{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "worldwide-blood",
   "metadata": {},
   "source": [
    "# A Data Science Investigation About Fatal Car Crashes in America "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "understanding-numbers",
   "metadata": {},
   "source": [
    "*✏️ Write 2-3 sentences describing your research.*\n",
    "\n",
    "It's a collection of data on the reasons fatal car crashes occur in every state of America, and it will be used to determine which region of America is the deadliest. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greater-circular",
   "metadata": {},
   "source": [
    "## Overarching Question: What is the deadliest region in America to drive on?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appreciated-testimony",
   "metadata": {},
   "source": [
    "*✏️ Write 2-3 sentences explaining why this question.*\n",
    "\n",
    "I am interested in this because I live on the Northeast Coast and we have a lot of car \n",
    "accidents. People drive very fast here. The roads are not always paved properly and maintained. I want to know if it's just bad luck when people get into accidents or if it's their own fault. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "permanent-pollution",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "technical-evans",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Include any import statements you will need\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "overhead-sigma",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
    "\n",
    "file_name = \"B_D - bad-drivers.csv\"\n",
    "dataset_path = \"data/\" + file_name\n",
    "\n",
    "df = pd.read_csv(dataset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "heated-blade",
   "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>State</th>\n",
       "      <th>number_drivers_fatal_billion_miles</th>\n",
       "      <th>percentage_drivers_fatal_speeding</th>\n",
       "      <th>percentage_drivers_fatal_alcohol_impaired</th>\n",
       "      <th>percentage_drivers_fatal_not_distracted</th>\n",
       "      <th>percentage_drivers_fatal_no_previous_accidents</th>\n",
       "      <th>car_insurance_premiums</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>18.8</td>\n",
       "      <td>39</td>\n",
       "      <td>30</td>\n",
       "      <td>96</td>\n",
       "      <td>80</td>\n",
       "      <td>784.55</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>18.1</td>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "      <td>1053.48</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>18.6</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>84</td>\n",
       "      <td>96</td>\n",
       "      <td>899.47</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>22.4</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>94</td>\n",
       "      <td>95</td>\n",
       "      <td>827.34</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>12.0</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>878.41</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  number_drivers_fatal_billion_miles  \\\n",
       "0     Alabama                                18.8   \n",
       "1      Alaska                                18.1   \n",
       "2     Arizona                                18.6   \n",
       "3    Arkansas                                22.4   \n",
       "4  California                                12.0   \n",
       "\n",
       "   percentage_drivers_fatal_speeding  \\\n",
       "0                                 39   \n",
       "1                                 41   \n",
       "2                                 35   \n",
       "3                                 18   \n",
       "4                                 35   \n",
       "\n",
       "   percentage_drivers_fatal_alcohol_impaired  \\\n",
       "0                                         30   \n",
       "1                                         25   \n",
       "2                                         28   \n",
       "3                                         26   \n",
       "4                                         28   \n",
       "\n",
       "   percentage_drivers_fatal_not_distracted  \\\n",
       "0                                       96   \n",
       "1                                       90   \n",
       "2                                       84   \n",
       "3                                       94   \n",
       "4                                       91   \n",
       "\n",
       "   percentage_drivers_fatal_no_previous_accidents  car_insurance_premiums  \\\n",
       "0                                              80                  784.55   \n",
       "1                                              94                 1053.48   \n",
       "2                                              96                  899.47   \n",
       "3                                              95                  827.34   \n",
       "4                                              89                  878.41   \n",
       "\n",
       "      region  \n",
       "0  Southeast  \n",
       "1       West  \n",
       "2  Southeast  \n",
       "3  Southeast  \n",
       "4       West  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continental-franklin",
   "metadata": {},
   "source": [
    "**Data Overview**\n",
    "\n",
    "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*\n",
    "\n",
    "### When is this data set from?\n",
    "\n",
    "I got the data set from FiveThirtyEight. It was used for an article called\n",
    "\"Dear Mona, Which state has the worst drivers?\" in October 2014. The person who wrote the article is Mona Chalabi, they are a data editor at the Guardian US, \n",
    "a columnist at New York Magazine, and a lead news writer for FiveThirtyEight.\n",
    "\n",
    "The date is about fatal collisions in each state. There are 8 rows:\n",
    "\n",
    "1. State\n",
    "2. Number of drivers involved in fatal collisions per billion miles\n",
    "3. Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n",
    "4. Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n",
    "5. Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n",
    "6. Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n",
    "7. Car Insurance Premiums ($)\n",
    "8. Region\n",
    "\n",
    "### How did this data set get clean?\n",
    "\n",
    "I did not need to do much cleaning of the data myself, but I did add a column called \"Region\" to separate the state into 5 different regions: Northwest, Midwest, Southeast, West, and Northeast. I also excluded data on Losses incurred by insurance companies for collisions per insured driver because insurance companies are well known for finding ways to get out of paying customers for collisions, thus it is not an accurate representation of fatal car crashes.    \n",
    "\n",
    "## What specific research questions will you investigate?\n",
    "\n",
    "1. What region has the highest drinking and driving cause of fatal collisions?\n",
    "\n",
    "2. What region has the highest car insurance premiums?\n",
    "\n",
    "3. What region is the most unlucky state for fatal collisions?\n",
    "\n",
    "4. Is there a connection between the speed and the roads that are causing fatal collisions, that would make the Car Insurance Premiums more expensive?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7bba5f3-5911-4a76-ad43-f6ce78cd4fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'number_drivers_fatal_billion_miles',\n",
       "       'percentage_drivers_fatal_speeding',\n",
       "       'percentage_drivers_fatal_alcohol_impaired',\n",
       "       'percentage_drivers_fatal_not_distracted',\n",
       "       'percentage_drivers_fatal_no_previous_accidents',\n",
       "       'car_insurance_premiums', 'region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infinite-instrument",
   "metadata": {},
   "source": [
    "# Methods and Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "basic-canadian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function seaborn.rcmod.set_theme(context='notebook', style='darkgrid', palette='deep', font='sans-serif', font_scale=1, color_codes=True, rc=None)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recognized-positive",
   "metadata": {},
   "source": [
    "## First Research Question: What region has the highest drinking and driving cause of fatal collisions?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-palmer",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "endless-variation",
   "metadata": {},
   "source": [
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?* \n",
    "  \n",
    "✏️ *Write your answer below:*\n",
    "\n",
    "To answer this question, I will organize the data for each state by the region it is in. Then, calculate the average percentage of drivers involved in fatal collisions who were alcohol-impaired. Finally, I will make a bar plot to compare the average number of fatal collisions that involved drinking and driving for each of the regions\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-japan",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "negative-highlight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region\n",
       "Southeast    29.687500\n",
       "West         30.363636\n",
       "Northwest    31.000000\n",
       "Northeast    31.444444\n",
       "Midwest      31.666667\n",
       "Name: percentage_drivers_fatal_alcohol_impaired, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#######################################################################\n",
    "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
    "### \n",
    "### Your data analysis may include a statistic and/or a data visualization\n",
    "#######################################################################\n",
    "\n",
    "region = df.groupby(\"region\").percentage_drivers_fatal_alcohol_impaired.mean().sort_values()\n",
    "region\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "victorian-burning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='region', ylabel='percentage_drivers_fatal_alcohol_impaired'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x=\"region\", y=\"percentage_drivers_fatal_alcohol_impaired\", errorbar=\"sd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collectible-puppy",
   "metadata": {},
   "source": [
    "## Second Research Question: What region has the highest car insurance premiums?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "demographic-future",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporate-roller",
   "metadata": {},
   "source": [
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?* \n",
    "\n",
    "✏️ *Write your answer below:*\n",
    "\n",
    "To answer this question, I will organize the data for each state by the region it is in. Then, compare the average cost of car insurance and see which region is the highest.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "juvenile-creation",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "pursuant-surrey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region\n",
       "Midwest       756.630833\n",
       "West          855.624545\n",
       "Southeast     905.472500\n",
       "Northeast     975.038889\n",
       "Northwest    1160.163333\n",
       "Name: car_insurance_premiums, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#######################################################################\n",
    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
    "###\n",
    "### Your data analysis may include a statistic and/or a data visualization\n",
    "#######################################################################\n",
    "\n",
    "df.groupby(\"region\").car_insurance_premiums.mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "located-night",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='region', ylabel='car_insurance_premiums'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x=\"region\", y=\"car_insurance_premiums\", errorbar=\"sd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab785a8-ac72-4fec-8d4b-7ad7f93de32d",
   "metadata": {},
   "source": [
    "## Third Research Question: What region is the most unlucky state for fatal collisions?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810dc600-da04-437d-a546-4d6c5bec01c6",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be64d030-0f40-4c32-ac3e-be494e64b3a7",
   "metadata": {},
   "source": [
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?* \n",
    "\n",
    "✏️ *Write your answer below:*\n",
    "\n",
    "To answer this question, I will organize the data for each state by the region it is in. Then, compare the average percentage of Drivers Involved In Fatal Collisions Who Were Not Distracted and the average percentage of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "096fe314-2953-4644-86e0-cd717f77eb8f",
   "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>percentage_drivers_fatal_not_distracted</th>\n",
       "      <th>percentage_drivers_fatal_no_previous_accidents</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Midwest</th>\n",
       "      <td>88.833333</td>\n",
       "      <td>86.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northeast</th>\n",
       "      <td>87.777778</td>\n",
       "      <td>85.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwest</th>\n",
       "      <td>91.333333</td>\n",
       "      <td>93.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southeast</th>\n",
       "      <td>83.000000</td>\n",
       "      <td>89.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>91.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           percentage_drivers_fatal_not_distracted  \\\n",
       "region                                               \n",
       "Midwest                                  88.833333   \n",
       "Northeast                                87.777778   \n",
       "Northwest                                91.333333   \n",
       "Southeast                                83.000000   \n",
       "West                                     84.000000   \n",
       "\n",
       "           percentage_drivers_fatal_no_previous_accidents  \n",
       "region                                                     \n",
       "Midwest                                         86.666667  \n",
       "Northeast                                       85.111111  \n",
       "Northwest                                       93.666667  \n",
       "Southeast                                       89.312500  \n",
       "West                                            91.727273  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = df.groupby(\"region\")\n",
    "region_mean = region[[\"percentage_drivers_fatal_not_distracted\", \"percentage_drivers_fatal_no_previous_accidents\"]].mean().sort_values(\"region\")\n",
    "region_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3777e6d1-08a5-4747-9100-9af86f88e90a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='region', ylabel='percentage_drivers_fatal_not_distracted'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=region_mean, x=\"region\", y=\"percentage_drivers_fatal_not_distracted\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "21d5122b-9973-43f9-b4f0-7db7a6c936d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='region', ylabel='percentage_drivers_fatal_no_previous_accidents'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=region_mean, x=\"region\", y=\"percentage_drivers_fatal_no_previous_accidents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d66967db-fe78-4889-824e-f7bce4e02cc8",
   "metadata": {},
   "source": [
    "## Fourth Research Question: Is there a connection between the average Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding and the region with the most expensive car insurance premiums?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9661b6d4-3c4f-42a2-8916-b9df38375760",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc44ade7-3ae9-44a0-b821-29ecb1b66385",
   "metadata": {},
   "source": [
    "Explain how you will approach this research question below. Consider the following:\n",
    "\n",
    "Which aspects of the dataset will you use?\n",
    "How will you reorganize/store the data?\n",
    "What data science tools/functions will you use and why?\n",
    "✏️ Write your answer below:\n",
    "\n",
    "To answer this question, I will organize the data for each state by the region it is in. Then, compare the average Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding to see if there is a connection with the region with the highest car insurance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b921b74c-951a-4f30-a42e-292f011fd61a",
   "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>percentage_drivers_fatal_speeding</th>\n",
       "      <th>car_insurance_premiums</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Midwest</th>\n",
       "      <td>27.166667</td>\n",
       "      <td>756.630833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northeast</th>\n",
       "      <td>32.444444</td>\n",
       "      <td>975.038889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwest</th>\n",
       "      <td>39.333333</td>\n",
       "      <td>1160.163333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southeast</th>\n",
       "      <td>27.687500</td>\n",
       "      <td>905.472500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>39.909091</td>\n",
       "      <td>855.624545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           percentage_drivers_fatal_speeding  car_insurance_premiums\n",
       "region                                                              \n",
       "Midwest                            27.166667              756.630833\n",
       "Northeast                          32.444444              975.038889\n",
       "Northwest                          39.333333             1160.163333\n",
       "Southeast                          27.687500              905.472500\n",
       "West                               39.909091              855.624545"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = df.groupby(\"region\")\n",
    "region_speed_insur = region[[\"percentage_drivers_fatal_speeding\", \"car_insurance_premiums\"]].mean().sort_values(\"region\")\n",
    "region_speed_insur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c242ede2-9440-4d49-8967-e6dfce650ec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='region', ylabel='percentage_drivers_fatal_speeding'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=region_speed_insur, y=\"percentage_drivers_fatal_speeding\", x=\"region\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c8079c91-3eb2-4dfd-88c0-53f8a81c9892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='region', ylabel='car_insurance_premiums'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=region_speed_insur, x=\"region\", y=\"car_insurance_premiums\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infectious-symbol",
   "metadata": {},
   "source": [
    "# Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "furnished-camping",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "## Considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bearing-stadium",
   "metadata": {},
   "source": [
    "*It's important to recognize the limitations of our research.\n",
    "Consider the following:*\n",
    "\n",
    "- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
    "- *What were limitations of your datset?*\n",
    "- *Are there any known biases in the data?*\n",
    "\n",
    "✏️ *Write your answer below:*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beneficial-invasion",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "about-raise",
   "metadata": {},
   "source": [
    "*Summarize what you discovered through the research. Consider the following:*\n",
    "\n",
    "- *What did you learn about your media consumption/digital habits?*\n",
    "- *Did the results make sense?*\n",
    "- *What was most surprising?*\n",
    "- *How will this project impact you going forward?*\n",
    "\n",
    "✏️ *Write your answer below:*"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
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  "toc": {
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   "nav_menu": {},
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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