new_read.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import scipy\n",
    "import scipy.stats\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import sklearn as sk\n",
    "\n",
    "pd.options.display.max_columns = 50\n",
    "pd.options.mode.chained_assignment = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Major libraries\n",
    "import scipy\n",
    "import bottleneck # for speeding up pandas operations\n",
    "import numexpr # ditto\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# Mining / EDA / dimensionality reduction\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.preprocessing import scale\n",
    "from scipy.spatial.distance import euclidean\n",
    "\n",
    "# Supervised learning\n",
    "from sklearn import metrics\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.cross_validation import train_test_split, cross_val_score, cross_val_predict\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Unsupervised learning\n",
    "from sklearn.cluster import DBSCAN, KMeans\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "%matplotlib inline\n",
    "rng = np.random.RandomState(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read the file and create a pandas dataframe\n",
    "data = pd.read_csv('D:/shweta/Re-admission/Diabetes_read/diabetic_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101766, 50)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the dimensions of the data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data type\n",
    "#data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>payer_code</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>max_glu_serum</th>\n",
       "      <th>A1Cresult</th>\n",
       "      <th>metformin</th>\n",
       "      <th>repaglinide</th>\n",
       "      <th>nateglinide</th>\n",
       "      <th>chlorpropamide</th>\n",
       "      <th>glimepiride</th>\n",
       "      <th>acetohexamide</th>\n",
       "      <th>glipizide</th>\n",
       "      <th>glyburide</th>\n",
       "      <th>tolbutamide</th>\n",
       "      <th>pioglitazone</th>\n",
       "      <th>rosiglitazone</th>\n",
       "      <th>acarbose</th>\n",
       "      <th>miglitol</th>\n",
       "      <th>troglitazone</th>\n",
       "      <th>tolazamide</th>\n",
       "      <th>examide</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2278392</td>\n",
       "      <td>8222157</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>?</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>Pediatrics-Endocrinology</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.83</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>276</td>\n",
       "      <td>250.01</td>\n",
       "      <td>255</td>\n",
       "      <td>9</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64410</td>\n",
       "      <td>86047875</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>648</td>\n",
       "      <td>250</td>\n",
       "      <td>V27</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>250.43</td>\n",
       "      <td>403</td>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16680</td>\n",
       "      <td>42519267</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[40-50)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>157</td>\n",
       "      <td>250</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   encounter_id  patient_nbr             race  gender      age weight  \\\n",
       "0       2278392      8222157        Caucasian  Female   [0-10)      ?   \n",
       "1        149190     55629189        Caucasian  Female  [10-20)      ?   \n",
       "2         64410     86047875  AfricanAmerican  Female  [20-30)      ?   \n",
       "3        500364     82442376        Caucasian    Male  [30-40)      ?   \n",
       "4         16680     42519267        Caucasian    Male  [40-50)      ?   \n",
       "\n",
       "   admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "0                  6                        25                    1   \n",
       "1                  1                         1                    7   \n",
       "2                  1                         1                    7   \n",
       "3                  1                         1                    7   \n",
       "4                  1                         1                    7   \n",
       "\n",
       "   time_in_hospital payer_code         medical_specialty  num_lab_procedures  \\\n",
       "0                 1          ?  Pediatrics-Endocrinology                  41   \n",
       "1                 3          ?                         ?                  59   \n",
       "2                 2          ?                         ?                  11   \n",
       "3                 2          ?                         ?                  44   \n",
       "4                 1          ?                         ?                  51   \n",
       "\n",
       "   num_procedures  num_medications  number_outpatient  number_emergency  \\\n",
       "0               0                1                  0                 0   \n",
       "1               0               18                  0                 0   \n",
       "2               5               13                  2                 0   \n",
       "3               1               16                  0                 0   \n",
       "4               0                8                  0                 0   \n",
       "\n",
       "   number_inpatient  diag_1  diag_2 diag_3  number_diagnoses max_glu_serum  \\\n",
       "0                 0  250.83       ?      ?                 1          None   \n",
       "1                 0     276  250.01    255                 9          None   \n",
       "2                 1     648     250    V27                 6          None   \n",
       "3                 0       8  250.43    403                 7          None   \n",
       "4                 0     197     157    250                 5          None   \n",
       "\n",
       "  A1Cresult metformin repaglinide nateglinide chlorpropamide glimepiride  \\\n",
       "0      None        No          No          No             No          No   \n",
       "1      None        No          No          No             No          No   \n",
       "2      None        No          No          No             No          No   \n",
       "3      None        No          No          No             No          No   \n",
       "4      None        No          No          No             No          No   \n",
       "\n",
       "  acetohexamide glipizide glyburide tolbutamide pioglitazone rosiglitazone  \\\n",
       "0            No        No        No          No           No            No   \n",
       "1            No        No        No          No           No            No   \n",
       "2            No    Steady        No          No           No            No   \n",
       "3            No        No        No          No           No            No   \n",
       "4            No    Steady        No          No           No            No   \n",
       "\n",
       "  acarbose miglitol troglitazone tolazamide examide citoglipton insulin  \\\n",
       "0       No       No           No         No      No          No      No   \n",
       "1       No       No           No         No      No          No      Up   \n",
       "2       No       No           No         No      No          No      No   \n",
       "3       No       No           No         No      No          No      Up   \n",
       "4       No       No           No         No      No          No  Steady   \n",
       "\n",
       "  glyburide-metformin glipizide-metformin glimepiride-pioglitazone  \\\n",
       "0                  No                  No                       No   \n",
       "1                  No                  No                       No   \n",
       "2                  No                  No                       No   \n",
       "3                  No                  No                       No   \n",
       "4                  No                  No                       No   \n",
       "\n",
       "  metformin-rosiglitazone metformin-pioglitazone change diabetesMed readmitted  \n",
       "0                      No                     No     No          No         NO  \n",
       "1                      No                     No     Ch         Yes        >30  \n",
       "2                      No                     No     No         Yes         NO  \n",
       "3                      No                     No     Ch         Yes         NO  \n",
       "4                      No                     No     Ch         Yes         NO  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 5 rows of data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>number_diagnoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.395987</td>\n",
       "      <td>43.095641</td>\n",
       "      <td>1.339730</td>\n",
       "      <td>16.021844</td>\n",
       "      <td>0.369357</td>\n",
       "      <td>0.197836</td>\n",
       "      <td>0.635566</td>\n",
       "      <td>7.422607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.985108</td>\n",
       "      <td>19.674362</td>\n",
       "      <td>1.705807</td>\n",
       "      <td>8.127566</td>\n",
       "      <td>1.267265</td>\n",
       "      <td>0.930472</td>\n",
       "      <td>1.262863</td>\n",
       "      <td>1.933600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time_in_hospital  num_lab_procedures  num_procedures  num_medications  \\\n",
       "count     101766.000000       101766.000000   101766.000000    101766.000000   \n",
       "mean           4.395987           43.095641        1.339730        16.021844   \n",
       "std            2.985108           19.674362        1.705807         8.127566   \n",
       "min            1.000000            1.000000        0.000000         1.000000   \n",
       "25%            2.000000           31.000000        0.000000        10.000000   \n",
       "50%            4.000000           44.000000        1.000000        15.000000   \n",
       "75%            6.000000           57.000000        2.000000        20.000000   \n",
       "max           14.000000          132.000000        6.000000        81.000000   \n",
       "\n",
       "       number_outpatient  number_emergency  number_inpatient  number_diagnoses  \n",
       "count      101766.000000     101766.000000     101766.000000     101766.000000  \n",
       "mean            0.369357          0.197836          0.635566          7.422607  \n",
       "std             1.267265          0.930472          1.262863          1.933600  \n",
       "min             0.000000          0.000000          0.000000          1.000000  \n",
       "25%             0.000000          0.000000          0.000000          6.000000  \n",
       "50%             0.000000          0.000000          0.000000          8.000000  \n",
       "75%             0.000000          0.000000          1.000000          9.000000  \n",
       "max            42.000000         76.000000         21.000000         16.000000  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['time_in_hospital','num_lab_procedures','num_procedures','num_medications',\n",
    "        'number_outpatient','number_emergency','number_inpatient',\n",
    "        'number_diagnoses']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71518"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#no of unique patient\n",
    "len(np.unique(data['patient_nbr']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.drop_duplicates(subset = 'patient_nbr', keep = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['readmitted'] = pd.Series([0 if val in ['NO', '>30'] else  val \n",
    "                         for val in data['readmitted']], index=data.index)\n",
    "data['readmitted'] = pd.Series([1 if val in ['<30'] else  val \n",
    "                         for val in data['readmitted']], index=data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    65225\n",
       "1     6293\n",
       "Name: readmitted, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.readmitted.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "414      5233\n",
       "428      3980\n",
       "786      3040\n",
       "410      2902\n",
       "486      2439\n",
       "427      2053\n",
       "715      1908\n",
       "434      1581\n",
       "682      1470\n",
       "780      1421\n",
       "491      1334\n",
       "276      1204\n",
       "996      1125\n",
       "38       1116\n",
       "250.8    1084\n",
       "599       998\n",
       "584       963\n",
       "820       824\n",
       "574       775\n",
       "435       754\n",
       "562       711\n",
       "577       690\n",
       "493       677\n",
       "518       664\n",
       "722       660\n",
       "V57       659\n",
       "296       635\n",
       "250.6     634\n",
       "433       616\n",
       "440       613\n",
       "         ... \n",
       "217         1\n",
       "791         1\n",
       "839         1\n",
       "911         1\n",
       "971         1\n",
       "975         1\n",
       "605         1\n",
       "131         1\n",
       "703         1\n",
       "145         1\n",
       "917         1\n",
       "637         1\n",
       "V25         1\n",
       "366         1\n",
       "684         1\n",
       "114         1\n",
       "804         1\n",
       "160         1\n",
       "691         1\n",
       "299         1\n",
       "923         1\n",
       "57          1\n",
       "939         1\n",
       "982         1\n",
       "833         1\n",
       "V70         1\n",
       "832         1\n",
       "207         1\n",
       "133         1\n",
       "V66         1\n",
       "Name: diag_1, Length: 697, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=data.diag_1.value_counts()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove irrelevant features\n",
    "data.drop(['encounter_id','patient_nbr', 'weight', 'payer_code','max_glu_serum','A1Cresult'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove rows that have NA in 'race', 'diag_1', 'diag_2', or 'diag_3' and 'gender'\n",
    "# remove rows that have invalid values in 'gender'\n",
    "data = data[data['race'] != '?']\n",
    "data = data[data['diag_1'] != '?']\n",
    "data = data[data['diag_2'] != '?']\n",
    "data = data[data['diag_3'] != '?']\n",
    "data = data[data['gender'] != 'Unknown/Invalid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Recategorize 'age' so that the population is more evenly distributed\n",
    "data['age'] = pd.Series(['[20-60)' if val in ['[20-30)', '[30-40)', '[40-50)', '[50-60)'] else val \n",
    "                         for val in data['age']], index=data.index)\n",
    "data['age'] = pd.Series(['[60-100)' if val in ['[60-70)','[70-80)','[80-90)', '[90-100)'] else val \n",
    "                         for val in data['age']], index=data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove the other medications\n",
    "data.drop(['metformin', 'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride', \n",
    "           'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide', 'pioglitazone', \n",
    "           'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone', 'tolazamide', 'examide', \n",
    "           'citoglipton', 'glyburide-metformin', 'glipizide-metformin', 'glimepiride-pioglitazone',\n",
    "           'metformin-rosiglitazone', 'metformin-pioglitazone','insulin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Recategorize 'age' so that the population is more evenly distributed\n",
    "data['discharge_disposition_id'] = pd.Series(['Home' if val in [1] else val \n",
    "                         for val in data['discharge_disposition_id']], index=data.index)\n",
    "\n",
    "data['discharge_disposition_id'] = pd.Series(['Anather' if val in [2,3,4,5,6] else val \n",
    "                         for val in data['discharge_disposition_id']], index=data.index)\n",
    "\n",
    "data['discharge_disposition_id'] = pd.Series(['Expired' if val in [11,19,20,21] else val \n",
    "                         for val in data['discharge_disposition_id']], index=data.index)\n",
    "\n",
    "data['discharge_disposition_id'] = pd.Series(['NaN' if val in [18,25,26] else val\n",
    "                         for val in data['discharge_disposition_id']], index=data.index)\n",
    "                        \n",
    "data['discharge_disposition_id'] = pd.Series(['other' if val in [7,8,9,10,12,13,14,15,16,17,22,23,24,27,28,29,30] else val \n",
    "                         for val in data['discharge_disposition_id']], index=data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# original 'admission_source_id' contains 25 levels\n",
    "# reduce 'admission_source_id' into 3 categories\n",
    "data['admission_source_id'] = pd.Series(['Emergency Room' if val == 7 else 'Referral' if val in [1,2,3] else \n",
    "                                         'NaN' if val in [15,17,20,21] else 'Other source' \n",
    "                                              for val in data['admission_source_id']], index=data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# original 'admission_type_id' contains 8 levels\n",
    "# reduce 'admission_type_id' into 2 categories\n",
    "data['admission_type_id'] = pd.Series(['Emergency' if val == 1 else 'Other type' \n",
    "                                              for val in data['admission_type_id']], index=data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Extract codes related to heart disease\n",
    "data = data.loc[data['diag_1'].isin(['410','411','412','413','414','415','420','421','422','423','424','425','426','427','428','429','430']) | data['diag_2'].isin(['410','411','412','413','414','415','420','421','422','423','424','425','426','427','428','429','430']) | data['diag_3'].isin(['410','411','412','413','414','415','420','421','422','423','424','425','426','427','428','429','430'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26703, 21)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                        object\n",
       "gender                      object\n",
       "age                         object\n",
       "admission_type_id           object\n",
       "discharge_disposition_id    object\n",
       "admission_source_id         object\n",
       "time_in_hospital             int64\n",
       "medical_specialty           object\n",
       "num_lab_procedures           int64\n",
       "num_procedures               int64\n",
       "num_medications              int64\n",
       "number_outpatient            int64\n",
       "number_emergency             int64\n",
       "number_inpatient             int64\n",
       "diag_1                      object\n",
       "diag_2                      object\n",
       "diag_3                      object\n",
       "number_diagnoses             int64\n",
       "change                      object\n",
       "diabetesMed                 object\n",
       "readmitted                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data type\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[20-60)</td>\n",
       "      <td>Other type</td>\n",
       "      <td>Home</td>\n",
       "      <td>Referral</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>411</td>\n",
       "      <td>250</td>\n",
       "      <td>9</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-100)</td>\n",
       "      <td>Other type</td>\n",
       "      <td>Home</td>\n",
       "      <td>Referral</td>\n",
       "      <td>4</td>\n",
       "      <td>?</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>411</td>\n",
       "      <td>V45</td>\n",
       "      <td>7</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-100)</td>\n",
       "      <td>Emergency</td>\n",
       "      <td>Home</td>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>5</td>\n",
       "      <td>?</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>492</td>\n",
       "      <td>250</td>\n",
       "      <td>8</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[60-100)</td>\n",
       "      <td>Other type</td>\n",
       "      <td>Home</td>\n",
       "      <td>Other source</td>\n",
       "      <td>13</td>\n",
       "      <td>?</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>398</td>\n",
       "      <td>427</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-60)</td>\n",
       "      <td>Emergency</td>\n",
       "      <td>Anather</td>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>7</td>\n",
       "      <td>Family/GeneralPractice</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>250.43</td>\n",
       "      <td>250.6</td>\n",
       "      <td>8</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         race  gender       age admission_type_id discharge_disposition_id  \\\n",
       "5   Caucasian    Male   [20-60)        Other type                     Home   \n",
       "6   Caucasian    Male  [60-100)        Other type                     Home   \n",
       "7   Caucasian    Male  [60-100)         Emergency                     Home   \n",
       "8   Caucasian  Female  [60-100)        Other type                     Home   \n",
       "12  Caucasian  Female   [20-60)         Emergency                  Anather   \n",
       "\n",
       "   admission_source_id  time_in_hospital       medical_specialty  \\\n",
       "5             Referral                 3                       ?   \n",
       "6             Referral                 4                       ?   \n",
       "7       Emergency Room                 5                       ?   \n",
       "8         Other source                13                       ?   \n",
       "12      Emergency Room                 7  Family/GeneralPractice   \n",
       "\n",
       "    num_lab_procedures  num_procedures  num_medications  number_outpatient  \\\n",
       "5                   31               6               16                  0   \n",
       "6                   70               1               21                  0   \n",
       "7                   73               0               12                  0   \n",
       "8                   68               2               28                  0   \n",
       "12                  60               0               15                  0   \n",
       "\n",
       "    number_emergency  number_inpatient diag_1  diag_2 diag_3  \\\n",
       "5                  0                 0    414     411    250   \n",
       "6                  0                 0    414     411    V45   \n",
       "7                  0                 0    428     492    250   \n",
       "8                  0                 0    398     427     38   \n",
       "12                 1                 0    428  250.43  250.6   \n",
       "\n",
       "    number_diagnoses change diabetesMed  readmitted  \n",
       "5                  9     No         Yes           0  \n",
       "6                  7     Ch         Yes           0  \n",
       "7                  8     No         Yes           0  \n",
       "8                  8     Ch         Yes           0  \n",
       "12                 8     Ch         Yes           1  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "#create variable emergency visits\n",
    "data['emergency_visits'] = [random.randint(0, 5) for _ in range(26703)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create variable emergency visits\n",
    "data['acuity_of_admission'] = [random.randint(1, 5) for _ in range(26703)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create variable emergency visits\n",
    "data['comorbidity'] = [random.randint(1, 15) for _ in range(26703)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[\"age\"] = data[\"age\"].astype('category')\n",
    "data[\"race\"] = data[\"race\"].astype('category')\n",
    "data[\"gender\"] = data[\"gender\"].astype('category')\n",
    "data[\"medical_specialty\"] = data[\"medical_specialty\"].astype('category')\n",
    "data[\"change\"] = data[\"change\"].astype('category')\n",
    "data[\"diabetesMed\"] = data[\"diabetesMed\"].astype('category')\n",
    "data[\"discharge_disposition_id\"] = data[\"discharge_disposition_id\"].astype('category')\n",
    "data[\"admission_source_id\"] = data[\"admission_source_id\"].astype('category')\n",
    "data[\"admission_type_id\"] = data[\"admission_type_id\"].astype('category')\n",
    "data[\"diag_1\"] = data[\"diag_1\"].astype('category')\n",
    "data[\"diag_2\"] = data[\"diag_2\"].astype('category')\n",
    "data[\"diag_3\"] = data[\"diag_3\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[\"age1\"] = data[\"age\"].cat.codes\n",
    "data[\"race1\"] = data[\"race\"].cat.codes\n",
    "data[\"gender1\"] = data[\"gender\"].cat.codes\n",
    "data[\"medical_specialty1\"] = data[\"medical_specialty\"].cat.codes\n",
    "data[\"change1\"] = data[\"change\"].cat.codes\n",
    "data[\"diabetesMed1\"] = data[\"diabetesMed\"].cat.codes\n",
    "data[\"discharge_disposition_id1\"] = data[\"discharge_disposition_id\"].cat.codes\n",
    "data[\"admission_source_id1\"] = data[\"admission_source_id\"].cat.codes\n",
    "data[\"admission_type_id1\"] = data[\"admission_type_id\"].cat.codes\n",
    "data[\"diag_11\"] = data[\"diag_1\"].cat.codes\n",
    "data[\"diag_21\"] = data[\"diag_2\"].cat.codes\n",
    "data[\"diag_31\"] = data[\"diag_3\"].cat.codes\n",
    "\n",
    "\n",
    "\n",
    "data.drop(['age', 'medical_specialty', 'race', 'gender','change', \n",
    "                  'diabetesMed', 'discharge_disposition_id', 'admission_source_id', \n",
    "                  'admission_type_id', 'diag_1', 'diag_2', 'diag_3'], axis=1, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# apply square root transformation on right skewed count data to reduce the effects of extreme values.\n",
    "# here log transformation is not appropriate because the data is Poisson distributed and contains many zero values.\n",
    "data['number_outpatient'] = data['number_outpatient'].apply(lambda x: np.sqrt(x + 0.5))\n",
    "data['number_emergency'] = data['number_emergency'].apply(lambda x: np.sqrt(x + 0.5))\n",
    "data['number_inpatient'] = data['number_inpatient'].apply(lambda x: np.sqrt(x + 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# feature scaling, features are standardized to have zero mean and unit variance\n",
    "feature_scale_cols = ['time_in_hospital', 'num_lab_procedures', 'num_procedures', 'num_medications', \n",
    "                      'number_diagnoses', 'number_inpatient', 'number_emergency', 'number_outpatient']\n",
    "\n",
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler().fit(data[feature_scale_cols])\n",
    "data_scaler = scaler.transform(data[feature_scale_cols])\n",
    "\n",
    "data_scaler_df = pd.DataFrame(data=data_scaler, columns=feature_scale_cols, index=data.index)\n",
    "data.drop(feature_scale_cols, axis=1, inplace=True)\n",
    "data = pd.concat([data, data_scaler_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create X (features) and y (response)\n",
    "X = data.drop(['readmitted'], axis=1)\n",
    "y = data['readmitted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split X and y into cross-validation (75%) and testing (25%) data sets\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_cv, X_test, y_cv, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 features are: \n",
      "num_lab_procedures\n",
      "num_medications\n",
      "diag_31\n",
      "diag_21\n",
      "diag_11\n",
      "comorbidity\n",
      "time_in_hospital\n",
      "emergency_visits\n",
      "acuity_of_admission\n",
      "medical_specialty1\n"
     ]
    },
    {
     "data": {
      "image/png": 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IkiRJ6gsmN5IkSZL6gvfcaNwW37KcOUedN9VhSJKkMXKpZPU7R24kSZIk9QWT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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xec9df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit Random Forest model to the cross-validation data\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_cv, y_cv)\n",
    "importances = forest.feature_importances_\n",
    "\n",
    "# make importance relative to the max importance\n",
    "feature_importance = 100.0 * (importances / importances.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "feature_names = list(X_cv.columns.values)\n",
    "feature_names_sort = [feature_names[indice] for indice in sorted_idx]\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "print('Top 10 features are: ')\n",
    "for feature in feature_names_sort[::-1][:10]:\n",
    "    print(feature)\n",
    "\n",
    "# plot the result\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, feature_names_sort)\n",
    "plt.title('Relative Feature Importance', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from collections import Counter\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "rf =  RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90675321433029588"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#regular logistic regression Method\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3,  random_state=55)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "acc3 = rf.score(X_test, y_test)\n",
    "acc3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.00076225082400172006"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cohen_kappa_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#data Preparation\n",
    "X1 = X\n",
    "y1 = y\n",
    "data = pd.concat([X,y], axis = 1)\n",
    "df = Counter(y)\n",
    "c = min(y, key=df.get)\n",
    "d = [i for i,x in enumerate(y) if x == c]  #row index having rare class\n",
    "d1 = [i for i,x in enumerate(y) if x != c]\n",
    "Rare_Class_data = data.iloc[d]\n",
    "Mejor_Class_data = data.iloc[d1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = Rare_Class_data\n",
    "a=Rare_Class_data.shape[0]\n",
    "b = Mejor_Class_data.shape[0]\n",
    "n = round(b/a)\n",
    "sm = SMOTE(random_state=12, ratio = 1.0)\n",
    "rf =  RandomForestClassifier()\n",
    "\n",
    "acc1=0\n",
    "acc2=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    " for i in range(1,n):\n",
    "             df1 = Rare_Class_data.copy()\n",
    "             df = data.append(df1)\n",
    "             data = df\n",
    "             n=df.shape[1]\n",
    "             X1=df.iloc[:,1:(n-2)]\n",
    "             y1=df.iloc[:,(n-1)]\n",
    "             \n",
    "             X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size = .3,random_state=i)\n",
    "             X_train_res, y_train_res = sm.fit_sample(X_train, y_train)\n",
    "             rf.fit(X_train_res, y_train_res)\n",
    "             y_pred = rf.predict(X_test)\n",
    "             acc1 = rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9936613958560524"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc1     # SMOTE method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98731947083350791"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cohen_kappa_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import datetime;\n",
    "a=datetime.datetime.strptime(\"2003-3-25\", '%Y-%m-%d')\n",
    "date = [(a - datetime.timedelta(days=x)).strftime('%Y-%m-%d') for x in range(-5000, 0)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2016-02-29', '2013-11-15', '2008-07-19', ..., '2006-09-30',\n",
       "       '2016-02-11', '2004-04-07'],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss=np.random.choice(date, 26703)\n",
    "ss.shape\n",
    "ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26703, 24)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create variable emergency visits\n",
    "data['date'] = ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "      <th>emergency_visits</th>\n",
       "      <th>acuity_of_admission</th>\n",
       "      <th>comorbidity</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[20-60)</td>\n",
       "      <td>Other type</td>\n",
       "      <td>Home</td>\n",
       "      <td>Referral</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>411</td>\n",
       "      <td>250</td>\n",
       "      <td>9</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-02-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-100)</td>\n",
       "      <td>Other type</td>\n",
       "      <td>Home</td>\n",
       "      <td>Referral</td>\n",
       "      <td>4</td>\n",
       "      <td>?</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>411</td>\n",
       "      <td>V45</td>\n",
       "      <td>7</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2013-11-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-100)</td>\n",
       "      <td>Emergency</td>\n",
       "      <td>Home</td>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>5</td>\n",
       "      <td>?</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>492</td>\n",
       "      <td>250</td>\n",
       "      <td>8</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2008-07-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[60-100)</td>\n",
       "      <td>Other type</td>\n",
       "      <td>Home</td>\n",
       "      <td>Other source</td>\n",
       "      <td>13</td>\n",
       "      <td>?</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>398</td>\n",
       "      <td>427</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2005-09-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-60)</td>\n",
       "      <td>Emergency</td>\n",
       "      <td>Anather</td>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>7</td>\n",
       "      <td>Family/GeneralPractice</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>250.43</td>\n",
       "      <td>250.6</td>\n",
       "      <td>8</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2010-09-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         race  gender       age admission_type_id discharge_disposition_id  \\\n",
       "5   Caucasian    Male   [20-60)        Other type                     Home   \n",
       "6   Caucasian    Male  [60-100)        Other type                     Home   \n",
       "7   Caucasian    Male  [60-100)         Emergency                     Home   \n",
       "8   Caucasian  Female  [60-100)        Other type                     Home   \n",
       "12  Caucasian  Female   [20-60)         Emergency                  Anather   \n",
       "\n",
       "   admission_source_id  time_in_hospital       medical_specialty  \\\n",
       "5             Referral                 3                       ?   \n",
       "6             Referral                 4                       ?   \n",
       "7       Emergency Room                 5                       ?   \n",
       "8         Other source                13                       ?   \n",
       "12      Emergency Room                 7  Family/GeneralPractice   \n",
       "\n",
       "    num_lab_procedures  num_procedures  num_medications  number_outpatient  \\\n",
       "5                   31               6               16                  0   \n",
       "6                   70               1               21                  0   \n",
       "7                   73               0               12                  0   \n",
       "8                   68               2               28                  0   \n",
       "12                  60               0               15                  0   \n",
       "\n",
       "    number_emergency  number_inpatient diag_1  diag_2 diag_3  \\\n",
       "5                  0                 0    414     411    250   \n",
       "6                  0                 0    414     411    V45   \n",
       "7                  0                 0    428     492    250   \n",
       "8                  0                 0    398     427     38   \n",
       "12                 1                 0    428  250.43  250.6   \n",
       "\n",
       "    number_diagnoses change diabetesMed  readmitted  emergency_visits  \\\n",
       "5                  9     No         Yes           0                 3   \n",
       "6                  7     Ch         Yes           0                 5   \n",
       "7                  8     No         Yes           0                 3   \n",
       "8                  8     Ch         Yes           0                 3   \n",
       "12                 8     Ch         Yes           1                 0   \n",
       "\n",
       "    acuity_of_admission  comorbidity        date  \n",
       "5                     1            3  2016-02-29  \n",
       "6                     1           11  2013-11-15  \n",
       "7                     1            7  2008-07-19  \n",
       "8                     2           11  2005-09-24  \n",
       "12                    1           11  2010-09-21  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>lenght_of_stay</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>readmitted</th>\n",
       "      <th>emergency_visits</th>\n",
       "      <th>acuity_of_admission</th>\n",
       "      <th>comorbidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-03-26</td>\n",
       "      <td>Referral</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>411</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-03-26</td>\n",
       "      <td>Referral</td>\n",
       "      <td>4</td>\n",
       "      <td>?</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>411</td>\n",
       "      <td>V45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-03-26</td>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>5</td>\n",
       "      <td>?</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>492</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-03-26</td>\n",
       "      <td>Other source</td>\n",
       "      <td>13</td>\n",
       "      <td>?</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>398</td>\n",
       "      <td>427</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-03-26</td>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>7</td>\n",
       "      <td>Family/GeneralPractice</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>250.43</td>\n",
       "      <td>250.6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date admission_source_id  lenght_of_stay       medical_specialty  \\\n",
       "0  2003-03-26            Referral               3                       ?   \n",
       "1  2003-03-26            Referral               4                       ?   \n",
       "2  2003-03-26      Emergency Room               5                       ?   \n",
       "3  2003-03-26        Other source              13                       ?   \n",
       "4  2003-03-26      Emergency Room               7  Family/GeneralPractice   \n",
       "\n",
       "   num_lab_procedures  num_procedures  number_outpatient  number_inpatient  \\\n",
       "0                  31               6                  0                 0   \n",
       "1                  70               1                  0                 0   \n",
       "2                  73               0                  0                 0   \n",
       "3                  68               2                  0                 0   \n",
       "4                  60               0                  0                 0   \n",
       "\n",
       "  diag_1  diag_2 diag_3  readmitted  emergency_visits  acuity_of_admission  \\\n",
       "0    414     411    250           0                 4                    1   \n",
       "1    414     411    V45           0                 1                    3   \n",
       "2    428     492    250           0                 0                    4   \n",
       "3    398     427     38           0                 5                    5   \n",
       "4    428  250.43  250.6           1                 3                    5   \n",
       "\n",
       "   comorbidity  \n",
       "0           14  \n",
       "1           12  \n",
       "2           10  \n",
       "3            1  \n",
       "4            8  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the file and create a pandas dataframe\n",
    "data7 = pd.read_csv('E:/xyz22.csv')\n",
    "data7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data7.index = data7['date'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data frame by month and item and extract a number of stats from each group\n",
    "d1=data7.resample('M').agg({'admission_source_id':'last',      \n",
    "                                     'lenght_of_stay': 'mean', \n",
    "                             'medical_specialty':'last',\n",
    "                              'num_lab_procedures':'last',\n",
    "                              'num_procedures':'last',\n",
    "                              'number_outpatient':sum,\n",
    "                              'number_inpatient':'sum',\n",
    "                             'diag_1':'last',\n",
    "                              'diag_2':'last',\n",
    "                              'diag_3':'last',\n",
    "                              'readmitted':'last',\n",
    "                              'emergency_visits':'last',\n",
    "                              'acuity_of_admission':'last',\n",
    "                              'comorbidity':'last'}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>lenght_of_stay</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>readmitted</th>\n",
       "      <th>emergency_visits</th>\n",
       "      <th>acuity_of_admission</th>\n",
       "      <th>comorbidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2003-03-31</th>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>?</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>564</td>\n",
       "      <td>414</td>\n",
       "      <td>794</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-04-30</th>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>4.790210</td>\n",
       "      <td>Family/GeneralPractice</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>428</td>\n",
       "      <td>250.02</td>\n",
       "      <td>401</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-31</th>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>4.791946</td>\n",
       "      <td>InternalMedicine</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>428</td>\n",
       "      <td>411</td>\n",
       "      <td>424</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-30</th>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>5.119497</td>\n",
       "      <td>?</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>414</td>\n",
       "      <td>411</td>\n",
       "      <td>424</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-07-31</th>\n",
       "      <td>Emergency Room</td>\n",
       "      <td>4.815642</td>\n",
       "      <td>?</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "      <td>428</td>\n",
       "      <td>707</td>\n",
       "      <td>416</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           admission_source_id  lenght_of_stay       medical_specialty  \\\n",
       "date                                                                     \n",
       "2003-03-31      Emergency Room        4.714286                       ?   \n",
       "2003-04-30      Emergency Room        4.790210  Family/GeneralPractice   \n",
       "2003-05-31      Emergency Room        4.791946        InternalMedicine   \n",
       "2003-06-30      Emergency Room        5.119497                       ?   \n",
       "2003-07-31      Emergency Room        4.815642                       ?   \n",
       "\n",
       "            num_lab_procedures  num_procedures  number_outpatient  \\\n",
       "date                                                                \n",
       "2003-03-31                  59               0                  3   \n",
       "2003-04-30                  51               0                  5   \n",
       "2003-05-31                  46               4                  5   \n",
       "2003-06-30                  46               6                  2   \n",
       "2003-07-31                  69               1                 16   \n",
       "\n",
       "            number_inpatient diag_1  diag_2 diag_3  readmitted  \\\n",
       "date                                                             \n",
       "2003-03-31                 5    564     414    794           0   \n",
       "2003-04-30                15    428  250.02    401           0   \n",
       "2003-05-31                24    428     411    424           0   \n",
       "2003-06-30                21    414     411    424           0   \n",
       "2003-07-31                35    428     707    416           0   \n",
       "\n",
       "            emergency_visits  acuity_of_admission  comorbidity  \n",
       "date                                                            \n",
       "2003-03-31                 5                    3            1  \n",
       "2003-04-30                 5                    1            9  \n",
       "2003-05-31                 2                    3           11  \n",
       "2003-06-30                 2                    4            7  \n",
       "2003-07-31                 4                    4           11  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(166, 14)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d.to_csv('E:/xy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[\"age\"] = data[\"age\"].astype('category')\n",
    "data[\"race\"] = data[\"race\"].astype('category')\n",
    "data[\"gender\"] = data[\"gender\"].astype('category')\n",
    "data[\"medical_specialty\"] = data[\"medical_specialty\"].astype('category')\n",
    "data[\"change\"] = data[\"change\"].astype('category')\n",
    "data[\"diabetesMed\"] = data[\"diabetesMed\"].astype('category')\n",
    "data[\"discharge_disposition_id\"] = data[\"discharge_disposition_id\"].astype('category')\n",
    "data[\"admission_source_id\"] = data[\"admission_source_id\"].astype('category')\n",
    "data[\"admission_type_id\"] = data[\"admission_type_id\"].astype('category')\n",
    "data[\"diag_1\"] = data[\"diag_1\"].astype('category')\n",
    "data[\"diag_2\"] = data[\"diag_2\"].astype('category')\n",
    "data[\"diag_3\"] = data[\"diag_3\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[\"age1\"] = data[\"age\"].cat.codes\n",
    "data[\"race1\"] = data[\"race\"].cat.codes\n",
    "data[\"gender1\"] = data[\"gender\"].cat.codes\n",
    "data[\"medical_specialty1\"] = data[\"medical_specialty\"].cat.codes\n",
    "data[\"change1\"] = data[\"change\"].cat.codes\n",
    "data[\"diabetesMed1\"] = data[\"diabetesMed\"].cat.codes\n",
    "data[\"discharge_disposition_id1\"] = data[\"discharge_disposition_id\"].cat.codes\n",
    "data[\"admission_source_id1\"] = data[\"admission_source_id\"].cat.codes\n",
    "data[\"admission_type_id1\"] = data[\"admission_type_id\"].cat.codes\n",
    "data[\"diag_11\"] = data[\"diag_1\"].cat.codes\n",
    "data[\"diag_21\"] = data[\"diag_2\"].cat.codes\n",
    "data[\"diag_31\"] = data[\"diag_3\"].cat.codes\n",
    "\n",
    "\n",
    "\n",
    "data.drop(['age', 'medical_specialty', 'race', 'gender','change', \n",
    "                  'diabetesMed', 'discharge_disposition_id', 'admission_source_id', \n",
    "                  'admission_type_id', 'diag_1', 'diag_2', 'diag_3'], axis=1, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# apply square root transformation on right skewed count data to reduce the effects of extreme values.\n",
    "# here log transformation is not appropriate because the data is Poisson distributed and contains many zero values.\n",
    "data['number_outpatient'] = data['number_outpatient'].apply(lambda x: np.sqrt(x + 0.5))\n",
    "data['number_emergency'] = data['number_emergency'].apply(lambda x: np.sqrt(x + 0.5))\n",
    "data['number_inpatient'] = data['number_inpatient'].apply(lambda x: np.sqrt(x + 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# feature scaling, features are standardized to have zero mean and unit variance\n",
    "feature_scale_cols = ['time_in_hospital', 'num_lab_procedures', 'num_procedures', 'num_medications', \n",
    "                      'number_diagnoses', 'number_inpatient', 'number_emergency', 'number_outpatient']\n",
    "\n",
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler().fit(data[feature_scale_cols])\n",
    "data_scaler = scaler.transform(data[feature_scale_cols])\n",
    "\n",
    "data_scaler_df = pd.DataFrame(data=data_scaler, columns=feature_scale_cols, index=data.index)\n",
    "data.drop(feature_scale_cols, axis=1, inplace=True)\n",
    "data = pd.concat([data, data_scaler_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create X (features) and y (response)\n",
    "X = data.drop(['readmitted'], axis=1)\n",
    "y = data['readmitted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split X and y into cross-validation (75%) and testing (25%) data sets\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_cv, X_test, y_cv, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 features are: \n",
      "num_lab_procedures\n",
      "diag_31\n",
      "diag_11\n",
      "num_medications\n",
      "diag_21\n",
      "comorbidity\n",
      "time_in_hospital\n",
      "emergency_visits\n",
      "acuity_of_admission\n",
      "number_diagnoses\n"
     ]
    },
    {
     "data": {
      "image/png": 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JjSRJkqShYHIjSZIkaSiY3EiSJEkaCiY3kiRJkoaCyc0slOSIJIcl+UCSZ01j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\no99pjrv8sQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc71a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit Random Forest model to the cross-validation data\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_cv, y_cv)\n",
    "importances = forest.feature_importances_\n",
    "\n",
    "# make importance relative to the max importance\n",
    "feature_importance = 100.0 * (importances / importances.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "feature_names = list(X_cv.columns.values)\n",
    "feature_names_sort = [feature_names[indice] for indice in sorted_idx]\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "print('Top 10 features are: ')\n",
    "for feature in feature_names_sort[::-1][:10]:\n",
    "    print(feature)\n",
    "\n",
    "# plot the result\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, feature_names_sort)\n",
    "plt.title('Relative Feature Importance', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from collections import Counter\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#data Preparation\n",
    "X1 = X\n",
    "y1 = y\n",
    "data = pd.concat([X,y], axis = 1)\n",
    "df = Counter(y)\n",
    "c = min(y, key=df.get)\n",
    "d = [i for i,x in enumerate(y) if x == c]  #row index having rare class\n",
    "d1 = [i for i,x in enumerate(y) if x != c]\n",
    "Rare_Class_data = data.iloc[d]\n",
    "Mejor_Class_data = data.iloc[d1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = Rare_Class_data\n",
    "a=Rare_Class_data.shape[0]\n",
    "b = Mejor_Class_data.shape[0]\n",
    "n = round(b/a)\n",
    "sm = SMOTE(random_state=12, ratio = 1.0)\n",
    "rf =  RandomForestClassifier()\n",
    "\n",
    "acc1=0\n",
    "acc2=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\shwetag\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    " for i in range(1,n):\n",
    "             df1 = Rare_Class_data.copy()\n",
    "             df = data.append(df1)\n",
    "             data = df\n",
    "             n=df.shape[1]\n",
    "             X1=df.iloc[:,1:(n-2)]\n",
    "             y1=df.iloc[:,(n-1)]\n",
    "             \n",
    "             X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size = .3,random_state=i)\n",
    "             X_train_res, y_train_res = sm.fit_sample(X_train, y_train)\n",
    "             rf.fit(X_train_res, y_train_res)\n",
    "             y_pred = rf.predict(X_test)\n",
    "             acc1 = rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ...Random forest using SMOTE method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9942066521264995"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc1     # SMOTE method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98841040862621266"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cohen_kappa_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.99      0.99      7255\n",
      "          1       0.99      1.00      0.99      7417\n",
      "\n",
      "avg / total       0.99      0.99      0.99     14672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/VxtWgfDosWZmdSpTozgKOBbYBLgf2CPP64k0zLibnszM6tL1iBsRD0TE9IiY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd80ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC curve\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred)\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.title('ROC curve for diabetes readmission')\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ...Random forest using Random over sampling method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.995637949836\n"
     ]
    }
   ],
   "source": [
    "#Random over sampling method\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "acc2 = rf.score(X_test, y_test)\n",
    "print(acc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.991273995309\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.99      1.00      7255\n",
      "          1       0.99      1.00      1.00      7417\n",
      "\n",
      "avg / total       1.00      1.00      1.00     14672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c3zgt6RzgshL73oQlE8pCYPeC9Y8ELmm1QNI0YBrAyi/amttvn8/As80nYr3w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\n/e/PwjQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdac6cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC curve\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred)\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.title('ROC curve for diabetes readmission')\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ...Regular Random forest Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90687804269129946"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#regular logistic regression Method\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3,  random_state=i)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "acc3 = rf.score(X_test, y_test)\n",
    "acc3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00045493120336337345"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cohen_kappa_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZY07Qd4nbGTstjp0H07ntrJbc3b8tFcuVDndYxhzD06NQVXWf35UW/jWbjDH5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\nMcYE9f+nM7ikdEXVAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe148048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC curve\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred)\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.title('ROC curve for diabetes readmission')\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      1.00      0.95      7272\n",
      "          1       0.11      0.00      0.00       739\n",
      "\n",
      "avg / total       0.83      0.91      0.86      8011\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "####Prediction on new data\n",
    "new=data[500:575]\n",
    "\n",
    "# create a (features) and b (response)\n",
    "p = new.drop(['readmitted'], axis=1)\n",
    "q = new['readmitted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75, 24)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48906, 24)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#y_pred_new = rf.predict(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred_new' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-a45449cd7ea9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred_new\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'y_pred_new' is not defined"
     ]
    }
   ],
   "source": [
    "y_pred_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}