Cardiac Arrhythmia Database_GH.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset can be found in https://www.kaggle.com/bulentesen/cardiac-arrhythmia-database\n",
    "\n",
    "The algorithm that I will use will be a supervised learning algorithm because I have a labelled dataset. Given the great number of features, it seems that a decision tree based model which inherently selects the best features (the most informative) can be a suitable choice for a baseline model. I will first try a random forest because it is applicable to categorical and continuous data and it is an ensemble (bad predictions from a single weaker model (tree) will be cancelled out and the final decision comes from the aggregation of the outputs of a set of models (trees)).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Loading the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = pd.read_csv('arrhythmia_column.names',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./arrhythmia.data', delimiter=',',names=column_names[0],na_values = ['?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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      ],
      "text/plain": [
       "     Age  Sex  Height  Weight  QRS Duration  P-R interval  Q-T interval  \\\n",
       "0     75    0     190      80            91           193           371   \n",
       "1     56    1     165      64            81           174           401   \n",
       "2     54    0     172      95           138           163           386   \n",
       "3     55    0     175      94           100           202           380   \n",
       "4     75    0     190      80            88           181           360   \n",
       "..   ...  ...     ...     ...           ...           ...           ...   \n",
       "447   53    1     160      70            80           199           382   \n",
       "448   37    0     190      85           100           137           361   \n",
       "449   36    0     166      68           108           176           365   \n",
       "450   32    1     155      55            93           106           386   \n",
       "451   78    1     160      70            79           127           364   \n",
       "\n",
       "     T interval  P interval  QRS  ...  V6_WA_Q Wave  V6_WA_R Wave  \\\n",
       "0           174         121  -16  ...           0.0           9.0   \n",
       "1           149          39   25  ...           0.0           8.5   \n",
       "2           185         102   96  ...           0.0           9.5   \n",
       "3           179         143   28  ...           0.0          12.2   \n",
       "4           177         103  -16  ...           0.0          13.1   \n",
       "..          ...         ...  ...  ...           ...           ...   \n",
       "447         154         117  -37  ...           0.0           4.3   \n",
       "448         201          73   86  ...           0.0          15.6   \n",
       "449         194         116  -85  ...           0.0          16.3   \n",
       "450         218          63   54  ...          -0.4          12.0   \n",
       "451         138          78   28  ...           0.0          10.4   \n",
       "\n",
       "     V6_WA_S Wave  V6_WA_R' Wave  V6_WA_S' Wave  V6_WA_P Wave  V6_WA_T Wave  \\\n",
       "0            -0.9            0.0            0.0           0.9           2.9   \n",
       "1             0.0            0.0            0.0           0.2           2.1   \n",
       "2            -2.4            0.0            0.0           0.3           3.4   \n",
       "3            -2.2            0.0            0.0           0.4           2.6   \n",
       "4            -3.6            0.0            0.0          -0.1           3.9   \n",
       "..            ...            ...            ...           ...           ...   \n",
       "447          -5.0            0.0            0.0           0.7           0.6   \n",
       "448          -1.6            0.0            0.0           0.4           2.4   \n",
       "449         -28.6            0.0            0.0           1.5           1.0   \n",
       "450          -0.7            0.0            0.0           0.5           2.4   \n",
       "451          -1.8            0.0            0.0           0.5           1.6   \n",
       "\n",
       "     V6_WA_QRSA  V6_WA_QRSTA  Class  \n",
       "0          23.3         49.4      8  \n",
       "1          20.4         38.8      6  \n",
       "2          12.3         49.0     10  \n",
       "3          34.6         61.6      1  \n",
       "4          25.4         62.8      7  \n",
       "..          ...          ...    ...  \n",
       "447        -4.4         -0.5      1  \n",
       "448        38.0         62.4     10  \n",
       "449       -44.2        -33.2      2  \n",
       "450        25.0         46.6      1  \n",
       "451        21.3         32.8      1  \n",
       "\n",
       "[452 rows x 280 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Check how the distribution of target class is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     245\n",
       "10     50\n",
       "2      44\n",
       "6      25\n",
       "16     22\n",
       "4      15\n",
       "3      15\n",
       "5      13\n",
       "9       9\n",
       "15      5\n",
       "14      4\n",
       "7       3\n",
       "8       2\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Class']!=1) #check how many occurences of non-normal arrythmia exists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 245 occurences of normal arrythmia and 207 occurences of non-normal arrythmia. As only patients with non-normal arrythmia are subjected to further examination (they are in risk of heart failure) it doesn't make sense to predict what class of arrythmia the patient has (even because there are certain types of arrythmia that don't even occur in this dataset, according with dataset description file \"arrhythmia.names\"). Furthermore, there aren't so many instances in this dataset that allows the algorithm to make a clear distinction (representativeness issue). For those reasons, I will transform this problem in a binary classification problem instead of a multi-class. This also generates a nearly balanced dataset from the target perspective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Normal    245\n",
       "Risk      207\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.copy() #create a copy of the original dataset\n",
    "\n",
    "#setting prediction values to \"Normal\" or \"Risk\" based on column scores values\n",
    "df1.loc[df1[\"Class\"] == 1,\"Class\"] = \"Normal\"         #class 1 is normal arrythmia\n",
    "df1.loc[df1[\"Class\"] != \"Normal\",\"Class\"] = \"Risk\"    #other classes are risk classes\n",
    "df1.Class.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking features that I am familiar with to see if there are any uncomon values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for Age:\n"
     ]
    },
    {
     "data": {
      "image/png": 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hj/z87p/B4PUEw9+xY1W1v9u+lWGgey393KuA71XVXFX9L3Ab8Jus8bXUQoA7ZX8R3VjuXuBIVX1o3lt3ALu617uA21e7tvWiqt5dVduraprhdfOlqnozcA/whm63TX2OAKrqv4AfJHlB1/RK4Ft4Lc33feCKJM/ofvdOnaM1vZaamImZ5DUMe06npuy/f41LWnNJfgv4V+Cb/Hx89z0Mx8FvAaYYXnRvrKofr0mR60iSVwB/XlWvTfJ8hj3yC4B7gd+vqsfXsr61lmQHwxu95wEPAn/AsIPntdRJ8j7g9xg+AXYv8EcMx7zX7FpqIsAlSadrYQhFkrQIA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ16v8ATiv2aOFru/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for Sex:\n"
     ]
    },
    {
     "data": {
      "image/png": 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PeF/fZ/zJ7s/joXH8XHv7AUlq0GqZlpEkLYLhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhr0f2pH9kqpX0gkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for Height:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for Weight:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for Heart rate bpm:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mtava\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Users\\mtava\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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G4J4kP0Z7fYFBzR/oln0+yWBlYT1j7MvYAryqbqyqjVU1yWCB/6NV9VrgLuC67rBtwL5x1bRYVfVV4MtJXtA1XQV8nsGlBLZ1bU30hcHSyeVJntnNHk70pblxmWO+sbgD+NXuUw+XA984sdSyWmXwxSlvBq6tqv+dddcdwA1JnpbkQgZvAH5yJWrso6o+V1Ubqmqyy4EjwKXdv6fmxgX4ZwYTUZI8HzibwdUIxzcuVTX2H+CVwIe628/rOvcQ8I/A01aipkX04WJgGri3G8hzGazpHwAe7LbrVrrOnn15K/AAcB/wd8DTWhoX4H0M1u+/wyAUts83Fgxe3r4T+CLwOQafvlnxPizQl4cYrKl+pvv5m1nHv6XryxeAV690/Qv1Zc79h4H1DY/L2cDfd/9u7gGuHPe4eCq9JDXKMzElqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWrU/wGOSo9/OAYmXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "familiar_features = ['Age','Sex','Height','Weight','Heart rate bpm'] #list of well known features with missing data\n",
    "\n",
    "#function that creates an histogram for a feature\n",
    "def print_hist(df,feature,nbins):\n",
    "    print(\"Histogram for \" + feature + \":\")\n",
    "    column = df[feature]\n",
    "    plt.hist(column,bins=nbins)\n",
    "    plt.show()\n",
    "\n",
    "for feature in familiar_features: print_hist(df1,feature,30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     2\n",
       "3     1\n",
       "7     3\n",
       "8     2\n",
       "     ..\n",
       "78    2\n",
       "79    1\n",
       "80    2\n",
       "81    1\n",
       "83    1\n",
       "Name: Age, Length: 77, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Age'].value_counts().sort_index() #get different values and count occurences for each value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>QRS Duration</th>\n",
       "      <th>P-R interval</th>\n",
       "      <th>Q-T interval</th>\n",
       "      <th>T interval</th>\n",
       "      <th>P interval</th>\n",
       "      <th>QRS</th>\n",
       "      <th>...</th>\n",
       "      <th>V6_WA_Q Wave</th>\n",
       "      <th>V6_WA_R Wave</th>\n",
       "      <th>V6_WA_S Wave</th>\n",
       "      <th>V6_WA_R' Wave</th>\n",
       "      <th>V6_WA_S' Wave</th>\n",
       "      <th>V6_WA_P Wave</th>\n",
       "      <th>V6_WA_T Wave</th>\n",
       "      <th>V6_WA_QRSA</th>\n",
       "      <th>V6_WA_QRSTA</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>316</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>608</td>\n",
       "      <td>10</td>\n",
       "      <td>83</td>\n",
       "      <td>126</td>\n",
       "      <td>232</td>\n",
       "      <td>128</td>\n",
       "      <td>60</td>\n",
       "      <td>125</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 280 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  Height  Weight  QRS Duration  P-R interval  Q-T interval  \\\n",
       "316    0    0     608      10            83           126           232   \n",
       "\n",
       "     T interval  P interval  QRS  ...  V6_WA_Q Wave  V6_WA_R Wave  \\\n",
       "316         128          60  125  ...          -0.7           4.5   \n",
       "\n",
       "     V6_WA_S Wave  V6_WA_R' Wave  V6_WA_S' Wave  V6_WA_P Wave  V6_WA_T Wave  \\\n",
       "316          -5.5            0.0            0.0           0.5           2.5   \n",
       "\n",
       "     V6_WA_QRSA  V6_WA_QRSTA  Class  \n",
       "316       -11.8          1.7   Risk  \n",
       "\n",
       "[1 rows x 280 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Age']==0] #select occurence with Age=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The presence of an individual with Age = 0 and Weight = 10 kgs (probably a baby) suggests that height = 608 cm is actually 61 cm. I should correct height for this instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[df1[\"Age\"] == 0, \"Height\"] = 61"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Height feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61      1\n",
       "105     1\n",
       "110     1\n",
       "119     1\n",
       "120     2\n",
       "124     1\n",
       "127     1\n",
       "130     2\n",
       "132     1\n",
       "133     1\n",
       "138     1\n",
       "140     1\n",
       "146     1\n",
       "148     1\n",
       "149     1\n",
       "150     9\n",
       "152     1\n",
       "153     5\n",
       "154     4\n",
       "155    23\n",
       "156    19\n",
       "157    10\n",
       "158     9\n",
       "159     6\n",
       "160    81\n",
       "161     5\n",
       "162    15\n",
       "163    16\n",
       "164     9\n",
       "165    46\n",
       "166     9\n",
       "167     8\n",
       "168    15\n",
       "169     7\n",
       "170    40\n",
       "171     9\n",
       "172    14\n",
       "173     7\n",
       "174     4\n",
       "175    21\n",
       "176    10\n",
       "177     4\n",
       "178     9\n",
       "179     1\n",
       "180     4\n",
       "181     1\n",
       "182     2\n",
       "184     1\n",
       "185     3\n",
       "186     3\n",
       "188     1\n",
       "190     3\n",
       "780     1\n",
       "Name: Height, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Height'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>QRS Duration</th>\n",
       "      <th>P-R interval</th>\n",
       "      <th>Q-T interval</th>\n",
       "      <th>T interval</th>\n",
       "      <th>P interval</th>\n",
       "      <th>QRS</th>\n",
       "      <th>...</th>\n",
       "      <th>V6_WA_Q Wave</th>\n",
       "      <th>V6_WA_R Wave</th>\n",
       "      <th>V6_WA_S Wave</th>\n",
       "      <th>V6_WA_R' Wave</th>\n",
       "      <th>V6_WA_S' Wave</th>\n",
       "      <th>V6_WA_P Wave</th>\n",
       "      <th>V6_WA_T Wave</th>\n",
       "      <th>V6_WA_QRSA</th>\n",
       "      <th>V6_WA_QRSTA</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>780</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>165</td>\n",
       "      <td>237</td>\n",
       "      <td>150</td>\n",
       "      <td>106</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.5</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 280 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  Height  Weight  QRS Duration  P-R interval  Q-T interval  \\\n",
       "141    1    1     780       6            85           165           237   \n",
       "\n",
       "     T interval  P interval  QRS  ...  V6_WA_Q Wave  V6_WA_R Wave  \\\n",
       "141         150         106   88  ...           0.0           5.0   \n",
       "\n",
       "     V6_WA_S Wave  V6_WA_R' Wave  V6_WA_S' Wave  V6_WA_P Wave  V6_WA_T Wave  \\\n",
       "141          -4.6            0.0            0.0           1.3           0.7   \n",
       "\n",
       "     V6_WA_QRSA  V6_WA_QRSTA  Class  \n",
       "141         2.7          5.5   Risk  \n",
       "\n",
       "[1 rows x 280 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Height']==780] #select occurence with Height=780"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The presence of another baby with incorrect height (780 cm) suggests another typo to correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[df1[\"Height\"] == 780, \"Height\"] = 78"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking for features with one-level cardinality (non-informative features):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"DI_WW_S' Wave\",\n",
       " \"AVL_WW_S' Wave\",\n",
       " 'AVL_WW_Ragged R wave',\n",
       " 'AVF_WW_Ragged P wave',\n",
       " 'V4_WW_Ragged P wave',\n",
       " 'V4_WW_Diphasic Deriviation P',\n",
       " \"V5_WW_S' Wave\",\n",
       " 'V5_WW_Ragged R wave',\n",
       " 'V5_WW_Ragged P wave',\n",
       " 'V5_WW_Ragged T Wave',\n",
       " \"V6_WW_S' Wave\",\n",
       " 'V6_WW_Diphasic Deriviation P',\n",
       " 'V6_WW_Ragged T Wave',\n",
       " \"DI_WA_S' Wave\",\n",
       " \"AVL_WA_S' Wave\",\n",
       " \"V5_WA_S' Wave\",\n",
       " \"V6_WA_S' Wave\"]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_levels = [] #create an empty list to append features with one level\n",
    "\n",
    "ncol = df1.shape[1] # store number of columns\n",
    "\n",
    "#for loop to add features names with one level to \"one_levels\" list \n",
    "for index in range(ncol):\n",
    "    if len(df1.iloc[:,index].unique()) == 1:\n",
    "        one_levels.append(df1.columns[index])\n",
    "\n",
    "one_levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All those features have only one level and therefore should be eliminated once they don't add knowledge to the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.copy()\n",
    "df2 = df1.drop(columns=one_levels) # delete one_level columns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "408"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum().sum() #total number of missing values is 408"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>T</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>P</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>QRST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>J</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Heart rate bpm</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "T                 8\n",
       "P                22\n",
       "QRST              1\n",
       "J               376\n",
       "Heart rate bpm    1"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print columns with missing values and its occurences\n",
    "missing = df2.isnull().sum()\n",
    "missing_df = pd.DataFrame(missing)\n",
    "missing_df[missing_df[0]!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the output above, there are few features with missing values. Furthermore, there's one feature (feature J) with 376 missing values. This represents 83% of the occurences in the dataset and for that reason I will delete this feature from the dataset. The rationale behind this is the rule of thumb of removing features with more than 60% of missing data (based on Kelleher et al, 2013 page 67)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>QRS Duration</th>\n",
       "      <th>P-R interval</th>\n",
       "      <th>Q-T interval</th>\n",
       "      <th>T interval</th>\n",
       "      <th>P interval</th>\n",
       "      <th>QRS</th>\n",
       "      <th>...</th>\n",
       "      <th>V6_WA_JJ Wave</th>\n",
       "      <th>V6_WA_Q Wave</th>\n",
       "      <th>V6_WA_R Wave</th>\n",
       "      <th>V6_WA_S Wave</th>\n",
       "      <th>V6_WA_R' Wave</th>\n",
       "      <th>V6_WA_P Wave</th>\n",
       "      <th>V6_WA_T Wave</th>\n",
       "      <th>V6_WA_QRSA</th>\n",
       "      <th>V6_WA_QRSTA</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>80</td>\n",
       "      <td>91</td>\n",
       "      <td>193</td>\n",
       "      <td>371</td>\n",
       "      <td>174</td>\n",
       "      <td>121</td>\n",
       "      <td>-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>49.4</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>174</td>\n",
       "      <td>401</td>\n",
       "      <td>149</td>\n",
       "      <td>39</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>38.8</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>95</td>\n",
       "      <td>138</td>\n",
       "      <td>163</td>\n",
       "      <td>386</td>\n",
       "      <td>185</td>\n",
       "      <td>102</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>12.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "      <td>202</td>\n",
       "      <td>380</td>\n",
       "      <td>179</td>\n",
       "      <td>143</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>34.6</td>\n",
       "      <td>61.6</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>80</td>\n",
       "      <td>88</td>\n",
       "      <td>181</td>\n",
       "      <td>360</td>\n",
       "      <td>177</td>\n",
       "      <td>103</td>\n",
       "      <td>-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>62.8</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <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",
       "      <td>447</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>199</td>\n",
       "      <td>382</td>\n",
       "      <td>154</td>\n",
       "      <td>117</td>\n",
       "      <td>-37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>448</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>137</td>\n",
       "      <td>361</td>\n",
       "      <td>201</td>\n",
       "      <td>73</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>38.0</td>\n",
       "      <td>62.4</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>449</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>68</td>\n",
       "      <td>108</td>\n",
       "      <td>176</td>\n",
       "      <td>365</td>\n",
       "      <td>194</td>\n",
       "      <td>116</td>\n",
       "      <td>-85</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>-28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-44.2</td>\n",
       "      <td>-33.2</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "      <td>55</td>\n",
       "      <td>93</td>\n",
       "      <td>106</td>\n",
       "      <td>386</td>\n",
       "      <td>218</td>\n",
       "      <td>63</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>46.6</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>451</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>127</td>\n",
       "      <td>364</td>\n",
       "      <td>138</td>\n",
       "      <td>78</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>21.3</td>\n",
       "      <td>32.8</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>452 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  Height  Weight  QRS Duration  P-R interval  Q-T interval  \\\n",
       "0     75    0     190      80            91           193           371   \n",
       "1     56    1     165      64            81           174           401   \n",
       "2     54    0     172      95           138           163           386   \n",
       "3     55    0     175      94           100           202           380   \n",
       "4     75    0     190      80            88           181           360   \n",
       "..   ...  ...     ...     ...           ...           ...           ...   \n",
       "447   53    1     160      70            80           199           382   \n",
       "448   37    0     190      85           100           137           361   \n",
       "449   36    0     166      68           108           176           365   \n",
       "450   32    1     155      55            93           106           386   \n",
       "451   78    1     160      70            79           127           364   \n",
       "\n",
       "     T interval  P interval  QRS  ...  V6_WA_JJ Wave  V6_WA_Q Wave  \\\n",
       "0           174         121  -16  ...           -0.3           0.0   \n",
       "1           149          39   25  ...           -0.5           0.0   \n",
       "2           185         102   96  ...            0.9           0.0   \n",
       "3           179         143   28  ...            0.1           0.0   \n",
       "4           177         103  -16  ...           -0.4           0.0   \n",
       "..          ...         ...  ...  ...            ...           ...   \n",
       "447         154         117  -37  ...            0.0           0.0   \n",
       "448         201          73   86  ...           -0.5           0.0   \n",
       "449         194         116  -85  ...            1.2           0.0   \n",
       "450         218          63   54  ...            0.2          -0.4   \n",
       "451         138          78   28  ...           -0.3           0.0   \n",
       "\n",
       "     V6_WA_R Wave  V6_WA_S Wave  V6_WA_R' Wave  V6_WA_P Wave  V6_WA_T Wave  \\\n",
       "0             9.0          -0.9            0.0           0.9           2.9   \n",
       "1             8.5           0.0            0.0           0.2           2.1   \n",
       "2             9.5          -2.4            0.0           0.3           3.4   \n",
       "3            12.2          -2.2            0.0           0.4           2.6   \n",
       "4            13.1          -3.6            0.0          -0.1           3.9   \n",
       "..            ...           ...            ...           ...           ...   \n",
       "447           4.3          -5.0            0.0           0.7           0.6   \n",
       "448          15.6          -1.6            0.0           0.4           2.4   \n",
       "449          16.3         -28.6            0.0           1.5           1.0   \n",
       "450          12.0          -0.7            0.0           0.5           2.4   \n",
       "451          10.4          -1.8            0.0           0.5           1.6   \n",
       "\n",
       "     V6_WA_QRSA  V6_WA_QRSTA   Class  \n",
       "0          23.3         49.4    Risk  \n",
       "1          20.4         38.8    Risk  \n",
       "2          12.3         49.0    Risk  \n",
       "3          34.6         61.6  Normal  \n",
       "4          25.4         62.8    Risk  \n",
       "..          ...          ...     ...  \n",
       "447        -4.4         -0.5  Normal  \n",
       "448        38.0         62.4    Risk  \n",
       "449       -44.2        -33.2    Risk  \n",
       "450        25.0         46.6  Normal  \n",
       "451        21.3         32.8  Normal  \n",
       "\n",
       "[452 rows x 262 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.copy()\n",
    "df3 = df3.drop(columns=['J'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling missing values: for the remaining features with missing values I will impute mean or median values depending on histogram distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for P:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for T:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for QRST:\n"
     ]
    },
    {
     "data": {
      "image/png": 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NqvsQ8PbubJSrgX89PdUyq5o4Rib0ie4b6f2Uego4CXy2a/9Z4AF6n+B+GfjpFfss0HuBHgV+l+4io2l/DepL99yNXb0PseKsGXqfuP9j99yN0+7DgH79EXAf8FV6/yG3DuvXrH618HqvUfsV3fHwle7YuLFrfwFwGHi4e7x42rX2qf0T9KZF/6s7RvYMqpvetMNHu/foPlac0TULXwP6MvPHiFdiSlKjvBJTkhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1Kj/AZRM4RBNezIrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for Heart rate bpm:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_features = ['P','T','QRST','Heart rate bpm'] #list pf features with missing data\n",
    "\n",
    "for feature in missing_features: print_hist(df3,feature,30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All histograms seem to be skewed so I will impute median values using sci-kit library \"impute\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "df4 = df3.copy() #create a new copy of the dataset\n",
    "X_df = df4.loc[:,df4.columns != 'Class'] #select all features except target feature\n",
    "X = np.array(X_df) #convert it to array (Simple Imputer doesn't work with dataframes)\n",
    "imp = SimpleImputer(missing_values=np.nan, strategy='median') #create imp object to impute median in all missing values \n",
    "imp = imp.fit(X) #calculate median values of the features with missing values\n",
    "X_imp = imp.transform(X) #fill dataset with median values wherever finds missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if there are any missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(X_imp).sum().sum() #total number of missing values in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will start by splitting the dataset in train and test sets. Note that I set random_state = 1 so I ensure that if I need to do another split later this seed will be kept constant so the same instances are splitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(316, 261)\n",
      "(136, 261)\n",
      "(316,)\n",
      "(136,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y = df4.iloc[:,-1] #subset target label\n",
    "\n",
    "# splitting train and test data with same seed (random_state=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_imp, y, test_size=0.3, random_state=1) #70%/30% splitting\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In code below I will count the number of instances in test set that belong to each class. This is important for the interpretation of the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal instances in test set: 73\n",
      "Risk instances in test set: 63\n"
     ]
    }
   ],
   "source": [
    "print('Normal instances in test set: ' + str(sum(y_test==\"Normal\")))\n",
    "print('Risk instances in test set: ' + str(sum(y_test==\"Risk\"))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In code below I will create my baseline model. I will use random_state = 1 in the classifier and in the further classifiers below so I can make a fair comparison when evaluating performance each time I fit the model to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[53 20]\n",
      " [18 45]]\n",
      "Accuracy: 72.06%\n"
     ]
    }
   ],
   "source": [
    "#create classifier object\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf1 = RandomForestClassifier(n_estimators=10,random_state=1) #create classifier object\n",
    "fit_model1 = clf1.fit(X_train,y_train) #train classifier\n",
    "y_pred = fit_model1.predict(X_test) #create predictions vector\n",
    "\n",
    "cm_m1 = metrics.confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion matrix:\")\n",
    "print(cm_m1)\n",
    "\n",
    "print(\"Accuracy: \" + str(round(metrics.accuracy_score(y_test, y_pred)*100,2))+\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As this is a high dimensional dataset (this is the case of a sparsely populated feature space where there are few instances for  many features) and based on the information that some attributes have strong correlation with others I will perform a feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will first normalize all the values using a range [0,1] normalization to avoid negative values in the chi-square test. Chi-square scores will give me an idea of how related each features are with the target feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler() #MinMaxScaler default is [0,1] normalization of features\n",
    "X_norm = scaler.fit_transform(X_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scores of each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.005425 10.138491  0.018572  0.012606  2.556128  0.028024  0.000001\n",
      "  1.56365   0.143958  0.073812  0.000271  0.000301  0.007586  0.233573\n",
      "  0.190674  0.027255  2.492172  0.916179  0.157892  1.183575  0.067676\n",
      "  0.067676  2.36715   2.36715   4.7343    0.909555  0.003411  3.753681\n",
      "  0.059854  1.437198  0.197977  0.056946  5.073967  1.183575  0.014236\n",
      "  0.014236  1.852709  0.496177  0.23938   0.238964  0.277537  1.118729\n",
      "  0.517553  0.844898  0.704348  0.844898  3.572947  0.406353  2.36715\n",
      "  0.118254  3.048393  1.073832  2.158335  0.844898  1.252266  1.340388\n",
      "  2.36715   0.014236  2.36715   2.36715   4.7343    0.025426  0.464783\n",
      "  2.107428  0.00007   1.466595  0.899182  0.844898  1.183575  0.187722\n",
      "  1.183575  5.140612  0.020649  2.156372  0.622138  2.689943  0.694774\n",
      "  2.36715   0.339195  0.844898  0.844898  1.183575  4.689063  0.087597\n",
      "  1.651834 17.237456  0.724852  4.643192  3.550725  8.285024  0.028473\n",
      "  1.374268  0.067676  0.071182 13.82464   0.007354  0.365345  9.666134\n",
      "  2.958937  0.474109  1.374268  4.7343    3.405712  4.7343    4.7343\n",
      "  2.748536 18.105852  0.315259  0.225852  2.737544  1.972625  0.109349\n",
      "  3.213898  2.36715   3.550725  4.7343    3.550725  5.917874  6.096161\n",
      "  0.167765  0.878211  0.282235  0.370542  0.062358  2.36715   1.052798\n",
      "  2.36715   5.605008  3.891718  0.010169  0.351802  0.584475  0.231885\n",
      "  3.550725  0.844898  0.526399  2.195923  0.014121  0.466136  0.262644\n",
      "  0.371034  0.844898  1.183575  0.014236  0.406353  0.0403    0.01769\n",
      "  0.033482  0.229586  1.121128  0.040023  0.528342  0.022433  1.083292\n",
      "  0.050136  0.24652   0.410608  0.236641  0.230704  0.008518  0.082191\n",
      "  0.587023  0.350228  1.109196  0.027941  0.137331  0.278962  0.13883\n",
      "  0.463704  0.007446  0.017471  0.025594  0.038996  0.000135  0.165177\n",
      "  0.544157  2.26926   0.0774    2.702956  0.001873  0.034507  1.717668\n",
      "  0.105319  1.413703  0.017348  0.004362  0.835871  0.21449   0.032287\n",
      "  0.016233  0.294177  0.008448  0.307977  0.022785  0.342492  0.020916\n",
      "  0.218951  1.183414  0.00983   0.021882  0.054217  0.270784  0.329812\n",
      "  0.036196  0.22161   0.88761   0.043715  7.26959   0.002291  0.042786\n",
      "  0.235681  0.004513  0.259459  0.019394  0.307332  0.191691  0.011071\n",
      "  4.500869  0.008518  0.000586  0.098582  0.058592  0.324924  0.058125\n",
      "  0.206855  0.27348   0.266124  1.721597  0.003115  0.014782  0.080351\n",
      "  0.217177  0.676734  0.018953  0.072868  0.036391  0.219033  1.045211\n",
      "  0.001024  0.014684  0.148367  0.318452  0.297353  0.068132  0.059458\n",
      "  0.003173  0.07642   1.007015  0.008527  0.688071  0.105122  0.513394\n",
      "  0.091068  0.249709  0.08808   0.030853  0.294286  0.008207  0.87088\n",
      "  0.061473  0.968501]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "feat_test = SelectKBest(score_func=chi2, k=100) # k = 100 is not important at this stage (I just want to check the scores)\n",
    "\n",
    "feat_fit = feat_test.fit(X_norm,y) # chi2 test over all normalised dataset\n",
    "\n",
    "np.set_printoptions(precision=6, suppress=True) # set precision and avoid scientific notation for scores print\n",
    "scores = feat_fit.scores_ \n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the output above, features with a higher score are more related with the target feature. There are some features where chi-square score was almost zero which mean that a feature selection process should eliminate those features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also antecipate from the correlation plot below that there are quite a few instances that are correlated (in yellow) and might be redundant and consequently eliminated in a feature selection process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(19, 15))\n",
    "plt.matshow(df4.corr(), fignum=f.number)\n",
    "#plt.xticks(range(df4.shape[1]), df4.columns, fontsize=14, rotation=45)\n",
    "#plt.yticks(range(df4.shape[1]), df4.columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title('Correlation Matrix', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before going to feature selection process itself I will train and evaluate again my model with normalized instances (and same seeds) to obtain a baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal instances in test set: 73\n",
      "Risk instances in test set: 63\n",
      "Confusion matrix (normalized dataset):\n",
      "[[52 21]\n",
      " [18 45]]\n",
      "Accuracy: 71.32%\n"
     ]
    }
   ],
   "source": [
    "# splitting train and test data with same seed (random_state=1)\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X_norm, y, test_size=0.3, random_state=1) #70%/30% splitting\n",
    "\n",
    "print('Normal instances in test set: ' + str(sum(y_test2==\"Normal\")))\n",
    "print('Risk instances in test set: ' + str(sum(y_test2==\"Risk\")))\n",
    "\n",
    "fit_model2 = clf1.fit(X_train2,y_train2) #train classifier\n",
    "y_pred2 = fit_model2.predict(X_test2) #create predictions vector\n",
    "\n",
    "cm_m2 = metrics.confusion_matrix(y_test2,y_pred2)\n",
    "print(\"Confusion matrix (normalized dataset):\")\n",
    "print(cm_m2)\n",
    "\n",
    "print(\"Accuracy: \" + str(round(metrics.accuracy_score(y_test2, y_pred2)*100,2))+\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the output above, accuracy has slighly decreased after normalization (there was an extra instance predicted as \"Risk\" being \"Normal\")."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code chunk below, I will go through the feature selection process. I will use Recursive Feature Elimination (RFE) which is a type of wrapper feature selection method. According with Kelleher et al, 2013 (Galaxy classification case study Page 503), feature selection approaches that search through subsets of features (known as wrapper approaches) are better at removing redundant features together than rank prune approaches because they consider groups of features together. <br>\n",
    "    \n",
    "It is important to refer that I will do a feature selection using the instances from the previous normalized training set and not the entire dataset. This ensures that not only predictions but also the feature selection process of my model are made over unseen instances and that avoids any potential bias arising from the feature selection process. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "#define a function to train and evaluate a random forest algorithm in a reduced feature space varying the number of features\n",
    "# and recursively selecting the optimal subset of features using RFE (Feature ranking with recursive feature elimination) \n",
    "#from skelearn\n",
    "\n",
    "def evaluate_feature_reduction_RFE(nfeat):\n",
    "\n",
    "    selector = RFE(clf1,n_features_to_select=nfeat) # n_features_to_select (number of features) will vary\n",
    "\n",
    "    best_feats = selector.fit(X_train2, y_train2) #get k best features from normalized training dataset (not all dataset!)\n",
    "\n",
    "    cols = best_feats.get_support(indices=True) # get best columns indexes to use later when selecting test subset\n",
    "\n",
    "    X_train_reduced = best_feats.transform(X_train2)  # get train dataframe with feature reduction\n",
    "\n",
    "    fit_model3 = clf1.fit(X_train_reduced, y_train2) # train model with feature reduction\n",
    "\n",
    "    X_test3 = X_test2[:,cols] #select same best columns from the unseen test set using indexes stored previously\n",
    "\n",
    "    y_pred3 = fit_model3.predict(X_test3) #make predictions with unseen instances and those that weren't used in feature selection\n",
    "\n",
    "    accuracy = round(metrics.accuracy_score(y_test2,y_pred3)*100,3) #compute accuracy for model with k subset of features\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "accuracies = [evaluate_feature_reduction_RFE(i) for i in range(1,X_train2.shape[1]+1)]\n",
    "\n",
    "print(accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting the maximum value of accuracy and number of features used to achieve it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest accuracy obtained with 40 features\n"
     ]
    }
   ],
   "source": [
    "best_nfeat = np.argmax(np.array(accuracies))+1\n",
    "print(\"Highest accuracy obtained with \" + str(best_nfeat) + \" features\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  \n",
    "### Evaluation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "On the evaluation step I will evaluate my model with the optimal reduced subset of features (40 features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix (reduced dataset):\n",
      "[[68  5]\n",
      " [18 45]]\n",
      "Accuracy: 83.088%\n"
     ]
    }
   ],
   "source": [
    "selector = RFE(clf1,n_features_to_select=best_nfeat) #best number of features\n",
    "best_feats = selector.fit(X_train2, y_train2) #get best features from normalized training dataset (not all dataset!)\n",
    "cols = best_feats.get_support(indices=True) # get best columns indexes to use later when selecting test subset\n",
    "X_train_reduced = best_feats.transform(X_train2)  # get train dataframe with feature reduction\n",
    "fit_model3 = clf1.fit(X_train_reduced, y_train2) # train model with feature reduction\n",
    "X_test3 = X_test2[:,cols] #select same best columns from the unseen test set using indexes stored previously\n",
    "y_pred3 = fit_model3.predict(X_test3) #make predictions with unseen instances and those that weren't used in feature selection\n",
    "accuracy = round(metrics.accuracy_score(y_test2,y_pred3)*100,3) #compute accuracy for model with 40 features\n",
    "\n",
    "cm_m3 = metrics.confusion_matrix(y_test2,y_pred3)\n",
    "print(\"Confusion matrix (reduced dataset):\")\n",
    "print(cm_m3)\n",
    "print(\"Accuracy: \" + str(accuracy)+\"%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will also compute average class accuracy in cell below. In fact there are some instances predicted as \"Normal\" when in fact they are \"Risk\" class instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average class accuracy: 82.29%\n"
     ]
    }
   ],
   "source": [
    "print(\"Average class accuracy: \" + str(round(calc_avg_class_acc_am(cm_m3)*100,2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, in cell below I compute the area under the Receiver Operating Characteristic Curve (ROC AUC) from the prediction scores. This model achieves a value (0.864) well above 0.5 (which would be a random prediction model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC:0.864\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "y_pred_prob = fit_model3.predict_proba(X_test3) #prediction probabilities vector [P(Normal), P(Risk)]\n",
    "\n",
    "print('ROC:' + str(round(roc_auc_score(y_test2, y_pred_prob[:,1]),3)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fact that the model uses only 40 features instead of the total 279 descriptive features is a very important gain in terms of computational resources saved, especially if the dataset increases or if the model becomes stale (due to concept drift, for example) and a lazy algorithm is preferred. I would monitor the cases of false negatives and if this figure increased I would investigate further, once it is an unwanted situation in medical diagnosis. I also would monitor the outputs of the model over time and if significant deviations from the original outputs occured, the implementation of a stability index would help me to identify concept drift. Finally, whenever the representativeness of all classes achieved acceptable levels, I could develop an extended version of the model to discriminate the different classes of arrythmia in a multi-class prediction problem."
   ]
  }
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