us-medical-insurance-costs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U.S. Medical Insurance Costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Look over your dataset\n",
    "# Open insurance.cvs and take a look at the file. Take note of how information is organized.\n",
    "# How will this affect how you analyze the data in Python?\n",
    "# Is there anything of particular interest to you in the dataset that you want to investigate?\n",
    "# Think about these things before you jump into Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "insurance_ls = []\n",
    "age = []\n",
    "sex = []\n",
    "bmi =  []\n",
    "children = []\n",
    "smoker = []\n",
    "region = []\n",
    "charges = []\n",
    "\n",
    "with open('insurance.csv') as insurance:\n",
    "    rows = csv.DictReader(insurance)\n",
    "    for i in rows:\n",
    "        insurance_ls.append(i)\n",
    "\n",
    "# print(insurance_ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To find the total and average for each numeric column and create seperate list of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people insured are 1338 .\n",
      "The highest charge is 63770.43 and the lowest charge is 1121.87 and the average of all charges is 13270.42\n",
      "The highest age is 64.0 and the lowest age is 18.0 and the average of all ages is 39\n",
      "Based on the numeric columns the average of each are: Average age - 39 Average BMI - 30.663 Average number of children - 1 Average charge - 13270.42.\n"
     ]
    }
   ],
   "source": [
    "num_of_insurees = 0\n",
    "sum_of_ages = 0\n",
    "total_charges = 0\n",
    "sum_of_bmi = 0\n",
    "sum_of_children = 0\n",
    "max_charge = 0\n",
    "max_age = 0\n",
    "\n",
    "\n",
    "for i in range(len(insurance_ls)):\n",
    "        total_charges += float(insurance_ls[i]['charges'])\n",
    "        sum_of_ages += int(insurance_ls[i]['age'])\n",
    "        sum_of_bmi+= float(insurance_ls[i]['bmi'])\n",
    "        sum_of_children += int(insurance_ls[i]['children'])\n",
    "        num_of_insurees += 1\n",
    "        \n",
    "        charges.append(float(insurance_ls[i]['charges']))\n",
    "        age.append(insurance_ls[i]['age'])\n",
    "        bmi.append(insurance_ls[i]['bmi'])\n",
    "        sex.append(insurance_ls[i]['sex'])\n",
    "        region.append(insurance_ls[i]['region'])\n",
    "        smoker.append(insurance_ls[i]['smoker'])\n",
    "        children.append(insurance_ls[i]['children'])\n",
    "\n",
    "# Thinking about focusing the differences in charges thought I would include highest and lowest charges to go with the average. \n",
    " \n",
    "        if float(insurance_ls[i]['age']) > float(max_age):\n",
    "           max_age = round(float(insurance_ls[i]['age']),2)\n",
    "        if float(insurance_ls[i]['charges']) > float(max_charge):\n",
    "           max_charge = round(float(insurance_ls[i]['charges']),2)\n",
    "lowest_age = max_age\n",
    "for i in range(len(insurance_ls)):       \n",
    "  if float(insurance_ls[i]['age']) < float(lowest_age):\n",
    "           lowest_age = round(float(insurance_ls[i]['age']),2)       \n",
    "lowest_charge = max_charge\n",
    "for i in range(len(insurance_ls)):       \n",
    "  if float(insurance_ls[i]['charges']) < float(lowest_charge):\n",
    "           lowest_charge = round(float(insurance_ls[i]['charges']),2)       \n",
    "  \n",
    "\n",
    "avg_age = round(sum_of_ages/len(insurance_ls))\n",
    "avg_bmi = round(sum_of_bmi/len(insurance_ls), 3)\n",
    "avg_num_children = round(sum_of_children/len(insurance_ls))\n",
    "avg_charge =  round(total_charges/len(insurance_ls), 2)\n",
    "\n",
    "\n",
    "print('Number of people insured are', num_of_insurees, '.')\n",
    "\n",
    "print('The highest charge is', max_charge, 'and the lowest charge is', lowest_charge, 'and the average of all charges is', avg_charge)\n",
    "\n",
    "print('The highest age is', max_age, 'and the lowest age is', lowest_age, 'and the average of all ages is', avg_age)\n",
    "\n",
    "print(\"Based on the numeric columns the average of each are: Average age - \" + str(avg_age) + ' Average BMI - '\n",
    "       + str(avg_bmi) + ' Average number of children - ' + str(avg_num_children) + ' Average charge - ' + str(avg_charge) + '.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now that i have the highest and lowest value charges along with the average. Now to find the average price per person per region, while creating a dictionary for those regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of  insurees in southwest is 325 and in the southeast is 364, and in the northwest is 325 and in the north east is 324\n",
      "The most expensive region is southeast at a total of 5363689.76 by average it is southeast 14735.41\n",
      "The least expensive region is southwest at a total of 4012754.65 by average it is southwest 12346.94\n"
     ]
    }
   ],
   "source": [
    "southwest_charges_ls = []\n",
    "southeast_charges_ls = []\n",
    "northwest_charges_ls = []\n",
    "northeast_charges_ls = []\n",
    "southwest_ls = []\n",
    "southeast_ls = []\n",
    "northwest_ls = []\n",
    "northeast_ls = []\n",
    "regional_dict = {'southwest' : southwest_ls,'southeast' : southeast_ls,'northwest' : northwest_ls,'northeast' : northeast_ls }\n",
    "regional_charges = {'southwest' : southwest_charges_ls,'southeast' : southeast_charges_ls,'northwest' : northwest_charges_ls,'northeast' : northeast_charges_ls } \n",
    "\n",
    "def count_per_region(dict):\n",
    "        southwest_c = 0\n",
    "        southeast_c = 0\n",
    "        northwest_c = 0\n",
    "        northeast_c = 0\n",
    "        for i in range(len(dict)):\n",
    "              if dict[i]['region'] == 'southwest':\n",
    "                southwest_c += 1\n",
    "                southwest_charges_ls.append(dict[i]['charges'])\n",
    "                southwest_ls.append(dict[i])\n",
    "              elif  dict[i]['region'] == 'southeast':\n",
    "                southeast_c += 1\n",
    "                southeast_charges_ls.append(dict[i]['charges'])\n",
    "                southeast_ls.append(dict[i])\n",
    "              elif dict[i]['region'] == 'northwest':\n",
    "                northwest_c += 1\n",
    "                northwest_charges_ls.append(dict[i]['charges'])\n",
    "                northwest_ls.append(dict[i])  \n",
    "              elif dict[i]['region'] == 'northeast':\n",
    "                northeast_c += 1\n",
    "                northeast_charges_ls.append(dict[i]['charges'])\n",
    "                northeast_ls.append(dict[i])\n",
    "              else:\n",
    "                  continue \n",
    "              \n",
    "        return print('Amount of  insurees in southwest is', southwest_c, 'and in the southeast is', str(southeast_c) + ', and in the northwest is', str(northwest_c),'and in the north east is',  str(northeast_c))\n",
    "count_per_region(insurance_ls)\n",
    "# print(southeast_ls)\n",
    "\n",
    "#to find the most expensive region for insurance                                \n",
    "\n",
    "def most_expensive(regional_charges):\n",
    "    r_one_sum =0\n",
    "    r_two_sum = 0\n",
    "    r_three_sum = 0\n",
    "    r_four_sum = 0\n",
    "    the_expensive = 0\n",
    "    for i in regional_charges['southwest']:\n",
    "        r_one_sum += float(i)\n",
    "    for i in regional_charges['southeast']:\n",
    "        r_two_sum += float(i)\n",
    "    for i in regional_charges['northwest']:\n",
    "        r_three_sum += float(i)\n",
    "    for i in regional_charges['northeast']:\n",
    "        r_four_sum += float(i)\n",
    "\n",
    "    if r_one_sum > r_two_sum and r_one_sum > r_three_sum and r_one_sum > r_four_sum:\n",
    "      the_expensive = r_one_sum\n",
    "      region = list(regional_charges.keys())[0]\n",
    "    elif r_two_sum > r_one_sum and r_two_sum > r_three_sum and r_two_sum > r_four_sum:\n",
    "      the_expensive = r_two_sum\n",
    "      region = list(regional_charges.keys())[1]\n",
    "    elif r_three_sum > r_one_sum and r_three_sum > r_two_sum and r_three_sum > r_four_sum:\n",
    "      the_expensive = r_three_sum\n",
    "      region = list(regional_charges.keys())[2]\n",
    "    elif r_four_sum > r_three_sum and r_four_sum > r_two_sum and r_four_sum > r_one_sum:\n",
    "      the_expensive = r_four_sum\n",
    "      region = list(regional_charges.keys())[3]\n",
    "    else:\n",
    "       pass\n",
    "    \n",
    "    #by average\n",
    "    r_one_avg =  r_one_sum/ len(regional_charges['southwest'])\n",
    "    r_two_avg = r_two_sum/len(regional_charges['southeast'])\n",
    "    r_three_avg = r_three_sum/len(regional_charges['northwest'])\n",
    "    r_four_avg = r_four_sum/len(regional_charges['northeast'])\n",
    "    \n",
    "    if r_one_avg > r_two_avg and r_one_avg > r_three_avg and r_one_avg > r_four_avg:\n",
    "      avg_most_expensive = r_one_avg\n",
    "      region2 = list(regional_charges.keys())[0]\n",
    "    elif r_two_avg > r_one_avg and r_two_avg > r_three_avg and r_two_avg > r_four_avg:\n",
    "      avg_most_expensive = r_two_avg\n",
    "      region2 = list(regional_charges.keys())[1]\n",
    "    elif r_three_avg > r_one_avg and r_three_avg > r_two_avg and r_three_avg > r_four_avg:\n",
    "      avg_most_expensive = r_three_avg\n",
    "      region2 = list(regional_charges.keys())[2]\n",
    "    elif r_four_avg > r_three_avg and r_four_avg > r_two_avg and r_four_avg > r_one_avg:\n",
    "      avg_most_expensive = r_four_avg\n",
    "      region2 = list(regional_charges.keys())[3]\n",
    "    else:\n",
    "       pass\n",
    "  \n",
    "  \n",
    "    return print('The most expensive region is', region, 'at a total of', round(the_expensive,2), 'by average it is', region2, round(avg_most_expensive,2))\n",
    "      \n",
    "\n",
    "most_expensive(regional_charges)\n",
    "\n",
    "# to find the least expensive\n",
    "\n",
    "def least_expensive(regional_charges):\n",
    "    r_one_sum =0\n",
    "    r_two_sum = 0\n",
    "    r_three_sum = 0\n",
    "    r_four_sum = 0\n",
    "    least_expensive = 0\n",
    "    for i in regional_charges['southwest']:\n",
    "        r_one_sum += float(i)\n",
    "    for i in regional_charges['southeast']:\n",
    "        r_two_sum += float(i)\n",
    "    for i in regional_charges['northwest']:\n",
    "        r_three_sum += float(i)\n",
    "    for i in regional_charges['northeast']:\n",
    "        r_four_sum += float(i)\n",
    "\n",
    "    if r_one_sum < r_two_sum and r_one_sum < r_three_sum and r_one_sum < r_four_sum:\n",
    "      least_expensive = r_one_sum\n",
    "      region = list(regional_charges.keys())[0]\n",
    "    elif r_two_sum < r_one_sum and r_two_sum < r_three_sum and r_two_sum < r_four_sum:\n",
    "      least_expensive = r_two_sum\n",
    "      region = list(regional_charges.keys())[1]\n",
    "    elif r_three_sum < r_one_sum and r_three_sum < r_two_sum and r_three_sum < r_four_sum:\n",
    "      least_expensive = r_three_sum\n",
    "      region = list(regional_charges.keys())[2]\n",
    "    elif r_four_sum < r_three_sum and r_four_sum < r_two_sum and r_four_sum < r_one_sum:\n",
    "      least_expensive = r_four_sum\n",
    "      region = list(regional_charges.keys())[3]\n",
    "    else:\n",
    "       pass\n",
    "\n",
    "#by average\n",
    "\n",
    "    r_one_avg =  r_one_sum/ len(regional_charges['southwest'])\n",
    "    r_two_avg = r_two_sum/len(regional_charges['southeast'])\n",
    "    r_three_avg = r_three_sum/len(regional_charges['northwest'])\n",
    "    r_four_avg = r_four_sum/len(regional_charges['northeast'])\n",
    "    \n",
    "    if r_one_avg < r_two_avg and r_one_avg < r_three_avg and r_one_avg < r_four_avg:\n",
    "      avg_least_expensive = r_one_avg\n",
    "      region_avg = list(regional_charges.keys())[0]\n",
    "    elif r_two_avg < r_one_avg and r_two_avg < r_three_avg and r_two_avg < r_four_avg:\n",
    "      avg_least_expensive = r_two_avg\n",
    "      region_avg = list(regional_charges.keys())[1]\n",
    "    elif r_three_avg < r_one_avg and r_three_avg < r_two_avg and r_three_avg < r_four_avg:\n",
    "      avg_least_expensive = r_three_avg\n",
    "      region_avg = list(regional_charges.keys())[2]\n",
    "    elif r_four_avg < r_three_avg and r_four_avg < r_two_avg and r_four_avg < r_one_avg:\n",
    "      avg_least_expensive = r_four_avg\n",
    "      region_avg = list(regional_charges.keys())[3]\n",
    "    else:\n",
    "       pass\n",
    "  \n",
    "    return print('The least expensive region is', region, 'at a total of', round(least_expensive,2), 'by average it is', region_avg, round(avg_least_expensive,2))\n",
    "\n",
    "\n",
    "\n",
    "least_expensive(regional_charges)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With this we can see that southeast with the most insurees not ony has the most but is also the most expensive overall by region total and average per person. \n",
    "# This is also above total average by over 1000 dollars.\n",
    "# Also with southwest with the least overall per region total and average per person.\n",
    "# which has a lower average than the average charge by a lil over 1000 dollars.\n",
    "# with this information I would say its safe to say to an extent that the southeast area can be seen as the more expensive area to live for inurance\n",
    "# but with so many factors such as children, smoker, age and whether the insuree is male or female can determine much more specifics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So now to look at the difference between male and female charges\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male count is 676 representing 50.52% vs the female count of 662 representing 49.48%.\n",
      "The males are charged a total of  9434763.8 with an average of 13956.75 per male, and women are charged a total of 8321061.19 with an average of 12569.58 per female\n"
     ]
    }
   ],
   "source": [
    "male_ls = []\n",
    "female_ls= []\n",
    "def male_vs_female_count(dict):\n",
    "  male_c = 0\n",
    "  female_c = 0\n",
    "  for i in range(len(dict)):\n",
    "    if dict[i]['sex'] == 'male':\n",
    "      male_c += 1\n",
    "      male_ls.append(dict[i])\n",
    "    elif  dict[i]['sex'] == 'female':\n",
    "      female_c += 1\n",
    "      female_ls.append(dict[i])\n",
    "    else:\n",
    "      pass\n",
    "            \n",
    "  return print('Male count is', male_c, 'representing',  (\"{:.2%}\".format(male_c/num_of_insurees)), 'vs the female count of', female_c, 'representing',  (\"{:.2%}\".format(female_c/num_of_insurees)) + '.')\n",
    "\n",
    "\n",
    "male_vs_female_count(insurance_ls)\n",
    "male_vs_female_dict= {'male' : male_ls, 'female' : female_ls}\n",
    "\n",
    "def male_vs_female_charges(male_vs_female_dict):\n",
    "   total_male_charges = 0\n",
    "   total_female_charges = 0\n",
    "   for i in male_vs_female_dict['male']:\n",
    "    total_male_charges += float(i['charges'])\n",
    "   for i in male_vs_female_dict['female']:\n",
    "    total_female_charges += float(i['charges'])\n",
    "         \n",
    "   avg_male_charge = total_male_charges/len(male_vs_female_dict['male'])\n",
    "   avg_female_charge = total_female_charges/len(male_vs_female_dict['female']) \n",
    "\n",
    "   return print('The males are charged a total of ', round(total_male_charges,2), 'with an average of',  round(avg_male_charge,2), 'per male, and women are charged a total of', round(total_female_charges,2), 'with an average of', round(avg_female_charge,2), 'per female') \n",
    "\n",
    "male_vs_female_charges(male_vs_female_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With this information  males represent a larger margin of the insurees but not by much only by 1.02% so I would say the women to men ratio over all is fairly equal\n",
    "# but men seem to spend more overall and by average per persons but it is not much higher than the overall avergae by a couple of hundred dollars, and for the women its less by overall and average per person.\n",
    "# with the average per person being lower than the overall average by a couple hundred dollars aswell.\n",
    "# With this inforation once again to some extent I could say men pay more, which looking at this could be true. Then again what region do they live in and do they tend have more children could also influence \n",
    "# the amount they are charged. Seeing this, I could cross this with regional charges and create a male vs female per region to further my reasoning by seeing if this concept continues. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now to look at the difference between those with children and those without.\n",
    "# Also I wanted to see if there was much difference between those with one child and those with more than one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of insurees with children 764 amount with more than one is 440 amount with just one child 324  and with no children are 574\n",
      "Total charges with no children: 7098070.0 with an average of 12365.98 Total charges with one child: 4124899.67 with an average of 12731.17 Total charges with more than one child 6532855.32 with an average of 14847.4 Total charges with all that have children are: 10657755.0 with an average of 13949.94\n"
     ]
    }
   ],
   "source": [
    "with_one_child_ls = []\n",
    "no_child_ls = []\n",
    "more_than_one_ls = []\n",
    "total_with_children_ls = []\n",
    "\n",
    "\n",
    "def child_or_not(dict):\n",
    "   with_one_child = 0\n",
    "   no_child = 0\n",
    "   more_than_one = 0\n",
    "   \n",
    "  \n",
    "   for i in range(len(dict)):\n",
    "    if dict[i]['children'] == '0':\n",
    "      no_child += 1\n",
    "      no_child_ls.append(dict[i])\n",
    "    elif dict[i]['children'] == '1':\n",
    "      with_one_child += 1\n",
    "      with_one_child_ls.append(dict[i])\n",
    "      total_with_children_ls.append(dict[i])\n",
    "    else:\n",
    "      more_than_one += 1\n",
    "      more_than_one_ls.append(dict[i])\n",
    "      total_with_children_ls.append(dict[i])\n",
    "   total_with_children = with_one_child + more_than_one\n",
    "   \n",
    "   return print('Amount of insurees with children', total_with_children, 'amount with more than one is', more_than_one,  'amount with just one child', with_one_child, ' and with no children are', no_child), total_with_children_ls, with_one_child_ls, no_child_ls, more_than_one_ls\n",
    "\n",
    "child_or_not(insurance_ls)\n",
    "\n",
    "child_or_not_dict = {'no child' : no_child_ls,'with one child' : with_one_child_ls, 'more than one' : more_than_one_ls, 'total with children' : total_with_children_ls }\n",
    "\n",
    "def price_vs_child_or_not(child_or_not_dict):\n",
    "  no_child_charges_total = 0\n",
    "  with_one_child_charges_total = 0\n",
    "  more_than_one_charges_total  = 0\n",
    "  total_with_children_charges_total = 0\n",
    "\n",
    "  for i in child_or_not_dict['no child']:\n",
    "    no_child_charges_total += float(i['charges'])\n",
    "  for i in child_or_not_dict['with one child']:\n",
    "    with_one_child_charges_total += float(i['charges'])      \n",
    "  for i in child_or_not_dict['more than one']:\n",
    "    more_than_one_charges_total += float(i['charges'])\n",
    "  for i in child_or_not_dict['total with children']:\n",
    "    total_with_children_charges_total += float(i['charges'])\n",
    "  \n",
    "  no_child_avg = round(no_child_charges_total/len(child_or_not_dict['no child']),2)\n",
    "  with_one_child_avg = round(with_one_child_charges_total/len(child_or_not_dict['with one child']),2)\n",
    "  more_than_one_avg = round(more_than_one_charges_total/len(child_or_not_dict['more than one']),2)\n",
    "  total_with_children_avg = round(total_with_children_charges_total/len(child_or_not_dict['total with children']),2)\n",
    "\n",
    "  return print('Total charges with no children:', round(no_child_charges_total,2),'with an average of', no_child_avg, 'Total charges with one child:', round(with_one_child_charges_total,2), 'with an average of', with_one_child_avg, 'Total charges with more than one child', round(more_than_one_charges_total,2), 'with an average of', more_than_one_avg, 'Total charges with all that have children are:', round(total_with_children_charges_total,2), 'with an average of', total_with_children_avg) \n",
    "\n",
    "price_vs_child_or_not(child_or_not_dict)\n",
    "\n",
    "#print(male_vs_female_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With this you can see that compared to the overall average charge those with no children and those with one child pay under overall average.\n",
    "# Those with no children are paying less. Also that the average of those with more than one child pay above overall average by more than 1000.\n",
    "# This possibly reveals that those with no children pay less than those with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the insurance.csv provided:\n",
    "\n",
    "Number of people insured are 1338 .\n",
    "\n",
    "Based on the numeric columns the average of each are: Average age - 39 Average BMI - 30.663 Average number of children - 1 Average charge - 13270.42.\n",
    "\n",
    "The highest charge is 63770.43 and the lowest charge is 1121.87 and the average of all charges is 13270.42\n",
    "\n",
    "The highest age is 64.0 and the lowest age is 18.0 and the average of all ages is 39\n",
    "\n",
    "Amount of  insurees in southwest is 325 and in the southeast is 364, and in the northwest is 325 and in the north east is 324\n",
    "The most expensive region is southeast at a total of 5363689.76 by average it is southeast 14735.41\n",
    "The least expensive region is southwest at a total of 4012754.65 by average it is southwest 12346.94\n",
    "\n",
    "Male count is 676 representing 50.52% vs the female count of 662 representing 49.48%.\n",
    "The males are charged a total of  9434763.8 with an average of 13956.75 per male, and women are charged a total of 8321061.19 with an average of 12569.58 per female.\n",
    "\n",
    "Amount of insurees with children 764 amount with more than one is 440 amount with just one child 324  and with no children are 574\n",
    "Total charges with no children: 7098070.0 with an average of 12365.98 Total charges with one child: 4124899.67 with an average of 12731.17 Total charges with more than one child 6532855.32 with an average of 14847.4 Total charges with all that have children are: 10657755.0 with an average of 13949.94\n",
    "The highest charge is 63770.43 and the lowest charge is 1121.87 and the average of all charges is 13270.42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What sticks out to me first about the different averages is the average age is 39, which seems high but seeing how the lowest age is 18 vs the highest age which is 64 the mean does not seem to be skewed nor is it far from the median, 41. This could mean people get their insurance and stuff together in their late 30s referring to the csv. To dive deeper I could compare multiple groups of years 18-28,28-38,38-48, and so on.\n",
    "The average number of children is 1 which could mean your average insuree could have at least one child, this seems likely because there are significantly more insurees with children according to the csv. \n",
    "\n",
    "Last but not insignificant with an average charge at a value of 13270.42 compared to a high of 63000 and a low of 1124 is not too far from the low but significantly far from the high compared to the low maybe anything around above a certain amount may particular outliers or have the worst factors that influence their cost. To get started, expenses by region at first glance southeast is the most expensive region to have insurance on average, and the southwest has the least expensive region to have insurance.\n",
    "\n",
    "Seeing that the southeast also has more insurees compared to the differences of the other regions perhaps simply because it is more, or the southeast has more outliers, or one could consider that it has more males in the area according to our breakdown of average male vs female. Which could help further my reasoning for both or prove my reasonings vague. Other possible outlooks could: be a variance of ages in the regions, who smoke, who has children, and possibly a particular BMI may be factored in for those regions, north vs. south areas and west vs. east areas because the south has the least expensive and most expensive areas in the southern hemisphere, and ranking the regions from most to least.\n",
    "\n",
    "Many of those outlooks could also provide further data on whether or not those factors, further my reasoning for the concept that males are charged more than females according to the csv, and the same with those with and without children, how those without children pay less. What regions have the least amount of children, that is male or female? Answering these questions will provide me with more information to better determine the most costly factors and possibly how much they can affect your overall cost whether it be age, sex, the region you live in, how many children you have, and BMI ( though not an accurate predictor of health but insurers still tend to use it)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "#possible improvements: more accurate numbers and percentages for differences, more highs and lows to compare the averages, diving deeper, \n",
    "# order the regions to get a better understanding of all of them and better organize later on, create an id system based on number of insurees\n",
    "# limit a random of selection or by range to possibly reduce possible skewage \n"
   ]
  }
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