us_medical_insurance_costs/us_medical_insurance_costs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U.S. Medical Insurance Costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project analyses a dataset of US Medical Insurance Costs. The dataset gives us data of \n",
    "age, sex, BMI, number of children, if the person is a smoker or not, region and the costs of insurance.\n",
    "\n",
    "The project answers the following questions:\n",
    "\n",
    "- What is the average age of people that get insurance?\n",
    "- Where is the majority from? \n",
    "- Do women pay more than men? \n",
    "- Does having more children affect the cost? \n",
    "- Does smoking affect the cost? \n",
    "- Does age affect the cost? \n",
    "- Does BMI affect the cost?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importing the dataset and saving it in a list of dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "insurance_data = []\n",
    "with open('insurance.csv', newline='') as dataset_csv:\n",
    "    dataset = csv.DictReader(dataset_csv)\n",
    "\n",
    "    for row in dataset:\n",
    "        insurance_data.append(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Saving the ages and regions in different lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = []\n",
    "regions = []\n",
    "\n",
    "for data in insurance_data:\n",
    "    ages.append(int(data['age']))\n",
    "    regions.append(data['region'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Calculating the average of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average age of people that get insurance is 39.\n"
     ]
    }
   ],
   "source": [
    "average_age = sum(ages) / len(ages)\n",
    "print('The average age of people that get insurance is {}.'.format(int(average_age)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Finding where the majority is located"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 364 users from southeast, 325 users from southwest, 324 users from northeast and 325 users from northwest. Thus the users are aproximately distributed by region.\n"
     ]
    }
   ],
   "source": [
    "southeast = 0\n",
    "southwest = 0\n",
    "northwest = 0\n",
    "northeast = 0\n",
    "\n",
    "for region in regions:\n",
    "    if region == 'southeast':\n",
    "        southeast += 1\n",
    "    elif region == 'southwest':\n",
    "        southwest += 1\n",
    "    elif region == 'northeast':\n",
    "        northeast += 1\n",
    "    elif region == 'northwest':\n",
    "        northwest += 1\n",
    "\n",
    "print('There are {se} users from southeast, {sw} users from southwest, {ne} users from northeast and \\\n",
    "{nw} users from northwest. Thus the users are aproximately distributed by region.'.format(\n",
    "    se=southeast, sw=southwest, ne=northeast, nw=northwest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pie chart\n",
    "def region_perc(region):\n",
    "    return 100 * region / len(insurance_data)\n",
    "\n",
    "se_p = region_perc(southeast)\n",
    "sw_p = region_perc(southwest)\n",
    "ne_p = region_perc(northeast)\n",
    "nw_p = region_perc(northwest)\n",
    "\n",
    "labels = 'SE', 'SW', 'NE', 'NW'\n",
    "sizes = [se_p, sw_p, ne_p, nw_p]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=False)\n",
    "plt.title('Users by region')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Do women pay more than men?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function counts the parameters that attend the options in options list, and when hasSomatory\n",
    "#is true, it sums the charges as well, storing the values in lists\n",
    "\n",
    "def count_and_total(dataset, parameter, options_list, hasSomatory):\n",
    "    counter = [0 for i in range(len(options_list))]\n",
    "    somatory = [0 for i in range(len(options_list))]\n",
    "    for data in dataset:\n",
    "        for i in range(len(options_list)):\n",
    "            if data[parameter] == options_list[i]:\n",
    "                counter[i] += 1\n",
    "                if hasSomatory:\n",
    "                    somatory[i] += float(data['charges'])\n",
    "    return counter, somatory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cost for women is $12569.58 and the average cost for men is $13956.75. \n",
      "Thus men pay more than women in this dataset.\n"
     ]
    }
   ],
   "source": [
    "users_by_gender, cost_by_gender = count_and_total(insurance_data, 'sex', ['female', 'male'], True)\n",
    "avg_women_cost = cost_by_gender[0] / users_by_gender[0] \n",
    "avg_men_cost = cost_by_gender[1] / users_by_gender[1]\n",
    "\n",
    "print('The average cost for women is ${} and the average cost for men is ${}. \\n\\\n",
    "Thus men pay more than women in this dataset.'.format(round(avg_women_cost, 2), round(avg_men_cost, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Does having more children affect the cost?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function accepts 4 ranges as a list (parameter options_list). \n",
    "#The options_list must be in ascendent order, where the numbers are inclusive in the range.\n",
    "\n",
    "def count_and_total_range(dataset, parameter, options_list, hasSomatory):\n",
    "    counter = [0, 0, 0, 0, 0]\n",
    "    somatory = [0, 0, 0, 0, 0]\n",
    "    for data in dataset:\n",
    "        if float(data[parameter]) > options_list[-1]:\n",
    "            counter[-1] += 1 \n",
    "            somatory[-1] += float(data['charges'])\n",
    "        elif float(data[parameter]) > options_list[-2] and float(data[parameter]) <= options_list[-1]:\n",
    "            counter[-2] += 1\n",
    "            somatory[-2] += float(data['charges'])\n",
    "        elif float(data[parameter]) > options_list[-3] and float(data[parameter]) <= options_list[-2]:\n",
    "            counter[-3] += 1\n",
    "            somatory[-3] += float(data['charges'])\n",
    "        elif float(data[parameter]) > options_list[-4] and float(data[parameter]) <= options_list[-3]:\n",
    "            counter[-4] += 1\n",
    "            somatory[-4] += float(data['charges'])\n",
    "        else:\n",
    "            counter[-5] += 1\n",
    "            somatory[-5] += float(data['charges'])\n",
    "    return counter, somatory  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding the number of people and cost by number of children\n",
    "#In this case, the last element in the resulting list will be for users with more than 3 children\n",
    "users_by_children, cost_by_children = count_and_total_range(insurance_data, 'children', [0, 1, 2, 3], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pie chart\n",
    "per_users_by_children = [100 * user / len(insurance_data) for user in users_by_children]\n",
    "        \n",
    "labels = 'None', '1', '2', '3', '4+'\n",
    "sizes = [per_users_by_children[0], per_users_by_children[1], \n",
    "         per_users_by_children[2], per_users_by_children[3], per_users_by_children[4]]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=False)\n",
    "plt.title('Users by number of children')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VEhFPRsTLwHRgrCQBBwEz8vpTgSNrdSxmZta+RvaJnAjcnKcHA4sKyxbnso7KtwOeKySktvJ2SZooaZ6kea2trd0UvpmZNSSJSPoqsA64uh77i4jJEdEcEc1NTU312KWZWZ9Q9yG+ko4HjgAOjojIxUuAXQrVhuQyOihfAQyU1D+3Ror1zcysTuraEpE0BjgV+HBErCksmgkcLWkzScOA4cB9wFxgeB6JtSmp831mTj63Ax/N648Hrq/XcZiZWVKzloika4ADgUGSFgNnkkZjbQbMTn3jzImIz0bEfEnXAQtIp7kmRcT6vJ3PA7OAfsCUiJifd/EVYLqk84AHgctrdSxm1nf1louMa3WBcc2SSEQc005xhx/0EXE+cH475TcBN7VT/iRp9JaZmTWIr1g3M7PSnETMzKw0JxEzMyvNScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxKcxIxM7PSnETMzKy0LpOIpAMkvTFPHyvpe5J2q31oZmb2eldNS+RSYI2kdwCnAE8A02oalZmZ9QjVJJF1+XG0Y4GLIuJi4E21DcvMzHqCap5s+IKk04HjgPdJegOwSW3DMjOznqCalsjHgbXAiRHxLDAE+HZXK0maImmZpEcLZdtKmi3p8fxzm1wuSRdKapH0sKR9CuuMz/UflzS+UP4uSY/kdS5Ufmi7mZnVT5dJJCeOnwOb5aLlwC+r2PaVwJiKstOAWyNiOHBrngc4FBieXxNJ/TBI2hY4E9iP9Dz1M9sST67z6cJ6lfsyM7Maq2Z01qeBGcAPc9Fg4FddrRcRdwErK4rHAlPz9FTgyEL5tEjmAAMl7QSMBmZHxMqIWAXMBsbkZQMiYk7ur5lW2JaZmdVJNaezJgEHAM8DRMTjwPYl97dDRCzN088CO+TpwcCiQr3Fuayz8sXtlLdL0kRJ8yTNa21tLRm6mZlVqiaJrI2Il9tmJPUHYmN3nFsQG72dKvc1OSKaI6K5qampHrs0M+sTqkkid0o6A9hC0iHAz4D/Lbm/v+RTUeSfy3L5EmCXQr0huayz8iHtlJuZWR1Vk0ROA1qBR4DPADcBXyu5v5lA2wir8cD1hfJxeZTWSGB1Pu01CxglaZvcoT4KmJWXPS9pZB6VNa6wLTMzq5MurxOJiFeAH+VX1SRdAxwIDJK0mDTK6hvAdZImAM8AR+XqNwGHAS3AGuCEvO+Vks4F5uZ650REW2f950gjwLYAbs4vMzOroy6TiKRHeG3fxWpgHnBeRKxob72IOKaDTR7cTt0gdeC3t50pwJR2yucBe3UcuZmZ1Vo1V6zfDKwHfprnjwa2JI2uuhL4UE0iMzOz171qksgHI2Kfwvwjkh6IiH0kHVurwMzM7PWvmo71fpL2bZuR9G6gX55dV5OozMysR6imJTIBuELSVnn+BWBCfsbI12sWmZmZve51mkQk9QPeFxFvk7Q1QESsLlS5rpbBmZnZ61unp7MiYj1wTJ5eXZFAzMysj6vmdNbvJF0EXAu81FYYEQ/ULCozM+sRqkkie+ef5xTKAjio26MxM7MepZor1j9Qj0DMzKznqaYlgqTDgT2BzdvKIuKcjtcwM7O+oJqHUl1GekTuSYCAjwG71TguMzPrAaq52PA9ETEOWBURZwP7A3vUNiwzM+sJqkkif80/10jaGfg7sFPtQjIzs56imj6RGyQNBL4NPEAamfXjWgZlZmY9QzWjs87Nkz+XdAOwuS86NDMzqH501nuAoW31JRER02oYl5mZ9QDVPJTqKuDNwEOk54pAOqXlJGJm1sdV0xJpBkbkpw+amZn9QzWjsx4FduzOnUr6kqT5kh6VdI2kzSUNk3SvpBZJ10raNNfdLM+35OVDC9s5PZc/Jml0d8ZoZmZd6zCJSPpfSTOBQcACSbMkzWx7ld2hpMHAF4DmiNiL9ICro4FvAhdExO7AKtJzTMg/V+XyC3I9JI3I6+0JjAEuybeuNzOzOunsdNZ3arzfLST9nfS89qWkGzp+Ii+fCpwFXAqMzdMAM4CLJCmXT4+ItcBTklqAfYF7ahi3mZkVdJhEIuJOAEnDgKUR8bc8vwWwQ9kdRsQSSd8B/kS6kPEW4H7guYhoe9zuYmBwnh4MLMrrrpO0Gtgul88pbLq4zqtImghMBNh1113Lhm5mZhWq6RP5GfBKYX59LitF0jakVsQwYGfgjaTTUTUTEZMjojkimpuammq5KzOzPqWaJNI/Il5um8nTm27EPj8IPBURrRHxd+AXwAHAQEltLaMhwJI8vQTYBSAv3xpYUSxvZx0zM6uDapJIq6QPt81IGgss34h9/gkYKWnL3LdxMLAAuB34aK4zHrg+T8/M8+Tlt+XhxjOBo/PorWHAcOC+jYjLzMw2UDXXiXwWuDo/IhdS38NxZXcYEfdKmkG6D9c64EFgMnAjMF3Sebns8rzK5cBVueN8JWlEFhExX9J1pAS0DpiUnwlvZmZ1Us29s54gtRy2yvMvbuxOI+JM4MyK4idJo6sq6/6N9AyT9rZzPnD+xsZjZmblVHXvLOie5GFmZr1LNX0iZmZm7XISMTOz0krdCh7wreDNzMy3gjczs/J8K3gzMyutIbeCNzOz3qGalkjbreDvA9a2FUbEhztexczM+oJqkshZtQ7CzMx6pmquWL+zHoGYmVnP02WfiKSRkuZKelHSy5LWS3q+HsGZmdnrWzUd6xcBxwCPA1sAnwIurmVQZmbWM1R1xXpEtAD9ImJ9RFxBjR8iZWZmPUM1HetrJG0KPCTpW6Tnoft2KWZmVlUyOC7X+zzwEulpgv9Ry6DMzKxnqGZ01jOStgB2ioiz6xCTmZn1ENWMzvoQ6b5Zv87ze0uaWeO4zMysB6jmdNZZpCcOPgcQEQ8Bw2oWkZmZ9RjVJJG/R8TqirKNuhmjpIGSZkj6g6SFkvaXtK2k2ZIezz+3yXUl6UJJLZIelrRPYTvjc/3HJY3fmJjMzGzDVZNE5kv6BNBP0nBJ/wPcvZH7/QHw64h4K/AOYCFwGnBrRAwHbs3zAIcCw/NrInApgKRtSc9p34/UUjqzLfGYmVl9VJNETgL2JN188RrgeeCLZXcoaWvg/cDlABHxckQ8B4wFpuZqU4Ej8/RYYFokc4CBknYCRgOzI2JlRKwCZuPrV8zM6qqa0VlrgK/mV3cYBrQCV0h6B3A/cDKwQ0QszXWeBXbI04OBRYX1F+eyjspfQ9JEUiuGXXfdtXuOwszMOk4iXY3A2ohbwfcH9gFOioh7Jf2Af566att2SOq2h2BFxGRgMkBzc7MfrmVm1k06a4nsT/qmfw1wL6Bu2udiYHFE3JvnZ5CSyF8k7RQRS/PpqmV5+RLSBY5thuSyJcCBFeV3dFOMZmZWhc76RHYEzgD2InWEHwIsj4g7N+b28BHxLLBI0lty0cHAAmAm0DbCajxwfZ6eCYzLo7RGAqvzaa9ZwChJ2+QO9VG5zMzM6qTDlkhErCddYPhrSZuR7uR7h6SzI+KijdzvScDV+Z5cTwInkBLadZImAM8AR+W6NwGHAS3AmlyXiFgp6Vxgbq53TkSs3Mi4zMxsA3TasZ6Tx+GkBDIUuBD45cbuNF+w2NzOooPbqRvApA62MwWYsrHxmJlZOZ11rE8jncq6CTg7Ih6tW1RmZtYjdNYSOZZ0196TgS9I/+hXF6mBMKDGsZmZ2etcZ30ifmaImZl1yonCzMxKcxIxM7PSnETMzKw0JxEzMyvNScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxKcxIxM7PSnETMzKy0hiURSf0kPSjphjw/TNK9klokXZufv46kzfJ8S14+tLCN03P5Y5JGN+hQzMz6rEa2RE4GFhbmvwlcEBG7A6uACbl8ArAql1+Q6yFpBHA0sCcwBrhEUr86xW5mZjQoiUgaAhwO/DjPCzgImJGrTAWOzNNj8zx5+cG5/lhgekSsjYingBZg37ocgJmZAY1riXwfOBV4Jc9vBzwXEevy/GJgcJ4eDCwCyMtX5/r/KG9nnVeRNFHSPEnzWltbu/EwzMz6tronEUlHAMsi4v567TMiJkdEc0Q0NzU11Wu3Zma9Xv8G7PMA4MOSDgM2BwYAPwAGSuqfWxtDgCW5/hJgF2CxpP7A1sCKQnmb4jpmZlYHdW+JRMTpETEkIoaSOsZvi4hPArcDH83VxgPX5+mZeZ68/LaIiFx+dB69NQwYDtxXp8MwMzMa0xLpyFeA6ZLOAx4ELs/llwNXSWoBVpISDxExX9J1wAJgHTApItbXP2wzs76roUkkIu4A7sjTT9LO6KqI+BvwsQ7WPx84v3YRmplZZ3zFupmZleYkYmZmpTmJmJlZaU4iZmZWmpOImZmV5iRiZmalOYmYmVlpTiJmZlaak4iZmZXmJGJmZqU5iZiZWWlOImZmVpqTiJmZleYkYmZmpTmJmJlZaU4iZmZWmpOImZmV5iRiZmal1T2JSNpF0u2SFkiaL+nkXL6tpNmSHs8/t8nlknShpBZJD0vap7Ct8bn+45LG1/tYzMz6uka0RNYBp0TECGAkMEnSCOA04NaIGA7cmucBDgWG59dE4FJISQc4E9iP9Gz2M9sSj5mZ1Ufdk0hELI2IB/L0C8BCYDAwFpiaq00FjszTY4FpkcwBBkraCRgNzI6IlRGxCpgNjKnfkZiZWUP7RCQNBd4J3AvsEBFL86JngR3y9GBgUWG1xbmso/L29jNR0jxJ81pbW7vvAMzM+riGJRFJWwE/B74YEc8Xl0VEANFd+4qIyRHRHBHNTU1N3bVZM7M+ryFJRNImpARydUT8Ihf/JZ+mIv9clsuXALsUVh+SyzoqNzOzOmnE6CwBlwMLI+J7hUUzgbYRVuOB6wvl4/IorZHA6nzaaxYwStI2uUN9VC4zM7M66d+AfR4AHAc8IumhXHYG8A3gOkkTgGeAo/Kym4DDgBZgDXACQESslHQuMDfXOyciVtblCMzMDGhAEomI3wLqYPHB7dQPYFIH25oCTOm+6MzMbEP4inUzMyvNScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxKcxIxM7PSnETMzKw0JxEzMyvNScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSuvxSUTSGEmPSWqRdFqj4zEz60t6dBKR1A+4GDgUGAEcI2lEY6MyM+s7enQSAfYFWiLiyYh4GZgOjG1wTGZmfYYiotExlCbpo8CYiPhUnj8O2C8iPl9RbyIwMc++BXisroFumEHA8kYH0UB9+fj78rFD3z7+nnDsu0VEU2Vh/0ZEUm8RMRmY3Og4qiFpXkQ0NzqORunLx9+Xjx369vH35GPv6aezlgC7FOaH5DIzM6uDnp5E5gLDJQ2TtClwNDCzwTGZmfUZPfp0VkSsk/R5YBbQD5gSEfMbHNbG6hGn3WqoLx9/Xz526NvH32OPvUd3rJuZWWP19NNZZmbWQE4iZmZWmpNIjUkKSd8tzP+XpLMaGNLrkqQpkpZJerTRsdSbpF0k3S5pgaT5kk5udEz1ImlzSfdJ+n0+9rMbHVOtSeon6UFJNzQ6lu7gJFJ7a4GPSBrU6EBe564ExjQ6iAZZB5wSESOAkcCkPnT7nrXAQRHxDmBvYIykkY0NqeZOBha2t0DS0/UNZeM5idTeOtLIiy9VLpA0VNJtkh6WdKukXXP5lZIulHS3pCfzlflt63xZ0ty8Tq/51hYRdwErGx1HI0TE0oh4IE+/QPqAGdzYqOojkhfz7Cb51WtH+0gaAhwO/LjRsXQXJ5H6uBj4pKStK8r/B5gaEW8HrgYuLCzbCXgvcATwDQBJo4DhpHuG7Q28S9L7axu61ZOkocA7gXsbHErd5NM7DwHLgNkR0ZuP/fvAqcArDY6j2ziJ1EFEPA9MA75QsWh/4Kd5+ipS0mjzq4h4JSIWADvkslH59SDwAPBWUlKxXkDSVsDPgS/mv5k+ISLWR8TepDtO7CtprwaHVBOSjgCWRcT9FeUXS3ooJ9Kd26YlfbUhgW6gHn2xYQ/zfdIH/xVV1l9bmFbh59cj4ofdGJe9DkjahJRAro6IXzQ6nkaIiOck3U7qG+uNAywOAD4s6TBgc2CApJ9ExLFtFSQ9nRNqj+GWSJ1ExErgOmBCofhu0q1aAD4J/F8Xm5kFnJi/sSJpsKTtuztWqy9JAi4HFkbE9xodTz1JapI0ME9vARwC/KGhQdVIRJweEUMiYijp//62YgLpqZxE6uu7pFs+tzkJOEHSw8BxpFEbHYqIW0inv+6R9AgwA3hTjWKtK0nXAPcAb5G0WNKErtbpRQ4g/f4PKpzKOKzRQdXJTsDt+X9gLqlPpFcMfe0rfNsTMzMrzS0RMzMrzUnEzMxKcxIxM7PSnETMzKw0JxEzMyvNScTMzEpzEjEzs9L+P2C8OyvVK949AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mean cost by number of children\n",
    "mean_cost_by_children = [cost_by_children[i] / users_by_children[i] for i in range(len(cost_by_children))]\n",
    "\n",
    "labels = ('None', '1', '2', '3', '4+')\n",
    "y_pos = range(0,5,1)\n",
    "mean = [mean_cost_by_children[0], mean_cost_by_children[1], \n",
    "        mean_cost_by_children[2], mean_cost_by_children[3], mean_cost_by_children[4]]\n",
    "\n",
    "plt.bar(y_pos, mean, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, labels)\n",
    "plt.ylabel('Mean charges')\n",
    "plt.title('Mean Charges by Number of Children')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first, it seems like having more children doesn't affect the cost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Does smoking affect the cost?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "users_by_smoker, cost_by_smoker = count_and_total(insurance_data, 'smoker', ['yes', 'no'], True)\n",
    "per_user_by_smoker = [100 * user / len(insurance_data) for user in users_by_smoker]\n",
    "\n",
    "#pie chart\n",
    "labels = 'Smoker', 'Non-smoker'\n",
    "sizes = [per_user_by_smoker[0], per_user_by_smoker[1]]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=False)\n",
    "plt.title('Users by smoker status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean charges for smokers is $32050.23 and for non-smokers is $8434.27. \n",
      "This may imply that smokers tend to pay more than those who don't smoke.\n"
     ]
    }
   ],
   "source": [
    "mean_cost_by_smoker = [cost_by_smoker[i] / users_by_smoker[i] for i in range(len(cost_by_smoker))]\n",
    "\n",
    "print(\"The mean charges for smokers is ${} and for non-smokers is ${}. \\n\\\n",
    "This may imply that smokers tend to pay more than those who don't smoke.\".format(round(mean_cost_by_smoker[0], 2), \n",
    "                                                             round(mean_cost_by_smoker[1], 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Does age affect the cost?\n",
    "\n",
    "The ages will be evaluated according to the following ranges:\n",
    "\n",
    "$young: age \\leq 33$\n",
    "\n",
    "$adult: 33 < age \\leq 48$\n",
    "\n",
    "$senior: 48 < age \\leq 64$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "#In this case the range will be [17, 33, 48, 64].\n",
    "#As I know there are no users under 17 and above 64, the first and last values will be 0.\n",
    "users_by_age, cost_by_age = count_and_total_range(insurance_data, 'age', [17, 33, 48, 64], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pie chart\n",
    "per_users_by_age = [100 * user / len(insurance_data) for user in users_by_age]\n",
    "\n",
    "labels = '(17, 33]', '(33, 48]', '(48, 64]'\n",
    "sizes = [per_users_by_age[1], per_users_by_age[2], per_users_by_age[3]]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=False)\n",
    "plt.title('Users by age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean charges for people under 33 is $9576.86, for people between 33 and 48 years \n",
      "old is $13679.49 and for people above 48 the mean charges is $17549.57. \n",
      "This may imply that the charges tend to increase with the age of the user.\n"
     ]
    }
   ],
   "source": [
    "mean_cost_by_age = [cost_by_age[i] / users_by_age[i] for i in range(1, len(cost_by_age)-1)]\n",
    "\n",
    "print(\"The mean charges for people under 33 is ${}, for people between 33 and 48 years \\n\\\n",
    "old is ${} and for people above 48 the mean charges is ${}. \\n\\\n",
    "This may imply that the charges tend to increase with the age of the user.\".format(round(mean_cost_by_age[0], 2), \n",
    "                                                             round(mean_cost_by_age[1], 2),\n",
    "                                                             round(mean_cost_by_age[2], 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Does the BMI affect the cost?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the National Heart, Lung and Blood Institute (NIH), the BMI can be classified as:\n",
    "\n",
    "$Underweight: BMI < 18.5$\n",
    "\n",
    "$Normal weight: 18.5 \\leq BMI \\leq 24.9$\n",
    "\n",
    "$Overweight: 25 \\leq BMI \\leq 29.9$\n",
    "\n",
    "$Obesity: BMI \\geq 30$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "#In this case the range will be [0, 18.4, 24.9, 29.9]. As there are no negative BMI values, the first value\n",
    "#will be zero.\n",
    "users_by_bmi, cost_by_bmi = count_and_total_range(insurance_data, 'bmi', [0, 18.4, 24.9, 29.9], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P9UbWCRAihqM5JGzQ+Cv85+FM5maG0Pdr61q8rqGffkt5Qb/O4qhqLfA4Tths8zY7hha5AOeDP1D5OOHUICKlwEm7WW4uTiC9idPyORZoV9UGnPAZJSKHAohIpojsHbH+cmCaiExxv/9qH2prBEb04730SUIGjb/CPwun084MoSzVVWc2NR/idR39lIUznk1+P9f7PdD97NNVwCUishT4GnDNQAtS1SU4wRHEOSTa3YWGc3EOm950D83WAW+52+gAzgL+W0SWAB/gHC52308rzhmkF0VkEU6I7HHgd1WtAeaJyIfR7AxOuCuD3YGrFuAcn3qmo6aDqr9U0bW1C4CRx4yk5IsltK5tZUPFBrRTIR3GfX0cudN67pMMtYZYcd0K8g/MZ9zXxhHuDFN5WyWddZ0UHVdE8Rec2V6qHqii6NgicqZ4daW/4/K6hrnfrm/o7+wG8eJxyhv68hc9qYhInqo2iYjgdHSvUNVbh7qORGzR/DcehwyApAtjzh3DzJtnMu36adS+WktbVRubHt/E6NNHM+OmGZR+pZRNj23a7TY2P7WZ4bN2dAM0fdhE7l65zLhpBvVv1wPQWtmKhtXzkElT3fjN+obPeVrE4JxDecGFXhfhgW+6ncUf4Qxbe68XRSRU0Pgr/HOAb3tdB0BmYeb2D396TjrZ47LpqutCRAi3hgGnxZI5sufRKVrXtNLV0EXe3jtmjJV0IdwRRkPq3JeME0alZ8TsZECffaWx+ZMs5zAkkd1OeUG8XWQYU6p6q9tZPFtVL1BVT/rYEiZo3LmX7iUOa+7Y0kHb2jZypucw5vwxbHpsE8HvBdn0j02UnrVrSGhY2fjoRsacu/M1b3l759FZ3cmqm1ZRfEIxW9/fyrDJw3YbVkNGteEHtXUHeltEVIzEGZvIDLG4+9DuwXeAuPtlD7WFqLyzkjHnjyE9J53a12oZc94Yyv5Qxtjzx1J1f9Uu69S+VsuI/UaQWbRzgEi6MPHyicy4cQYFBxdQ81INJSeWsPHRjVTeWcnW97cO1dvayVGtbe/nqUb9TIRHTqa84FKvi0g1CRE0/gr/eOAmr+uIpF3KujvXUXhoIQVznAtJ6+fVkz/HOcGRf3A+ratad1mv5dMWal+pZfn3l7PpsU3Uz6tn0+M79+XUvFZD4WGFtK5sJT0nnYlXTqT6xerYv6lIqu03VNfO7n3BhHIr5QXjvS4ilcTFTWB9cBsxOLc/GKpK1f1VZI/NpuTEHWdBMwszaQ42k+fLoznQTFbprt0aEy+fuP1x3dw6Wte0MuacHYdRoeYQjUsamfL9KTR+0Lh9wAvtGPozhPt0dCwYHQol6pmm3ckHbgC+5XUhqSLuT2/7K/yn4Ez0FleaP2lm9c2ryZ6QjXPmEErPKiUtJ42Nf98IYZBMYdzXx5EzJYfW1a3Uvl7L+Et3/kO6LWjGfW1HH+XGRzYy4oAR5PnyCHeEWXvbWrrquig6tojiE4qH7k2qhp+p2lg5vbNrytDtdMh0AbMpb1jhdSGpIK6Dxl/hz2Xn+0PMEJrY2fnu/67f+Hmv64ihf1DecJ7XRaSCeO+j+TEWMp75ZXVtf6+mTTRfpbxgf6+LSAVxGzT+Cn8+g7jM2wzOyFDo/YPb2pOtEziSEDG0gomNuA0anHs0IscEMUPkJzV1od6XSgonU15gIwDEWFwGjXs/00Bu7zdRMCwcXn5yc8ugxq1NMDd7XUCyi8ugwbk9f7TXRaSqK+sbPLhgx1NHUl6wu6EaTBTE3Vknf4U/A/gUmOx1LakoXXX9ojXrxg7xFLfx4APgQMob4usDkSTisUVzARYynjlva+PKFAwZgP2BM70uIlnFVdD4K/xpOIMjGw+Ias3VdQ0He12Hh/7L6wKSVVwFDXA6zoDKxgNfbG5ZlqPar5kDLn22ldG3NLLP3U07PX/H/A7K7mxi77ub+NHLbT2uW9+mnPV4C2V3NuG7q4l31jmDiP345Tb2vaeJrz+94z6xh5d28Md323vcThQdR3lB5ADfJgriLWgiR543Q0W15Wc1df2eUvbi/TN58cKds+n11V08u7yTJZcP56Mr8/jBYT0PY3PNi22cOCOD4HfyWHL5cHyj0mloUxZvCrH0ijyy0mHZZyFaO5UHPujk2wfHfDicNCAVB8eKubgJGvcO7WS7eS9hzGlrXzgyHC7q73pHTc6gKGfnac7vea+DnxyRTXaG8/zo4bv+mjW0KW+u7eKyA5yhMrLShcJhQppAZ8i5abWlU8lMh9+93cFVh2SRmT4k06lfNBQ7STVxEzTAOcRXPalDtevG6toBz1kd6ZOaMHPXdvG5+5o4+sFmFlbteu3f6vowo3KFS55t44B7m/jGc600dygjsoWTZ2ZwwL3NjM1LoyBbmF8V4vSyIRv8q4zygkQbgD3uxdMH225u88j0zq75E7u6otY30RWG2lbl3cuGc8sJwzjniRYiL6PoCsPijWGumJPJ+/+Vx/BM4bdvOX0wPzo8mw8uz+P3XxrG9a+3c+Ox2dy3uINz/tnCr96MeT8NwNeHYiepJC6Cxl/hnwak8tkOT920pSZyqtRBmZAvnOHLREQ4ZHw6aQLVLbrLMhPyhc9NcIZEOmt2Bos3hXda5v2NIVRhVnEa//y4k8fPzmVlXZgVNTG/O+I8ygsSfXzkuBIXQcOOibnMEBvV1fWev6Njr2hu8/SyTF5f45xB+qQmREcISnJ37l8Zk5fGxII0llc7ofHq6i5ml+z863j96+3cdFw2nWEIuRmUBrR0RrPaHhUBp8Z8LykkXoLGDps8cn1N3aA6P857soVD/9rM8powE/7QyF8Xd3DpAZmsqlP2ubuJc59opeL0HESEDY1hTv77jkH47zhpGBc81cq+9zTxwaYw1x2Zvf21Z4KdzBmXxrgRaRQOE/Yfk47/nibaQsp+Y4bkekLrFI4iz29B8Ff49wY+9LSIFDU8HP7o3bXrI6dRNY5OYBzlKXffV0zEQ4vGDps88t3a+kava4hjmcBpXheRLOIhaM7wuoBUlKm6+uzGJjuNu2c2Tk2UeBo0/gr/aCDZR3GLSxc3bF2fFh9/aOLZYV4XkCy8/kU7yuP9p6Q01c2X1zVYa6Z3e1FeUNL7YqY3FjQp6MtNzR9nQXbvSxqsVRMVXgfN0R7vP/Wobv1hbd0BXpeRQA73uoBk4NlMlYEyX94jaeRvLGLe+9MkNL8srXTlWGaE0yQVB10aMke0ti3OD+sxXteRQKxFEwWeXUcTKPMdDbzR/TmFpq25rFg+XrYumCW5i6fLjKZcGelJgclIteOVdRvqSkOhUq9LSSBtQAHlDR1eF5LIvJx7e5d7mwTyClo44JAVyiErFAXtSmf1+hKq3p8uOn9W2tjVpUzfPget6RdfR+f80uSbRzvWhgEHAu96XUgii6ugiSQgmSGmTv2MqVM/U854O4RCQ/1wVgQmStOCWZL//jSZ0TpMkn1GxcFTDf9qS42NHjcwh2NBMyheBs2BA1lJoGBkM3MOCyqHBRWFcEcGK9aNYtOiGWmyYC8Zv260TI12sYlufFdowV6dnck8j3YsHQb83usiEpknfTSBMl8azrFvTEYzCkNt3QhWfjRJmueXSeHSKTKzPUuGx2JfieK+jZ999Lm2druvaWDWUt4wxesiEplXQTMOqBqq/SmE2jNZsWY0m9/bKy194UyZtLFYJg7V/r1WEAoteauyaj+v60hgXcAwyhtSZZrgqPPq0GlIP+QC6cM6KSuroqysKsyFr0NY2FKdz6plU6R1/iwp/miyzOzMkGFDWddQ+VFtnZ0xGZwMYBywzutCEpVXQTPJo/1ul6aMGt3AqC8sUb6wRFHobM3io1VjpGbhXpK5cC+ZUl0gY72uc7Cyw+EVX25qsdELB28iFjQDlrJBE0kgM7eDvfepVPapVC55BULCxs2FrFkyVToWzJKS4ESZ2ZUuCTXE4+X1WzcDM72uIwlMAt72uohElRKHTgOVrowdW8fYsXXKiYsVhbbmbJZ+Ok7qFuwl2YtmyrS6ETLa6zp3J1216uKGrZ/zuo5Ep4q2kzm2L8fVIjIFeF5V9+n2XDnQpKq/68v+ROQN4Aeq+t4Ayu0XEfkyMFtVf7uHZY5x69lleFMRuRb4s6q2RL7WnbVo+kFgWF47++6/Wtl/tcK/oSuNdZtGsu6DadI1f1baqBXjmRlOEy8vG9junMamTzNgvNd1xCtVWrpIr24hu6Fe85o3U9hRpSVaqaPTK7U0uzI8Oq9KS0Z+xsjiLjImrfG64N0QkXRVHVBHtao+Bzw3iN1fCzwMWNDEUkaYiRNqmDihRjl1YQiF5sYcPvxkvDQsmCU5i2bIjMZc6ffEbIMlqnXX1tbPGer99ubSZ1t5/pMuRg8XPrwyb5fX31jTxWn/aGFqoXO/7xm+TH5xdDZbmsN85bFW6tuUXx2XvX2ep9P+0cI9pwxj3AhneVVCYaS6jay6RnKbajS/baMWhyp1NGu1NLtSR+eu01EFG7W4uJmcPPr+uzjolqvbUpkPHAsUApep6lwRyQEeAPYDgkBOt3W+CPwS5277lcAlqtokImuAx4ATgD+KyDWqepCI7Ad8AExW1UoRWQn4geHAn9jxfq9V1XkicjEwR1W/IyLTgb+7yz7rLrPtPylPRJ4A9gEW4czoeRVOJ/nrIlKtqsfu7r17FTRJ+1dWYHh+K/vP+VSZ86lz6UBnOms2FLP+/Wmi88vSxqwaw3QViemd819oaV2aqxp3d8dfvH8m3zkka6d5tSMdOSmD58/feZrdh5Z0tl6wX3bjKXsXbDzlodqpo2fOfv+RpW3FtSM6Cn6TffrmyvbRI6q0pKiG/CIlrRSI9v1c0fpjkaGqh4jIycANwPHAFUCLqvpEZF9gMYCIlAA/B45X1WYR+THwPeBGd1s1qnqgu+yPRSQfZ7bX94AjReQtYLOqtojIfcCtqvqWiEwC/g34Imq7DbhNVR8VkcsjXjsA2BvYAMwDDlfV20Xke8CxqrrHsZW9CpqkPI28O5khpkzezJTJm5XT3w2hsLVhOCuCE6Rx/iwZ8f50mdEyTAqitkPV1p9X18blxXlHTc5gTb0zd4oq7SHStrSS3dDA8OYtWtj+aksof3nbxok/7bzg00otHb5eSwo3aVFxtbySQ2dazs1tR4yuzrqZ89p/evTmJdcz6swbWRceNhR9fn29zWV3F6Zte/4p999FwBT38VHA7QCqulRElrrPfx5nBMp57u19WcA73bb5WLfHb+PcKnEUcDNwIiDAXPf144HZ3W4TzBeRyCblocDp7uNHgO59SgtUdT2AiHzg1v7Wbt7rLrwKGq/HwfGUQH5hMwd9frny+eXOzaOd6axcN4oNi2eIzJ+VNq5yFFMHevPoAe3tC4vDYU8GFVMlrEhtO5l1jeRsrdH8tk1a1LVeR7FWSzMrtTQ32KpFn4bvmzC1/d5sYIL7BUBbxlK2bPkNv//L04ek5xUz8thLyRqVxfDZR1P93C00LXmRwqMvpnHxCwzf+zjSMofsb9aux3k9qwEiRxwoAla7j7dNtRmi98+fAC+r6u6mI2ru9vhNnNbMZJzDnh/jhNsL7utpwOdVtW2nHfT9V6z7FKF9qX0nXgWNjTnTjYBkhZg+fRPTp29Szn4rhEJdXR4rP54kTfNnScGSaTKzLWuXv0C7Ug3dtKU26vd6qdLUSXpNC8Ma6jWv5TNGtldpCZU6On1tuHRYpY7O26AlIzdTWBwivQTY7RCYXWmfEd7NsENZpTMYf8X9pGXl0LpyIVue/hXjv/UX0rKHM/rscgBCbU1sffcJRp3xM2r+73bCbU3kH/IVssdHHglEVW7vi4Dbf7JRRI5T1ddEpAindXEbcMluVnsTOB94TUT2AfZ1n38XuEtEZqjqpyIyHBivqp/0sI25wK+BN1U1LCK1wMnAT93XX8LpU7kFQET2V9UPIrbxLnAmTkupr7OTNAIjgLg8dLKg6YXAyKIm5hzxsXLEx7rtNopP1o5m06IZaekLZsmEDcUyOXK9KZ1d8yd3dfVpsCZVOsOk1bS6HafVWtC+QYtC63S0rNXSrLVaOny9jirYpEXFLQzLo+9/1QcsLXvH5zln+sHoS/cQamkgPXfHkWXDvEcpOOwcmj/+D9kT9iZ31uFsefrXlH71pliW1p/rp76OExB/cL//paqu3EPr4R7gAREJAAGcwypUdYvbWfuoiGwbevXnwC5Bo6prxNnBm+5TbwETVLXO/f5qt6alOJ/7N4HIfphrgYdF5GfAi0BDH97rn4EXRWRDPHYGW9D0k3sbxV6zqthrVlWY8//j3EZRM4LVH06WlvllUvThZJl5U3VNUVip6yCztomcrXU6onWTjuys0lGs1dEZlVqaU6mjR1RpSXEtI0aCjAHGeP3+tgk11ZE2vBARoX3DclAlLWdH90hnbRWhxhqGTdqXjs2rkYwsENCumN9l0efDfVX9GOfMUuTzx3R7XI3bR6OqreymBaGqr9HDkCqqOqWH5yZ2e3wzTl9N9/19tYd1HgQedL+twjm8UhE5F5jlLvMG3QapU9XvdHt8B3BHT7V359VNlV1Y2ESdop0gjd7OPbpn36uqylvU2pJZHwpJUXpG+IqS4tZOt+ALRo5sf6i2dtg/G+qz04HstDT90ajRLQfl5nZtW/+aqvV515aMapmanR2u7uqSq6rWj2gKh+XK4pLWk/LzY5Y2Kmkb9gl85I/V9uOBiBwJ3InTN1QPXKqqn0Zl2x4FTTx/FozpyWpfMDDN6yIS1ZCf/XHHojEm0XT1vojZHS8+9Dber0lEnV4XkMiGPGh8wUAIaBrq/RozSDamzyB4dRiz0aP9GjNQm70uIJF5FTSbPNqvMQM1ZEPPJiMLGmP6xoJmECxojOkbC5pBsKAxpm8saAbBOoON6RsLmkGwFo0xfWNBMwjWojGmd53Y6e1B8SpoVgBhj/ZtTH9t8AUDdn/eIHgSNL5goBlnoGVjEsESrwtIdF7e4PiBh/s2pj/me11AovMyaOyvhEkUC7wuINF5GTSLPdy3MX2lwEKvi0h0XgaNNUdNIljuCwb6Mnau2QPPgsYXDNTinH0yJp7ZH8Qo8Hq0u3d6X8QYT1n/TBRY0BizZ9aiiQKvg+ZFj/dvzJ60Akt7Xcr0ytOg8QUDa7DraUz8etEXDNhYwVHgdYsG4GmvCzBmN57wuoBkEQ9B84zXBRjTg3bgea+LSBaeB40vGFgKrPK6DmMivOwLBrZ6XUSy8DxoXM94XYAxEZ70uoBkEi9BY/00Jp50As96XUQyiZegeRsbWMjEj9d9wUCd10Ukk7gIGl8wEAae8roOY1x2tinK4iJoXHd5XYAxOGeb7FA+yuImaHzBwIfAa17XYVLeY75goNrrIpJN3ASN6zavCzAp7w6vC0hG8RY0z2PX1BjvvOsLBt7zuohkFFdB43YK218U45XbvS4gWcVV0LjuBxq9LsKknDXA414XkaziLmjcy74f8LoOk3J+7wsGQl4XkaziLmhcd2ATzJmhswX4q9dFJLO4DBpfMPApUOF1HSZl3OYLBlq9LiKZxWXQuH4GNHtdhEl664BbvS4i2cVt0PiCgY3Af3tdh0l6P/YFAy1eF5Hs4jZoXL/D+YtjTCy85QsGHvW6iFQQ10HjHjf/1Os6TFIKA9d4XUSqiOugcT2Cza1jou8BXzBg0zIPkbgPGl8woMD3vK7DJJUG4Dqvi0glcR80AL5gYB7wmNd1mKRxky8YsIHWhlBCBI3rGsBu3zeDFcDuaRpyoqpe19BngTLfmSTZ6GfHr/yU4WnppAlkIPxzyhRur97Ca41NiEBxejo3jx3L6IzMXdbdZ3mQmdnZAIzLyOSuCRMA+OGGDaxob+fovDy+O2oUAH+qqWZGVjbHjxgxdG8u/rQDn/MFA0u8LiTVZHhdQH/4goEnA2W+h4ELva4lmh6cOJGRGTv+Ky4dWcTVJU5A/K2ulruraygfM2aX9bJFeHrK1J2eW97WxrA04ZmpU7lsXSWNoRBtqixtbeXy4pLYvpH490MLGW8k0qHTNt8BKr0uIpby0tO3P24NK9KPdTNEaAsrYVW6VEkT4Y7qLXzHDa4U9pwvGLAhSDyScEHjCwYagPOBLq9riQYR4Rvr13HWmtU8Xl+//fk/btnCcSs/5fmtDVxV0nNLpEOVs9es4dy1a3il0RlZY3p2NkUZ6Zy5dg3H5OVR2dFBGJg9bNgQvJu4VQVc6nURqSyh+mi6C5T5fgrc7HUdg/VZZyelmZnUdHXxjfXr+NnoUubk5m5//c81NbRrmKt6aJFsW3ddRweXrKvk/omTmJSVtdMyV65fR/mYMTzV0MDy9nYOyx3O2YWFsX5b8SQMHOcLBv7jdSGpLOFaNN38FnjJ6yIGqzTT6eQtzsjgC3l5LG3b+SbiU/Pzebmx53HAtq07MSuLQ3JzCbS37fT6q42NzB42jJawsq6jk1vHjeelxkZawyk1AsevLGS8l7BB417I9zVgtde1DFRLOExzOLT98dvNLczMzmZNR8f2ZV5ramRaVvYu6zaEQnS4gVHX1cXi1lamd1uuU5W/1dVxWVExbeEw4nb0hFA6E7QVOwBvATd6XYRJsLNOkXzBwOZAme9EnJkui72up79qurq4ekMVAF2qnJKfz5HD87imaj2rOzpIQxiXmcENpc4Zpw/bWnmsvp6bxoxlVUc75Zs+I00grPDNomJmZO8Imkfr6jitIJ+ctDRmZWfTFg5z2urVHJU3nPxunc1JbBVwlo2aFx8Sto+mu0CZ71DgVSDH61pMXNgMHOYLBlZ6XYhxJOyhU3e+YOAdnDNRKdX5YHrUCJxkIRNfkiJoAHzBwDPA1V7XYTzVAZxhd2XHn6QJGgBfMHAXNipfqlLgIl8w8IrXhZhdJVXQuH4KPOx1EWbIfdcXDPzD6yJMz5IuaNzT3pdiw0qkkt/6ggGbtz2OJV3QAPiCgU6czuG7vK7FxFy5Lxiw4V7jXFKc3t6TQJnvBqDc6zpM1Clwjd0omRiSPmgAAmW+K4A7SdIWXArqAi7xBQPWF5cgUiJoAAJlvrNxOomzelvWxLWtOFf8vux1IabvUiZoAAJlvi8AzwB5HpdiBqYSOMUXDHzodSGmf1LqUMIXDLwKHAXYVaOJZxHweQuZxJRSQQPgCwbeBw4E7JqLxHEHcLg7TbJJQCl16BQpUOa7DGdE/NzeljWe2ILT6fuC14WYwUnpoAEIlPn2xrm4b2+vazE7eRnnlgJrxSSBlDt0iuQLBj4CDgbu87oWA0An8CPgSxYyySPlWzTdBcp85wJ3AyO9riVFrQDO8wUDi7wuxERXyrdounNvypsFVHhdS4rpAG4BDrSQSU7WotmNQJnvSOAerO8m1p7GmdjNLjlIYhY0exAo82UAVwI3AEUel5NslgLX+oKB170uxMSeBU0fBMp8I4FfAN8Gdp0E2/THFuDnwH2+YMCGXk0RFjT9ECjzzQCuAy7A7pnqr1acjvab3NlGTQqxoBmAQJlvLHAVcDl2hqo3G3DGBbrXFwzUeF2M8YYFzSAEynzDgcuAa4Gp3lYTdxYBtwKPuwORmRRmQRMFgTJfOnAm8AOci/9SVRh4FrjVFwzM9boYEz8saKIsUOabA5ztfqVKK2c18DjO4VHCTlFsYseCJoYCZb6D2BE60zwuJ9rW4YTLY75gYKHXxZj4ZkEzRAJlvgPZETrTPS5nIBRYDDwH/MsdbsOYPrGg8UCgzDcF+Hy3rwOIv9PlLTjBstD9+o8vGNjgbUkmUVnQxIFAmS8bJ2y2Bc/ngMmADFEJnThX6i7s9vWxLxgIDdH+TZKzoIlTgTJfDk7YTHW/JgNjgTHdvnJxwijN/TfysQDNwEac61m2/Rv5eK0vGGgfordmUpAFjTEm5myYCGNMzFnQGGNizoLGGBNzFjQ9EJEJIvKsiKwQkZUicpuIZInIxSJyZxS2f6OIHO8+vlZEbBYGk9QsaCKIiABPAc+o6kxgL5yZLX8drX2o6i9U9RX322ux6V5MkrOg2dVxQJuqPgCgqiHgu8ClOIEwUUTecFs7N2xbSUQuFJEFIvKBiNwrIunu14Mi8qGILBOR77rLPigiZ4nI1cA44HUReV1ELhWRP3bb5jdF5Nahe+vGxEaG1wXEob1xhjjYTlW3ikglzs/rEGAfnCtnF4rICzjXqnwVOFxVO0XkbpzBsT4CxqvqPgAiUhix3dtF5HvAsapaLSJ5wM9E5Ieq2glcAvxXDN+rMUPCgqb/XlbVGgAReQo4AugCDsIJHoAcYDPwL2CaiNwBvAC8tKcNq2qTiLwGnCoiASBTVZfF7J0YM0QsaHb1MXBW9ydEJB+YhBMokVc4Ks4VuBWq+tPIjYnIfsCXcEbjOwfnEGxP7sMZLjQIPDCA+o2JO9ZHs6tXgVwR+TqAiKQDvwcexDlcOkFEikQkBzgdmOeuc5aIjHbXKRKRySJSAqSp6pM4A3If2MP+GoER275R1fnAROB84NGYvENjhpgFTQR17sn4CnC2iKwAPgHacFoZAAuAJ3FuQnxSVd9T1Y9xguQlEVmKM2/0WGA88IaIfAA8DOzS4gH+DLwoIt2nHXkcmKeqddF+f8Z4we51ikMi8jxwq6q+6nUtxkSDtWjiiIgUisgnQKuFjEkm1qIxxsSctWiMMTFnQWOMiTkLGmNMzFnQGGNizoLGGBNzFjTGmJizoDHGxJwFjTEm5ixojDExZ0FjjIk5CxpjTMxZ0BhjYs6CxhgTcxY0xpiY+/+POE010pM0/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pie chart\n",
    "per_users_by_bmi = [100 * user / len(insurance_data) for user in users_by_bmi]\n",
    "\n",
    "labels = 'Underweight', 'Normalweight', 'Overweight', 'Obesity'\n",
    "sizes = [per_users_by_bmi[1], per_users_by_bmi[2], per_users_by_bmi[3], per_users_by_bmi[4]]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=False)\n",
    "plt.title('Users by BMI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean charges for underweighted people is $8852.2, for normalweighted people $10379.5, \n",
      "for overweighted people $11006.81 and for obese people $15491.54. \n",
      "This may imply that the charges tend to increase with the BMI of the user.\n"
     ]
    }
   ],
   "source": [
    "mean_cost_by_bmi = [cost_by_bmi[i] / users_by_bmi[i] for i in range(1, len(cost_by_bmi))]\n",
    "\n",
    "print(\"The mean charges for underweighted people is ${}, for normalweighted people ${}, \\n\\\n",
    "for overweighted people ${} and for obese people ${}. \\n\\\n",
    "This may imply that the charges tend to increase with the BMI of the user.\".format(round(mean_cost_by_bmi[0], 2), \n",
    "                                                             round(mean_cost_by_bmi[1], 2),\n",
    "                                                             round(mean_cost_by_bmi[2], 2),\n",
    "                                                             round(mean_cost_by_bmi[3], 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project investigated the available dataset in order to find out how the parameters influence the insurance costs.\n",
    "\n",
    "According to the results, it seems like the most influencing parameters are **age**, **smoking** and the **BMI**, which makes sense because these are parameters that have influence on the user's health. \n",
    "\n",
    "The number of children doesn't seem to affect the cost and the gender also seems to have little influence on these costs. "
   ]
  }
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