{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indicators of Heavy Traffic on I-94\n",
    "\n",
    "In this project, we're going to analyze a dataset about the westbound traffic on the [I-94 Interstate highway](https://en.wikipedia.org/wiki/Interstate_94).\n",
    "\n",
    "The goal of our analysis is to determine a few indicators of heavy traffic on I-94. These indicators can be weather type, time of the day, time of the week, etc.\n",
    "\n",
    "## The I-94 Traffic Dataset\n",
    "\n",
    "John Hogue made the dataset available that we'll be working with, and you can download it from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_main</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>date_time</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>288.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>2012-10-02 09:00:00</td>\n",
       "      <td>5545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>289.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 10:00:00</td>\n",
       "      <td>4516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>289.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 11:00:00</td>\n",
       "      <td>4767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>290.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 12:00:00</td>\n",
       "      <td>5026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>291.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 13:00:00</td>\n",
       "      <td>4918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  holiday    temp  rain_1h  snow_1h  clouds_all weather_main  \\\n",
       "0    None  288.28      0.0      0.0          40       Clouds   \n",
       "1    None  289.36      0.0      0.0          75       Clouds   \n",
       "2    None  289.58      0.0      0.0          90       Clouds   \n",
       "3    None  290.13      0.0      0.0          90       Clouds   \n",
       "4    None  291.14      0.0      0.0          75       Clouds   \n",
       "\n",
       "  weather_description            date_time  traffic_volume  \n",
       "0    scattered clouds  2012-10-02 09:00:00            5545  \n",
       "1       broken clouds  2012-10-02 10:00:00            4516  \n",
       "2     overcast clouds  2012-10-02 11:00:00            4767  \n",
       "3     overcast clouds  2012-10-02 12:00:00            5026  \n",
       "4       broken clouds  2012-10-02 13:00:00            4918  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "i_94 = pd.read_csv('Metro_Interstate_Traffic_Volume.csv')\n",
    "i_94.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_main</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>date_time</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48199</th>\n",
       "      <td>None</td>\n",
       "      <td>283.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2018-09-30 19:00:00</td>\n",
       "      <td>3543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48200</th>\n",
       "      <td>None</td>\n",
       "      <td>282.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 20:00:00</td>\n",
       "      <td>2781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>None</td>\n",
       "      <td>282.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>proximity thunderstorm</td>\n",
       "      <td>2018-09-30 21:00:00</td>\n",
       "      <td>2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48202</th>\n",
       "      <td>None</td>\n",
       "      <td>282.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 22:00:00</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48203</th>\n",
       "      <td>None</td>\n",
       "      <td>282.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 23:00:00</td>\n",
       "      <td>954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      holiday    temp  rain_1h  snow_1h  clouds_all  weather_main  \\\n",
       "48199    None  283.45      0.0      0.0          75        Clouds   \n",
       "48200    None  282.76      0.0      0.0          90        Clouds   \n",
       "48201    None  282.73      0.0      0.0          90  Thunderstorm   \n",
       "48202    None  282.09      0.0      0.0          90        Clouds   \n",
       "48203    None  282.12      0.0      0.0          90        Clouds   \n",
       "\n",
       "          weather_description            date_time  traffic_volume  \n",
       "48199           broken clouds  2018-09-30 19:00:00            3543  \n",
       "48200         overcast clouds  2018-09-30 20:00:00            2781  \n",
       "48201  proximity thunderstorm  2018-09-30 21:00:00            2159  \n",
       "48202         overcast clouds  2018-09-30 22:00:00            1450  \n",
       "48203         overcast clouds  2018-09-30 23:00:00             954  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_94.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48204 entries, 0 to 48203\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   holiday              48204 non-null  object \n",
      " 1   temp                 48204 non-null  float64\n",
      " 2   rain_1h              48204 non-null  float64\n",
      " 3   snow_1h              48204 non-null  float64\n",
      " 4   clouds_all           48204 non-null  int64  \n",
      " 5   weather_main         48204 non-null  object \n",
      " 6   weather_description  48204 non-null  object \n",
      " 7   date_time            48204 non-null  object \n",
      " 8   traffic_volume       48204 non-null  int64  \n",
      "dtypes: float64(3), int64(2), object(4)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "i_94.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has 48,204 rows and 9 columns, and there are no null values. Each row describes traffic and weather data for a specific hour — we have data from 2012-10-02 09:00:00 until 2018-09-30 23:00:00.\n",
    "\n",
    "A station located approximately midway between Minneapolis and Saint Paul records the traffic data (see the [dataset documentation](https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume)). For this station, the direction of the route is westbound (i.e., cars moving from east to west). This means that the results of our analysis will be about the westbound traffic in the proximity of the station. In other words, we should avoid generalizing our results for the entire I-94 highway.\n",
    "\n",
    "## Analyzing Traffic Volume\n",
    "\n",
    "We're going to start our analysis by examining the distribution of the `traffic_volume` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "i_94['traffic_volume'].plot.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48204.000000\n",
       "mean      3259.818355\n",
       "std       1986.860670\n",
       "min          0.000000\n",
       "25%       1193.000000\n",
       "50%       3380.000000\n",
       "75%       4933.000000\n",
       "max       7280.000000\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_94['traffic_volume'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Between 2012-10-02 09:00:00 and 2018-09-30 23:00:00, the hourly traffic volume varied from 0 to 7,280 cars, with an average of 3,260 cars.\n",
    "\n",
    "About 25% of the time, there were only 1,193 cars or fewer passing the station each hour — this probably occurs during the night, or when a road is under construction. However, about 25% of the time, the traffic volume was four times as much (4,933 cars or more).\n",
    "\n",
    "This observation gives our analysis an interesting direction: comparing daytime data with nighttime data.\n",
    "\n",
    "## Traffic Volume: Day vs. Night\n",
    "\n",
    "We'll start by dividing the dataset into two parts:\n",
    "\n",
    "- Daytime data: hours from 7 AM to 7 PM (12 hours)\n",
    "- Nighttime data: hours from 7 PM to 7 AM (12 hours)\n",
    "\n",
    "While this is not a perfect criterion for distinguishing between nighttime and daytime, it's a good starting point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23877, 9)\n",
      "(24327, 9)\n"
     ]
    }
   ],
   "source": [
    "i_94['date_time'] = pd.to_datetime(i_94['date_time'])\n",
    "\n",
    "day = i_94.copy()[(i_94['date_time'].dt.hour >= 7) & (i_94['date_time'].dt.hour < 19)]\n",
    "print(day.shape)\n",
    "\n",
    "night = i_94.copy()[(i_94['date_time'].dt.hour >= 19) | (i_94['date_time'].dt.hour < 7)]\n",
    "print(night.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This significant difference in row numbers between `day` and `night` is due to a few hours of missing data. For instance, if you look at rows 176 and 177 (`i_94.iloc[176:178]`), you'll notice there's no data for two hours (4 and 5).\n",
    "\n",
    "## Traffic Volume: Day vs. Night (II)\n",
    "\n",
    "Now that we've isolated `day` and `night`, we're going to look at the histograms of traffic volume side-by-side by using a grid chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,3.5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(day['traffic_volume'])\n",
    "plt.xlim(-100, 7500)\n",
    "plt.ylim(0, 8000)\n",
    "plt.title('Traffic Volume: Day')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Traffic Volume')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(night['traffic_volume'])\n",
    "plt.xlim(-100, 7500)\n",
    "plt.ylim(0, 8000)\n",
    "plt.title('Traffic Volume: Night')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Traffic Volume')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23877.000000\n",
       "mean      4762.047452\n",
       "std       1174.546482\n",
       "min          0.000000\n",
       "25%       4252.000000\n",
       "50%       4820.000000\n",
       "75%       5559.000000\n",
       "max       7280.000000\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day['traffic_volume'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24327.000000\n",
       "mean      1785.377441\n",
       "std       1441.951197\n",
       "min          0.000000\n",
       "25%        530.000000\n",
       "50%       1287.000000\n",
       "75%       2819.000000\n",
       "max       6386.000000\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "night['traffic_volume'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram that shows the distribution of traffic volume during the day is left skewed. This means that most of the traffic volume values are high — there are 4,252 or more cars passing the station each hour 75% of the time (because 25% of values are less than 4,252).\n",
    "\n",
    "The histogram displaying the nighttime data is right skewed. This means that most of the traffic volume values are low — 75% of the time, the number of cars that passed the station each hour was less than 2,819.\n",
    "\n",
    "Although there are still measurements of over 5,000 cars per hour, the traffic at night is generally light. Our goal is to find indicators of heavy traffic, so we'll only focus on the daytime data moving forward.\n",
    "\n",
    "## Time Indicators\n",
    "\n",
    "One of the possible indicators of heavy traffic is time. There might be more people on the road in a certain month, on a certain day, or at a certain time of day.\n",
    "\n",
    "We're going to look at a few line plots showing how the traffic volume changes according to the following:\n",
    "\n",
    "- Month\n",
    "- Day of the week\n",
    "- Time of day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day['month'] = day['date_time'].dt.month\n",
    "by_month = day.groupby('month').mean()\n",
    "by_month['traffic_volume'].plot.line()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The traffic looks less heavy during cold months (November–February) and more intense during warm months (March–October), with one interesting exception: July. Is there anything special about July? Is traffic significantly less heavy in July each year?\n",
    "\n",
    "To answer the last question, let's see how the traffic volume changed each year in July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day['year'] = day['date_time'].dt.year\n",
    "only_july = day[day['month'] == 7]\n",
    "only_july.groupby('year').mean()['traffic_volume'].plot.line()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Typically, the traffic is pretty heavy in July, similar to the other warm months. The only exception we see is 2016, which had a high decrease in traffic volume. One possible reason for this is road construction — [this article from 2016](https://www.crainsdetroit.com/article/20160728/NEWS/160729841/weekend-construction-i-96-us-23-bridge-work-i-94-lane-closures-i-696) supports this hypothesis.\n",
    "\n",
    "As a tentative conclusion here, we can say that warm months generally show heavier traffic compared to cold months. In a warm month, you can can expect for each hour of daytime a traffic volume close to 5,000 cars.\n",
    "\n",
    "## Time Indicators (II)\n",
    "\n",
    "Let's now look at a more granular indicator: day number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day['dayofweek'] = day['date_time'].dt.dayofweek\n",
    "by_dayofweek = day.groupby('dayofweek').mean()\n",
    "by_dayofweek['traffic_volume'].plot.line()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traffic volume is significantly heavier on business days (Monday – Friday). Except for Monday, we only see values over 5,000 during business days. Traffic is lighter on weekends, with values below 4,000 cars.\n",
    "\n",
    "## Time Indicators (III)\n",
    "\n",
    "Let's now see what values we have based on time of the day. The weekends, however, will drag down the average values, so we're going to look only at the averages separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4/L/LmDRvPXf1PYn+HRPynOaE6hUYO6IHV/VM5o3vVnPpKz+wceeBYk4qeSjxtVNEpLCO2WyaWTWgF/AagHPukHNuB9AfGOVPNgoY4A/3B8Y65w4651YBK4BuZtYAqOacm+683WdHh80TcYvW7+Le9xZw9ciZajilxBjzwxpe+mYll3dvxPWnNz3qtPFxMdzfrw3PX9qJJRt2cf7z3/L9ii3FlFRyKy21U0SksPKzZLMJkAG8YWZzzOxVM6sM1HfObQDwr+v50ycAaWHzp/vjEvzh3OOLRduE6jx1SQd+WLWV4aPUcEr0+2LxJu7/YCFntqzHA/3akN81p/06NGTSjT2pUSmey1/7kRemrCAU0uGRAlAqaqeISGHlp9mMAzoDLzrnOgF78Vf7HEFe/xHdUcb/+gHMRphZqpmlZmRk5CNi/lzYKZGnBnVg+s9buXZ0Kgcy1XBKdJqXtoOb3p5D24TqPH9ZJ+JiC7YvX7N6Vfnghp6c264Bj3+2jBFjZrFzf2aE0soRFGvtjFTdFBEprPz8B0sH0p1zP/q3x+MV0E3+6h38681h0yeFzZ8IrPfHJ+Yx/leccy8751Kccyl169bN72vJl4s6J/LEwA58t3KLGk6JSmu37mP4qJnUqRrPa8O6Uin++I5QVrl8HM9f2okH+rVmyrLN9Ht+GovW7yzitHIUxVo7I1k3RUQK45jNpnNuI5BmZif5o84EFgOTgGH+uGHAB/7wJGCImZU3sxPxNmaf4a8u2m1m3f09KYeGzVOsBnZJ5PGL2zNthRpOiS7b9x7iyjdmkBVyjLyqG3Wrli/U45kZV/Y8kXeu686hrBAXvfA976amHXtGKbTSWDtFRI5HfheZ3AS8ZWbxwM/AVXiN6jgzGw6sBQYBOOcWmdk4vKKaBdzgnMvp5q4HRgIVgU/9SyAGpSThgLsnzGfEmFm8fEUXKpSLPeZ8IpFyIDOba0enkr5jP/+55mSa1i26A7R3aVyLj24+lZvfnsOd4+cza812HrigjT7zkVfqaqeISEFZtJ9XOSUlxaWmpkbs8cfNTOOuCfM5vUVd/q2GUwISCjlufHs2ny7cyL8u68y57RpE5HmyQ46nJy/jX1+vpG1CNV78XReSalWKyHNFOzOb5ZxLCTpHJES6bopI2XU8tbPMn0Hokq5JPHZRO775KYPfvzmLg1lapS7F75FPlvDJgo3cd26riDWaALExxp19WvLq0BTWbN3Hec99y1dLN0Xs+URERMp8swkwpFsj/nZRO6Ysy+D3Y9RwSvF6fdoqXpu2iitPSWb4qScWy3Oe1bo+H990Gkm1KnH1yFSe+nwZ2To8koiIRICaTd+l3Rrx6IXt+HpZBte/OVsNpxSLzxZu4KGPF9OnTX3+cn7rfB9Lsyg0ql2JCdefwuCUJJ7/agUPfbS42J5bRETKDjWbYS47uREPD2jLV0s3c8Nbs3V+aYmoWWu2ccvYuXRMqsGzQzoRG1P8p7uuUC6Wvw9sz8WdE3lnZhq7D+hYnCIiUrTUbOZyeffGPDSgLV8s2cwf1HBKhPycsYdrRqXSoHoFXh2aEviOab/r3oj9mdl8NH9DoDlERKT0UbOZhyu6N+av/dvwxZJN3PAfNZxStLbsOciVb8zEzBh5VTdqVyncsTSLQqekGrSoX4V3ZuoYnCIiUrTUbB7B0B7JPHhBGyYv3sRNb88mM1sNpxTe/kPZDB+VyubdB3htWArJdSoHHQnwDv5+SUoSc9N2sGzj7qDjiIhIKaJm8yiGnZLM/f1a899Fm7jpP3PUcEqhZIccN4+dw/z0HTw7pBOdGtUMOtJhLuqcSLlY09JNEREpUmo2j+Gqnifyf+e35rNFG7n5bTWccnycczz44SImL97EA/3a0KfNCUFH+pValeM5u3V9Js5J19EYRESkyKjZzIerTz2RP5/Xik8XbuSWsWo4pWCcczz35QpGT1/DiF5NGHZKctCRjmhw10Zs35fJF4s3Bx1FRERKifyeG73Mu+a0JgA8/PESjLk8O6QjcbHq1eXoDmWFuG/iAt6dlc6FnRK4p2/LoCMd1anN6tCwegXGzlzLee0jdyYjEREpO9RsFsA1pzXBOe/UgmbwzGA1nHJk2/Ye4vdjZjFj9TZuPrM5t57ZnJgAjqVZELExxsCUJJ7/ajnp2/eRWLNsnjddRESKjjqlArq2VxPuPaclH83fwG3j5pGlVeqShxWbdzPgX98xN30Hzw7pyO1nt4j6RjPHoC6JAIyflR5wEhERKQ3UbB6H605vyj3ntOTDeet54MNFOKdzSssvpv6UwYUvfM++Q9mMHdGd/h0Tgo5UIEm1KnFqszq8m5qu86WLiEihqdk8Tr8/vSnX9WrCmz+s5bVpq4KOI1Fi1PeruWrkTBJqVOSDG3vSOcoOb5Rfl6QksW7Hfr5bsSXoKCIiUsKp2SyEu/u25Jy2J/DIJ0v4bOHGoONIgLKyQ/zl/YXcP2kRvzmpLuOvP4WEGhWDjnXcftumPjUqleOdVB1zU0RECkfNZiHExBj/GNyRDok1uPWdOcxL2xF0JAnAzv2ZXDVyJmN+8A5t9O8rUqhSvmTve1c+LpYLOyUwedEmtu89FHQcEREpwdRsFlKFcrG8OiyFOlXKM3xUKmnb9gUdSYrRmq17ueiF75i+cit/v7gdfzq3FbElZEegYxncNYlD2SEmzlkXdBQRESnB1GwWgTpVyjPyqq4czMrm6pEz2bk/M+hIUgx+/HkrA/71HVv3HuLNa05mcNdGQUcqUi1PqEaHxOq8MzNNO8GJiMhxU7NZRJrVq8q/L+/Cqi17ueGt2TrLUCk3LjWNy1/7kVqV43n/Dz3p3qR20JEi4pKuSSzbtJt56TuDjiIiIiWUms0idEqzOvztonZMW7GFP09cqKVBpVB2yPHoJ0u4a/x8ujepzXt/6ElyncpBx4qYCzo0pGK5WN6ZqR2FRETk+KjZLGKDUpK4qXcz3klN44UpK4OOI0Vo78Esrhszi5en/swV3RvzxpVdqV6xXNCxIqpqhXKc264BH85bz75DWUHHERGREkjNZgTcfnYL+ndsyBP/XcaH89YHHUeKwLod+xn40nS+WrqJBy9ow0MD2paZU5UO7prEnoNZfDx/Q9BRRESkBCob/y2LmZnx94vb0zW5Jne8O49Za7YFHUkKYc7a7fT/53ekb9vHG1d1Y9gpyUFHKlZdk2vSpE5lxumYmyIichzUbEZIhXKx/PuKFBpWr8C1o2exZuveoCPJcZg0bz2DX/6BSvGxvPeHUzi9Rd2gIxU7M+OSrknMXL2dlRl7go4jIiIljJrNCKpVOZ43rupGyDmuemMmO/bp4NglhXOOpyf/xM1vz6FjYg3ev6EnzetXDTpWYC7qnEBsjDFOOwqJiEgBqdmMsBPrVOaVoSmkb9/PiDGzOJiVHXQkOYafNu1m6OszeO7L5QzsksiYa7pRq3J80LECVa9qBXq3rMeE2ek6rJeIiBSIms1i0DW5Fk8Mas+MVdu4Z8ICHRIpSmXsPsifJi6g7zNTmZe2gwcvaMMTA9tTPi426GhRYUjXJLbsOcRXSzcHHUVEREqQkn0C5xKkf8cE1m7dx1OTf6JRrUrcdnaLoCOJ70BmNq9NW8WLU1ZyIDOboT2SueXM5tQs40szczu9RV3qVS3PuJlp9GlzQtBxRESkhMjXkk0zW21mC8xsrpml+uNqmdlkM1vuX9cMm/5eM1thZsvMrE/Y+C7+46wws+fMrHScRDqfbuzdjIFdEnn2y+VMmJUedJwyLxRyfDB3HWc+9Q1P/HcZPZrW5vPbevHABW3UaOYhLjaGgV0S+XrZZjbuPBB0nBJBtVNEpGCr0X/jnOvonEvxb98DfOmcaw586d/GzFoDQ4A2QF/gBTPLWQ/5IjACaO5f+hb+JZQcZsajF7ajR5Pa3PPefKav3Bp0pDIrdfU2Lnzxe24ZO5calcrx9rXdeWVoCk3qVgk6WlS7JCWJkIMJs/VjqQBUO0WkTCvMNpv9gVH+8ChgQNj4sc65g865VcAKoJuZNQCqOeemO2+jxdFh85QZ8XExvHR5FxrVqsR1Y1JZsVmHkilOa7fu4w9vzWLgS9PZuHM/Tw7qwIc3nkqPpqXz3OZFLblOZbo3qcW41DRCIW17fJxUO0WkTMlvs+mAz81slpmN8MfVd85tAPCv6/njE4Dw46Ok++MS/OHc48uc6pXKMfKqbpSLjeHqkTPZuudg0JFKvZ37M3nk48Wc9fQ3fL00g9vOasHXfzyDgV0SiYnRGsmCGNw1iTVb9/HDKi2ZzwfVThEp8/LbbPZ0znUGzgFuMLNeR5k2r//c7ijjf/0AZiPMLNXMUjMyMvIZsWRJqlWJV4alsGnXAa4dncqBTB0SKRIys0OM+n41ZzzxNa9OW8WATg2ZcucZ3HJWcyrFa/+443FO2wZUrRCnY27mT7HVzrJQN0WkZMpXs+mcW+9fbwYmAt2ATf7qHfzrnOOhpANJYbMnAuv98Yl5jM/r+V52zqU451Lq1i29Z2zp3Kgm/xjckdlrd3DHu/O0WrIIOef4YvEm+jwzlfsnLaJVg2p8dNOpPD6wA/WrVQg6XolWoVws/Ts25NOFG9m5PzPoOFGtOGtnWambIlLyHLPZNLPKZlY1Zxj4LbAQmAQM8ycbBnzgD08ChphZeTM7EW9j9hn+6qLdZtbd35NyaNg8Zda57Rpw7zkt+Xj+Bp74fFnQcUqFhet2ctkrP3LN6FQAXh2awlvXnEybhtUDTlZ6DOnaiINZISbNXRd0lKil2iki4snPesT6wET/SBtxwH+cc5+Z2UxgnJkNB9YCgwCcc4vMbBywGMgCbnDO5awjvh4YCVQEPvUvZd6IXk1YvXUfL05ZSc1K5RjRq2nQkUqkTbsO8OR/lzF+djo1Kpbjr/3bcGm3RpSL1bkLilrbhOq0blCNsTPTuKJHctBxopVqp4gI+Wg2nXM/Ax3yGL8VOPMI8zwCPJLH+FSgbcFjlm5mxkP927DrQCaPfrKUrJDjD2c0CzpWxBzIzOaj+Rv4fuUWQiFHyEHIOZwDhyMU8m6HnLc6PGc45J95KeR+mca5nGkdSzbsJjvkuPa0Jtzwm2ZUr1gu4Fdaug3umsT9kxaxcN1O2iZoqXFuqp0iIh7tIREl4mJjeHZwR2LNePyzZYRCjht7Nw86VpFav2M/b/6whrEz09i29xB1qpSnUnwsMQYxZph/HT78yzivKY/JPU0MxFkMMWac374BN/VuTqPalYJ+qWXCgI4JPPLJEsalpqnZFBGRI1KzGUXiYmN4+pIOxBg8+flPZIfglrNKdsPpnOOHn7cx6vvVfL54IwBntarPsFOSOaVpbXQilJKreqVynNP2BN6fs44/nduKCuV0DnkREfk1NZtRJi42hqcu6UhMjPGPL34i2zluO6t5iWvK9h7M4v256xj9/RqWbdpNDX9b1N+d3IikWlryWFoMTknig7nr+WzhRgZ00qEfRUTk19RsRqHYGOOJgR2INeO5L5cTCjnu+G2LEtFwrt6yl9HT1/DurDR2H8iiTcNqPD6wPRd0aKglX6VQ9ya1SapVkXdmpqnZFBGRPKnZjFKxMcbfL25PbIzxz69XkO0cd/U5KSobzlDI8c3yDEZ9v5opyzKIizHOadeAK09pTOdGNaMysxSNmBhjcEoST37+E2u27qVx7cpBRxIRkSijZjOKxcQYj17YjtgY48UpK8kOOe49p2XUNG8792cyflY6Y6avZvXWfdStWp5bzmzO705uRD0dOL3MGNgliacn/8S41DTu7NMy6DgiIhJl1GxGuZgY4+EBbYkx4+WpP5Mdcvz5vFaBNpzLNu5m9PTVTJyzjn2HsunSuCa3nd2Cc9o2ID5Ox7Qsa06oXoHTW9Rl/Kx0bjurBXE6rqmIiIRRs1kCmBl/7d+G2BjjtWmryA457u/XulgbzuyQY/LijYz6fg3Tf95KfFwM/Ts0ZNgpyTrsjTC4axK/f3M2U5dn0Ltl/aDjiIhIFFGzWUKYGff3a/2/hjPkHA9e0CbiDWdmdoj356zjhSkrWbVlLwk1KnJ335YM7ppErcrxEX1uKTl6t6xPnSrxvDMzTc2miIgcRs1mCWJm/Pm8VsTG/LJK/aH+bYmJKfqG82BWNuNnpfPilJWkb99P6wbVeOF3nflt6/paTSq/Eh8Xw0WdE3l92ioydh+kbtXyQUcSEZEooWazhDEz7j2n5WE7DT16YbsiazgPZGbz9oy1/Pubn9m46wAdkmrw4AVt6N2yXtTsmCTR6ZKUJF6e+jPvzU7nutObBh1HRESihJrNEsjMuKvPScSad1ikkHM8dlH7QjWcew9m8daPa3h56iq27DlIt+RaPDGoPac2q6MmU/KlWb0qpDSuyTupaYzo1USfGxERAdRsllhmxh2/bUFMjHfg9+wQPD7QOy5nQew6kMno71fz2rRVbN+XyanN6nBj7050b1I7QsmlNLukaxJ3jZ/PrDXbSUmuFXQcERGJAmo2SzAz4/azWxBr3qktQ87x5KAO+Wo4d+w7xOvTVvHG96vZfSCL3i3rccNvmtGlcc1iSC6l1XntGvDgpEWMnZmmZlNERAA1m6XCLWc1JzYGnvz8J7JDjqcv6XDEnXi27DnIq9+uYsz01ew9lE2fNvW5qXdzHb5IikTl8nH069CQD+au5/5+ralaoVzQkUREJGBqNkuJG3s3JzYmhr9/tpRs53hmcEfKhTWcm3Yd4N/f/Mx/ZqzhYFaI89s35IbfNKXlCdUCTC2l0eCuSYydmcZH8zdwabdGQccREZGAqdksRa4/oymxMfDoJ0sJhRzPXdqJTbsO8NI3Kxk3M51s5xjQMYE//KYpTetWCTqulFIdk2rQqkE11mzdF3QUERGJAmo2S5kRvZoSY8bDHy/h/OemsTJjD2YwsEsi15/ejEa1KwUdUUo5M+ODG3rq1KUiEbBt7yF+2rSbuBgjLjaGuBijXGwMcbFGuRjvOny4nD9NbIzpCBESGDWbpdA1pzUhLsZ46vOf+N3Jjbju9KY0rFEx6FhShqjRFClaoZDjrRlrefzTpew+mHVcj1Eu1ojL1YTGxRixsUaMeRcz/GEwwm7H4N+fcx+/nsefJi7GKB8XS/lyMcTHxlC+XIx3Oy7mf+NzhuPjcoZjKF8u9pfhsPnjYg3De178XDHm/bA1wHKGw3JZWH7L9VoKetQWKTw1m6XUlT1PZNgpyfolKyJSwi3duIt731vAnLU7OKVpbUb0akKMGVmhEJnZjqxsFzYcIjPkXWdlOzJD3nX4+Ex/+qxsR2a2I+QczjlCDn/Yuw7545zDv//X0+RcZ4e8x8r2hw9mhjiYlc3BrJB3yczmkP/cQWuXUJ0BnRLo16EB9apWCDpOmaBmsxRToykiUnLtP5TNc18t55WpP1OtYjmevqQDF3ZKKNG1PTvkOJQV1ojmbkpzj88Mke03tQ6v2cU5HN7SXv8mIec1seHT5QznNMvOwcGsEFOXZ/DQR4t55OPF9GxWhws7JdCnzQlULq+WKFL0zoqIiESZb37K4M/vLyBt234GdknkT+e2olbl+KBjFVpsjFExPpaK8bGBZfhjn5NYsXkPH8xdx8Q567h93DwqllvI2a3rc2GnBE5tXuewo7lI4anZFBERiRIZuw/y0EeLmTRvPU3qVObta7vTo6nO6FbUmtWrwh2/PYnbz27BrDXbmThnHR8v2MCkeeupXTmefh0aMqBTAh0Sq5foJcnRQs2miIhIwEIhxzupafztkyUcyAxxy5nN+cNvmlI+LrglgGWBmZGSXIuU5Frc368N3/yUwftz1vGfGWsZ+f1qkmtXYkCnBAZ0TCC5TuWg45ZYajZFREQCtHzTbv40cQEzV2/n5BNr8ciF7WhWT8dCLm7xcTGc3bo+Z7euz64DmXy2YCPvz13Hs18u55kvltOpUQ0u7JTAee0aULtK+aDjlihqNkVERAJwIDObf329gpe+WUnl8nE8PrA9g7okarVtFKhWoRyXdE3ikq5JbNi5n0lz1zNxzjr+74NF/PXDxfRqUZcBnRI4u1X9QLc/LSnUbIqIiBSz71Zs4b6JC1i9dR8XdUrgvvNaaWlZlGpQvSLXnd6U605vypINu3h/7jomzV3PV0s3Uzk+lhG9mnL9GU11fOGjULMpIiJSTLbuOcgjnyzhvdnraFy7Em8OP5lTm9cJOpbkU6sG1WjVoBp392nJj6u2MeaH1fzji5/4ZMEGHh/Yng5JNYKOGJXUbIqIiESYc453Z6Xz6CdL2Hswixt/04wbezejQjmtgi2JYmKMHk1r06Npbb5cson7Ji7kwhe+Y/ipJ3L72Sdp1Xou+V7ma2axZjbHzD7yb9cys8lmtty/rhk27b1mtsLMlplZn7DxXcxsgX/fc6YNU0SkFFPdFICVGXsY8vIP3DV+Ps3qVuHjm0/jj31OUqNZSpzZqj6f396LId0a8cq3q+j77FS+X7kl6FhRpSAbGNwCLAm7fQ/wpXOuOfClfxszaw0MAdoAfYEXzCznG/UiMAJo7l/6Fiq9iEh0U90sw7KyQ/zzq+Wc88y3LNmwi79d1I5x1/WgRf2qQUeTIlatQjkevbAdb1/bHYDLXvmRe99bwK4DmQEniw75ajbNLBE4D3g1bHR/YJQ/PAoYEDZ+rHPuoHNuFbAC6GZmDYBqzrnpzjkHjA6bR0SkVFHdLNtWbN7DxS9N58nPf+LsNvX54o7TubRbI2JitGC6NOvRtDaf3dKLEb2a8M7MtZz99Dd8sXhT0LECl98lm88AdwGhsHH1nXMbAPzrev74BCAtbLp0f1yCP5x7vIhIafQMqptlTijkeH3aKs577lvWbN3LPy/rxL8u60y9qhWCjibFpGJ8LH86txUT/9CTmpXiuWZ0Kje/PYetew4GHS0wx2w2zex8YLNzblY+HzOvn23uKOPzes4RZpZqZqkZGRn5fFoRkeigulk2pW3bx2Wv/sBfP1pMz2Z1+PzWXpzfvmHQsSQgHZJqMOnGU7ntrBZ8unADZz39DR/MXYe3kqJsyc+SzZ7ABWa2GhgL9DazN4FN/ioe/OvN/vTpQFLY/InAen98Yh7jf8U597JzLsU5l1K3bt0CvBwRkaigulmGOOd4Z+Zaznn2Wxau28XjF7fntWEp1KumpZllXXxcDLec1ZyPbz6NxrUrc8vYuQwflcr6HfuDjlasjtlsOufudc4lOueS8TZg/8o5dzkwCRjmTzYM+MAfngQMMbPyZnYi3gbtM/xVRrvNrLu/N+XQsHlEREoN1c2yY/OuAwwflcrdExbQNqEan95yGpd0TdJZgOQwLepXZcL1p/CX81szfeVWfvuPqbz14xpCobKxlLMwx9l8DBhnZsOBtcAgAOfcIjMbBywGsoAbnHPZ/jzXAyOBisCn/kVEpKxQ3SxFPpy3nr98sJD9h7K5v19rhvVI1g5AckSxMcbwU0/k7Fb1uXfifO6buJBJc9fz2MXtObFO5aDjRZRF+7YDKSkpLjU1NegYIlLKmNks51xK0DkiQXUzsrbvPcRfPljIR/M30CGpBk9f0oGmdasEHUtKEOcc41LTePjjJRzKCnH72S0YfuqJxMVG/ykvj6d26gxCIiIi+fTV0k3cPWEBO/Yd4o+/bcHvT29aIhoEiS5mxuCujTjjpHr8+f2F/O3TpXy8YAN/v7g9rRpUCzpekdM3RERE5Bh2H8jk7vHzuXpkKrUrx/P+DT25sXdzNZpSKPWrVeDlK7rwz8s6sW77fvo9P40Js9KPPWMJoyWbIiIiRzF95Vb++O48Nuzcz/VnNOXWs5pTPk6nmpSiYWac374hPZvW4dFPltClcc1jz1TCqNkUERHJw4HMbB7/bBmvf7eK5NqVePf3PejSuFbQsaSUqlk5nicGdQg6RkSo2RQREcllXtoObh83l5UZexnWozF3n9OSSvH6lylyPPTNERER8R3KCvH8V8t5YcpK6lctz5vDT+bU5nWCjiVSoqnZFBERATKzQ1z/5iy+XLqZizsncv8FralWoVzQsURKPDWbIiJS5jnnuGfCAr5cupmH+rfhih7JQUcSKTV0zAYRESnzHvt0KRNmp3PbWS3UaIoUMTWbIiJSpr0y9Wf+PfVnhvZozM1nNgs6jkipo2ZTRETKrAmz0nnkkyWc164B9/drg5nObS5S1NRsiohImfTV0k3cNWE+PZvV5unBHYiNUaMpEglqNkVEpMyZtWYbf3hrNq0aVOXfV6TojEAiEaRmU0REypSfNu3m6pGpnFCtAiOv6kaV8jowi0gkqdkUEZEyY92O/Qx9bQbxcTGMGX4ydaqUDzqSSKmnZlNERMqEbXsPMfS1H9l7KIvRV3cjqValoCOJlAladyAiIqXe3oNZXD1yJmnb9zPm6m60alAt6EgiZYaWbIqISKl2KCvE9W/NZn76Dv55aSdOblI76EgiZYqWbIqISKkVCjnuHD+PqT9l8NhF7fhtmxOCjiRS5mjJpoiIlErOOR7+eAkfzF3PnX1OYki3RkFHEimT1GyKiEip9OI3K3n9u1Vc1TOZP5zRNOg4ImWWmk0RESl13pm5lsc/W0b/jg35y3mtdRpKkQCp2RQRkVLl80Ubufe9BfRqUZcnBnYgRqehFAmUmk0RESk1Zqzaxk1vz6FdYg1e/F1n4uP0b04kaPoWiohIqbBkwy6Gj5pJQs2KvHFlVyrrNJQiUUHNpoiIlHhp2/Yx7PUZVI6PY/TV3ahVOT7oSCLi088+EREp0bbsOcjQ12dwMCvEu7/vQWJNnYZSJJpoyaaIiJRYew5mcdUbM9mwcz+vX5lCi/pVg44kIrloyaaIiJRYf564gMUbdvHK0C50aVwr6DgikodjLtk0swpmNsPM5pnZIjN70B9fy8wmm9ly/7pm2Dz3mtkKM1tmZn3CxncxswX+fc+ZDnwmIqWUamfkfbZwA+/PXc9NvZvRu2X9oOOIyBHkZzX6QaC3c64D0BHoa2bdgXuAL51zzYEv/duYWWtgCNAG6Au8YGax/mO9CIwAmvuXvkX3UkREoopqZwRt3XOQ+yYupG1CNW74TbOg44jIURyz2XSePf7Ncv7FAf2BUf74UcAAf7g/MNY5d9A5twpYAXQzswZANefcdOecA0aHzSMiUqqodkaOc477Ji5k94EsnhrUkXKx2v1AJJrl6xtqZrFmNhfYDEx2zv0I1HfObQDwr+v5kycAaWGzp/vjEvzh3ONFREol1c7ImDRvPZ8t2shtZ7fgpBO0Q5BItMtXs+mcy3bOdQQS8X5ptz3K5HltS+SOMv7XD2A2wsxSzSw1IyMjPxFFRKJOcdbOslI3N+06wP99sIhOjWowoleToOOISD4UaN2Dc24HMAVve6FN/uod/OvN/mTpQFLYbInAen98Yh7j83qel51zKc65lLp16xYkoohI1CmO2lkW6qZzjnsmzOdgVjZPDepArM55LlIi5Gdv9LpmVsMfrgicBSwFJgHD/MmGAR/4w5OAIWZW3sxOxNuYfYa/umi3mXX396QcGjaPiEipotpZ9N5NTefrZRnc3bclTepWCTqOiORTfo6z2QAY5e8VGQOMc859ZGbTgXFmNhxYCwwCcM4tMrNxwGIgC7jBOZftP9b1wEigIvCpfxERKY1UO4tQ+vZ9/PWjxXRvUothPZKDjiMiBWDezo3RKyUlxaWmpgYdQ0RKGTOb5ZxLCTpHJJS2uhkKOa54/Ufmrt3BZ7f2IqmWTkcpEpTjqZ06XoSIiES1t35cw3crtnLfea3VaIqUQGo2RUQkaq3espdHP1lKrxZ1ubRb0rFnEJGoo2ZTRESiUnbIcef4ecTFGn+/uB06S6dIyZSfHYRERESK3evTVjFz9XaeGtSBBtUrBh1HRI6TlmyKiEjUWbF5N098voyzWtXnos5l+oRJIiWemk0REYkqWdkh7hg3j8rxsTx6UVutPhcp4bQaXUREospL36xkXvpO/nlZJ+pVrRB0HBEpJC3ZFBGRqLF4/S6e/XI557dvwPntGwYdR0SKgJpNERGJCoeyQtw+bi7VK8bzUP+2QccRkSKi1egiIhIVnv9qOUs37uaVoSnUrBwfdBwRKSJasikiIoGbl7aDF6as5OLOiZzdun7QcUSkCKnZFBGRQB3IzOaOd+dRt0p5/q9f66DjiEgR02p0EREJ1FOfL2PF5j2Mvrob1SuWCzqOiBQxLdkUEZHAzFy9jVenreKykxvRq0XdoOOISASo2RQRkUDsO5TFH9+dR2LNivzp3FZBxxGRCNFqdBERCcRjny5lzdZ9jB3RnSrl9e9IpLTSkk0RESl2363Ywujpa7iqZzLdm9QOOo6IRJCaTRERKVa7D2Ry1/j5NKlTmbv6tAw6johEmNZbiIhIsXr4oyVs2Lmf8defQsX42KDjiEiEacmmiIgUm6+WbuKd1DSuO70pnRvVDDqOiBQDNZsiIlIstu45yF3jF9DyhKrcelbzoOOISDHRanQREYk45xz3vLeAXfszefOabpSP0+pzkbJCSzZFRCTixqWmMXnxJu7scxItT6gWdBwRKUZqNkVEJKLWbN3Lgx8upkeT2gw/9cSg44hIMVOzKSIiEZOVHeK2d+YSG2M8dUkHYmIs6EgiUsy0zaaIiETMi1NWMnvtDp4d0pGGNSoGHUdEAqAlmyIiEhHz03fw7JfL6dehIf07JgQdR0QComZTRESK3P5D2dz6zlzqVi3Pw/3bBh1HRAKk1egiIlLkHv1kCT9n7OWta06meqVyQccRkQAdc8mmmSWZ2ddmtsTMFpnZLf74WmY22cyW+9c1w+a518xWmNkyM+sTNr6LmS3w73vOzLSluIiUSmW5dn69bDNjfljD8FNPpGezOkHHEZGA5Wc1ehZwh3OuFdAduMHMWgP3AF8655oDX/q38e8bArQB+gIvmFnO0XtfBEYAzf1L3yJ8LSIi0aRM1s5tew9x1/j5tKhfhTv7nBR0HBGJAsdsNp1zG5xzs/3h3cASIAHoD4zyJxsFDPCH+wNjnXMHnXOrgBVANzNrAFRzzk13zjlgdNg8IiKlSlmsnc457n1vPjv2HeKZwZ2oUE5nCRKRAu4gZGbJQCfgR6C+c24DeEUVqOdPlgCkhc2W7o9L8IdzjxcRKdXKSu18d1Y6/120iTt+exKtG+osQSLiyXezaWZVgAnArc65XUebNI9x7ijj83quEWaWamapGRkZ+Y0oIhJ1iqt2Bl0307bt48FJi+h2Yi2uPa1JsT+/iESvfDWbZlYOr1i+5Zx7zx+9yV+9g3+92R+fDiSFzZ4IrPfHJ+Yx/leccy8751Kccyl169bN72sREYkqxVk7g6yb2SHHbe/MJcaMpy/pQKzOEiQiYfKzN7oBrwFLnHNPh901CRjmDw8DPggbP8TMypvZiXgbs8/wVxftNrPu/mMODZtHRKRUKUu186VvVpK6ZjsP9m9DYs1KQccRkSiTn+Ns9gSuABaY2Vx/3J+Ax4BxZjYcWAsMAnDOLTKzccBivL0xb3DOZfvzXQ+MBCoCn/oXEZHSqEzUzoXrdvKPyT9xXrsGXNgpKjclFZGAmbdzY/RKSUlxqampQccQkVLGzGY551KCzhEJxVU3D2Rmc/7z09h9IJP/3tqLGpXiI/6cIhKs46mdOoOQiIgcl8c+XcqKzXsYM7ybGk0ROSKdG11ERAps6k8ZjPx+NVeeksxpzbUjp4gcmZpNEREpkO17D/HHd+fRvF4V7jmnZdBxRCTKaTW6iIjkm3OO+95fwPZ9h3j9yq46S5CIHJOWbIqISL69N3sdnyzYyG1nt6BtQvWg44hICaBmU0RE8iVt2z7un7SIbsm1uK5X06DjiEgJoWZTRESOKTvkuGPcPACe0lmCRKQAtM2miIgc08tTf2bG6m08OagDSbV0liARyT8t2RQRkaNauG4nT09exjltT+DizjpLkIgUjJpNERE5ogOZ2dz2zlxqVorn0Qvb4Z2eXUQk/7QaXUREjmjSvPUs37yHUVd3o2ZlnSVIRApOzaaIiBzRoC6JtKhflY5JNYKOIiIllFaji4jIEZmZGk0RKRQ1myIiIiISMeacCzrDUZnZbmBZ0DmOog6wJegQRxDN2UD5Ckv5Cuck51zVoENEgupmoSlf4Shf4UR7vgLXzpKwzeYy51xK0CGOxMxSozVfNGcD5Sss5SscM0sNOkMEqW4WgvIVjvIVTknIV9B5tBpdRERERCJGzaaIiIiIRExJaDZfDjrAMURzvmjOBspXWMpXONGerzCi/bUpX+EoX+EoX+EUOF/U7yAkIiIiIiVXSViyKSIiIiIlVNQ2m2ZWw8zGm9lSM1tiZj2CzhTOzG4zs0VmttDM3jazCgHned3MNpvZwrBxtcxsspkt969rRlm+J/y/73wzm2hmNaIpX9h9fzQzZ2Z1gsjmZ8gzn5ndZGbL/M/i49GUz8w6mtkPZjbXzFLNrFtA2ZLM7Gu/jiwys1v88VHz/ShKqp0FzqPaWcT5wu5T7Sxgtmipm36WIqudUdtsAs8CnznnWgIdgCUB5/kfM0sAbgZSnHNtgVhgSLCpGAn0zTXuHuBL51xz4Ev/dlBG8ut8k4G2zrn2wE/AvcUdKsxIfp0PM0sCzgbWFnegXEaSK5+Z/QboD7R3zrUBngwgV46R/Pr9exx40DnXEfg//3YQsoA7nHOtgO7ADWbWmuj6fhQl1c6CGYlqZ2GMRLXzeI0keusmFGHtjMpm08yqAb2A1wCcc4ecczsCDfVrcUBFM4sDKgHrgwzjnJsKbMs1uj8wyh8eBQwozkzh8srnnPvcOZfl3/wBSCz2YL9kyev9A/gHcBcQ6MbNR8h3PfCYc+6gP83mYg/mO0I+B1Tzh6sT0HfEObfBOTfbH96N13wlEEXfj6Ki2llwqp2Fo9p5/KK5bkLR1s6obDaBJkAG8IaZzTGzV82sctChcjjn1uH9EloLbAB2Ouc+DzZVnuo75zaA96EB6gWc52iuBj4NOkQ4M7sAWOecmxd0liNoAZxmZj+a2Tdm1jXoQLncCjxhZml435cgl74AYGbJQCfgR0rW9yO/VDuLRkn6bKh2Flw0185bibK6CYWvndHabMYBnYEXnXOdgL1E0Souf/uE/sCJQEOgspldHmyqksvM7sNbXP9W0FlymFkl4D681RjRKg6oibd6405gnJlZsJEOcz1wm3MuCbgNf2lbUMysCjABuNU5tyvILBGk2lmGqHYet2iunVFVN6Foame0NpvpQLpz7kf/9ni8AhotzgJWOecynHOZwHvAKQFnyssmM2sA4F8Htpr1SMxsGHA+8DsXXcfhaor3D3Gema3GW00128xOCDTV4dKB95xnBhDCO6dutBiG990AeBcIckP3cnjF8i3nXE6mqP9+HAfVzqIR9Z8N1c5CiebaGTV1E4qudkZls+mc2wikmdlJ/qgzgcUBRsptLdDdzCr5v4bOJIo2wg8zCe+Di3/9QYBZfsXM+gJ3Axc45/YFnSecc26Bc66ecy7ZOZeMV5w6+5/NaPE+0BvAzFoA8cCWIAPlsh443R/uDSwPIoT/HX0NWOKcezrsrqj+fhwP1c4iE9WfDdXOQnuf6K2dUVE3oYhrp3MuKi9ARyAVmI/3wagZdKZc+R4ElgILgTFA+YDzvI23DVQm3pd7OFAbb0+x5f51rSjLtwJIA+b6l5eiKV+u+1cDdaIpH16BfNP/DM4GekdZvlOBWcA8vO18ugSU7VS8je7nh33Wzo2m70cRv17VzoLlUe0s4ny57lftLFi2qKibfr4iq506g5CIiIiIRExUrkYXERERkdJBzaaIiIiIRIyaTRERERGJGDWbIiIiIhIxajZFREREJGLUbErUM7NkM1sYdA4RkZJCdVOiiZpNKZPMLC7oDCIiJYnqphwvNZtSUsSa2StmtsjMPjezimbW0cx+MLP5ZjbRP+8yZjbFzFL84Tr+KdMwsyvN7F0z+xD4PLiXIiJSLFQ3JSqo2ZSSojnwL+dcG2AHcDEwGrjbOdceWADcn4/H6QEMc871jlRQEZEoobopUUHNppQUq5xzc/3hWUBToIZz7ht/3CigVz4eZ7JzblsE8omIRBvVTYkKajalpDgYNpwN1DjKtFn88tmukOu+vUWYSUQkmqluSlRQsykl1U5gu5md5t++Asj5tb4a6OIPDyzmXCIi0Up1UwKhPcukJBsGvGRmlYCfgav88U8C48zsCuCroMKJiEQh1U0pduacCzqDiIiIiJRSWo0uIiIiIhGjZlNEREREIkbNpoiIiIhEjJpNEREREYkYNZsiIiIiEjFqNkVEREQkYtRsioiIiEjEqNkUERERkYj5f1u6F1OZNPx6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day['hour'] = day['date_time'].dt.hour\n",
    "bussiness_days = day.copy()[day['dayofweek'] <= 4] # 4 == Friday\n",
    "weekend = day.copy()[day['dayofweek'] >= 5] # 5 = Saturday\n",
    "by_hour_business = bussiness_days.groupby('hour').mean()\n",
    "by_hour_weekend = weekend.groupby('hour').mean()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(11,3.5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "by_hour_business['traffic_volume'].plot.line()\n",
    "plt.xlim(6,20)\n",
    "plt.ylim(1500,6500)\n",
    "plt.title('Traffic Volume By Hour: Monday–Friday')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "by_hour_weekend['traffic_volume'].plot.line()\n",
    "plt.xlim(6,20)\n",
    "plt.ylim(1500,6500)\n",
    "plt.title('Traffic Volume By Hour: Weekend')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At each hour of the day, the traffic volume is generally higher during business days compared to the weekends. As somehow expected, the rush hours are around 7 and 16 — when most people travel from home to work and back. We see volumes of over 6,000 cars at rush hours.\n",
    "\n",
    "To summarize, we found a few time-related indicators of heavy traffic:\n",
    "\n",
    "- The traffic is usually heavier during warm months (March–October) compared to cold months (November–February).\n",
    "- The traffic is usually heavier on business days compared to weekends.\n",
    "- On business days, the rush hours are around 7 and 16.\n",
    "\n",
    "## Weather Indicators\n",
    "\n",
    "Another possible indicator of heavy traffic is weather. The dataset provides us with a few useful columns about weather: `temp`, `rain_1h`, `snow_1h`, `clouds_all`, `weather_main`, `weather_description`.\n",
    "\n",
    "A few of these columns are numerical, so let's start by looking up their correlation values with `traffic_volume`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp              0.128317\n",
       "rain_1h           0.003697\n",
       "snow_1h           0.001265\n",
       "clouds_all       -0.032932\n",
       "traffic_volume    1.000000\n",
       "month            -0.022337\n",
       "year             -0.003557\n",
       "dayofweek        -0.416453\n",
       "hour              0.172704\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day.corr()['traffic_volume']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temperature shows the strongest correlation with a value of just +0.13. The other relevant columns (`rain_1h`, `snow_1h`, `clouds_all`) don't show any strong correlation with `traffic_value`.\n",
    "\n",
    "Let's generate a scatter plot to visualize the correlation between `temp` and `traffic_volume`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day.plot.scatter('traffic_volume', 'temp')\n",
    "plt.ylim(230, 320) # two wrong 0K temperatures mess up the y-axis\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can conclude that temperature doesn't look like a solid indicator of heavy traffic.\n",
    "\n",
    "Let's now look at the other weather-related columns: `weather_main` and `weather_description`.\n",
    "\n",
    "\n",
    "## Weather Types\n",
    "\n",
    "To start, we're going to group the data by `weather_main` and look at the `traffic_volume` averages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb8AAAD4CAYAAAB8OegSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAda0lEQVR4nO3dfZRcVZ3u8e9jCwkIJCOJGIGhgRV8i0wkBYIgBi+i14COIw64HHnRMYNv6HgdJw5zMTj3OhmZ5YzCVYyv+Aq+gBOJI0EgoIhANyTpgLwIhAUBQUQbEIwQnvvH2U0qsdPpIl1d1Zzns1atOrXP3qd+Zy/Cr/c+p86WbSIiIurkGZ0OICIiYrwl+UVERO0k+UVERO0k+UVERO0k+UVERO08s9MBxMamTZvm3t7eTocRETFh9Pf33297eittkvy6TG9vL319fZ0OIyJiwpB0R6ttMu0ZERG1k+QXERG1k+QXERG1k2t+XWZg7SC9C5Z2OoyIiJatWTSv0yGMWkZ+ERFRO21LfpJ2lrSivH4laW3Z/p2kG9r0nSdIOnMMjjNV0rvHIqaIiOg+bUt+tn9je7bt2cBZwH+U7dnAE+363lZI2ty071SgpeQnqWerA4qIiHHRqWnPHkmfl3S9pGWStgOQtFxSo2xPk7SmbJ8g6TxJP5J0i6RPDB1I0omSbpZ0GXBwU/l0Sd+TdE15HVzKF0paLGkZ8FVJL5Z0dRmVrpI0E1gE7F3KTlfldEmrJQ1IOqYca66kSyV9Exgony+T9O0S0yJJby3HH5C09/h0b0REjKRTN7zMBN5i+52Svg28Cfj6FtrMBl4KrANuknQG8DhwGjAHGAQuBa4r9T9FNdr8qaQ/By4EXlj2zQEOsf1oOc6nbH9D0rZAD7AAmFVGqkh6U/n+vwCmAddIurwc64BS93ZJc0udFwIPALcBX7B9gKT3A+8DPrDpiUmaD8wH6NmppYcURETEU9Cp5He77RVlux/oHUWbi20PApRrhntQJaLltn9dys8F9in1DwdeJGmo/U6SdizbS2w/WravBE6RtBtwnu1bmtoMOQT4lu31wL1llLk/8CBwte3bm+peY/ueEs+twLJSPgAcNtyJ2V4MLAaYNGNmVheOiGizTiW/dU3b64HtyvbjbJiKnbyFNkOxby5ZPAM4qCnJAVAS2++HPtv+pqSrgHnAhZL+lmrEtlGzzZ5J07GGifOJps9PkJ+WRER0hW77qcMaqilJgKNHUf8qYG65s3Qb4M1N+5YB7x36IGn2cAeQtBdwm+1PA0uAfYGHgB2bql0OHCOpR9J04FDg6tGcUEREdJ9uS37/DrxL0s+opjRHVKYXF1JNXf4YuLZp98lAo9zEcgNw0mYOcwywWtIK4AXAV23/Brii3OByOnA+sApYCVwCfNj2r57C+UVERBeQnUtM3aTRaDirOkREjJ6kftuNVtp028gvIiKi7ZL8IiKidpL8IiKidpL8IiKidpL8IiKidpL8IiKidpL8IiKidpL8IiKidpL8IiKidvKg5S4zsHaQ3gVLOx1GRDwNrFk0r9MhdK2M/CIionZqnfwknVJWk19VVm1/2Rgff42kaWX74bE8dkREPHW1nfaUdBBwJLCf7XUlSW3b4bAiImIc1HnkNwO43/Y6ANv3275b0msl3Sjpp5I+LekCAEkLJX1oqHFZ7qi3bH9fUn8ZRc7vxMlERMTo1Tn5LQN2l3SzpM9IeqWkycDngaOAVwDPHeWx3m57DtAATpa0cyuBSJovqU9S3/pHBltpGhERT0Ftk5/th6lWjZ8P/Bo4l2rB29tt3+JqocOvj/JwJ0taCfwc2B2Y2WIsi203bDd6tp/SStOIiHgKanvND8D2emA5sFzSAHA8sLnVfR9n4z8WJgNImgscDhxk+xFJy4f2RUREd6rtyE/S8yU1j9BmA/cCe0rau5S9pWn/GmC/0nY/YM9SPgX4bUl8LwAObGfcERGx9eo88tsBOEPSVKpR3S+ppkC/CyyVdD/wU2BWqf894DhJK4BrgJtL+Y+AkyStAm6imvqMiIgupurSVgynTGl+yPaR4/WdjUbDfX194/V1ERETnqR+241W2tR22jMiIuqrztOeW2R7OdUNMRER8TSSkV9ERNROkl9ERNROkl9ERNROkl9ERNROkl9ERNROkl9ERNROkl9ERNROfufXZQbWDtK7YGmnw4iIGlqzaF6nQxg3GflFRETtJPk1kXRKWY19laQVkl7W6ZgiImLsZdqzkHQQcCSwn+11kqYB23Y4rIiIaIOM/DaYAdxvex2A7ftt3y1pjaTTJF0raaCs2YekZ0v6fhkl/lzSvqV8QNJUVX4j6bhS/jVJh3fs7CIi4klJfhssA3aXdLOkz0h6ZdO++23vB3wW+FApOw24zva+wD8BXy3lVwAHAy8GbgNeUcoPZDNr/UmaL6lPUt/6RwbH9KQiIuJPJfkVth8G5lAtaPtr4FxJJ5Td55X3fqC3bB8CfK20vQTYWdIU4CfAoeX1WeAlknYFHijfMdx3L7bdsN3o2X7KWJ9aRERsIsmvie31tpfb/ijwXuBNZde68r6eDddJNdwhgMupRnuvoFoO6dfA0VRJMSIiukCSXyHp+ZJmNhXNBu4YocnlwFtL27lUU6MP2r4TmAbMtH0b8FOqqdIkv4iILpG7PTfYAThD0lTgceCXVFOgR26m/kLgy5JWAY8AxzftuwroKds/Af6VKglGREQXkO1OxxBNGo2G+/r6Oh1GRMSEIanfdqOVNpn2jIiI2knyi4iI2knyi4iI2knyi4iI2knyi4iI2knyi4iI2knyi4iI2knyi4iI2knyi4iI2snjzbrMwNpBehcs7XQYEREjWrNoXqdD2Cq1HPlJOkXS9WUh2hWSXraVx5sr6YKxii8iItqrdiM/SQdRPax6P9vrJE0Dtu1wWBERMY7qOPKbQbX80DoA2/fbvlvSGkkfl3RlWVV9P0kXSrpV0kkAqpwuabWkAUnHbHpwSftLuk7SXpLmSLpMUn851oxxPteIiBhGHZPfMmB3STdL+oykVzbtu9P2QVTLEH2FahHaA4GPlf1/RbXO318AhwOnNyc0SS8HzgLeANwJnAEcbXsO8CXg/7bxvCIiYpRqN+1p+2FJc6hWWj8MOFfSgrJ7SXkfAHaw/RDwkKQ/lHX+DgG+ZXs9cK+ky4D9gQeBFwKLgSPKSHIWMAu4SBJU6/vdM1xMkuZTrR1Iz07Tx/qUIyJiE7VLfgAleS0HlksaYMNCtOvK+xNN20OfnwlohMPeA0wGXgrcXepeX0aSW4pnMVXiZNKMmVlgMSKizWo37Snp+ZJmNhXNBu4YZfPLgWMk9UiaDhwKXF32/Q6YB3xc0lzgJmB6ucEGSdtIevFWn0BERGy12iU/YAfgbEk3SFoFvAhYOMq25wOrgJXAJcCHbf9qaKfte4GjgP9HNQI8Gvg3SSuBFcDLx+gcIiJiK8jOLFs3aTQa7uvr63QYEREThqR+241W2tRx5BcRETWX5BcREbUz6uQn6a8k3SJpUNKDkh6S9GA7g4uIiGiHVn7q8AngKNu/aFcwERER46GVac97k/giIuLpoJWRX5+kc4Hv0/QDcNvnjXVQERER7dRK8tsJeAQ4oqnMQJJfRERMKKNOfrZPbGcgERER42WLyU/Sh21/QtIZVCO9jdg+uS2RRUREtMloRn5DN7nksSPjYGDtIL0LlnY6jIiItlqzaF5Hv3+Lyc/2D8r72e0PJyIiov1Gfc2vrGLwj1QPgp48VG77VW2Iq2tIWk+1vt8zgduBt9n+3Qj1G8BxmQ6OiOherfzO7xtUU6B7AqcBa4Br2hBTt3nU9mzbs4AHgPeMVNl2XxJfRER3ayX57Wz7i8Bjti+z/XbgwDbF1a2uBHYFkHSApJ9Juq68P7+Uz5V0QdleKOlLkpZLuk1SkmJERBdo5Xd+j5X3eyTNo1qtfLexD6k7SeoB/gfwxVJ0I3Co7cclHQ58HHjTME1fABwG7AjcJOmzth9rriBpPjAfoGen6W06g4iIGNJK8vs/kqYA/ws4g+pH73/flqi6y3aSVgC9QD9wUSmfQrUo7kyqn4Bss5n2S22vA9ZJug/YBbiruYLtxcBigEkzZmaBxYiINhv1tKftC2wP2l5t+zDbc2wvaWdwXeJR27OBPYBt2XDN71+AS8u1wKNougloE+uattfT2h8cERHRBq3c7bkn8D6qEdCT7Wy/fuzD6j62B8s1u/+S9Fmqkd/asvuEjgUWEREta2UU8n2q610/AJ5oSzRdzvZ1klYCx1It8XS2pA8Cl3Q2soiIaIXs0V1iknSV7Ze1OZ7aazQa7uvLw3QiIkZLUr/tRittWhn5fUrSR4FlbLyk0bWtfGFERESntZL8XgK8DXgVG6Y9XT5HRERMGK0kvzcCe9n+Y7uCiYiIGA+tPOFlJTC1TXFERESMm1ZGfrsAN0q6ho2v+dXipw4REfH00Ury+2jbooiIiBhHo05+ti8bab+kK20ftPUhRUREtFcr1/y2ZHOP94qIiOgqY5n88kDmiIiYEPKQ5S4zsHaQ3gVLOx1GRERL1iya1+kQWjKWIz+N4bEiIiLaZlTJT1KPpB9vodrbxiCeriTJkr7W9PmZkn7dtGL76yUtGKH9bEmvG49YIyJiy0aV/GyvBx4pi9lurs7qMYuq+/wemCVpu/L51WxYzgjbS2wvGqH9bCDJLyKiS7Qy7fkHYEDSFyV9eujVrsC60H8DQ5PabwG+NbRD0gmSzizbb5a0WtJKSZdL2hb4GHCMpBWSjhn3yCMiYiOt3PCytLzq6hzg1DLVuS/wJeAVw9Q7FXiN7bWSptr+o6RTgYbt9w53YEnzgfkAPTtNb0/0ERHxpFZ+5H52mfb7c9s3tTGmrmR7laReqlHfD0eoegXwFUnfBs4b5bEXA4sBJs2YmZ+MRES02ainPSUdBawAflQ+z5a0pE1xdaslwL/TNOW5KdsnAf8M7A6skLTzOMUWERGj1Mq050LgAGA5gO0VkvZsQ0zd7EvAoO0BSXOHqyBpb9tXAVeVPxh2Bx4Cdhy3KCMiYkSt3PDyuO3BTcpqNUVn+y7bn9pCtdMlDUhaDVxOtRTUpcCLcsNLRER3kD26/CXpi8DFwALgTcDJwDZlmi/GSKPRcF9fX6fDiIiYMCT122600qaVkd/7gBdTreX3LeBB4AOtfFlEREQ3aOVuz0eAU8orIiJiwhp18pO0D/AhoLe5ne1XjX1YERER7dPK3Z7fAc4CvgCsb084ERER7ddK8nvc9mfbFklERMQ42WLyk/TssvkDSe8Gzqe66QUA2w+0KbaIiIi2GM3Ir5/q93xD6/X9Q9M+A3uNdVARERHttMXkZ3tPAEmTbf+heZ+kye0KLCIiol1a+Z3fz0ZZFhER0dVGc83vucCuwHaSXsqG6c+dgO3bGFstDawdpHdBnVeOioiJZs2ieVuu1GVGc83vNcAJwG7AJ5vKHwL+qQ0xRUREtNVorvmdDZwt6U22vzcOMXWcpIdt79D0+QRGWIw2IiImllYeb/Y9SfOonu85uan8Y+0ILCIiol1aWcz2LOAYqgdcC3gzsEeb4upako6SdJWk6yT9WNIupfyHZcmiFZIGJR0vqUfS6ZKukbRK0t91Ov6IiGjtbs+X2z4O+K3t04CDqBZqfTrarimRrQCaR7c/BQ60/VLgHODDALZfZ3s28A7gDuD7ZXvQ9v7A/sA7h1sAWNJ8SX2S+tY/sumSiRERMdZaebzZo+X9EUnPA34DPF1Xcn+0JDJgwzW/8nE34FxJM4Btgdub6k0Dvgb8te1BSUcA+0o6ulSZAsxsbgNgezGwGGDSjJm1WiA4IqITWkl+F0iaCpwOXEv1dJcvtCOoLncG8EnbSyTNBRYCSOqhGgl+zPbqUlfA+2xf2IE4IyJiM1q54eVfyub3JF0ATLZdxzm6KcDasn18U/kiYJXtc5rKLgTeJekS24+VZaHW2v79OMUaERHDaOWGl+0l/W9Jn7e9DniOpCPbGFu3Wgh8R9JPgPubyj8EHNF0rfD1VCPjG4BrJa0GPkdro+2IiGgD2aO7xCTpXKqHXB9ne5ak7YArm6+NxdZrNBru6+vrdBgREROGpH7bjS3X3KCVuz33tv0J4DEA24+y4VFnERERE0Yrye+PZbRnAEl707SuX0RExETRyvWnjwI/AnaX9A3gYKpnfkZEREworSS/44ClwHeB24D3275/5CYRERHdp5Xk92XgEODVVKu3r5B0ue1PtSWyiIiINmnld36XSLqM6jFdhwEnUT3kOskvIiImlFEnP0kXA88CrgR+Auxv+752BRYREdEurdztuQr4IzAL2BcY+q1fRETEhNLKtOffA0jaATiR6hrgc4FJ7QktIiKiPVqZ9nwv8ApgDtWSPV+imv6MMTSwdpDeBUs7HUZE1MiaRfM6HcK4a+Vuz+2ATwL9th9vUzxdT9J6YKCp6C9tr+lQOBER8RS0Mu15ejsDmUAezfNMIyImtlZueInNkDRb0s8lrZJ0vqQ/K+X7l7IrJZ1eVnaIiIgOS/Jr3XZNyxadX8q+Cvyj7X2ppkQ/Wsq/DJxk+yBgfQdijYiIYWRtudZtNO0paQow1fZlpehsqvX+pgI72v5ZKf8mMOz6h5LmA/MBenaa3qawIyJiSEZ+7TPq5Z5sL7bdsN3o2X5KO2OKiAiS/Laa7UHgt5JeUYreBlxm+7fAQ5IOLOXHdiTAiIj4E5n2HBvHA2dJ2p5qxYsTS/k7gM9L+j2wHBjsTHgREdEsya9FtncYpmwFcOCf1ub6chMMkhYAfe2NLiIiRiPJr73mSfoIVT/fwSgW/33JrlPoq+HTFiIixlOSXxvZPhc4t9NxRETExnLDS0RE1E6SX0RE1E6SX0RE1E6SX0RE1E6SX0RE1E6SX0RE1E6SX0RE1E5+59dlBtYO0rtgaafDiIgYF2s69FCPjPwiIqJ2apf8JK0vC9FeL2mlpA9KGrYfJD1P0nefwnc8vPWRRkREu9Rx2vPJxWglPYdqkdkpbFh9nbLvmbbvBo4e9wgjIqKt6pj8nmT7vrKK+jWSFlItTTQPmAw8S9LbgQtsz5L0BaBRmu4KnAn0AK8vZdOBZbZPbP4OSf8A/DUwCTjf9kZJNiIixl+tkx+A7dvKtOdzStFBwL62H5DU21TvbwEk7QFcCHzF9h3AqZKmAD+hSohPknQEMBM4gGpl9yWSDrV9+Sb15gPzAXp2mj72JxkRERup3TW/zVDT9kW2Hxi2kjQZ+A7w3pL4kCTgG8B/2O7fpMkR5XUdcC3wAqpkuBHbi203bDd6tp+y1ScTEREjq/3IT9JewHrgvlL0+xGqnwWcZ/vHTWULgbtsf3m4wwP/avtzYxFrRESMjVqP/CRNp0poZ9r2Fuq+B9jR9qKmsiOBVwMnb6bZhcDbJe1Q6u9abrKJiIgOquPIbztJK4BtgMeBrwGfHEW7DwGPlbZQJc1jgOcBV1eznyyxfepQA9vLJL0QuLLsfxj4GzaMMiMiogO0hQFPjLNGo+G+vr5OhxERMWFI6rfd2HLNDWo97RkREfWU5BcREbWT5BcREbWT5BcREbWT5BcREbWT5BcREbWT5BcREbWT5BcREbWT5BcREbVTx8ebdbWBtYP0Llja6TAiIsbNmkXzxv07M/KLiIjaqV3yk/RcSedIulXSDZJ+KGkfSavH6PgnSDpzyzUjIqJTajXtWRaePR842/axpWw2sEsn44qIiPFVt5HfYcBjts8aKrC9Arhz6LOkyZK+LGlA0nWSDivlG43oJF0gaW7ZPlHSzZIuAw5uqvNmSaslrZR0ebtPLiIiRqdWIz9gFtC/hTrvAbD9EkkvAJZJ2mdzlSXNAE4D5gCDwKXAdWX3qcBrbK+VNHWEY8wH5gP07DR9dGcSERFPWd1GfqNxCNUCt9i+EbgD2GzyA14GLLf9a9t/BM5t2ncF8BVJ7wR6NncA24ttN2w3erafstUnEBERI6tb8rueaoQ2Em2m/HE27q/JTdvDrghs+yTgn4HdgRWSdh5lnBER0UZ1S36XAJPKSAwASfsDezTVuRx4a9m3D/DnwE3AGmC2pGdI2h04oNS/CpgraWdJ2wBvbjr23ravsn0qcD9VEoyIiA6r1TU/25b0RuA/JS0A/kCV1D7QVO0zwFmSBqhGeyfYXifpCuB2YABYDVxbjnmPpIXAlcA9pXxoivN0STOpRpMXAyvbeoIRETEqsoedsYsOaTQa7uvr63QYEREThqR+241W2tRt2jMiIiLJLyIi6ifJLyIiaifJLyIiaifJLyIiaifJLyIiaifJLyIiaifJLyIiaifJLyIiaqdWjzebCAbWDtK7YGmnw4iIaIs1i+Z1OgQgI7+IiKihJL9NSHqupHMk3SrpBkk/lLSPpNWdji0iIsZGpj2bSBJwPnC27WNL2WxglzE8vmw/MRbHi4iIpyYjv40dBjxm+6yhAtsrgDuHPkvqkXS6pGskrZL0d6V8B0kXS7pW0oCkN5TyXkm/kPQZquWOsqZfRESHZeS3sVlA/xbqvAMYtL2/pEnAFZKWUSXIN9p+UNI04OeSlpQ2zwdOtP3u4Q4oaT4wH6Bnp+ljcR4RETGCJL/WHQHsK+no8nkKMBO4C/i4pEOBJ4Bd2TBdeoftn2/ugLYXA4sBJs2YmQUWIyLaLMlvY9cDR2+hjoD32b5wo0LpBGA6MMf2Y5LWAJPL7t+PcZwREbEVcs1vY5cAkyS9c6hA0v7AHk11LgTeJWmbsn8fSc+iGgHeVxLfYZu0iYiILpLk18S2gTcCry4/dbgeWAjc3VTtC8ANwLXl5w+foxpBfwNoSOoD3grcOJ6xR0TE6Kn6/310i0aj4b6+vk6HERExYUjqt91opU1GfhERUTtJfhERUTtJfhERUTtJfhERUTu54aXLSHoIuKnTcXTYNOD+TgfRBdIP6YMh6YeR+2AP2y09His/cu8+N7V619LTjaS+uvcBpB8gfTAk/TD2fZBpz4iIqJ0kv4iIqJ0kv+6zuNMBdIH0QSX9kD4Ykn4Y4z7IDS8REVE7GflFRETtJPlFRETtJPl1CUmvlXSTpF9KWtDpeMaapC9Juq+shDFU9mxJF0m6pbz/WdO+j5S+uEnSa5rK50gaKPs+LUnjfS5PlaTdJV0q6ReSrpf0/lJem36QNFnS1ZJWlj44rZTXpg+GSOqRdJ2kC8rnOvbBmhL/irIizvj1g+28OvwCeoBbgb2AbYGVwIs6HdcYn+OhwH7A6qayTwALyvYC4N/K9otKH0wC9ix901P2XQ0cRLWo8H8D/7PT59ZCH8wA9ivbOwI3l3OtTT+UeHco29sAVwEH1qkPmvrig8A3gQvK5zr2wRpg2iZl49IPGfl1hwOAX9q+zfYfgXOAN3Q4pjFl+3LggU2K3wCcXbbPBv6yqfwc2+ts3w78EjhA0gxgJ9tXuvov/qtNbbqe7XtsX1u2HwJ+AexKjfrBlYfLx23Ky9SoDwAk7QbMo1ofdEit+mAE49IPSX7dYVfgzqbPd5Wyp7tdbN8DVWIAnlPKN9cfu5btTcsnHEm9wEupRj616ocy3bcCuA+4yHbt+gD4T+DDwBNNZXXrA6j+8FkmqV/S/FI2Lv2Qx5t1h+Hmp+v8G5TN9cfTop8k7QB8D/iA7QdHuDzxtOwH2+uB2ZKmAudLmjVC9addH0g6ErjPdr+kuaNpMkzZhO6DJgfbvlvSc4CLJN04Qt0x7YeM/LrDXcDuTZ93A+7uUCzj6d4yZUF5v6+Ub64/7irbm5ZPGJK2oUp837B9XimuXT8A2P4dsBx4LfXqg4OB10taQ3WJ41WSvk69+gAA23eX9/uA86kuAY1LPyT5dYdrgJmS9pS0LXAssKTDMY2HJcDxZft44L+ayo+VNEnSnsBM4OoyBfKQpAPL3VzHNbXpeiXmLwK/sP3Jpl216QdJ08uID0nbAYcDN1KjPrD9Edu72e6l+rd+ie2/oUZ9ACDpWZJ2HNoGjgBWM1790Om7ffJ68g6n11Hd/XcrcEqn42nD+X0LuAd4jOovtXcAOwMXA7eU92c31T+l9MVNNN25BTTKP5BbgTMpTymaCC/gEKrpmFXAivJ6XZ36AdgXuK70wWrg1FJemz7YpD/msuFuz1r1AdXd7SvL6/qh/++NVz/k8WYREVE7mfaMiIjaSfKLiIjaSfKLiIjaSfKLiIjaSfKLiIjaSfKLiIjaSfKLiIja+f8EmZDdpglbpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_weather_main = day.groupby('weather_main').mean()\n",
    "by_weather_main['traffic_volume'].plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there's no weather type where traffic volume exceeds 5,000 cars. This makes finding a heavy traffic indicator more difficult. Let's also group by `weather_description`, which has a more granular weather classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_weather_description = day.groupby('weather_description').mean()\n",
    "by_weather_description['traffic_volume'].plot.barh(figsize=(5,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there are three weather types where traffic volume exceeds 5,000:\n",
    "\n",
    "- Shower snow\n",
    "- Light rain and snow\n",
    "- Proximity thunderstorm with drizzle\n",
    "\n",
    "It's not clear why these weather types have the highest average traffic values — this is bad weather, but not that bad. Perhaps more people take their cars out of the garage when the weather is bad instead of riding a bike or walking.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "In this project, we tried to find a few indicators of heavy traffic on the I-94 Interstate highway. We managed to find two types of indicators:\n",
    "\n",
    "- Time indicators\n",
    "    - The traffic is usually heavier during warm months (March–October) compared to cold months (November–February).\n",
    "    - The traffic is usually heavier on business days compared to the weekends.\n",
    "    - On business days, the rush hours are around 7 and 16.\n",
    "- Weather indicators\n",
    "    - Shower snow\n",
    "    - Light rain and snow\n",
    "    - Proximity thunderstorm with drizzle"
   ]
  }
 ],
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