwY/xy9X1cY+a2xR756qOibJHOAHwKFV9VySAHf3+W+lq3cB8E+B7wLvAVYBP2IQ/B+uqpv7rKcxqKrd/g+wfgzHXA3s1z1eCKxkEPYAd42h3l0zPe6WV/Vc60XAbzN45vCmbuyhMf8dTf/+/gqY6B6/FFjdc6210x7fOc6f5ebvrfuZvgu4BNgIfAtYDOw/hnprgL2BA4CfAgd24y+e/r33WG81sFf3eF/g5u7xgjH9X3g5sAy4H/jb7s/abmxu3/Vm6eUvxnDMlwG/C/xX4P1brPuzcXwfY7uOvm9J7tnaKuDgMZTcq7rpmqp6JMmJwNVJXtXV7NvPk+xbVc8A/2TzYJKXA8/3Waiqngc+m+TL3dcnGeN7KjovSnIAg0BMdWe8VfWzJJt6rrVm2rO8u5NMVtXKJEcCf99zLYDqfqbXA9d38+SnAecCfwDMei+SHXQJgxDcC/g08OUkDwHHM7hT7DjMAZ5j8Ixzf4CqWj+m1wSuYjCNeGJVPQGQ5JUMfnF+GXhnn8WSbO3ZeRg8y+7bZQymvr4C/GaSf84g8J9l8HfYu91m6qYLo3czeMr4D1YB/7OqDu253k3Ax6pq1bSxOcClwAeqaq+e6+3T/UVvOT4POKSqVvdZb4sapwMnVNWnxljjEQa/sMJguuhXq+qJJPsB36uq3v5Ddb8c/wh4G4MbRR3L4G6qjwIXVNXdfdXq6t1VVW/eyrqXVNXf9VmvO+6hAFX1WJK5DF7LWV9Vt4+h1keB84BbgbcDn6mqy5JMAF+pqrf3XO+Bqnrtjq4bod5zDKalZjqBO76qXtJzvVXT/70n+TSDKbEzgBuq52lh2L2C/hLgsqr63gzr/ltVvb/nevOBTZvPKLZYd0JVfb/PenuqJPsCB1fVw2M49v7AqxmcjU5V1ZN91+jqHFlVfz2OY+8qkhwNvJ7Bi+f3j7nW9cC3gRWb/86SHAz8BvDOqjql53prgF+rqgdnWPdoVR0+w26j1FsLHN09C9w8thj4OIPp4lf1WQ92o6CXtGfopviWMviwooO64ScZ3BhxWVVt+ax+1HrvY/A60T+6VXqSs6rq6z3X+z3g+qr69hbjpwL/uaoW9VkPDHpJu5FxXGG3J9Qz6CXtNpKsr6oF1tsxu81VN5L2DC/0FXat1wODXtKu52C2cYWd9XacQS9pV/MNBlefrNpyRZKbrbfjnKOXpMZ590pJapxBL0mNM+ilISSZm+TD05ZPTPKNndmTtDUGvTScucCHZ91K2gUY9GpekoVJ7k/y+SRrklyR5JQk30/yYPfJXgcm+XqSe5LcmuSYbt+Lklya5OYkD3X3ZofBLXNfk2RVkt/vxvZLcnVX64ru/vDSTuflldpT/CJwNrCEwf3w3w+8lcEdAz/F4M6Wd1XVWUlOAi7n/9+i9nUMPoFrf+CBJJ9jcC+WN2y+C2F3G+s3A0cDjwHfB04A/tFN+KQXmmf02lM8XFWruzsG3gvcWINri1cz+GCZtzL4IAiq6ibgFd3tjgGuq6pnq+opYANbf/fi7VU11dVY1R1X2ukMeu0ppt/r//lpy88zeGY70zTL5jeZTN/3Obb+THh7t5NeUAa9NHAL8AH4f9MwT1XVT7ax/U/pPmlJ2tV5xiENXARc1t1w6hkGH1u3VVX1t92LuWuAvwCuG3+L0nC8BYIkNc6pG0lqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Lj/i+mm4zdPG6GHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[train.year==2013].groupby('month')['Count'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1192389e8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+SXIIcExVfWeCNRwG/ByDxdm2qrpvUrW0ep5fVf8xyRoOFEleCBzP4E392ydSw4Ec9JKkmXkevSR1zqCXpM4Z9NJeSLIoyduHtl+Z5OpJ1iTtiUEv7Z1FwNtnHCXtBwx6dS/J0iS3J/lIkluTXJbkjCRfS3JHuw/QkUn+KcktSa5L8uL2uRcmuSTJ+iR3tisdAS4CnptkY5I/b32HJrmiPddl7YpMaeIO9PPopVE9DzgPWMngqtc3Ai8HXgu8l8F56TdX1blJTgMuZXDbCIAXMLgPz2HAt5J8mME9Z36hqk6EwaEb4KXACxn8H8lfA04Fdjv1V5pvruj1VPGdqtrULhi7Dbi2BucWb2Jwq4qXAx8DqKp1wFHtymGAz1bVo1X1AHA/e77q+oaq2taeY2ObV5o4g15PFY8OtZ8Y2n6CwSvb6Q6z7LzIZPhzH2fPr4RHHSfNK4NeGvgK8Cb438MwD1TVk92S4fu0+5ZI+ztXHNLAhcBH243yHmGGewBV1X+3N3NvBf4F+OzclyjtHW+BIEmd89CNJHXOoJekzhn0ktQ5g16SOmfQS1LnDHpJ6pxBL0md+x9iedH2Zqr0ggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[train.year==2014].groupby('month')['Count'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119238cc0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby(['year', 'month'])['Count'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b74c668>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby(['year', 'month','day'])['Count'].mean().plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b84d8d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('day')['Count'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b9a1908>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('Hour')['Count'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation from above plot : Traffic during the night hours is low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ba49cc0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('day_of_week')['Count'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation from above plot : Traffic on weekdays is more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From EDA we have observed following things\n",
    "    1. Traffic will increase as the years pass by\n",
    "    2. Traffic will be high from May to October\n",
    "    3. Traffic during the night hours is low (12AM to 9 AM)\n",
    "    4. Traffic on weekdays is more"
   ]
  }
 ],
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