us-medical-insurance-costs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U.S. Medical Insurance Costs\n",
    "(This is a work in progress, it will be improved as I learn more)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 1. Overview\n",
    "\n",
    "This project aims to showcase some investgative skills learnt so far (45% complete) through the Data Science Career Path on Codecademy. It will involve a CSV file which I will analyze to come up with some findings and potitnital use cases for this dataset. (This is a work in progress so may be improved over time with more experience) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Contents\n",
    "\n",
    "1. Overview  \n",
    "&ensp;1.1 Contents  \n",
    "&ensp;1.2 Observing the Data  \n",
    "&ensp;1.3 Goals\n",
    "2. Descriptive Analysis  \n",
    "&ensp;2.1 Helper Funcitons  \n",
    "&ensp;2.2 Describing the Variables  \n",
    "&ensp;&ensp;&ensp;2.2.1 Age  \n",
    "&ensp;&ensp;&ensp;2.2.2 BMI  \n",
    "&ensp;&ensp;&ensp;2.2.3 Children  \n",
    "&ensp;&ensp;&ensp;2.2.4 Charges  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Observing the Data\n",
    "From observing the data set, it is structured as follows:\n",
    "|Name|Type|\n",
    "|:---|:---|\n",
    "|age|numerical (int)|\n",
    "|sex|categorical (male/female)|\n",
    "|bmi|numerical (float)|\n",
    "|children|numerical (int)|\n",
    "|smoker|categorical (yes/no)|\n",
    "|region|categorical|\n",
    "|charges|numerical (float)|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[comment]: <> (For analysis the data will need to be slightly adjusted to allow for easy processing. \"smoker\" and \"sex\" can be changed to a binay category value. 1 = \"yes\" and \"male\". )\n",
    "There doesn't seem to be any missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#import csv into variable\n",
    "import csv\n",
    "\n",
    "insurance_data = csv.DictReader(open(\"insurance.csv\"))\n",
    "\n",
    "#store columns as perameters\n",
    "age = []\n",
    "sex = []\n",
    "bmi = []\n",
    "children = []\n",
    "smoker = []\n",
    "region = []\n",
    "charges = []\n",
    "\n",
    "for row in insurance_data:\n",
    "    age.append(int(row['age']))\n",
    "    sex.append(row['sex'])\n",
    "    bmi.append(float(row['bmi']))\n",
    "    children.append(int(row['children']))\n",
    "    smoker.append(row['smoker'])\n",
    "    region.append(row['region'])\n",
    "    charges.append(float(row['charges']))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Goals\n",
    "- [X] Descriptive Analysis, (mean, mode, range, count, of each variable)\n",
    "- [ ] Exploratory Analysis, do some investication into correlations between columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 2 Descriptive Analysis\n",
    "\n",
    "I will first do some descriptive analysis to understand the dataset better. Will inlude varius displays of values and charts.\n",
    "\n",
    "numerical:\n",
    "\n",
    "- mean - center of the distribution\n",
    "- standard deviation - sread of the distribution (lower the less variability)\n",
    "- skewness \n",
    "-\n",
    "- mode - peak of the distribution\n",
    "-\n",
    "- median - middle value of data, robust\n",
    "- interquartile range - robust\n",
    "-\n",
    "- range\n",
    "- variance\n",
    "\n",
    "categorical:\n",
    "\n",
    "- frequency - the count\n",
    "- proprtion - frequency/total\n",
    "- percentage - proportion converted\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Helper Funcitons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "#helper funcitons\n",
    "import statistics\n",
    "from scipy import stats\n",
    "from collections import Counter\n",
    "\n",
    "#numerical data\n",
    "def get_mean(data): return sum(data)/len(data)\n",
    "\n",
    "def get_median(data): return sorted(data)[len(data)//2]\n",
    "\n",
    "def get_mode(data): return Counter(data).most_common(1)[0][0]\n",
    "\n",
    "def get_range(data): return abs(max(data) - min(data))\n",
    "\n",
    "def get_iqr(data): return stats.iqr(data)\n",
    "\n",
    "def get_stdev(data): return statistics.stdev(data)\n",
    "\n",
    "def get_variance(data): return statistics.variance(data)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#categorical data\n",
    "def get_frequency(data): return dict(Counter(data))\n",
    "\n",
    "def get_proportion(frequency): return {key:frequency[key]/sum(frequency.values()) for key in dict(frequency)}\n",
    "\n",
    "def get_percentage(proportion): return {key: proportion[key]*100 for key in proportion}\n",
    "\n",
    "                                                                                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_numerical_variable_info(data):\n",
    "    variable_info = {\n",
    "        \"mean\":get_mean(data),\n",
    "        \"median\":get_median(data),\n",
    "        \"mode\":get_mode(data),\n",
    "        \"range\":get_range(data),\n",
    "        \"iqr\":get_iqr(data),\n",
    "        \"stdev\":get_stdev(data),\n",
    "        \"variance\":get_variance(data),\n",
    "        \"max\":max(data),\n",
    "        \"min\":min(data)\n",
    "    }\n",
    "    return variable_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_categorical_variable_info(data):\n",
    "    variable_info = {\n",
    "        \"frequency\" : get_frequency(data)\n",
    "    }\n",
    "    variable_info[\"proportion\"] = get_proportion(variable_info['frequency'])\n",
    "    variable_info[\"percentage\"] = get_percentage(variable_info['proportion'])\n",
    "    return variable_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'frequency': {'southwest': 325,\n",
       "  'southeast': 364,\n",
       "  'northwest': 325,\n",
       "  'northeast': 324},\n",
       " 'proportion': {'southwest': 0.2428998505231689,\n",
       "  'southeast': 0.27204783258594917,\n",
       "  'northwest': 0.2428998505231689,\n",
       "  'northeast': 0.242152466367713},\n",
       " 'percentage': {'southwest': 24.28998505231689,\n",
       "  'southeast': 27.204783258594915,\n",
       "  'northwest': 24.28998505231689,\n",
       "  'northeast': 24.2152466367713}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_variable_info(region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_numeric_plots(data):\n",
    "    import matplotlib.pyplot as plt\n",
    "    #to fix errors when making labels\n",
    "    from importlib import reload\n",
    "    variable_info = get_numerical_variable_info(data)\n",
    "    print(variable_info)\n",
    "    \n",
    "    plt=reload(plt)\n",
    "\n",
    "    fig, ax = plt.subplots(2,1)\n",
    "    fig.tight_layout()\n",
    "\n",
    "\n",
    "    bg_color = [1,1,1,0.5]\n",
    "\n",
    "    #distribution analysis histogram\n",
    "    bin_num = round(variable_info['range'])/2 if len(data) < 50 else 50\n",
    "    ax[0].hist(data, bins=bin_num, edgecolor='k')\n",
    "    ax[0].axvline(variable_info['mean'], color='k', linestyle='dashed')\n",
    "    ax[0].text(variable_info['mean'] + 0.6, 0, f'mean: {round(variable_info[\"mean\"])}', rotation=90, va='bottom', backgroundcolor=bg_color)\n",
    "    ax[0].axvline(variable_info['mode'], color='k', linestyle='dashed')\n",
    "    ax[0].text(variable_info['mode'] + 0.6, 0, f'mode: {round(variable_info[\"mode\"])}', rotation=90, va='bottom', backgroundcolor=bg_color)\n",
    "    ax[0].axvline(variable_info['median'], color='k', linestyle='dashed')\n",
    "    ax[0].text(variable_info['median'] - 1.6, 0, f'median: {round(variable_info[\"median\"])}', rotation=90, va='bottom', backgroundcolor=bg_color)\n",
    "\n",
    "    #outlier analysis box plot\n",
    "    ax[1].boxplot(data, vert= 0, widths=0.5)\n",
    "    ax[1].text(variable_info['median'] + 0.5, 1.3, f'iqr: {round(variable_info[\"iqr\"])}', va='center', ha='center')\n",
    "    ax[1].text(variable_info['max'], 1, f'max: {round(variable_info[\"max\"])}', va='center', ha='center', rotation=90,backgroundcolor=bg_color)\n",
    "    ax[1].text(variable_info['min'], 1, f'min: {round(variable_info[\"min\"])}', va='center', ha='center', rotation=90,backgroundcolor=bg_color)\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Describing the variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 Age\n",
    "\n",
    "- [ ] somethings happening at 18\n",
    "\n",
    "- [ ] nothing after 64 or before 18\n",
    "\n",
    "- [ ] even spread apart form 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mean': 39.20702541106129, 'median': 39, 'mode': 18, 'range': 46, 'iqr': 24.0, 'stdev': 14.049960379216156, 'variance': 197.40138665754378, 'max': 64, 'min': 18}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_numeric_plots(age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the top two graphs show that the spread of the data is quite even across the age groups, with a spike at age 18. understanding if this is a good sample of ages needs investigation to find if this has a similar spread to the spread of the ages of total population of the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "us_population_data = csv.DictReader(open(\"other datasets/us-population-gender.csv\"))\n",
    "\n",
    "bins = [15,20,25,30,35,40,45,50,55,60,65]\n",
    "#process us population data for spine graph\n",
    "labels = []\n",
    "total_male = 0\n",
    "total_female = 0\n",
    "us_totals = {}\n",
    "for row in us_population_data:\n",
    "    label = row['ï»¿label'].split(' ')[0]\n",
    "    if label != \"Under\":\n",
    "        label = int(label)\n",
    "    else:\n",
    "        label = 0\n",
    "        \n",
    "    if label in bins and label != 65:    \n",
    "        us_totals.setdefault(label, 0)\n",
    "        us_totals[label] = (int(row[\"total\"].replace(',', '')))\n",
    "        total_male += int(row['male'].replace(',', ''))\n",
    "        total_female += int(row['female'].replace(',', ''))\n",
    "\n",
    "us_totals[15] = (us_totals[15]/5) * 2 # make an estimate for only 18 and 19 year olds\n",
    "us_totals[60] = (us_totals[60]/5) * 3 # make an estimage removing above 64\n",
    "\n",
    "#process age data for spine graph\n",
    "age_totals ={}\n",
    "for each in age:\n",
    "    bin = 0\n",
    "    for i in range(len(bins), 0, -1):\n",
    "        if i > len(bins) -1:\n",
    "            continue\n",
    "        if each < bins[i]:\n",
    "            bin = bins[i-1]\n",
    "        \n",
    "    total = age_totals.setdefault(bin,0)\n",
    "    age_totals[bin] += 1\n",
    "\n",
    "\n",
    "#turn into usable lists for plotting\n",
    "us_total_num = sum(list(us_totals.values())[3:13])\n",
    "us_percentages = {key:(us_totals[key]/us_total_num)*100 for key in us_totals} \n",
    "\n",
    "age_total_num = sum(age_totals.values())\n",
    "age_percentages = {key:(age_totals[key]/age_total_num)*100 for key in age_totals}\n",
    "\n",
    "us_percentages_list = list(zip(us_percentages.keys(), us_percentages.values()))\n",
    "age_percentages_list =list(zip(age_percentages.keys(), age_percentages.values()))\n",
    "\n",
    "us_percentages_list.sort()\n",
    "age_percentages_list.sort()\n",
    "\n",
    "us_bars = [i[1] for i in us_percentages_list]\n",
    "age_bars = [i[1] for i in age_percentages_list]\n",
    "\n",
    "x_values = np.arange(len(age_bars))\n",
    "bins.pop(0)\n",
    "bins.insert(0,18) #rename lowest bin\n",
    "\n",
    "plt.bar(x_values+0.2, us_bars, 0.4, label='US age distribition')\n",
    "plt.bar(x_values-0.2, age_bars, 0.4, label='Data age distribution')\n",
    "\n",
    "plt.xticks(x_values, bins[:-1]) \n",
    "plt.xlabel(\"Start of Age Groups (upto not including next bin num)\") \n",
    "plt.ylabel(\"Percentage of Populaition\") \n",
    "plt.title(\"Distibution of Age Compared to US total pupulation\") \n",
    "plt.legend() \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the spread of the ages in our data with the general population ([as of 222](https://data.census.gov/table/ACSST1Y2022.S0101?q=population%20by%20age)), we can see that this spike at 18-20 is not aligned with the general population trends. the data also seems to have more over 60 data points compared to the proportions within the general population. The US age distribution dataset includes the total population, not just those with medical insurane. this dataset may not be proportionally representitive of all age groups. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mean': 30.663396860986538, 'median': 30.4, 'mode': 32.3, 'range': 37.17, 'iqr': 8.3975, 'stdev': 6.098186911679014, 'variance': 37.18788360977324, 'max': 53.13, 'min': 15.96}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_numeric_plots(bmi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These plots show that within this dataset, the average bmi is 31. For adults 20 years or older, the bmi chart is as follows:\n",
    "|BMI|Weight Status|\n",
    "|:---|:---|\n",
    "|Below 18.5|Underweight|\n",
    "|18.5 – 24.9|Healthy Weight|\n",
    "|25.0 – 29.9|Overweight|\n",
    "|30.0 and Above|Obesity|\n",
    "\n",
    "The data that we have does include ages below 20, but a general observation is that the trend in this dataset is that, with a median of 30, over half of the observations are included in the \"Obecity\" weight status. with the majority of poeple between 25 and 35, which is another observation showing the above healthy weight trend.\n",
    "\n",
    "there are also a few outliers on the high end that are VERY HIGH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mean': 1.0949177877429, 'median': 1, 'mode': 0, 'range': 5, 'iqr': 2.0, 'stdev': 1.2054927397819137, 'variance': 1.4532127456669048, 'max': 5, 'min': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "children_info=get_categorical_variable_info(children)\n",
    "children_numerical_info = get_numerical_variable_info(children)\n",
    "print(children_numerical_info)\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.pie(children_info['percentage'].values(), labels = children_info['percentage'].keys(), autopct='%1.0f%%')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chart shows that most people in this data set have no children, the average number of children for this dataset is 1.1. in 2020, the fertility rate (number of childen born per woman) is [1.64](https://en.wikipedia.org/wiki/Demographics_of_the_United_States#Vital_statistics:~:text=1.93-,1.64,-Life%20expectancy%20at).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Charges\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mean': 13270.422265141257, 'median': 9386.1613, 'mode': 1639.5631, 'range': 62648.554110000005, 'iqr': 11899.625365, 'stdev': 12110.011236694001, 'variance': 146652372.15285498, 'max': 63770.42801, 'min': 1121.8739}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_numeric_plots(charges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this is an interesting graph, there is a strong scew to the right depicting that most poeple pay less than ~10,000. there are also 3 modes, one very stron one at the low end, one around ~20,000, and a third one ~40,000, with a few very high outliers above $60,000.\n",
    "\n",
    "Thes multiple modes could be cause from other variables in the data. investication is needed to see if these smaller distibutions can be mapped via other variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "remove_mode_1 = [i for i in charges if i > 15000] #rough estimate\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(remove_mode_1, bins = 50)\n",
    "plt.title(\"Charges histogram minus the large distribution\")\n",
    "plt.xlabel(\"Chrage\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removeing the large distribution form the data shows to more very clear distributions that will need to be investigated for corelation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_info=get_categorical_variable_info(sex)\n",
    "\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.pie(sex_info['percentage'].values(), labels = sex_info['percentage'].keys(), autopct='%1.0f%%')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this shows a very even split between males and females within this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.pie([total_female,total_male], labels = ['female', 'male'], autopct='%1.0f%%')\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "comparing these two plots the male/female split in this dataset is aligned with the total us population data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smoker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smoker_info=get_categorical_variable_info(smoker)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.pie(smoker_info['percentage'].values(), labels = smoker_info['percentage'].keys(), autopct='%1.0f%%')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there is a majority of non smokers within this dataset. this is one variable that should be tested to see corellations against the charges variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_info=get_categorical_variable_info(region)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.pie(region_info['percentage'].values(), labels = region_info['percentage'].keys(),  autopct='%1.0f%%')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this shows a very even split over the regions apart from a slight increase in the south east region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploritory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions\n",
    "\n",
    "### Questions to ask about data collection\n",
    "- Why is there a spike of people of ages 19-20?\n",
    "- why are there no data points ager age 64?\n",
    "- Why is there slightly more data points in the south east reagion? \n",
    "\n",
    "### Trends to analyse  \n",
    "i will analyse the charges variable first as this is an important vaiable about Health Insurance. \n",
    "- age -> charges\n",
    "- sex -> charges\n",
    "- bmi -> charges\n",
    "- children -> charges\n",
    "- smoker -> charges  \n",
    "\n",
    "From here i will see if there are any strong correlations and correlate those variables with others to see if that shows any other interesting features. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}