ML_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "from sodapy import Socrata"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    }
   ],
   "source": [
    "# Unauthenticated client only works with public data sets. Note 'None'\n",
    "# in place of application token, and no username or password:\n",
    "client = Socrata(\"chronicdata.cdc.gov\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>year</th>\n",
       "      <th>stateabbr</th>\n",
       "      <th>statedesc</th>\n",
       "      <th>locationname</th>\n",
       "      <th>datasource</th>\n",
       "      <th>category</th>\n",
       "      <th>measure</th>\n",
       "      <th>data_value_unit</th>\n",
       "      <th>data_value_type</th>\n",
       "      <th>data_value</th>\n",
       "      <th>low_confidence_limit</th>\n",
       "      <th>high_confidence_limit</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>locationid</th>\n",
       "      <th>categoryid</th>\n",
       "      <th>measureid</th>\n",
       "      <th>datavaluetypeid</th>\n",
       "      <th>short_question_text</th>\n",
       "      <th>geolocation</th>\n",
       "      <th>:@computed_region_bxsw_vy29</th>\n",
       "      <th>:@computed_region_he4y_prf8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>High cholesterol among adults aged &gt;=18 years ...</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude prevalence</td>\n",
       "      <td>32.1</td>\n",
       "      <td>31.2</td>\n",
       "      <td>33.1</td>\n",
       "      <td>7760</td>\n",
       "      <td>56043</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>HIGHCHOL</td>\n",
       "      <td>CrdPrv</td>\n",
       "      <td>High Cholesterol</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-107.669052,...</td>\n",
       "      <td>14</td>\n",
       "      <td>3127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Laramie</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Risk Behaviors</td>\n",
       "      <td>No leisure-time physical activity among adults...</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude prevalence</td>\n",
       "      <td>23.8</td>\n",
       "      <td>21.5</td>\n",
       "      <td>26.2</td>\n",
       "      <td>100595</td>\n",
       "      <td>56021</td>\n",
       "      <td>RISKBEH</td>\n",
       "      <td>LPA</td>\n",
       "      <td>CrdPrv</td>\n",
       "      <td>Physical Inactivity</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-104.660395,...</td>\n",
       "      <td>14</td>\n",
       "      <td>3119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Carbon</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Status</td>\n",
       "      <td>Mental health not good for &gt;=14 days among adu...</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude prevalence</td>\n",
       "      <td>12.9</td>\n",
       "      <td>12.1</td>\n",
       "      <td>13.7</td>\n",
       "      <td>14711</td>\n",
       "      <td>56007</td>\n",
       "      <td>HLTHSTAT</td>\n",
       "      <td>MHLTH</td>\n",
       "      <td>CrdPrv</td>\n",
       "      <td>Mental Health</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-106.9331526...</td>\n",
       "      <td>14</td>\n",
       "      <td>3081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Sublette</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>Depression among adults aged &gt;=18 years</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude prevalence</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>9856</td>\n",
       "      <td>56035</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>DEPRESSION</td>\n",
       "      <td>CrdPrv</td>\n",
       "      <td>Depression</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-109.9161701...</td>\n",
       "      <td>14</td>\n",
       "      <td>3124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Uinta</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>Stroke among adults aged &gt;=18 years</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude prevalence</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>20215</td>\n",
       "      <td>56041</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>STROKE</td>\n",
       "      <td>CrdPrv</td>\n",
       "      <td>Stroke</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-110.5589468...</td>\n",
       "      <td>14</td>\n",
       "      <td>893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year stateabbr statedesc locationname datasource               category  \\\n",
       "0  2019        WY   Wyoming     Washakie      BRFSS        Health Outcomes   \n",
       "1  2020        WY   Wyoming      Laramie      BRFSS  Health Risk Behaviors   \n",
       "2  2020        WY   Wyoming       Carbon      BRFSS          Health Status   \n",
       "3  2020        WY   Wyoming     Sublette      BRFSS        Health Outcomes   \n",
       "4  2020        WY   Wyoming        Uinta      BRFSS        Health Outcomes   \n",
       "\n",
       "                                             measure data_value_unit  \\\n",
       "0  High cholesterol among adults aged >=18 years ...               %   \n",
       "1  No leisure-time physical activity among adults...               %   \n",
       "2  Mental health not good for >=14 days among adu...               %   \n",
       "3            Depression among adults aged >=18 years               %   \n",
       "4                Stroke among adults aged >=18 years               %   \n",
       "\n",
       "    data_value_type data_value low_confidence_limit high_confidence_limit  \\\n",
       "0  Crude prevalence       32.1                 31.2                  33.1   \n",
       "1  Crude prevalence       23.8                 21.5                  26.2   \n",
       "2  Crude prevalence       12.9                 12.1                  13.7   \n",
       "3  Crude prevalence       16.0                 15.0                  17.1   \n",
       "4  Crude prevalence        3.0                  2.7                   3.4   \n",
       "\n",
       "  totalpopulation locationid categoryid   measureid datavaluetypeid  \\\n",
       "0            7760      56043    HLTHOUT    HIGHCHOL          CrdPrv   \n",
       "1          100595      56021    RISKBEH         LPA          CrdPrv   \n",
       "2           14711      56007   HLTHSTAT       MHLTH          CrdPrv   \n",
       "3            9856      56035    HLTHOUT  DEPRESSION          CrdPrv   \n",
       "4           20215      56041    HLTHOUT      STROKE          CrdPrv   \n",
       "\n",
       "   short_question_text                                        geolocation  \\\n",
       "0     High Cholesterol  {'type': 'Point', 'coordinates': [-107.669052,...   \n",
       "1  Physical Inactivity  {'type': 'Point', 'coordinates': [-104.660395,...   \n",
       "2        Mental Health  {'type': 'Point', 'coordinates': [-106.9331526...   \n",
       "3           Depression  {'type': 'Point', 'coordinates': [-109.9161701...   \n",
       "4               Stroke  {'type': 'Point', 'coordinates': [-110.5589468...   \n",
       "\n",
       "  :@computed_region_bxsw_vy29 :@computed_region_he4y_prf8  \n",
       "0                          14                        3127  \n",
       "1                          14                        3119  \n",
       "2                          14                        3081  \n",
       "3                          14                        3124  \n",
       "4                          14                         893  "
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return results as csv from API / converted to Python list of\n",
    "# dictionaries by sodapy.\n",
    "results = client.get(\"swc5-untb\", limit=2000000)\n",
    "\n",
    "# Convert to pandas DataFrame\n",
    "results_df = pd.DataFrame.from_records(results)\n",
    "\n",
    "# View all columns in dataframes\n",
    "pd.set_option('display.max_columns', None)  \n",
    "\n",
    "# Display dataframe\n",
    "results_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2019', '2020'], dtype=object)"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View how many years worth of data we have\n",
    "results_df['year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                              0\n",
       "stateabbr                         0\n",
       "statedesc                         0\n",
       "locationname                     60\n",
       "datasource                        0\n",
       "category                          0\n",
       "measure                           0\n",
       "data_value_unit                   0\n",
       "data_value_type                   0\n",
       "data_value                        0\n",
       "low_confidence_limit              0\n",
       "high_confidence_limit             0\n",
       "totalpopulation                   0\n",
       "locationid                        0\n",
       "categoryid                        0\n",
       "measureid                         0\n",
       "datavaluetypeid                   0\n",
       "short_question_text               0\n",
       "geolocation                      60\n",
       ":@computed_region_bxsw_vy29    1192\n",
       ":@computed_region_he4y_prf8     660\n",
       "dtype: int64"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View null values by year\n",
    "results_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                           object\n",
       "stateabbr                      object\n",
       "statedesc                      object\n",
       "locationname                   object\n",
       "datasource                     object\n",
       "category                       object\n",
       "measure                        object\n",
       "data_value_unit                object\n",
       "data_value_type                object\n",
       "data_value                     object\n",
       "low_confidence_limit           object\n",
       "high_confidence_limit          object\n",
       "totalpopulation                object\n",
       "locationid                     object\n",
       "categoryid                     object\n",
       "measureid                      object\n",
       "datavaluetypeid                object\n",
       "short_question_text            object\n",
       "geolocation                    object\n",
       ":@computed_region_bxsw_vy29    object\n",
       ":@computed_region_he4y_prf8    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data types for results_df\n",
    "results_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Health Outcomes', 'Health Risk Behaviors', 'Health Status',\n",
       "       'Prevention'], dtype=object)"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View health categories - (which include metrics we could use as features for machine learning)\n",
    "results_df['category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LPA', 'MHLTH', 'DENTAL', 'BINGE', 'COREW', 'CERVICAL', 'PHLTH',\n",
       "       'BPMED', 'COLON_SCREEN', 'CHOLSCREEN', 'COREM', 'SLEEP',\n",
       "       'CSMOKING', 'MAMMOUSE', 'ACCESS2', 'GHLTH', 'CHECKUP'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View contributing health metrics (possible features for machine learning)\n",
    "results_df.loc[results_df['category'].isin(['Prevention', 'Health Risk Behaviors', 'Health Status'])]['measureid'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HIGHCHOL', 'LPA', 'MHLTH', 'DEPRESSION', 'STROKE', 'CASTHMA',\n",
       "       'BPHIGH', 'DIABETES', 'DENTAL', 'BINGE', 'COPD', 'COREW',\n",
       "       'CERVICAL', 'CHD', 'PHLTH', 'BPMED', 'COLON_SCREEN', 'KIDNEY',\n",
       "       'CHOLSCREEN', 'COREM', 'SLEEP', 'CSMOKING', 'MAMMOUSE',\n",
       "       'ARTHRITIS', 'ACCESS2', 'TEETHLOST', 'CANCER', 'GHLTH', 'CHECKUP',\n",
       "       'OBESITY'], dtype=object)"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View disease outcomes (possible targets for machine learning)\n",
    "# This analysis will focus on COPD (Chronic Obstructive Pulmonary Disease)\n",
    "# However, this notebook will make it easy to analyze other diseases\n",
    "results_df['measureid'].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                             int64\n",
       "stateabbr                       object\n",
       "statedesc                       object\n",
       "locationname                    object\n",
       "datasource                      object\n",
       "category                        object\n",
       "measure                         object\n",
       "data_value_unit                 object\n",
       "data_value_type                 object\n",
       "data_value                     float64\n",
       "low_confidence_limit           float64\n",
       "high_confidence_limit          float64\n",
       "totalpopulation                  int64\n",
       "locationid                      object\n",
       "categoryid                      object\n",
       "measureid                       object\n",
       "datavaluetypeid                 object\n",
       "short_question_text             object\n",
       "geolocation                     object\n",
       ":@computed_region_bxsw_vy29     object\n",
       ":@computed_region_he4y_prf8     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of columns from results_df we want to convert from object to numeric\n",
    "# This will allow us to perform calculations on the data\n",
    "numeric_list = ['year', 'data_value', 'low_confidence_limit', 'high_confidence_limit', 'totalpopulation']\n",
    "\n",
    "# Convert columns in numeric_list to numeric data type in results_df\n",
    "results_df[numeric_list] = results_df[numeric_list].apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "# View data types for results_df\n",
    "results_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LPA_df - ['No leisure-time physical activity among adults aged >=18 years']\n",
      "MHLTH_df - ['Mental health not good for >=14 days among adults aged >=18 years']\n",
      "DENTAL_df - ['Visits to dentist or dental clinic among adults aged >=18 years']\n",
      "BINGE_df - ['Binge drinking among adults aged >=18 years']\n",
      "COREW_df - ['Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years']\n",
      "CERVICAL_df - ['Cervical cancer screening among adult women aged 21-65 years']\n",
      "PHLTH_df - ['Physical health not good for >=14 days among adults aged >=18 years']\n",
      "BPMED_df - ['Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure']\n",
      "COLON_SCREEN_df - ['Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years']\n",
      "CHOLSCREEN_df - ['Cholesterol screening among adults aged >=18 years']\n",
      "COREM_df - ['Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening']\n",
      "SLEEP_df - ['Sleeping less than 7 hours among adults aged >=18 years']\n",
      "CSMOKING_df - ['Current smoking among adults aged >=18 years']\n",
      "MAMMOUSE_df - ['Mammography use among women aged 50-74 years']\n",
      "ACCESS2_df - ['Current lack of health insurance among adults aged 18-64 years']\n",
      "GHLTH_df - ['Fair or poor self-rated health status among adults aged >=18 years']\n",
      "CHECKUP_df - ['Visits to doctor for routine checkup within the past year among adults aged >=18 years']\n"
     ]
    }
   ],
   "source": [
    "# Create a list of the health metrics which will become our features and make them into dataframes\n",
    "# And print the feature and the measure of each dataframe as a key to more easily understand the data\n",
    "\n",
    "# Add the health outcomes to a list\n",
    "features_list = results_df.loc[results_df['category'].isin(['Prevention', 'Health Risk Behaviors', 'Health Status'])]['measureid'].unique()\n",
    "\n",
    "# Create unique dataframes for each feature in the features_list\n",
    "for feature in features_list:\n",
    "    globals()[feature + \"_df\"] = results_df.loc[results_df['measureid'] == feature]\n",
    "\n",
    "# Create a list that contains the names of the dataframes made in the step above\n",
    "dataframes_list = [feature + \"_df\" for feature in features_list]\n",
    "\n",
    "# Print the feature and the measure of each dataframe\n",
    "for dataframe in dataframes_list:\n",
    "    print(f\"{dataframe} - {globals()[dataframe]['measure'].unique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>year</th>\n",
       "      <th>stateabbr</th>\n",
       "      <th>statedesc</th>\n",
       "      <th>locationname</th>\n",
       "      <th>datasource</th>\n",
       "      <th>category</th>\n",
       "      <th>measure</th>\n",
       "      <th>data_value_unit</th>\n",
       "      <th>data_value_type</th>\n",
       "      <th>data_value</th>\n",
       "      <th>low_confidence_limit</th>\n",
       "      <th>high_confidence_limit</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>locationid</th>\n",
       "      <th>categoryid</th>\n",
       "      <th>measureid</th>\n",
       "      <th>datavaluetypeid</th>\n",
       "      <th>short_question_text</th>\n",
       "      <th>geolocation</th>\n",
       "      <th>:@computed_region_bxsw_vy29</th>\n",
       "      <th>:@computed_region_he4y_prf8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Niobrara</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>Chronic obstructive pulmonary disease among ad...</td>\n",
       "      <td>%</td>\n",
       "      <td>Age-adjusted prevalence</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2275</td>\n",
       "      <td>56027</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>COPD</td>\n",
       "      <td>AgeAdjPrv</td>\n",
       "      <td>COPD</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-104.4683727...</td>\n",
       "      <td>14</td>\n",
       "      <td>3121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Laramie</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>Chronic obstructive pulmonary disease among ad...</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude prevalence</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>100595</td>\n",
       "      <td>56021</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>COPD</td>\n",
       "      <td>CrdPrv</td>\n",
       "      <td>COPD</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-104.660395,...</td>\n",
       "      <td>14</td>\n",
       "      <td>3119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Sheridan</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>Chronic obstructive pulmonary disease among ad...</td>\n",
       "      <td>%</td>\n",
       "      <td>Age-adjusted prevalence</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.6</td>\n",
       "      <td>30863</td>\n",
       "      <td>56033</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>COPD</td>\n",
       "      <td>AgeAdjPrv</td>\n",
       "      <td>COPD</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-106.8812114...</td>\n",
       "      <td>14</td>\n",
       "      <td>3123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year stateabbr statedesc locationname datasource         category  \\\n",
       "12  2020        WY   Wyoming     Niobrara      BRFSS  Health Outcomes   \n",
       "21  2020        WY   Wyoming      Laramie      BRFSS  Health Outcomes   \n",
       "38  2020        WY   Wyoming     Sheridan      BRFSS  Health Outcomes   \n",
       "\n",
       "                                              measure data_value_unit  \\\n",
       "12  Chronic obstructive pulmonary disease among ad...               %   \n",
       "21  Chronic obstructive pulmonary disease among ad...               %   \n",
       "38  Chronic obstructive pulmonary disease among ad...               %   \n",
       "\n",
       "            data_value_type  data_value  low_confidence_limit  \\\n",
       "12  Age-adjusted prevalence         8.4                   7.3   \n",
       "21         Crude prevalence         6.3                   5.5   \n",
       "38  Age-adjusted prevalence         5.7                   4.9   \n",
       "\n",
       "    high_confidence_limit  totalpopulation locationid categoryid measureid  \\\n",
       "12                    9.5             2275      56027    HLTHOUT      COPD   \n",
       "21                    7.3           100595      56021    HLTHOUT      COPD   \n",
       "38                    6.6            30863      56033    HLTHOUT      COPD   \n",
       "\n",
       "   datavaluetypeid short_question_text  \\\n",
       "12       AgeAdjPrv                COPD   \n",
       "21          CrdPrv                COPD   \n",
       "38       AgeAdjPrv                COPD   \n",
       "\n",
       "                                          geolocation  \\\n",
       "12  {'type': 'Point', 'coordinates': [-104.4683727...   \n",
       "21  {'type': 'Point', 'coordinates': [-104.660395,...   \n",
       "38  {'type': 'Point', 'coordinates': [-106.8812114...   \n",
       "\n",
       "   :@computed_region_bxsw_vy29 :@computed_region_he4y_prf8  \n",
       "12                          14                        3121  \n",
       "21                          14                        3119  \n",
       "38                          14                        3123  "
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the COPD dataframe by filtering results_df which will be our taget\n",
    "COPD_df = results_df.loc[results_df[\"measureid\"] == \"COPD\", :]\n",
    "\n",
    "# Display dataframe and view all columns\n",
    "COPD_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LPA_df',\n",
       " 'MHLTH_df',\n",
       " 'DENTAL_df',\n",
       " 'BINGE_df',\n",
       " 'COREW_df',\n",
       " 'CERVICAL_df',\n",
       " 'PHLTH_df',\n",
       " 'BPMED_df',\n",
       " 'COLON_SCREEN_df',\n",
       " 'CHOLSCREEN_df',\n",
       " 'COREM_df',\n",
       " 'SLEEP_df',\n",
       " 'CSMOKING_df',\n",
       " 'MAMMOUSE_df',\n",
       " 'ACCESS2_df',\n",
       " 'GHLTH_df',\n",
       " 'CHECKUP_df']"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View dataframes list\n",
    "dataframes_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>year</th>\n",
       "      <th>stateabbr</th>\n",
       "      <th>locationname</th>\n",
       "      <th>COPD_data_value</th>\n",
       "      <th>totalpopulation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Niobrara</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sheridan</td>\n",
       "      <td>5.7</td>\n",
       "      <td>30863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year stateabbr locationname  COPD_data_value  totalpopulation\n",
       "12  2020        WY     Niobrara              8.4             2275\n",
       "38  2020        WY     Sheridan              5.7            30863\n",
       "39  2020        WY  Hot Springs              6.8             4425"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add COPD_df to dataframes_list\n",
    "dataframes_list.append(\"COPD_df\")\n",
    "\n",
    "# Filter dataframes to only include datavaluetypeid AgeAdjPrv\n",
    "for dataframe in dataframes_list:\n",
    "    globals()[dataframe] = globals()[dataframe].loc[globals()[dataframe]['datavaluetypeid'] == 'AgeAdjPrv']\n",
    "\n",
    "# Change the name of data_value to the name of the measureid + data_value for all dataframes\n",
    "for dataframe in dataframes_list:\n",
    "    globals()[dataframe] = globals()[dataframe].rename(columns={'data_value': globals()[dataframe]['measureid'].unique()[0] + '_data_value'})\n",
    "\n",
    "# Save a copy of each dataframe as dataframe_name + _copy\n",
    "for dataframe in dataframes_list:\n",
    "    globals()[dataframe + \"_copy\"] = globals()[dataframe].copy()\n",
    "\n",
    "# Drop unnecessary columns for all dataframes in dataframes_list\n",
    "for dataframe in dataframes_list:\n",
    "    globals()[dataframe] = globals()[dataframe].drop(columns=['statedesc', 'datasource', 'category', 'measure', \n",
    "                                                              'data_value_unit', 'locationid', 'categoryid', \n",
    "                                                              'measureid', 'data_value_type', 'low_confidence_limit', \n",
    "                                                              'high_confidence_limit', 'short_question_text', 'geolocation', \n",
    "                                                              'datavaluetypeid', ':@computed_region_bxsw_vy29', \n",
    "                                                              ':@computed_region_he4y_prf8'], axis=1)\n",
    "\n",
    "# Display dataframe and view all columns\n",
    "COPD_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LPA_df',\n",
       " 'MHLTH_df',\n",
       " 'DENTAL_df',\n",
       " 'BINGE_df',\n",
       " 'COREW_df',\n",
       " 'CERVICAL_df',\n",
       " 'PHLTH_df',\n",
       " 'BPMED_df',\n",
       " 'COLON_SCREEN_df',\n",
       " 'CHOLSCREEN_df',\n",
       " 'COREM_df',\n",
       " 'SLEEP_df',\n",
       " 'CSMOKING_df',\n",
       " 'MAMMOUSE_df',\n",
       " 'ACCESS2_df',\n",
       " 'GHLTH_df',\n",
       " 'CHECKUP_df',\n",
       " 'COPD_df']"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View dataframes list\n",
    "dataframes_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>year</th>\n",
       "      <th>stateabbr</th>\n",
       "      <th>locationname</th>\n",
       "      <th>COPD_data_value</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>LPA_data_value</th>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <th>DENTAL_data_value</th>\n",
       "      <th>BINGE_data_value</th>\n",
       "      <th>COREW_data_value</th>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <th>BPMED_data_value</th>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <th>CHOLSCREEN_data_value</th>\n",
       "      <th>COREM_data_value</th>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Niobrara</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2275</td>\n",
       "      <td>28.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>26.7</td>\n",
       "      <td>78.2</td>\n",
       "      <td>11.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.4</td>\n",
       "      <td>33.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>59.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>67.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sheridan</td>\n",
       "      <td>5.7</td>\n",
       "      <td>30863</td>\n",
       "      <td>20.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>81.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>69.2</td>\n",
       "      <td>14.7</td>\n",
       "      <td>10.8</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4425</td>\n",
       "      <td>24.6</td>\n",
       "      <td>14.1</td>\n",
       "      <td>61.8</td>\n",
       "      <td>17.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>80.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>19.9</td>\n",
       "      <td>60.1</td>\n",
       "      <td>16.2</td>\n",
       "      <td>12.9</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year stateabbr locationname  COPD_data_value  totalpopulation  \\\n",
       "0  2020        WY     Niobrara              8.4             2275   \n",
       "1  2020        WY     Sheridan              5.7            30863   \n",
       "2  2020        WY  Hot Springs              6.8             4425   \n",
       "\n",
       "   LPA_data_value  MHLTH_data_value  DENTAL_data_value  BINGE_data_value  \\\n",
       "0            28.2              16.0               58.0              16.2   \n",
       "1            20.4              13.3               65.3              19.3   \n",
       "2            24.6              14.1               61.8              17.8   \n",
       "\n",
       "   COREW_data_value  CERVICAL_data_value  PHLTH_data_value  BPMED_data_value  \\\n",
       "0              26.7                 78.2              11.6               NaN   \n",
       "1              32.3                 81.9               8.4               NaN   \n",
       "2              28.8                 80.7               9.7               NaN   \n",
       "\n",
       "   COLON_SCREEN_data_value  CHOLSCREEN_data_value  COREM_data_value  \\\n",
       "0                     57.9                    NaN              38.4   \n",
       "1                     63.4                    NaN              50.0   \n",
       "2                     61.5                    NaN              41.4   \n",
       "\n",
       "   SLEEP_data_value  CSMOKING_data_value  MAMMOUSE_data_value  \\\n",
       "0              33.1                 22.6                 59.4   \n",
       "1              31.9                 17.6                 69.2   \n",
       "2              32.3                 19.9                 60.1   \n",
       "\n",
       "   ACCESS2_data_value  GHLTH_data_value  CHECKUP_data_value  \n",
       "0                17.5              15.6                67.9  \n",
       "1                14.7              10.8                64.0  \n",
       "2                16.2              12.9                62.4  "
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copy the COPD_df dataframe to a new dataframe\n",
    "ml_df = COPD_df.copy()\n",
    "\n",
    "# Merge the dataframes in dataframes_list to ml_df\n",
    "for dataframe in dataframes_list:\n",
    "    ml_df = ml_df.merge(globals()[dataframe], how='left', on=['year', 'totalpopulation', 'stateabbr', 'locationname'])\n",
    "\n",
    "# Rename the column COPD_data_value_x to COPD_data_value\n",
    "ml_df = ml_df.rename(columns={'COPD_data_value_x': 'COPD_data_value'})\n",
    "\n",
    "# Drop the column COPD_data_value_y\n",
    "ml_df = ml_df.drop(columns=['COPD_data_value_y'], axis=1)\n",
    "\n",
    "# Display dataframe and view all columns\n",
    "ml_df.head(3)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>year</th>\n",
       "      <th>COPD_data_value</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>LPA_data_value</th>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <th>DENTAL_data_value</th>\n",
       "      <th>BINGE_data_value</th>\n",
       "      <th>COREW_data_value</th>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <th>BPMED_data_value</th>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <th>CHOLSCREEN_data_value</th>\n",
       "      <th>COREM_data_value</th>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3144.0</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3.144000e+03</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "      <td>3144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>7.183938</td>\n",
       "      <td>2.102205e+05</td>\n",
       "      <td>25.709796</td>\n",
       "      <td>15.730184</td>\n",
       "      <td>58.988963</td>\n",
       "      <td>17.834669</td>\n",
       "      <td>36.866349</td>\n",
       "      <td>81.565331</td>\n",
       "      <td>10.963836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.734765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.759351</td>\n",
       "      <td>34.471947</td>\n",
       "      <td>20.034256</td>\n",
       "      <td>70.444593</td>\n",
       "      <td>16.797328</td>\n",
       "      <td>16.032029</td>\n",
       "      <td>72.762309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.747671</td>\n",
       "      <td>5.918720e+06</td>\n",
       "      <td>5.192709</td>\n",
       "      <td>2.040912</td>\n",
       "      <td>7.488897</td>\n",
       "      <td>3.020080</td>\n",
       "      <td>4.477454</td>\n",
       "      <td>2.344340</td>\n",
       "      <td>2.152859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.738214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.143715</td>\n",
       "      <td>3.637180</td>\n",
       "      <td>4.105682</td>\n",
       "      <td>3.959298</td>\n",
       "      <td>6.606075</td>\n",
       "      <td>4.442595</td>\n",
       "      <td>4.045601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>33.200000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>68.400000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>58.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>1.088425e+04</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>53.600000</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>80.100000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.300000</td>\n",
       "      <td>31.900000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>67.900000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>69.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.562050e+04</td>\n",
       "      <td>25.200000</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>81.800000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.700000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>19.850000</td>\n",
       "      <td>70.700000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>73.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>6.829725e+04</td>\n",
       "      <td>29.200000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>64.525000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>83.200000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>73.300000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>75.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>3.314493e+08</td>\n",
       "      <td>47.200000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>80.700000</td>\n",
       "      <td>27.600000</td>\n",
       "      <td>53.400000</td>\n",
       "      <td>87.700000</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.600000</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>83.400000</td>\n",
       "      <td>53.300000</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>82.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year  COPD_data_value  totalpopulation  LPA_data_value  \\\n",
       "count  3144.0      3144.000000     3.144000e+03     3144.000000   \n",
       "mean   2020.0         7.183938     2.102205e+05       25.709796   \n",
       "std       0.0         1.747671     5.918720e+06        5.192709   \n",
       "min    2020.0         2.600000     8.700000e+01       10.200000   \n",
       "25%    2020.0         5.900000     1.088425e+04       22.000000   \n",
       "50%    2020.0         7.000000     2.562050e+04       25.200000   \n",
       "75%    2020.0         8.200000     6.829725e+04       29.200000   \n",
       "max    2020.0        16.500000     3.314493e+08       47.200000   \n",
       "\n",
       "       MHLTH_data_value  DENTAL_data_value  BINGE_data_value  \\\n",
       "count       3144.000000        3144.000000       3144.000000   \n",
       "mean          15.730184          58.988963         17.834669   \n",
       "std            2.040912           7.488897          3.020080   \n",
       "min            8.300000          33.200000          8.200000   \n",
       "25%           14.300000          53.600000         15.700000   \n",
       "50%           15.700000          59.800000         17.600000   \n",
       "75%           17.200000          64.525000         19.700000   \n",
       "max           23.300000          80.700000         27.600000   \n",
       "\n",
       "       COREW_data_value  CERVICAL_data_value  PHLTH_data_value  \\\n",
       "count       3144.000000          3144.000000       3144.000000   \n",
       "mean          36.866349            81.565331         10.963836   \n",
       "std            4.477454             2.344340          2.152859   \n",
       "min           16.200000            68.400000          5.600000   \n",
       "25%           33.900000            80.100000          9.300000   \n",
       "50%           36.800000            81.800000         10.800000   \n",
       "75%           39.800000            83.200000         12.400000   \n",
       "max           53.400000            87.700000         20.900000   \n",
       "\n",
       "       BPMED_data_value  COLON_SCREEN_data_value  CHOLSCREEN_data_value  \\\n",
       "count               0.0              3144.000000                    0.0   \n",
       "mean                NaN                67.734765                    NaN   \n",
       "std                 NaN                 4.738214                    NaN   \n",
       "min                 NaN                49.000000                    NaN   \n",
       "25%                 NaN                64.800000                    NaN   \n",
       "50%                 NaN                68.100000                    NaN   \n",
       "75%                 NaN                71.300000                    NaN   \n",
       "max                 NaN                82.100000                    NaN   \n",
       "\n",
       "       COREM_data_value  SLEEP_data_value  CSMOKING_data_value  \\\n",
       "count       3144.000000       3144.000000          3144.000000   \n",
       "mean          42.759351         34.471947            20.034256   \n",
       "std            5.143715          3.637180             4.105682   \n",
       "min           19.800000         23.800000             5.800000   \n",
       "25%           39.300000         31.900000            17.500000   \n",
       "50%           42.700000         34.400000            19.850000   \n",
       "75%           46.000000         36.800000            22.600000   \n",
       "max           60.600000         48.400000            41.100000   \n",
       "\n",
       "       MAMMOUSE_data_value  ACCESS2_data_value  GHLTH_data_value  \\\n",
       "count          3144.000000         3144.000000       3144.000000   \n",
       "mean             70.444593           16.797328         16.032029   \n",
       "std               3.959298            6.606075          4.442595   \n",
       "min              50.600000            5.700000          6.500000   \n",
       "25%              67.900000           12.100000         12.600000   \n",
       "50%              70.700000           15.000000         15.300000   \n",
       "75%              73.300000           19.500000         19.000000   \n",
       "max              83.400000           53.300000         36.800000   \n",
       "\n",
       "       CHECKUP_data_value  \n",
       "count         3144.000000  \n",
       "mean            72.762309  \n",
       "std              4.045601  \n",
       "min             58.300000  \n",
       "25%             69.800000  \n",
       "50%             73.200000  \n",
       "75%             75.600000  \n",
       "max             82.600000  "
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics for all columns\n",
    "ml_df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning - RandomForestRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies for machine learning\n",
    "# import tensorflow as tf\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>stateabbr</th>\n",
       "      <th>locationname</th>\n",
       "      <th>COPD_data_value</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>LPA_data_value</th>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <th>DENTAL_data_value</th>\n",
       "      <th>BINGE_data_value</th>\n",
       "      <th>COREW_data_value</th>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <th>BPMED_data_value</th>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <th>CHOLSCREEN_data_value</th>\n",
       "      <th>COREM_data_value</th>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Niobrara</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2275</td>\n",
       "      <td>28.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>26.7</td>\n",
       "      <td>78.2</td>\n",
       "      <td>11.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.4</td>\n",
       "      <td>33.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>59.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>67.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sheridan</td>\n",
       "      <td>5.7</td>\n",
       "      <td>30863</td>\n",
       "      <td>20.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>81.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>69.2</td>\n",
       "      <td>14.7</td>\n",
       "      <td>10.8</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>WY</td>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4425</td>\n",
       "      <td>24.6</td>\n",
       "      <td>14.1</td>\n",
       "      <td>61.8</td>\n",
       "      <td>17.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>80.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>19.9</td>\n",
       "      <td>60.1</td>\n",
       "      <td>16.2</td>\n",
       "      <td>12.9</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year stateabbr locationname  COPD_data_value  totalpopulation  \\\n",
       "0  2020        WY     Niobrara              8.4             2275   \n",
       "1  2020        WY     Sheridan              5.7            30863   \n",
       "2  2020        WY  Hot Springs              6.8             4425   \n",
       "\n",
       "   LPA_data_value  MHLTH_data_value  DENTAL_data_value  BINGE_data_value  \\\n",
       "0            28.2              16.0               58.0              16.2   \n",
       "1            20.4              13.3               65.3              19.3   \n",
       "2            24.6              14.1               61.8              17.8   \n",
       "\n",
       "   COREW_data_value  CERVICAL_data_value  PHLTH_data_value  BPMED_data_value  \\\n",
       "0              26.7                 78.2              11.6               NaN   \n",
       "1              32.3                 81.9               8.4               NaN   \n",
       "2              28.8                 80.7               9.7               NaN   \n",
       "\n",
       "   COLON_SCREEN_data_value  CHOLSCREEN_data_value  COREM_data_value  \\\n",
       "0                     57.9                    NaN              38.4   \n",
       "1                     63.4                    NaN              50.0   \n",
       "2                     61.5                    NaN              41.4   \n",
       "\n",
       "   SLEEP_data_value  CSMOKING_data_value  MAMMOUSE_data_value  \\\n",
       "0              33.1                 22.6                 59.4   \n",
       "1              31.9                 17.6                 69.2   \n",
       "2              32.3                 19.9                 60.1   \n",
       "\n",
       "   ACCESS2_data_value  GHLTH_data_value  CHECKUP_data_value  \n",
       "0                17.5              15.6                67.9  \n",
       "1                14.7              10.8                64.0  \n",
       "2                16.2              12.9                62.4  "
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View ml_df\n",
    "ml_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                          0\n",
       "stateabbr                     0\n",
       "locationname                  1\n",
       "COPD_data_value               0\n",
       "totalpopulation               0\n",
       "LPA_data_value                0\n",
       "MHLTH_data_value              0\n",
       "DENTAL_data_value             0\n",
       "BINGE_data_value              0\n",
       "COREW_data_value              0\n",
       "CERVICAL_data_value           0\n",
       "PHLTH_data_value              0\n",
       "BPMED_data_value           3144\n",
       "COLON_SCREEN_data_value       0\n",
       "CHOLSCREEN_data_value      3144\n",
       "COREM_data_value              0\n",
       "SLEEP_data_value              0\n",
       "CSMOKING_data_value           0\n",
       "MAMMOUSE_data_value           0\n",
       "ACCESS2_data_value            0\n",
       "GHLTH_data_value              0\n",
       "CHECKUP_data_value            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "ml_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop BPMED_data_value and CHOLSCREEN_data_value columns\n",
    "ml_df = ml_df.drop(columns=['BPMED_data_value', 'CHOLSCREEN_data_value'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>stateabbr</th>\n",
       "      <th>locationname</th>\n",
       "      <th>COPD_data_value</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>LPA_data_value</th>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <th>DENTAL_data_value</th>\n",
       "      <th>BINGE_data_value</th>\n",
       "      <th>COREW_data_value</th>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <th>COREM_data_value</th>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2020</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.6</td>\n",
       "      <td>331449281</td>\n",
       "      <td>22.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>64.5</td>\n",
       "      <td>16.7</td>\n",
       "      <td>37.4</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>70.6</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>14.6</td>\n",
       "      <td>77.8</td>\n",
       "      <td>13.5</td>\n",
       "      <td>13.7</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year stateabbr locationname  COPD_data_value  totalpopulation  \\\n",
       "365  2020        US          NaN              5.6        331449281   \n",
       "\n",
       "     LPA_data_value  MHLTH_data_value  DENTAL_data_value  BINGE_data_value  \\\n",
       "365            22.9              13.9               64.5              16.7   \n",
       "\n",
       "     COREW_data_value  CERVICAL_data_value  PHLTH_data_value  \\\n",
       "365              37.4                 83.7               9.4   \n",
       "\n",
       "     COLON_SCREEN_data_value  COREM_data_value  SLEEP_data_value  \\\n",
       "365                     70.6              44.0              33.3   \n",
       "\n",
       "     CSMOKING_data_value  MAMMOUSE_data_value  ACCESS2_data_value  \\\n",
       "365                 14.6                 77.8                13.5   \n",
       "\n",
       "     GHLTH_data_value  CHECKUP_data_value  \n",
       "365              13.7                73.0  "
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the null value in the locationname column\n",
    "ml_df.loc[ml_df['locationname'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                       0\n",
       "stateabbr                  0\n",
       "locationname               0\n",
       "COPD_data_value            0\n",
       "totalpopulation            0\n",
       "LPA_data_value             0\n",
       "MHLTH_data_value           0\n",
       "DENTAL_data_value          0\n",
       "BINGE_data_value           0\n",
       "COREW_data_value           0\n",
       "CERVICAL_data_value        0\n",
       "PHLTH_data_value           0\n",
       "COLON_SCREEN_data_value    0\n",
       "COREM_data_value           0\n",
       "SLEEP_data_value           0\n",
       "CSMOKING_data_value        0\n",
       "MAMMOUSE_data_value        0\n",
       "ACCESS2_data_value         0\n",
       "GHLTH_data_value           0\n",
       "CHECKUP_data_value         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the null value in the locationname column\n",
    "ml_df = ml_df.dropna()\n",
    "\n",
    "# Check for null values\n",
    "ml_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['stateabbr', 'locationname']"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create label encoder instance\n",
    "le = LabelEncoder()\n",
    "\n",
    "# Create a copy of ml_df\n",
    "ml_df_encoded = ml_df.copy()\n",
    "\n",
    "# Generate our categorical variable lists\n",
    "dataset_cat = ml_df_encoded.dtypes[ml_df_encoded.dtypes == \"object\"].index.tolist()\n",
    "\n",
    "# View the list of categorical variables\n",
    "dataset_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>year</th>\n",
       "      <th>stateabbr</th>\n",
       "      <th>locationname</th>\n",
       "      <th>COPD_data_value</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>LPA_data_value</th>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <th>DENTAL_data_value</th>\n",
       "      <th>BINGE_data_value</th>\n",
       "      <th>COREW_data_value</th>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <th>COREM_data_value</th>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>50</td>\n",
       "      <td>1179</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2275</td>\n",
       "      <td>28.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>26.7</td>\n",
       "      <td>78.2</td>\n",
       "      <td>11.6</td>\n",
       "      <td>57.9</td>\n",
       "      <td>38.4</td>\n",
       "      <td>33.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>59.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>67.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>50</td>\n",
       "      <td>1509</td>\n",
       "      <td>5.7</td>\n",
       "      <td>30863</td>\n",
       "      <td>20.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>81.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>63.4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>69.2</td>\n",
       "      <td>14.7</td>\n",
       "      <td>10.8</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>50</td>\n",
       "      <td>779</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4425</td>\n",
       "      <td>24.6</td>\n",
       "      <td>14.1</td>\n",
       "      <td>61.8</td>\n",
       "      <td>17.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>80.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>61.5</td>\n",
       "      <td>41.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>19.9</td>\n",
       "      <td>60.1</td>\n",
       "      <td>16.2</td>\n",
       "      <td>12.9</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  stateabbr  locationname  COPD_data_value  totalpopulation  \\\n",
       "0  2020         50          1179              8.4             2275   \n",
       "1  2020         50          1509              5.7            30863   \n",
       "2  2020         50           779              6.8             4425   \n",
       "\n",
       "   LPA_data_value  MHLTH_data_value  DENTAL_data_value  BINGE_data_value  \\\n",
       "0            28.2              16.0               58.0              16.2   \n",
       "1            20.4              13.3               65.3              19.3   \n",
       "2            24.6              14.1               61.8              17.8   \n",
       "\n",
       "   COREW_data_value  CERVICAL_data_value  PHLTH_data_value  \\\n",
       "0              26.7                 78.2              11.6   \n",
       "1              32.3                 81.9               8.4   \n",
       "2              28.8                 80.7               9.7   \n",
       "\n",
       "   COLON_SCREEN_data_value  COREM_data_value  SLEEP_data_value  \\\n",
       "0                     57.9              38.4              33.1   \n",
       "1                     63.4              50.0              31.9   \n",
       "2                     61.5              41.4              32.3   \n",
       "\n",
       "   CSMOKING_data_value  MAMMOUSE_data_value  ACCESS2_data_value  \\\n",
       "0                 22.6                 59.4                17.5   \n",
       "1                 17.6                 69.2                14.7   \n",
       "2                 19.9                 60.1                16.2   \n",
       "\n",
       "   GHLTH_data_value  CHECKUP_data_value  \n",
       "0              15.6                67.9  \n",
       "1              10.8                64.0  \n",
       "2              12.9                62.4  "
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For loop to encode text columns to numerical values\n",
    "for textColumn in dataset_cat:\n",
    "    ml_df_encoded[textColumn] = le.fit_transform(ml_df_encoded[textColumn])\n",
    "\n",
    "# View ml_df\n",
    "ml_df_encoded.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of columns to be our features\n",
    "feature_cols = [i for i in ml_df_encoded.columns if i not in ('COPD_data_value')]\n",
    "X = ml_df_encoded[feature_cols]\n",
    "\n",
    "# Create our target column\n",
    "y = ml_df_encoded['COPD_data_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into testing and training datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=10, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Random Forest Regression model on the training set\n",
    "\n",
    "# Fitting Random Forest Regression to the dataset\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.00476101, 0.00125623, 0.00128928, 0.00257245,\n",
       "       0.05371611, 0.00282396, 0.00642451, 0.00152489, 0.00305553,\n",
       "       0.13303906, 0.00336004, 0.00145186, 0.00232682, 0.76306983,\n",
       "       0.00189207, 0.0040931 , 0.00688048, 0.0064628 ])"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View feature importance\n",
    "regressor.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <td>0.763070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <td>0.133039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <td>0.053716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <td>0.006880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "      <td>0.006463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BINGE_data_value</th>\n",
       "      <td>0.006425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stateabbr</th>\n",
       "      <td>0.004761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <td>0.004093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <td>0.003360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <td>0.003056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DENTAL_data_value</th>\n",
       "      <td>0.002824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LPA_data_value</th>\n",
       "      <td>0.002572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <td>0.002327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <td>0.001892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COREW_data_value</th>\n",
       "      <td>0.001525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COREM_data_value</th>\n",
       "      <td>0.001452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalpopulation</th>\n",
       "      <td>0.001289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>locationname</th>\n",
       "      <td>0.001256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         importance\n",
       "CSMOKING_data_value        0.763070\n",
       "PHLTH_data_value           0.133039\n",
       "MHLTH_data_value           0.053716\n",
       "GHLTH_data_value           0.006880\n",
       "CHECKUP_data_value         0.006463\n",
       "BINGE_data_value           0.006425\n",
       "stateabbr                  0.004761\n",
       "ACCESS2_data_value         0.004093\n",
       "COLON_SCREEN_data_value    0.003360\n",
       "CERVICAL_data_value        0.003056\n",
       "DENTAL_data_value          0.002824\n",
       "LPA_data_value             0.002572\n",
       "SLEEP_data_value           0.002327\n",
       "MAMMOUSE_data_value        0.001892\n",
       "COREW_data_value           0.001525\n",
       "COREM_data_value           0.001452\n",
       "totalpopulation            0.001289\n",
       "locationname               0.001256\n",
       "year                       0.000000"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of the feature importance\n",
    "feature_importance_df = pd.DataFrame(regressor.feature_importances_, index = X_train.columns, columns=['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "# View the dataframe\n",
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import table\n",
    "\n",
    "# Create a bargraph of the feature importance\n",
    "feature_importance_df.plot(kind='bar', figsize=(10,5), color='red', fontsize=15)\n",
    "plt.title('Feature Importance', fontsize=25)\n",
    "plt.xlabel('Features', fontsize=25)\n",
    "plt.ylabel('Importance', fontsize=25)\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('./Images/feature_importance.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error: 0.29850208050111154\n",
      "r2 score:  0.9678719254226114\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "import numpy as np\n",
    "from sklearn import metrics \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "# Evaluate the model\n",
    "from sklearn.metrics import r2_score\n",
    "print('r2 score: ', r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residuals\n",
    "plt.scatter(y_test, y_test - y_pred, c=\"red\", label=\"Testing Data\")\n",
    "plt.hlines(y=0, xmin=y_test.min(), xmax=y_test.max())\n",
    "plt.title(\"Residual Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>8.4</td>\n",
       "      <td>8.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>5.6</td>\n",
       "      <td>5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>9.5</td>\n",
       "      <td>9.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2481</th>\n",
       "      <td>6.9</td>\n",
       "      <td>6.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>7.8</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>4.3</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>7.4</td>\n",
       "      <td>7.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>7.8</td>\n",
       "      <td>7.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2720</th>\n",
       "      <td>9.5</td>\n",
       "      <td>9.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>9.1</td>\n",
       "      <td>8.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted\n",
       "2997     8.4       8.37\n",
       "423      5.6       5.74\n",
       "644      9.5       9.36\n",
       "2481     6.9       6.86\n",
       "1056     7.8       7.35\n",
       "588      4.3       4.47\n",
       "1633     7.4       7.23\n",
       "962      7.8       7.89\n",
       "2720     9.5       9.59\n",
       "1050     9.1       8.76"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Import dependencies\n",
    "# import dataframe_image as dfi\n",
    "\n",
    "# Create a dataframe of the actual and predicted values\n",
    "actual_predicted_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "\n",
    "# View the dataframe\n",
    "actual_predicted_df.head(10)\n",
    "\n",
    "# # View the dataframe\n",
    "# ap_df = actual_predicted_df.head(10)\n",
    "\n",
    "# # Export the dataframe as a png\n",
    "# dfi.export(ap_df, './Images/dataframe.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                0\n",
      "count  315.000000\n",
      "mean     7.139302\n",
      "std      1.627300\n",
      "min      3.780000\n",
      "25%      5.895000\n",
      "50%      6.910000\n",
      "75%      8.155000\n",
      "max     13.610000\n"
     ]
    }
   ],
   "source": [
    "# View summary statistics for the predicted results\n",
    "print(pd.DataFrame(y_pred).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       COPD_data_value\n",
      "count       315.000000\n",
      "mean          7.160000\n",
      "std           1.667998\n",
      "min           2.600000\n",
      "25%           5.950000\n",
      "50%           7.000000\n",
      "75%           8.200000\n",
      "max          13.600000\n"
     ]
    }
   ],
   "source": [
    "# View summary statistics for the actual results\n",
    "print(pd.DataFrame(y_test).describe())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning - Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a linear regression model using the training sets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Create a linear regression model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error: 0.3384720262847948\n",
      "r2 score:  0.9586918698125257\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "# Evaluate the model\n",
    "print('r2 score: ', r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <td>2.816506e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <td>2.027922e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <td>1.720454e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "      <td>2.466950e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COREM_data_value</th>\n",
       "      <td>1.368507e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COREW_data_value</th>\n",
       "      <td>8.928655e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <td>8.226542e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LPA_data_value</th>\n",
       "      <td>7.775069e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalpopulation</th>\n",
       "      <td>4.109984e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>locationname</th>\n",
       "      <td>-9.873649e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stateabbr</th>\n",
       "      <td>-8.673330e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <td>-3.452509e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <td>-1.774754e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <td>-2.574826e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DENTAL_data_value</th>\n",
       "      <td>-2.748139e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BINGE_data_value</th>\n",
       "      <td>-4.023459e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <td>-4.072035e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <td>-4.073300e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           importance\n",
       "PHLTH_data_value         2.816506e-01\n",
       "MHLTH_data_value         2.027922e-01\n",
       "CSMOKING_data_value      1.720454e-01\n",
       "CHECKUP_data_value       2.466950e-02\n",
       "COREM_data_value         1.368507e-02\n",
       "COREW_data_value         8.928655e-03\n",
       "SLEEP_data_value         8.226542e-03\n",
       "LPA_data_value           7.775069e-03\n",
       "totalpopulation          4.109984e-08\n",
       "year                     0.000000e+00\n",
       "locationname            -9.873649e-06\n",
       "stateabbr               -8.673330e-04\n",
       "COLON_SCREEN_data_value -3.452509e-03\n",
       "MAMMOUSE_data_value     -1.774754e-02\n",
       "CERVICAL_data_value     -2.574826e-02\n",
       "DENTAL_data_value       -2.748139e-02\n",
       "BINGE_data_value        -4.023459e-02\n",
       "GHLTH_data_value        -4.072035e-02\n",
       "ACCESS2_data_value      -4.073300e-02"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the feature importance\n",
    "feature_importance = regressor.coef_\n",
    "\n",
    "# Create a dataframe of the feature importance\n",
    "feature_importance_df = pd.DataFrame(feature_importance, index = X_train.columns, columns=['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "# View the dataframe\n",
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bargraph of the feature importance\n",
    "feature_importance_df.plot(\n",
    "    figsize=(10,5),\n",
    "    kind='bar', \n",
    "    color='red', \n",
    "    fontsize=15)\n",
    "\n",
    "# Give x-axis a darker line\n",
    "plt.axhline(color='black', linewidth=.5)\n",
    "\n",
    "# Add legend\n",
    "plt.legend(loc='upper right', fontsize=15)\n",
    "\n",
    "# Add title and axis names\n",
    "plt.title('Feature Importance', fontsize=15)\n",
    "plt.xlabel('Features', fontsize=15)\n",
    "plt.ylabel('Importance', fontsize=15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['year',\n",
       " 'locationname',\n",
       " 'stateabbr',\n",
       " 'COLON_SCREEN_data_value',\n",
       " 'MAMMOUSE_data_value',\n",
       " 'CERVICAL_data_value',\n",
       " 'DENTAL_data_value',\n",
       " 'BINGE_data_value',\n",
       " 'GHLTH_data_value',\n",
       " 'ACCESS2_data_value']"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add features with importance less than or equal to 0 to a list\n",
    "features_to_drop = []\n",
    "\n",
    "# For loop to add features with importance less than or equal to 0 to a list\n",
    "for index, row in feature_importance_df.iterrows():\n",
    "    if row['importance'] <= 0:\n",
    "        features_to_drop.append(index)\n",
    "\n",
    "# View the list of features to drop\n",
    "features_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>COPD_data_value</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>LPA_data_value</th>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <th>COREW_data_value</th>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <th>COREM_data_value</th>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.4</td>\n",
       "      <td>2275</td>\n",
       "      <td>28.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>11.6</td>\n",
       "      <td>38.4</td>\n",
       "      <td>33.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>67.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.7</td>\n",
       "      <td>30863</td>\n",
       "      <td>20.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>8.4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.8</td>\n",
       "      <td>4425</td>\n",
       "      <td>24.6</td>\n",
       "      <td>14.1</td>\n",
       "      <td>28.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>41.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>19.9</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COPD_data_value  totalpopulation  LPA_data_value  MHLTH_data_value  \\\n",
       "0              8.4             2275            28.2              16.0   \n",
       "1              5.7            30863            20.4              13.3   \n",
       "2              6.8             4425            24.6              14.1   \n",
       "\n",
       "   COREW_data_value  PHLTH_data_value  COREM_data_value  SLEEP_data_value  \\\n",
       "0              26.7              11.6              38.4              33.1   \n",
       "1              32.3               8.4              50.0              31.9   \n",
       "2              28.8               9.7              41.4              32.3   \n",
       "\n",
       "   CSMOKING_data_value  CHECKUP_data_value  \n",
       "0                 22.6                67.9  \n",
       "1                 17.6                64.0  \n",
       "2                 19.9                62.4  "
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop features with importance less than or equal to 0 from ml_df_encoded\n",
    "ml_df_encoded = ml_df_encoded.drop(columns=features_to_drop, axis=1)\n",
    "\n",
    "# View ml_df_encoded\n",
    "ml_df_encoded.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set X and y values for machine learning\n",
    "X = ml_df_encoded.drop(columns=['COPD_data_value'], axis=1)\n",
    "y = ml_df_encoded['COPD_data_value']\n",
    "\n",
    "# Split the data into testing and training datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=0)\n",
    "\n",
    "# Create a linear regression model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Predict the results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the feature importance\n",
    "feature_importance = regressor.coef_\n",
    "\n",
    "# Create a dataframe of the feature importance\n",
    "feature_importance_df = pd.DataFrame(feature_importance, index = X_train.columns, columns=['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "# Create a bargraph of the feature importance\n",
    "feature_importance_df.plot(\n",
    "    figsize=(10,5),\n",
    "    kind='bar', \n",
    "    color='red', \n",
    "    fontsize=15)\n",
    "\n",
    "# Give x-axis a darker line\n",
    "plt.axhline(color='black', linewidth=.5)\n",
    "\n",
    "# Add legend\n",
    "plt.legend(loc='upper right', fontsize=15)\n",
    "\n",
    "# Add title and axis names\n",
    "plt.title('Feature Importance', fontsize=15)\n",
    "plt.xlabel('Features', fontsize=15)\n",
    "plt.ylabel('Importance', fontsize=15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error: 0.3971228002805095\n",
      "r2 score:  0.9431357102796543\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "# Evaluate the model\n",
    "print('r2 score: ', r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residuals\n",
    "plt.scatter(y_test, y_test - y_pred, c=\"red\", label=\"Testing Data\")\n",
    "plt.hlines(y=0, xmin=y_test.min(), xmax=y_test.max())\n",
    "plt.title(\"Residual Plot\")\n",
    "plt.show()\n",
    "\n",
    "# Export the plot as a png\n",
    "plt.savefig('./Images/residual_plot.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.7</td>\n",
       "      <td>5.504212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.1</td>\n",
       "      <td>5.500097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6.8</td>\n",
       "      <td>6.214123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.4</td>\n",
       "      <td>6.660828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5.3</td>\n",
       "      <td>5.701097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3116</th>\n",
       "      <td>7.3</td>\n",
       "      <td>6.906869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3120</th>\n",
       "      <td>8.8</td>\n",
       "      <td>8.329371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127</th>\n",
       "      <td>8.6</td>\n",
       "      <td>8.016815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3130</th>\n",
       "      <td>5.6</td>\n",
       "      <td>5.317236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3140</th>\n",
       "      <td>8.5</td>\n",
       "      <td>7.957035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted\n",
       "1        5.7   5.504212\n",
       "10       6.1   5.500097\n",
       "22       6.8   6.214123\n",
       "23       6.4   6.660828\n",
       "30       5.3   5.701097\n",
       "...      ...        ...\n",
       "3116     7.3   6.906869\n",
       "3120     8.8   8.329371\n",
       "3127     8.6   8.016815\n",
       "3130     5.6   5.317236\n",
       "3140     8.5   7.957035\n",
       "\n",
       "[315 rows x 2 columns]"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of the actual and predicted values\n",
    "actual_predicted_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "\n",
    "# View the dataframe ordered by index in ascending order\n",
    "actual_predicted_df.sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>year</th>\n",
       "      <th>stateabbr</th>\n",
       "      <th>locationname</th>\n",
       "      <th>COPD_data_value</th>\n",
       "      <th>totalpopulation</th>\n",
       "      <th>LPA_data_value</th>\n",
       "      <th>MHLTH_data_value</th>\n",
       "      <th>DENTAL_data_value</th>\n",
       "      <th>BINGE_data_value</th>\n",
       "      <th>COREW_data_value</th>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <th>COREM_data_value</th>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>8.4</td>\n",
       "      <td>8.658487</td>\n",
       "      <td>2020</td>\n",
       "      <td>AR</td>\n",
       "      <td>Drew</td>\n",
       "      <td>8.4</td>\n",
       "      <td>17977</td>\n",
       "      <td>34.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>52.4</td>\n",
       "      <td>14.3</td>\n",
       "      <td>35.5</td>\n",
       "      <td>80.8</td>\n",
       "      <td>13.4</td>\n",
       "      <td>67.8</td>\n",
       "      <td>40.2</td>\n",
       "      <td>36.9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>71.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>77.5</td>\n",
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       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>5.6</td>\n",
       "      <td>5.955038</td>\n",
       "      <td>2020</td>\n",
       "      <td>TX</td>\n",
       "      <td>Medina</td>\n",
       "      <td>5.6</td>\n",
       "      <td>52358</td>\n",
       "      <td>26.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>53.2</td>\n",
       "      <td>19.1</td>\n",
       "      <td>34.1</td>\n",
       "      <td>79.2</td>\n",
       "      <td>10.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>38.9</td>\n",
       "      <td>33.3</td>\n",
       "      <td>16.1</td>\n",
       "      <td>70.6</td>\n",
       "      <td>33.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>9.5</td>\n",
       "      <td>9.151721</td>\n",
       "      <td>2020</td>\n",
       "      <td>TN</td>\n",
       "      <td>DeKalb</td>\n",
       "      <td>9.5</td>\n",
       "      <td>20837</td>\n",
       "      <td>27.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>51.8</td>\n",
       "      <td>14.6</td>\n",
       "      <td>34.9</td>\n",
       "      <td>80.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>69.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.2</td>\n",
       "      <td>25.2</td>\n",
       "      <td>69.8</td>\n",
       "      <td>20.2</td>\n",
       "      <td>18.6</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted  year stateabbr locationname  COPD_data_value  \\\n",
       "2997     8.4   8.658487  2020        AR         Drew              8.4   \n",
       "423      5.6   5.955038  2020        TX       Medina              5.6   \n",
       "644      9.5   9.151721  2020        TN       DeKalb              9.5   \n",
       "\n",
       "      totalpopulation  LPA_data_value  MHLTH_data_value  DENTAL_data_value  \\\n",
       "2997            17977            34.6              17.4               52.4   \n",
       "423             52358            26.9              14.9               53.2   \n",
       "644             20837            27.7              18.2               51.8   \n",
       "\n",
       "      BINGE_data_value  COREW_data_value  CERVICAL_data_value  \\\n",
       "2997              14.3              35.5                 80.8   \n",
       "423               19.1              34.1                 79.2   \n",
       "644               14.6              34.9                 80.2   \n",
       "\n",
       "      PHLTH_data_value  COLON_SCREEN_data_value  COREM_data_value  \\\n",
       "2997              13.4                     67.8              40.2   \n",
       "423               10.7                     62.7              38.9   \n",
       "644               12.5                     69.5              40.0   \n",
       "\n",
       "      SLEEP_data_value  CSMOKING_data_value  MAMMOUSE_data_value  \\\n",
       "2997              36.9                 22.7                 71.9   \n",
       "423               33.3                 16.1                 70.6   \n",
       "644               35.2                 25.2                 69.8   \n",
       "\n",
       "      ACCESS2_data_value  GHLTH_data_value  CHECKUP_data_value  \n",
       "2997                16.0              21.0                77.5  \n",
       "423                 33.6              17.6                69.4  \n",
       "644                 20.2              18.6                73.0  "
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge actual_predicted_df with copd_df_copy on index\n",
    "merged_df = pd.merge(actual_predicted_df, ml_df, left_index=True, right_index=True)\n",
    "\n",
    "# View the merged dataframe\n",
    "merged_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>COREW_data_value</th>\n",
       "      <th>CERVICAL_data_value</th>\n",
       "      <th>PHLTH_data_value</th>\n",
       "      <th>COLON_SCREEN_data_value</th>\n",
       "      <th>COREM_data_value</th>\n",
       "      <th>SLEEP_data_value</th>\n",
       "      <th>CSMOKING_data_value</th>\n",
       "      <th>MAMMOUSE_data_value</th>\n",
       "      <th>ACCESS2_data_value</th>\n",
       "      <th>GHLTH_data_value</th>\n",
       "      <th>CHECKUP_data_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Actual, Predicted, year, stateabbr, locationname, COPD_data_value, totalpopulation, LPA_data_value, MHLTH_data_value, DENTAL_data_value, BINGE_data_value, COREW_data_value, CERVICAL_data_value, PHLTH_data_value, COLON_SCREEN_data_value, COREM_data_value, SLEEP_data_value, CSMOKING_data_value, MAMMOUSE_data_value, ACCESS2_data_value, GHLTH_data_value, CHECKUP_data_value]\n",
       "Index: []"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View rows where Actual does not equal COPD_data_value\n",
    "merged_df.loc[merged_df['Actual'] != merged_df['COPD_data_value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>8.4</td>\n",
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       "      <td>2020</td>\n",
       "      <td>AR</td>\n",
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       "      <th>423</th>\n",
       "      <td>5.6</td>\n",
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       "      <td>TX</td>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "      Actual  Predicted  year stateabbr locationname  COPD_data_value  \\\n",
       "2997     8.4   8.658487  2020        AR         Drew              8.4   \n",
       "423      5.6   5.955038  2020        TX       Medina              5.6   \n",
       "644      9.5   9.151721  2020        TN       DeKalb              9.5   \n",
       "\n",
       "      totalpopulation  \n",
       "2997            17977  \n",
       "423             52358  \n",
       "644             20837  "
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop everything after 7th column\n",
    "merged_df = merged_df.iloc[:, :7]\n",
    "\n",
    "# View the dataframe\n",
    "merged_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>categoryid</th>\n",
       "      <th>measureid</th>\n",
       "      <th>datavaluetypeid</th>\n",
       "      <th>short_question_text</th>\n",
       "      <th>geolocation</th>\n",
       "      <th>:@computed_region_bxsw_vy29</th>\n",
       "      <th>:@computed_region_he4y_prf8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.4</td>\n",
       "      <td>8.658487</td>\n",
       "      <td>2020</td>\n",
       "      <td>AR</td>\n",
       "      <td>Drew</td>\n",
       "      <td>8.4</td>\n",
       "      <td>17977</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>Chronic obstructive pulmonary disease among ad...</td>\n",
       "      <td>%</td>\n",
       "      <td>Age-adjusted prevalence</td>\n",
       "      <td>7.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>05043</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>COPD</td>\n",
       "      <td>AgeAdjPrv</td>\n",
       "      <td>COPD</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-91.7227781,...</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.6</td>\n",
       "      <td>5.955038</td>\n",
       "      <td>2020</td>\n",
       "      <td>TX</td>\n",
       "      <td>Medina</td>\n",
       "      <td>5.6</td>\n",
       "      <td>52358</td>\n",
       "      <td>Texas</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>Chronic obstructive pulmonary disease among ad...</td>\n",
       "      <td>%</td>\n",
       "      <td>Age-adjusted prevalence</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>48325</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>COPD</td>\n",
       "      <td>AgeAdjPrv</td>\n",
       "      <td>COPD</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-99.1110845,...</td>\n",
       "      <td>23</td>\n",
       "      <td>777</td>\n",
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       "      <th>2</th>\n",
       "      <td>9.5</td>\n",
       "      <td>9.151721</td>\n",
       "      <td>2020</td>\n",
       "      <td>TN</td>\n",
       "      <td>DeKalb</td>\n",
       "      <td>9.5</td>\n",
       "      <td>20837</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>BRFSS</td>\n",
       "      <td>Health Outcomes</td>\n",
       "      <td>Chronic obstructive pulmonary disease among ad...</td>\n",
       "      <td>%</td>\n",
       "      <td>Age-adjusted prevalence</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>47041</td>\n",
       "      <td>HLTHOUT</td>\n",
       "      <td>COPD</td>\n",
       "      <td>AgeAdjPrv</td>\n",
       "      <td>COPD</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-85.8335959,...</td>\n",
       "      <td>39</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted  year stateabbr locationname  COPD_data_value  \\\n",
       "0     8.4   8.658487  2020        AR         Drew              8.4   \n",
       "1     5.6   5.955038  2020        TX       Medina              5.6   \n",
       "2     9.5   9.151721  2020        TN       DeKalb              9.5   \n",
       "\n",
       "   totalpopulation  statedesc datasource         category  \\\n",
       "0            17977   Arkansas      BRFSS  Health Outcomes   \n",
       "1            52358      Texas      BRFSS  Health Outcomes   \n",
       "2            20837  Tennessee      BRFSS  Health Outcomes   \n",
       "\n",
       "                                             measure data_value_unit  \\\n",
       "0  Chronic obstructive pulmonary disease among ad...               %   \n",
       "1  Chronic obstructive pulmonary disease among ad...               %   \n",
       "2  Chronic obstructive pulmonary disease among ad...               %   \n",
       "\n",
       "           data_value_type  low_confidence_limit  high_confidence_limit  \\\n",
       "0  Age-adjusted prevalence                   7.4                    9.5   \n",
       "1  Age-adjusted prevalence                   4.9                    6.2   \n",
       "2  Age-adjusted prevalence                   8.1                   10.8   \n",
       "\n",
       "  locationid categoryid measureid datavaluetypeid short_question_text  \\\n",
       "0      05043    HLTHOUT      COPD       AgeAdjPrv                COPD   \n",
       "1      48325    HLTHOUT      COPD       AgeAdjPrv                COPD   \n",
       "2      47041    HLTHOUT      COPD       AgeAdjPrv                COPD   \n",
       "\n",
       "                                         geolocation  \\\n",
       "0  {'type': 'Point', 'coordinates': [-91.7227781,...   \n",
       "1  {'type': 'Point', 'coordinates': [-99.1110845,...   \n",
       "2  {'type': 'Point', 'coordinates': [-85.8335959,...   \n",
       "\n",
       "  :@computed_region_bxsw_vy29 :@computed_region_he4y_prf8  \n",
       "0                          15                          15  \n",
       "1                          23                         777  \n",
       "2                          39                        2006  "
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge merged_df with copd_df_copy on year, stateabbr, locationname, totalpopulation, and COPD_data_value\n",
    "merged_df = pd.merge(merged_df, COPD_df_copy, on=['year', 'stateabbr', 'locationname', 'totalpopulation', 'COPD_data_value'])\n",
    "\n",
    "# View the dataframe\n",
    "merged_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7476714066947174"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the standard deviation of the COPD_data_value column\n",
    "COPD_df_copy['COPD_data_value'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a row that answers True if the predicted value lies between the high and low confidence_limit values\n",
    "merged_df['within_confidence_limit'] = np.where(\n",
    "    (merged_df['Predicted'] >= merged_df['low_confidence_limit']) & \n",
    "    (merged_df['Predicted'] <= merged_df['high_confidence_limit']), True, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     306\n",
       "False      9\n",
       "Name: within_confidence_limit, dtype: int64"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of True values and False values\n",
    "merged_df['within_confidence_limit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     97.142857\n",
       "False     2.857143\n",
       "Name: within_confidence_limit, dtype: float64"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of predicted values within the confidence limit\n",
    "merged_df['within_confidence_limit'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    315\n",
       "Name: Within 1 Standard Deviation, dtype: int64"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column that answers true if the predicted value is within 1 standard deviation of the actual value\n",
    "merged_df['Within 1 Standard Deviation'] = np.where(\n",
    "    (merged_df['Actual'] >= merged_df['Predicted'] - COPD_df_copy['COPD_data_value'].std()) & \n",
    "    (merged_df['Actual'] <= merged_df['Predicted'] + COPD_df_copy['COPD_data_value'].std()), True, False)\n",
    "\n",
    "# Count the number of true values in the Within 1 Standard Deviation column\n",
    "merged_df['Within 1 Standard Deviation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The standard deviation of the actual data is 1.75.\n",
      "The percentage of predicted values within one standard deviation is True    100.0\n",
      "Name: Within 1 Standard Deviation, dtype: float64%.\n"
     ]
    }
   ],
   "source": [
    "# Use an f string to print the standard deviation of the actual data rounded to 2 decimal places\n",
    "print(f\"The standard deviation of the actual data is {round(COPD_df_copy['COPD_data_value'].std(), 2)}.\")\n",
    "\n",
    "# Use an f string to print the percentage of predicted values within one standard deviation rounded to 2 decimal places\n",
    "print(f\"The percentage of predicted values within one standard deviation is {round(merged_df['Within 1 Standard Deviation'].value_counts(normalize=True)*100, 2)}%.\")"
   ]
  }
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