{"cells":[{"metadata":{},"cell_type":"markdown","source":"# PREDICTING MEDICAL COSTS WITH REGRESSION MODELS"},{"metadata":{},"cell_type":"markdown","source":"In this notebook we will look at samples of medical costs of hospital patients along with other variables such as age, sex, body mass index, smoker status. \n\nOnce a thorough exploratory data analysis is completed, various machine learning models will be implemented to predict medical costs using the feature variables contained within the dataset. \n\nThank you for taking the time to view this kernel and please offer any feedback.\n\n-Milton Suggs"},{"metadata":{},"cell_type":"markdown","source":"<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Table of Contents</h3>\n\n1. [Libraries & Packages](#libraries)\n2. [Initial Insights](#insights)\n3. [Data Preprocessing & Feature Engineering](#preprocessing)\n4. [Exploratory Data Analysis](#explore)\n5. [Feature Selection](#features)  \n6. [Model Building](#models)  \n    A. [Multiple Linear Regression](#lr)  \n    B. [Ridge Regression](#ridge)  \n    C. [Lasso Regression](#lasso)  \n    D. [Random Forest Regression](#rf)  \n    E. [Polynomial Regression](#poly)  \n7. [Model Evaluation](#modeleval)\n8. [Conclusion](#conclusion) "},{"metadata":{},"cell_type":"markdown","source":"<a id=\"libraries\"></a>\n## IMPORT LIBARARIES AND PACKAGES"},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install seaborn\n!pip install chart_studio","execution_count":248,"outputs":[{"output_type":"stream","text":"Requirement already satisfied: seaborn in /opt/conda/lib/python3.7/site-packages (0.10.0)\nRequirement already satisfied: numpy>=1.13.3 in /opt/conda/lib/python3.7/site-packages (from seaborn) (1.18.5)\nRequirement already satisfied: pandas>=0.22.0 in /opt/conda/lib/python3.7/site-packages (from seaborn) (1.1.2)\nRequirement already satisfied: matplotlib>=2.1.2 in /opt/conda/lib/python3.7/site-packages (from seaborn) (3.2.1)\nRequirement already satisfied: scipy>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from seaborn) (1.4.1)\nRequirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.22.0->seaborn) (2.8.1)\nRequirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.22.0->seaborn) (2019.3)\nRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=2.1.2->seaborn) (2.4.7)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=2.1.2->seaborn) (0.10.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=2.1.2->seaborn) (1.2.0)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=0.22.0->seaborn) (1.14.0)\n\u001b[33mWARNING: You are using pip version 20.2.3; however, version 20.2.4 is available.\nYou should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\nRequirement already satisfied: chart_studio in /opt/conda/lib/python3.7/site-packages (1.1.0)\nRequirement already satisfied: plotly in /opt/conda/lib/python3.7/site-packages (from chart_studio) (4.10.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from chart_studio) (2.23.0)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from chart_studio) (1.14.0)\nRequirement already satisfied: retrying>=1.3.3 in /opt/conda/lib/python3.7/site-packages (from chart_studio) (1.3.3)\nRequirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (3.0.4)\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (2.9)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (2020.6.20)\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (1.24.3)\n\u001b[33mWARNING: You are using pip version 20.2.3; however, version 20.2.4 is available.\nYou should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n","name":"stdout"}]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import r2_score\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import cross_val_predict\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import GridSearchCV\nimport math  \n\n\n# Plotly Packages\nfrom plotly import tools\nimport chart_studio.plotly as py\nimport plotly.figure_factory as ff\nimport plotly.graph_objects as go\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\nimport plotly.express as px\nfrom plotly.subplots import make_subplots\ninit_notebook_mode(connected=True)\n\n\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport missingno as msno\n\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":249,"outputs":[{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}},{"output_type":"stream","text":"/kaggle/input/insurance/insurance.csv\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"insights\"></a>\n## INITIAL INSIGHTS"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/insurance/insurance.csv')\ndf.head()","execution_count":250,"outputs":[{"output_type":"execute_result","execution_count":250,"data":{"text/plain":"   age     sex     bmi  children smoker     region      charges\n0   19  female  27.900         0    yes  southwest  16884.92400\n1   18    male  33.770         1     no  southeast   1725.55230\n2   28    male  33.000         3     no  southeast   4449.46200\n3   33    male  22.705         0     no  northwest  21984.47061\n4   32    male  28.880         0     no  northwest   3866.85520","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.900</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92400</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.770</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55230</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.000</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46200</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.705</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47061</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.880</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.85520</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.info()","execution_count":251,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1338 entries, 0 to 1337\nData columns (total 7 columns):\n #   Column    Non-Null Count  Dtype  \n---  ------    --------------  -----  \n 0   age       1338 non-null   int64  \n 1   sex       1338 non-null   object \n 2   bmi       1338 non-null   float64\n 3   children  1338 non-null   int64  \n 4   smoker    1338 non-null   object \n 5   region    1338 non-null   object \n 6   charges   1338 non-null   float64\ndtypes: float64(2), int64(2), object(3)\nmemory usage: 73.3+ KB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.describe()","execution_count":252,"outputs":[{"output_type":"execute_result","execution_count":252,"data":{"text/plain":"               age          bmi     children       charges\ncount  1338.000000  1338.000000  1338.000000   1338.000000\nmean     39.207025    30.663397     1.094918  13270.422265\nstd      14.049960     6.098187     1.205493  12110.011237\nmin      18.000000    15.960000     0.000000   1121.873900\n25%      27.000000    26.296250     0.000000   4740.287150\n50%      39.000000    30.400000     1.000000   9382.033000\n75%      51.000000    34.693750     2.000000  16639.912515\nmax      64.000000    53.130000     5.000000  63770.428010","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1338.000000</td>\n      <td>1338.000000</td>\n      <td>1338.000000</td>\n      <td>1338.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>39.207025</td>\n      <td>30.663397</td>\n      <td>1.094918</td>\n      <td>13270.422265</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>14.049960</td>\n      <td>6.098187</td>\n      <td>1.205493</td>\n      <td>12110.011237</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>18.000000</td>\n      <td>15.960000</td>\n      <td>0.000000</td>\n      <td>1121.873900</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>27.000000</td>\n      <td>26.296250</td>\n      <td>0.000000</td>\n      <td>4740.287150</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>39.000000</td>\n      <td>30.400000</td>\n      <td>1.000000</td>\n      <td>9382.033000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>51.000000</td>\n      <td>34.693750</td>\n      <td>2.000000</td>\n      <td>16639.912515</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>64.000000</td>\n      <td>53.130000</td>\n      <td>5.000000</td>\n      <td>63770.428010</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# VIEW CORRELATION BETWEEN EACH FEATURE TO THE TARGET VARIABLE (CHARGES)\n\ndf.corr()['charges'].sort_values().to_frame()","execution_count":253,"outputs":[{"output_type":"execute_result","execution_count":253,"data":{"text/plain":"           charges\nchildren  0.067998\nbmi       0.198341\nage       0.299008\ncharges   1.000000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>children</th>\n      <td>0.067998</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>0.198341</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>0.299008</td>\n    </tr>\n    <tr>\n      <th>charges</th>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# CREATE HEATMAP TO VIEW CORRELATIONS\n\nf, ax = plt.subplots(figsize=(10, 8))\ncorr = df.corr()\nsns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap='YlGnBu', square=True, ax=ax)","execution_count":254,"outputs":[{"output_type":"execute_result","execution_count":254,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f6f8c8a78d0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x576 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAiEAAAHYCAYAAACbYjjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3de3RV5Z3/8c8+B8I1F+EHGG6DokAEBSpSoYrlYgOSFDKOMkYdLMu4hgKj1CIWyq0wYoCxY5NatLOgaKxaL5USMLUqUwdFEBWMoVFBKCghWEgaIBGSk+f3h8MZMZDsQHb24z7vV9dei7PPPjvfndODXz7P8+zjGGOMAAAAmlnI7wIAAEBsogkBAAC+oAkBAAC+oAkBAAC+oAkBAAC+oAkBAAC+oAkBAABnlZ2drVGjRqlv37766KOPznhMJBLRokWLNGbMGF1//fV69tlnXZ2bJgQAAJzV6NGj9eSTT6pbt25nPWbdunXat2+fXn75ZT3zzDPKycnRp59+2uC5WzRloQAAwH4VFRWqqKiosz8hIUEJCQmn7RsyZEiD59uwYYNuuukmhUIhdejQQWPGjFFBQYHuvPPOel/XrE1Im563NOePw3kq3T3F7xLQSG3Dnf0uAY2wfv8Bv0vAOZjwD+Oa7Wd59d/NZbOGKzc3t87+6dOna8aMGY0+X0lJibp27Rp9nJycrIMHDzb4OpIQAABizOTJk5WRkVFn/9dTEK/RhAAAEGPONOxyPpKTk3XgwAFdccUVkuomI2fDxFQAACzlOCFPtqY2duxYPfvss6qtrdWRI0f0yiuvKDU1tcHX0YQAAGApRyFPtsZYsmSJRowYoYMHD+oHP/iBxo8fL0nKyspSYWGhJGnChAnq3r27vve97+nmm2/WtGnT1KNHj4avzxhjGv9rOTdMTP1mYWLqNw8TU79ZmJj6zdScE1Pb/cPtnpz3+F+f8OS8jcWcEAAALOXF0IlNgn11AADAWiQhAABYKuhJCE0IAACWchzH7xI8FewWCwAAWIskBAAAawU7Kwj21QEAAGuRhAAAYKmgT0wN9tUBAABrkYQAAGCpoCchNCEAAFiqsd/z8k0T7KsDAADWIgkBAMBSQR+OCfbVAQAAa5GEAABgqaAnITQhAABYKuhNSLCvDgAAWIskBAAASzniW3QBAACaHEkIAACWCvqcEJoQAAAsFfQmJNhXBwAArEUSAgCApUhCAAAAPEASAgCAtYKdFdCEAABgKYZjAAAAPEASAgCApUhCAAAAPEASAgCApZyAZwXBvjoAAGAtkhAAACwV9DkhNCEAAFjKcRy/S/BUsFssAABgLZIQAAAsFfThmGBfHQAAsBZJCAAAlgr6El2aEAAALMVwDAAAgAdIQgAAsBRJCAAAgAdIQgAAsBQTUwEAgD8YjgEAAGh6JCEAAFiKiakAAAAeIAkBAMBSfIsuAACAB1w1IXv37tUtt9yiUaNGSZKKioqUk5PjaWEAAMQ6RyFPNlu4qmThwoWaOnWq4uPjJUkpKSkqKCjwtDAAAGKd44Q82WzhqpKjR49qxIgR0bGpUCikli1beloYAAAINlcTU8PhsKqrq6NNSGlpqUIhezopAAACiYmpUmZmpqZPn66ysjLl5OQoMzNTU6ZM8bo2AAAQYK6SkIkTJ6p79+7auHGjqqqqlJ2drSFDhnhdGwAAsS3ggw6u7xMyZMgQGg8AAJpTwIdjXDUhN954Y50bpsTHx2vQoEG688471a5dO0+KAwAAweUq6Bk2bJiSk5M1depUTZ06VV27dtWAAQNUWlqqhQsXelwiAAAxynG82SzhKgl5++239cwzz0Qfjxw5UnfccYfWrFmjG264wbPiAABAcLlqQsrKynTixAm1atVKknTy5EmVlpbKcRy1bt3a0wIBAIhZTEyVxo0bp0mTJmncuHFyHEcvvfSSUlNTdfz4cXXr1s3rGgEAiEnGoqETL7hqQmbOnKlBgwZpy5YtkqR/+7d/08iRIyVJubm53lUHAAACy/Vt2999913t3r1bRUVFWr16tf7lX/7F69oAAIhtjkebJVw1IXPmzFE4HNbevXs1adIkhcNhXXHFFV7XBgAAAsxVE/LXv/5V99xzj1q3bq20tDQ9+uij+uCDD7yuDQCA2BZyvNks4aoJiYuLkyS1bNlS5eXlatmypQ4ePOhpYQAAINhcTUzt1auXysvLlZ6erkmTJik+Pl4pKSle1wYAQGxjdYy0YsUKSdIPfvADXX755Tp69KhGjBjhaWEAAMS8YPcg7r/A7hS+xA4AADSFRjchAACgmVg0idQLAb8hLAAAsBVJCAAAtmJiKgAA8EWwexCGYwAAgD9IQgAAsBUTUwEAAJoeSQgAALYKdhBCEwIAgK1MwFfHMBwDAAB8QRICAICtAj4xlSYEAACc1Z49e3T//fervLxcSUlJys7OVq9evU475vDhw/rJT36ikpISVVdX6+qrr9ZPf/pTtWhRf5vBcAwAALZyPNoaYcGCBcrMzNQf//hHZWZmav78+XWOWblypXr37q1169Zp3bp1Kioq0ssvv9zguWlCAADAGR0+fFg7d+5UWlqaJCktLU07d+7UkSNHTjvOcRwdP35ctbW1OnnypKqrq9WlS5cGz89wDAAAtvJodUxFRYUqKirq7E9ISFBCQkL0cUlJibp06aJwOCxJCofD6ty5s0pKStShQ4focT/84Q81Y8YMXXPNNaqqqtKtt96qK6+8ssE6aEIAALCVRxNT16xZo9zc3Dr7p0+frhkzZjT6fAUFBerbt6/WrFmj48ePKysrSwUFBRo7dmy9r6MJAQAgxkyePFkZGRl19n81BZGk5ORklZaWKhKJKBwOKxKJ6NChQ0pOTj7tuLy8PD3wwAMKhUKKj4/XqFGjtGXLlgabEOaEAABgK48mpiYkJKh79+51tq83IR07dlRKSory8/MlSfn5+UpJSTltKEaSunfvrtdff12SdPLkSW3evFmXXnppg5dHEwIAAM5q4cKFysvLU2pqqvLy8rRo0SJJUlZWlgoLCyVJc+bM0TvvvKP09HRNnDhRvXr10s0339zguRmOAQDAVhbctr1379569tln6+z/9a9/Hf1zz549tXr16kafmyYEAABbWdCEeInhGAAA4AuSEAAAbBXwqCDglwcAAGxFEgIAgK0CPieEJgQAAFsFuwdhOAYAAPiDJAQAAEsZj747xhYkIQAAwBckIQAA2CrgE1NJQgAAgC9IQgAAsFWwgxCaEAAArMXEVAAAgKZHEgIAgK0CPjG1WZuQ0t1TmvPH4Tx16b3K7xLQSFX7FvldAhrhsqSI3yUAviIJAQDAVsEOQmhCAACwFhNTAQAAmh5JCAAAtiIJAQAAaHokIQAAWMoEOwihCQEAwFoMxwAAADQ9khAAAGwV8DumkoQAAABfkIQAAGAr5oQAAAA0PZIQAABsFfCogCYEAABbMTEVAACg6ZGEAABgKyamAgAAND2SEAAALGUCPieEJgQAAFsFfLwi4JcHAABsRRICAICtmJgKAADQ9EhCAACwFRNTAQCALxiOAQAAaHokIQAA2CrYQQhJCAAA8AdJCAAAljLMCQEAAGh6JCEAANgq4EkITQgAALYK+H1CGI4BAAC+IAkBAMBWAY8KAn55AADAViQhAADYKuBzQmhCAACwVcBXxzAcAwAAfEESAgCArUhCAAAAmh5JCAAAljJMTAUAAL4I+HhFwC8PAADYiiQEAABbBXw4hiQEAAD4giQEAABbsUQXAACg6ZGEAABgq4AnITQhAADYKtg9CMMxAADAHyQhAABYygR8OIYkBAAA+IIkBAAAWwX8ZmU0IQAA2CrgwzH1NiGzZs3S8uXLdeONN8o5Qzf23HPPeVYYAAAItnqbkMmTJ0uSZs+e3SzFAACArwh2EFJ/EzJgwABJ0tChQ5ulGAAAEDtczQn55JNPtHLlSu3bt081NTXR/QzHAADgnVDA17C6akLuvvtuTZgwQRkZGQqHw17XBAAAFPjFMe6akBYtWujOO+/0uhYAABBDXAU91157rV5//XWvawEAAF/hON5stnCVhAwbNkw//OEPFQqFFBcXJ2OMHMfR5s2bva4PAAAElKsmZP78+Vq6dKn69++vUNBnyQAAYIkz3aMrSFw1IYmJiRo7dqzXtQAAgK8IeA/ibk7ImDFj9NRTT6m8vFxVVVXRDQAABNuePXs0adIkpaamatKkSdq7d+8Zj9uwYYPS09OVlpam9PR0/e1vf2vw3K6SkP/8z/+UJC1atCi6z3Ec/eUvf3HzcgAAcA5sSEIWLFigzMxMTZgwQWvXrtX8+fP1+OOPn3ZMYWGhcnNztWbNGnXq1ElHjx5VXFxcg+d21YQUFxefW+UAAOAb6/Dhw9q5c6dWr14tSUpLS9PixYt15MgRdejQIXrcb37zG02ZMkWdOnWSJMXHx7s6v+tv0T1y5Ih27Nghx3E0cOBAXXDBBY25DgAA0EiOR2tBKioqVFFRUWd/QkKCEhISoo9LSkrUpUuX6I1Kw+GwOnfurJKSktOakN27d6t79+669dZbVVlZqeuvv15Tp05tcGKtqybk5Zdf1rx589S/f38ZY1RcXKzFixdrzJgxri4WAADYY82aNcrNza2zf/r06ZoxY0ajzxeJRPThhx9q9erVOnnypO6880517dpVEydOrPd1rpqQn//853r66ad10UUXSZL27t2rqVOn0oQAAOAhr+aETJ48WRkZGXX2fzUFkaTk5GSVlpYqEokoHA4rEono0KFDSk5OPu24rl27auzYsYqLi1NcXJxGjx6t999/v8EmxFXQk5iYGG1AJKlXr15KSkpy81IAAHCOQo43W0JCgrp3715n+3oT0rFjR6WkpCg/P1+SlJ+fr5SUlNOGYqQv54ps2rRJxhhVV1frrbfeUr9+/Rq+vvqePLUU95prrtGvfvUrff755zp06JBWrlyp66+/vrG/SwAA8A2zcOFC5eXlKTU1VXl5edGVsllZWSosLJQkjR8/Xh07dtQNN9ygiRMn6pJLLtE//dM/NXhuxxhjzvZkv3795DiOznTIuSzRraj+U6OOh7+69F7ldwlopKp9ixo+CNb4+O8f+V0CzsGliWnN9rMuW+XN97btnDLCk/M2Vr1zQliaCwAAvOJ6iS4AAGheNtyszEs0IQAAWCroX2DHV+ICAABfkIQAAGApr+6YaouAXx4AALAVSQgAAJYK+JQQmhAAAGwV9CaE4RgAAOALkhAAACxFEgIAAOABkhAAACwVIgkBAABoeiQhAABYKuhzQmhCAACwVNCbEIZjAACAL0hCAACwlBPwmakkIQAAwBckIQAAWCroc0JoQgAAsFTQmxCGYwAAgC9IQgAAsBRJCAAAgAdIQgAAsFTAV+jShAAAYCuGYwAAADxAEgIAgKWcgEcFAb88AABgK5IQAAAsxZwQAAAAD5CEAABgKSfgUQhNCAAAlgp4D8JwDAAA8AdJCAAAliIJAQAA8ECzJiFtw52b88fhPFXtW+R3CWikNj0X+F0CGqFsz91+lwDLBT0JYTgGAABLBf0L7BiOAQAAviAJAQDAUiQhAAAAHiAJAQDAUiHH+F2Cp2hCAACwFMMxAAAAHiAJAQDAUkFPCoJ+fQAAwFIkIQAAWCroE1NJQgAAgC9IQgAAsFTQV8fQhAAAYKmgD1cE/foAAIClSEIAALBU0IdjSEIAAIAvSEIAALCUE/AlujQhAABYiuEYAAAAD5CEAABgqaAnBUG/PgAAYCmSEAAALBX0746hCQEAwFJMTAUAAPAASQgAAJYKelIQ9OsDAACWIgkBAMBSzAkBAADwAEkIAACWYokuAADwBcMxAAAAHiAJAQDAUkFPCoJ+fQAAwFIkIQAAWIqJqQAAwBdMTAUAAPAASQgAAJYiCQEAAPAASQgAAJYKelJAEwIAgKWCvjom6E0WAACwFEkIAACWYmIqAACAB0hCAACwVNCTgqBfHwAAOA979uzRpEmTlJqaqkmTJmnv3r1nPfaTTz7RwIEDlZ2d7ercNCEAAFgq5HizNcaCBQuUmZmpP/7xj8rMzNT8+fPPeFwkEtGCBQs0ZswY1+dmOAYAAEs5Hi3RraioUEVFRZ39CQkJSkhIiD4+fPiwdu7cqdWrV0uS0tLStHjxYh05ckQdOnQ47bWPPfaYvvvd76qyslKVlZWu6iAJAQAgxqxZs0ajR4+us61Zs+a040pKStSlSxeFw2FJUjgcVufOnVVSUnLaccXFxdq0aZPuuOOORtVBEgIAgKW8WqI7efJkZWRk1Nn/1RTErerqas2bN09Lly6NNitu0YQAABBjvj7scjbJyckqLS1VJBJROBxWJBLRoUOHlJycHD3m888/1759+3TXXXdJ+nKoxxijY8eOafHixfWenyYEAABL+T1nomPHjkpJSVF+fr4mTJig/Px8paSknDYfpGvXrtqyZUv0cU5OjiorKzV79uwGz+/39QEAgLMIOcaTrTEWLlyovLw8paamKi8vT4sWLZIkZWVlqbCw8LyuzzHGNNu349TU7miuH4Um0CLUxu8S0Ehtei7wuwQ0Qtmeu/0uAeegdfjqZvtZ8955xZPzLr7S/TJaLzEcAwCApYL+3TGumpDDhw/riSee0P79+1VTUxPd//DDD3tWGAAACDZXTciMGTPUu3dvDRs2rNHLbwAAwLkhCdGXy20aWmYDAACaVtD/2e9qdcyll16q0tJSr2sBAAAxxHUS8v3vf1+DBw9Wq1atovuZEwIAgHcau5z2m8ZVE5KWlqa0tDSvawEAADHEVRNypvvLAwAAbwV9YqqrOSF79+7VLbfcolGjRkmSioqKlJOT42lhAAAg2Fw1IQsXLtTUqVMVHx8vSUpJSVFBQYGnhQEAEOtCjjebLVw1IUePHtWIESPkOF9WHgqF1LJlS08LAwAg1oUdbzZbuGpCwuGwqquro01IaWmpQiG++w4AAJw7VxNTMzMzNX36dJWVlSknJ0cvvviiZs6c6XVtAADENJuGTrzgqgmZOHGiunfvro0bN6qqqkrZ2dkaMmSI17UBAIAAa7AJiUQiuvnmm/X888/TeAAA0Ixi/mZl4XBYF1xwgU6cOHHa3VIBAIC3GI6R1KtXL916661KTU1V27Zto/tvvfVWzwoDAADB5qoJOX78uC699FJ98sknXtcDAAD+V9C/RddVE7J06VKv6wAAADGm3ibkySefrPfFDMcAAOCdmJ4T8sEHH0iSysrKtHXrVg0bNkyStHnzZl1zzTU0IQAAeCimV8ecGoaZNm2a1q5dqx49ekiS9u/fr4ceesj76gAAQGC5mhPy2WefRRsQSerRo4f27NnjWVEAAMCu73nxgqsvgElKStIvf/lLHTp0SIcOHdKvfvUrJSUleV0bAAAIMFdNSHZ2tj788EOlp6crPT1dxcXFys7O9ro2AABiWsjxZrOFq+GYLl266Be/+IXXtQAAgBhSbxPy5z//ud4XX3fddU1aDAAA+D82pRZeqLcJ+a//+q+zPuc4Dk0IAAAeiukm5IknnmiuOgAAQIyptwnZv3+/evTooV27dp3x+UsuucSTogAAgBSO5ZuVLVmyRI8++qjuuuuuOs85jqNXX33Vs8IAAECw1duEPProo5Kk1157rVmKAQAA/8fVfTS+wVwt0ZWkqqoqHTx4UJFIJLqP4RgAALwT0xNTT3nyySe1YsUKJSYmKhT6si9jOAYAAJwPV03IqlWrlJ+fr27dunldDwAA+F9BT0JcDTd16tSJBgQAADSpepOQU0tzhw8frmXLlmn8+PFq1apV9HnmhAAA4J2YXqL79aW5BQUF0T8zJwQAAG8FfTim3ibk1NLcN954Q5dffrkSEhIkSRUVFSoqKvK+OgAAEFiu5oQsX75c8fHx0cft27fXsmXLPCsKAAB8mYR4sdnCVRNijJHj/F/VoVDotPuFAAAANJarJqRdu3basWNH9PGOHTvUtm1bz4oCAADBT0Jc3Sdk1qxZmjZtWnQ1zK5du5Sbm+tpYQAAxLqwRQ2DF1w1IYMHD9b69eu1fft2GWM0ePBgJSYmel0bAAAIMNffHZOYmKjrrrvOy1oAAMBXhAJ+n5Cgf0EfAACwlOskBAAANK+gJwVBvz4AAGApkhAAACxl03JaL9CEAABgqaAv0WU4BgAA+IIkBAAAS7FEFwAAwAMkIQAAWIqJqQAAwBc0IU1o/f4DzfnjcJ4uS4r4XQIaqWzP3X6XgEa44KKH/S4B56Bq39V+lxAYJCEAAFgq6BM3g359AADAUiQhAABYymFOCAAA8EPAexCGYwAAgD9IQgAAsFTQh2NIQgAAgC9IQgAAsFTQk4KgXx8AALAUSQgAAJZyAv4tujQhAABYKuDzUhmOAQAA/iAJAQDAUizRBQAA8ABJCAAAlgp4EEITAgCArUIB70IYjgEAAL4gCQEAwFIBD0JIQgAAgD9IQgAAsFTQl+jShAAAYKmA9yAMxwAAAH+QhAAAYKmgJyE0IQAA4Kz27Nmj+++/X+Xl5UpKSlJ2drZ69ep12jG//OUvtWHDBoXDYbVo0UIzZ87Utdde2+C5aUIAALCUDTcrW7BggTIzMzVhwgStXbtW8+fP1+OPP37aMVdccYWmTJmiNm3aqLi4WLfddps2bdqk1q1b13tu5oQAABBjKioq9Omnn9bZKioqTjvu8OHD2rlzp9LS0iRJaWlp2rlzp44cOXLacddee63atGkjSerbt6+MMSovL2+wDpIQAAAs5VUQsmbNGuXm5tbZP336dM2YMSP6uKSkRF26dFE4HJYkhcNhde7cWSUlJerQocMZz/3iiy+qZ8+euvDCCxusgyYEAABLOY7x5LyTJ09WRkZGnf0JCQnndd6tW7fq4Ycf1qpVq1wdTxMCAECMSUhIcNVwJCcnq7S0VJFIROFwWJFIRIcOHVJycnKdY9977z3NmjVLjzzyiC6++GJXdTAnBAAASzkebW517NhRKSkpys/PlyTl5+crJSWlzlDM+++/r5kzZ+oXv/iF+vfv7/r8NCEAAOCsFi5cqLy8PKWmpiovL0+LFi2SJGVlZamwsFCStGjRIn3xxReaP3++JkyYoAkTJujDDz9s8NyOMcabAaczWPvXl5rrR6EJXJYU8bsENFKP9v/P7xLQCBdc9LDfJeAcVO17qtl+1idH13ly3ovj0z05b2MxJwQAAEsFfbgi6NcHAAAsRRICAIClHAvumOolkhAAAOALkhAAACwV8CCEJgQAAFsxHAMAAOABkhAAACwV8CCEJAQAAPiDJAQAAEuFAh6FkIQAAABfkIQAAGCpgAchNCEAANjKcZrtO2Z9wXAMAADwBUkIAACWCvpwDEkIAADwBUkIAACWCvpt22lCAACwVMB7EIZjAACAP0hCAACwVNCTgqBfHwAAsFSjm5DDhw9r+/btXtQCAAC+wnG82WzhqgnJzMzU0aNHVVFRoYkTJ2ru3LnKzs72ujYAAGKc49FmB1dNSGVlpeLj47Vx40alp6dr3bp12rRpk9e1AQCAAHPVhJw8eVKStGXLFg0fPlyhUEjhcNjTwgAAiHWOR/+zhasmZOjQoUpNTdW2bds0dOhQVVRUKBRiTisAADh3rpboLliwQMXFxerRo4fi4uJ07NgxLVmyxOvaAACIaY4T7H/wu7o6x3FUXl6uF198UZJUW1urNm3aeFoYAAAINldNyGOPPabc3Fw9/vjjkqSamhrNmTPH08IAAACrY5Sfn6/f/OY3atu2rSTpwgsv1LFjxzwtDACAWMfEVEmtW7dWy5YtT9vn2HS3EwAA8I3jamLqhRdeqG3btslxHNXW1mrlypW69NJLva4NAIAYF+x/8LtKQubNm6dHHnlEH3/8sQYOHKi3336bOSEAAOC8uEpCOnXqpFWrVqmqqkq1tbVq166d13UBABDzgr5E11UT8uc//7nOvvbt26tPnz6Kj49v8qIAAIAU9OEYV03II488osLCQvXt21eS9NFHH6lfv346ePCglixZopEjR3paJAAACB5XTUjPnj01b948DRgwQJJUVFSkZ555RsuWLdOPfvQjmhAAADxg03JaL7gabCouLo42IJLUv39/FRUVqXfv3jLGeFYcAAAILldNSJs2bZSfnx99nJ+fH/0WXe4XAgCAN4J+szJXwzFLly7VrFmzNGfOHDmOo969e2vZsmWqqqrSfffd53WNAADEqBhfHROJRLRt2za98MIL0Vu1t2/fPvr8d77zHe+qAwAAgdVgixUOh7V27VpJXzYfX21AAACAdxzH8WSzhaucZ/jw4SooKPC6FgAAEENczQnJy8tTeXm5WrdurTZt2sgYI8dxtHnzZq/rAwAghtmTWnjBVRPy/PPPe10HAACIMa6akG7dunldBwAA+BqbltN6wVUTUlJSouXLl6u4uFgnTpyI7n/11Vc9KwwAAAR7ia6rq5szZ46GDRsmY4xWrFihK6+8UhkZGV7XBgAAAsxVE1JWVqabbrpJLVq00ODBg/Xggw9q69atXtcGAEBMC/odU101IS1btpQktW3bVgcOHFBNTY0OHDjgaWEAACDYXM0JGTJkiMrLy3XLLbfoH//xHxUXF6fU1FSvawMAIKbZdGMxL7hqQmbPni1JmjhxooYOHapjx46pT58+nhYGAACC3YS4nnZbVVWlPXv2qLKyUqFQSLt27fKyLgAAEHCukpAnn3xSK1asUFJSUjQachyHJboAAHjICfgSXVdNyKpVq5Sfn89NywAAQJNx1YR06tSJBgQAgGYX7Dkh9TYhp+Z9DB8+XMuWLdP48ePVqlWr6POXXHKJt9UBABDDYnp1zF133XXa44KCguifmRMCAADOR71NyGuvvSZJeuONN3T55ZcrISFBklRRUaGioiLvqwMAIKYFOwlxNe12+fLlio+Pjz5u3769li1b5llRAAAg+FxNTDXGnDYuFQqFFIlEPCsKAAAEf4muq6tr166dduzYEX28Y8cOtW3b1rOiAABA8LlKQmbNmqVp06ZFV8Ps2rVLubm5nhYGAACCPSfEVRMyePBgrV+/Xtu3b5cxRoMHD1ZiYqLXtQEAENMcmpAvJSYm6rrrrvOyFgAAEENcNyEAAKB5Bf1mZcGedgsAAKxFEgIAgNdfxUAAAAgsSURBVLWCnRXQhAAAYKmgT0wNdosFAACsRRICAIC1SEIAAACaHEkIAACWCvoSXZoQAACsFewBi2BfHQAAsBZJCAAAlgr6El3HGGP8LgIAAMQehmMAAIAvaEIAAIAvaEIAAIAvaEIAAIAvaEIAAIAvaEIAAIAvaEIAAIAvaEIAAIAvaEIAAIAvaEIAAIAvaEIQKH379tXx48fP6xyFhYW69957m6ginDJq1Ch99NFHZ3wuKytL+/btkyTdfvvt2rhx4xmPu//++5WXl+dZjThdfe8Z0BT4Ajvgay6//HL9x3/8h99lxJRf//rX5/X6mpoatWjBX2c24r1Bffh/RgPuvfde7dmzR9XV1erZs6ceeOABJSYm6uc//7k2bNigpKQkDR06VJs3b9YLL7wgSfr973+v3/72t4pEImrfvr0WLlyoiy++2OcriR2rVq3SG2+8obKyMv3oRz9SamqqpC9TknvuuUevvPKKysvLtWTJEr355pv6n//5H9XU1Ojhhx9W7969tWXLFmVnZ0ffTzTee++9p2XLlkVTqfvuu0+S9NJLL2nevHn6/PPPNWXKFN12222SvvwX98qVK9WnT5/TzlNaWqr77rtPZWVl6t69uyKRSPS5+++/X+3atdPevXtVVlamF1544ayfvRdeeEH5+flKSEjQxx9/rPj4eOXk5KhTp07N9BuxX2Pfs+zsbG3dulXV1dW64IIL9MADD6hbt2769NNPdeONN+q2227Tm2++qe9///u66qqr9JOf/ERVVVXq16+f9u3bp6lTp2rkyJE6dOiQlixZogMHDujEiRMaP368/vVf/1W1tbX62c9+prfeektxcXFq27atnn76ad9+P/CIQb0OHz4c/fNDDz1kli9fbl599VWTnp5ujh8/biKRiJk2bZrJyMgwxhjz9ttvm6ysLHPixAljjDH//d//bSZNmuRL7bGoT58+JicnxxhjzO7du83QoUPN3/72t+hzeXl5xhhjNmzYYAYNGmQ2btxojDHmscceM/fee68xxpi33nor+n6i8crKyszw4cPNO++8Y4wxpqamxpSXl5uRI0eaBx980BhjzP79+82gQYPMsWPHjDHGjBw50nz44YfGGGNuu+0289prrxljjJk+fXr0/dy3b58ZNGiQeeKJJ4wxxsyePdtkZGSY48ePG2Pq/+w9//zzZsiQIebAgQPGGGPmzp1rHnroIc9/F98U5/KeffXvxt/97nfmnnvuiR7Xp08fs379+ujzGRkZ5sUXXzTGGPP++++bfv36Rd/jO+64w2zdutUYY8yJEyfMLbfcYjZt2mSKiorM9773PROJRIwxxpSXl3v5K4BPSEIasHbtWq1bt07V1dWqrKxUr169VF1drXHjxqlt27aSpIkTJ+qRRx6RJL322msqLi7WTTfdJEkyxqiiosK3+mPRqd/9xRdfrMsuu0zbt2/X6NGjJUnjxo2TJPXv31+S9N3vfleSNGDAAP3pT39q/mIDaPv27erdu7e+9a1vSZLC4bASExMlSTfccIMkqXv37kpISNDBgwfVu3fvs55ry5Yt+ulPfypJ6tGjh4YNG3ba82PHjo1+Dhv67H3rW99ScnKyJGngwIF68803m+JyA+Fc3rPXX39dv/3tb1VZWamamprTzteqVavoZ+3YsWP66KOPlJ6eLunL4c6+fftKkiorK7V161YdOXIk+trjx49r9+7dysjIUCQS0dy5c/Xtb39bI0eO9PaXAF/QhNRj27Zteuqpp/T000+rQ4cOWrdunX73u9/JGCPHcc74GmOMbrzxRt19993NXC3O5OvvVatWrSRJoVBIcXFx0f2hUKjOX6Q4N8aYsz536vcvffkfuq8Or5yLUw3IqZ9b32evqX92kDT2Pfvss8+0dOlSPffcc+rRo4feffdd/fjHP44e16ZNm+jn7tRn8Ex/Z9bW1spxHD333HNq2bJlnefXr1+vLVu2aPPmzVqxYoV+//vfM4QWMKyOqUdFRYXat2+vpKQknTx5Us8//7wk6dvf/rYKCgpUVVWl2tpa/eEPf4i+ZtSoUVq7dq0OHjwoSYpEIvrggw98qT9WnXqf9u7dq7/85S8aOHCgzxXFlsGDB2v37t167733JH35Gfj73/9+Tue6+uqro+/n/v37tXnz5rMey2fv3DX2PTt27JhatmypTp06qba2tt65GvHx8brkkkuUn58vSSoqKoquuGnfvr2uvPJKPfbYY9HjS0pK9Pnnn+vIkSP64osvNGLECP34xz9WfHy89u/f3xSXC4uQhNRjxIgR+sMf/qBx48apS5cuGjBggAoLCzV69Gi99957mjBhgrp06aKBAwdGP7BXXXWV7rnnHk2dOlWRSETV1dUaO3asBgwY4PPVxI64uDj98z//s8rKyvSzn/1MHTt29LukmJKUlKScnBw9+OCDqqysVCgU0uzZs8/pXHPnztV9992ngoICXXTRRfrOd75z1mP57J27xr5nffv21dixYzV+/Hh17dpVV111lbZt23bW47OzszVnzhytXr1a/fv3V79+/RQfHy9JWrFihZYuXRodrmnXrp3+/d//XV988YXmzZunmpoaRSIRjRgxQoMGDWraC4fvHFNfDoezOnbsmNq3b6/a2lrNnTtXnTt31syZM/0uCwCsU1lZGR2i2bVrl26//XYVFBRE550gdpGEnKPZs2frs88+0xdffKH+/fsrKyvL75IAwErvvvuuli1bFp17snjxYhoQSCIJAQAAPmFiKgAA8AVNCAAA8AVNCAAA8AVNCAAA8AVNCAAA8MX/BwNlCi5tSyxvAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"preprocessing\"></a>\n## DATA PREPROCESSING & FEATURE ENGINEERING\n\n**Create new feature \"Age Range\" from the Age feature**<br>\n  * young adult: 18 - 35\n  * middle age: 36 - 55\n  * senior adult: 56+\n   \n**Create new feature \"weight status\" from the BMI feature**<br>\n  * underweight\n  * normal weight\n  * overweight\n  * obese"},{"metadata":{"trusted":true},"cell_type":"code","source":"df['age'] = df['age'].astype('int') #Convert data type of 'age' to int\ndf['children'] = df['children'].astype('int') #Convert data type of 'children' to int\n\n#Create new feature 'age_cat' to categorize values from 'age' colummn\n\ndf['age_cat'] = np.nan \n\nfor col in [df]:\n    col.loc[(col['age'] >= 18) & (col['age'] <= 35), 'age_cat'] = 'young adult'\n    col.loc[(col['age'] > 35) & (col['age'] <= 55), 'age_cat'] = 'middle age'\n    col.loc[col['age'] > 55, 'age_cat'] = 'senior adult'\n\n    \n#Create new feature 'weight_status' to categorize values from 'bmi' colummn\n\ndf[\"weight_status\"] = np.nan\n\nfor col in [df]:\n    col.loc[col[\"bmi\"] < 18.5, \"weight_status\"] = \"underweight\"\n    col.loc[(col[\"bmi\"] >= 18.5) & (col[\"bmi\"] < 24.986), \"weight_status\"] = \"normal weight\"\n    col.loc[(col[\"bmi\"] >= 25) & (col[\"bmi\"] < 29.926), \"weight_status\"] = \"overweight\"\n    col.loc[col[\"bmi\"] >= 30, \"weight_status\"] = \"obese\"\n    \ndf.head()","execution_count":255,"outputs":[{"output_type":"execute_result","execution_count":255,"data":{"text/plain":"   age     sex     bmi  children smoker     region      charges      age_cat  \\\n0   19  female  27.900         0    yes  southwest  16884.92400  young adult   \n1   18    male  33.770         1     no  southeast   1725.55230  young adult   \n2   28    male  33.000         3     no  southeast   4449.46200  young adult   \n3   33    male  22.705         0     no  northwest  21984.47061  young adult   \n4   32    male  28.880         0     no  northwest   3866.85520  young adult   \n\n   weight_status  \n0     overweight  \n1          obese  \n2          obese  \n3  normal weight  \n4     overweight  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>charges</th>\n      <th>age_cat</th>\n      <th>weight_status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.900</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92400</td>\n      <td>young adult</td>\n      <td>overweight</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.770</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55230</td>\n      <td>young adult</td>\n      <td>obese</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.000</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46200</td>\n      <td>young adult</td>\n      <td>obese</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.705</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47061</td>\n      <td>young adult</td>\n      <td>normal weight</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.880</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.85520</td>\n      <td>young adult</td>\n      <td>overweight</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"explore\"></a>\n## **EXPLORATORY DATA ANALYSIS**\n\nIn this section we'll perform an extensive exploratory data analyisis to visualize each of the feature variables. Starting with the 'charges' column, which is our target variable, we'll look at the distribution of values throughout the dataset. Afterward we'll perform univariate, bivariate, and multivariate data explorations. "},{"metadata":{},"cell_type":"markdown","source":"### VALUE DISTRIBUTION OF CHARGES VARIABLE"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Determine the distribution of charges\n\ncharge_dist = df[\"charges\"]\nlogcharge = np.log(df[\"charges\"])\n\ntrace0 = go.Histogram(x=charge_dist,\n                      histnorm='probability',\n                      name=\"Charges Distribution\",\n                      marker = dict(color = '#02919e',)\n                     )\n\ntrace1 = go.Histogram(x=logcharge,\n                      histnorm='probability',\n                      name=\"Charges Distribution using Log\",\n                      marker = dict(color = '#9e2602')\n                     )\n\nfig = make_subplots(rows=2, cols=1,\n                    subplot_titles=('Charge Distribution','Log Charge Distribution'),\n                    print_grid=False)\n\nfig.append_trace(trace0, 1, 1)\nfig.append_trace(trace1, 2, 1)\n\nfig['layout'].update(showlegend=True, title='Charge Distribution', bargap=0.05)\n\niplot(fig, filename='custom-sized-subplot-with-subplot-titles')","execution_count":256,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"linkText":"Export to plot.ly","plotlyServerURL":"https://plot.ly","showLink":false},"data":[{"histnorm":"probability","marker":{"color":"#02919e"},"name":"Charges 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(mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Percentage of smokers:\\n ', df.smoker.value_counts() / len(df))","execution_count":260,"outputs":[{"output_type":"stream","text":"Percentage of smokers:\n  no     0.795217\nyes    0.204783\nName: smoker, dtype: float64\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### DISTRIBUTION OF AGE & AGE_CAT VARIABLE\n\n* The majority of samples are in the range of age 36 to 64 years split fairly evenly between the categories \"middle age\" and \"senior adult\". \n* The young adult population is far less 16.1% of the dataset consisting of people ages 35 and below."},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = ff.create_distplot([df['age']], ['age'], bin_size=1, colors=['blue'])\nfig.update_layout(title_text=\"Distribution of Age\", title_x=0.5)\nfig.show()","execution_count":261,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"autobinx":false,"histnorm":"probability 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title_x=0.5)\nfig.show()","execution_count":263,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"autobinx":false,"histnorm":"probability 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percentage have 4 or more. \n* It doesn't seem likely that number of children will have any significant effect on charges. 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Becuase the 'smoker' variable is the most highly correlated with the 'charges' variable we will begin by exploring the relationship between 'smoker' and other variables such as 'age', 'bmi' & 'weight status', and 'region'"},{"metadata":{},"cell_type":"markdown","source":"### SMOKER STATUS BY SEX\n\n* The number of male smokers (159) outweights the number of female smokers (115)\n* Approx 23.5% of male patients are smokers as compared to 17.4% of female patients"},{"metadata":{"trusted":true},"cell_type":"code","source":"s_f = df[(df['smoker'] == 'yes') & (df['sex'] == 'female')]\ns_m = df[(df['smoker'] == 'yes') & (df['sex'] == 'male')]\n\nns_f = df[(df['smoker'] == 'no') & (df['sex'] == 'female')]\nns_m = df[(df['smoker'] == 'no') & (df['sex'] == 'male')]\n\nfig = go.Figure(data=[go.Bar(name='Female', x=df['smoker'].unique(), y=[len(s_f), len(ns_f)]),\n                      go.Bar(name='Male', x=df['smoker'].unique(), y=[len(s_m), len(ns_m)]),\n                     ]\n               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ages\", title_x=0.5)\nfig.show()","execution_count":269,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"autobinx":false,"histnorm":"probability 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data=smoker_age)","execution_count":270,"outputs":[{"output_type":"execute_result","execution_count":270,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f6f854ece90>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x576 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = df['smoker'].sort_values().unique()\nvalues_ya = df['smoker'].loc[df['age_cat'] == 'young adult'].value_counts()\nvalues_ma = df['smoker'].loc[df['age_cat'] == 'middle age'].value_counts()\nvalues_sa = df['smoker'].loc[df['age_cat'] == 'senior adult'].value_counts()\n\nfig = make_subplots(rows=1, cols=3, specs=[[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}]],\n                   subplot_titles=['Young Adult', 'Middle Age', 'Senior Adult'])\n\nfig.add_trace(go.Pie(labels=labels, values=values_ya, name='Young Adult Smokers'), 1,1)\nfig.add_trace(go.Pie(labels=labels, values=values_ma, name='Middle Age Smokers'), 1,2)\nfig.add_trace(go.Pie(labels=labels, values=values_sa, name='Senior Adult Smokers'), 1,3)\n\nfig.update_traces(hole=.4, hoverinfo=\"label+percent+name\", textinfo='value+percent')\n\nfig.update_layout(title_text=\"Smoker Percentages by Age Categories\")\nfig.show()","execution_count":271,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"domain":{"x":[0,0.2888888888888889],"y":[0,1]},"hole":0.4,"hoverinfo":"label+percent+name","labels":["no","yes"],"name":"Young Adult Smokers","textinfo":"value+percent","type":"pie","values":[453,121]},{"domain":{"x":[0.35555555555555557,0.6444444444444445],"y":[0,1]},"hole":0.4,"hoverinfo":"label+percent+name","labels":["no","yes"],"name":"Middle Age Smokers","textinfo":"value+percent","type":"pie","values":[435,113]},{"domain":{"x":[0.7111111111111111,1],"y":[0,1]},"hole":0.4,"hoverinfo":"label+percent+name","labels":["no","yes"],"name":"Senior Adult Smokers","textinfo":"value+percent","type":"pie","values":[176,40]}],"layout":{"annotations":[{"font":{"size":16},"showarrow":false,"text":"Young 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"},{"metadata":{"trusted":true},"cell_type":"code","source":"smoker_weight = df.groupby(['weight_status','smoker'])['smoker'].count().to_frame()\nsmoker_weight.rename(columns={smoker_weight.columns[0]: 'count'}, inplace=True)\nsmoker_weight = smoker_weight.reset_index()\n\nplt.figure(figsize=(14,8))\nsns.barplot(x='weight_status', y='count', hue='smoker', palette='mako', data=smoker_weight)","execution_count":272,"outputs":[{"output_type":"execute_result","execution_count":272,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f6f8514cc50>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x576 with 1 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= df['smoker'].sort_values().unique()\nvalues_uw = df['smoker'].loc[df['weight_status'] == 'underweight'].value_counts()\nvalues_nw = df['smoker'].loc[df['weight_status'] == 'normal weight'].value_counts()\nvalues_ow = df['smoker'].loc[df['weight_status'] == 'overweight'].value_counts()\nvalues_ob = df['smoker'].loc[df['weight_status'] == 'obese'].value_counts()\n\nfig = make_subplots(rows=2, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}], [{'type':'domain'}, {'type':'domain'}]],\n                   subplot_titles=['Underweight', 'Normal Weight', 'Overweight', 'Obese'])\n\nfig.add_trace(go.Pie(labels=labels, values=values_uw, name='Underweight Smokers'), 1,1)\nfig.add_trace(go.Pie(labels=labels, values=values_nw, name='Normal Weight Smokers'), 1,2)\nfig.add_trace(go.Pie(labels=labels, values=values_ow, name='Overweight Smokers'), 2,1)\nfig.add_trace(go.Pie(labels=labels, values=values_ob, name='Obese Smokers'), 2,2)\n\nfig.update_traces(hoverinfo=\"label+percent+name\", textinfo='value+percent')\n\nfig.update_layout(title_text=\"Smoker Percentages by Weight Status\")\nfig.show()","execution_count":273,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"domain":{"x":[0,0.45],"y":[0.625,1]},"hoverinfo":"label+percent+name","labels":["no","yes"],"name":"Underweight Smokers","textinfo":"value+percent","type":"pie","values":[15,5]},{"domain":{"x":[0.55,1],"y":[0.625,1]},"hoverinfo":"label+percent+name","labels":["no","yes"],"name":"Normal Weight Smokers","textinfo":"value+percent","type":"pie","values":[175,50]},{"domain":{"x":[0,0.45],"y":[0,0.375]},"hoverinfo":"label+percent+name","labels":["no","yes"],"name":"Overweight Smokers","textinfo":"value+percent","type":"pie","values":[312,74]},{"domain":{"x":[0.55,1],"y":[0,0.375]},"hoverinfo":"label+percent+name","labels":["no","yes"],"name":"Obese 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document.getElementById('cc336233-9ce5-475a-b019-9b8c18e27cc3');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### SMOKER STATUS BY REGION"},{"metadata":{"trusted":true},"cell_type":"code","source":"regions = df.groupby(['region', 'smoker'])['region'].count().to_frame()\nregions.rename(columns={regions.columns[0]: 'count'}, inplace=True)\nregions = regions.reset_index()\nregions = regions.sort_values(by='count')\n\nplt.figure(figsize=(14,8))\nsns.barplot(x='region', y='count', hue='smoker', palette='magma', data=regions)\nplt.title('Smoker Status by Region', fontsize=22)\n","execution_count":274,"outputs":[{"output_type":"execute_result","execution_count":274,"data":{"text/plain":"Text(0.5, 1.0, 'Smoker Status by Region')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### SMOKER STATUS BY NUMBER OF CHILDREN\n\n* number of children does not appear to have any influence on smoker status"},{"metadata":{"trusted":true},"cell_type":"code","source":"smoker_children = df.groupby(['children','smoker'])['smoker'].count().to_frame()\nsmoker_children.rename(columns={smoker_children.columns[0]: 'count'}, inplace=True)\nsmoker_children = smoker_children.reset_index()\n\nplt.figure(figsize=(14,8))\nsns.barplot(x='children', y='count', hue='smoker', palette='mako', data=smoker_children)","execution_count":275,"outputs":[{"output_type":"execute_result","execution_count":275,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f6f876d7a50>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### BMI DISTRIBUTION BY AGE\n\n* Body mass index and weight status appears to be fairly evenly distributed across all age categories. \n* Senior adults tend to have a slightly higher BMI while young adults tend to have a slightly lower BMI"},{"metadata":{"trusted":true},"cell_type":"code","source":"# BODY MASS INDEX AND AGE\n\nfig = px.scatter(df, x='age', y='bmi', color='weight_status')\nfig.update_layout(title='BMI Distribution by Age')\nfig.show()","execution_count":276,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"weight_status=overweight<br>age=%{x}<br>bmi=%{y}<extra></extra>","legendgroup":"overweight","marker":{"color":"#636efa","symbol":"circle"},"mode":"markers","name":"overweight","showlegend":true,"type":"scattergl","x":[19,32,31,37,37,60,25,62,37,59,18,19,63,24,48,40,43,28,19,40,31,53,44,29,31,56,46,55,59,30,61,34,20,19,29,55,60,29,49,20,26,19,59,19,20,22,22,34,29,30,19,48,40,18,50,42,32,37,47,38,54,46,58,44,26,36,25,56,45,28,59,35,24,34,53,26,47,59,59,18,19,55,63,29,55,58,20,49,18,50,25,47,40,30,46,63,52,29,22,25,19,47,48,36,28,50,44,34,26,29,52,56,60,62,46,24,60,34,36,19,50,30,46,47,21,23,50,64,63,38,18,42,31,35,26,56,60,56,40,39,20,56,19,30,60,19,54,61,24,19,61,18,21,20,45,43,38,24,57,39,50,42,51,18,59,38,46,49,25,32,31,58,22,59,33,58,42,56,19,27,28,47,56,49,59,29,36,24,39,42,39,18,52,57,49,40,36,53,42,21,34,47,49,36,60,50,41,48,27,31,50,34,37,21,31,47,33,27,45,42,53,19,53,50,31,35,62,19,27,42,23,25,47,44,36,36,56,57,20,57,38,51,40,26,23,49,41,37,23,25,32,57,64,36,19,26,26,22,59,44,43,61,62,46,59,44,18,53,25,30,34,24,29,35,44,21,50,26,36,54,31,44,45,38,38,36,24,48,28,47,51,38,59,19,30,22,19,35,28,43,64,49,47,55,48,57,36,29,42,54,32,21,32,55,55,53,37,49,50,48,28,41,63,55,50,44,48,18,41,35,18,43,38,22,40,31,24,48,23,43,23,19,40,31,18,37,40,50,45,58,45,33,24,20,40,34,54,41,43,45,43,35,26,32,39,25,47,36,46,58,18,28,33,19,43,24,32,42,33,18,39,62,31,57,21,61],"xaxis":"x","y":[27.9,28.88,25.74,27.74,29.83,25.84,26.22,26.29,28.025,27.72,26.315,28.6,28.31,26.6,28,28.69,27.36,25.935,28.9,26.315,28.5,28.1,27.4,29.59,26.885,27.2,27.74,26.98,29.83,25.46,29.92,27.5,28.025,28.4,27.94,29.7,28.595,27.94,27.17,28.975,28.785,28.3,26.505,25.555,28.785,28.05,25.175,25.3,29.735,28.69,28.4,29.7,25.46,25.175,27.83,26.6,29.8,29.64,28.215,27.835,29.2,28.9,28.595,26.41,29.92,26.2,25.74,26.6,28.6,28.88,26.4,27.74,28.5,26.73,26.6,29.92,25.46,27.83,27.5,26.73,29.07,26.8,27.74,28.975,25.365,25.2,26.84,25.84,29.37,27.455,27.55,26.6,28.12,27.645,26.62,26.22,26.4,29.64,28.82,26.8,27.7,25.41,28.88,27.55,27.5,26.6,27.645,25.27,29.64,27.2,27.36,28.785,25.74,27.55,27.72,27.6,27.55,29.26,29.92,27.835,25.6,27.7,27.6,29.83,26.4,28.31,25.365,25.6,26.98,27.265,29.165,26.9,28.595,28.9,29.45,26.695,29.64,25.65,29.6,29.6,29.735,25.935,25.175,28.38,28.7,29.8,25.1,28.31,28.5,28.7,25.08,25.08,25.7,27.93,28.7,26.03,28.93,25.27,28.7,28.3,27.36,26.07,25.8,25.46,29.7,28.025,28.05,25.84,28.595,28.93,29.1,27.17,26.84,25.46,28.27,29,29.48,25.3,28.31,25.175,26.98,29.37,28.595,25.6,28.785,26.03,28.88,29.3,26.41,28.31,26.22,28.5,25.3,28.785,28.7,29.81,29.7,26.41,26.125,27.36,26.41,29.545,28.69,27.74,28.9,27.075,28.405,27.265,29.15,26.62,26.41,27,26.4,28.975,27.645,27.83,27.1,26.03,25.175,29,26.7,28.88,28.88,27.6,29.26,27.7,29.92,27.6,28.5,26.18,26.51,25.84,26.125,29.81,28.025,25.84,28.31,29.81,29.6,28.1,28,25.4,29.9,29.48,28.12,27.1,28.8,29.5,27.36,26.695,28.93,28.975,26.885,29.04,25.555,27.265,29.355,28.31,26.695,27.5,26.885,28.2,25,25.8,27.5,29.735,26.18,29.48,29.7,28.405,27.835,26.79,25.9,27.1,25.365,25.745,28.16,29.15,29.92,25.46,25.9,25.8,27.645,27.835,28.27,26.885,25.8,29.6,29.26,28.215,27.74,27.6,25.46,26.03,27.93,28.88,27.265,28.025,25.8,25.08,26.41,29.83,29.8,27.645,28.9,27.94,28.595,25.6,25.3,28.88,29.59,26.03,27.835,28.975,29.83,28.6,29.8,29.925,26.22,25.85,26.315,29.64,25.08,29.9,28.12,25,27.93,28.215,28.31,26.125,27.36,26.7,29.26,27.1,27.4,29.81,29.925,27.36,28.49,25.27,28,27.93,29.355,25.8,27.28,25.555,25.08,25.3,27.83,28.215,25.7,29.4,29.83,27.3,29.3,27.72,27.645,28.05,29.9,27.5,25.52,27.61,27.06,29.735,29.925,26.79,27.645,25.9,25.745,25.175,26.125,26.51,27.455,25.745,27.8,27.72,28.12,26.315,26.695,28.31,26.315,26.695,25.935,25.74,25.8,29.07],"yaxis":"y"},{"hovertemplate":"weight_status=obese<br>age=%{x}<br>bmi=%{y}<extra></extra>","legendgroup":"obese","marker":{"color":"#EF553B","symbol":"circle"},"mode":"markers","name":"obese","showlegend":true,"type":"scattergl","x":[18,28,46,23,56,27,52,56,30,60,30,18,34,55,31,22,28,62,35,60,31,37,38,55,18,28,36,18,21,36,58,58,18,34,25,61,40,58,57,21,22,41,45,22,48,37,57,21,35,64,28,54,55,41,18,26,63,54,37,21,52,58,44,44,47,52,38,53,19,54,22,26,29,46,51,53,32,44,30,18,54,20,32,19,63,49,35,24,63,41,18,44,36,30,41,29,61,18,19,39,64,19,48,27,63,40,20,40,45,41,34,53,32,19,55,28,58,41,42,31,44,23,40,54,60,24,63,54,27,50,56,38,19,19,53,46,59,45,50,41,59,54,55,52,46,59,28,25,31,56,28,57,29,30,58,41,43,49,27,52,50,54,32,34,57,40,45,64,52,61,61,43,64,50,62,63,49,33,46,33,18,46,23,48,35,49,56,44,18,42,26,20,24,64,62,55,55,35,19,58,24,48,19,48,49,46,43,21,18,47,64,49,52,32,19,43,52,64,64,61,40,25,48,27,19,60,60,22,52,31,33,18,59,45,35,32,59,55,57,56,40,49,42,62,56,27,18,47,25,21,23,63,49,18,51,48,31,44,53,19,61,62,29,19,37,22,56,27,19,58,20,45,35,31,32,51,38,18,19,46,57,62,37,64,33,46,53,34,20,63,54,54,28,54,43,63,52,25,46,34,3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\"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Body Mass Index by Age Category\"}, \"xaxis\": {\"title\": {\"font\": {\"size\": 16}, \"text\": \"Age Category\"}}, \"yaxis\": {\"title\": {\"font\": {\"size\": 16}, \"text\": \"Body Mass Index\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('a39c26bc-ed27-45ba-a5cb-bbbe5d2fb73c');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### WEIGHT STATUS AND AGE CATEGORY\n\n* The Young Adult population is slightly more likely to have a normal weight status than the middle age population. \n* The middle age population is slight more lokely to have an obese weight status than the young adult population. \n"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(df['age_cat'].value_counts())\n\nweights = df.groupby(['weight_status','age_cat'])['age_cat'].count().to_frame()\nweights.rename(columns={weights.columns[0]: 'count'}, inplace=True)\nweights = weights.reset_index()\nweights = weights.sort_values(by='count')\n\nplt.figure(figsize=(14,8))\nsns.barplot(x='weight_status', y='count', hue='age_cat', palette='mako', data=weights)\n","execution_count":278,"outputs":[{"output_type":"stream","text":"young adult     574\nmiddle age      548\nsenior adult    216\nName: age_cat, dtype: int64\n","name":"stdout"},{"output_type":"execute_result","execution_count":278,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f6f874c0090>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x576 with 1 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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### WEIGHT STATUS PER REGION\n\n* the Northwest region has a significantly higher number of obese people than the other regions"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Obesity per region\nregion_weight = pd.crosstab(df['weight_status'], df['region']).apply(lambda x: x/x.sum() * 100)\nregion_weight = round(region_weight, 2)\nregion_weight","execution_count":279,"outputs":[{"output_type":"execute_result","execution_count":279,"data":{"text/plain":"region         northeast  northwest  southeast  southwest\nweight_status                                            \nnormal weight      22.53      19.38      11.26      14.77\nobese              44.14      45.54      66.76      53.23\noverweight         30.25      32.92      21.98      31.08\nunderweight         3.09       2.15       0.00       0.92","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>region</th>\n      <th>northeast</th>\n      <th>northwest</th>\n      <th>southeast</th>\n      <th>southwest</th>\n    </tr>\n    <tr>\n      <th>weight_status</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>normal weight</th>\n      <td>22.53</td>\n      <td>19.38</td>\n      <td>11.26</td>\n      <td>14.77</td>\n    </tr>\n    <tr>\n      <th>obese</th>\n      <td>44.14</td>\n      <td>45.54</td>\n      <td>66.76</td>\n      <td>53.23</td>\n    </tr>\n    <tr>\n      <th>overweight</th>\n      <td>30.25</td>\n      <td>32.92</td>\n      <td>21.98</td>\n      <td>31.08</td>\n    </tr>\n    <tr>\n      <th>underweight</th>\n      <td>3.09</td>\n      <td>2.15</td>\n      <td>0.00</td>\n      <td>0.92</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ne_under = df[(df['weight_status'] == 'underweight') & (df['region'] == 'northeast')]\nne_normal = df[(df['weight_status'] == 'normal weight') & (df['region'] == 'northeast')]\nne_over = df[(df['weight_status'] == 'overweight') & (df['region'] == 'northeast')]\nne_obese = df[(df['weight_status'] == 'obese') & (df['region'] == 'northeast')]\n\nnw_under = df[(df['weight_status'] == 'underweight') & (df['region'] == 'northwest')]\nnw_normal = df[(df['weight_status'] == 'normal weight') & (df['region'] == 'northwest')]\nnw_over = df[(df['weight_status'] == 'overweight') & (df['region'] == 'northwest')]\nnw_obese = df[(df['weight_status'] == 'obese') & (df['region'] == 'northwest')]\n\nse_under = df[(df['weight_status'] == 'underweight') & (df['region'] == 'southeast')]\nse_normal = df[(df['weight_status'] == 'normal weight') & (df['region'] == 'southeast')]\nse_over = df[(df['weight_status'] == 'overweight') & (df['region'] == 'southeast')]\nse_obese = df[(df['weight_status'] == 'obese') & (df['region'] == 'southeast')]\n\nsw_under = df[(df['weight_status'] == 'underweight') & (df['region'] == 'southwest')]\nsw_normal = df[(df['weight_status'] == 'normal weight') & (df['region'] == 'southwest')]\nsw_over = df[(df['weight_status'] == 'overweight') & (df['region'] == 'southwest')]\nsw_obese = df[(df['weight_status'] == 'obese') & (df['region'] == 'southwest')]\n\nfig = go.Figure(data=[go.Bar(name='Underweight', x=df['region'].unique(), y=[len(ne_under), len(nw_under), len(se_under), len(sw_under)]),\n                      go.Bar(name='Normal Weight', x=df['region'].unique(), y=[len(ne_normal), len(nw_normal), len(se_normal), len(sw_normal)]),\n                      go.Bar(name='Overweight', x=df['region'].unique(), y=[len(ne_over), len(nw_over), len(se_over), len(sw_over)]),\n                      go.Bar(name='Obese', x=df['region'].unique(), y=[len(ne_obese), len(nw_obese), len(se_obese), len(sw_obese)])\n                     ]\n               )\n\n# Change the bar mode\nfig.update_layout(barmode='group', title_text = \"Weight Status by Region\")\nfig.show()","execution_count":280,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"name":"Underweight","type":"bar","x":["southwest","southeast","northwest","northeast"],"y":[10,7,0,3]},{"name":"Normal 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\"yes\")].values\nsa_smoker = df[\"bmi\"].loc[(df[\"age_cat\"] == \"middle age\") & (df[\"smoker\"] == \"yes\")].values\ne_smoker = df[\"bmi\"].loc[(df[\"age_cat\"] == \"senior adult\") & (df[\"smoker\"] == \"yes\")].values\n\n# Non-Smokers\nya_nonsmoker = df[\"bmi\"].loc[(df[\"age_cat\"] == \"young adult\") & (df[\"smoker\"] == \"no\")].values\nsa_nonsmoker = df[\"bmi\"].loc[(df[\"age_cat\"] == \"middle age\") & (df[\"smoker\"] == \"no\")].values\ne_nonsmoker = df[\"bmi\"].loc[(df[\"age_cat\"] == \"senior adult\") & (df[\"smoker\"] == \"no\")].values","execution_count":281,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_data = ['Smoker', 'Non-Smoker']\n\ny0 = bmi_smoker\ny1 = bmi_nonsmoker\n\ny_data = [y0,y1]\n\ncolors = ['rgba(251, 43, 43, 0.5)', 'rgba(125, 251, 137, 0.5)', \n          'rgba(251, 43, 43, 0.5)', 'rgba(125, 251, 137, 0.5)', \n          'rgba(251, 43, 43, 0.5)', 'rgba(125, 251, 137, 0.5)']\n\ntraces = []\n\nfor xd, yd, cls in zip(x_data, y_data, colors):\n        traces.append(go.Box(y=yd, name=xd, boxpoints='all', jitter=0.5, whiskerwidth=0.2,\n                             fillcolor=cls, marker=dict(size=2,), line=dict(width=1))\n                     )\n\nlayout = go.Layout(title='Body Mass Index of Smokers Status by Age Category',\n                   xaxis=dict(title=\"Smoker Status\", titlefont=dict(size=16)),\n                   yaxis=dict(title=\"Body Mass Index\", autorange=True, showgrid=True, \n                              zeroline=True, dtick=5, gridcolor='rgb(255, 255, 255)',\n                              gridwidth=1, zerolinecolor='rgb(255, 255, 255)', zerolinewidth=2,\n                              titlefont=dict(size=16)),\n                   margin=dict(l=40,r=30,b=80,t=100,),\n                   paper_bgcolor='rgb(255, 255, 255)',\n                   plot_bgcolor='rgb(255, 245, 201)',\n                   showlegend=False\n                  )\n\nfig = go.Figure(data=traces, layout=layout)\niplot(fig)","execution_count":282,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"linkText":"Export to plot.ly","plotlyServerURL":"https://plot.ly","showLink":false},"data":[{"boxpoints":"all","fillcolor":"rgba(251, 43, 43, 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=== 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### AGE CATEGORIES BY REGION"},{"metadata":{"trusted":true},"cell_type":"code","source":"region_age = df.groupby(['region','age_cat'])['age_cat'].count().to_frame()\nregion_age.rename(columns={region_age.columns[0]: 'count'}, inplace=True)\nregion_age = region_age.reset_index()\nregion_age = region_age.sort_values(by='count')\n\nplt.figure(figsize=(14,8))\nsns.barplot(x='region', y='count', hue='age_cat', palette='mako', data=region_age)","execution_count":283,"outputs":[{"output_type":"execute_result","execution_count":283,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f6f8766b610>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_data = ['Young A. Smoker', 'Young A. Non-Smoker',\n          'Senior A. Smoker', 'Senior A. Non-Smoker',\n          'Elder Smoker', 'Elder Non-Smoker',]\n\ny0 = ya_smoker\ny1 = ya_nonsmoker\ny2 = sa_smoker\ny3 = sa_nonsmoker\ny4 = e_smoker\ny5 = e_nonsmoker\n\ny_data = [y0,y1,y2,y3,y4,y5]\n\ncolors = ['rgba(251, 43, 43, 0.5)', 'rgba(125, 251, 137, 0.5)', \n          'rgba(251, 43, 43, 0.5)', 'rgba(125, 251, 137, 0.5)', \n          'rgba(251, 43, 43, 0.5)', 'rgba(125, 251, 137, 0.5)']\n\ntraces = []\n\nfor xd, yd, cls in zip(x_data, y_data, colors):\n        traces.append(go.Box(y=yd,\n                             name=xd,\n                             boxpoints='all',\n                             jitter=0.5,\n                             whiskerwidth=0.2,\n                             fillcolor=cls,\n                             marker=dict(size=2,),\n                             line=dict(width=1),\n                            )\n                     )\n\nlayout = go.Layout(title='Body Mass Index of Smokers Status by Age Category',\n                   xaxis=dict(title=\"Status\", titlefont=dict(size=16)),\n                   yaxis=dict(title=\"Body Mass Index\",\n                              autorange=True, \n                              showgrid=True, \n                              zeroline=True,\n                              dtick=5,\n                              gridcolor='rgb(255, 255, 255)',\n                              gridwidth=1,\n                              zerolinecolor='rgb(255, 255, 255)',\n                              zerolinewidth=2,\n                              titlefont=dict(size=16)\n                             ),\n                   margin=dict(l=40,r=30,b=80,t=100,),\n                   paper_bgcolor='rgb(255, 255, 255)',\n                   plot_bgcolor='rgb(255, 245, 201)',\n                   showlegend=False\n                  )\n\nfig = go.Figure(data=traces, layout=layout)\niplot(fig)","execution_count":284,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"linkText":"Export to plot.ly","plotlyServerURL":"https://plot.ly","showLink":false},"data":[{"boxpoints":"all","fillcolor":"rgba(251, 43, 43, 0.5)","jitter":0.5,"line":{"width":1},"marker":{"size":2},"name":"Young A. Smoker","type":"box","whiskerwidth":0.2,"y":[27.9,42.13,35.3,31.92,36.3,35.6,36.4,36.67,31.68,22.42,23.98,24.75,37.62,28.025,27.94,28.3,17.765,25.3,28.69,25.175,35.53,36.85,36.08,27.74,34.8,29.07,36.67,17.29,31.92,26.84,36.955,45.54,27.7,34.39,31.4,30.8,21.7,32.9,28.31,40.15,17.955,17.195,33.5,28.38,28.5,25.7,34.4,30.25,34.105,38.095,30.21,21.85,28.31,37.8,33.11,33.535,31.13,39.05,21.755,24.42,31.73,35.5,29.15,38.17,27.1,28.88,24.4,28.5,24.795,42.24,35.53,32.78,24.13,28.93,24.6,31.73,29.7,27.835,22.895,25.9,20.52,20.045,22.99,32.7,31.02,26.03,21.565,37.07,28.025,30.685,52.58,31.68,30.4,19.095,33.33,35.75,31.4,42.75,32.49,44.88,27.36,29.81,28.49,35.625,30.02,27.28,24.42,21.8,33.63,29.83,27.3,31.065,27.06,21.66,39.4,34.9,21.85,28.12,30.2,34.7,23.655]},{"boxpoints":"all","fillcolor":"rgba(125, 251, 137, 0.5)","jitter":0.5,"line":{"width":1},"marker":{"size":2},"name":"Young A. 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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"CHARGES BY SMOKER STATUS\"}, \"xaxis\": {\"anchor\": \"y\", \"categoryarray\": [\"yes\", \"no\"], \"categoryorder\": \"array\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"smoker\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"charges\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('8ffd4822-b246-47d2-877f-4c3e648b4697');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### CHARGES BY SEX\n\n* Females tend to have lower healthcare charges than their male counterparts."},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.box(df, x=\"sex\", y='charges', color='sex', points=\"all\")\nfig.update_layout(title='CHARGES BY SEX')\nfig.show()","execution_count":286,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"alignmentgroup":"True","boxpoints":"all","hovertemplate":"sex=%{x}<br>charges=%{y}<extra></extra>","legendgroup":"female","marker":{"color":"#636efa"},"name":"female","notched":false,"offsetgroup":"female","orientation":"v","showlegend":true,"type":"box","x":["female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","female","fema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\"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"smoker\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"charges\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('4d9437cc-7559-4eeb-bf39-e3410d035f36');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    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adult\"].mean()\navg_charge_sa = df[\"charges\"].loc[df[\"age_cat\"] == \"middle age\"].mean()\navg_charge_e = df[\"charges\"].loc[df[\"age_cat\"] == \"senior adult\"].mean()\n\n# Median\nmed_charge_ya = df[\"charges\"].loc[df[\"age_cat\"] == \"young adult\"].median()\nmed_charge_sa = df[\"charges\"].loc[df[\"age_cat\"] == \"middle age\"].median()\nmed_charge_e = df[\"charges\"].loc[df[\"age_cat\"] == \"senior adult\"].median()\n\n# Determine the distribution of age according to charge\n\naverage_plot = go.Bar(x=['young adult', 'middle age', 'senior adult'],\n                      y=[avg_charge_ya, avg_charge_sa, avg_charge_e],\n                      name='Mean',\n                      marker=dict(color=\"#18b5c9\")\n                     )\n\nmed_plot = go.Bar(x=['young adult', 'middle age', 'senior adult'],\n                  y=[med_charge_ya, med_charge_sa, med_charge_e],\n                  name='Median',\n                  marker=dict(color=\"#c93318\")\n                 )\n\nfig = 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Means\navg_charge_uw = df[\"charges\"].loc[df[\"weight_status\"] == \"underweight\"].mean()\navg_charge_nw = df[\"charges\"].loc[df[\"weight_status\"] == \"normal weight\"].mean()\navg_charge_ow = df[\"charges\"].loc[df[\"weight_status\"] == \"overweight\"].mean()\navg_charge_ob = df[\"charges\"].loc[df[\"weight_status\"] == \"obese\"].mean()\n\n# Median\nmed_charge_uw = df[\"charges\"].loc[df[\"weight_status\"] == \"underweight\"].median()\nmed_charge_nw = df[\"charges\"].loc[df[\"weight_status\"] == \"normal weight\"].median()\nmed_charge_ow = df[\"charges\"].loc[df[\"weight_status\"] == \"overweight\"].median()\nmed_charge_ob = df[\"charges\"].loc[df[\"weight_status\"] == \"obese\"].median()\n\n\n\naverage_plot = go.Bar(x=['Underweight', 'Normal Weight', 'Overweight', 'Obese'],\n                      y=[avg_charge_uw, avg_charge_nw, avg_charge_ow, avg_charge_ob],\n                      name='Mean',\n                      marker=dict(color=\"#18b5c9\")\n                     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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Create subpplots\nf, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18,8))\n\nsns.stripplot(x=\"age_cat\", y=\"charges\", hue=\"weight_status\", data=df, ax=ax1, linewidth=1, palette=\"Set2\")\nax1.set_title(\"Relationship of Weight Condition, Age and Charges\")\nax1.legend_.remove()\n\nsns.stripplot(x=\"smoker\", y=\"charges\", hue=\"weight_status\", data=df, ax=ax2, linewidth=1, palette=\"Set2\")\nax2.set_title(\"Relationship between Smokers and Charges\")\n\nplt.show()","execution_count":296,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x576 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = ff.create_facet_grid(df,\n                           x='age',\n                           y='charges',\n                           color_name='weight_status',\n                           show_boxes=False,\n                           marker={'size': 10, 'opacity': 1.0},\n                           colormap={'underweight': '#cff682', 'normal weight': '#a7f682',\n                                     'overweight': '#fbe8ee', 'obese': '#fd2f1c'}\n                          )\n\nfig['layout'].update(title=\"Weight Status vs Charges\", width=800, height=600, plot_bgcolor='#fbfbfb', \n                     paper_bgcolor='#ffffff')\n\n\niplot(fig, filename='facet - custom colormap')","execution_count":297,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"linkText":"Export to plot.ly","plotlyServerURL":"https://plot.ly","showLink":false},"data":[{"marker":{"color":"rgb(167, 246, 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            \"meanline\": {\"visible\": True},\n              \"line\": {\"color\": '#00FF40'},\n              \"marker\": {\"line\": {\"width\": 2,\"color\": '#81F781'}},\n              \"span\": [0],\n              \"showlegend\": showLegend[i]\n             }\n    \n    data.append(female)\n        \n\nfig = {\"data\": data,\n       \"layout\" : {\"title\": \"Charges Distribution of Obese Patients<br><i>Group by Smoking Status\",\n                   \"yaxis\": {\"zeroline\": False,\n                             \"title\": \"Patient Charges\",\n                             \"titlefont\": {\"size\": 16}\n                            },\n                   \"violingap\": 0,\n                   \"violingroupgap\": 0,\n                   \"violinmode\": \"overlay\"\n                  }\n      }\n\niplot(fig, filename='violin/advanced', validate = False)","execution_count":298,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"linkText":"Export to 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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Charges by BMI with smoker distinction\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"age\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"charges\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('57383697-4238-438f-9a77-eac855f0d2f0');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### MEDIAN AND MEAN CHARGE BY REGION"},{"metadata":{"trusted":true},"cell_type":"code","source":"region_charges = df.groupby(['region', 'charges'], as_index=False).mean().groupby('region')['charges'].mean().to_frame()\nregion_charges.rename(columns={'charges':'mean'}, inplace=True)\nregion_charges.reset_index()\n\nregion_median = df.groupby(['region', 'charges'], as_index=False).median().groupby('region')['charges'].median().to_frame()\n\nregion_charges['median'] = region_median['charges']\nregion_charges = region_charges.reset_index()\nregion_charges = region_charges.melt(id_vars='region')\n\nfig, ax1 = plt.subplots(figsize=(14, 7))\n\nsns.barplot(x='region', y='value', hue='variable', data=region_charges, ax=ax1)\nplt.title('Mean & Median Charges by Region')\nplt.ylabel('Charges')\nsns.despine(fig)\n","execution_count":301,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x504 with 1 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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### AVERAGE PATIENT CHARGES ORGANIZED BY WEIGHT STATUS & REGION"},{"metadata":{"trusted":true},"cell_type":"code","source":"weight_region = df.groupby(['region', 'weight_status'], as_index=False)['charges'].mean()\nweight_region.rename(columns={'charges':'mean'}, inplace=True)\nweight_region\n\nsns.set(font_scale=1)  \ng = sns.FacetGrid(weight_region, col=\"region\", col_wrap=2, height=6)\ng.map(sns.barplot, \"weight_status\", \"mean\", alpha=.7)\ng.fig.subplots_adjust(wspace=.1, hspace=.15)\ng.add_legend()\nplt.subplots_adjust(top=0.9)\ng.fig.suptitle('AVERAGE PATIENT CHARGES BY WEIGHT STATUS, ORGANIZED BY REGION')\n","execution_count":302,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py:728: UserWarning:\n\nUsing the barplot function without specifying `order` is likely to produce an incorrect plot.\n\n","name":"stderr"},{"output_type":"execute_result","execution_count":302,"data":{"text/plain":"Text(0.5, 0.98, 'AVERAGE PATIENT CHARGES BY WEIGHT STATUS, ORGANIZED BY REGION')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 872.8x864 with 4 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"features\"></a>\n## **FEATURE SELECTION**\n\nBased on our exploratory data analysis we were able to identify the **smoker**, **bmi**, and **age**, variables as most significant in predicting our target variable. \n\nIn this section we will encode our categorical variables using the LabelEncoder in preparation to run our models. "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\n\n# sex, female = 0, male = 1\nle = LabelEncoder()\nle.fit(df.sex.drop_duplicates()) \ndf.sex = le.transform(df.sex)\n\n# smoker/non-smoker - non-smoker=0, smoker=1\nle.fit(df.smoker.drop_duplicates()) \ndf.smoker = le.transform(df.smoker)\n\n#region - Northeast = 0, Northwest = 1, Southeast = 2, Southwest = 3\nle.fit(df.region.drop_duplicates()) \ndf.region = le.transform(df.region)\n\n","execution_count":303,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"f, ax = plt.subplots(figsize=(10, 8))\ncorr = df.corr()\nsns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap='YlGnBu', square=True, ax=ax)","execution_count":304,"outputs":[{"output_type":"execute_result","execution_count":304,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f6f84690410>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x576 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"models\"></a>\n## **MODEL BUILDING**\n\nWe will implement several different models in order to predict our target variable using our selected features.\n\nThe models used will consist of:\n* Multiple Linear Regression\n* Lasso Regression\n* Ridge Regression\n* Random Forest Regression\n* Polynomial Regression "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.metrics import r2_score,mean_squared_error, mean_absolute_error\nfrom sklearn.ensemble import RandomForestRegressor","execution_count":305,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"lr\"></a>\n### MULTIPLE LINEAR REGRESSION"},{"metadata":{"trusted":true},"cell_type":"code","source":"X = df.drop(['charges', 'age_cat', 'weight_status', 'region', 'children'], axis = 1)\ny = df.charges\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)\n\nlr = LinearRegression()\nlr.fit(X_train, y_train)\n\nyhat_train = lr.predict(X_train)\nyhat_lr = lr.predict(X_test)\n\nmse_lr = mean_squared_error(y_test, yhat_lr)\nrmse_lr = math.sqrt(mean_squared_error(y_test, yhat_lr))\nmae_lr = mean_absolute_error(y_test, yhat_lr)\nr2_lr = r2_score(y_test, yhat_lr)\nrss_lr = np.mean((yhat_lr - y_test) * 2)\nevs_lr = lr.score(X_test, y_test)\n\n\nprint('Intercept: ', lr.intercept_)\nprint('Coeficient ', lr.coef_)","execution_count":306,"outputs":[{"output_type":"stream","text":"Intercept:  -11239.832486084832\nCoeficient  [  254.284314      29.68735411   314.96997768 23658.69608788]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"ridge\"></a>\n### RIDGE REGRESSION"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import Ridge\n\nridge = Ridge(alpha=0.5)\nridge.fit(X_train, y_train)\nyhat_ridge = ridge.predict(X_test)\n\nmse_ridge = mean_squared_error(y_test, yhat_ridge)\nrmse_ridge = math.sqrt(mean_squared_error(y_test, yhat_ridge))\nmae_ridge = mean_absolute_error(y_test, yhat_ridge)\nr2_ridge = r2_score(y_test, yhat_ridge)\nrss_ridge = np.mean((yhat_ridge - y_test) * 2)\nevs_ridge = ridge.score(X_test, y_test)\n\n\nprint('Intercept: ', ridge.intercept_)\nprint('Coeficient ', ridge.coef_)","execution_count":307,"outputs":[{"output_type":"stream","text":"Intercept:  -11221.06824811431\nCoeficient  [  254.17604953    33.71257705   314.91644826 23585.30143641]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"lasso\"></a>\n### LASSO REGRESSION"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import Lasso\n\nlasso = Lasso(alpha=0.2, fit_intercept=True, normalize=False, precompute=False, max_iter=1000,\n              tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')\nlasso.fit(X_train, y_train)\nyhat_lasso = lasso.predict(X_test)\n\nmse_lasso = mean_squared_error(y_test, yhat_lasso)\nrmse_lasso = math.sqrt(mean_squared_error(y_test, yhat_lasso))\nmae_lasso = mean_absolute_error(y_test, yhat_lasso)\nr2_lasso = r2_score(y_test, yhat_lasso)\nrss_lasso = np.mean((yhat_lasso - y_test) * 2)\nevs_lasso = lasso.score(X_test, y_test)\n\nprint('Intercept: ', lasso.intercept_)\nprint('Coeficient ', lasso.coef_)","execution_count":308,"outputs":[{"output_type":"stream","text":"Intercept:  -11239.025039007314\nCoeficient  [  254.28194957    28.95462174   314.96623994 23657.51537436]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"rf\"></a>\n### RANDOM FOREST REGRESSION"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import RandomForestRegressor\nfrom sklearn import metrics\n\nrf = RandomForestRegressor(n_estimators = 100, criterion = 'mse', random_state = 1, n_jobs = -1)\nrf.fit(X_train, y_train)\nyhat_rf = rf.predict(X_test)\n\nmse_rf = mean_squared_error(y_test, yhat_rf)\nrmse_rf = math.sqrt(mean_squared_error(y_test, yhat_rf))\nmae_rf = mean_absolute_error(y_test, yhat_rf)\nr2_rf = r2_score(y_test, yhat_rf)\nrss_rf = np.mean((yhat_rf - y_test) * 2)\nevs_rf = rf.score(X_test, y_test)","execution_count":309,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"poly\"></a>\n### POLYNOMIAL REGRESSION"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import PolynomialFeatures\n\n\npol = PolynomialFeatures(degree = 2)\nX_pol = pol.fit_transform(X)\npol_reg = LinearRegression()\npol_reg.fit(X_train, y_train)\n\nyhat_poly = pol_reg.predict(X_test)\n\nmse_poly = mean_squared_error(y_test, yhat_poly)\nrmse_poly = math.sqrt(mean_squared_error(y_test, yhat_poly))\nmae_poly = mean_absolute_error(y_test, yhat_poly)\nr2_poly = r2_score(y_test, yhat_poly)\nrss_poly = np.mean((yhat_poly - y_test) * 2)\nevs_poly = pol_reg.score(X_test, y_test)\n\nprint(pol_reg.intercept_)\nprint(pol_reg.coef_)\nprint(pol_reg.score(X_test, y_test))\nprint('')\n\n","execution_count":310,"outputs":[{"output_type":"stream","text":"-11239.832486084832\n[  254.284314      29.68735411   314.96997768 23658.69608788]\n0.7923828990966664\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"modeleval\"></a>\n## MODEL EVALUATION\n\nThe **Random Forest Regression Model** is the model that gives us the best results with an R Squared score of 86% and a lower error scores than the other models. "},{"metadata":{"trusted":true},"cell_type":"code","source":"#MEAN SQUARED ERROR\nprint('MEAN SQUARED ERROR')\nprint('Linear Regression | Mean Squared Error: %.2f ' % mse_lr)\nprint('Ridge Regression | Mean Squared Error: %.2f ' % mse_ridge)\nprint('Lasso Regression | Mean Squared Error: %.2f ' % mse_lasso)\nprint('Random Forest | Mean Squared Error: %.2f ' % mse_rf)\nprint('Polynomial Regression | Mean Squared Error: %.2f ' % mse_poly)\nprint('')\n\n#ROOT MEAN SQUARED ERROR\nprint('ROOT MEAN SQUARED ERROR')\nprint('Linear Regression | Root Mean Squared Error: %.2f ' % rmse_lr)\nprint('Ridge Regression | Root Mean Squared Error: %.2f ' % rmse_ridge)\nprint('Lasso Regression | Root Mean Squared Error: %.2f ' % rmse_lasso)\nprint('Random Forest | Root Mean Squared Error: %.2f ' % rmse_rf)\nprint('Polynomial Regression | Root Mean Squared Error: %.2f ' % rmse_poly)\nprint('')\n\n#MEAN ABSOLUTE ERROR\nprint('MEAN ABSOLUTE ERROR')\nprint('Linear Regression | Mean Absolute Error: %.2f ' % mae_lr)\nprint('Ridge Regression | Mean Absolute Error: %.2f ' % mae_ridge)\nprint('Lasso Regression | Mean Absolute Error: %.2f ' % mae_lasso)\nprint('Random Forest | Mean Absolute Error: %.2f ' % mae_rf)\nprint('Polynomial Regression | Mean Absolute Error: %.2f ' % mae_poly)\nprint('')\n\n#R2 SCORE\nprint('R2 SCORE')\nprint('Linear Regression | R2 Score: %.4f ' % r2_lr)\nprint('Ridge Regression | R2 Score: %.4f ' % r2_ridge)\nprint('Lasso Regression | R2 Score: %.4f ' % r2_lasso)\nprint('Random Forest | R2 Score: %.4f ' % r2_rf)\nprint('Polynomial Regression | R2 Score: %.4f ' % r2_poly)\nprint('')\n\n#RESIDUAL SUM OF SQUARES\nprint('RESIDUAL SUM OF SQUARES')\nprint('Linear Regression | Residual Sum of Squares %.2f ' % rss_lr)\nprint('Ridge Regression | Residual Sum of Squares %.2f ' % rss_ridge)\nprint('Lasso Regression | Residual Sum of Squares %.2f ' % rss_lasso)\nprint('Random Forest | Residual Sum of Squares %.2f ' % rss_rf)\nprint('Polynomial Regression | Residual Sum of Squares %.2f ' % rss_poly)\nprint('')\n\n#EXPLAINED VARIANCE SCORE\nprint('EXPLAINED VARIANCE SCORE')\nprint('Linear Regression | Explained Variance Score: %.4f ' % evs_lr)\nprint('Ridge Regression | Explained Variance Score: %.4f ' % evs_ridge)\nprint('Lasso Regression | Explained Variance Score: %.4f ' % evs_lasso)\nprint('Random Forest | Explained Variance Score: %.4f ' % evs_rf)\nprint('Polynomial Regression | Explained Variance Score: %.4f ' % evs_poly)\n","execution_count":311,"outputs":[{"output_type":"stream","text":"MEAN SQUARED ERROR\nLinear Regression | Mean Squared Error: 32686097.13 \nRidge Regression | Mean Squared Error: 32704209.28 \nLasso Regression | Mean Squared Error: 32686187.91 \nRandom Forest | Mean Squared Error: 22017828.32 \nPolynomial Regression | Mean Squared Error: 32686097.13 \n\nROOT MEAN SQUARED ERROR\nLinear Regression | Root Mean Squared Error: 5717.18 \nRidge Regression | Root Mean Squared Error: 5718.76 \nLasso Regression | Root Mean Squared Error: 5717.18 \nRandom Forest | Root Mean Squared Error: 4692.32 \nPolynomial Regression | Root Mean Squared Error: 5717.18 \n\nMEAN ABSOLUTE ERROR\nLinear Regression | Mean Absolute Error: 4003.45 \nRidge Regression | Mean Absolute Error: 4009.98 \nLasso Regression | Mean Absolute Error: 4003.51 \nRandom Forest | Mean Absolute Error: 2735.80 \nPolynomial Regression | Mean Absolute Error: 4003.45 \n\nR2 SCORE\nLinear Regression | R2 Score: 0.7924 \nRidge Regression | R2 Score: 0.7923 \nLasso Regression | R2 Score: 0.7924 \nRandom Forest | R2 Score: 0.8601 \nPolynomial Regression | R2 Score: 0.7924 \n\nRESIDUAL SUM OF SQUARES\nLinear Regression | Residual Sum of Squares -180.29 \nRidge Regression | Residual Sum of Squares -181.23 \nLasso Regression | Residual Sum of Squares -180.38 \nRandom Forest | Residual Sum of Squares 1165.48 \nPolynomial Regression | Residual Sum of Squares -180.29 \n\nEXPLAINED VARIANCE SCORE\nLinear Regression | Explained Variance Score: 0.7924 \nRidge Regression | Explained Variance Score: 0.7923 \nLasso Regression | Explained Variance Score: 0.7924 \nRandom Forest | Explained Variance Score: 0.8601 \nPolynomial Regression | Explained Variance Score: 0.7924 \n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## CONCLUSION\n\nIn the United States healthcare continues to be a highly contested issue. The rising costs of healthcare puts many people at financial risk for even the smallest of illnesses. Analysis of this dataset has provided great insight to some of the factors that have a profound effect on the cost of healthcare, chief among them being whether or not a person smokes. \n\nWith this information and information that may be gained in the future, one can hope that individuals will make appropriate decisions regarding their health and that as a society we develop ways to better aid each other in the ways that are needed. \n\nThis notebook will continue to be updated, but please feel free to leave feedback!\n\nThanks for stopping by, \n\nMilton"}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}