Capstone 1 Heart Disease.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capstone 1: Heart Disease "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thinkful : Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "April 24, 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By: Corey Coole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset used in this project comes from four different sources.\n",
    "    \n",
    "     1. Cleveland Clinic Foundation \n",
    "     2. Hungarian Institute of Cardiology, Budapest \n",
    "     3. V.A. Medical Center, Long Beach, CA \n",
    "     4. University Hospital, Zurich, Switzerland \n",
    "\n",
    "The raw dataset contains 76 attributes, however all published experiments refer to using a set of 14 chosen from the larger collection. Thise 14 data indicators are age, sex, chest pain type, resting blood pressure, cholesterol level, fasting blood sugar, resting electrocardiographic results maximum heart rate achieved, exercize induced angina(true/false), ST depression induced by exercize related to rest, the slope of the peak exercize ST segment, number of major vessels colored by fluorosopy, thallium test result, heart disease risk value. The attributes were gathered to study and try to predict the presence of heart disease in a patient. The risk value refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. This report does not investigate any further than how such risk values were attributed. \n",
    "   \n",
    "It is unclear however, if the patients were admitted into the hospital's care because of suspected heart disease risk or if the patients come from a more general selection pool. Thusly, these results may not reflect a wide percentage of the population at hand. It is also recognizd that this dataset was curated no later then July 1988, as such this dataset would not reflect the results of present day trends of health and culture with respect to heart disease. \n",
    "\n",
    "With that said, this report addresses three seperate questions relating to the nature and statistical relationship between the distinct locations of the data as well as some of the attributes therein. The questions are as follows:\n",
    "\n",
    "Question 1 - Does there seem to be an average prediction risk value of heart disease shared among the data, or does one location stand out from the rest?\n",
    "\n",
    "Question 2 - Does the data reflect an increased risk for heart disease in older patients?\n",
    "\n",
    "Question 3 - How probable is a patient in the dataset to have a higher than average risk of heart disease if their cholesterol level is above 200 mg/dL?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we are to ascertain any results based upon averages and standard deviations wih respect to the dataset, we would first like to address the normality of the data set. Plotting each location's Age data frame against a sample set in a QQplot will tell us if the collected age instances of each data set tend too much to the older or younger years, as well as highlight the presence of any outliers we may have to deal with going forward. Although each qqplot has a generally linear trend, we note that all data sets in this study contain traces of tails, and inherently outliers with respect to age. Overall the linear trend of each qqplot seem to suggest a high probability of normality, thusly we continue on to address some finer points of interest in this study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # Import necessary libraries\n",
    "import pandas as pd  \n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "    \n",
    "df_clv = pd.read_csv('cleveland.names.csv', sep=',') # Read the four raw .csv file, store into data frames\n",
    "df_clv.replace('?', 0, inplace=True)\n",
    "\n",
    "df_lngb = pd.read_csv('va.names.csv', sep=',')\n",
    "df_lngb.replace('?', 0, inplace=True)\n",
    "\n",
    "df_swtz = pd.read_csv('switz.names.csv', sep=',')\n",
    "df_swtz.replace('?', 0, inplace=True)\n",
    "\n",
    "df_hun = pd.read_csv('hungarian.names.csv', sep=',')\n",
    "df_hun.replace('?', 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def select_columns(data_frame, column_names): # create a function to select specific data column from data frame\n",
    "    new_frame = data_frame.loc[:, column_names]\n",
    "    return new_frame\n",
    "\n",
    "selected_columns = ['Age']\n",
    "\n",
    "clv_ages = select_columns(df_clv, selected_columns)\n",
    "sorted_clv_ages = clv_ages.sort_values(by='Age', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "\n",
    "lngb_ages = select_columns(df_lngb, selected_columns)\n",
    "sorted_lngb_ages = lngb_ages.sort_values(by='Age', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    " \n",
    "norm1 = np.random.normal(0, 1, len(sorted_clv_ages)) # Create sets of random normal data to chart against actual data\n",
    "norm1.sort() \n",
    "norm2 = np.random.normal(0, 1, len(sorted_lngb_ages))\n",
    "norm2.sort()\n",
    "\n",
    "plt.figure(figsize=(10, 5)) # Create QQplot to investigate the normality of age data \n",
    "           \n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(norm1, sorted_clv_ages , \"o\") \n",
    "plt.title('Cleveland Age QQPlot')           \n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(norm2, sorted_lngb_ages , \"o\")  \n",
    "plt.title('Long Beach Va Age QQPlot') \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def select_columns(data_frame, column_names): # create a function to select specific data column from data frame\n",
    "    new_frame = data_frame.loc[:, column_names]\n",
    "    return new_frame\n",
    "\n",
    "selected_columns = ['Age']\n",
    "\n",
    "swtz_ages = select_columns(df_swtz, selected_columns)\n",
    "sorted_swtz_ages = swtz_ages.sort_values(by='Age', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "\n",
    "hun_ages = select_columns(df_hun, selected_columns)\n",
    "sorted_hun_ages = hun_ages.sort_values(by='Age', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "\n",
    "norm3 = np.random.normal(0, 1, len(sorted_swtz_ages)) # Create sets of random normal data to chart against actual data \n",
    "norm3.sort()\n",
    "norm4 = np.random.normal(0, 1, len(sorted_hun_ages))\n",
    "norm4.sort()\n",
    "\n",
    "plt.figure(figsize=(10, 5)) # Create QQplot to investigate the normality of age data \n",
    "           \n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(norm3, sorted_swtz_ages , \"o\")\n",
    "plt.title('Switzerland Age QQPlot') \n",
    "           \n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(norm4, sorted_hun_ages , \"o\") \n",
    "plt.title('Hungary Age QQPlot') \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see the Age data plotted against a rando normal variable, each location's QQplot follows a relatively linear trend. In this way, I am encouraged to go ahead and continue analysis. However there are noticeable outliers present in the tails of the data. We will take care of these values later.\n",
    "\n",
    "I also create histograms based upon the age data to get a sense of the shape and spread of each dataset. The collections do not seem to reflect harsh skewness and there seems to be a fair amount of overlap in each locatino's age data. Though we cannot infer much else until we have done sufficient calculations for interpreting the averages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle \n",
    "\n",
    "plt.figure(figsize=(9,5)) # Create Histogram to investigate the spread and shape of Age data \n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(df_clv[\"clv_age\"], normed=True, color='purple', bins=np.arange(35, 80), alpha=.5) \n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(df_lngb[\"lngb_age\"], normed=True, color='green', bins=np.arange(35, 80), alpha=.5)\n",
    "\n",
    "plt.xlabel('Patient Age')\n",
    "plt.title('Cleveland vs Long Beach VA Age Distribution(Normed)')\n",
    "plt.legend(['Cleveland', 'Long Beach'])\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_swtz[\"swtz_age\"] = pd.read_csv('switz.names.csv', usecols=[0])\n",
    "df_hun[\"hun_age\"] = pd.read_csv('hungarian.names.csv', usecols=[0])\n",
    "\n",
    "plt.figure(figsize=(9,5)) # Create Histogram to investigate the spread and shape of Age data\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(df_swtz[\"swtz_age\"], normed=True, color='red', bins=np.arange(35, 80), alpha=.5) \n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(df_hun[\"hun_age\"], normed=True, color='blue', bins=np.arange(35, 80), alpha=.5)\n",
    "\n",
    "plt.xlabel('Patient Age')\n",
    "plt.title('Switzerland vs Hunagry Age Distribution(Normed)')\n",
    "plt.legend(['Switzerland', 'Hungary'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If each location's risk avergae is to be compared, we must take cre of any outliers that might hold too much influence over the calculations. Using the age data as in the QQplots above, outliers + or - 2 standard deviations of the theoretical mean are taken out of the parent dataset. In application through boxplot analysis, the VA Medical Center,California data and University Hospital, Switzerland were found to have outliers present and thus only these datasets required outlier extraction.\n",
    "\n",
    "Using the Age dat from each location, respective box and whisker plots show that no more outliers with respect to age  remain in the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clv[\"clv_age\"] = pd.read_csv('cleveland.names.csv', usecols=[0]) # Different method to extract column data\n",
    "\n",
    "df_lngb[\"lngb_age\"] = pd.read_csv('va.names.csv', usecols=[0])\n",
    "df_lngb_remove_outliers = df_lngb[((df_lngb.Age - df_lngb.Age.mean()) / df_lngb.Age.std()).abs() < 2] \n",
    "                                                    # Remove outliers from long beach data(+-2 stds of the mean)\n",
    "df_lngb_clean = df_lngb_remove_outliers[\"Age\"]\n",
    "\n",
    "plt.figure(figsize=(6,6)) # Create Box and Whisker plots to check for outliers in the data \n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.boxplot(df_clv[\"clv_age\"])\n",
    "plt.ylabel('Age')\n",
    "plt.title('Cleveland')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.boxplot(df_lngb_clean)\n",
    "plt.ylabel('Age')\n",
    "plt.title('Long Beach VA')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_swtz[\"switz_age\"] = pd.read_csv('switz.names.csv', usecols=[0])\n",
    "df_swtz_remove_outliers = df_swtz[((df_swtz.Age - df_swtz.Age.mean()) / df_swtz.Age.std()).abs() < 2]\n",
    "                                                    # Remove outliers from long beach data(+-2 stds of the mean)\n",
    "df_swtz_clean = df_swtz_remove_outliers[\"Age\"]\n",
    "\n",
    "df_hun[\"hun_age\"] = pd.read_csv('hungarian.names.csv', usecols=[0])\n",
    "                                                    \n",
    "plt.figure(figsize=(6,6)) # Create Box and Whisker plots to check for outliers in the data \n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.boxplot(df_swtz_clean)\n",
    "plt.ylabel('Age')\n",
    "plt.title('Switzerland')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.boxplot(df_hun[\"hun_age\"])\n",
    "plt.ylabel('Age')\n",
    "plt.title('Hungary')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I check also the risk value data of each location for outliers as well. With none found present, I am more encouraged that calculations will result in meaningful means and standard deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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91SPNLHtJmsw4Q3wHDM3eDngAcLfeKpJmlL0kTWacIb6LGYybh8FwxFeBp/VZlDSj7CVpAuMM8d1jKQqRZp29JE1mwYBK8rhRT6yqd+7+cqTZYy9JizPqCOrRI9YVYFNJ47GXpEVYMKCq6uTbsuMkhwLnMvgQ+MfAmVX1t3O2OQp4N4OxeIB3VtUZt+V1pdbYS9LijHOSBEl+jcElWvbauWyMX/4dwHOq6pIkdwYuTvKBqvr8nO0+WlWPmqRoaVrZS9L4xrkf1KuB32BwHbEATwQO29Xzqurqqrqkm/4OcAVw8G2qVppi9pI0mXEuFvuLVXUCsL2qTgceBBw6yYskWQf8PHDRPKsflOQzSd6X5D4LPH9Dkk1JNm3dunWSl5ZaYi9JExgnoL7X/fndJHcHfgiMfbpskjsB7wBOqaob5qy+BDisqu4HvAx413z7qKozq2p9Va1fs2bNuC8ttcZekiYwTkCdn2Q/4K8YNMHXGNwqYJeSrGbQUG+a71Taqrqhqm7spi8AVic5cMzapWljL0kTGPU9qNVV9cOq+vNu0TuSnA/sNc5dQZMEOAu4oqr+ZoFt7gZ8q6oqyZEMAvPaiX8KqWH2krQ4o87i+0aSdzN4h/ehGriZwc3WxvFg4DeBzya5tFv2fGAtQFW9GngC8HtJdjAY/jiuqm51OwJpytlL0iKMCqifYfBL/2fAuUnOA95SVfN9OHsrVfUxBmcqjdrm5cDLx6xVmlb2krQIC34GVVXXVtVrqupXgCMZfAHwpUm+nOQvlqxCacrZS9LijHOSBFV1FYMx8FcB3wF+q8+ipFllL0njGxlQSfZK8sQk7wS+DDwM+BPg7ktRnDQr7CVpcqPO4nszcDTwEeDNwPFV9f2lKkyaFfaStDijTpJ4P/A73aVVJC2evSQtwqirmZ+zlIVIs8pekhZnrJMkJElaaosKqCR77u5CpJXIXpIWNs7tNl43Z/5OwAW9VSTNKHtJmsw4R1DfSPIqgCT7A/8EvLHXqqTZZC9JE9hlQFXVnwE3dDdb+yfgxVX1+t4rk2aMvSRNZtT3oB43NPspBtcR+xRQSR433yX/Jd2avSQtzqjvQT16zvy/Aau75QXYVNJ47CVpEUZ9D+rkpSxEmlX2krQ445zF96Ik+yRZneRfknw7yVOXojhplthL0mTGOYvvv1fVDcCjgC3ATwPP7bUqaTbZS9IExgmo1d2fj2Rwk7VtPdYjzTJ7SZrAqJMkdnpPki8wuI307ydZA3glZmly9pI0gXG+B/U84EHA+qr6IXATcGzfhUmzxl6SJjPqe1APraoPDn+HI8nwJp4aK43BXpIWZ9QQ3y8DH+TW3+EAv7shTcJekhZh1PegTu3+9Dsc0m1gL0mLM/IzqCR7JDlwaP72STYkuaL/0qTZYS9Jk1swoJIcB2wDLktyYZJfAb4CPAJ4yhLVJ009e0lanFGfQf0p8ICq2pzk/sAngOOq6h+WpjRpZthL0iKMGuL7QVVtBqiqS4Cv2lDSothL0iKMOoK6a5JnD83faXi+qv6mv7KkmWIvSYswKqBeC9x5xLyk8dhL0iKMOs389KUsRJpV9pK0OONcLFaSpCVnQEmSmmRASZKaNM4ddX8iyVlJ3tfN3zvJ0/ovTZot9pI0mXGOoM4G3g/cvZv/d+CUvgqSZtjZ2EvS2MYJqAOr6u3AjwGqagfwo109KcmhST6U5Ioklyd51jzbJMnfJdmc5LLuW/bSrLKXpAmMc0fdm5LchcFtAUjyQOD6MZ63A3hOVV2S5M7AxUk+UFWfH9rmEcDh3eMXgFd1f0qzyF6SJjBOQD0b2Aj8VJKPA2uAJ+zqSVV1NXB1N/2d7qrNBwPDTXUscG5VFfDJJPslOah7rjRr7CVpArsMqO5d2y8D9wICfLG7XfXYkqwDfh64aM6qg4Erh+a3dMtu0VRJNgAbANauXTvJS8+8Aw44gO3bt0/0nDl3cx1p//33Z9u2bZOWpXnYS9JkxjmL74nA3lV1OfBY4G2TjG8nuRPwDuCUqrph7up5nlK3WlB1ZlWtr6r1a9asGfelV4Tt27dTVb09Jg0/LcxekiYzzkkSf9YNKzwEeDhwDoPx7V1KsppBQ72pqua7rfUW4NCh+UOAq8bZtzSF7CVpAuME1M6zjH4NeFVVvRu4/a6elME40lnAFSOu1rwROKE7A+mBwPWOmWuG2UvSBMY5SeIbSV4DHA28MMmejBdsDwZ+E/hskku7Zc8H1gJU1auBC4BHApuB7wInT1a+NFXsJWkC4wTUk4BjgL+uquuSHAQ8d1dPqqqPMf+4+PA2BTx9nEKlGWAvSRPY5bu3qvpuN+Z9fZK1wGrgC71XJs0Ye0mazDhn8T0myZeArwIXdn++r+/CpFljL0mTGWf8+8+BBwL/XlX3YDB+/vFeq5Jmk70kTWCcgPphVV0L3C7J7arqQ8ARPdclzSJ7SZrAOCdJXNd9QfCjwJuSXMPg2mCSJmMvSRMY5wjqWAanrZ4C/CPwZeDRfRYlzSh7SZrAONfiuynJYcDhVXVOkjsAe/RfmjRb7CVpMuOcxffbwHnAa7pFBwPv6rMoaRbZS9JkxhniezqDb7LfAFBVXwLu2mdR0oyyl6QJjBNQN1fVD3bOJFnFPFdJlrRL9pI0gXEC6sIkzwf2TvKrwN8D7+m3LGkm2UvSBMYJqOcBW4HPAr/D4KKUf9pnUdKMspekCYxzFt+PgdcCr01yAHBId2FKSROwl6TJjHMW34eT7NM11KXA65MsdE8aSQuwl6TJjDPEt293e+nHAa+vqgcwuIaYpMnYS9IExgmoVd19a54EnN9zPdIss5ekCYwTUGcA7wc2V9Wnk/wk8KV+y5Jmkr0kTWCckyT+nsHpsDvnvwI8vs+ipFlkL0mTWTCgkvxRVb0oycuY58uEVfXMXiuTZoS9JC3OqCOoK7o/Ny1FIdIMs5ekRVgwoKrqPd2f5yxdOdLssZekxRl5kkSSE5NckuSm7rEpyQlLVZw0K+wlaXKjPoM6gcGN1Z4NXAIEuD/wV0moqnOXpkRputlL0uKMOoL6feDXq+pDVXV9VV1XVR9kcNbR7y9NedJMsJekRRgVUPtU1dfmLuyW7dNXQdIMspekRRgVUN9b5DpJt2QvSYsw6jTzn0ly2TzLA/xkT/VIs8hekhZhZEAtWRXSbLOXpEUY9T2ory9lIdKsspekxRnnYrGSJC05A0qS1KRx7qj7rHGWSRrNXpImM84R1InzLDtpN9chrQT2kjSBUZc6ejJwPPCTSTYOrbozcG3fhUmzwl6SFmfUaeb/ClwNHAi8eGj5d4D5vtNxC0leBzwKuKaq7jvP+qOAdwNf7Ra9s6rOGK9saarYS9IijDzNPMkW4KaqunAR+z4beDkw6kKYH62qRy1i39LUsJekxRn5GVRV/Qj4bpJ9J91xVX0E2LbYwqRZYi9Jkxs1xLfT94HPJvkAcNPOhbvpNtUPSvIZ4CrgD6vq8vk2SrIB2ACwdu3a3fCy0rKwl6QJjBNQ7+0eu9slwGFVdWOSRwLvAg6fb8OqOhM4E2D9+vXVQy3SUrCXpAnsMqD6uk11Vd0wNH1BklcmObCqvt3H60nLzV6SJrPLgEpyOPCXwL2BvXYur6rbdBXmJHcDvlVVleRIBp+HecqtZpa9JE1mnCG+1wOnAi8BfgU4mcFtAkZK8hbgKODA7gymU4HVAFX1auAJwO8l2cHgnjjHVZVDDppl9pI0gXECau+q+pck6a7KfFqSjzJokgVV1ZN3sf7lDE6dlVYKe0mawFhn8SW5HfClJM8AvgHctd+ypJlkL0kTGOdafKcAdwCeCTwAeCrzX1NM0mj2kjSBcc7i+zTAYFSiTu6/JGk22UvSZMa53caDknweuKKbv1+SV/ZemTRj7CVpMuMM8b0UeDjdaatV9Rngl/osSppR9pI0gbHuqFtVV85Z9KMeapFmnr0kjW+cs/iuTPKLQCW5PYMPeK/otyxpJtlL0gTGOYL6XeDpwMHAFuCIbl7SZOwlaQLjnMX3beApS1CLNNPsJWkyo275/jJgwcul7KZbBEgzz16SFmfUEdSmoenT2cXlWCQtyF6SFmHULd///60BkpzS160CpFlnL0mLM9Zp5owYnpA0EXtJGtO4ASVJ0pIadZLEd/jPd3t3SLLzrp0Bqqr26bs4aRbYS9LijPoM6s5LWYg0q+wlaXEc4pMkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDWpt4BK8rok1yT53ALrk+TvkmxOclmS+/dVizTN7CWtVH0eQZ0NHDNi/SOAw7vHBuBVPdYiTbOzsZe0AvUWUFX1EWDbiE2OBc6tgU8C+yU5qK96pGllL2mlWrWMr30wcOXQ/JZu2dVzN0yygcE7Q9auXbskxUlTxF7aDerUfeC0ffvbtya2nAGVeZbVfBtW1ZnAmQDr16+fdxtpBbOXdofTrh970yRU+dfXt+U8i28LcOjQ/CHAVctUizTN7CXNpOUMqI3ACd0ZSA8Erq+qWw1JSNole0kzqbchviRvAY4CDkyyBTgVWA1QVa8GLgAeCWwGvguc3Fct0jSzl7RS9RZQVfXkXawv4Ol9vb40K+wlrVReSUKS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KReAyrJMUm+mGRzkufNs/6kJFuTXNo9fqvPeqRpZS9pJVrV146T7AG8AvhVYAvw6SQbq+rzczZ9W1U9o686pGlnL2ml6vMI6khgc1V9pap+ALwVOLbH15Nmlb2kFanPgDoYuHJofku3bK7HJ7ksyXlJDp1vR0k2JNmUZNPWrVv7qFVqmb2kFanPgMo8y2rO/HuAdVX1c8A/A+fMt6OqOrOq1lfV+jVr1uzmMqXm2UtakfoMqC3A8Lu4Q4Crhjeoqmur6uZu9rXAA3qsR5pW9pJWpD4D6tPA4UnukeT2wHHAxuENkhw0NPsY4Ioe65Gmlb2kFam3s/iqakeSZwDvB/YAXldVlyc5A9hUVRuBZyZ5DLAD2Aac1Fc90rSyl7RSpWruUHbb1q9fX5s2bVruMpqRhD7/Dfve/7RJcnFVrV/uOnYHe2nx7IvbZtw+8koSkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJvUaUEmOSfLFJJuTPG+e9XsmeVu3/qIk6/qsR5pW9pJWot4CKskewCuARwD3Bp6c5N5zNnsasL2q7gm8BHhhX/VI08pe0krV5xHUkcDmqvpKVf0AeCtw7JxtjgXO6abPAx6WJD3WJE0je0krUp8BdTBw5dD8lm7ZvNtU1Q7geuAuPdYkTSN7SSvSqh73Pd+7t1rENiTZAGwAWLt27W2vbIbUqfvAafv2u38tN3tpmYw6CF1oXdWt/tq1SH0G1Bbg0KH5Q4CrFthmS5JVwL7Atrk7qqozgTMB1q9f77/+sNOuX+4K1D97aZkYNsurzyG+TwOHJ7lHktsDxwEb52yzETixm34C8MHyN0Kay17SitTbEVRV7UjyDOD9wB7A66rq8iRnAJuqaiNwFvCGJJsZvNs7rq96pGllL2ml6nOIj6q6ALhgzrIXDE1/H3hinzVIs8Be0krklSQkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNyrRdkT/JVuDry13HFDvOWmEbAAAA5ElEQVQQ+PZyFzHFDquqNctdxO5gL90m9tFtM1YfTV1A6bZJsqmq1i93HdI0s4+WhkN8kqQmGVCSpCYZUCvPmctdgDQD7KMl4GdQkqQmeQQlSWqSASVJapIBtUIkeV2Sa5J8brlrkaaVfbS0DKiV42zgmOUuQppyZ2MfLRkDaoWoqo8A25a7Dmma2UdLy4CSJDXJgJIkNcmAkiQ1yYCSJDXJgFohkrwF+ARwryRbkjxtuWuSpo19tLS81JEkqUkeQUmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmvT/AFkdSUE+eCvfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clv[\"clv_hdv\"] = pd.read_csv('cleveland.names.csv', usecols=[13])\n",
    "\n",
    "df_lngb_clean = df_lngb_remove_outliers[\"PredHrtDis\"]\n",
    "\n",
    "plt.figure(figsize=(6,6)) # Create Box and Whisker plot of Heart Disease Risk to check for the presence of outliers\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.boxplot(df_clv[\"clv_hdv\"])\n",
    "plt.ylabel('Heart Disease Risk Value')\n",
    "plt.title('Cleveland')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.boxplot(df_lngb_clean)\n",
    "plt.ylabel('Heart Disease Risk Value')\n",
    "plt.title('Long Beach VA')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_swtz_clean = df_swtz_remove_outliers[\"PredHrtDis\"]\n",
    "\n",
    "df_hun[\"hun_hdv\"] = pd.read_csv('hungarian.names.csv', usecols=[13])\n",
    "\n",
    "plt.figure(figsize=(6,6)) # Create Box and Whisker plot of Heart Disease Risk to check for the presence of outliers\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.boxplot(df_swtz_clean)\n",
    "plt.ylabel('Heart Disease Risk Value')\n",
    "plt.title('Switzerland')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.boxplot(df_hun[\"hun_hdv\"])\n",
    "plt.ylabel('Heart Disease Risk Value')\n",
    "plt.title('Hungary')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_clv_hd_values = df_clv[\"clv_hdv\"].sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "\n",
    "sorted_lngb_hd_values = df_lngb_clean.sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "                                                            # Sort Heart Disease Values numerically with quicksort \n",
    "norm1 = np.random.normal(0, 1, len(sorted_clv_hd_values)) # Create random normal set to chart against HD data\n",
    "norm1.sort()\n",
    "norm2 = np.random.normal(0, 1, len(df_lngb_clean))\n",
    "norm2.sort()\n",
    "\n",
    "plt.figure(figsize=(10, 5)) # Create QQplot to investigate the normality of Heart Disease data \n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(norm1, sorted_clv_hd_values, \"o\")\n",
    "plt.ylabel('Heart Disease Risk Value')\n",
    "plt.title('Cleveland')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(norm2, sorted_lngb_hd_values, \"o\")\n",
    "plt.ylabel('Heart Disease Risk Value')\n",
    "plt.title('California')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_swtz_hd_values = df_swtz_clean.sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "\n",
    "sorted_hun_hd_values = df_hun[\"PredHrtDis\"].sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "                                                            # Sort Heart Disease Values numerically with quicksort\n",
    "norm3 = np.random.normal(0, 1, len(df_swtz_clean)) # Create random normal set to chart against HD data \n",
    "norm3.sort()\n",
    "norm4 = np.random.normal(0, 1, len(sorted_hun_hd_values))\n",
    "norm4.sort()\n",
    "\n",
    "plt.figure(figsize=(10, 5)) # Create QQplot to investigate the normality of Heart Disease data \n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(norm3, sorted_swtz_hd_values, \"o\",)\n",
    "plt.ylabel('Heart Disease Risk Value')\n",
    "plt.title('Switzerland')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(norm4, sorted_hun_hd_values, \"o\",)\n",
    "plt.ylabel('Heart Disease Risk Value')\n",
    "plt.title('Hungary')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a simple function to return the means and standard deviations of a risk value data frame with respect to each location, we already see the Hungarian Institute with an average risk value and standard deviation set itself far apart from the other three locations. While the Cleveland dataset's average is also well below the remaining contenders, its standard deviation follows inline with California and Switzedland. \n",
    "Thus far, we have an idea from our analysis that these calculations speak to the true results of each dataset, however I believe a T-test is necessary to investigate further. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleveland mean and standard deviation:\n",
      "mean = 0.9372937293729373\n",
      "std = 1.2285356879701044\n",
      "\n",
      "Long Beach VA mean and standard deviation:\n",
      "mean = 1.5591397849462365\n",
      "std = 1.2032601263674862\n",
      "\n",
      "Switzerland mean and standard deviation:\n",
      "mean = 1.8220338983050848\n",
      "std = 1.018053748452664\n",
      "\n",
      "Hungary mean and standard deviation:\n",
      "mean = 0.36054421768707484\n",
      "std = 0.4809770804966045\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def mean_and_std(location,df): # Create function to return mean and standard deviation of given data\n",
    "    return (location + \" mean and standard deviation:\\nmean = {}\\nstd = {}\\n\".format(np.mean(df),df.std()))\n",
    "\n",
    "print(mean_and_std(\"Cleveland\",df_clv[\"PredHrtDis\"]))\n",
    "print(mean_and_std(\"Long Beach VA\",df_lngb_clean))\n",
    "print(mean_and_std(\"Switzerland\",df_swtz_clean))\n",
    "print(mean_and_std(\"Hungary\",df_hun[\"PredHrtDis\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating another function using the t-test module from the SciPy library, each locations risk value data frame are compared to every other location. We remember a t-statistic refects how many standard errors worth of space separates the compared means. And that a small p-value reflects a specific sample to be more likely a meaningful description of its respective population, and not a characteristic of variablilty in the sample collection.\n",
    "\n",
    "As such, the T-test results return overall values that trend in small p-values and large t-statistics. From these calculations I infer that the means and standard devaitons that were calculated previously speak to the true true measures of each location's dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleveland vs Long beach VA:\n",
      "Ttest_indResult(statistic=-5.5038714016807715, pvalue=6.670879099259733e-08)\n",
      "\n",
      "Cleveland vs Switzerland:\n",
      "Ttest_indResult(statistic=-7.541108281440142, pvalue=8.145666912713882e-13)\n",
      "\n",
      "Cleveland vs Hungary:\n",
      "Ttest_indResult(statistic=7.594033970762158, pvalue=2.265481917609552e-13)\n",
      "\n",
      "Long beach VA vs Switzerland:\n",
      "Ttest_indResult(statistic=-2.042461010769659, pvalue=0.04204871155403733)\n",
      "\n",
      "Long beach VA vs Hungary:\n",
      "Ttest_indResult(statistic=12.946691381955537, pvalue=5.867544146933868e-29)\n",
      "\n",
      "Switzerland vs Hungary:\n",
      "Ttest_indResult(statistic=14.939470844555224, pvalue=1.1675453409399648e-30)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind # import scipy library for t-test \n",
    "\n",
    "def t_test(df_1, df_2): # Create function to run two data sets and return test statistic and p-value \n",
    "        return ttest_ind(df_1, df_2, equal_var=False) \n",
    "\n",
    "print(\"Cleveland vs Long beach VA:\\n{}\\n\".format(t_test(df_clv[\"PredHrtDis\"], df_lngb_clean)))\n",
    "print(\"Cleveland vs Switzerland:\\n{}\\n\".format(t_test(df_clv[\"PredHrtDis\"], df_swtz_clean)))\n",
    "print(\"Cleveland vs Hungary:\\n{}\\n\".format(t_test(df_clv[\"PredHrtDis\"], df_hun[\"PredHrtDis\"])))\n",
    "print(\"Long beach VA vs Switzerland:\\n{}\\n\".format(t_test(df_lngb_clean, df_swtz_clean)))\n",
    "print(\"Long beach VA vs Hungary:\\n{}\\n\".format(t_test(df_lngb_clean, df_hun[\"PredHrtDis\"])))\n",
    "print(\"Switzerland vs Hungary:\\n{}\\n\".format(t_test(df_swtz_clean, df_hun[\"PredHrtDis\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With outliers removed and in consideration of the T-test relults, I conclude that all datasets reflect meaningful averages and standard deviations, and further the calculations for the Hungarian Institute of Cardiology reflect divergent result from the remaining location's. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To image the relationship between a location's age data and heart disease risk data, I create a simple scatter plot  from age and risk data frames of each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4)) # Create Scatter plot to investigate relationship between Age and Heart Disease data\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x=df_clv['Age'], y=df_clv['PredHrtDis'])\n",
    "plt.title('Cleveland: Age vs Heart Disease Risk')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Risk Value')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(x=df_lngb['Age'], y=df_lngb['PredHrtDis'])\n",
    "plt.title('Long Beach VA: Age vs Heart Disease Risk')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Risk Value')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4)) # Create Scatter plot to investigate relationship between Age and Heart Disease data\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x=df_swtz['Age'], y=df_swtz['PredHrtDis'])\n",
    "plt.title('Switzerland: Age vs Heart Disease Risk')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Risk Value')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(x=df_hun['Age'], y=df_hun['PredHrtDis'])\n",
    "plt.title('Hungary: Age vs Heart Disease Risk')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Risk Value')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These scatter plots show us the predicted heart disease risk vs age. If age of was a standout indicator for predicting heart disease we would notice a definite trend in the data points on the plot. We see a slight tendency in the data of the Long Beach VA dataset, as there is a higher concentration of data points in older ages. However, in the majority of instances, the spread of risk values seem to be fairly evenly distributed across the years. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the probability a patient with cholesterol levels greater than 200 mg/dL would be assigned a risk value greater than the location average, I construct a simple result of data frame counts with respect to cholesterol level and the amount of those counts that come from patients with greater than average risk levels. \n",
    "\n",
    "The Switzerland dataset could not be used in this calculation since it did ot include patient cholesterol level data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleveland average heart disease risk = 0.9372937293729373\n",
      "\n",
      "Total number of patients with levels over 200 mg/dL:\n",
      "Chol          253\n",
      "PredHrtDis    253\n",
      "dtype: int64\n",
      "\n",
      "Total number of patients with chol. levels over 200 and given a Heart Disease value over the group average:\n",
      "Chol          70\n",
      "PredHrtDis    70\n",
      "dtype: int64\n",
      "\n",
      "Probability of a greater than average risk Heart Diease:\n",
      "Chol          0.27668\n",
      "PredHrtDis    0.27668\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "clv_chol_v_hrt_dis = df_clv[['Chol','PredHrtDis']] # Create parsed data frame with only Cholesterol and HD data\n",
    "\n",
    "clv_hrt_dis = df_clv['PredHrtDis']\n",
    "print(\"Cleveland average heart disease risk = {}\\n\".format(np.mean(clv_hrt_dis))) # Calculate mean HD risk\n",
    "\n",
    "clv_hdv_grt_avrg = clv_chol_v_hrt_dis[clv_chol_v_hrt_dis.PredHrtDis > 1]\n",
    "\n",
    "clv_chol_grt200 = clv_chol_v_hrt_dis[clv_chol_v_hrt_dis.Chol > 200] # Count patients with >200 chol. levels \n",
    "\n",
    "print(\"Total number of patients with levels over 200 mg/dL:\\n{}\\n\".format(clv_chol_grt200.count()))\n",
    "\n",
    "print(\"Total number of patients with chol. levels over 200 and given a Heart Disease value over the group average:\\n{}\".format(clv_chol_grt200[clv_chol_grt200.PredHrtDis > 1.0].count()))\n",
    "                            # Count the amount of >200 patients that also have greater than average HD risk \n",
    "\n",
    "prob_risk = clv_chol_grt200[clv_chol_grt200.PredHrtDis > 1.0].count() / clv_chol_grt200.count()\n",
    "                            # Calculate the percentage of >200 chol. with >avg Heart Disease risk\n",
    "\n",
    "print(\"\\nProbability of a greater than average risk Heart Diease:\\n{}\".format(prob_risk))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Long Beach VA average heart disease risk = 1.5591397849462365\n",
      "\n",
      "Total number of patients with levels over 200 mg/dL:\n",
      "Chol          117\n",
      "PredHrtDis    117\n",
      "dtype: int64\n",
      "\n",
      "Total number of patients with chol. levels over 200 and given a Heart Disease value over the group average:\n",
      "Chol          35\n",
      "PredHrtDis    35\n",
      "dtype: int64\n",
      "\n",
      "Probability of a greater than average risk Heart Diease:\n",
      "Chol          0.299145\n",
      "PredHrtDis    0.299145\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_lngb = pd.read_csv('cleaned.va.csv', sep=',')\n",
    "\n",
    "lngb_chol_v_hrt_dis = df_lngb[['Chol','PredHrtDis']] # Create parsed data frame with only Cholesterol and HD data\n",
    "\n",
    "lngb_hrt_dis = df_lngb_clean\n",
    "print(\"Long Beach VA average heart disease risk = {}\\n\".format(np.mean(lngb_hrt_dis))) # Calculate mean HD risk\n",
    "\n",
    "lngb_chol_grt200 = lngb_chol_v_hrt_dis[lngb_chol_v_hrt_dis.Chol > 200] # Count patients with >200 chol. levels\n",
    "\n",
    "print(\"Total number of patients with levels over 200 mg/dL:\\n{}\\n\".format(lngb_chol_grt200.count()))\n",
    "\n",
    "print(\"Total number of patients with chol. levels over 200 and given a Heart Disease value over the group average:\\n{}\".format(lngb_chol_grt200[lngb_chol_grt200.PredHrtDis > 2.0].count()))\n",
    "                            # Count the amount of >200 patients that also have greater than average HD risk \n",
    "\n",
    "prob_risk = lngb_chol_grt200[lngb_chol_grt200.PredHrtDis > 2.0].count() / lngb_chol_grt200.count()\n",
    "                            # Calculate the percentage of >200 chol. with >avg Heart Disease risk\n",
    "print(\"\\nProbability of a greater than average risk Heart Diease:\\n{}\".format(prob_risk))\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hungarian average heart disease risk = 0.36054421768707484\n",
      "\n",
      "Total number of patients with levels over 200 mg/dL:\n",
      "Chol          216\n",
      "PredHrtDis    216\n",
      "dtype: int64\n",
      "\n",
      "Total number of patients with chol. levels over 200 and given a Heart Disease value over the group average:\n",
      "Chol          88\n",
      "PredHrtDis    88\n",
      "dtype: int64\n",
      "\n",
      "Probability of a greater than average risk Heart Diease:\n",
      "Chol          0.407407\n",
      "PredHrtDis    0.407407\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_hun = pd.read_csv('cleaned.hungarian.csv', sep=',')\n",
    "\n",
    "hun_chol_v_hrt_dis = df_hun[['Chol','PredHrtDis']] # Create parsed data frame with only Cholesterol and HD data\n",
    "\n",
    "hun_hrt_dis = df_hun['PredHrtDis']\n",
    "print(\"Hungarian average heart disease risk = {}\\n\".format(np.mean(hun_hrt_dis))) # Calculate mean HD risk\n",
    "\n",
    "hun_chol_grt200 = hun_chol_v_hrt_dis[hun_chol_v_hrt_dis.Chol > 200] # Count patients with >200 chol. levels\n",
    "\n",
    "print(\"Total number of patients with levels over 200 mg/dL:\\n{}\\n\".format(hun_chol_grt200.count()))\n",
    "\n",
    "print(\"Total number of patients with chol. levels over 200 and given a Heart Disease value over the group average:\\n{}\".format(hun_chol_grt200[hun_chol_grt200.PredHrtDis > 0].count()))\n",
    "                            # Count the amount of >200 patients that also have greater than average HD risk \n",
    "\n",
    "prob_risk = hun_chol_grt200[hun_chol_grt200.PredHrtDis > 0.0].count() / hun_chol_grt200.count()\n",
    "                            # Calculate the percentage of >200 chol. with >avg Heart Disease risk\n",
    "    \n",
    "print(\"\\nProbability of a greater than average risk Heart Diease:\\n{}\".format(prob_risk))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these results we see that the patients from the California VA at .277 and Cleveland Clinic .299 were similar in probability that a patient with a cholesterol level of 200 mg/dL would have a higher than average assigned risk value. And further we see patients from the Hungarian Instititute with a much higher probability at .407. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout investigation of each location's data, small differences in results have combined to form a broader image of the true nature of each dataset. Suprisingly regardless of age, we see similar distributions of Heart Disease risk shared amongst the patient collection. Markedly, the Hungarian Institute's results diverge from the remaining locations. An explanation may be that when the heart disease risk values were assigned, they may of been given on a binary scale, as such a 0 value for no suspected risk and a 1 value for a suspected risk, regardless of severity of such a risk. This would also explain the higher probablity of a risk value over the mean risk if a patient has a cholesterol level over 200 in Hungarian patients. \n",
    "\n",
    "Further or more rigous research might include an explanation of the patient selection process. As the locations and institutes vary so greatly, surley this information would benefit from more context. I would also like to see a study conducted present day, a contemporary investigation may include more scientifically advanced indictors and more meaningful attributes.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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