Introductory statistical data analysis with pandas PyData Berlin 2017.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "I recommend installing Anaconda for ease:\n",
    "https://www.continuum.io/downloads\n",
    "certain packages i.e. seaborn, do not come with the standard installation. A simple search for\n",
    "how to 'install seaborn with anaconda' will easily find the installation instructions.\n",
    "i.e. in this case:\n",
    "http://seaborn.pydata.org/installing.html\n",
    "in terminal (mac):\n",
    "conda install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Alexandru Agachi\n",
    "m.a.agachi@gmail.com  \n",
    "Background: pursued degrees and graduate and postgraduate diplomas in international relations, energy science,\n",
    "surgical robotics, neuro anatomy and imagery, and biomedical innovation\n",
    "Working with a data driven firm in London that operates on financial markets, and teaching a big data module in the\n",
    "biomedical innovation degree at Pierre et Marie Curie University in Paris."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Why"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Why statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Why focus on data\n",
    "> Better to be in an expanding world and not quite in exactly the right field, than to be in a contracting world \n",
    "> where people's worst behavior comes out.  \n",
    "Eric Weinstein, Fellow University of Oxford, MD Thiel Capital."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Why healthcare\n",
    "Ripe for data analysis\n",
    "30% of world's data\n",
    "> The biomedical sciences have been the pillar of the health care system for a long time now. The new system will have \n",
    "> two equal pillars — the biomedical sciences and the data sciences.  \n",
    "Dr Scott Zeger, Johns Hopkins University."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Roadmap - Blueprint for statistical data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#import the packages we will use\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#use simplest tool available\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.stats.multicomp as multi\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "import sklearn.metrics\n",
    "from sklearn import datasets\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#from sklearn import preprocessing\n",
    "#from sklearn.linear_model import LassoLarsCV\n",
    "#from sklearn.cluster import KMeans\n",
    "%matplotlib inline\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "\n",
    "#Whenever you are not sure of a function or parameter used, please verify documentation for that function at:\n",
    "#http://pandas.pydata.org/pandas-docs/stable/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "All credit for the data, and our many thanks, go to the principal investigators who collected this data \n",
    "and made it available:\n",
    "\n",
    "1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.\n",
    "2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.\n",
    "3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.\n",
    "4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Online repositories for data used and for this notebook:\n",
    "\n",
    "http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/\n",
    "https://github.com/AlexandruAgachi/introductory_statistics_tutorial/blob/master/Introductory%20statistical%20data%20analysis%20with%20pandas%20PyData%20Berlin%202017.ipynb\n",
    "\n",
    "here specify your own directory for where you stored the datasets on your computer:\n",
    "\n",
    "For me: cd Downloads/Heart disease dataset\n",
    "\n",
    "note: when downloading the datasets, some people had to rename them manually with a 'txt' ending\n",
    "the four data files that interest us should be named:\n",
    "\n",
    "processed.cleveland.data.txt\n",
    "processed.hungarian.data.txt\n",
    "processed.switzerland.data.txt\n",
    "processed.va.data.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Data management is an integral part of your research process  \n",
    "Choices you make here will influence your entire study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#let's load the first dataset in pandas with the read_csv function. This will create pandas dataframe objects per below.\n",
    "cleveland = pd.read_csv('processed.cleveland.data.txt', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
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  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "switzerland = pd.read_csv('processed.switzerland.data.txt', header=None)"
   ]
  },
  {
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   "execution_count": 28,
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    "collapsed": false
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "va = pd.read_csv('processed.va.data.txt', header=None)"
   ]
  },
  {
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   "execution_count": 31,
   "metadata": {
    "collapsed": false
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  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.concat([cleveland, hungary, va, switzerland])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
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    "collapsed": false
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140</td>\n",
       "      <td>268</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>54.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150</td>\n",
       "      <td>283</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120</td>\n",
       "      <td>284</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>132</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130</td>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120</td>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>110</td>\n",
       "      <td>167</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>-.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>-.5</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>-.9</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>74.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4  5  6    7  8    9  10   11   12  13\n",
       "0    63.0  1.0  1.0  145  233  1  2  150  0  2.3  3  0.0  6.0   0\n",
       "1    67.0  1.0  4.0  160  286  0  2  108  1  1.5  2  3.0  3.0   2\n",
       "2    67.0  1.0  4.0  120  229  0  2  129  1  2.6  2  2.0  7.0   1\n",
       "3    37.0  1.0  3.0  130  250  0  0  187  0  3.5  3  0.0  3.0   0\n",
       "4    41.0  0.0  2.0  130  204  0  2  172  0  1.4  1  0.0  3.0   0\n",
       "5    56.0  1.0  2.0  120  236  0  0  178  0  0.8  1  0.0  3.0   0\n",
       "6    62.0  0.0  4.0  140  268  0  2  160  0  3.6  3  2.0  3.0   3\n",
       "7    57.0  0.0  4.0  120  354  0  0  163  1  0.6  1  0.0  3.0   0\n",
       "8    63.0  1.0  4.0  130  254  0  2  147  0  1.4  2  1.0  7.0   2\n",
       "9    53.0  1.0  4.0  140  203  1  2  155  1  3.1  3  0.0  7.0   1\n",
       "10   57.0  1.0  4.0  140  192  0  0  148  0  0.4  2  0.0  6.0   0\n",
       "11   56.0  0.0  2.0  140  294  0  2  153  0  1.3  2  0.0  3.0   0\n",
       "12   56.0  1.0  3.0  130  256  1  2  142  1  0.6  2  1.0  6.0   2\n",
       "13   44.0  1.0  2.0  120  263  0  0  173  0    0  1  0.0  7.0   0\n",
       "14   52.0  1.0  3.0  172  199  1  0  162  0  0.5  1  0.0  7.0   0\n",
       "15   57.0  1.0  3.0  150  168  0  0  174  0  1.6  1  0.0  3.0   0\n",
       "16   48.0  1.0  2.0  110  229  0  0  168  0    1  3  0.0  7.0   1\n",
       "17   54.0  1.0  4.0  140  239  0  0  160  0  1.2  1  0.0  3.0   0\n",
       "18   48.0  0.0  3.0  130  275  0  0  139  0  0.2  1  0.0  3.0   0\n",
       "19   49.0  1.0  2.0  130  266  0  0  171  0  0.6  1  0.0  3.0   0\n",
       "20   64.0  1.0  1.0  110  211  0  2  144  1  1.8  2  0.0  3.0   0\n",
       "21   58.0  0.0  1.0  150  283  1  2  162  0    1  1  0.0  3.0   0\n",
       "22   58.0  1.0  2.0  120  284  0  2  160  0  1.8  2  0.0  3.0   1\n",
       "23   58.0  1.0  3.0  132  224  0  2  173  0  3.2  1  2.0  7.0   3\n",
       "24   60.0  1.0  4.0  130  206  0  2  132  1  2.4  2  2.0  7.0   4\n",
       "25   50.0  0.0  3.0  120  219  0  0  158  0  1.6  2  0.0  3.0   0\n",
       "26   58.0  0.0  3.0  120  340  0  0  172  0    0  1  0.0  3.0   0\n",
       "27   66.0  0.0  1.0  150  226  0  0  114  0  2.6  3  0.0  3.0   0\n",
       "28   43.0  1.0  4.0  150  247  0  0  171  0  1.5  1  0.0  3.0   0\n",
       "29   40.0  1.0  4.0  110  167  0  2  114  1    2  2  0.0  7.0   3\n",
       "..    ...  ...  ...  ...  ... .. ..  ... ..  ... ..  ...  ...  ..\n",
       "93   62.0  0.0  4.0  120    0  ?  1  123  1  1.7  3    ?    ?   1\n",
       "94   62.0  1.0  1.0  120    0  ?  2  134  0  -.8  2    2    ?   1\n",
       "95   62.0  1.0  3.0  160    0  0  0   72  1    0  2    ?    3   3\n",
       "96   62.0  1.0  4.0  115    0  ?  0  128  1  2.5  3    ?    ?   2\n",
       "97   62.0  1.0  4.0  115    0  ?  0   72  1  -.5  2    ?    3   1\n",
       "98   62.0  1.0  4.0  150    0  ?  1   78  0    2  2    ?    7   3\n",
       "99   63.0  1.0  4.0  100    0  ?  0  109  0  -.9  2    ?    ?   1\n",
       "100  63.0  1.0  4.0  140    0  ?  2  149  0    2  1    ?    ?   2\n",
       "101  63.0  1.0  4.0  150    0  0  0   86  1    2  2    ?    ?   3\n",
       "102  63.0  1.0  4.0  150    0  ?  1  154  0  3.7  1    ?    ?   3\n",
       "103  63.0  1.0  4.0  185    0  0  0   98  1    0  1    ?    7   1\n",
       "104  64.0  0.0  4.0  200    0  0  0  140  1    1  2    ?    3   3\n",
       "105  64.0  0.0  4.0   95    0  ?  0  145  0  1.1  3    ?    ?   1\n",
       "106  64.0  1.0  4.0  110    0  ?  0  114  1  1.3  3    ?    ?   1\n",
       "107  65.0  1.0  4.0  115    0  0  0   93  1    0  2    ?    7   1\n",
       "108  65.0  1.0  4.0  145    0  ?  1   67  0    ?  ?    ?    6   3\n",
       "109  65.0  1.0  4.0  155    0  ?  0  154  0    1  1    ?    ?   0\n",
       "110  65.0  1.0  4.0  160    0  1  1  122  0    ?  ?    ?    7   3\n",
       "111  66.0  0.0  4.0  155    0  ?  0   90  0    0  ?    ?    7   1\n",
       "112  66.0  1.0  4.0  150    0  0  0  108  1    2  2    ?    7   3\n",
       "113  67.0  1.0  1.0  145    0  0  2  125  0    0  2    ?    3   2\n",
       "114  68.0  1.0  4.0  135    0  0  1  120  1    0  1    ?    7   3\n",
       "115  68.0  1.0  4.0  145    0  ?  0  136  0  1.8  1    ?    ?   1\n",
       "116  69.0  1.0  4.0  135    0  0  0  130  0    0  2    ?    6   1\n",
       "117  69.0  1.0  4.0    ?    0  0  1    ?  ?    ?  ?    ?    7   3\n",
       "118  70.0  1.0  4.0  115    0  0  1   92  1    0  2    ?    7   1\n",
       "119  70.0  1.0  4.0  140    0  1  0  157  1    2  2    ?    7   3\n",
       "120  72.0  1.0  3.0  160    0  ?  2  114  0  1.6  2    2    ?   0\n",
       "121  73.0  0.0  3.0  160    0  0  1  121  0    0  1    ?    3   1\n",
       "122  74.0  1.0  2.0  145    0  ?  1  123  0  1.3  1    ?    ?   1\n",
       "\n",
       "[920 rows x 14 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#we rename all columns to make them more legible\n",
    "df.columns = ['age', 'sex', 'chest_pain', 'rest_bp', 'cholesterol', 'fasting_bs', 'rest_ecg', 'max_heart_rate', 'exercise_angina', 'st_depression', 'slope', 'fluoroscopy',\n",
    "             'defect', 'diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest_pain</th>\n",
       "      <th>rest_bp</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting_bs</th>\n",
       "      <th>rest_ecg</th>\n",
       "      <th>max_heart_rate</th>\n",
       "      <th>exercise_angina</th>\n",
       "      <th>st_depression</th>\n",
       "      <th>slope</th>\n",
       "      <th>fluoroscopy</th>\n",
       "      <th>defect</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>203.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>155.0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex chest_pain  rest_bp  cholesterol fasting_bs rest_ecg  \\\n",
       "0  63.0  1.0        1.0    145.0        233.0        1.0      2.0   \n",
       "1  67.0  1.0        4.0    160.0        286.0        0.0      2.0   \n",
       "2  67.0  1.0        4.0    120.0        229.0        0.0      2.0   \n",
       "3  37.0  1.0        3.0    130.0        250.0        0.0      0.0   \n",
       "4  41.0  0.0        2.0    130.0        204.0        0.0      2.0   \n",
       "5  56.0  1.0        2.0    120.0        236.0        0.0      0.0   \n",
       "6  62.0  0.0        4.0    140.0        268.0        0.0      2.0   \n",
       "7  57.0  0.0        4.0    120.0        354.0        0.0      0.0   \n",
       "8  63.0  1.0        4.0    130.0        254.0        0.0      2.0   \n",
       "9  53.0  1.0        4.0    140.0        203.0        1.0      2.0   \n",
       "\n",
       "   max_heart_rate exercise_angina  st_depression slope fluoroscopy defect  \\\n",
       "0           150.0             0.0            2.3   3.0         0.0    6.0   \n",
       "1           108.0             1.0            1.5   2.0         3.0    3.0   \n",
       "2           129.0             1.0            2.6   2.0         2.0    7.0   \n",
       "3           187.0             0.0            3.5   3.0         0.0    3.0   \n",
       "4           172.0             0.0            1.4   1.0         0.0    3.0   \n",
       "5           178.0             0.0            0.8   1.0         0.0    3.0   \n",
       "6           160.0             0.0            3.6   3.0         2.0    3.0   \n",
       "7           163.0             1.0            0.6   1.0         0.0    3.0   \n",
       "8           147.0             0.0            1.4   2.0         1.0    7.0   \n",
       "9           155.0             1.0            3.1   3.0         0.0    7.0   \n",
       "\n",
       "  diagnosis  \n",
       "0         0  \n",
       "1         2  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "6         3  \n",
       "7         0  \n",
       "8         2  \n",
       "9         1  "
      ]
     },
     "execution_count": 603,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's look at our dataframe. the head(10) option tells pandas to return only the first ten rows of our dataframe.\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# What sort of study was this?\n",
    "\n",
    "A. Observational or experimental\n",
    "\n",
    "Observational and experimental data\n",
    "\n",
    "to know if your data is obersevational or experimental, you ask if the explanatory variable was manipulated or not\n",
    "\n",
    "In a true experiment, 3 conditions:\n",
    "1. only one variable is manipulated\n",
    "2. we have a control group\n",
    "3. random* assignment (analysis stage in randomized trials: check clss imbalances, and if any found consider incl. \n",
    "in model as explanatory variables, for statistical control). In theory, in this case one could determine causality.\n",
    "\n",
    "Quasi experiment:\n",
    "1. only one variable is manipulated\n",
    "2. control group\n",
    "3. no random assignment; groups pre selected. i.e. drug users study.\n",
    "To improve a quasi experimental design: add confounding variables; have a control group; use a pre-test/post-test \n",
    "design confounder=control variable=covariate=third variable=lurking variable\n",
    "\n",
    "in an observational study the regression line only describes data you see. it cannot be used to predict result of \n",
    "intervention\n",
    "\n",
    "\n",
    "*randomization works best as your sample size approaches infinity. for small sizes, imbalances in the groups\n",
    "can occur.\n",
    "\n",
    "B. Longitudinal or cross sectional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Then data analysis? Wrong!\n",
    "\n",
    "Background research. Always starts with background research. Domain knowledge.\n",
    "\n",
    "We quickly find three studies using this dataset:\n",
    "\n",
    "Detrano et al.: http://www.ajconline.org/article/0002-9149(89)90524-9/pdf\n",
    "Sundaram and Kakade: http://www2.rmcil.edu/dataanalytics/v2015/papers/Clinical_Decision_Support_For_Heart_Disease.pdf\n",
    "Soni et al.: http://www.enggjournals.com/ijcse/doc/IJCSE11-03-06-120.pdf\n",
    "\n",
    "Value the time of domain experts.\n",
    "Dataset we'll focus on today. look at txt file AND at explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "First stage in a study is always exploratory data analysis\n",
    "We typically receive the data without context, in a raw file that we cannot easily interpret\n",
    "The five steps of exploratory data analysis\n",
    "1. ?\n",
    "2. organizing and summarizing the data\n",
    "3. Looking for important features and patterns\n",
    "4. Looking for exceptions\n",
    "5. Interpreting these findings in the context of the research question at hand\n",
    "\n",
    "This can be summarized in a notebook or even an initial data report for everyone involved in the data project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "920\n"
     ]
    }
   ],
   "source": [
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "print(len(df.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "number of observations to variables largely exceeds heuristic rule of 5 to 1  \n",
    "see Victoria Stodden (2006) for a discussion of why this is important  \n",
    "https://web.stanford.edu/~vcs/thesis.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "convert all variables to numeric ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#mark all variables as numeric data, and signify, for the relevant ones, that they are categorical rather than \n",
    "#quantitative variables\n",
    "#errors='coerce' tells pandas to return invalid values as NaN rather than as the input values themselves\n",
    "#crucial to do this step otherwise subsequent analyses will not work properly. i.e. pandas would interpret missing \n",
    "#values as strings\n",
    "df['age'] = pd.to_numeric(df['age'], errors='coerce')\n",
    "df['sex'] = pd.to_numeric(df['sex'], errors='coerce').astype('category')\n",
    "df['chest_pain'] = pd.to_numeric(df['chest_pain'], errors='coerce').astype('category')\n",
    "df['rest_bp'] = pd.to_numeric(df['rest_bp'], errors='coerce')\n",
    "df['cholesterol'] = pd.to_numeric(df['cholesterol'], errors='coerce')\n",
    "df['fasting_bs'] = pd.to_numeric(df['fasting_bs'], errors='coerce').astype('category')\n",
    "df['rest_ecg'] = pd.to_numeric(df['rest_ecg'], errors='coerce').astype('category')\n",
    "df['max_heart_rate'] = pd.to_numeric(df['max_heart_rate'], errors='coerce')\n",
    "df['exercise_angina'] = pd.to_numeric(df['exercise_angina'], errors='coerce').astype('category')\n",
    "df['st_depression'] = pd.to_numeric(df['st_depression'], errors='coerce')\n",
    "df['slope'] = pd.to_numeric(df['slope'], errors='coerce').astype('category')\n",
    "df['fluoroscopy'] = pd.to_numeric(df['fluoroscopy'], errors='coerce').astype('category')\n",
    "df['defect'] = pd.to_numeric(df['defect'], errors='coerce').astype('category')\n",
    "df['diagnosis'] = pd.to_numeric(df['diagnosis'], errors='coerce').astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# let's check for missing/outlier values\n",
    "\n",
    "Why bother?\n",
    "Nearly all statistical methods assume complete information. In the presence of missing data:\n",
    "1. parameter estimates may be biased*\n",
    "2. statistical power weakens\n",
    "3. precision of confidence intervals is diminished\n",
    "\n",
    "Three types of missing variables:\n",
    "1. Missing completely at random (MCAR): p(missing data on Y) independent of p(Y value) or p(other variables values)\n",
    "but: p(missing data on Y) may be linked to p(missing data on other variables in dataset))\n",
    "2. Missing at random (MAR): p(missing data on Y) independent of value of Y after controlling for other variables\n",
    "3. Not missing at random (NMAR)\n",
    "\n",
    "If MAR, missing data is ignorable and there is no need to model missing data mechanism\n",
    "if NMAR, missing data mechanism is not ignorable and one must develop v good understanding of missing data process to model it\n",
    "\n",
    "Standard options:\n",
    "1. listwise deletion. works well if MCAR, which is rarely true, and can delete significant part of our sample\n",
    "2. imputation.\n",
    "2.a. marginal mean imputation: leads to biased estimates of variance and covariance and should be avoided\n",
    "2.b. conditional mean imputation: we regress missing values on values of all other variables present in dataset.\n",
    "     MCAR assumption. Generalized least squares usually shows good results.\n",
    "Overall issue with imputation methods: underestimate standard errors and overstimate test statistics.\n",
    "\n",
    "Advanced options:\n",
    "1. Multiple imputation\n",
    "2. Maximum likelihood\n",
    "3. Bayesian simulation\n",
    "4. Hot deck (selects at random, with replacement, a value from observations with similar values for other variables)\n",
    "\n",
    "For a great discussion of missing data:\n",
    "http://www.bu.edu/sph/files/2014/05/Marina-tech-report.pdf\n",
    "\n",
    "Statsmodels and Scikit learn imputation functions:\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html\n",
    "http://www.statsmodels.org/dev/imputation.html\n",
    "\n",
    "At this point in time, pandas, scikit learn and statsmodels offer relatively poor methods of data imputation. You\n",
    "therefore will need to manually create your processes. But this will be worth your time!\n",
    "\n",
    "\"The only really good solution to the missing data problem is not to have any. So in the design and execution of \n",
    "research projects, it is essential to put great effort into minimizing the occurrence of missing data. \n",
    "Statistical adjustments can never make up for sloppy research.\" Paul Allison, 2001.\n",
    "\n",
    "*Bias affects all measurements the same way, while chance errors vary from measurement to measurement\n",
    "therefore bias cannot be noticed by looking just at measurements, we need an external/theoretical\n",
    "benchmark as well. Bias is often discussed in conjunction with variance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Now we go through each variable in our dataset one by one. We check for missing values if any, how many, and what %\n",
    "of total values they represent for each variable. For this we use:\n",
    "isnull() function checks for missing values\n",
    "value_counts() counts them\n",
    "we also look at what values our variables take, by applying the value_counts() function directly, without isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-7467e92da54e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df['age'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    920\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    726\n",
       "0.0    194\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    0.78913\n",
       "0.0    0.21087\n",
       "Name: sex, dtype: float64"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the normalize = True parameter returns for us the % of total rather than the absolute number.\n",
    "df['sex'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    920\n",
       "Name: chest_pain, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['chest_pain'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0    496\n",
       "3.0    204\n",
       "2.0    174\n",
       "1.0     46\n",
       "Name: chest_pain, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['chest_pain'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    861\n",
       "True      59\n",
       "Name: rest_bp, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_bp'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.93587\n",
       "True     0.06413\n",
       "Name: rest_bp, dtype: float64"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we divide by len(df), to manually calculate the % of total observations that this represents each time.\n",
    "df['rest_bp'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    890\n",
       "True      30\n",
       "Name: cholesterol, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cholesterol'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.967391\n",
       "True     0.032609\n",
       "Name: cholesterol, dtype: float64"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cholesterol'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    830\n",
       "True      90\n",
       "Name: fasting_bs, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fasting_bs'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.902174\n",
       "True     0.097826\n",
       "Name: fasting_bs, dtype: float64"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fasting_bs'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    692\n",
       "1.0    138\n",
       "Name: fasting_bs, dtype: int64"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#standard value_counts() function drops missing values. To avoid this you can add dropna=False argument to function.\n",
    "df['fasting_bs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.997826\n",
       "True     0.002174\n",
       "Name: rest_ecg, dtype: float64"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_ecg'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    551\n",
       "2.0    188\n",
       "1.0    179\n",
       "Name: rest_ecg, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_ecg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    865\n",
       "True      55\n",
       "Name: max_heart_rate, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['max_heart_rate'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.940217\n",
       "True     0.059783\n",
       "Name: max_heart_rate, dtype: float64"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['max_heart_rate'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    865\n",
       "True      55\n",
       "Name: exercise_angina, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['exercise_angina'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.940217\n",
       "True     0.059783\n",
       "Name: exercise_angina, dtype: float64"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['exercise_angina'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    528\n",
       "1.0    337\n",
       "Name: exercise_angina, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['exercise_angina'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    858\n",
       "True      62\n",
       "Name: st_depression, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['st_depression'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.932609\n",
       "True     0.067391\n",
       "Name: st_depression, dtype: float64"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['st_depression'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    611\n",
       "True     309\n",
       "Name: slope, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['slope'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.66413\n",
       "True     0.33587\n",
       "Name: slope, dtype: float64"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['slope'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    345\n",
       "1.0    203\n",
       "3.0     63\n",
       "Name: slope, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['slope'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     611\n",
       "False    309\n",
       "Name: fluoroscopy, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fluoroscopy'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     0.66413\n",
       "False    0.33587\n",
       "Name: fluoroscopy, dtype: float64"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fluoroscopy'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    181\n",
       "1.0     67\n",
       "2.0     41\n",
       "3.0     20\n",
       "Name: fluoroscopy, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fluoroscopy'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     486\n",
       "False    434\n",
       "Name: defect, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['defect'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     0.528261\n",
       "False    0.471739\n",
       "Name: defect, dtype: float64"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['defect'].isnull().value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    196\n",
       "7.0    192\n",
       "6.0     46\n",
       "Name: defect, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['defect'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    920\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    411\n",
       "1    265\n",
       "2    109\n",
       "3    107\n",
       "4     28\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#the variables slope, defect and fluoroscopy have 33-47% of missing values\n",
    "#context. Why?\n",
    "#can we correct for this without introducing other biases into our dataset?\n",
    "#Here we will decide to eliminate these variables.\n",
    "#Data analysts are mere mortals too. Cannot fix everything.\n",
    "df_red = df[['age', 'sex', 'chest_pain', 'rest_bp', 'cholesterol', 'fasting_bs', 'rest_ecg', 'max_heart_rate', \n",
    "            'exercise_angina', 'st_depression', 'diagnosis']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "920"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#rest_bp, cholesterol, fasting_bs, rest_ecg, max_heart_rate, exercise_angina, st_depression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.6/site-packages/pandas/core/generic.py:3295: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "#rest_ecg is a categorical variable with only 2% of missing values.\n",
    "#we make the choice to impute missing values with a straightforward method: the mode\n",
    "#we are conscious this may introduce biases but due to low number of missing values, in what is\n",
    "#a small range categorical variable (no extreme outliers possible) we feel comfortable doing this\n",
    "df_red['rest_ecg'].fillna(df_red['rest_ecg'].mode().iloc[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    920\n",
       "Name: rest_ecg, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_red['rest_ecg'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#let's see if there is any overlap between missing variables relative to observations, and what would happen if we \n",
    "#were to limit our dataset to observations with non missing values\n",
    "df_clean = df_red[df_red['rest_bp'].notnull() & df_red['cholesterol'].notnull() & df_red['fasting_bs'].notnull() & df['max_heart_rate'].notnull() & df['exercise_angina'].notnull() &\n",
    "                    df['st_depression'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "741"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                False\n",
       "sex                False\n",
       "chest_pain         False\n",
       "rest_bp            False\n",
       "cholesterol        False\n",
       "fasting_bs         False\n",
       "rest_ecg           False\n",
       "max_heart_rate     False\n",
       "exercise_angina    False\n",
       "st_depression      False\n",
       "diagnosis          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "this is not approach we would use in a real world study, but for exploratory purposes in this tutorial\n",
    "we can retain 85% of observations that were properly recorded regarding all variables\n",
    "I will add here an example with conditional mean imputation for one of the variables\n",
    "Statsmodels very limited in options = very manual work.\n",
    "From version 0.8.0 onwards MICE function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "now that we cleaned the data we can move on to univariate analysis - one variable at a time\n",
    "\n",
    "data reporting tells you what is happening, but data analysis tells you why it is happening\n",
    "\n",
    "\n",
    "Descriptive statistics\n",
    "\n",
    "a parameter is calculated from the population, while a statistic from a sample\n",
    "\n",
    "In studying samples, we assume the Central Limit Theorem holds: if you draw enough samples, from\n",
    "a population, and each sample is large enough, the distribution of the statistics of the samples\n",
    "will be normally distributed.\n",
    "normal curve discovered around 1720 by Abraham de Moivre. Around 1870, Adolph Quetelet thought\n",
    "of using it as the curve of an ideal histogram\n",
    "\n",
    "center-spread-shape\n",
    "3 measures of center: mean, median and mode\n",
    "1 measure of spread: standard deviation\n",
    "2 attributes of shape: \n",
    "1. symmetry or skewness. skewed right (i.e. salaries), skewed left (i.e. age of natural deaths)\n",
    "2. modality or peakness: unimodal, bimodal, uniform...\n",
    "\n",
    "now we will aim to get an idea of the shape, center, and spread of these variables\n",
    "we will analyze the shape visually by checking for modality and skewness\n",
    "we will check for measures of center such as mean, median and mode\n",
    "we will check the spread through the standard deviation for quantitative variables\n",
    "\n",
    "standard deviation: how far away observations are from their average\n",
    "in a normal distribution roughly 68% are within 1 SD and 95% within 2 SDs.\n",
    "\n",
    "Quantitative variables\n",
    "shape, center and spread \n",
    "histogram\n",
    "\n",
    "Categorical variables\n",
    "mode\n",
    "bar chart or frequency distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# rules for visualizing data:\n",
    "\n",
    "for visualizing one variable: \n",
    "if it is categorical we use a bar chart i.e. sns's countplot function\n",
    "if it is quantitative, we can combine a kernel density estimate and a histogram with sns's distplot function\n",
    "\n",
    "for visualizing two variables:\n",
    "C-Q: bivariate bar graph with sns factorplot (bin/collapse explanatory variable), categories on x axis, and mean of \n",
    "response variable on y axis\n",
    "Q-Q: scatterplot with sns regplot\n",
    "C-C: you can plot them one a time. problem with a bivariate graph is that mean has no meaning in context of a \n",
    "categorical variable\n",
    "\n",
    "for further reading:\n",
    "http://sphweb.bumc.bu.edu/otlt/mph-modules/bs/datapresentation/DataPresentation7.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116ca35f8>"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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fvr1XBwMAANdXoZCPGjVK69ev14cffig/Pz/1799fHTp08PZsAADgOir8Paud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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137bd1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gender\n",
    "#given that gender is a categorical variable, we use a countplot to visualize it\n",
    "#we always plot the variable of interest on the x axis, and the count or frequency on the y axis\n",
    "sns.countplot(x='sex', data = df_clean)\n",
    "plt.suptitle('Frequency of observations by gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "the results definitely suggest a discussion about class imbalance is in order here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.481781\n",
       "1    0.276653\n",
       "2    0.106613\n",
       "3    0.105263\n",
       "4    0.029690\n",
       "Name: diagnosis, dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Diagnosis\n",
    "#we check the counts per each value of the variable. sort=False tells pandas not to sort the results by values. \n",
    "#normalize = True tells it to return the relative frequencies rather than the absolute counts\n",
    "#if we had not cleaned the data, we could add parameter dropna=False so that value_counts does not drop null values\n",
    "df_clean['diagnosis'].value_counts(sort=False, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1137dfb00>"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LYmJiNGnSpGLbd+7cWaGhoZIky7Lk6uqqAwcOKDg4WJLUtm1bbd++XRUqVFBg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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147b9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='diagnosis', data=df_clean)\n",
    "plt.suptitle('Frequency distribution of diagnosis state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    741.000000\n",
       "mean      53.099865\n",
       "std        9.402028\n",
       "min       28.000000\n",
       "25%       46.000000\n",
       "50%       54.000000\n",
       "75%       60.000000\n",
       "max       77.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's look at age now\n",
    "#the describe request gives us the count, mean, std, min, max, as well as the quartiles for the\n",
    "#respective value distribution\n",
    "df_clean['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11429c748>"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ykJOTAwDYtm0b1qxZg6CgoAmKTkTjrb6lB7XNRkQFeyIiSGeX19SpFZgc54+uHhOKDW12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\nyTMqZxERkTyjchYREckzKmcREZE88zckO7N3YbcvVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114676208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns distplot function combines matplotlib hist() function with sns kdeplot() function.\n",
    "sns.distplot(df_clean['age'])\n",
    "plt.suptitle('Distribution of age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    741.000000\n",
       "mean     138.740891\n",
       "std       25.828809\n",
       "min       60.000000\n",
       "25%      120.000000\n",
       "50%      140.000000\n",
       "75%      159.000000\n",
       "max      202.000000\n",
       "Name: max_heart_rate, dtype: float64"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['max_heart_rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1153af5c0>"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sWZgwYQLuu+8+7N69G/Pnz8crr7yCTZs2IT09HZs2bcLKlSuRnp5+Qz8Pcj0SgVf5ExER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\nB/QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153a4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_clean['max_heart_rate'])\n",
    "plt.suptitle('Distribution of maximal heart rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1158a2fd0>"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IJBPzXxc9mlEoVfpJH5dJwowKlwRcix7nGmEnKiI04Fp0BM0JTxKxMOAwCa0T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\nq7E3ORo7iQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157f8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot('diagnosis', data=df_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# rules for visualizing two variables (recap). \n",
    "Their order does not matter here, hence Q-C and C-Q use the same visualization rule\n",
    "\n",
    "C-Q: bivariate bar graph with sns factorplot, categories on x axis, and mean of response variable on y axis\n",
    "Q-Q: scatterplot with sns regplot\n",
    "C-C: you can plot them one a time. problem with a bivariate graph is that mean has no meaning in context of a \n",
    "categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116ccc240>"
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3/J5+/fph/fr1XZ7Pycm5t5US0QPPqiMUBwcHNDc3KxdUa2pqeP8IEXVh1RFKfHw89Ho9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\niEia/wNtMTw/PQ3z7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116cd87b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#kind = bar asks for a bar graph and ci=None suppresses error bars\n",
    "sns.factorplot(x='sex', y='age', kind='bar', data=df_clean, ci=None)\n",
    "plt.suptitle('Gender vs age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x116e33b00>"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ks9kkSQ6HQ/39/SHta7QnNcLurjHNivCEvB7h6tgRmf1iBFPrF9Wg7NmzRzabTYcPH9ax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\nneIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11705ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#categorical explanatory variable 'sex' and quantitative response variable 'rest_bp'\n",
    "sns.factorplot(x='sex', y='rest_bp', data=df_clean, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['chest_pain'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115bf69b0>"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ISMB7R2NoaWnBskUN4LwDpqvQO29KdECzMs5McIcH4P5gL6JCDKIowG6zw+1y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\niIhIYQzkRERECvt/1P4NzC86NW4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d0bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We use a regplot to plot two quantitative variables, age and cholesterol, while also having a regression line suggesting\n",
    "#any association present\n",
    "#we always plot the explanatory variable on the x axis, and the response variable on the y axis\n",
    "sns.regplot(x='age', y='cholesterol', data=df_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We see that several observations have a cholesterol of \"0\". This will need to be investigated subsequently and treated\n",
    "#as missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  diagnosis\n",
       "0.0  0            0.142391\n",
       "     1            0.028261\n",
       "     3            0.008696\n",
       "     2            0.007609\n",
       "     4            0.002174\n",
       "1.0  0            0.245652\n",
       "     1            0.194565\n",
       "     2            0.078261\n",
       "     3            0.076087\n",
       "     4            0.021739\n",
       "Name: diagnosis, dtype: float64"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how can we gain a better idea of how two categorical variables interact?\n",
    "df_clean.groupby('sex')['diagnosis'].value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1172e3438>"
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACG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\neAMAYBjCGwAAwxDeAAAYhvAGAMAw/w8RD6+7kfUESwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b2a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#common statistical boxplot visualization\n",
    "sns.boxplot(x='cholesterol', y = 'sex', data=df_clean)\n",
    "plt.suptitle('Cholesterol levels by gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>max_heart_rate</th>\n",
       "      <th>rest_bp</th>\n",
       "      <th>st_depression</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosis</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">0</th>\n",
       "      <th>count</th>\n",
       "      <td>357.000000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>357.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.302521</td>\n",
       "      <td>233.336134</td>\n",
       "      <td>149.291317</td>\n",
       "      <td>129.871148</td>\n",
       "      <td>0.425210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.417789</td>\n",
       "      <td>66.596253</td>\n",
       "      <td>23.643876</td>\n",
       "      <td>16.566861</td>\n",
       "      <td>0.712199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1</th>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.946341</td>\n",
       "      <td>229.443902</td>\n",
       "      <td>131.541463</td>\n",
       "      <td>134.502439</td>\n",
       "      <td>1.088293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.577333</td>\n",
       "      <td>101.127628</td>\n",
       "      <td>23.681806</td>\n",
       "      <td>19.045514</td>\n",
       "      <td>1.021369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">2</th>\n",
       "      <th>count</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>58.430380</td>\n",
       "      <td>182.607595</td>\n",
       "      <td>127.797468</td>\n",
       "      <td>135.392405</td>\n",
       "      <td>1.394937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.041422</td>\n",
       "      <td>122.971168</td>\n",
       "      <td>22.807095</td>\n",
       "      <td>15.723099</td>\n",
       "      <td>1.204787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.500000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>261.500000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.500000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">3</th>\n",
       "      <th>count</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59.179487</td>\n",
       "      <td>178.205128</td>\n",
       "      <td>122.769231</td>\n",
       "      <td>136.102564</td>\n",
       "      <td>1.598718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.870285</td>\n",
       "      <td>116.047165</td>\n",
       "      <td>24.381664</td>\n",
       "      <td>24.938673</td>\n",
       "      <td>1.352006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>105.750000</td>\n",
       "      <td>122.250000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>219.500000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>267.750000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">4</th>\n",
       "      <th>count</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59.227273</td>\n",
       "      <td>206.045455</td>\n",
       "      <td>130.545455</td>\n",
       "      <td>142.227273</td>\n",
       "      <td>2.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.286143</td>\n",
       "      <td>112.888684</td>\n",
       "      <td>24.595252</td>\n",
       "      <td>21.080191</td>\n",
       "      <td>1.283798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>56.250000</td>\n",
       "      <td>174.750000</td>\n",
       "      <td>116.250000</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.500000</td>\n",
       "      <td>230.500000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>141.500000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>63.750000</td>\n",
       "      <td>283.500000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>157.500000</td>\n",
       "      <td>3.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>4.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        age  cholesterol  max_heart_rate     rest_bp  \\\n",
       "diagnosis                                                              \n",
       "0         count  357.000000   357.000000      357.000000  357.000000   \n",
       "          mean    50.302521   233.336134      149.291317  129.871148   \n",
       "          std      9.417789    66.596253       23.643876   16.566861   \n",
       "          min     28.000000     0.000000       69.000000   94.000000   \n",
       "          25%     43.000000   202.000000      135.000000  120.000000   \n",
       "          50%     51.000000   230.000000      152.000000  130.000000   \n",
       "          75%     57.000000   269.000000      168.000000  140.000000   \n",
       "          max     76.000000   564.000000      202.000000  190.000000   \n",
       "1         count  205.000000   205.000000      205.000000  205.000000   \n",
       "          mean    52.946341   229.443902      131.541463  134.502439   \n",
       "          std      8.577333   101.127628       23.681806   19.045514   \n",
       "          min     31.000000     0.000000       82.000000   92.000000   \n",
       "          25%     47.000000   203.000000      115.000000  120.000000   \n",
       "          50%     54.000000   243.000000      130.000000  130.000000   \n",
       "          75%     59.000000   279.000000      150.000000  145.000000   \n",
       "          max     75.000000   603.000000      195.000000  200.000000   \n",
       "2         count   79.000000    79.000000       79.000000   79.000000   \n",
       "          mean    58.430380   182.607595      127.797468  135.392405   \n",
       "          std      7.041422   122.971168       22.807095   15.723099   \n",
       "          min     42.000000     0.000000       60.000000  100.000000   \n",
       "          25%     55.000000     0.000000      112.500000  124.000000   \n",
       "          50%     59.000000   219.000000      130.000000  134.000000   \n",
       "          75%     63.000000   261.500000      144.000000  144.500000   \n",
       "          max     74.000000   409.000000      180.000000  180.000000   \n",
       "3         count   78.000000    78.000000       78.000000   78.000000   \n",
       "          mean    59.179487   178.205128      122.769231  136.102564   \n",
       "          std      7.870285   116.047165       24.381664   24.938673   \n",
       "          min     39.000000     0.000000       63.000000    0.000000   \n",
       "          25%     54.000000     0.000000      105.750000  122.250000   \n",
       "          50%     60.000000   219.500000      120.000000  135.000000   \n",
       "          75%     64.000000   267.750000      140.000000  150.000000   \n",
       "          max     77.000000   353.000000      173.000000  200.000000   \n",
       "4         count   22.000000    22.000000       22.000000   22.000000   \n",
       "          mean    59.227273   206.045455      130.545455  142.227273   \n",
       "          std      8.286143   112.888684       24.595252   21.080191   \n",
       "          min     38.000000     0.000000       84.000000  112.000000   \n",
       "          25%     56.250000   174.750000      116.250000  126.250000   \n",
       "          50%     60.500000   230.500000      130.000000  141.500000   \n",
       "          75%     63.750000   283.500000      145.000000  157.500000   \n",
       "          max     77.000000   407.000000      182.000000  190.000000   \n",
       "\n",
       "                 st_depression  \n",
       "diagnosis                       \n",
       "0         count     357.000000  \n",
       "          mean        0.425210  \n",
       "          std         0.712199  \n",
       "          min        -0.500000  \n",
       "          25%         0.000000  \n",
       "          50%         0.000000  \n",
       "          75%         0.800000  \n",
       "          max         4.200000  \n",
       "1         count     205.000000  \n",
       "          mean        1.088293  \n",
       "          std         1.021369  \n",
       "          min         0.000000  \n",
       "          25%         0.000000  \n",
       "          50%         1.000000  \n",
       "          75%         2.000000  \n",
       "          max         5.000000  \n",
       "2         count      79.000000  \n",
       "          mean        1.394937  \n",
       "          std         1.204787  \n",
       "          min        -1.000000  \n",
       "          25%         0.000000  \n",
       "          50%         1.400000  \n",
       "          75%         2.100000  \n",
       "          max         4.000000  \n",
       "3         count      78.000000  \n",
       "          mean        1.598718  \n",
       "          std         1.352006  \n",
       "          min         0.000000  \n",
       "          25%         0.500000  \n",
       "          50%         1.500000  \n",
       "          75%         2.150000  \n",
       "          max         6.200000  \n",
       "4         count      22.000000  \n",
       "          mean        2.363636  \n",
       "          std         1.283798  \n",
       "          min         0.000000  \n",
       "          25%         1.250000  \n",
       "          50%         2.500000  \n",
       "          75%         3.450000  \n",
       "          max         4.400000  "
      ]
     },
     "execution_count": 617,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describing the dataset/the variables of the dataset, after we group the values in the dataset by diagnostic category\n",
    "#the describe function gives us the key statistics for each quantitative variable in the dataset\n",
    "df_clean.groupby('diagnosis').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#before starting to manipulate the dataset itself, we make a copy, and will work on the copy rather than the original\n",
    "df_clean_copy = df_clean.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Now we can move to inferential statistics\n",
    "\n",
    "We call them inferential statistics because we try to infer the population's paramters from our sample\n",
    "\n",
    "The process is always the same and involves hypothesis testing:\n",
    "1. define null hypothesis and alternate hypothesis\n",
    "2. analyze evidence\n",
    "3. interpret results\n",
    "\n",
    "Typical H0: there is no relationship between the explanatory and response variable\n",
    "Typical H1: there is a statistically significant relationship\n",
    "\n",
    "Bivariate statistical tools:\n",
    "Three main tools: ANOVA; chi-square; correlation coefficient\n",
    "\n",
    "Type 1 vs Type 2 errors\n",
    "Type 1 error: the incorrect rejection of a true null hypothesis\n",
    "Type 2 error: retaining a false null hypothesis\n",
    "\n",
    "we will test the null hypothesis that age and diagnosis are not related.\n",
    "the type of variables we have (explanatory/response and categorical/quantitative for each) determines the type of \n",
    "statistical tools we will use:\n",
    "\n",
    "Explanatory categorical and response quantitative: ANOVA\n",
    "Explanatory categorical and response categorical: Chi Square test\n",
    "Explanatory quantitative and response categorical: classify/bin explanatory variable and use chi square test\n",
    "Explanatory quantitative and response quantitative: pearson correlation\n",
    "\n",
    "a result is statistically significant if it is unlikely to be the result of chance\n",
    "\n",
    "before performing these analyses, one needs to use the .dropna() function to include only valid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    age   R-squared:                       0.134\n",
      "Model:                            OLS   Adj. R-squared:                  0.129\n",
      "Method:                 Least Squares   F-statistic:                     28.42\n",
      "Date:                Fri, 23 Jun 2017   Prob (F-statistic):           5.56e-22\n",
      "Time:                        18:09:46   Log-Likelihood:                -2658.2\n",
      "No. Observations:                 741   AIC:                             5326.\n",
      "Df Residuals:                     736   BIC:                             5350.\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept            50.3025      0.464    108.322      0.000        49.391    51.214\n",
      "C(diagnosis)[T.1]     2.6438      0.769      3.438      0.001         1.134     4.153\n",
      "C(diagnosis)[T.2]     8.1279      1.091      7.450      0.000         5.986    10.270\n",
      "C(diagnosis)[T.3]     8.8770      1.097      8.095      0.000         6.724    11.030\n",
      "C(diagnosis)[T.4]     8.9248      1.927      4.630      0.000         5.141    12.709\n",
      "==============================================================================\n",
      "Omnibus:                        2.010   Durbin-Watson:                   1.203\n",
      "Prob(Omnibus):                  0.366   Jarque-Bera (JB):                1.920\n",
      "Skew:                          -0.060   Prob(JB):                        0.383\n",
      "Kurtosis:                       2.782   Cond. No.                         6.38\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Going the wrong way?\n",
    "#In regression analysis you can change your predictor and response variables. This is because they may be correlated,\n",
    "#in which case X is correlated to Y and by definition Y is correlated with X. There is no directionality implied, which\n",
    "#is also why you cannot talk of causation, but only of correlation.\n",
    "test1 = smf.ols(formula = 'age ~ C(diagnosis)', data = df_clean_copy).fit()\n",
    "print(test1.summary())\n",
    "\n",
    "#*Whenever we have an explanatory categorical variable with more than two levels, we need to explicitly state this for\n",
    "#statsmodels. Here we state it by adding 'C'and the variable name in parentheses. By definition, statsmodels then\n",
    "#converts the first level of the variable into the reference group, therefore showing only n-1 levels in the results.\n",
    "#each level is in brief a comparison to the reference group. Here the reference group becomes a diagnosis of 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "What interests us in an ANOVA table are:\n",
    "R squared\n",
    "The F statistic\n",
    "The p value (here called 'Prob (F-statistic)\n",
    "The coefficients of each explanatory variable and the associated p value for it (here 'coef' and 'P>|t| columns)\n",
    "The 95% confidence interval for our coefficients. Here called '[95.0% Conf. Int.]\n",
    "\n",
    "The 'R Squared' statistic. This is a measure of how much of the variability in the response variable, age here, is \n",
    "explained by our model. We see this here it is 0.134 only. So diagnostic group only helps us explain 13.4% of the \n",
    "variability in age. This can indicate that either we omit important explanatory variables that we can add to the model, or that we miss the structure of the association.\n",
    "\n",
    "The F statistic:\n",
    "An F test is any statistical test in which the test statistic has an F distribution under the null hypothesis\n",
    "The F statistic = variation among sample means/variation within sample groups\n",
    "ANOVA F Test = are the differences among the sample means due to true differences among the population means, \n",
    "or merely due to sampling variability?\n",
    "\n",
    "p value in an ANOVA table = probability of getting an F value as large or larger if H0 is true\n",
    "probability of finding this value if there is no difference between sample means.\n",
    "I would like to thank M.N. for sending me the below blog, which contains a couple of great posts on common statistical concepts:\n",
    "\n",
    "http://blog.minitab.com/blog/adventures-in-statistics-2/how-to-correctly-interpret-p-values\n",
    "\n",
    "\n",
    "In an ANOVA table, we can decide if the relationship is statistically significant by checking the value of the F\n",
    "statistic against the relevant statistical table, F=28.42 above, or by looking at the p value. The latter is easiest.\n",
    "A long tradition in statistics is to consider a p value (also called alpha) below 0.05 as indicative of a significant\n",
    "relationship. When conducting such tests, you need to decide what p value/alpha value you feel comfortable with, and from then onwards you create  a binary framework: either a statistic has an associated p value below the value you decided on at the beginning, or not. In this sense, a p value of 0.0001 is not indicative of a 5 times stronger relationship than a p value of 0.0005. \n",
    "In our case here a p value of 5.56e-22 means the relationship is statistically significant (5.56e-22 < 0.05).\n",
    "\n",
    "The coefficients for each variable. Here for example our model would be:\n",
    "age = 50.3025 + 2.6438*diagnosis1 + 8.1279* diagnosis2 + 8.8770 * diagnosis3 + 8.9248 * diagnosis4\n",
    "We can also say that being having a diagnosis of 1 increases someone's age by 2.6438 assuming we hold all other\n",
    "explanatory variables in our model constant at 0.\n",
    "a negative but significant coefficient indicates that our explanatory variable and the respons variable are negatively\n",
    "associated: as one increases, the other decreases.\n",
    "the higher the coefficient, the more impact it will have on the value of our response variable based on our model.\n",
    "However, these coefficients result from our sample, meaning that the population parameters may differ from these.\n",
    "The confidence intervals give us a range in which these parameters can be for the population, with 95% confidence.\n",
    "For example we can be 95% confident that the population parameter for diagnosis1 is between 1.134 and 4.153."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Whenever the explanatory variable has more than 2 levels, we need to also perform post hoc statistical tests to \n",
    "better understand the relationship between the explanatory variable and the response variable\n",
    "we know the groups tested are different overall, but not exactly where/how they are different\n",
    "for explanatory variables with multiples levels, F test and p value do not tell us why the group means are not equal, \n",
    "or how. there are many ways in which this can be the case.\n",
    "\n",
    "How are the response and explanatory variables associated per level of the explanatory variable?\n",
    "\n",
    "post hoc tests aim to protect against type 1 error when explanatory variable is multilevel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diagnosis\n",
      "0    50.302521\n",
      "1    52.946341\n",
      "2    58.430380\n",
      "3    59.179487\n",
      "4    59.227273\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#now we examine the means and standard deviations\n",
    "grouped1_mean = df_clean_copy.groupby('diagnosis').mean()['age']\n",
    "print(grouped1_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diagnosis\n",
      "0    9.417789\n",
      "1    8.577333\n",
      "2    7.041422\n",
      "3    7.870285\n",
      "4    8.286143\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "grouped1_std = df_clean_copy.groupby('diagnosis').std()['age']\n",
    "print(grouped1_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple Comparison of Means - Tukey HSD,FWER=0.05\n",
      "=============================================\n",
      "group1 group2 meandiff  lower   upper  reject\n",
      "---------------------------------------------\n",
      "  0      1     2.6438   0.5412  4.7464  True \n",
      "  0      2     8.1279   5.1446 11.1111  True \n",
      "  0      3     8.877    5.8781 11.8758  True \n",
      "  0      4     8.9248   3.654  14.1955  True \n",
      "  1      2     5.484    2.3067  8.6614  True \n",
      "  1      3     6.2331   3.0411  9.4252  True \n",
      "  1      4     6.2809   0.898  11.6639  True \n",
      "  2      3     0.7491  -3.0808  4.579  False \n",
      "  2      4     0.7969  -4.9872  6.5809 False \n",
      "  3      4     0.0478  -5.7443  5.8399 False \n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#given that we have an explanatory categorical variable with multiple levels, we use the \n",
    "#tuckey hsd test\n",
    "#other tests:\n",
    "#Holm T\n",
    "#Least Significant Difference\n",
    "\n",
    "tuckey1 = multi.MultiComparison(df_clean_copy['age'], df_clean_copy['diagnosis'])\n",
    "res1 = tuckey1.tukeyhsd()\n",
    "print(res1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We will now test another hypothesis:  \n",
    "Hypothesis(0)(a): the presence of chest pain and the diagnosis (0 or 1) are independent  \n",
    "Alternative Hypothesis 1: presence of chest pain and diagnosis are not independent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Feature engineering\n",
    "\n",
    "Paradox: you can get better results with great feature engineering and a poor model than with poor feature engineering\n",
    "but a great model\n",
    "> A feature is an attribute that is useful to your problem  \n",
    "Dr. Jason Brownlee\n",
    "> \"The algorithms we used are very standard for Kagglers...We spent most of our efforts in feature engineering.  \n",
    "Xavier Conort, #1 Kaggler in 2013\n",
    "\n",
    "Aims to convert data attributes into data features\n",
    "\n",
    "Aims to optimize data modelling\n",
    "\n",
    "Requires understanding of the dataset and research problem, and understanding of model you plan on using can be \n",
    "domain driven, or data driven\n",
    "i.e. for SVM with linear kernel you need to manually construct nonlinear interactions between features and feed them \n",
    "as input to your SVM model. An SVM with polynomial kernel will naturally capture them.\n",
    "other example: SVMs are very sensitive to dimensions of features, while DT/RFs are not\n",
    "With tabular data you combine, aggregate, split and/or decompose features in order to create new ones\n",
    "Given an output y and a feature x, you can try the following transforms first:\n",
    "e^x, log(x), x^2, x^3\n",
    "an indicator of the usefulness of the transformation is if the correlation between y and x'\n",
    "is higher than the correlation between y and x\n",
    "best way to validate this is to check your model error with or without the transformed feature\n",
    "http://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/\n",
    "http://trevorstephens.com/kaggle-titanic-tutorial/r-part-4-feature-engineering/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_clean_copy' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-23a8553fc454>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdiagnosis_dic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_clean_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'diagnosis_binary'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_clean_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'diagnosis'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiagnosis_dic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df_clean_copy' is not defined"
     ]
    }
   ],
   "source": [
    "diagnosis_dic = {0:0, 1:1, 2:1, 3:1, 4:1}\n",
    "df_clean_copy['diagnosis_binary'] = df_clean_copy['diagnosis'].map(diagnosis_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    384\n",
       "0    357\n",
       "Name: diagnosis_binary, dtype: int64"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy['diagnosis_binary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chest_pain        1.0  2.0  3.0  4.0\n",
      "diagnosis_binary                    \n",
      "0                  23  128  112   94\n",
      "1                  15   22   49  298\n"
     ]
    }
   ],
   "source": [
    "#contingency table of observed counts\n",
    "#the crosstab function allows us to cross one variable with another\n",
    "#when creating contingency tables, we put the response variable first (therefore vertical in table), \n",
    "#and the explanatory variable second, therefore horizontal at the top of the table.\n",
    "ct1 = pd.crosstab(df_clean_copy['diagnosis_binary'], df_clean_copy['chest_pain'])\n",
    "print(ct1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chest_pain             1.0       2.0       3.0       4.0\n",
      "diagnosis_binary                                        \n",
      "0                 0.605263  0.853333  0.695652  0.239796\n",
      "1                 0.394737  0.146667  0.304348  0.760204\n"
     ]
    }
   ],
   "source": [
    "#column percentages\n",
    "colsum = ct1.sum(axis=0)\n",
    "colpct = ct1/colsum\n",
    "print(colpct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square value, p value, expected counts\n",
      "(206.69693667917477, 1.5066891002705367e-44, 3, array([[  18.30769231,   72.26720648,   77.56680162,  188.8582996 ],\n",
      "       [  19.69230769,   77.73279352,   83.43319838,  203.1417004 ]]))\n"
     ]
    }
   ],
   "source": [
    "#chi square test\n",
    "#Expected counts: p assuming events are independent. p(1) * p(2) | column total*row total/table total\n",
    "#Chi square statistic summarizes this. difference between our obersavtion and what we would expect if H0 is true\n",
    "#We rely on the p value, as different distributions define whether the chi square itself is large or not\n",
    "\n",
    "print('chi-square value, p value, expected counts')\n",
    "cs1 = scipy.stats.chi2_contingency(ct1)\n",
    "print(cs1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Explanatory variable with multiple levels!\n",
    "#we would have to do a pairwise comparison between every two groups of the explanatory\n",
    "#variable, vs the response variable\n",
    "#This would be a Bonferroni adjustment - we adjust p value we use by number of pairwise comparisons, and test these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'chest_pain', 'rest_bp', 'cholesterol', 'fasting_bs',\n",
       "       'rest_ecg', 'max_heart_rate', 'exercise_angina', 'st_depression',\n",
       "       'diagnosis', 'diagnosis_binary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex               0.0  1.0\n",
      "diagnosis_binary          \n",
      "0                 131  226\n",
      "1                  43  341\n"
     ]
    }
   ],
   "source": [
    "ct2 = pd.crosstab(df_clean_copy['diagnosis_binary'], df_clean_copy['sex'])\n",
    "print(ct2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex                    0.0       1.0\n",
      "diagnosis_binary                    \n",
      "0                 0.752874  0.398589\n",
      "1                 0.247126  0.601411\n"
     ]
    }
   ],
   "source": [
    "#column percentages\n",
    "colsum2 = ct2.sum(axis=0)\n",
    "colpct2 = ct2/colsum2\n",
    "print(colpct2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square value, p value, expected counts\n",
      "(65.523823461676344, 5.741569147761909e-16, 1, array([[  83.82995951,  273.17004049],\n",
      "       [  90.17004049,  293.82995951]]))\n"
     ]
    }
   ],
   "source": [
    "#chi square test\n",
    "print('chi-square value, p value, expected counts')\n",
    "cs2 = scipy.stats.chi2_contingency(ct2)\n",
    "print(cs2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Moderators\n",
    "#a moderator is a third variable that affects the direction and/or strength between your explanatory and response variables\n",
    "#the question is, is our response variable associated with our explanatory variable, for each level of our third variable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's see if sex is a moderator in the statistically significant relationship between chest pain and diagnosis\n",
    "df_clean_copy_men = df_clean_copy[df_clean_copy['sex'] == 0]\n",
    "len(df_clean_copy_men)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "567"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy_women = df_clean_copy[df_clean_copy['sex'] == 1]\n",
    "len(df_clean_copy_women)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chest_pain        1.0  2.0  3.0  4.0\n",
      "diagnosis_binary                    \n",
      "0                   9   47   44   31\n",
      "1                   0    4    6   33\n"
     ]
    }
   ],
   "source": [
    "#contingency table of observed counts\n",
    "#when creating contingency tables, we put the response variable first (therefore vertical in table), \n",
    "#and the explanatory variable second, therefore horizontal at the top of the table.\n",
    "ct3 = pd.crosstab(df_clean_copy_men['diagnosis_binary'], df_clean_copy_men['chest_pain'])\n",
    "print(ct3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chest_pain        1.0       2.0   3.0       4.0\n",
      "diagnosis_binary                               \n",
      "0                 1.0  0.921569  0.88  0.484375\n",
      "1                 0.0  0.078431  0.12  0.515625\n"
     ]
    }
   ],
   "source": [
    "#column percentages\n",
    "colsum = ct3.sum(axis=0)\n",
    "colpct = ct3/colsum\n",
    "print(colpct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square value, p value, expected counts\n",
      "(39.896349270579876, 1.1207983387044401e-08, 3, array([[  6.77586207,  38.39655172,  37.64367816,  48.18390805],\n",
      "       [  2.22413793,  12.60344828,  12.35632184,  15.81609195]]))\n"
     ]
    }
   ],
   "source": [
    "#chi square test\n",
    "print('chi-square value, p value, expected counts')\n",
    "cs3 = scipy.stats.chi2_contingency(ct3)\n",
    "print(cs3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chest_pain        1.0  2.0  3.0  4.0\n",
      "diagnosis_binary                    \n",
      "0                  14   81   68   63\n",
      "1                  15   18   43  265\n"
     ]
    }
   ],
   "source": [
    "#contingency table of observed counts\n",
    "#when creating contingency tables, we put the response variable first (therefore vertical in table), \n",
    "#and the explanatory variable second, therefore horizontal at the top of the table.\n",
    "ct4 = pd.crosstab(df_clean_copy_women['diagnosis_binary'], df_clean_copy_women['chest_pain'])\n",
    "print(ct4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chest_pain             1.0       2.0       3.0       4.0\n",
      "diagnosis_binary                                        \n",
      "0                 0.482759  0.818182  0.612613  0.192073\n",
      "1                 0.517241  0.181818  0.387387  0.807927\n"
     ]
    }
   ],
   "source": [
    "#column percentages\n",
    "colsum = ct4.sum(axis=0)\n",
    "colpct = ct4/colsum\n",
    "print(colpct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square value, p value, expected counts\n",
      "(153.13334878752136, 5.556477341199484e-33, 3, array([[  11.55908289,   39.46031746,   44.24338624,  130.7372134 ],\n",
      "       [  17.44091711,   59.53968254,   66.75661376,  197.2627866 ]]))\n"
     ]
    }
   ],
   "source": [
    "#chi square test\n",
    "print('chi-square value, p value, expected counts')\n",
    "cs4 = scipy.stats.chi2_contingency(ct4)\n",
    "print(cs4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chest_pain  diagnosis\n",
       "1.0         0             14\n",
       "            1              9\n",
       "            2              3\n",
       "            3              2\n",
       "            4              1\n",
       "2.0         0             81\n",
       "            1             14\n",
       "            2              2\n",
       "            3              2\n",
       "3.0         0             68\n",
       "            1             22\n",
       "            3             11\n",
       "            2              8\n",
       "            4              2\n",
       "4.0         1            134\n",
       "            0             63\n",
       "            2             59\n",
       "            3             55\n",
       "            4             17\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy_women.groupby('chest_pain')['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "the relationship between chest pain and the diagnosis holds for both levels of the sex variables, hence it is not a \n",
    "moderator.  \n",
    "we would test for moderator variables in the case of a quantitative response variable the same way:  \n",
    "1. divide the population into the sublevels of the third variables\n",
    "2. conduct an smf.ols test for each to see if the relationship is statistically significant for each level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "identifying a confounding variable does not allow to establish causation, just to get closer to a causal connection.\n",
    "due to infinite number of possible lurking variables, observational studies cannot rly establish causation\n",
    "a lurking of confounding variable is a third variable that is associated with both the explanatory and response \n",
    "variables.\n",
    "i.e. x=firefighters; y=damage caused by a fire. plot would suggest more firefighters causes more fire damage.\n",
    "in reality there is a third confounding variable that influences both, seriousness of the fire.\n",
    "In a study we want to demonstrate that our statistical relationship is valid even after controlling for confounders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "now we will test whether there is a relationship between two quantitative variables, age and cholesterol\n",
    "for this we use the pearson correlation test\n",
    "r, going from -1 to 1 only tells us whether the two variables are linearly related. they may be related in nonlinear ways\n",
    "therefore it's always important to look at r in parallel with a scatterplot of the two variables\n",
    "r squared is a measure of how much variability in one variable can be explained by the other variable\n",
    "to calculate the pearson coefficient we need to remove all missing values\n",
    "Please remember that when two variables are correlated it is possible that:\n",
    "X causes Y or Y causes X\n",
    "Z causes both X and Y\n",
    "X and Y are correlated by chance - a spurious correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'chest_pain', 'rest_bp', 'cholesterol', 'fasting_bs',\n",
       "       'rest_ecg', 'max_heart_rate', 'exercise_angina', 'st_depression',\n",
       "       'diagnosis', 'diagnosis_binary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115abeac8>"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZQJg1axZ4nsfixYuRk5ODQ4cOYenSpXA4HIiPj1fd31y6d++O06dP4+DBgygsLMR7772H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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ac3b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scat1 = sns.regplot(x='age', y = 'cholesterol', fit_reg=True, data = df_clean_copy)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Cholesterol')\n",
    "plt.suptitle('Relationship between age and cholesterol')\n",
    "scat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "association between age and cholesterol\n",
      "(-0.068169552180946086, 0.063640508704807752)\n"
     ]
    }
   ],
   "source": [
    "#the r coefficient is a measure of association, of how closely points are clustered around a line\n",
    "#correlations are always between -1 and 1\n",
    "#r measures solely linear association\n",
    "#association, not causation\n",
    "#it is a number without units\n",
    "#it can mislead in presence of outliers or non linear association -> always draw a scatter plot as well and check visually\n",
    "#when you look at a scatter plot you look at direction form and strength of relationship\n",
    "#if you identify a nonlinear association, with one or more curves, then you know you ought to add nonlinear explanatory\n",
    "#variables to your regression model, to aim to capture this nonlinear association as well.\n",
    "#ecological correlations based on averages can be misleading and overstate strength of associations for individual \n",
    "#units\n",
    "print('association between age and cholesterol')\n",
    "print(scipy.stats.pearsonr(df_clean_copy['age'], df_clean_copy['cholesterol']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Linear regression:\n",
    "\n",
    "multivariate linear regression for quantitative response variable\n",
    "logistic regression for binary categorical response variable\n",
    "\n",
    "Assumptions of these types of models:\n",
    "\n",
    "Normality: residuals from our linear regression model are normally distributed. if they are not,\n",
    "our model may be misspecified\n",
    "\n",
    "Linearity: association between explanatory and response variable is linear\n",
    "\n",
    "Homoscedasticity (or assumption of constant variance): variability in the response variable is the same at all levels\n",
    "of the explanatory variable. i.e. if residuals are spread around the regression line in a \n",
    "similar manner as you move along the x axis (values of the explanatory variable)\n",
    "\n",
    "Independence: observations are not correlated with each other. Longitudinal data can violate this assumption, as well \n",
    "as hierarchical nesting/clustering data i.e. looking at students by classes. this assumption is the most serious\n",
    "to be violated, and also cannot be fixed by modifying the variables. the data structure itself is the problem.\n",
    "\n",
    "We have to contend with:\n",
    "\n",
    "Multicollinearity: explanatory variables are highly correlated with each other. this can mess up your parameter \n",
    "estimates or make them highly unstable. Signs: \n",
    "1. highly associated variable not significant. \n",
    "2. negative regression \n",
    "coefficient that should be positive \n",
    "3. taking out an explanatory variable drastically changes the results\n",
    "\n",
    "Outliers: can affect your regression line\n",
    "\n",
    "multiple regression model allows us to find the relationship between one explanatory variable and the \n",
    "reponse variable, while controlling (holding constant at 0) all the other variables.\n",
    "for interpretability of our model, each variable needs to include a meaningful value of 0, so as to make it easier \n",
    "to interpret the coefficients (what does it mean to hold cholesterol constant at 0 if its range has no value of 0?)\n",
    "for a categorical variable, we can just recode one of the values to be 0\n",
    "for a quantitative variable, we have to center it. Centering = subtracting the mean of a variable from the value \n",
    "of the variable. We are therefore recoding it so that its mean=0.\n",
    "if a quantitative explanatory variable includes a meaningful value of 0 already, we may not need to center it.\n",
    "in linear regression we only center explanatory variables not response one\n",
    "in logistic regression we always need to code response binary variable so that 0 means no outcome and 1 outcome occurred\n",
    "this is true whether outcome is positive or negative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We will create a multiple regression model, investigating the relationship between our explanatory variables and \n",
    "our response variable diagnosis\n",
    "we will first center the explanatory variables. for categorical variables, one of the categories needs to be 0, for \n",
    "quantitative variables, we need to subtract the mean from each value.\n",
    "\n",
    "Notes: \n",
    "do not center the response variable.\n",
    "is using logistic regression, do recode the binary response variable to make sure one class is coded as 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'chest_pain', 'rest_bp', 'cholesterol', 'fasting_bs',\n",
       "       'rest_ecg', 'max_heart_rate', 'exercise_angina', 'st_depression',\n",
       "       'diagnosis', 'diagnosis_binary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#categorical variables: sex, chest_pain, fasting_bs, rest_ecg, exercise_angina\n",
    "#quantitative variables: age, rest_bp, cholesterol, max_heart_rate, st_depression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0    392\n",
       "3.0    161\n",
       "2.0    150\n",
       "1.0     38\n",
       "Name: chest_pain, dtype: int64"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy['chest_pain'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "recode_chest_pain = {1:0, 2:1, 3:2, 4:3}\n",
    "df_clean_copy['chest_pain_p'] = df_clean_copy['chest_pain'].map(recode_chest_pain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    692\n",
       "1.0    138\n",
       "Name: fasting_bs, dtype: int64"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fasting_bs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    551\n",
       "2.0    188\n",
       "1.0    179\n",
       "Name: rest_ecg, dtype: int64"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_ecg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    528\n",
       "1.0    337\n",
       "Name: exercise_angina, dtype: int64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['exercise_angina'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_clean_copy['age_c'] = df_clean_copy['age'] - df_clean_copy['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_clean_copy['rest_bp_c'] = df_clean_copy['rest_bp'] - df_clean_copy['rest_bp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_clean_copy['cholesterol_c'] = df_clean_copy['cholesterol'] - df_clean_copy['cholesterol'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_clean_copy['max_heart_rate_c'] = df_clean_copy['max_heart_rate'] - df_clean_copy['max_heart_rate'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_clean_copy['st_depression_c'] = df_clean_copy['st_depression'] - df_clean_copy['st_depression'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'chest_pain', 'rest_bp', 'cholesterol', 'fasting_bs',\n",
       "       'rest_ecg', 'max_heart_rate', 'exercise_angina', 'st_depression',\n",
       "       'diagnosis', 'chest_pain_p', 'age_c', 'rest_bp_c', 'cholesterol_c',\n",
       "       'max_heart_rate_c', 'st_depression_c', 'diagnosis_binary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_clean_copy_c = df_clean_copy[['age_c', 'sex', 'chest_pain_p', 'rest_bp_c', 'cholesterol_c',\n",
    "                                       'fasting_bs', 'rest_ecg', 'max_heart_rate_c', 'exercise_angina',\n",
    "                                       'st_depression_c', 'diagnosis_binary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age_c', 'sex', 'chest_pain_p', 'rest_bp_c', 'cholesterol_c',\n",
       "       'fasting_bs', 'rest_ecg', 'max_heart_rate_c', 'exercise_angina',\n",
       "       'st_depression_c', 'diagnosis_binary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy_c.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  age_c   R-squared:                       0.001\n",
      "Model:                            OLS   Adj. R-squared:                 -0.000\n",
      "Method:                 Least Squares   F-statistic:                    0.8054\n",
      "Date:                Tue, 27 Jun 2017   Prob (F-statistic):              0.370\n",
      "Time:                        19:31:51   Log-Likelihood:                -2711.1\n",
      "No. Observations:                 741   AIC:                             5426.\n",
      "Df Residuals:                     739   BIC:                             5435.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.5596      0.713     -0.785      0.433        -1.959     0.840\n",
      "sex[T.1.0]     0.7314      0.815      0.897      0.370        -0.868     2.331\n",
      "==============================================================================\n",
      "Omnibus:                       11.248   Durbin-Watson:                   1.086\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):                8.518\n",
      "Skew:                          -0.160   Prob(JB):                       0.0141\n",
      "Kurtosis:                       2.583   Cond. No.                         3.91\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model1 = smf.ols(formula = 'age_c ~ sex', data = df_clean_copy_c).fit()\n",
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We now add explanatory variables to our model one at a time. In doing this, we keep an eye on what impact adding an\n",
    "explanatory variable will have on our model. Sometimes for example we add a variable, and another variable that was\n",
    "statistically significant (had a p value below 0.05), suddenly becomes insignificant. This means the new variable\n",
    "is a confounder, a moderator, variable in the relationship between that other variable and the response variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  age_c   R-squared:                       0.005\n",
      "Model:                            OLS   Adj. R-squared:                  0.002\n",
      "Method:                 Least Squares   F-statistic:                     1.889\n",
      "Date:                Tue, 27 Jun 2017   Prob (F-statistic):              0.152\n",
      "Time:                        19:32:53   Log-Likelihood:                -2709.6\n",
      "No. Observations:                 741   AIC:                             5425.\n",
      "Df Residuals:                     738   BIC:                             5439.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept        -0.3644      0.721     -0.506      0.613        -1.780     1.051\n",
      "sex[T.1.0]        0.4762      0.827      0.576      0.565        -1.148     2.100\n",
      "cholesterol_c    -0.0065      0.004     -1.724      0.085        -0.014     0.001\n",
      "==============================================================================\n",
      "Omnibus:                       10.509   Durbin-Watson:                   1.107\n",
      "Prob(Omnibus):                  0.005   Jarque-Bera (JB):                8.122\n",
      "Skew:                          -0.158   Prob(JB):                       0.0172\n",
      "Kurtosis:                       2.597   Cond. No.                         288.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model2 = smf.ols(formula = 'age_c ~ sex + cholesterol_c', data=df_clean_copy_c).fit()\n",
    "print(model2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#here we will skip a couple of steps and create the overall model with all variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  age_c   R-squared:                       0.278\n",
      "Model:                            OLS   Adj. R-squared:                  0.266\n",
      "Method:                 Least Squares   F-statistic:                     21.58\n",
      "Date:                Tue, 27 Jun 2017   Prob (F-statistic):           1.25e-43\n",
      "Time:                        20:35:33   Log-Likelihood:                -2590.6\n",
      "No. Observations:                 741   AIC:                             5209.\n",
      "Df Residuals:                     727   BIC:                             5274.\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------------------\n",
      "Intercept                 -0.9208      1.486     -0.619      0.536        -3.839     1.997\n",
      "sex[T.1.0]                -1.5406      0.744     -2.071      0.039        -3.001    -0.080\n",
      "C(chest_pain_p)[T.1]      -1.5128      1.507     -1.004      0.316        -4.471     1.446\n",
      "C(chest_pain_p)[T.2]       0.5994      1.477      0.406      0.685        -2.300     3.499\n",
      "C(chest_pain_p)[T.3]      -0.5931      1.446     -0.410      0.682        -3.432     2.246\n",
      "fasting_bs[T.1.0]          3.9777      0.853      4.664      0.000         2.303     5.652\n",
      "C(rest_ecg)[T.1.0]         1.8683      0.851      2.196      0.028         0.198     3.539\n",
      "C(rest_ecg)[T.2.0]         3.9081      0.742      5.268      0.000         2.452     5.364\n",
      "exercise_angina[T.1.0]     0.0511      0.769      0.066      0.947        -1.458     1.561\n",
      "rest_bp_c                  0.0754      0.017      4.508      0.000         0.043     0.108\n",
      "cholesterol_c             -0.0042      0.003     -1.251      0.211        -0.011     0.002\n",
      "max_heart_rate_c          -0.1093      0.013     -8.152      0.000        -0.136    -0.083\n",
      "st_depression_c            0.9892      0.319      3.097      0.002         0.362     1.616\n",
      "diagnosis_binary           1.4426      0.796      1.813      0.070        -0.119     3.005\n",
      "==============================================================================\n",
      "Omnibus:                        5.205   Durbin-Watson:                   1.369\n",
      "Prob(Omnibus):                  0.074   Jarque-Bera (JB):                3.958\n",
      "Skew:                           0.047   Prob(JB):                        0.138\n",
      "Kurtosis:                       2.654   Cond. No.                         870.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model3 = smf.ols(formula = 'age_c ~ sex + C(chest_pain_p) + (rest_bp_c) + cholesterol_c + fasting_bs + C(rest_ecg) + \\\n",
    "                            max_heart_rate_c + exercise_angina + st_depression_c + diagnosis_binary', data = df_clean_copy_c).fit()\n",
    "print(model3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We identified some explanatory variables that are associated with age, but our model overall barely explains 27%\n",
    "of the variation in the response variable\n",
    "let's run some diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b67UoEWm4PlppY9+J4rLvTYaT/tm7GJ6+CX+jlGP+MSyxJnAyILrCOE2ni9QP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\nERFxMAW5iIiIg30CASp4eT9h3+wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bf6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Q-Q plot for normality\n",
    "fig1 = sm.qqplot(model3.resid, line='r')\n",
    "#red line represents residuals we would expect if model residuals were normally distributed\n",
    "#our residuals below deviate somewhat from red line, especially at lower and higher quantiles, meaning they do not\n",
    "#follow a normal distribution. This means the curvilinear association in our model is not fully explained by our model.\n",
    "#We could add more explanatory variables in this case to try to better explain any curvilinear association."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x117457978>"
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TMc+1nDjbIWXxqo3SsW/QeqhD6biL6EZ6pv33v/8djz32GNra2pBKpZBMJrF9+3b89a9/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\nIaEthBBCOAgJbSGEEMJBSGgLIYQQDuI/pWpLRhwbSWgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11730bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#simple plot of residuals\n",
    "stdres = pd.DataFrame(model3.resid_pearson)\n",
    "plt.plot(stdres, 'o', ls='None')\n",
    "l = plt.axhline(y=0, color='r')\n",
    "plt.ylabel('Standardized Residual')\n",
    "plt.xlabel('Observation Number')\n",
    "#resid_pearson normalizes our model's residuals\n",
    "#ls='none' means points will not be connected\n",
    "\n",
    "#we expect most residuals to fall within 2sd of the mean. More than 2 indicate there are outliers, and more than 3 \n",
    "#extreme outliers.\n",
    "#if more than 1% of our observations have standardized residuals with an absolute value greater than 2.5, or more than 5%\n",
    "#have one greater than or equal to 2, there is evidence that the fit of the model is poor. top cause of this is \n",
    "#ommission of important explanatory variables in our model.\n",
    "\n",
    "#standardized residuals in linear regression will always be linear, and the line will be horizontal\n",
    "\n",
    "#normalizing or standardizing residuals amounts to making them have a mean of 0 and sd of 1 so as to fit a normal \n",
    "#standard distribution\n",
    "\n",
    "#if residuals show a strong pattern (up, down, polynomial) then it is a good indication of nonlinearity\n",
    "#in the underlying relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(576x396)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7676XTwscPXo0TZs2ZcuWLUycOBFJksRcdEEQ7pgI5NXUl19+ibu7u00ddbD2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\n1i5LlqtZUrksrhmJPWuLgb0yGdY3i31Hrf1zq59OwuOyU5DtR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zdTXl6OJEncfffdPPLI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\nnpyczOXLl4m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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11534bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#leverage plot\n",
    "fig4 = sm.graphics.influence_plot(model3, size=8)\n",
    "print(fig4)\n",
    "\n",
    "#leverage, always between 0 and 1. At 0, a standardized residual has no influence on our model.\n",
    "#leverage is a measure of how much influence a specific residual (and therefore observation) has on our model.\n",
    "#we see that we have extreme outliers, but they are low leverage, meaning they do not have an undue influence on our\n",
    "#estimation of the regression model.\n",
    "#we have an observation that is both high leverage and and outlier, observation 33. We would need to further investigate\n",
    "#the corresponding observation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#now let's focus on our actual response variable in the study\n",
    "#since it is a binary variable, we will need to use a logistic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.543711\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:       diagnosis_binary   No. Observations:                  741\n",
      "Model:                          Logit   Df Residuals:                      737\n",
      "Method:                           MLE   Df Model:                            3\n",
      "Date:                Sat, 24 Jun 2017   Pseudo R-squ.:                  0.2148\n",
      "Time:                        13:48:58   Log-Likelihood:                -402.89\n",
      "converged:                       True   LL-Null:                       -513.13\n",
      "                                        LLR p-value:                 1.581e-47\n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "----------------------------------------------------------------------------------------\n",
      "Intercept               -0.4274      0.332     -1.288      0.198        -1.078     0.223\n",
      "C(chest_pain_p)[T.1]    -1.3335      0.404     -3.299      0.001        -2.126    -0.541\n",
      "C(chest_pain_p)[T.2]    -0.3992      0.373     -1.069      0.285        -1.131     0.333\n",
      "C(chest_pain_p)[T.3]     1.5812      0.352      4.488      0.000         0.891     2.272\n",
      "========================================================================================\n"
     ]
    }
   ],
   "source": [
    "lreg1 = smf.logit(formula = 'diagnosis_binary ~ C(chest_pain_p)', data = df_clean_copy_c).fit()\n",
    "print(lreg1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.488979\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:       diagnosis_binary   No. Observations:                  741\n",
      "Model:                          Logit   Df Residuals:                      734\n",
      "Method:                           MLE   Df Model:                            6\n",
      "Date:                Tue, 27 Jun 2017   Pseudo R-squ.:                  0.2939\n",
      "Time:                        19:24:14   Log-Likelihood:                -362.33\n",
      "converged:                       True   LL-Null:                       -513.13\n",
      "                                        LLR p-value:                 3.728e-62\n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "----------------------------------------------------------------------------------------\n",
      "Intercept               -1.6537      0.408     -4.058      0.000        -2.452    -0.855\n",
      "sex[T.1.0]               1.4468      0.233      6.216      0.000         0.991     1.903\n",
      "C(chest_pain_p)[T.1]    -1.0153      0.430     -2.362      0.018        -1.858    -0.173\n",
      "C(chest_pain_p)[T.2]    -0.3127      0.400     -0.781      0.435        -1.097     0.472\n",
      "C(chest_pain_p)[T.3]     1.6327      0.378      4.315      0.000         0.891     2.374\n",
      "age_c                    0.0583      0.011      5.538      0.000         0.038     0.079\n",
      "cholesterol_c           -0.0015      0.001     -1.495      0.135        -0.004     0.000\n",
      "========================================================================================\n"
     ]
    }
   ],
   "source": [
    "#again, normally we would add variables one at a time, but here we will go faster.\n",
    "lreg2 = smf.logit(formula = 'diagnosis_binary ~ age_c + sex + C(chest_pain_p) + cholesterol_c', data = df_clean_copy_c).fit()\n",
    "print(lreg2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.421593\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:       diagnosis_binary   No. Observations:                  741\n",
      "Model:                          Logit   Df Residuals:                      727\n",
      "Method:                           MLE   Df Model:                           13\n",
      "Date:                Tue, 27 Jun 2017   Pseudo R-squ.:                  0.3912\n",
      "Time:                        20:35:53   Log-Likelihood:                -312.40\n",
      "converged:                       True   LL-Null:                       -513.13\n",
      "                                        LLR p-value:                 1.100e-77\n",
      "==========================================================================================\n",
      "                             coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------------------\n",
      "Intercept                 -1.9267      0.465     -4.139      0.000        -2.839    -1.014\n",
      "sex[T.1.0]                 1.3101      0.256      5.110      0.000         0.808     1.813\n",
      "C(chest_pain_p)[T.1]      -0.5553      0.478     -1.162      0.245        -1.492     0.381\n",
      "C(chest_pain_p)[T.2]      -0.1410      0.443     -0.318      0.750        -1.009     0.727\n",
      "C(chest_pain_p)[T.3]       1.3009      0.430      3.023      0.003         0.458     2.144\n",
      "fasting_bs[T.1.0]          0.5082      0.292      1.742      0.081        -0.063     1.080\n",
      "C(rest_ecg)[T.1.0]        -0.0877      0.302     -0.290      0.772        -0.680     0.505\n",
      "C(rest_ecg)[T.2.0]         0.2337      0.253      0.925      0.355        -0.261     0.729\n",
      "exercise_angina[T.1.0]     1.0054      0.231      4.353      0.000         0.553     1.458\n",
      "age_c                      0.0217      0.013      1.723      0.085        -0.003     0.046\n",
      "rest_bp_c                  0.0062      0.006      1.089      0.276        -0.005     0.017\n",
      "cholesterol_c             -0.0018      0.001     -1.576      0.115        -0.004     0.000\n",
      "max_heart_rate_c          -0.0142      0.005     -3.119      0.002        -0.023    -0.005\n",
      "st_depression_c            0.6158      0.114      5.402      0.000         0.392     0.839\n",
      "==========================================================================================\n"
     ]
    }
   ],
   "source": [
    "lreg3 = smf.logit(formula = 'diagnosis_binary ~ age_c + sex + C(chest_pain_p) + rest_bp_c + \\\n",
    "                  cholesterol_c + fasting_bs + C(rest_ecg) + max_heart_rate_c + \\\n",
    "                  exercise_angina + st_depression_c', data = df_clean_copy_c).fit()\n",
    "print(lreg3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Odds Ratios\n",
      "Intercept                    0.145625\n",
      "C(sex)[T.1.0]                3.706415\n",
      "C(chest_pain_p)[T.1]         0.573894\n",
      "C(chest_pain_p)[T.2]         0.868495\n",
      "C(chest_pain_p)[T.3]         3.672428\n",
      "C(fasting_bs)[T.1.0]         1.662336\n",
      "C(rest_ecg)[T.1.0]           0.916029\n",
      "C(rest_ecg)[T.2.0]           1.263268\n",
      "C(exercise_angina)[T.1.0]    2.732984\n",
      "age_c                        1.021933\n",
      "rest_bp_c                    1.006246\n",
      "cholesterol_c                0.998173\n",
      "max_heart_rate_c             0.985909\n",
      "st_depression_c              1.851057\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#however, for logistic regression it makes much more sense to calculate the odds ratio\n",
    "#this is because in binary logistic regression, we only deal with probabilities, of an outcome (response variable) \n",
    "#being 0 or 1.\n",
    "#odds are calculated as the exponentiation of our coefficients as calculated in a normal ANOVA table.\n",
    "\n",
    "#Odds ratio (OR) for an explanatory variable:\n",
    "#if OR=1, there is no association meaningful association between explanatory and response variables\n",
    "#if OR<1, the response variable becomes less likely as the explanatory one increases\n",
    "#if OR>1, the response variable becomes more likely as the explanatory one increases\n",
    "print('Odds Ratios')\n",
    "print(np.exp(lreg3.params))\n",
    "#Interpretation of OR\n",
    "#Here we would say that based on our sample, women were 3.7 times more likely than men to have a diagnosis of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Lower CI  Upper CI        OR\n",
      "Intercept                  0.058480  0.362629  0.145625\n",
      "C(sex)[T.1.0]              2.242497  6.125988  3.706415\n",
      "C(chest_pain_p)[T.1]       0.224975  1.463965  0.573894\n",
      "C(chest_pain_p)[T.2]       0.364589  2.068857  0.868495\n",
      "C(chest_pain_p)[T.3]       1.580165  8.535010  3.672428\n",
      "C(fasting_bs)[T.1.0]       0.938503  2.944433  1.662336\n",
      "C(rest_ecg)[T.1.0]         0.506524  1.656603  0.916029\n",
      "C(rest_ecg)[T.2.0]         0.769914  2.072760  1.263268\n",
      "C(exercise_angina)[T.1.0]  1.737951  4.297705  2.732984\n",
      "age_c                      0.997020  1.047468  1.021933\n",
      "rest_bp_c                  0.995035  1.017583  1.006246\n",
      "cholesterol_c              0.995907  1.000445  0.998173\n",
      "max_heart_rate_c           0.977157  0.994740  0.985909\n",
      "st_depression_c            1.480481  2.314391  1.851057\n"
     ]
    }
   ],
   "source": [
    "# odd ratios with 95% confidence intervals\n",
    "params = lreg3.params\n",
    "conf = lreg3.conf_int()\n",
    "conf['OR'] = params\n",
    "conf.columns = ['Lower CI', 'Upper CI', 'OR']\n",
    "print (np.exp(conf))\n",
    "#we have 95% confidence that the sex odds ratio will be between 2.24 and 6.12 for the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1137df2e8>"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Y1/K+uI1yv1KJkYvOiqtWy0Wt5ya1Y7T0Oj5LN8BM/TyivW7RmNpfeQT/a1B0\nQ4l8ffV9rcSSRHsETP2MRnNSGcBqtTCNVTIz0pCZkea4cnWyjV6jZ6ApT0WYLW7jJoZK+8svv8Ty\n5ctRUlKCjIwMzJs3DyNGjPCibY4isu71MNoTjRdyVOc3zQj1tNQgGhrChsFk6v0nM+41SQIkSXLN\nFeckPbt1cDWbmx77K4+0ONcMtA7M0To+ZSUyWmt/02lD1Ergo12BbsXI69lNO9WmleDPaD0uamPJ\naIwp55nou59s2R1Zzdpx2cuYDSK1w5ZtB1A0Ns8wwCzRcLL0rRGGSjsQCODYsWORSl/qql9+INoS\nlvHOTarzm2aE+l2jc7Fo9WZLnodEpLBggGHpRDdR1+gVsXB1WURpmwkoUrvd1atOec9VdJ0AgC4G\n6S7TUoMtgsK07mPm/LHT6J0YMIudbQJ1G5SYNbT0zuXL70J2u1tx9yuRDQo3VsTf7anWzKQmH1tN\nVLwsIKJ9hkPBuHHjcNNNN2Hfvn146KGHMGrUKN9V+YrVSsoLtPZSehkMoNyfdUV+bg9L9bQTETkS\n3CinuB06tHM25kOpPM1Uh8vMSGsVLV6+qypiJKxevx2FBQPQp7t2ecHjstPQPkO/dKuWwlbfx+vV\nlux1ijatrlwIR+v6eqQEA7bmpIx8Ll+EUpZFU77VrVKtotzok68/1ZX7xQtelr41VNojR47EzJkz\ncdttt6Fnz5545plnUFhY6EXbHMPsBO7TPcv0kSgnnA19umdh+Nl9o/q91j6KaAClBAMYfnZfzJpw\nJgBr9bT9jOh88ZZtB1y5X0owgL/NvhRFY/McO2KnJD+3B4rG5gkVLtDSXaeXJUo0Vs4eJE5qoWyH\n+npm6dAuTfhezCDqV9nrZFZJAk3Gmzz3+3TPQtHYPNx65ZBIHwcDiHyu9mppYWVOqlEGemqhfK/K\ncWBlnMkrYeVvjYwRJXpyS9R2+X6idsrGjl475Pdg9km7ZmfAhenXCvX2pNsI3eOSJGH9+vXo2LEj\nhgwZghNOOAEA8O9//xu33HILFixYYPlm4XAYM2bMwNdff4309HTMmTMHvXtrW7ROYsadqIz21HM7\nyVHRj/7tM2gF3ssR59/urkJKShANjWHkdMzQTBwiBxjJGc6s7lPpTRDAOF3kOYM6eBqA1aFdOqpr\nj3l2PxnRuVhZAP5QddTR+8mrDbm/ndg/7qoSZrLgNVN+Ui+jlGisZIb3ILS9QXceqLGyup141RBL\nKWbVyCvRBaqfAAAgAElEQVQ30Ri3sm8vKroi97FWJO9ji0s13eciN6m6n/Xy1+fn9hDuCauvr3RF\nm3GXK9+blhtbbp/IlR0MNLvnZblltlZ1fm4P4TtpDEu680XpvTB6TnXWs8unrIw6Uj4lGBC+MzOG\nnJMIlfaMGTNQUlKCo0ePYvr06cjPz8f//M//YNmyZbjqqqts3ey9997DsWPH8Oqrr2LTpk145JFH\n8Mwzz9huvFnMTGClAhR9XzlwREcbeh+vfdxEfWZUXdxC6cpUTlh5b1K+p5UJYmT9ndKnHfr16xu5\nblBwLlp5frRTVlvUHWuMKN+c7Az07NYeW7YdQH1DWHi+UnR2dnD/LpHzrFoTKyUYQFiSLGWRUtKh\nXRq6dMzQVWxWs2cZoa5lrpem0SzqvNPKexmlKDXKPa41VkKhPbrzRstgFN1H7/yxKFubXn8pE9yI\nxrj6uiIlZGe1r6dc9FbJekpS3S+iI25617cq5/TaJ1KMSu+clZS0ZjGz4DAjn5X0Pj5LmFmvoTFs\nqNCVCzrlO8vJSsW44UM8T74iPKd9/vnn44033sAPP/yAadOmoaqqCl27dsW0adMiq26rzJ07F0OG\nDMGvf/1rAMA555yDDz/8UPc3oVAI0l/+z9b9lByta0Tt0Xo0NIYR/NFnEg43KYN2bdPQtk2K8Pta\n3zla14iqw63TuWZltml1rXilru4Y2rRp3nt16pmM+s7O70RtMyIrsw0A6D6X6NoZbVJR3xCOtEcO\nTlK2D4CpZ1U+mxXM9J8bY1UeG0frGlFz5FjkXHcwGED7jHThMzo1J7T6y8pYUlN9+BiO1DW0+jyj\nTSo6ZOrHH6jniVYbo2mbCDvXF40zvfcmuo7Wu2zXJoj2mfZjQPZXHtHMERC06KK30jdG49JoborG\nr2hcOEFg4m+tn9Pu0KEDMjMzkZmZiW3btmHixIm48cYbo2pITU0N2rdvH/l/SkoKGhoaDLOr1dVF\n71INAMhsG4RyG7++IYyj9RKqDteh6nDTZ8FgAG3TflTqP9ozYUlCQ0M96tDY4nrt2gRxtF5COCxF\nfhdAI+rqmr8Xa+RnVLZRubpQ9q2ZZ6pvCOPIMSmyNRAIBJCR3vKarfvaXJ/o/U7UNgCRz+RTDZLU\nsu0weC7RtdNSgfTUlmOm5f+brm3mWeVnq28AauvEirtdm6DG6k+//4z62+5Yras7hgCADhlqgSV+\nRqfmhNZ81bu3EempQGM4iGP1zX2fnhZEeqo5+aL1Hbvj3Cx2ri/uN2vtE8+JYFTyuG1aALV1rZV2\n27SApeta6Rujcam8lpa81Bu/TugmLfTMIqG2VB7r6tKlS9QKGwDat2+Pw4cPR/4fDodNpUM964Xn\nor63jLw/VVJagUct7qXl/JhOUT5Xe57JZBJm0tw5nfNWr1pV7s+64mBVneU0fHpn3fUyAvkhn28o\nFMJ5P44Lt9sauYci7sGtdIjKe76peq6zdO4lysikHleJUDXJCCtZGY3Gj9W/y9tH0Y5Hp66r1RdW\n54zetoCfcDpbp/raIkwp7bQ0Z6KMTz31VKxbtw6XXXYZNm3ahBNPPNGR69ph4eoyy79RV8IBzBdh\n/6r8h0jAi9YE0irzqXV9MxgF9yhzkcv3mv9ySKg4zJSrLH4phIWry1oJ8FgVpbejXMy01ayA0vue\nvBeo/E7NkXosXF3WquqWXlvNCko7z5XXNxVqeaQ1ruSkIMrxbYRbhlGsjUOjfjb6u9ooVmfgsjt3\ntBPNRH9d0bWNruXEXnis33UsESrtr776CieddBKAJnej8t+BQABfffWV5ZtdeOGF+Oc//4nRo0dD\nkiQ8/PDDNpttD7lO8L5Xoo8mBJoU1WOLS9HQGI6k+xQpttXrt+OkPp0jv5NxKjVetJW85OpYWsLc\nbBSuVtm7WBSlt6tcRG19bHFp5N9mBJRZJSnKNKd3Xa0ASKvfVz5L8UuhVjm/5exh/fpVmM60J49v\nMx4lN4w4p66rpQwyTf7WaKzr/R3QT6WppPilEJa+/43malm+npnStKJ2WsHomdwyzpwyrq1+Nx4Q\nKu2tW7c6frNgMIhZs2Y5fl0z2EljagY5otlMEgmrSV6UZzL1Bpb62eyUDVXyvCL7lp3ENMrJb6WA\nvRF6q2ezRouechG1VS4/2KGdtsdJLexEfbbQYr/+798+i6y8zVQyUrbDyngX5fxWP5fRkS4zQt8t\nI86J64qUwagzO7fyOmhhNNb1/m7V86e3WlZ/ZjanhJU5aWQQ7thd5YpxBoi9pPK7tmLA6XlGndiW\ncANv6mvGmHhJY1q+q8pSUpZgMIArila1KqmnHIROHCdSs6/yCG6a/Q5uGj7IVmYp5eQ3Om6kRMsw\nAcTlL+XVs1pYGRktWquAvL6phke/RMqtfFcVRtyzMmJE6KWhLCltWr2a6Vf5mJKeN0bdDsC58S4/\nl1w606h/zAh9J404p68rUvzry6oxfpTxisxorIv+Ljr/6xRmj0uaTcVpxvsmuqfsJYhmb17UV3Lx\nH6VnTInSgDMyOpzcrnSapFDa8ZTG1Ipb3mgV72ZSflkhisol6tEYljBp/joM7t8FBw5pt6+wYEAL\nIahnmDiN1iqgfBeEK2mzmOkzLSPDSZ5dvtnxTG/1DWGsXr8duT/rqlt0IxgM4PIpK5GqE1xnxYiT\nUZa0FF3bznXViBT/vkP1uqs3oNmw1EI2PmNVMdDsMUOzWdvMyFO9e2rF+ZhF796dstrq9q+y+pud\n96DltYmFaz0plHa0eYjjFS/yOouUTyCgb4DorQ7lFIhWq4c5hWgV4FTN9FjWXl/zyQ7XCjPIFZxE\npVnlPlWWMpSF8ydbduu+Y1lhGAVpqq8NNK1+RApxcP8upgWrSPF37Zimu+Wh9VzBQFPJUqNEMkYF\na+STHtEk/+l9fJZhtkUrZ6T15KmcMdJMXW31VpWZ9xSNLJcNODtByEBrr80X5bV4/SPvg2yFSnvF\nihW6Pxw5cqTjjXELpzNeJRtaGa0A++k513yyI6bbFVYTnKjpmq2dljYeqG8Io0937QxQTlxbHfm+\nY1eVYZlH0btWKzajKGct5NWPKPPc6vXbTbs6RYp/b2U9JEE1PJEh0kuQGVErclqk4HKyMyJ1Auyu\nDgGYUqJaQaQiRPJUmTkMMCcfrO5D62XdM0pHXFgwAM8u32x7gaD22nxYpm1AuF1bW5jD79NPP8Wn\nn36KpUuXYv78+di4cSM+++wz/PnPf8Zbb73lWoPcQOT2yf1ZV49b4ixmreNoi1bIg3zy9afiiSnD\nIoLHTrECIPpaxdEw/Oy+kUh/u2RmpOkW67BDIKCdz9sOg/t3ceQ6WpSUVgBoEqRPTBkWyS5oh17H\nZ2Fw/y54bHEpRtyz0pZSUq5+Ptmy2/TvtFbOyjGtjD2x47ewspcukk/KnNbq+SYX7VAXOlF/R86f\nYNb1XfxSCJPmr4u8ZyvtVafvNSpqAzT3k1EUutG9bxo+SFgkJi01iKKxTZGERosFK8VQRFUS3a6t\nLVxpz507FwBwww03YNWqVejcuem40qFDh/D73//e1UY5jdIt9e3uqhbWvd2atLFEmY/cjKALO3C+\nTcvyNZOrOBbIAWGAdg5jK8Jdi+/2VGPy9ac6uj8p56y/omiVqeh/vX3z1eu3G9bLlldF109/y5I7\nX33uOBpXvNkAOz3kYM1e3TpYWkGJBKs8ps2O5xxBzXEre+lm8m0r2ybCKDcAYC4OxsjNK2ov0CQH\nlPd/YsowXS+B3E9mAwmN+krrPnIJ2Unz1+k+tzJBlJliKF07pmGvhgfG7drahnvae/fuRXZ2duT/\nGRkZ2Ldvn84v4hN5wKuz2IhcYqLJqCYlGMDk6091JSisa3YG9h860moval/lkUgylOFn99W1HovG\n5um6xvp0z4qc9zQjpJTHwZR4EWRj9p0ok6io98se/dtnUR+J69mtA/JzxZWY7CALPbNbOROvGqJ7\ncmDLtgO6AlO+38Srhlh+b0bnjr1EucdtBSPBqrd3mhIMGG4TWa2vHG3CETPuZatGtp6bV91e0f3l\nhEsiOWU07kX1ua0YE0bHT4HW5TXNvA9RlUS3a2sbKu3zzjsPN910Ey666CKEw2G8/fbbuPTSS11t\nlJfoWY1mhJkswO2cj5Xd2yLBn5mRBknwdzkZSvmuKqEyU9bb1nqWUWd2xvhR57T4zOgoxD7FsSUl\nZpRYSjBge2WmXD2LyiLKqIVNNPuBWsjjQ1SJyQwpwQAkSWoVrGS1pORbH5VrGiHf7q6KrHpysjMQ\nQFMJUlGVLSsGiNG5Yz+gPr2gXpma3beViXVaTqvn1M2MMytuXlFwl7xXXjQ2T3f1aqZanRn0lK2e\nQWynvKa6SqJX795QaU+bNg1r1qzBhg0bEAgEcPPNN6OgoMDVRsUKpehTK/POWW2FNbGV3HrlEMNV\nq+ze1grAUBKt61ApmAKBpqjpxsZwRFFkhve0+o0Z9+Bji0s1020aBYLonUeVK2iJUE7+u0bnmjra\nIRPtirDrj0EuIoVnZESo0cvTnp/bA/NfDhkeDfzfHyOORYIoLDWPH7nPRfe16hJ2q6Sp2+itkM1G\no9ccqW9ltEa7SnYCq+fUlfJN9A7l96w0bnKyUnFjsKKVUWxk8C19/5tIPIxRe9xSgKJ3qiz1apVY\nvHtTR75ycnJwwgkn4KqrrsLmzZvdbpOnGLmV1IPTzKDSq/cKNFml8nndnOwMW2ehlRysOhpxg+t5\nC2TFIrc7FGqttI2eQXkddV8ZCXG9iR0OS4aufqB58gNiZal0qZWUVkSlWIaf3Vf3LKmeJ0OEUZSu\nqP6vksawhOKXQhh+dl/Tz2cU1Wp2lS+PrcH9u/hGaatXyKL9TWU0OtDaA2ElytpL7JxTV54EEK1y\n1X/bW1lvOlWxEjOrdrcVoBeGgRcYKu1Fixbhvffew969e3HppZfiwQcfxNVXX41bbrnFi/a5jhW3\nknKQqxWv1j6qGZe7E3uiahe9jJFg0iM/twf+92+fmXJny9eLZl+7U1ZbU9sK8uTXU5bKM7967REd\n3dI6Y6uH+n0HgwHDlbfeO7DSj/LZaeVYE53JNRKc+bk9dO8rn8GV2x1tEhc5nuLvH5W7drZcRu0R\nM7MylfftteaomcxeXibeiMa9rKfMzMgQM9skbgZnWennePCKRIuh0l6+fDmWLFmCa665BtnZ2Xjt\ntddQWFiYMEpbNODKd1Vp7t3qFaMAtIM+ZPSiF7tmZyAzI83WykU0MaNN7Wg26lytSOXJ74YgVk5+\nI8vZaAVw9FiD5ueiM7Z6KN/3FUWrDL+v9w7yc5vOHK/5ZIeh8v9uT3WrsSZycxsJTr1jPgBa9Um0\ne9ryuzqpT2ehZ8pMkg41cuS83mrK7MpU7xnt5LTW+q4TRLuKFCkzMzLEzDaJW8FZsaoiGEsMlXYw\nGER6enrk/23atEFKSoqrjfISvQGn9fL1FIHRClZPAPxQdRTPTb/I1tEpOQjEqmD6orwWz69b1yI9\npHyG+Yeqo1HlLJYg3qfWStailxVKyY4fA6xkgaRnOev1t54rPtpzlmaEmFHaTrPBjFrXsbvq0hvb\nXTVyAuglutBKxmPVuJJXslrPkhIMICxJ6NJRHGSnh9k+MvMutea9kQfPjVW4G6tIM8aNqC+teqzM\noO63miPmit0kEoZK+7TTTsO8efNw5MgRvPfee3j11VcxdOhQL9rmKsp8xnpYqXRkJOz1BIA8Cey4\nmEX7bHqCqaS0osVxBa0852aPRonc0aLfa9W1Fq2o0lKbjAl50S8qIaqFXgSwnms3WleemXeop0Ct\nBM7VHKmPnFNWGjLydaysuvTG9niN6FrRc54++Hhs2XagVWCnVeNK9iIAzc8iBzPKXhyjIDsRZvvI\nbpS13jP5aXVoJEMiAWo6JxScQqvfRLid4CSWGCrte++9F0uWLMHPfvYzrFixAueeey6uu+46L9rm\nGlaOAKlfvhnFK0JPAMhZrMxGrWuhNjDs7FVpkZYaRDgsoeePuaBFrkeRslH+Xj2ZjYynu0bnChW6\nnjVdUlohtMKNVvbRuvK0FI0VgWbG7SwHL8pKS6uAhdVVnN7KWRR1Lt9LHg/qXOFmFJLRak6p8CfN\nX6e5x2xnZaUVaKpODpKf2wP//e92hLY3WJr3es8kmidydSqt54hVzWf1O87JSsW44U2BmVq1A6wa\nT1awYsy6neAklpgKRBszZgxGjx4d+ay4uBhFRUWuNsxNonn5eorXSNjLg1mr4IIyeb5e1LrePnH5\nrpauY/meVvaqtAiHJawovtzwe6Jrin4vMp4CAbSo4iRSsCJrWnRd5VE7vXzPTgidaFyVZs4IT5q/\nTvPEwfOry1oYeVaUuWhs651h1dpP12KhICmP3n215pNbpT316ipvKKvG/qoGYT4ErXbqHRkTBZ/K\nddsBtJr/sVyZK99xKBRCnskANaexIrPcTnASS4S5x2Uee+wxjBkzBnv2NB8P+uijj1xtlFuUlFbg\n6bf2WNozVr/8/NymnLrKvN9dszNMW5j5uT2Eua9FxkR+blOe5xXFlxvm8pUntCioSF5NWMkKZtZq\nFeX+Ff1e9LyShBYKxcp19erpZmakRa5pJt9zrDCT21lYRlKgEJ7/8bRD+a4qhMOS5jiRx3af7lkI\n/pgL3erKyaiWuBbK+6pzZquxOsbMIhqLq9dvx97KeoTDUkTZds3OMGyn+pmMEinptcVsXm4vcct4\n0kP07nOyM0yNnUTBcKXdt29fTJgwAWPGjMEjjzyCX/7yl160y3HMuMS1AmhEEzKaQRHNgDe75y2q\n/WrnSJbZAhRWA6D0LGdl+81e1+j51Md55Pso3X5679VJF6WZPNF6+61WE5uIlPnC1WWt2vHElGGt\n0v2aRa9dVtJiinAqc5YaK6u4zIw0PDf9IsPvmXHra6GWA7FQkEY4Ub/cKnqeoERW0moMlXYgEMCF\nF16Inj174s4778S4ceOQlpbmRdscxYxV6tXLj2bAy+0zSjup5SrX64O01KZsaZkZrRO9qOveGrXN\nbACUnoDXU7Ci6xq9Y3X/arn9RDjporSaJ1oLp3K97688orkvnmnzenrtckLJRHu0SYuS0oqmaHST\n3ic7z2HFKFCP01goSCPcMp70cOPd+xFDpS39GLY7cOBAvPLKK7jzzjvx1Vdfud4wpzFTuN2rl29m\nwButxJQWvEjxqZWBqA9SggEsmzcicj2tvVKze1VWvBB6Al5PwYowEozRCBSruZ3dvpZagBmdiTdb\nbEW+5vhh9sqE5ueKc9A7pWSsjDEj74gd75Od57DiGVGP01goSCNipUCj9XImAoZK+6mnnor8u3Pn\nzli4cCHefvttVxvlBlYLALiJ0YC3sqozs+KSlYEZi91LV1x+bg9hpSotgWQkgEXPl5YajJTns4uZ\nfjHrPneqj80Yb/KzA+ZTrTa1w35tb1EhFbNGqVPbEGbmkd6Jh4tP7216bBohmqfKFMbKQEkl8brC\npAKNDUKl/cQTT2DSpEl48sknNf8+fPhw1xrlBnasVTePWcgDXlkyUk4mYTW1qvw3I1ezmT7w2hV3\n65VDTNWuNSOARc8XrcIGjPvFiqHlRh+Lnr1j+zYt2qDsZ1Ekc7Tv2kjJqCvhqSPcRVHcejngtTAz\nj/ROPMhj84XVm7G/qiEqZanuE/m8udKrpXe0M5kVZKyOu8UrQqV98sknA2hKrpIIyC/Z7AT04piF\n6B6BgPb39Sr25Of2wE2z39EUwp2y2ka+B+gHX8Vqr8qoT80aMko3sGjlYgdRv8gZ2qxkZnKjj+V7\nqI98qRPvmHENFxYMADQqwFltj1a/62V704tJkOMq5O+ZEeDRpOBUnhHPDO+xFZSnxkxgWiJn8rJD\nrI+7xSNCpT1w4EDs3LkzIbKfyViZgE7uYVq9hyh9qN0VkNIGMAq+ildXnJEA1lJA6pVLNBa7sl++\n3V2FsCpDmwgtQ8utPs7PbTp/rrViM/LSqNuhVwEuGvQUs9Ger3qv3EiAixSybMQCsdsvjseI8HjE\nCznsN4RKe+zYsQgEAqirq8OBAwfQs2dPBINBfPvtt+jVq5cv97WtYGVS2VUGons0NGrn+zYSJKJ6\n1qLP5dzj6nbHoyvOaEVkJtdztBa73C9W8sOLDK1o+lhvvNmpq2ymHU65KKMpMiIKpNM6tpafK646\nJ58ZVz6710ZqPEaExyM0blojVNpr164FANx9990YM2ZM5Hz25s2b8f/+3//zpnUxxOykikYZiO4h\nZwOzKkisCAJ17vF4dzsZrYiMJreTFrsTmZnsKkGj8eaGMnDSRWn1fLkZRMfW9CLZle89FkZqPEaE\nxyM0blpjmBFt27ZtLRKqDBkyBNu3m6tA5GfMZKUCostWpHeP/NzmLGhPTBlmSqiYbbNe+2KZZUmP\n/Fz9rFlGmbKsWuxy5rgrilZh0vx1LbJ5RZuZSVaCetnJRBi9NytjwCxOjpVo2pGjUWlMhNw2kZcp\n1is1o/FMmnBjPPsdwyNfxx9/PB5//HFcdtllCIfDWLVqFfr06eNB02KLWbdZNO4bp11zVq7nR7eT\n3orIaOVi1Quht7KMNjOTSNmJ3LxKjN6bG+5eJ8eKun3BYEAzfkOuL68sG2sFuW3xvFKLx22oeCNe\nY2xiiaHSLi4uxp///GdMnjwZAHDWWWdh7ty5rjcsHjAzqaIVCk5PXLPXi2dhZgejyW3FHWnkSo9W\nkOjl5xa5eWXMvDenx1Q0Y0W0DSC3TxTBLpcCLX4p1KpsbNfsjEjFNKNja3RD+x8aNy0xVNqPPPJI\n0ihpKxiVlIwnoaAlOBNRmOlNbqe9ENEIEiv7uuo991i8N7v3NJuuFbBWPlaZ+1v32JrB9QnxI4ZK\n+9///jcOHz6MzEy72YgTD7MlJeMBkeAsGpuHUWd2Rmh7Q9IIs3jxQljJG652QcdCCdm9p9ngP9F7\nMWs8GbWNKzWSSBgq7WAwiGHDhqFv375o06ZN5PMXXnjB1YbFMyJh1Pt471OiGqEnOMcPy8L4UdEn\njUg03F7N6kU1q9EyFGKhhOzcM9q9cLPGE5UySSYMlXZRUZEX7fAV8RDE5Uyea3P5pZMtjaAXq1lR\nVLMaP29XROuxSMQtHEKixVBpn3baaSgrK0NtbS0kSUJjYyMqKip8l95UuQfde11VVEI41kFcXua5\nTtY0gm6v3vSKm4TDUkJsV0SrdLkfTUhrDJX2fffdh9LSUhw6dAj9+vXD1q1bceqpp+Lqq6/2on2O\n4LTiifUKwEqiEN22msgvzTSC1jHjmXCzuEm84ITSpeubkJYYKu2NGzdizZo1mD17NsaNGwdJkjBr\n1iwv2uYYTiseI2HktjvZinter61m8kvHw1aAnzBrICbLKpJKlxBnMVTaxx13HNLS0tC/f398/fXX\n+PWvf43Dhw970TbHcEPxiISRF+5kqy5vN44n+fU8txKlcZWTlYobgxVRvyOrZVWp0AghVjBMY9qt\nWzc8++yzyM3NxeLFi/Hmm2+itrbWi7Y5hlGKSyfxIj2ol6n9EjWNoDqV6N7KetOpRPWgZ4IQ4iaG\nK+2HHnoIH3zwAYYMGYKLLroIq1evxowZMzxomnOY3YN2wq3thdD20rUaazeuW1sNbu3VJ7JnghAS\ne4RKe+fOnZF/5+bmYufOnSgoKEBBQYHtm1VXV6OoqAg1NTWor6/H1KlTkZuba/t6ZlEqnm93V6GX\nRgIUp9zaXgltL12rsXLjurnV4JZxFesgxWQm2Y4mkuTEcj3t7777Dj169MCaNWss32zhwoU4/fTT\nMX78ePz3v//FPffcg+XLl0f1AGaRFU8oFEJeXuuEIk6tvCi0ncPNyHW3jKtYeyaSlWQ9mkiSD0/r\naY8fPx7p6ekAgMbGxhYZ1mKNUysvCm3ncHOrwU3jigFm3sOjiSRZCEiSJOl94fLLL8eqVatafDZi\nxAi88cYbuhdeunQpFi1a1OKzhx9+GEOGDMG+ffvw29/+Fvfff79hkpZQyFyO5mh5+q092FtZ3+rz\nbtlpuO2ybp60gbTE7XfyRXkt1pdVY9+henTtmIazB3XAKX3aRX1d4j0zX6mAliQLBoAHr6PSJv5D\nyyMMuFhPu7CwEIWFha0+//rrrzF58mTce++9prOqiRpvB5F7/MagdhGQccOHIC9BLXVRX8QLbr+T\nvDxg/Kimf8d7X3iN3/qj97oqze2OXsdnRf0cfusLN2FfNONmX+gtVi3X0z7zzDNtl+r8z3/+gzvv\nvBOPPfYYBg4caOsabkG3dvzBd0LMwlgSkiwYKu2OHTti+vTpjtzs0UcfxbFjx/DQQw8BANq3b49n\nnnnGkWs7Afci4w++E2IGGngkWTBU2suWLcO8efNQVdXkepIkCYFAAF999ZXlm8WTgiaJBY/7EBp4\nJBkwVNpPPfUUXnzxRZx44oletIcQy/C4DyEkWTCVxpQKm8QzXqSOJYSQeMBwpX3yySfjjjvuwFln\nndXiXPXIkSNdbRghZmG+b0JIsmCotGtqapCZmYlNmza1+JxKuyXcU40dzPftXzhvCLGGodLWOt51\n9OhRVxrjV7inGlt43MefcN4QYh1Dpb1mzRo89dRTqK2thSRJCIfDOHr0KD7++GMv2ucLmEIxtvC4\njz9xYt5wpe497PPYYiq5ypw5c7Bw4UJMnDgR69evx8GDB71om2/gnqqz2BEKPO7jP6KdN1ype88X\n5bV4/SP2eSwxVNpZWVk4/fTT8dlnn6G6uhqTJk3CVVdd5UXbXMcpi5F7qs5BQZxY6M2xaOcNPVze\n82GZtkHFPvcOwyNfbdu2xfbt29G/f39s2LABx44dQ3W1/1eQsnIo31WFcFiKKIeS0grL1xLtnXJP\n1To8vpU4GM2xaOcNPVzes+9Q6wI+APvcSwyV9l133YXHHnsMw4YNw8cff4yzzjoLF1xwgRdtcxUn\nlUN+bg8Ujc1Dn+5ZCASAtNQgAoGma9kxApIZCuLEwWiOKedNSjCAPt2zUDQ2z/SKrZdgRU4Pl3t0\n7S+30AkAABNvSURBVJim+Tn73DsM3eOdOnXC448/DgB4/fXXcejQIWzfvt31hrmN08pBFjTFL4VQ\n3xAGQNeuHbjVkDiYmWPRxCLw1ID3nDOoA17/6IdWn7PPvUO40g6FQti4cSNuv/12/Otf/8LGjRux\nceNGlJWV4b777vOyja7ghpVO1270cKshcXB7JRztSp1Y55Q+7djnMUa40v7oo4+wYcMG7N27N7LS\nBoDU1FRce+21njTOTdyw0unajR4e30ocvFgJ89SA97DPY4tQaU+aNAkAsGLFioTMfuaGcqBr1xko\nFBIDGmCEOI/unva6deuQl5cHAHjvvffw2muvYdCgQfjd736H1FTD7fC4x2nlwD024nfkI1o7dleh\n97qqqJUsDTBCnEW4p71gwQI8+eSTqKurw9atWzFlyhQUFBTg8OHDmDdvnpdt9A3cYyN+RnlES5IQ\n1TFIQog7CJfLK1euxKuvvoqMjAzMnz8f559/PgoLCyFJEi677DIv2+gruLIgfoXJSogIpz0wxD5C\npR0IBJCRkQEA+PTTT3H99ddHPieE+ButTGUMpCRaxCJLIfObixEq7ZSUFFRVVaG2thZfffUVzjrr\nLADA999/nxD72YQkKyIhnJOdgf2VR1p9n4GUyY3XHhimMtZHqH0nTJiAkSNHoqGhAVdffTWOO+44\nvPXWW/jTn/6E3//+9162kRDiIFbzBjCQMrnx2gPDbRp9hEr7kksuQW5uLg4ePIiBAwcCADIzMzFn\nzhwMHTrUswYSQpxFJIQPVh1F0di8Jrfk7ir0Oj6Lbkni+VFWbtPoo+vn7tatG7p16xb5/7nnnut6\ngwgh7qInhOVAylAoFDnuSZIbr4+yMt+FPoYFQwghiUWipYotKa3ApPnrcEXRKkyav45H1BxGeZQ1\nGIDrR1kTbXw6DSPKCEkyEilTGYOWvMFLD0wijU83oNImJAlJlHwCDFpKTBJlfLoB3eOEEN/CoCWS\nbFBpE0J8i9vlPwmJN6i0CSG+hUFLJNngnjYhxLcwaIkkG1TahBBfw6AlkkzQPU4IIYT4BCptQggh\nxCdQaRNCCCE+gUqbEEII8QlU2oQQQohPoNImhBBCfAKVNiGEEOITqLQJIYQQn8DkKj6lpLQCS9//\nBt/uqUavBMsClcjPRggh0RCTlfa2bduQl5eHurq6WNze98g1hMt3VSEcliI1hEtKK2LdtKhJ5Gcj\nhJBo8Vxp19TUYN68eUhPT/f61gmDXg1hv5PIz0YIIdHiqdKWJAnTp0/H5MmTkZGR4eWtE4pEriGc\nyM9GCCHR4tqe9tKlS7Fo0aIWn/3kJz/BZZddhoEDB1q6VigUcrJpjl/Pa3KyUrG3sl7zc6vPFm99\n4eSzWSXe+iLWsD+aYV80w75oJhZ9EZAkSfLqZhdeeCGOP/54AMCmTZswZMgQvPzyy7q/CYVCyMvL\nc6wNTl8vFsj7vmqKxuZZCtiKx75w6tmsEo99EUvYH82wL5phXzTjZl/oXdvT6PF333038u/zzz8f\nzz33nJe3TxgSuYZwIj8bIYREC498+ZREriGcyM9GCCHREDOlvXbt2ljd2tfwDDMhhCQvXGn7CPV+\nr3yGGQAVNyGEJAFMY+ojeIaZEEKSGyptH8EzzIQQktxQafuIXt06aH7eU/A5IYSQxIJK20cUFgyw\n9DkhhJDEgoFoPoJnmAkhJLmh0vYZPMNMCCHJC93jhBBCiE+g0iaEEEJ8ApU2IYQQ4hOotAkhhBCf\nwEA0QhIA5qQnJDmg0ibE5zAnPSHJA93jhPgc5qQnJHmg0ibE5zAnPSHJA5U2IT6HOekJSR6otAnx\nOcxJT0jywEA0QnwOc9ITkjxQaROSADAnPSHJAd3jhBBCiE+g0iaEEEJ8ApU2IYQQ4hOotAkhhBCf\nQKVNCCGE+AQqbUIIIcQnUGkTQgghPoFKmxBCCPEJVNqEEEKIT6DSJoQQQnwClTYhhBDiE6i0CSGE\nEJ9ApU0IIYT4BCptQgghxCdQaRNCCCE+gUqbEEII8QlU2oQQQohPoNImhBBCfAKVNiGEEOITqLQJ\nIYQQn5Dq5c0aGxsxd+5cbNmyBceOHcOkSZMwbNgwL5tACCGE+BZPlfbKlSvR0NCAxYsXY8+ePfj7\n3//u5e0JIYQQX+Op0l6/fj0GDBiACRMmQJIkTJ8+3cvbE0IIIb4mIEmS5MaFly5dikWLFrX4rFOn\nTujRowcefvhhbNy4EY8//jhefvll3euEQiE3mkcIIYTELXl5eZqfu7bSLiwsRGFhYYvP7r77bpx3\n3nkIBAI47bTTUF5ebupaosbbIRQKOXo9P8O+aIZ90RL2RzPsi2bYF8242Rd6i1VPo8fz8vLwwQcf\nAAC2bt2K7t27e3l7QgghxNd4qrSvueYaSJKEa665BtOnT8fMmTO9vD0hhBDiazwNREtPT8fcuXO9\nvCUhhBCSMDC5CiGEEOITqLQJIYQQn0ClTQghhPgET/e0CSHeUFJagaXvf4Nv91SjV7cOKCwYgPzc\nHrFuFiEkSqi0CUkwSkorUPxS8znP8l1Vkf9TcRPib+geJyTBWPr+N5Y+J4T4ByptQhKMb/dUa37+\nneBzQoh/oNImJMHo1a2D5uc9BZ8TQvwDlTYhCUZhwQBLnxNC/AMD0QhJMORgs6Xvf4Pv9lSjJ6PH\nCUkYqLQJSUDyc3tQSROSgNA9TgghhPgEKm1CCCHEJ1BpE0IIIT6BSpsQQgjxCVTahBBCiE+g0iaE\nEEJ8ApU2IYQQ4hOotAkhhBCfQKVNCCGE+ISAJElSrBuhRygUMv4SIYQQkkDk5eVpfh73SpsQQggh\nTdA9TgghhPgEKm1CCCHEJ1BpE0IIIT6BSpsQQgjxCVTahBBCiE9IjXUDvCIcDmPGjBn4+uuvkZ6e\njjlz5qB3796xbpanXHnllWjfvj0AoEePHpg4cSKmTp2KQCCAAQMG4I9//COCwcS24z7//HPMnz8f\nL774Inbs2KH5/EuWLMHixYuRmpqK2267DcOGDYt1s11B2RdlZWW49dZb0adPHwDAddddh8suuywp\n+qK+vh73338/vv/+exw7dgy33XYbTjjhhKQcG1p90b1796QcG42NjXjggQewfft2BAIBzJw5E23a\ntIn9uJCShDVr1kj33XefJEmSVFpaKk2cODHGLfKWo0ePSldccUWLz2699Vbpk08+kSRJkqZPny69\n8847sWiaZ/z1r3+Vhg8fLhUWFkqSpP38e/fulYYPHy7V1dVJVVVVkX8nGuq+WLJkibRgwYIW30mW\nvnjttdekOXPmSJIkSQcPHpTOPffcpB0bWn2RrGPj3XfflaZOnSpJkiR98skn0sSJE+NiXCT2skpB\nKBTCOeecAwD4xS9+gS1btsS4Rd6ydetWHDlyBDfffDPGjRuHTZs24csvv8Rpp50GAMjPz8dHH30U\n41a6S69evfDEE09E/q/1/Js3b0Zubi7S09PRoUMH9OrVC1u3bo1Vk11D3RdbtmzBP/7xD4wZMwb3\n338/ampqkqYvLrnkEtx5550AAEmSkJKSkrRjQ6svknVsXHDBBZg9ezYAYOfOncjKyoqLcZE0Srum\npibiGgaAlJQUNDQ0xLBF3tK2bVvccsstWLBgAWbOnIkpU6ZAkiQEAgEAQGZmJqqrq2PcSne5+OKL\nkZravCOk9fw1NTXo0KFD5DuZmZmoqanxvK1uo+6LIUOG4N5778XLL7+Mnj174qmnnkqavsjMzET7\n9u1RU1ODO+64A3fddVfSjg2tvkjmsZGamor77rsPs2fPxogRI+JiXCSN0m7fvj0OHz4c+X84HG4h\ntBKdvn374vLLL0cgEEDfvn2RnZ2NAwcORP5++PBhZGVlxbCF3qPcv5efXz1ODh8+3GJCJioXXngh\nBg8eHPl3WVlZUvXFrl27MG7cOFxxxRUYMWJEUo8NdV8k+9iYN28e1qxZg+nTp6Ouri7yeazGRdIo\n7VNPPRUlJSUAgE2bNuHEE0+McYu85bXXXsMjjzwCANizZw9qampw1lln4dNPPwUAlJSU4Je//GUs\nm+g5gwYNavX8Q4YMQSgUQl1dHaqrq7Ft27akGCu33HILNm/eDAD4+OOPcfLJJydNX+zfvx8333wz\nioqKcPXVVwNI3rGh1RfJOjZWrFiBZ599FgCQkZGBQCCAwYMHx3xcJE3ucTl6/N///jckScLDDz+M\n/v37x7pZnnHs2DFMmzYNO3fuRCAQwJQpU9CpUydMnz4d9fX16NevH+bMmYOUlJRYN9VVKioqMHny\nZCxZsgTbt2/XfP4lS5bg1VdfhSRJuPXWW3HxxRfHutmuoOyLL7/8ErNnz0ZaWhpycnIwe/ZstG/f\nPin6Ys6cOfj73/+Ofv36RT77wx/+gDlz5iTd2NDqi7vuugvFxcVJNzZqa2sxbdo07N+/Hw0NDfjt\nb3+L/v37x1xmJI3SJoQQQvxO0rjHCSGEEL9DpU0IIYT4BCptQgghxCdQaRNCCCE+gUqbEEII8QlU\n2oQ4zOHDhzFz5kxceOGFuPzyy3H99dfj448/jvz9hhtuiJz1jBVr167FwoULAQCvvPIKXnnllaiv\nOXXqVIwZMwbKAynLli3D1KlTo762U9chxO8kT0owQjxAkiRMnDgRJ510Et58802kp6ejrKwMEyZM\nwKOPPoqhQ4fGuokAmvKuy1x33XWOXffzzz/HCy+8gBtvvNGxaxJCmqHSJsRBNmzYgJ07d+KFF16I\n5CgeNGgQbrvtNjz99NMRpb1kyRI88sgjkCQJ06ZNw9ChQ/Hxxx+juLgYANCxY0c8+uij6Ny5M1as\nWIFFixYhHA7j5JNPxh//+Ee0adMGp59+Ok4++WTs378fPXr0wIgRI3DJJZcAAK666irMnj0bhw8f\nxp/+9CccPXoUhw4dQlFREQYMGIDFixcDAH7yk59g586dAIBJkyZh3bp1eOyxxxAOh9GzZ0/MmjUL\nOTk5OP/883H55Zdj/fr1OHLkCObNmxdJbanklltuwTPPPIPzzjuvVenbG264AbfffjuGDh2KiooK\njBs3DmvXrsXUqVORkZGBUCiE6upq3H///Vi5ciW2bt2KCy64ILLC3rFjB8aMGYPKykoMGzYM99xz\nDwKBgKn+ee2115CWlubCGyfEW+geJ8RBvvjiCwwePDiisGV+9atf4Ysvvoj8v127dli+fDkeeeQR\n3HvvvTh27BiefvppzJgxA8uWLcOwYcNQVlaGb775JlKrd+XKlejSpQsWLFgAADh48CAmTJiAlStX\nYuTIkXjrrbcAAOXl5airq8PJJ5+Ml156CXPmzMHy5cvx0EMP4emnn8YJJ5yA0aNHY/To0Rg1alSk\nTQcOHMCDDz6Ip556Cm+88QZOPfVUzJo1K/L37OxsvPbaaxg9enQkvaOa3r17Y+LEibj//vthJW/T\n3r17sWrVKtxxxx2YNm0aZs6ciRUrVmDJkiWRQjYVFRV44oknsHz5coRCIbz//vum+4cKmyQKXGkT\n4iCBQACNjY2tPq+vr2/xfzmv88CBA9G5c2f897//RUFBAW6//XZccMEFKCgowFlnnYWXXnoJO3bs\nwDXXXBO5zqBBgyLX+fnPfw4AOPfcczF79mzU1NRg9erVGDFiBACguLgY69atw9tvv43PP/+8RWED\nNZs3b8aQIUPQo0cPAMC1116Lv/71r5G/y6VtBwwYgHfeeUd4nXHjxuGdd97BCy+8YLpwQn5+PoCm\nlf+AAQPQpUsXAE2GwqFDhwAA559/Pjp37gwAuPTSS7Fhwwbs3r3bVP8QkihQaRPiID//+c/x4osv\nor6+vsXqbtOmTTjllFMi/1fmeJckCampqRg/fjyGDRuGdevWobi4GJs3b0a7du1w6aWX4oEHHgDQ\nFOSmNAratm0LAEhPT8d5552HtWvX4u23346shK+//noMHToUQ4cOxRlnnIEpU6YI2x4Oh1v8X5Kk\nFuVr27RpAwCtvAhqgsEg5s6di2uvvRbXXnttq2sCaFUWV9lXoup76rKqqampaGxsNNU/hCQKdI8T\n4iC//OUvccIJJ+Dhhx+OrK63bNmCZ555Br/73e8i33vjjTcANLnTa2pq0Lt3bxQWFuLw4cMYP348\nxo8fj7KyMgwdOhTvvvsuDhw4AEmSMGPGDCxatEjz3ldccQUWLlyIjh074qc//SkqKytRXl6OO++8\nE+eeey7++c9/RhSaVj35n//85/j8889RUVEBAHj11VdtB87JbnLZVQ0AnTp1wn/+8x8AwHvvvWf5\nmh988AGqqqpQV1eHN998E2eeeaal/iEkEeBKmxCHefLJJ/GnP/0Jw4cPR0pKCjp27Iji4uIWCrC2\nthYjR45EMBjEo48+irS0NEyePBlTp05Famoq2rRpg5kzZ+LEE0/E7bffjhtvvBHhcBgnnXQSJkyY\noHnfvLw8VFdXY/To0QCaXMuFhYX49a9/jfbt2+MXv/gFjh49itraWvzqV7/Cfffdh5ycnMjvc3Jy\nMGvWLNx+++2or6/HT37yEzz00EO2+0F2k8v85je/wdSpU/H666+joKDA8vX69euHCRMmoKqqCsOH\nD8fZZ58NAKb7h5BEgFW+CCGEEJ9A9zghhBDiE6i0CSGEEJ9ApU0IIYT4BCptQgghxCdQaRNCCCE+\ngUqbEEII8QlU2oQQQohPoNImhBBCfML/B/yaWpunmPyeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137f2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#statsmodels offers significantly less residuals analysis options for logistic regression compared to linear one\n",
    "#yet there is no reason we cannot get information by studying the residuals\n",
    "#simple plot of residuals\n",
    "stdres = pd.DataFrame(lreg3.resid_pearson)\n",
    "plt.plot(stdres, 'o', ls='None')\n",
    "l = plt.axhline(y=0, color='r')\n",
    "plt.ylabel('Standardized Residual')\n",
    "plt.xlabel('Observation Number')\n",
    "#resid_pearson normalizes our model's residuals\n",
    "#ls='none' means points will not be connected\n",
    "#we expect most residuals to fall within 2sd of the mean. More than 2 are outliers, and more than 3 extreme outliers.\n",
    "#if more than 1% of our observations have standardized residuals with an absolute value greater than 2.5, or more than 5%\n",
    "#have one greater than or equal to 2, there is evidence that the fit of the model is poor. largest cause of this is ommission\n",
    "#of important explanatory variables in our model.\n",
    "#standardized residuals in linear regression will always be linear, and the line will be horizontal\n",
    "#if residuals show a strong pattern (up, down, polynomial) then it is a good indication of nonlinearity\n",
    "#in the underlying relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Statsmodels and scikit learn still offer few and poor 'off the shelf' options for imputing missing values in more\n",
    "#statistically sound ways.\n",
    "#As of statsmodels 0.8.0 at least, statsmodels offers the MICE imputation function\n",
    "#a description is available here: http://www.statsmodels.org/dev/imputation.html\n",
    "#and the details behind the implementation are here: \n",
    "#http://www.statsmodels.org/dev/_modules/statsmodels/imputation/mice.html\n",
    "#to be able to use MICE you will need to update the statsmodels coming with Anaconda, to 0.8.0\n",
    "#I am yet to find nice tutorials/examples of the mice function\n",
    "#Let's create here a dataset with some missing values for one of our variables\n",
    "df_clean_mice = df_red[df_red['rest_bp'].notnull() & df_red['cholesterol'].notnull() & df_red['fasting_bs'].notnull() & \\\n",
    "                  df['max_heart_rate'].notnull() & df['exercise_angina'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                False\n",
       "sex                False\n",
       "chest_pain         False\n",
       "rest_bp            False\n",
       "cholesterol        False\n",
       "fasting_bs         False\n",
       "rest_ecg           False\n",
       "max_heart_rate     False\n",
       "exercise_angina    False\n",
       "st_depression       True\n",
       "diagnosis          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 674,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_mice.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>rest_bp</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>max_heart_rate</th>\n",
       "      <th>st_depression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>745.000000</td>\n",
       "      <td>745.000000</td>\n",
       "      <td>745.000000</td>\n",
       "      <td>745.000000</td>\n",
       "      <td>741.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.130201</td>\n",
       "      <td>132.771812</td>\n",
       "      <td>219.923490</td>\n",
       "      <td>138.817450</td>\n",
       "      <td>0.893117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.392743</td>\n",
       "      <td>18.599746</td>\n",
       "      <td>93.713004</td>\n",
       "      <td>25.825905</td>\n",
       "      <td>1.086922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>271.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age     rest_bp  cholesterol  max_heart_rate  st_depression\n",
       "count  745.000000  745.000000   745.000000      745.000000     741.000000\n",
       "mean    53.130201  132.771812   219.923490      138.817450       0.893117\n",
       "std      9.392743   18.599746    93.713004       25.825905       1.086922\n",
       "min     28.000000    0.000000     0.000000       60.000000      -1.000000\n",
       "25%     46.000000  120.000000   197.000000      120.000000       0.000000\n",
       "50%     54.000000  130.000000   231.000000      140.000000       0.500000\n",
       "75%     60.000000  140.000000   271.000000      160.000000       1.500000\n",
       "max     77.000000  200.000000   603.000000      202.000000       6.200000"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_mice.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we create a dataframe with only quantitative variables\n",
    "df_clean_mice = df_clean_mice[['age', 'rest_bp', 'cholesterol', 'max_heart_rate', 'st_depression']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.imputation.mice as mice\n",
    "import statsmodels\n",
    "from statsmodels.base.model import LikelihoodModelResults\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "from collections import defaultdict\n",
    "#we wrap our dataframe in a MICEData object\n",
    "imp = mice.MICEData(df_clean_mice)\n",
    "#we specify our analysis model\n",
    "formula_mice = 'diagnosis_binary ~ age + rest_bp + cholesterol + max_heart_rate + st_depression_c'\n",
    "#We now take the MICEData object, and the formula specification and run both the imputation and the analysis\n",
    "mice = mice.MICE(formula_mice, smf.logit, imp)\n",
    "#various plots and summary statistics are available and/or being developed with the MICE package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Machine learning for data exploration\n",
    "\n",
    "For me machine learning is simply the branch of statistics that has traditionally focused on working with large, \n",
    "heterogenous datasets, characterized by numerous variables that interact in nonlinear ways.\n",
    "It is a collection of tools, just like statistics in general.\n",
    "\n",
    "An amazing article on traditional parametric statistics vs machine learning is:\n",
    "\"Statistical Modeling: The Two Cultures,\" Leo Breiman, 2001.\n",
    "\n",
    "Machine learning can be used for:\n",
    "1. regression\n",
    "2. classification\n",
    "3. feature engineering and/or selection\n",
    "\n",
    "Accuracy = test error rate. The rate at which an algorithm correctly classifies or estimates.\n",
    "Goal is to minimize test error rate.\n",
    "(in linear regression, which we saw before, accuracy was the mean squared error)\n",
    "(in logistic regression: accuracy = how well the model classifies observations)\n",
    "\n",
    "Supervised vs unsupervised learning\n",
    "In supervised learning we work with labeled data\n",
    "In unsupervised learning we aim to find patterns in unlabeled data\n",
    "\n",
    "In machine learning we will regularly face the bias-variance trade off:\n",
    "Variance = change in parameter estimates across different data sets\n",
    "Bias = how far off model estimated values are from true values\n",
    "ideally we want low variance and low bias, but they are negatively associated. As one decreases, the other increases.\n",
    "Generally, as complexity of model increases, this leads to higher variance and lower bias\n",
    "Simple models will have lower variance, but also be more biased."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We will briefly apply three machine learning algorithms here, which we could use in our exploratory data analysis:\n",
    "\n",
    "Decision trees\n",
    "Random forests\n",
    "Support vector machines\n",
    "\n",
    "I intend to expand this section in the future. It is difficult today to not take advantage of some of the\n",
    "machine learning tools available, including when it comes to data exploration\n",
    "We often forget that some key applications of machine learning tools are to help us gain insights faster than with\n",
    "more manual methods, and in feature engineering and/or selection\n",
    "\n",
    "In the data exploration phase, you would use very raw, off the shelf, machine learning algorithms, simply for \n",
    "exploratory/descriptive purposes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Decision trees\n",
    "\n",
    "Note: decision trees cannot handle missing data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'chest_pain', 'rest_bp', 'cholesterol', 'fasting_bs',\n",
       "       'rest_ecg', 'max_heart_rate', 'exercise_angina', 'st_depression',\n",
       "       'diagnosis', 'chest_pain_p', 'age_c', 'rest_bp_c', 'cholesterol_c',\n",
       "       'max_heart_rate_c', 'st_depression_c', 'diagnosis_binary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_copy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#although decision trees and random forests are particularly suitable for dealing with categorical variables, and in\n",
    "#many statistical packages we can input the categorical variables directly, because of how they are implemented in\n",
    "#scikit learn, in this package we cannot input categorical variables directly. We first have to encode them in a \n",
    "#process called one hot encoding. This creates a separate column (variable) for each value of our explanatory\n",
    "#categorical variable. For this we use pandas' get_dummies function\n",
    "\n",
    "df_sex = pd.get_dummies(df_clean_copy['sex'], prefix = 'sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_chest_pain = pd.get_dummies(df_clean_copy['chest_pain'], prefix = 'chest_pain')\n",
    "df_fasting_bs = pd.get_dummies(df_clean_copy['fasting_bs'], prefix = 'fasting_bs')\n",
    "df_rest_ecg = pd.get_dummies(df_clean_copy['rest_ecg'], prefix = 'rest_ecg')\n",
    "df_exercise_angina = pd.get_dummies(df_clean_copy['exercise_angina'], prefix='exercise_angina')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_merged = pd.concat([df_clean_copy, df_sex, df_chest_pain, df_fasting_bs, df_rest_ecg, df_exercise_angina], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'chest_pain', 'rest_bp', 'cholesterol', 'fasting_bs',\n",
       "       'rest_ecg', 'max_heart_rate', 'exercise_angina', 'st_depression',\n",
       "       'diagnosis', 'chest_pain_p', 'age_c', 'rest_bp_c', 'cholesterol_c',\n",
       "       'max_heart_rate_c', 'st_depression_c', 'diagnosis_binary', 'sex_0.0',\n",
       "       'sex_1.0', 'chest_pain_1.0', 'chest_pain_2.0', 'chest_pain_3.0',\n",
       "       'chest_pain_4.0', 'fasting_bs_0.0', 'fasting_bs_1.0', 'rest_ecg_0.0',\n",
       "       'rest_ecg_1.0', 'rest_ecg_2.0', 'exercise_angina_0.0',\n",
       "       'exercise_angina_1.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_dt = df_merged[['age', 'sex_0.0', 'sex_1.0', 'chest_pain_1.0', 'chest_pain_2.0', 'chest_pain_3.0', \\\n",
    "                       'chest_pain_4.0', 'rest_bp', 'cholesterol', 'fasting_bs_0.0', 'fasting_bs_1.0', \\\n",
    "                      'rest_ecg_1.0', 'rest_ecg_2.0', 'max_heart_rate', 'exercise_angina_0.0', 'exercise_angina_1.0',\\\n",
    "                       'st_depression', 'diagnosis_binary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#here we select as predictors all variables in our dataset expect for the response one\n",
    "predictors = df_dt.ix[:, df_dt.columns != 'diagnosis_binary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex_0.0</th>\n",
       "      <th>sex_1.0</th>\n",
       "      <th>chest_pain_1.0</th>\n",
       "      <th>chest_pain_2.0</th>\n",
       "      <th>chest_pain_3.0</th>\n",
       "      <th>chest_pain_4.0</th>\n",
       "      <th>rest_bp</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting_bs_0.0</th>\n",
       "      <th>fasting_bs_1.0</th>\n",
       "      <th>rest_ecg_1.0</th>\n",
       "      <th>rest_ecg_2.0</th>\n",
       "      <th>max_heart_rate</th>\n",
       "      <th>exercise_angina_0.0</th>\n",
       "      <th>exercise_angina_1.0</th>\n",
       "      <th>st_depression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex_0.0  sex_1.0  chest_pain_1.0  chest_pain_2.0  chest_pain_3.0  \\\n",
       "0  63.0        0        1               1               0               0   \n",
       "1  67.0        0        1               0               0               0   \n",
       "2  67.0        0        1               0               0               0   \n",
       "3  37.0        0        1               0               0               1   \n",
       "4  41.0        1        0               0               1               0   \n",
       "\n",
       "   chest_pain_4.0  rest_bp  cholesterol  fasting_bs_0.0  fasting_bs_1.0  \\\n",
       "0               0    145.0        233.0               0               1   \n",
       "1               1    160.0        286.0               1               0   \n",
       "2               1    120.0        229.0               1               0   \n",
       "3               0    130.0        250.0               1               0   \n",
       "4               0    130.0        204.0               1               0   \n",
       "\n",
       "   rest_ecg_1.0  rest_ecg_2.0  max_heart_rate  exercise_angina_0.0  \\\n",
       "0             0             1           150.0                    1   \n",
       "1             0             1           108.0                    0   \n",
       "2             0             1           129.0                    0   \n",
       "3             0             0           187.0                    1   \n",
       "4             0             1           172.0                    1   \n",
       "\n",
       "   exercise_angina_1.0  st_depression  \n",
       "0                    0            2.3  \n",
       "1                    1            1.5  \n",
       "2                    1            2.6  \n",
       "3                    0            3.5  \n",
       "4                    0            1.4  "
      ]
     },
     "execution_count": 577,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we select as the target our response variable\n",
    "target = df_dt['diagnosis_binary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we create our training and testing datasets. Each will have its predictors and its target (response variable).\n",
    "pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, target, test_size = 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(444, 17) (297, 17) (444,) (297,)\n"
     ]
    }
   ],
   "source": [
    "print(pred_train.shape, pred_test.shape, tar_train.shape, tar_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = DecisionTreeClassifier()\n",
    "classifier = classifier.fit(pred_train, tar_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = classifier.predict(pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100,  46],\n",
       "       [ 35, 116]])"
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.confusion_matrix(tar_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.10711089  0.03075379  0.02813197  0.01287773  0.03156475  0.02747934\n",
      "  0.10905395  0.09986029  0.11723441  0.01573098  0.02055796  0.01537355\n",
      "  0.01416915  0.12507431  0.06819212  0.06646467  0.11037014]\n"
     ]
    }
   ],
   "source": [
    "#let's look at which of our variables it considers most important:\n",
    "print(classifier.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78787878787878785"
      ]
     },
     "execution_count": 645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.accuracy_score(tar_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Random forests\n",
    "\n",
    "Random forests, an ensemble learning method, are a more sophisticated method of using trees\n",
    "Pioneered by Tin Kam Ho, Leo Breiman (who also created 'bagging') and Adele Cutler\n",
    "For a full discussion, please see\n",
    "https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier2 = RandomForestClassifier(n_estimators = 25)\n",
    "classifier2 = classifier2.fit(pred_train, tar_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions2 = classifier2.predict(pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[102,  44],\n",
       "       [ 32, 119]])"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.confusion_matrix(tar_test, predictions2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.10483732  0.02897734  0.02174692  0.01403641  0.04168387  0.02074735\n",
      "  0.09862823  0.09633414  0.10519325  0.01756028  0.01531064  0.00897264\n",
      "  0.01828645  0.14053416  0.03421658  0.10297459  0.12995983]\n"
     ]
    }
   ],
   "source": [
    "#let's look at which of our variables the random forest considers most important:\n",
    "#(these follow the order in which we input them to the random forest i.e. our dataset)\n",
    "print(classifier2.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74410774410774416"
      ]
     },
     "execution_count": 656,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.accuracy_score(tar_test, predictions2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "random forests typically outperform decision trees, in particular when our decision tree model exhibits instability\n",
    "the lower accuracy here is perhaps a result of our implementation of the random forest - we would need to tune its\n",
    "parameters in order to optimize it for the problem at hand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Support vector machines\n",
    "\n",
    "SVMs are a set of supervised learning methods, used for classification and regression\n",
    "They handle nonlinearity, through kernalization, much better than a standard logistic classifier\n",
    "\n",
    "A couple of very good explanations:\n",
    "http://www.kdnuggets.com/2016/07/support-vector-machines-simple-explanation.html\n",
    "http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf\n",
    "http://scikit-learn.org/stable/modules/svm.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "classifier3 = svm.SVC()\n",
    "classifier3 = classifier3.fit(pred_train, tar_train)\n",
    "predictions3 = classifier3.predict(pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8, 138],\n",
       "       [  4, 147]])"
      ]
     },
     "execution_count": 658,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.confusion_matrix(tar_test, predictions3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52188552188552184"
      ]
     },
     "execution_count": 659,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.accuracy_score(tar_test, predictions3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "we obtain a relatively low accuracy - once more, as for random forests, the more complex a machine learning tool is,\n",
    "the more work we will need to do in order to adapt it to our problem and hopefully reach a high accuracy for our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# How to go further\n",
    "\n",
    "MOOCS\n",
    "\n",
    "This is perhaps the best introductory data analysis course I found online, and will cover many of the topics covered\n",
    "here in more depth, covering both SAS and Pandas\n",
    "https://www.coursera.org/learn/data-visualization\n",
    "\n",
    "A similarly good course but in R:\n",
    "https://www.coursera.org/specializations/jhu-data-science\n",
    "\n",
    "Another course I really like is Bill Chambers' Udemy one:\n",
    "https://www.udemy.com/data-analysis-in-python-with-pandas/\n",
    "\n",
    "For machine learning algorithms and their implementation:\n",
    "https://www.coursera.org/specializations/machine-learning\n",
    "    \n",
    "Statistics books - classics:\n",
    "\n",
    "https://www.amazon.com/Statistics-4th-David-Freedman/dp/0393929728/ref=sr_1_1?ie=UTF8&qid=1499250394&sr=8-1&keywords=David+freedman+statistics\n",
    "\n",
    "https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/ref=sr_1_1?ie=UTF8&qid=1499250450&sr=8-1&keywords=an+introduction+to+statistical+learning\n",
    "\n",
    "https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=sr_1_5?ie=UTF8&qid=1499250450&sr=8-5&keywords=an+introduction+to+statistical+learning\n",
    "\n",
    "Machine learning readings\n",
    "\n",
    "https://www.amazon.com/Machine-Learning-Python-Techniques-Predictive/dp/1118961749/ref=sr_1_1?ie=UTF8&qid=1499250696&sr=8-1&keywords=machine+learning+in+python+bowles\n",
    "https://www.amazon.com/Machine-Learning-Hands-Developers-Professionals/dp/1118889061/ref=sr_1_1?ie=UTF8&qid=1499250725&sr=8-1&keywords=Machine+learning+hands+on+for+developers+and+technical+professionals+Bell\n",
    "https://www.amazon.com/Fundamentals-Machine-Learning-Predictive-Analytics/dp/0262029448/ref=sr_1_1?ie=UTF8&qid=1499251042&sr=8-1&keywords=fundamentals+of+machine+learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Following R.S.'s question on how to get more practice/go further after this tutorial: I genuinely believe that there is no teacher like experience. What I find most useful, is to go to a large online repository of datasets i.e. \n",
    "\n",
    "https://archive.ics.uci.edu/ml/datasets.html\n",
    "\n",
    "or\n",
    "\n",
    "https://bigml.com/gallery/datasets\n",
    "\n",
    "Choose datasets that 1. focus on a topic that you find interesting, and 2. are relatively clean i.e. it’s very frustrating to spend a lot of time just cleaning data  - that is an exercise in itself, and something you can learn by itself at your convenience (unless you are already a pro at it of course).\n",
    "\n",
    "And then take one dataset from the sources above each week, and analyze it: Import it, visualize it, manage your data, analyze it with regression, try to improve your models with feature engineering, and then apply any machine learning methods to it as well. And every week make sure you do something new/add something i.e. one different type of graph, one different type of residuals test for your regression, (feature engineering by definition will be different for each dataset), one new machine learning model.\n",
    "\n",
    "Document each one of them, build a notebook for each, and upload them to your github/portfolio!\n",
    "\n",
    "I genuinely believe this is the best, and the most ‘realistic’ way of learning and becoming an applied data analyst as well.\n",
    "\n",
    "Once you are comfortable with such tasks, you can of course try to compete in Kaggle for example, whether to replicate past performances or in current competitions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Thank you and please don't hesitate to contact me if I can assist you further."
   ]
  }
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