us-medical-insurance-costs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U.S. Medical Insurance Costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all packages here\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'charges': '7281.5056'}, {'age': '37', 'sex': 'male', 'bmi': '29.83', 'children': '2', 'smoker': 'no', 'region': 'northeast', 'charges': '6406.4107'}, {'age': '60', 'sex': 'female', 'bmi': '25.84', 'children': '0', 'smoker': 'no', 'region': 'northwest', 'charges': '28923.13692'}, {'age': '25', 'sex': 'male', 'bmi': '26.22', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '2721.3208'}, {'age': '62', 'sex': 'female', 'bmi': '26.29', 'children': '0', 'smoker': 'yes', 'region': 'southeast', 'charges': '27808.7251'}, {'age': '23', 'sex': 'male', 'bmi': '34.4', 'children': '0', 'smoker': 'no', 'region': 'southwest', 'charges': '1826.843'}, {'age': '56', 'sex': 'female', 'bmi': '39.82', 'children': '0', 'smoker': 'no', 'region': 'southeast', 'charges': '11090.7178'}, {'age': '27', 'sex': 'male', 'bmi': '42.13', 'children': '0', 'smoker': 'yes', 'region': 'southeast', 'charges': '39611.7577'}, {'age': '19', 'sex': 'male', 'bmi': '24.6', 'children': '1', 'smoker': 'no', 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'4747.0529'}, {'age': '26', 'sex': 'male', 'bmi': '27.06', 'children': '0', 'smoker': 'yes', 'region': 'southeast', 'charges': '17043.3414'}, {'age': '57', 'sex': 'male', 'bmi': '23.7', 'children': '0', 'smoker': 'no', 'region': 'southwest', 'charges': '10959.33'}, {'age': '22', 'sex': 'female', 'bmi': '30.4', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '2741.948'}, {'age': '32', 'sex': 'female', 'bmi': '29.735', 'children': '0', 'smoker': 'no', 'region': 'northwest', 'charges': '4357.04365'}, {'age': '39', 'sex': 'male', 'bmi': '29.925', 'children': '1', 'smoker': 'yes', 'region': 'northeast', 'charges': '22462.04375'}, {'age': '25', 'sex': 'female', 'bmi': '26.79', 'children': '2', 'smoker': 'no', 'region': 'northwest', 'charges': '4189.1131'}, {'age': '48', 'sex': 'female', 'bmi': '33.33', 'children': '0', 'smoker': 'no', 'region': 'southeast', 'charges': '8283.6807'}, {'age': '47', 'sex': 'female', 'bmi': '27.645', 'children': '2', 'smoker': 'yes', 'region': 'northwest', 'charges': '24535.69855'}, {'age': '18', 'sex': 'female', 'bmi': '21.66', 'children': '0', 'smoker': 'yes', 'region': 'northeast', 'charges': '14283.4594'}, {'age': '18', 'sex': 'male', 'bmi': '30.03', 'children': '1', 'smoker': 'no', 'region': 'southeast', 'charges': '1720.3537'}, {'age': '61', 'sex': 'male', 'bmi': '36.3', 'children': '1', 'smoker': 'yes', 'region': 'southwest', 'charges': '47403.88'}, {'age': '47', 'sex': 'female', 'bmi': '24.32', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '8534.6718'}, {'age': '28', 'sex': 'female', 'bmi': '17.29', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '3732.6251'}, {'age': '36', 'sex': 'female', 'bmi': '25.9', 'children': '1', 'smoker': 'no', 'region': 'southwest', 'charges': '5472.449'}, {'age': '20', 'sex': 'male', 'bmi': '39.4', 'children': '2', 'smoker': 'yes', 'region': 'southwest', 'charges': '38344.566'}, {'age': '44', 'sex': 'male', 'bmi': '34.32', 'children': '1', 'smoker': 'no', 'region': 'southeast', 'charges': '7147.4728'}, {'age': '38', 'sex': 'female', 'bmi': '19.95', 'children': '2', 'smoker': 'no', 'region': 'northeast', 'charges': '7133.9025'}, {'age': '19', 'sex': 'male', 'bmi': '34.9', 'children': '0', 'smoker': 'yes', 'region': 'southwest', 'charges': '34828.654'}, {'age': '21', 'sex': 'male', 'bmi': '23.21', 'children': '0', 'smoker': 'no', 'region': 'southeast', 'charges': '1515.3449'}, {'age': '46', 'sex': 'male', 'bmi': '25.745', 'children': '3', 'smoker': 'no', 'region': 'northwest', 'charges': '9301.89355'}, {'age': '58', 'sex': 'male', 'bmi': '25.175', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '11931.12525'}, {'age': '20', 'sex': 'male', 'bmi': '22', 'children': '1', 'smoker': 'no', 'region': 'southwest', 'charges': '1964.78'}, {'age': '18', 'sex': 'male', 'bmi': '26.125', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '1708.92575'}, {'age': '28', 'sex': 'female', 'bmi': '26.51', 'children': '2', 'smoker': 'no', 'region': 'southeast', 'charges': '4340.4409'}, {'age': '33', 'sex': 'male', 'bmi': '27.455', 'children': '2', 'smoker': 'no', 'region': 'northwest', 'charges': '5261.46945'}, {'age': '19', 'sex': 'female', 'bmi': '25.745', 'children': '1', 'smoker': 'no', 'region': 'northwest', 'charges': '2710.82855'}, {'age': '45', 'sex': 'male', 'bmi': '30.36', 'children': '0', 'smoker': 'yes', 'region': 'southeast', 'charges': '62592.87309'}, {'age': '62', 'sex': 'male', 'bmi': '30.875', 'children': '3', 'smoker': 'yes', 'region': 'northwest', 'charges': '46718.16325'}, {'age': '25', 'sex': 'female', 'bmi': '20.8', 'children': '1', 'smoker': 'no', 'region': 'southwest', 'charges': '3208.787'}, {'age': '43', 'sex': 'male', 'bmi': '27.8', 'children': '0', 'smoker': 'yes', 'region': 'southwest', 'charges': '37829.7242'}, {'age': '42', 'sex': 'male', 'bmi': '24.605', 'children': '2', 'smoker': 'yes', 'region': 'northeast', 'charges': '21259.37795'}, {'age': '24', 'sex': 'female', 'bmi': '27.72', 'children': '0', 'smoker': 'no', 'region': 'southeast', 'charges': '2464.6188'}, {'age': '29', 'sex': 'female', 'bmi': '21.85', 'children': '0', 'smoker': 'yes', 'region': 'northeast', 'charges': '16115.3045'}, {'age': '32', 'sex': 'male', 'bmi': '28.12', 'children': '4', 'smoker': 'yes', 'region': 'northwest', 'charges': '21472.4788'}, {'age': '25', 'sex': 'female', 'bmi': '30.2', 'children': '0', 'smoker': 'yes', 'region': 'southwest', 'charges': '33900.653'}, {'age': '41', 'sex': 'male', 'bmi': '32.2', 'children': '2', 'smoker': 'no', 'region': 'southwest', 'charges': '6875.961'}, {'age': '42', 'sex': 'male', 'bmi': '26.315', 'children': '1', 'smoker': 'no', 'region': 'northwest', 'charges': '6940.90985'}, {'age': '33', 'sex': 'female', 'bmi': '26.695', 'children': '0', 'smoker': 'no', 'region': 'northwest', 'charges': '4571.41305'}, {'age': '34', 'sex': 'male', 'bmi': '42.9', 'children': '1', 'smoker': 'no', 'region': 'southwest', 'charges': '4536.259'}, {'age': '19', 'sex': 'female', 'bmi': '34.7', 'children': '2', 'smoker': 'yes', 'region': 'southwest', 'charges': '36397.576'}, {'age': '30', 'sex': 'female', 'bmi': '23.655', 'children': '3', 'smoker': 'yes', 'region': 'northwest', 'charges': '18765.87545'}, {'age': '18', 'sex': 'male', 'bmi': '28.31', 'children': '1', 'smoker': 'no', 'region': 'northeast', 'charges': '11272.33139'}, {'age': '19', 'sex': 'female', 'bmi': '20.6', 'children': '0', 'smoker': 'no', 'region': 'southwest', 'charges': '1731.677'}, {'age': '18', 'sex': 'male', 'bmi': '53.13', 'children': '0', 'smoker': 'no', 'region': 'southeast', 'charges': '1163.4627'}, {'age': '35', 'sex': 'male', 'bmi': '39.71', 'children': '4', 'smoker': 'no', 'region': 'northeast', 'charges': '19496.71917'}, {'age': '39', 'sex': 'female', 'bmi': '26.315', 'children': '2', 'smoker': 'no', 'region': 'northwest', 'charges': '7201.70085'}, {'age': '31', 'sex': 'male', 'bmi': '31.065', 'children': '3', 'smoker': 'no', 'region': 'northwest', 'charges': '5425.02335'}, {'age': '62', 'sex': 'male', 'bmi': '26.695', 'children': '0', 'smoker': 'yes', 'region': 'northeast', 'charges': '28101.33305'}, {'age': '62', 'sex': 'male', 'bmi': '38.83', 'children': '0', 'smoker': 'no', 'region': 'southeast', 'charges': '12981.3457'}, {'age': '42', 'sex': 'female', 'bmi': '40.37', 'children': '2', 'smoker': 'yes', 'region': 'southeast', 'charges': '43896.3763'}, {'age': '31', 'sex': 'male', 'bmi': '25.935', 'children': '1', 'smoker': 'no', 'region': 'northwest', 'charges': '4239.89265'}, {'age': '61', 'sex': 'male', 'bmi': '33.535', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '13143.33665'}, {'age': '42', 'sex': 'female', 'bmi': '32.87', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '7050.0213'}, {'age': '51', 'sex': 'male', 'bmi': '30.03', 'children': '1', 'smoker': 'no', 'region': 'southeast', 'charges': '9377.9047'}, {'age': '23', 'sex': 'female', 'bmi': '24.225', 'children': '2', 'smoker': 'no', 'region': 'northeast', 'charges': '22395.74424'}, {'age': '52', 'sex': 'male', 'bmi': '38.6', 'children': '2', 'smoker': 'no', 'region': 'southwest', 'charges': '10325.206'}, {'age': '57', 'sex': 'female', 'bmi': '25.74', 'children': '2', 'smoker': 'no', 'region': 'southeast', 'charges': '12629.1656'}, {'age': '23', 'sex': 'female', 'bmi': '33.4', 'children': '0', 'smoker': 'no', 'region': 'southwest', 'charges': '10795.93733'}, {'age': '52', 'sex': 'female', 'bmi': '44.7', 'children': '3', 'smoker': 'no', 'region': 'southwest', 'charges': '11411.685'}, {'age': '50', 'sex': 'male', 'bmi': '30.97', 'children': '3', 'smoker': 'no', 'region': 'northwest', 'charges': '10600.5483'}, {'age': '18', 'sex': 'female', 'bmi': '31.92', 'children': '0', 'smoker': 'no', 'region': 'northeast', 'charges': '2205.9808'}, {'age': '18', 'sex': 'female', 'bmi': '36.85', 'children': '0', 'smoker': 'no', 'region': 'southeast', 'charges': '1629.8335'}, {'age': '21', 'sex': 'female', 'bmi': '25.8', 'children': '0', 'smoker': 'no', 'region': 'southwest', 'charges': '2007.945'}, {'age': '61', 'sex': 'female', 'bmi': '29.07', 'children': '0', 'smoker': 'yes', 'region': 'northwest', 'charges': '29141.3603'}]\n"
     ]
    }
   ],
   "source": [
    "def filename(file):\n",
    "    with open(file) as insurance_csv:\n",
    "        insurance_file = csv.DictReader(insurance_csv)\n",
    "        rows = []\n",
    "        for row in insurance_file:\n",
    "            rows.append(row)\n",
    "    return rows\n",
    "main_file = filename(\"insurance.csv\")\n",
    "print(main_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n1. Find out the average age of the patients in the dataset.\\n2. Analyze where a majority of the individuals are from.\\n3. Look at the different costs between smokers vs. non-smokers.\\n4. Figure out what the average age is for someone who has at least one child in this dataset.\\n5. return the number of males vs. females counted in the dataset\\n6. find geographical location of the patients\\n7. return the average yearly medical charges of the patients\\n8. creating a dictionary that contains all patient information\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Analysis\n",
    "\"\"\"\n",
    "1. Find out the average age of the patients in the dataset.\n",
    "2. Analyze where a majority of the individuals are from.\n",
    "3. Look at the different costs between smokers vs. non-smokers.\n",
    "4. Figure out what the average age is for someone who has at least one child in this dataset.\n",
    "5. return the number of males vs. females counted in the dataset\n",
    "6. find geographical location of the patients\n",
    "7. return the average yearly medical charges of the patients\n",
    "8. creating a dictionary that contains all patient information\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The average age is: 39.21 years'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Analysis - 1\n",
    "def avg_age(dataset):\n",
    "    total_age = 0\n",
    "    for age in dataset:\n",
    "        total_age += int(age.get(\"age\"))\n",
    "    average_age = total_age/len(dataset)\n",
    "    return \"The average age is: \" + str(round(average_age,2)) + \" years\"\n",
    "avg_age(main_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "southeast 364\n"
     ]
    }
   ],
   "source": [
    "# Data Analysis - 2\n",
    "# 2. Analyze where a majority of the individuals are from.\n",
    "# for main_dict in main_file:\n",
    "#     print(main_dict['region'])\n",
    "def majority_individuals_region(dataset):\n",
    "    unique_regions = dict()\n",
    "    for keys in dataset:\n",
    "        regions = (keys['region'])\n",
    "        if regions not in unique_regions:\n",
    "            unique_regions[regions] = 1\n",
    "        else:\n",
    "            unique_regions[regions] += 1\n",
    "    return unique_regions\n",
    "\n",
    "all_major_regions = majority_individuals_region(main_file)\n",
    "majority = max(zip(all_major_regions.values(), all_major_regions.keys()))\n",
    "print(majority[1], majority[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'southeast': 364, 'southwest': 325, 'northwest': 325, 'northeast': 324}\n"
     ]
    }
   ],
   "source": [
    "#Testing a different approach\n",
    "t = (set(sub['region'] for sub in main_file))\n",
    "regions_lst = [i['region'] for i in main_file]\n",
    "count = [regions_lst.count(x) for x in t]\n",
    "# for x in t:\n",
    "#     count.append(regions_lst.count(x))\n",
    "\n",
    "z = dict(zip(t,count))\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cost of insurance who smokes is: 6563.35\n",
      "The average cost of insurance who does not smoke is: 6707.07\n"
     ]
    }
   ],
   "source": [
    "# Data Analysis - 3\n",
    "# 3. Look at the different costs between smokers vs. non-smokers.\n",
    "def smoker_non_charges(datafile):\n",
    "    smoker_charges = 0\n",
    "    non_smoker_charges = 0\n",
    "    for data_points in datafile:\n",
    "        if data_points['smoker']=='yes':\n",
    "            smoker_charges += float(data_points.get('charges'))/len(datafile)\n",
    "        else:\n",
    "            non_smoker_charges += float(data_points.get('charges'))/len(datafile)\n",
    "    return smoker_charges, non_smoker_charges\n",
    "calculating_charges = smoker_non_charges(main_file)\n",
    "\n",
    "print(\"The average cost of insurance who smokes is: {}\".format(round(calculating_charges[0],2)))\n",
    "print(\"The average cost of insurance who does not smoke is: {}\".format(round(calculating_charges[1],2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average is: 39.21\n"
     ]
    }
   ],
   "source": [
    "# Data Analysis - 4\n",
    "# 4. Figure out what the average age is for someone who has at least one child in this dataset.\n",
    "child = 0\n",
    "for i in main_file:\n",
    "    if i['children']!=0:\n",
    "        child += int(i.get('age'))/len(main_file)\n",
    "print(\"The average is: {:.2f}\".format(child))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The nunmber of males counted are: 676 and the number of females counted are: 662'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data analysis - 5\n",
    "# 5. return the number of males vs. females counted in the dataset\n",
    "\n",
    "def count(dataset):\n",
    "    male_count = 0\n",
    "    female_count = 0\n",
    "    for no_of_ppl in dataset:\n",
    "        sex = no_of_ppl.get(\"sex\")\n",
    "        if sex == 'male':\n",
    "            male_count += 1\n",
    "        else:\n",
    "            female_count += 1\n",
    "    return \"The nunmber of males counted are: \" + str(male_count) + \" and the number of females counted are: \" + str(female_count)\n",
    "count(main_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Unique regions are: ['southwest', 'southeast', 'northwest', 'northeast']\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data analysis - 6\n",
    "# 6. find geographical location of the patients\n",
    "\n",
    "def unique(dataset):\n",
    "    unique_region = []\n",
    "    for regions in dataset:\n",
    "        geo_regions = regions.get(\"region\")\n",
    "        if geo_regions not in unique_region:\n",
    "            unique_region.append(geo_regions)\n",
    "    return \"Unique regions are: \" + str(unique_region)\n",
    "    \n",
    "unique(main_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13270.42"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data analysis - 7\n",
    "# 7. return the average yearly medical charges of the patients\n",
    "def average_yearly_medical_charges(dataset):\n",
    "    total_charges = 0\n",
    "    length_charges = []\n",
    "    for yearly_charges in dataset:\n",
    "        main_key = yearly_charges[\"charges\"]\n",
    "        length_charges.append(main_key)\n",
    "        total_charges += float(main_key)\n",
    "    average_medical_charge = total_charges/len(length_charges)\n",
    "    return round(average_medical_charge,2)\n",
    "\n",
    "\n",
    "average_yearly_medical_charges(main_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: {'age': 19, 'sex': 'female', 'bmi': 27.9, 'children': 0, 'smoker': 'yes', 'region': 'southwest', 'charges': 16884.924}, 2: {'age': 18, 'sex': 'male', 'bmi': 33.77, 'children': 1, 'smoker': 'no', 'region': 'southeast', 'charges': 1725.5523}, 3: {'age': 28, 'sex': 'male', 'bmi': 33.0, 'children': 3, 'smoker': 'no', 'region': 'southeast', 'charges': 4449.462}, 4: {'age': 33, 'sex': 'male', 'bmi': 22.705, 'children': 0, 'smoker': 'no', 'region': 'northwest', 'charges': 21984.47061}, 5: {'age': 32, 'sex': 'male', 'bmi': 28.88, 'children': 0, 'smoker': 'no', 'region': 'northwest', 'charges': 3866.8552}, 6: {'age': 31, 'sex': 'female', 'bmi': 25.74, 'children': 0, 'smoker': 'no', 'region': 'southeast', 'charges': 3756.6216}, 7: {'age': 46, 'sex': 'female', 'bmi': 33.44, 'children': 1, 'smoker': 'no', 'region': 'southeast', 'charges': 8240.5896}, 8: {'age': 37, 'sex': 'female', 'bmi': 27.74, 'children': 3, 'smoker': 'no', 'region': 'northwest', 'charges': 7281.5056}, 9: {'age': 37, 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'children': 3, 'smoker': 'no', 'region': 'northwest', 'charges': 10600.5483}, 1335: {'age': 18, 'sex': 'female', 'bmi': 31.92, 'children': 0, 'smoker': 'no', 'region': 'northeast', 'charges': 2205.9808}, 1336: {'age': 18, 'sex': 'female', 'bmi': 36.85, 'children': 0, 'smoker': 'no', 'region': 'southeast', 'charges': 1629.8335}, 1337: {'age': 21, 'sex': 'female', 'bmi': 25.8, 'children': 0, 'smoker': 'no', 'region': 'southwest', 'charges': 2007.945}, 1338: {'age': 61, 'sex': 'female', 'bmi': 29.07, 'children': 0, 'smoker': 'yes', 'region': 'northwest', 'charges': 29141.3603}}\n"
     ]
    }
   ],
   "source": [
    "# Data analysis - 8\n",
    "# 8. creating a dictionary that contains all patient information\n",
    "\n",
    "ages_list = [int(ages['age']) for ages in main_file]\n",
    "sex_list = [(ages['sex']) for ages in main_file]\n",
    "bmi_list = [float(ages['bmi']) for ages in main_file]\n",
    "children_list = [int(ages['children']) for ages in main_file]\n",
    "smoker_list = [(ages['smoker']) for ages in main_file]\n",
    "region_list = [(ages['region']) for ages in main_file]\n",
    "charges_list = [float(ages['charges']) for ages in main_file]\n",
    "def create_dictionary(ages, sex, bmi, children, smoker, region, charges):\n",
    "    dictionary = dict()\n",
    "    for index in range(len(main_file)):\n",
    "        dictionary[index+1] = {\"age\":ages[index], \"sex\":sex[index], \"bmi\":bmi[index], \"children\":children[index],\n",
    "                              \"smoker\":smoker[index], \"region\":region[index], \"charges\":charges[index]}\n",
    "    return dictionary\n",
    "final_dictionary = create_dictionary(ages_list,sex_list,bmi_list,children_list,smoker_list,region_list,charges_list)\n",
    "print(final_dictionary)"
   ]
  }
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