Diabetes_Readmission_Rates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sklearn packages\n",
    "from sklearn import metrics, ensemble\n",
    "from sklearn.preprocessing import PolynomialFeatures, LabelEncoder, label_binarize, MinMaxScaler\n",
    "from sklearn.feature_selection import SelectKBest, f_regression, RFECV\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# import utility libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from itertools import cycle\n",
    "import pickle\n",
    "\n",
    "# import other modules\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "# set pandas and seaborn params and matplotlib magic\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "sns.set(style=\"whitegrid\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning and Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load and explore dataset\n",
    "df = pd.read_csv('data/diabetic_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn target into binary rather than three classifications\n",
    "df['readmitted'] = np.where(df.readmitted == '<30', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total number of hospital visits in the past year\n",
    "df['total_hosp_visits'] = df['number_outpatient'] + df['number_emergency'] + df['number_inpatient']\n",
    "\n",
    "# visualize total hospital visits\n",
    "plt.scatter(df['total_hosp_visits'],df['readmitted'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# >40 hostital visits in the past year, based on distribution above\n",
    "df['forty_hosp_visits'] = np.where(df['total_hosp_visits'] >= 40, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There appear to be extreme outliers for all the columns pertaining to square footage.\n",
    "fig, ax = plt.subplots(nrows=1, ncols=10, figsize=(20,9), )\n",
    "\n",
    "ax[0].hist(df.time_in_hospital)\n",
    "ax[0].set(xlabel='df.time_in_hospital')\n",
    "ax[1].hist(df.num_lab_procedures)\n",
    "ax[1].set(xlabel='df.num_lab_procedures')\n",
    "ax[2].hist(df.num_procedures)\n",
    "ax[2].set(xlabel='df.num_procedures')\n",
    "ax[3].hist(df.num_medications)\n",
    "ax[3].set(xlabel='df.num_medications')\n",
    "ax[4].hist(df.number_outpatient)\n",
    "ax[4].set(xlabel='df.number_outpatient')\n",
    "ax[5].hist(df.number_emergency)\n",
    "ax[5].set(xlabel='df.number_emergency')\n",
    "ax[6].hist(df.number_inpatient)\n",
    "ax[6].set(xlabel='df.number_inpatient')\n",
    "ax[7].hist(df.number_diagnoses)\n",
    "ax[7].set(xlabel='df.number_diagnoses')\n",
    "ax[8].hist(df.total_hosp_visits)\n",
    "ax[8].set(xlabel='df.total_hosp_visits')\n",
    "ax[9].hist(df.forty_hosp_visits)\n",
    "ax[9].set(xlabel='df.forty_hosp_visits')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=10, figsize=(20,9))\n",
    "\n",
    "ax[0].boxplot(df.time_in_hospital)\n",
    "ax[0].set(xlabel='df.time_in_hospital')\n",
    "ax[1].boxplot(df.num_lab_procedures)\n",
    "ax[1].set(xlabel='df.num_lab_procedures')\n",
    "ax[2].boxplot(df.num_procedures)\n",
    "ax[2].set(xlabel='df.num_procedures')\n",
    "ax[3].boxplot(df.num_medications)\n",
    "ax[3].set(xlabel='df.num_medications')\n",
    "ax[4].boxplot(df.number_outpatient)\n",
    "ax[4].set(xlabel='df.number_outpatient')\n",
    "ax[5].boxplot(df.number_emergency)\n",
    "ax[5].set(xlabel='df.number_emergency')\n",
    "ax[6].boxplot(df.number_inpatient)\n",
    "ax[6].set(xlabel='df.number_inpatient')\n",
    "ax[7].boxplot(df.number_diagnoses)\n",
    "ax[7].set(xlabel='df.number_diagnoses')\n",
    "ax[8].boxplot(df.total_hosp_visits)\n",
    "ax[8].set(xlabel='df.total_hosp_visits')\n",
    "ax[9].boxplot(df.forty_hosp_visits)\n",
    "ax[9].set(xlabel='df.forty_hosp_visits')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove outliers based on distributions above\n",
    "cols = ['time_in_hospital', 'num_lab_procedures', 'num_procedures', 'num_medications',\n",
    "        'number_outpatient', 'number_emergency', 'number_inpatient', 'number_diagnoses', 'total_hosp_visits']\n",
    "\n",
    "df[cols].head()\n",
    "\n",
    "df['time_in_hospital'] = np.where(df['time_in_hospital'] > 12, 12, df['time_in_hospital'])\n",
    "df['num_lab_procedures'] = np.where(df['num_lab_procedures'] > 95, 95, df['num_lab_procedures'])\n",
    "df['num_procedures'] = np.where(df['num_procedures'] > 5, 5, df['num_procedures'])\n",
    "df['num_medications'] = np.where(df['num_medications'] > 55, 55, df['num_medications'])\n",
    "df['number_diagnoses'] = np.where(df['number_diagnoses'] > 13, 13, df['number_diagnoses'])\n",
    "df['number_diagnoses'] = np.where(df['number_diagnoses'] < 2, 132, df['number_diagnoses'])\n",
    "df['total_hosp_visits'] = np.where(df['total_hosp_visits'] > 40, 40, df['total_hosp_visits'])\n",
    "\n",
    "# Examine new distributions\n",
    "fig, ax = plt.subplots(nrows=1, ncols=10, figsize=(20,9))\n",
    "\n",
    "ax[0].hist(df.time_in_hospital)\n",
    "ax[0].set(xlabel='df.time_in_hospital')\n",
    "ax[1].hist(df.num_lab_procedures)\n",
    "ax[1].set(xlabel='df.num_lab_procedures')\n",
    "ax[2].hist(df.num_procedures)\n",
    "ax[2].set(xlabel='df.num_procedures')\n",
    "ax[3].hist(df.num_medications)\n",
    "ax[3].set(xlabel='df.num_medications')\n",
    "ax[4].hist(df.number_outpatient)\n",
    "ax[4].set(xlabel='df.number_outpatient')\n",
    "ax[5].hist(df.number_emergency)\n",
    "ax[5].set(xlabel='df.number_emergency')\n",
    "ax[6].hist(df.number_inpatient)\n",
    "ax[6].set(xlabel='df.number_inpatient')\n",
    "ax[7].hist(df.number_diagnoses)\n",
    "ax[7].set(xlabel='df.number_diagnoses')\n",
    "ax[8].hist(df.total_hosp_visits)\n",
    "ax[8].set(xlabel='df.total_hosp_visits')\n",
    "ax[9].hist(df.forty_hosp_visits)\n",
    "ax[9].set(xlabel='df.forty_hosp_visits')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=10, figsize=(20,9))\n",
    "\n",
    "ax[0].boxplot(df.time_in_hospital)\n",
    "ax[0].set(xlabel='df.time_in_hospital')\n",
    "ax[1].boxplot(df.num_lab_procedures)\n",
    "ax[1].set(xlabel='df.num_lab_procedures')\n",
    "ax[2].boxplot(df.num_procedures)\n",
    "ax[2].set(xlabel='df.num_procedures')\n",
    "ax[3].boxplot(df.num_medications)\n",
    "ax[3].set(xlabel='df.num_medications')\n",
    "ax[4].boxplot(df.number_outpatient)\n",
    "ax[4].set(xlabel='df.number_outpatient')\n",
    "ax[5].boxplot(df.number_emergency)\n",
    "ax[5].set(xlabel='df.number_emergency')\n",
    "ax[6].boxplot(df.number_inpatient)\n",
    "ax[6].set(xlabel='df.number_inpatient')\n",
    "ax[7].boxplot(df.number_diagnoses)\n",
    "ax[7].set(xlabel='df.number_diagnoses')\n",
    "ax[8].boxplot(df.total_hosp_visits)\n",
    "ax[8].set(xlabel='df.total_hosp_visits')\n",
    "ax[9].boxplot(df.forty_hosp_visits)\n",
    "ax[9].set(xlabel='df.forty_hosp_visits')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop cols w/ many missing vals\n",
    "df.drop(columns=['weight', 'payer_code'], inplace=True)\n",
    "\n",
    "# drop unique id's\n",
    "df.drop(columns=['encounter_id', 'patient_nbr'],  inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert primary diagnosis code strs to floats in order to bin using numeric ranges\n",
    "def bin_diag_codes(diag):\n",
    "    df[diag] = np.where(df[diag].str.startswith('V'), '10'+df[diag].str[1:], df[diag])\n",
    "    df[diag] = np.where(df[diag].str.startswith('E'), '1'+df[diag].str[1:], df[diag])\n",
    "    df[diag] = np.where(df[diag] == '?', '2000', df[diag])\n",
    "    df[diag] = df[diag].astype(np.float64)\n",
    "\n",
    "# bin primary-df[diag]nosis-codes using numeric ranges\n",
    "    conditions = [(df[diag] >= 1) & (df[diag] <= 139), (df[diag] >= 140) & (df[diag] <= 239),\n",
    "                  (df[diag] >= 240) & (df[diag] <= 279), (df[diag] >= 280) & (df[diag] <= 289),\n",
    "                  (df[diag] >= 290) & (df[diag] <= 319), (df[diag] >= 320) & (df[diag] <= 389),\n",
    "                  (df[diag] >= 390) & (df[diag] <= 459), (df[diag] >= 460) & (df[diag] <= 519),\n",
    "                  (df[diag] >= 520) & (df[diag] <= 579), (df[diag] >= 580) & (df[diag] <= 629),\n",
    "                  (df[diag] >= 630) & (df[diag] <= 679), (df[diag] >= 680) & (df[diag] <= 709),\n",
    "                  (df[diag] >= 710) & (df[diag] <= 739), (df[diag] >= 740) & (df[diag] <= 759),\n",
    "                  (df[diag] >= 760) & (df[diag] <= 779), (df[diag] >= 780) & (df[diag] <= 799),\n",
    "                  (df[diag] >= 800) & (df[diag] <= 999), (df[diag] >= 1001) & (df[diag] <= 1091),\n",
    "                  (df[diag] >= 1800) & (df[diag] <= 1999), df[diag] == 2000]\n",
    "    \n",
    "    choices = ['Inf/Par', 'Neoplasms', 'End/Nut/Met/Imm', 'Blood', 'Mental', 'Nervous',\n",
    "           'Circulatory', 'Respiratory', 'Digestive', 'Genitourinary', 'Pre/Chi/Pue',\n",
    "           'Skin', 'Musculoskeletal', 'Congenital', 'Newborn', 'Ill-Defined',\n",
    "           'Inj/Poi', 'Other Factors', 'Inj/Poi Supp.', 'Unknown']\n",
    "    \n",
    "    return np.select(conditions, choices)\n",
    "\n",
    "df['diag_1'] = bin_diag_codes('diag_1')\n",
    "df['diag_2'] = bin_diag_codes('diag_2')\n",
    "df['diag_3'] = bin_diag_codes('diag_3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin discharge disposition codes using domain knowledge   \n",
    "conditions = [df.discharge_disposition_id==1,\n",
    "              df.discharge_disposition_id.isin([11, 19, 20, 21]),\n",
    "              df.discharge_disposition_id.isin([18, 25, 26]),\n",
    "              df.discharge_disposition_id.isin([2, 3, 4, 5, 9, 10, 15, 22, 23, 24]),\n",
    "              df.discharge_disposition_id.isin([16, 17]),\n",
    "              df.discharge_disposition_id.isin([27, 28, 29, 30]),\n",
    "              df.discharge_disposition_id.isin([6, 8]),\n",
    "              df.discharge_disposition_id == 7,\n",
    "              df.discharge_disposition_id.isin([13, 14]),\n",
    "              df.discharge_disposition_id == 12]\n",
    "\n",
    "choices = ['Home, no Care', 'Expired', 'Unknown', 'Inpatient',\n",
    "          'Outpatient', 'Unknown Status Facility', 'Home Care', 'AMA',\n",
    "          'Hospice', 'To Return']\n",
    "\n",
    "df.discharge_disposition_id = np.select(conditions, choices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin admission-source id codes using domain knowledge      \n",
    "conditions = [df.admission_source_id.isin([1,2,3]),\n",
    "              df.admission_source_id.isin([4,5,6,10,18,19,22,25,26]),\n",
    "              df.admission_source_id == 7,\n",
    "              df.admission_source_id == 8,\n",
    "              df.admission_source_id.isin([9,15,17,20,21]),\n",
    "              df.admission_source_id.isin([11,12,13,14,23,24])]\n",
    "\n",
    "choices = ['Referral', 'Transfer', 'ER', 'Court/Law Enf',\n",
    "          'Unknown', 'Birth-Related']\n",
    "\n",
    "df.admission_source_id = np.select(conditions, choices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert medical_specialty values to medical_specialty.values\n",
    "df.medical_specialty = df.medical_specialty.values\n",
    "\n",
    "# bin medical specialties based on domain knowledge of similar/overlapping specialties\n",
    "conditions = [df.medical_specialty.isin(x) for x in [\n",
    "    ['Orthopedics', 'Orthopedics-Reconstructive', 'PhysicalMedicineandRehabilitation', 'SportsMedicine'],\n",
    "    ['Cardiology', 'Surgery-Cardiovascular/Thoracic', 'Surgery-Thoracic', 'Surgery-Cardiovascular','Cardiology-Pediatric'],\n",
    "    ['Pediatrics-Endocrinology', 'Endocrinology', 'Endocrinology-Metabolism'],\n",
    "    ['Oncology + Hematology/Oncology + Hematology', 'Obsterics&Gynecology-GynecologicOnco', 'Pediatrics-Hematology-Oncology'],\n",
    "    ['Neurology', 'Surgery-Neuro', 'Pediatrics-Neurology', 'Neurophysiology'],\n",
    "    ['Psychiatry', 'Psychology', 'Psychiatry-Child/Adolescent', 'Psychiatry-Addictive'],\n",
    "    ['ObstetricsandGynecology', 'Gynecology', 'Obstetrics', 'Perinatology'],\n",
    "    ['Pulmonology', 'Pediatrics-Pulmonology'],\n",
    "    ['Surgery-General', 'Surgeon', 'Surgery-Pediatric'],\n",
    "    ['Hospitalist', 'InternalMedicine'],\n",
    "    ['Anesthesiology', 'Anesthesiology-Pediatric'],\n",
    "    ['Surgery-Colon&Rectal', 'Proctology'],\n",
    "    ['Surgery-Maxillofacial', 'Surgery-Plastic', 'Surgery-PlasticwithinHeadandNeck'],\n",
    "    ['AllergyandImmunology', 'Pediatrics-AllergyandImmunology'],\n",
    "    ['Emergency/Trauma', 'Pediatrics-EmergencyMedicine'],\n",
    "    ['DCPTEAM', 'Resident'],\n",
    "    ['Pediatrics-InfectiousDiseases', 'InfectiousDiseases'],\n",
    "    ['Radiologist', 'Radiology']\n",
    "]]\n",
    "\n",
    "choices = ['Ortho/Sports', 'Cardio/Thoracic', 'Endo', 'Onc/Hema', 'Neuro', 'Psych', 'OBGYN', 'Pulmo', 'Surgery',\n",
    "           'Int Med', 'Anesth', 'ColoRectal', 'Plastic Surg', 'Immun', 'Emergency', 'Other', 'Infect', 'Radio']\n",
    "\n",
    "df.medical_specialty = np.select(conditions, choices, default=df.medical_specialty) #<-- set default since not all values binned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin ages based on log likelihood of being readmitted, per the following figure: https://www.hindawi.com/journals/bmri/2014/781670/fig2/\n",
    "df['binned_age'] = 'None'\n",
    "df['binned_age'] = np.where(df['age'].isin(['[0-10)', '[10-20)', '[20-30)']), '[0-30)', df['binned_age'])\n",
    "df['binned_age'] = np.where(df['age'].isin(['[30-40)', '[40-50)', '[50-60)']), '[30-60)', df['binned_age'])\n",
    "df['binned_age'] = np.where(df['age'].isin(['[60-70)', '[70-80)', '[80-90)', '[90-100)']), '[60-100)', df['binned_age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize barplots for features with clearest trends,\n",
    "# which we expect to have the greatest effect\n",
    "\n",
    "sns.catplot(x=\"discharge_disposition_id\", y=\"readmitted\", kind=\"bar\", data=df, ci=None, aspect=3, )\n",
    "sns.catplot(x=\"glipizide-metformin\", y=\"readmitted\", kind=\"bar\", data=df, ci=None, aspect=3)\n",
    "sns.catplot(x=\"glyburide\", y=\"readmitted\", kind=\"bar\", data=df, ci=None, aspect=3)\n",
    "sns.catplot(x=\"miglitol\", y=\"readmitted\", kind=\"bar\", data=df, ci=None, aspect=3)\n",
    "sns.catplot(x=\"acarbose\", y=\"readmitted\", kind=\"bar\", data=df, ci=None, aspect=3)\n",
    "sns.catplot(x=\"tolbutamide\", y=\"readmitted\", kind=\"bar\", data=df, ci=None, aspect=3)\n",
    "sns.catplot(x=\"number_inpatient\", y=\"readmitted\", kind=\"bar\", data=df, ci=None, aspect=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90409\n",
       "1    11357\n",
       "Name: readmitted, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    0.888401\n",
       "1    0.111599\n",
       "Name: readmitted, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check for class imbalance\n",
    "display(df.readmitted.value_counts(), df.readmitted.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# under sample majority class to eliminate class imbalance\n",
    "minority = df[df['readmitted'] == 1]\n",
    "undersampled_majority = df[df['readmitted'] == 0].sample(n=len(minority))\n",
    "df = pd.concat([minority, undersampled_majority])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11357\n",
       "0    11357\n",
       "Name: readmitted, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1    0.5\n",
       "0    0.5\n",
       "Name: readmitted, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confirm new class distribution\n",
    "display(df.readmitted.value_counts(), df.readmitted.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write cleaned date to csv\n",
    "df.to_csv('data/diabetic_data_clean.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load clean data\n",
    "df = pd.read_csv('data/diabetic_data_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set X and y\n",
    "X = df.drop(columns='readmitted')\n",
    "y = df.readmitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create label-encoded copy of X\n",
    "le_X = X.copy()\n",
    "le = LabelEncoder()\n",
    "le_X = pd.DataFrame(le_X.apply(le.fit_transform))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define function to determine feature importances\n",
    "def plot_feature_importances(model):\n",
    "    n_features = X_train.shape[1]\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center') \n",
    "    plt.yticks(np.arange(n_features), X_train.columns.values) \n",
    "    plt.xlabel('Feature importance')\n",
    "    plt.ylabel('Feature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grid Search for Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'UNCOMMENT THIS CELL TO RUN GRID SEARCH'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"UNCOMMENT THIS CELL TO RUN GRID SEARCH\"\"\"\n",
    "# # create a dictionary of all the parameters you want to tune\n",
    "# param_grid = {'max_depth': list(range(5,10)),\n",
    "#               'min_samples_split': [5,6,7],\n",
    "#               'min_samples_leaf': [2, 3],\n",
    "#               'max_features': [4,5,6,7,8]}\n",
    "\n",
    "# # train test split\n",
    "# X_train, X_test, y_train, y_test = train_test_split(le_X, y, test_size=.2, random_state=42)\n",
    "\n",
    "# # create a grid search object and fit it to the data\n",
    "# # score using recall to maximize likelihood of predicting all people who will be readmitted\n",
    "# grid_tree=GridSearchCV(DecisionTreeClassifier(random_state=42), param_grid, cv=3, scoring='recall', verbose=1, n_jobs=-1)\n",
    "# grid_tree.fit(X_train, y_train)\n",
    "\n",
    "# # pickle grid parameters\n",
    "# pickle_out = open(\"Pickles/dt_grid.pickle\",\"wb\")\n",
    "# pickle.dump(grid_tree, pickle_out)\n",
    "# pickle_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision tree using grid search:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[1569  685]\n",
      " [1088 1201]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision:  0.6398067128118062\n",
      "Testing Precision:  0.6367974549310711\n",
      "Training Recall:  0.540251433612704\n",
      "Testing Recall:  0.5246832678025338\n",
      "Training Accuracy:  0.6187881789664851\n",
      "Testing Accuracy:  0.6097292537970505\n",
      "Training F1-Score:  0.585829596412556\n",
      "Testing F1-Score:  0.5753293413173652\n"
     ]
    }
   ],
   "source": [
    "# load grid parameters\n",
    "grid_tree = pd.read_pickle('Pickles/dt_grid.pickle')\n",
    "\n",
    "# train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(le_X, y, test_size=.2, random_state=42)\n",
    "\n",
    "# build baseline decision tree using grid search to determine optimal parameters \n",
    "dtc = DecisionTreeClassifier(max_depth=grid_tree.best_params_['max_depth'],\n",
    "                             max_features=grid_tree.best_params_['max_features'],\n",
    "                             min_samples_leaf=grid_tree.best_params_['min_samples_leaf'],\n",
    "                             min_samples_split=grid_tree.best_params_['min_samples_split'],\n",
    "                             random_state=42, class_weight = 'balanced')\n",
    "# fit to the training data\n",
    "model_log = dtc.fit(X_train, y_train)\n",
    "\n",
    "# use fitted model to predict on the test data\n",
    "y_hat_train = dtc.predict(X_train)\n",
    "y_hat_test  = dtc.predict(X_test)\n",
    "\n",
    "# print confusion matrix\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_hat_test)\n",
    "print('Confusion Matrix:\\n', cnf_matrix)\n",
    "\n",
    "# visualize confusion matrix of the test set\n",
    "metrics.plot_confusion_matrix(dtc, X_test, y_test,\n",
    "                              display_labels=['Yes', 'No'],\n",
    "                              cmap=plt.cm.Blues)\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "\n",
    "print('Training Precision: ', metrics.precision_score(y_train, y_hat_train))\n",
    "print('Testing Precision: ', metrics.precision_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Recall: ', metrics.recall_score(y_train, y_hat_train))\n",
    "print('Testing Recall: ', metrics.recall_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Accuracy: ', metrics.accuracy_score(y_train, y_hat_train))\n",
    "print('Testing Accuracy: ', metrics.accuracy_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training F1-Score: ', metrics.f1_score(y_train, y_hat_train))\n",
    "\n",
    "print('Testing F1-Score: ', metrics.f1_score(y_test, y_hat_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6103895487193681"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ROC/AUC score\n",
    "y_pred = dtc.predict(X_test)\n",
    "fpr,tpr,thresh = roc_curve(y_test,y_pred)\n",
    "auc(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.6365002095202432\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve\n",
    "dt_probs = dtc.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, dt_probs)\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "print('AUC: {}'.format(auc(fpr, tpr)))\n",
    "plt.figure(figsize=(10,8))\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='mediumorchid',\n",
    "         lw=lw, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='lightskyblue', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.yticks([i/20.0 for i in range(21)])\n",
    "plt.xticks([i/20.0 for i in range(21)])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# determine feature importance\n",
    "plot_feature_importances(dtc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grid Search for Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'UNCOMMENT THIS CELL TO RUN GRID SEARCH'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"UNCOMMENT THIS CELL TO RUN GRID SEARCH\"\"\"\n",
    "# # create a dictionary of all the parameters to tune\n",
    "# param_grid = {'n_estimators': [100,300,500,800],\n",
    "#     'criterion': ['gini', 'entropy'],\n",
    "#     'max_depth': [None,2,4,6,10],\n",
    "#     'max_features': [None,4,6,8]}\n",
    "\n",
    "# # train test split\n",
    "# X_train, X_test, y_train, y_test = train_test_split(le_X, y, test_size=.2, random_state=42)\n",
    "\n",
    "# # Create a grid search object and fit it to the data\n",
    "# grid_forest=GridSearchCV(ensemble.RandomForestClassifier(random_state=42), param_grid, cv=3, scoring='recall', verbose=1, n_jobs=-1)\n",
    "# grid_forest.fit(X_train, y_train)\n",
    "\n",
    "# # pickle grid parameters\n",
    "# pickle_out = open(\"Pickles/rf_grid.pickle\",\"wb\")\n",
    "# pickle.dump(grid_forest, pickle_out)\n",
    "# pickle_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest with Grid Search Parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[1410  844]\n",
      " [ 880 1409]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision:  0.6281211852180668\n",
      "Testing Precision:  0.6253883710608078\n",
      "Training Recall:  0.6241729157476842\n",
      "Testing Recall:  0.6155526430755789\n",
      "Training Accuracy:  0.6280336800396236\n",
      "Testing Accuracy:  0.6205150781421968\n",
      "Training F1-Score:  0.6261408263731402\n",
      "Testing F1-Score:  0.6204315279612506\n"
     ]
    }
   ],
   "source": [
    "# load grid parameters\n",
    "grid_forest = pd.read_pickle('Pickles/rf_grid.pickle')\n",
    "\n",
    "# train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(le_X, y, test_size=.2, random_state=42)\n",
    "\n",
    "# instantiate RFC classifier\n",
    "rfc = ensemble.RandomForestClassifier(criterion = grid_forest.best_params_['criterion'],\n",
    "                                      max_depth = grid_forest.best_params_['max_depth'],\n",
    "                                      max_features = grid_forest.best_params_['max_features'],\n",
    "                                      n_estimators = grid_forest.best_params_['n_estimators'],\n",
    "                                      random_state = 42)\n",
    "\n",
    "# fit to the training data\n",
    "model_forest = rfc.fit(X_train, y_train)\n",
    "\n",
    "# use fitted model to predict on the test data\n",
    "y_hat_train = rfc.predict(X_train)\n",
    "y_hat_test  = rfc.predict(X_test)\n",
    "\n",
    "# print confusion matrix\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_hat_test)\n",
    "print('Confusion Matrix:\\n', cnf_matrix)\n",
    "\n",
    "# visualize confusion matrix of the test set\n",
    "metrics.plot_confusion_matrix(rfc, X_test, y_test,\n",
    "                              display_labels=['Yes', 'No'],\n",
    "                              cmap=plt.cm.Blues)\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "\n",
    "print('Training Precision: ', metrics.precision_score(y_train, y_hat_train))\n",
    "print('Testing Precision: ', metrics.precision_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Recall: ', metrics.recall_score(y_train, y_hat_train))\n",
    "print('Testing Recall: ', metrics.recall_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Accuracy: ', metrics.accuracy_score(y_train, y_hat_train))\n",
    "print('Testing Accuracy: ', metrics.accuracy_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training F1-Score: ', metrics.f1_score(y_train, y_hat_train))\n",
    "print('Testing F1-Score: ', metrics.f1_score(y_test, y_hat_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6205536063647638"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ROC/AUC score\n",
    "y_pred = rfc.predict(X_test)\n",
    "fpr,tpr,thresh = roc_curve(y_test,y_pred)\n",
    "auc(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.6641988438203932\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve\n",
    "rf_probs = rfc.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, rf_probs)\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "print('AUC: {}'.format(auc(fpr, tpr)))\n",
    "plt.figure(figsize=(10,8))\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='mediumorchid',\n",
    "         lw=lw, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='lightskyblue', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.yticks([i/20.0 for i in range(21)])\n",
    "plt.xticks([i/20.0 for i in range(21)])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# determine feature importance\n",
    "plot_feature_importances(rfc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grid Search for XG Boost**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'UNCOMMENT THIS CELL TO RUN GRID SEARCH'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"UNCOMMENT THIS CELL TO RUN GRID SEARCH\"\"\"\n",
    "# # Create a dictionary of all the parameters you want to tune\n",
    "# param_grid = {'n_estimators': [100,300,500,800],\n",
    "#     'criterion': ['gini', 'entropy'],\n",
    "#     'max_depth': [None,2,4,6,10],\n",
    "#     'max_features': [None,4,6,8]}\n",
    "\n",
    "# # train test split\n",
    "# X_train, X_test, y_train, y_test = train_test_split(le_X, y, test_size=.2, random_state=42)\n",
    "\n",
    "# # Create a grid search object and fit it to the data\n",
    "# grid_xg=GridSearchCV(XGBClassifier(), param_grid, cv=3, scoring='recall', verbose=1, n_jobs=-1)\n",
    "# grid_xg.fit(X_train, y_train)\n",
    "\n",
    "# # pickle grid parameters\n",
    "# pickle_out = open(\"Pickles/xg_grid.pickle\",\"wb\")\n",
    "# pickle.dump(grid_xg, pickle_out)\n",
    "# pickle_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XG Boost with Grid Search Parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:892: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:50:36] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:541: \n",
      "Parameters: { criterion } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[12:50:36] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Training:\n",
      "0    12071\n",
      "1     6100\n",
      "Name: readmitted, dtype: int64\n",
      "0    0.6643\n",
      "1    0.3357\n",
      "Name: readmitted, dtype: float64\n",
      "Testing\n",
      "0    2833\n",
      "1    1710\n",
      "Name: readmitted, dtype: int64\n",
      "0    0.623597\n",
      "1    0.376403\n",
      "Name: readmitted, dtype: float64\n",
      "Confusion Matrix:\n",
      " [[1411  843]\n",
      " [ 867 1422]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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JSUk+bTNmzPD53KJFC1q0aAHAoUOHWLp0KbNmzSI/P5/Q0FBvv9DQUPLy8jh8+DButxuXy+XTbuaMCbhbt244HA4Mw6iyz+FwsHv3btODi4jUNKfT/Cab8+ef7ZcuXYrH4/HZFxwc7Pe58vLyGDFiBAkJCdxwww1s27bNp7xhGIY3T/667OFPGeSMCfjbb7/1O0gRkbricIDZJIdTuS88PPycz/Ovf/2LESNGMGTIEIYPHw5A8+bNKSgo8PYpLCwkLCyMkJAQiouL8Xg8BAQEUFBQ4C1nnI1pDbiyspJXXnmF5ORkSkpKWLx4cZV/UURE6kpt3IT7tZKSEu69917GjBnjTb5wsjQRFBTEtm3bAFi1ahWRkZEEBgbSpUsX1q5dC0BGRgaRkZGm5zFNwHPnzmXPnj1s374dwzD49NNPmTVr1rlel4jIeXHgx0248zzH8uXLKSwsZMmSJfTr149+/fqxcOFCAObNm8esWbOIjo6mtLSUoUOHApCSkkJaWhoxMTFs3bqVsWPHml+Lcboi7y/069ePlStXEh8fT0ZGBidOnKBfv368//7753mJ9U+7vtPZn3vY6jDET4c/e9rqEMRPDiCohh77Gr50O/klJ87aJ8zdgFcTr6uZE9Yi078Sl8uF0/nvgXKDBg28d/pEROqa048asE0ehDNPwG3btvXeSfz+++957bXXaN++fV3EJiJSxUX1KPLEiRPZuXMnBw8eZODAgRw7dowJEybURWwiIlU4fp4HfLbtglmMx+12M3PmzLqIRUTE1IW0HrDpCPjgwYM88sgj3HDDDURERDBhwoTTPsonIlIX6mIaWl0xTcCTJk2iVatWLF++nDfeeINLL72UKVOm1EVsIiJVVGctiPrOtASRk5PDokWLvJ+feOIJ4uLiajUoEZEzCcBBgEmGDTjvmcB1w3QEHBYWRlZWlvfzjz/+6LMYhYhInfKn/GCTIfAZR8APPPAAcHIloDvuuIPu3bvjdDrZvHkz7dq1q7MARUR+6aKYB3xqXctf69GjR23FIiJi6uQA1+ydcHUUzHk6YwK+8847T9tuGAb79u2rtYBERM7mQpqGZnoT7u2332bu3Ln89NNP3raQkBA2btxYq4GJiJzORfVW5BdffJElS5awaNEixo4dy8cff8yPP/5YF7GJiFThdDoIuFgeRW7atCnXXXcdV199NQcPHmTUqFFs2bKlLmITEanC4edmB6YJ2OVycfToUVq3bs3XX38NoAXZRcQyTszXgnDaJAWbJuC7776b+++/nx49erBs2TLi4+Np06ZNXcQmIlLFRfUkXP/+/YmJiaFRo0YsW7aMb775hptvvrkuYhMRqeKiuAm3ZMmSM37pzTffZNiwYbUSkIjI2VwU09D+8Y9/1GUcIiJ+uZBmQZi+E+5iUvSTh0r9bdhG+E1jrA5B/HR5eAh71k6rkWNNXvcdh0rLz9onpFEg06OuqpHz1Sa93E1EbMWJ+ewB09kF9YQSsIjYykVxE05EpD5y4sdqaHUSyfkzjbOyspKXX36ZJ554gpKSEhYvXqwHMUTEMk4nBPx8I+5Mm9MmGdh0BDx37lwOHTrEN998A8Cnn35KQUEBkyZNqvXgRER+7UJaD9j034nPPvuM2bNnExQUhNvt5tVXX9VKaCJimYvqSTiXy4XzF+P5Bg0a4HKpdCwi1ji13oNZHzswzaRt27Zl6dKleDwevv/+e1577TXat29fF7GJiFThwPxHd3ukXz9KEBMnTmTnzp0cPHiQgQMHcuzYMSZMmFAXsYmIVFFbJYiSkhJiY2PJzs72tj3++OOsWLHC+/nAgQMkJiYSHR3NqFGjOHbsGABFRUWMHDmS22+/ncTERAoKCvw6p2kCdrvdzJw5k02bNrF582bmz5/PZZddVt1rExGpEQGOs8+ACHCav7b+17Zv387AgQPZu3cvAHl5eTzwwAOsW7fOp9/UqVMZNGgQmZmZdOjQgdTUVAAWLFhAly5deP/997nrrruYMWOGX+c1LUE89dRTp23XLAgRsUJtzIJIS0sjJSWFxx9/HIA1a9bQq1cvmjZt6u1TXl7Oli1beP755wGIj49n8ODBjBs3jk8++YSlS5cCEBsby7Rp0ygvLycwMPCs5zVNwL8O4OOPP6Zr167VuzoRkRri8OMm3Kkn4XJzc6s8txAcHExwcLBP269HrCNGjABg27Zt3rbDhw/jdru9kxBCQ0PJy8sDID8/n9DQUODkxAW3282hQ4do1qzZWeM0TcCjR4/2+XzfffcxatQos6+JiNSK6ixHmZiYSE5Ojs++0aNHk5SUVO3zGoZR5RHnMz3ybBiGz+yxM6n2fDK3201+fn51vyYiUiOqU4I4NYPrl349+vVXSEgIxcXFeDweAgICKCgoICwsDICwsDAKCwtp3rw5FRUVHDt2zKd6cCamCXj69OneLG8YBjt37tQriUTEMo6ff5n1AQgPD6+x8wYGBtKlSxfWrl1LXFwcGRkZREZGAnDLLbeQkZHBAw88wNq1a+nSpYtp/Rf8SMC/nvHQt29f+vbte46XICJyfgKc4DL56T6gltaCSElJITk5mUWLFhEeHs7TTz8NwJgxY0hOTqZPnz40adKEefPm+XU80wXZH3/8cebOnXv+kduAFmS3Fy3Ibh81uSD74s/3U1RWcdY+wZe4uL/b5TVyvtpkOgL+9ttvT1t8FhGxwoW0HKVpAg4NDaVPnz5cd911NG7c2NuuecAiYoWL4qWcJ06coEGDBnTq1IlOnTrVZUwiImd0USzGM2DAAFauXFllHrCIiJVOLshu3scOzpiA9bJkEamPnDhwmkxDM9tfX5wxAR8/fpxdu3adMRFfc801tRaUiMiZXBQ14KysLJKSkk6bgB0OBx9++GGtBiYicjoX0iuJzpiAr7rqKjIyMuoyFhERUw6H+U0224+ARUTqIwd+lCDqJJLzd8YE3KVLl7qMQ0TEL6cWXTfrYwdnTMB60EJE6iMn5k+62WQWmkoQImIzDof50gg2KQIrAYuIrTgwr/HaI/0qAYuIzVwUjyKLiNRHGgGLiFjE6XTgNJnlYLa/vlACFhFbcWA+y8Ee6VcJWERsxuHHLAi7vEBCCVhEbEU1YBERizjwYwRskxSsBCwithLgcBBgkoDN9tcXSsAiYisqQYiIWMWPBdntkoGVgEXEVk4uxmP2SiJ7UAIWEVu5KF5JJCJSHzl+/mXWxw6UgEXEVjQLQkTEKroJJyJijQupBmyXm4UiIsC/a8Bmv6qrpKSE2NhYsrOzAdi0aRNxcXH07t2bZ555xttv9+7dxMfHExUVxcSJE6moqADgwIEDJCYmEh0dzahRozh27JjpOZWARcRWnA7/turYvn07AwcOZO/evQCUlZUxYcIEUlNTWbt2LTt27GD9+vUAjBs3jilTprBu3ToMwyAtLQ2AqVOnMmjQIDIzM+nQoQOpqanm11K9MEVErOXE4X0rxhm3n0fAubm5ZGdn+2xFRUVVjpmWlkZKSgphYWEAfP3117Ru3ZpWrVrhcrmIi4sjMzOTnJwcysrK6NixIwDx8fFkZmZSXl7Oli1biIqK8mk3oxqwiNhKdaahJSYmkpOT47Nv9OjRJCUl+bTNmDHD53N+fj6hoaHez2FhYeTl5VVpDw0NJS8vj8OHD+N2u3G5XD7tZpSAba68wsOYp94gK/cQTqeTecn3cFlwIx6b/TZHin+isrKSZycNpqS0jCnPrvR+74ude3l11gh6drvawugvPp2vac2TSXcQ98BCb1v/qC7cd/ctRN07H4BRA28lvndnAD7YuJO5L79PcONLWDz9DzRpfAmBLheTFqxgyzc/WHINVnP4UWI4dRNu6dKleDwen33BwcGm56isrPRZcc0wDBwOxxnbT/3uG4N5HUQJ2OY+/GwXFZ5K1iz+I+v/37fMXvwu7kaXEN+7C317dWLjtn/y3f48ft/9GlY8d/Jf/TUffUnz3wYr+daxh4f8ngExXTn203FvW4e2LRjc70Zvwmjd4jfcFX09vx/2JwwD3n9pLO99sp24nh1Zv2UPL7z1CVe1DuPlp4bRY8gci67EWtUZAYeHh5/TOZo3b05BQYH3c0FBAWFhYVXaCwsLCQsLIyQkhOLiYjweDwEBAd7+ZlQDtrk2rUKpqKiksrKS4mNlBLoC2PLN9xwoOMLdY55nxV+30r3TVd7+pT8d50+vvM/0sQkWRn1x+iG7kCGPv+T9fNmljUkZ3Y8J89O9bTk/Hqb/w89TWWlgGAYuVwBlJypIffNjXluxEQBXgJPjJ8rrPP764tQ0NLPtfFx33XX88MMP7Nu3D4/Hw7vvvktkZCQtWrQgKCiIbdu2AbBq1SoiIyMJDAykS5curF27FoCMjAwiIyNNz6MEbHONGwaR9eMhbh40k3FzlnHvXbeQlXuIpk0akrbwIVo0u4zn3vjQ2//Ndz8n7taO/Kap28KoL05rPv6K8oqTPw47nQ7+PGkQE59Op7i0zNunwlPJoaMnpy9NG3Mn3+zJ5l/78ykq+Ymy4+WE/aYJi6f9ganPr7bkGuoDh5/b+QgKCmL27NkkJSURExNDmzZtiI6OBmDevHnMmjWL6OhoSktLGTp0KAApKSmkpaURExPD1q1bGTt2rOl5VIKwuReXfUKPru2ZOCqOnLzD3PXwc1x2aWN6R/w3ALdFdGD24ne9/Vf8dRsvPTXMqnDlZx3bX06by8OYn3wPQQ1ctLuiOTMfSWDC0+kENXDx3OSTdftH5yzzfue/rvwdL88cxpSFK9n0xXcWRm+t2nwU+aOPPvL++cYbb2T16qr/0LVv357ly5dXaW/RogWvv/56tc6nBGxzlzZpRKDr5A8ylwU3oryiks4d/oMPP9vFXdHX8/lX39HuipN1sKKSnzhxooIWzS6zMmQBvti1j+4DTt55bxUewiszhjHh6ZOliKXz7ufTrXtY+Je/efu3u6I5S2YP594JS9jxz5zTHvOicQGtyK4EbHP3D+jBH2e9Sb9RCymv8DD+/j5cf20bHpv1Fn9ZuYEm7oakppz8Een7rHxahodYHLGcTZ8e13LT/1xFUAMXv+9+DQDTnl/N2D/cxiUNApn1aH/g5D+miY+9aGWolrLLamdmHIZhGFYHUV8U/eShUn8bthF+0xirQxA/XR4ewp6102rkWF/tL+JExdn/R23gctDxcvPpZlbTCFhEbOUCqkAoAYuIzVxAGVgJWERs5eR6D+Z97EAJWERs5QIaACsBi4gN2SXDmlACFhFb8We5dbtMU1MCFhFbuZBeSaQELCL2opdyiohYQyUIERGLqAQhImIRTUMTEbHKBZSBlYBFxFZUAxYRsYjTj5dymu2vL5SARcReVIIQEbGGShAiIlbRgxgiIta4gCoQSsAiYkN2ybAmlIBFxFa0ILuIiEVUghARscoFlIGVgEXEVjQNTUTEIloNTUTEIhdSAnZaHYCISHU5TH5V14svvkhUVBRxcXEsWrQIgE2bNhEXF0fv3r155plnvH13795NfHw8UVFRTJw4kYqKinO+DiVgEbGVUyNgs81fmzZtYs2aNaSnp5ORkcH27dtZvXo1EyZMIDU1lbVr17Jjxw7Wr18PwLhx45gyZQrr1q3DMAzS0tLO+VqUgEXEVhx+bv7atWsXERERuN1uAgICuPnmm3nnnXdo3bo1rVq1wuVyERcXR2ZmJjk5OZSVldGxY0cA4uPjyczMPOdrUQIWEVupzgg4NzeX7Oxsn62oqMjneNdccw0bNmzgyJEjHD9+nI8++ogvvviC0NBQb5+wsDDy8vLIz8/3aQ8NDSUvL++cr0U34UTEZvwf3yYmJpKTk+PTNnr0aJKSkryfb7zxRuLj4xkyZAhNmzblxhtvZMOGDTh+UccwDAOHw0FlZeVp28+VErCI2IrTAYafC7IvXboUj8fjsy84ONjnc0lJCb1792bYsGEAvPzyy3Tt2pWCggJvn4KCAsLCwmjevLlPe2FhIWFhYed+Lef8TRERCzjwowTxc9/w8HBatmzps/06AWdnZ/Pggw9SUVFBcXExy5cvZ+zYsfzwww/s27cPj8fDu+++S2RkJC1atCAoKIht27YBsGrVKiIjI8/5WjQCFhFb8e9JOP+1b9+e3r1707dvX5GD0igAAAjpSURBVDweD//7v/9L586dmT17NklJSRw/fpxbbrmF6OhoAObNm8ekSZMoKSnhmmuuYejQoed+LYZhGOf87QtM0U8eKvW3YRvhN42xOgTx0+XhIexZO61GjlVQXI7H5P/TAAeENgmskfPVJo2ARcR2bPKgmyklYBGxlV/WeM/Wxw6UgEXEVhwOP2rASsAiIjXvAloOWAlYROxFJQgREYvU9DQ0KykBi4itaAQsImIRJWAREcvY5Y1v5pSARcRW/BndagQsIlIL/MmtNsm/SsAiYkN2ybAmlIBFxFb8qQDbJT8rAYuIrTgdYLZooRKwiEhtsEt29YMS8C84HHpFiJ1cHh5idQjipxZhTWvsWE6HA7NVzO0yC0ILsouIWEQDPhERiygBi4hYRAlYRMQiSsAiIhZRAhYRsYgSsIiIRZSARUQsogQsImIRJWAREYsoAYuIWEQJuB6bNm0aDz/8sE/bhg0b6NWrFyUlJdU+XnZ2Nu3atWPjxo0+7T179iQ7O/u8YhWR6lMCrsceffRRduzYwYcffghAaWkpTz75JDNnzsTtdp/TMQMDA5k8efI5JXARqVlKwPVY48aNeeqpp5g+fTqlpaU8++yz9OzZk4YNGzJw4EDuvPNOhg8fTlZWFgBLliyhb9++3HHHHUyZMuW0xwwLC6N79+7MmTPntPtfeOEFYmJiiIuLY/bs2Xg8nlq7PpGLnRJwPde9e3ciIiIYP348GzduJCkpiUmTJjF//nxWrlzJsGHDmDx5Mh6Ph8WLF5Oens6KFSsoLy8nLy/vtMdMTk5mw4YNVUoR69ev56OPPiI9PZ2VK1eyb98+3n777bq4TJGLktYDtoHk5GR69OjB888/T25uLllZWYwaNcq7v6SkhICAADp16kT//v3p1asXw4YNo1mzZqc9ntvtZvr06UyePJnVq1d72z///HP69OlDw4YNAUhISCAjI4PExMTavUCRi5QSsA243W6Cg4Np0aIFJSUltGzZklWrVgHg8XgoLCwEIDU1la+++oq///3vjBgxgnnz5tG1a9fTHjMiIqJKKaKysrJKv4qKilq4IhEBlSBsp02bNhw9epStW7cCkJ6ezmOPPcahQ4eIiYmhbdu2jBkzhptuuok9e/ac9VinShH5+fkAdOvWjffee4+ysjIqKipIT0+nW7dutX5NIhcrjYBtpkGDBixcuJAZM2Zw/Phx3G43c+bMISQkhAEDBtC/f38aNmzIFVdcQUJCAnl5eYwcOdI7Yv6lU6WIe++9F4Bbb72V3bt3k5CQQEVFBREREQwePLiuL1HkoqFXEl0EJkyYwMyZM60OQ0R+RSWIC1xpaSm33nqr1WGIyGloBCwiYhGNgEVELKIELCJiESVgOaPs7Gyuvvpq+vXr59369u3L8uXLz/vY999/PytWrACgX79+FBUVnbFvcXExQ4cOrfY5MjMzGTJkSJX2zZs3Exsba/r9du3acejQoWqdMzk5mVdeeaVa35GLl6ahyVldcsklPlPY8vLyiI2NpUOHDrRv375GznG6KXK/dPToUb755psaOZdIfaIELNXSrFkzWrduzd69e9m1axfLly/np59+wu128/rrr/POO+/w1ltvUVlZSdOmTZk8eTJXXnkleXl5JCcnk5+fz+9+9zsOHjzoPWa7du347LPPCAkJYfHixaxcuRKXy0Xr1q2ZPXs248ePp6ysjH79+rFixQr27t3LjBkzOHLkCB6PhyFDhtC/f38AFi5cyJo1a2jatCmtW7c2vZ4ffviBadOmcezYMQoKCmjfvj0LFiwgKCgIgAULFvDNN99QWVnJ2LFjvTNKznSdItViiJxBVlaW0bFjR5+2L774wrj++uuNAwcOGOnp6cb1119vFBcXG4ZhGJs3bzYGDRpklJaWGoZhGJ9++qkRHR1tGIZhPPjgg8YzzzxjGIZh7N271+jYsaORnp5uGIZhtG3b1jh48KDxt7/9zejdu7dx5MgRwzAMY+bMmUZqaqpPHOXl5UZMTIyxY8cOwzAMo6ioyLj99tuNL7/80vjggw+MmJgYo7i42CgvLzdGjhxpDB48uMp1ff7550afPn0MwzCM2bNnGxkZGYZhGMaJEyeM2NhYIzMz0xvX4sWLDcMwjD179hhdu3Y1Dh48eNbrfOKJJ4yXX375/P7i5aKhEbCc1amRJ5xcd+Kyyy7jT3/6E+Hh4cDJ0euptYk/+eQT9u3bxz333OP9flFREUeOHGHTpk088cQTALRu3Zobbrihyrk+++wzoqOjufTSSwEYP348gM9i8Xv37mX//v1MmDDBJ8Zdu3bxr3/9i9tuu80bT0JCAq+//vpZr2/cuHFs3LiRl156ib1795Kfn09paal3/8CBAwFo27YtV155JV9++SXbtm0743WKVIcSsJzVr2vAv9aoUSPvnysrK+nXrx/jxo3zfs7Pz+fSSy/F4XBg/GLKuctV9T+9gIAAHA6H93NRUVGVm3Mej4cmTZr4xFRYWEiTJk2YO3euzzkCAgJMr++RRx7B4/Fw++2306NHD3Jzc32O4XT++z51ZWUlLpfrrNcpUh2aBSE1JiIigvfee8+7uM9bb73FH/7wBwBuvvlmli1bBsCBAwfYvHlzle93796dDz74wPu2jj//+c+89tpruFwuPB4PhmFwxRVX+PyjkJubS2xsLDt27CAyMpLMzEyKioqorKw0vbkHJ1/x9NBDDxETEwPA9u3bfRahX7lyJQA7d+5k//79XHfddWe9TpHq0AhYakxERAT33Xcfw4cPx+Fw4Ha7ee6553A4HKSkpDB+/Hhuv/12mjdvftoZFLfccgvfffed98f+q666iunTp9OwYUOuvfZa+vTpw9KlS0lNTWXGjBm8/PLLVFRUMGbMGDp37gzAnj17SEhIIDg4mPbt23P48OGzxvzHP/6Rhx56iEaNGuF2u7n++uvZv3+/d39WVhZ33HEHDoeDp59+mqZNm571OkWqQ48ii4hYRCUIERGLKAGLiFhECVhExCJKwCIiFlECFhGxiBKwiIhFlIBFRCyiBCwiYpH/DwSao14fOE/+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision:  0.6654034774855104\n",
      "Testing Precision:  0.6278145695364239\n",
      "\n",
      "\n",
      "\n",
      "Training Recall:  0.6583590648434053\n",
      "Testing Recall:  0.6212319790301442\n",
      "\n",
      "\n",
      "\n",
      "Training Accuracy:  0.6643002586538991\n",
      "Testing Accuracy:  0.6235967422408101\n",
      "\n",
      "\n",
      "\n",
      "Training F1-Score:  0.6618625277161861\n",
      "Testing F1-Score:  0.624505928853755\n"
     ]
    }
   ],
   "source": [
    "# load grid parameters\n",
    "grid_xg = pd.read_pickle('Pickles/xg_grid.pickle')\n",
    "\n",
    "# train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(le_X, y, test_size=.2, random_state=42)\n",
    "\n",
    "# instantiate the model\n",
    "xgb = XGBClassifier(n_estimators=100,\n",
    "                            max_depth=2,\n",
    "                            max_features=None,\n",
    "                            random_state=42)\n",
    "\n",
    "xgb = XGBClassifier(n_estimators=grid_xg.best_params_['n_estimators'],\n",
    "                            criterion=grid_xg.best_params_['criterion'],\n",
    "                            max_depth=grid_xg.best_params_['max_depth'],\n",
    "                            max_features=grid_xg.best_params_['max_features'],\n",
    "                            random_state=42)\n",
    "\n",
    "\n",
    "\n",
    "# fit the model to training data\n",
    "model_xg = xgb.fit(X_train, y_train)\n",
    "\n",
    "# generate predictions\n",
    "y_hat_train = xgb.predict(X_train)\n",
    "y_hat_test = xgb.predict(X_test)\n",
    "\n",
    "residuals = np.abs(y_train - y_hat_train)\n",
    "print('Training:')\n",
    "print(pd.Series(residuals).value_counts())\n",
    "print(pd.Series(residuals).value_counts(normalize=True))\n",
    "\n",
    "residuals = np.abs(y_test - y_hat_test)\n",
    "print('Testing')\n",
    "print(pd.Series(residuals).value_counts())\n",
    "print(pd.Series(residuals).value_counts(normalize=True))\n",
    "\n",
    "# print confusion matrix\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_hat_test)\n",
    "print('Confusion Matrix:\\n', cnf_matrix)\n",
    "\n",
    "# visualize confusion matrix of the test set\n",
    "metrics.plot_confusion_matrix(xgb, X_test, y_test,\n",
    "                              display_labels=[\"Yes,\" \"No\"],\n",
    "                              cmap=plt.cm.Blues)\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "\n",
    "print('Training Precision: ', metrics.precision_score(y_train, y_hat_train))\n",
    "print('Testing Precision: ', metrics.precision_score(y_test, y_hat_test))\n",
    "print('\\n\\n')\n",
    "\n",
    "print('Training Recall: ', metrics.recall_score(y_train, y_hat_train))\n",
    "print('Testing Recall: ', metrics.recall_score(y_test, y_hat_test))\n",
    "print('\\n\\n')\n",
    "\n",
    "print('Training Accuracy: ', metrics.accuracy_score(y_train, y_hat_train))\n",
    "print('Testing Accuracy: ', metrics.accuracy_score(y_test, y_hat_test))\n",
    "print('\\n\\n')\n",
    "\n",
    "print('Training F1-Score: ', metrics.f1_score(y_train, y_hat_train))\n",
    "print('Testing F1-Score: ', metrics.f1_score(y_test, y_hat_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6236151022036257"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ROC/AUC score\n",
    "y_pred = xgb.predict(X_test)\n",
    "fpr,tpr,thresh = roc_curve(y_test,y_pred)\n",
    "auc(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.6655840808031003\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve\n",
    "rf_probs = xgb.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, rf_probs)\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "print('AUC: {}'.format(auc(fpr, tpr)))\n",
    "plt.figure(figsize=(10,8))\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='mediumorchid',\n",
    "         lw=lw, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='lightskyblue', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.yticks([i/20.0 for i in range(21)])\n",
    "plt.xticks([i/20.0 for i in range(21)])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# determine feature importance\n",
    "plot_feature_importances(xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n",
      "-------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision:  0.6398067128118062\n",
      "Testing Precision:  0.6367974549310711\n",
      "Training Recall:  0.540251433612704\n",
      "Testing Recall:  0.5246832678025338\n",
      "Training Accuracy:  0.6187881789664851\n",
      "Testing Accuracy:  0.6097292537970505\n",
      "Training F1-Score:  0.585829596412556\n",
      "Testing F1-Score:  0.5753293413173652\n",
      "\n",
      "\n",
      "Random Forest\n",
      "-------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision:  0.6281211852180668\n",
      "Testing Precision:  0.6253883710608078\n",
      "Training Recall:  0.6241729157476842\n",
      "Testing Recall:  0.6155526430755789\n",
      "Training Accuracy:  0.6280336800396236\n",
      "Testing Accuracy:  0.6205150781421968\n",
      "Training F1-Score:  0.6261408263731402\n",
      "Testing F1-Score:  0.6204315279612506\n",
      "\n",
      "\n",
      "XGBoost\n",
      "-------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision:  0.6654034774855104\n",
      "Testing Precision:  0.6278145695364239\n",
      "Training Recall:  0.6583590648434053\n",
      "Testing Recall:  0.6212319790301442\n",
      "Training Accuracy:  0.6643002586538991\n",
      "Testing Accuracy:  0.6235967422408101\n",
      "Training F1-Score:  0.6618625277161861\n",
      "Testing F1-Score:  0.624505928853755\n"
     ]
    }
   ],
   "source": [
    "# Compare results for all three models\n",
    "\n",
    "# train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(le_X, y, test_size=.2, random_state=42)\n",
    "\n",
    "# use fitted model to predict on the test data\n",
    "y_hat_train = dtc.predict(X_train)\n",
    "y_hat_test  = dtc.predict(X_test)\n",
    "\n",
    "print('Decision Tree')\n",
    "print('-------------')\n",
    "# visualize confusion matrix of the test set\n",
    "metrics.plot_confusion_matrix(dtc, X_test, y_test,\n",
    "                              display_labels=['Yes', 'No'],\n",
    "                              cmap=plt.cm.Blues)\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "print('Training Precision: ', metrics.precision_score(y_train, y_hat_train))\n",
    "print('Testing Precision: ', metrics.precision_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Recall: ', metrics.recall_score(y_train, y_hat_train))\n",
    "print('Testing Recall: ', metrics.recall_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Accuracy: ', metrics.accuracy_score(y_train, y_hat_train))\n",
    "print('Testing Accuracy: ', metrics.accuracy_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training F1-Score: ', metrics.f1_score(y_train, y_hat_train))\n",
    "print('Testing F1-Score: ', metrics.f1_score(y_test, y_hat_test))\n",
    "\n",
    "\n",
    "# use fitted model to predict on the test data\n",
    "y_hat_train = rfc.predict(X_train)\n",
    "y_hat_test  = rfc.predict(X_test)\n",
    "\n",
    "print('\\n\\nRandom Forest')\n",
    "print('-------------')\n",
    "# visualize confusion matrix of the test set\n",
    "metrics.plot_confusion_matrix(rfc, X_test, y_test,\n",
    "                              display_labels=['Yes', 'No'],\n",
    "                              cmap=plt.cm.Blues)\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "print('Training Precision: ', metrics.precision_score(y_train, y_hat_train))\n",
    "print('Testing Precision: ', metrics.precision_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Recall: ', metrics.recall_score(y_train, y_hat_train))\n",
    "print('Testing Recall: ', metrics.recall_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Accuracy: ', metrics.accuracy_score(y_train, y_hat_train))\n",
    "print('Testing Accuracy: ', metrics.accuracy_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training F1-Score: ', metrics.f1_score(y_train, y_hat_train))\n",
    "print('Testing F1-Score: ', metrics.f1_score(y_test, y_hat_test))\n",
    "\n",
    "\n",
    "\n",
    "# use fitted model to predict on the test data\n",
    "y_hat_train = xgb.predict(X_train)\n",
    "y_hat_test  = xgb.predict(X_test)\n",
    "\n",
    "print('\\n\\nXGBoost')\n",
    "print('-------')\n",
    "\n",
    "# visualize confusion matrix of the test set\n",
    "metrics.plot_confusion_matrix(xgb, X_test, y_test,\n",
    "                              display_labels=['Yes', 'No'],\n",
    "                              cmap=plt.cm.Blues)\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "print('Training Precision: ', metrics.precision_score(y_train, y_hat_train))\n",
    "print('Testing Precision: ', metrics.precision_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Recall: ', metrics.recall_score(y_train, y_hat_train))\n",
    "print('Testing Recall: ', metrics.recall_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training Accuracy: ', metrics.accuracy_score(y_train, y_hat_train))\n",
    "print('Testing Accuracy: ', metrics.accuracy_score(y_test, y_hat_test))\n",
    "\n",
    "print('Training F1-Score: ', metrics.f1_score(y_train, y_hat_train))\n",
    "print('Testing F1-Score: ', metrics.f1_score(y_test, y_hat_test))"
   ]
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