Predicting Hospital ReAdmissions with KNN Classification .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "17ccb77a",
   "metadata": {},
   "source": [
    "## KNN Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82384896",
   "metadata": {},
   "source": [
    "### Import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cdbb510b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries and packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d7b9c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CaseOrder', 'Customer_id', 'Interaction', 'UID', 'City', 'State',\n",
       "       'County', 'Zip', 'Lat', 'Lng', 'Population', 'Area', 'TimeZone', 'Job',\n",
       "       'Children', 'Age', 'Income', 'Marital', 'Gender', 'ReAdmis',\n",
       "       'VitD_levels', 'Doc_visits', 'Full_meals_eaten', 'vitD_supp',\n",
       "       'Soft_drink', 'Initial_admin', 'HighBlood', 'Stroke',\n",
       "       'Complication_risk', 'Overweight', 'Arthritis', 'Diabetes',\n",
       "       'Hyperlipidemia', 'BackPain', 'Anxiety', 'Allergic_rhinitis',\n",
       "       'Reflux_esophagitis', 'Asthma', 'Services', 'Initial_days',\n",
       "       'TotalCharge', 'Additional_charges', 'Item1', 'Item2', 'Item3', 'Item4',\n",
       "       'Item5', 'Item6', 'Item7', 'Item8'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data and check out the columns\n",
    "data = pd.read_csv('medical_clean.csv')\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5d7dbb",
   "metadata": {},
   "source": [
    "### Create a new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f21310b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new dataset with the features to predict with\n",
    "df = data[['ReAdmis','Initial_days','Age','VitD_levels','TotalCharge',\n",
    "          'Additional_charges','Gender','Initial_admin','Services', 'Complication_risk','HighBlood','Stroke',\n",
    "           'Overweight','Diabetes','Asthma']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e57eb04d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a copy of the dataset to work on\n",
    "df = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d82a87b",
   "metadata": {},
   "source": [
    "### Check for duplicates, null values and data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e181d930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fa7172a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 15 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ReAdmis             10000 non-null  object \n",
      " 1   Initial_days        10000 non-null  float64\n",
      " 2   Age                 10000 non-null  int64  \n",
      " 3   VitD_levels         10000 non-null  float64\n",
      " 4   TotalCharge         10000 non-null  float64\n",
      " 5   Additional_charges  10000 non-null  float64\n",
      " 6   Gender              10000 non-null  object \n",
      " 7   Initial_admin       10000 non-null  object \n",
      " 8   Services            10000 non-null  object \n",
      " 9   Complication_risk   10000 non-null  object \n",
      " 10  HighBlood           10000 non-null  object \n",
      " 11  Stroke              10000 non-null  object \n",
      " 12  Overweight          10000 non-null  object \n",
      " 13  Diabetes            10000 non-null  object \n",
      " 14  Asthma              10000 non-null  object \n",
      "dtypes: float64(4), int64(1), object(10)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check for null values and data types\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d07f553a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ReAdmis                  2\n",
       "Initial_days          9997\n",
       "Age                     72\n",
       "VitD_levels           9976\n",
       "TotalCharge           9997\n",
       "Additional_charges    9418\n",
       "Gender                   3\n",
       "Initial_admin            3\n",
       "Services                 4\n",
       "Complication_risk        3\n",
       "HighBlood                2\n",
       "Stroke                   2\n",
       "Overweight               2\n",
       "Diabetes                 2\n",
       "Asthma                   2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4615e958",
   "metadata": {},
   "source": [
    "### Outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bba1be1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check for distribution and outliers\n",
    "df.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c253990",
   "metadata": {},
   "source": [
    "### Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49b8785f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initial_days</th>\n",
       "      <th>Age</th>\n",
       "      <th>VitD_levels</th>\n",
       "      <th>TotalCharge</th>\n",
       "      <th>Additional_charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.455299</td>\n",
       "      <td>53.511700</td>\n",
       "      <td>17.964262</td>\n",
       "      <td>5312.172769</td>\n",
       "      <td>12934.528587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.309341</td>\n",
       "      <td>20.638538</td>\n",
       "      <td>2.017231</td>\n",
       "      <td>2180.393838</td>\n",
       "      <td>6542.601544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.001981</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.806483</td>\n",
       "      <td>1938.312067</td>\n",
       "      <td>3125.703000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.896215</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>16.626439</td>\n",
       "      <td>3179.374015</td>\n",
       "      <td>7986.487755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.836244</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>17.951122</td>\n",
       "      <td>5213.952000</td>\n",
       "      <td>11573.977735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.161020</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>19.347963</td>\n",
       "      <td>7459.699750</td>\n",
       "      <td>15626.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>71.981490</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>26.394449</td>\n",
       "      <td>9180.728000</td>\n",
       "      <td>30566.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Initial_days           Age   VitD_levels   TotalCharge  \\\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000   \n",
       "mean      34.455299     53.511700     17.964262   5312.172769   \n",
       "std       26.309341     20.638538      2.017231   2180.393838   \n",
       "min        1.001981     18.000000      9.806483   1938.312067   \n",
       "25%        7.896215     36.000000     16.626439   3179.374015   \n",
       "50%       35.836244     53.000000     17.951122   5213.952000   \n",
       "75%       61.161020     71.000000     19.347963   7459.699750   \n",
       "max       71.981490     89.000000     26.394449   9180.728000   \n",
       "\n",
       "       Additional_charges  \n",
       "count        10000.000000  \n",
       "mean         12934.528587  \n",
       "std           6542.601544  \n",
       "min           3125.703000  \n",
       "25%           7986.487755  \n",
       "50%          11573.977735  \n",
       "75%          15626.490000  \n",
       "max          30566.070000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics of features\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7395945",
   "metadata": {},
   "source": [
    "### Visual evalutaion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c247d6ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visually evaluate the total distribution of the classes for readmission\n",
    "sns.countplot(x=df.ReAdmis);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6315be66",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "814dc8d2",
   "metadata": {},
   "source": [
    "### P-values and contingency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ebb299b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ReAdmis p-value: 0.0\n",
      " Initial_days p-value: 0.48683689071721553\n",
      " Age p-value: 0.18551330450513348\n",
      " VitD_levels p-value: 0.5065427582250107\n",
      " TotalCharge p-value: 0.48683689071721553\n",
      " Additional_charges p-value: 0.005178117979576193\n",
      " Gender p-value: 0.4525370014241822\n",
      " Initial_admin p-value: 0.14298951184306222\n",
      " Services p-value: 0.03075281113212747\n",
      " Complication_risk p-value: 0.923567890607327\n",
      " HighBlood p-value: 0.8368656684578771\n",
      " Stroke p-value: 0.9474770077616069\n",
      " Overweight p-value: 0.4032948387365497\n",
      " Diabetes p-value: 0.7775238586920562\n",
      " Asthma p-value: 0.090951450679407\n"
     ]
    }
   ],
   "source": [
    "# Evaluating p-values of the chosen variables\n",
    "var_cols = ['ReAdmis', 'Initial_days', 'Age', 'VitD_levels', 'TotalCharge',\n",
    "       'Additional_charges', 'Gender', 'Initial_admin', 'Services',\n",
    "       'Complication_risk', 'HighBlood', 'Stroke', 'Overweight', 'Diabetes',\n",
    "       'Asthma']\n",
    "for cols in var_cols:\n",
    "    ct = pd.crosstab(df['ReAdmis'], df[cols])\n",
    "    print(f\" {cols} p-value:\",chi2_contingency(ct)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984a625c",
   "metadata": {},
   "source": [
    "### Re-expression of categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7dddd7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dummy encoding categorical features\n",
    "df = pd.get_dummies(df, columns=['Gender','Initial_admin','Services', 'Complication_risk'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "51c45a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lable encoding categorical features\n",
    "categorical = ['ReAdmis','HighBlood','Stroke','Overweight','Diabetes','Asthma']\n",
    "cat_dict = {'Yes':1,'No':0} \n",
    "for cat in categorical:\n",
    "    df[cat] = df[cat].replace(cat_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a001d1d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ReAdmis</th>\n",
       "      <th>Initial_days</th>\n",
       "      <th>Age</th>\n",
       "      <th>VitD_levels</th>\n",
       "      <th>TotalCharge</th>\n",
       "      <th>Additional_charges</th>\n",
       "      <th>HighBlood</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>Overweight</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>...</th>\n",
       "      <th>Initial_admin_Elective Admission</th>\n",
       "      <th>Initial_admin_Emergency Admission</th>\n",
       "      <th>Initial_admin_Observation Admission</th>\n",
       "      <th>Services_Blood Work</th>\n",
       "      <th>Services_CT Scan</th>\n",
       "      <th>Services_Intravenous</th>\n",
       "      <th>Services_MRI</th>\n",
       "      <th>Complication_risk_High</th>\n",
       "      <th>Complication_risk_Low</th>\n",
       "      <th>Complication_risk_Medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9284</th>\n",
       "      <td>1</td>\n",
       "      <td>63.166250</td>\n",
       "      <td>27</td>\n",
       "      <td>18.562810</td>\n",
       "      <td>7712.909000</td>\n",
       "      <td>5174.54000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4367</th>\n",
       "      <td>0</td>\n",
       "      <td>1.324426</td>\n",
       "      <td>67</td>\n",
       "      <td>18.366589</td>\n",
       "      <td>2256.634146</td>\n",
       "      <td>21900.27451</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6817</th>\n",
       "      <td>1</td>\n",
       "      <td>69.196010</td>\n",
       "      <td>64</td>\n",
       "      <td>18.409380</td>\n",
       "      <td>8295.831000</td>\n",
       "      <td>21397.12000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ReAdmis  Initial_days  Age  VitD_levels  TotalCharge  \\\n",
       "9284        1     63.166250   27    18.562810  7712.909000   \n",
       "4367        0      1.324426   67    18.366589  2256.634146   \n",
       "6817        1     69.196010   64    18.409380  8295.831000   \n",
       "\n",
       "      Additional_charges  HighBlood  Stroke  Overweight  Diabetes  ...  \\\n",
       "9284          5174.54000          0       0           0         1  ...   \n",
       "4367         21900.27451          1       0           1         0  ...   \n",
       "6817         21397.12000          1       0           0         0  ...   \n",
       "\n",
       "      Initial_admin_Elective Admission  Initial_admin_Emergency Admission  \\\n",
       "9284                                 0                                  1   \n",
       "4367                                 0                                  0   \n",
       "6817                                 0                                  1   \n",
       "\n",
       "      Initial_admin_Observation Admission  Services_Blood Work  \\\n",
       "9284                                    0                    1   \n",
       "4367                                    1                    1   \n",
       "6817                                    0                    0   \n",
       "\n",
       "      Services_CT Scan  Services_Intravenous  Services_MRI  \\\n",
       "9284                 0                     0             0   \n",
       "4367                 0                     0             0   \n",
       "6817                 0                     0             1   \n",
       "\n",
       "      Complication_risk_High  Complication_risk_Low  Complication_risk_Medium  \n",
       "9284                       0                      0                         1  \n",
       "4367                       0                      0                         1  \n",
       "6817                       0                      0                         1  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb9503d",
   "metadata": {},
   "source": [
    "### Heat map and correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04f3081b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a heat map to see feature correlation\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, cmap='Blues');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "41196c84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename some of the features for readability\n",
    "df.rename(columns={'Initial_admin_Emergency Admission':'Emergency_admission', \n",
    "                   'Initial_admin_Elective Admission':'Elective_admission',\n",
    "                   'Initial_admin_Observation Admission':'Observation_admission',\n",
    "                   'Services_Blood Work':'Blood_work','Services_CT Scan':'CT_scan','Services_MRI':'MRI',\n",
    "                   'Services_Intravenous':'Intravenous'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97e6ce3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save cleaned data to excel\n",
    "df.to_excel('D209-Task1_cleaned_data.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434248e2",
   "metadata": {},
   "source": [
    "## ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c1a2c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate target variable and predictors\n",
    "X = df.drop('ReAdmis',axis=1)\n",
    "y = df['ReAdmis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6560f15a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Initial_days', 'TotalCharge', 'Emergency_admission', 'CT_scan',\n",
       "       'Intravenous'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select 5 best features and print the selected feature names\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif\n",
    "\n",
    "skbest = SelectKBest(score_func=f_classif, k=5)\n",
    "X_new = skbest.fit_transform(X,y)\n",
    "print(X_new.shape)\n",
    "\n",
    "features = X.columns[(skbest.get_support())]\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1bdfddcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new X dataset with the selected 5 features\n",
    "X_new = X[['Initial_days', 'TotalCharge', 'Emergency_admission', 'CT_scan', 'Intravenous']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1fb4f804",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save new dataset to excel\n",
    "X_new.to_excel('X_new.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d54edc0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train and test data stratify readmission data so it is evenly ditributed to test and train\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_new,y, test_size=0.25, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ef60c5",
   "metadata": {},
   "source": [
    "- Scale the data to reduce bias of variables with higher values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e1934d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale train and test data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2980cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check to see if admitted ratio is the same for training and test\n",
    "\n",
    "print(y_train.value_counts())\n",
    "print(y_test.value_counts())\n",
    "print('Training admitted ratio:', sum(y_train==1)/len(y_train))\n",
    "print('Test admitted ratio:', sum(y_test==1)/len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2d7526f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7500, 5), (2500, 5))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe for train and test data\n",
    "\n",
    "train_data = pd.DataFrame(X_train, y_train, columns=X_new.columns)\n",
    "test_data = pd.DataFrame(X_test, y_test, columns=X_new.columns)\n",
    "train_data.shape, test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d414647c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save train and test data to excel\n",
    "\n",
    "train_data.to_excel('PA_train_data.xlsx')\n",
    "test_data.to_excel('PA_test_data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e41e6b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initial_days</th>\n",
       "      <th>TotalCharge</th>\n",
       "      <th>Emergency_admission</th>\n",
       "      <th>CT_scan</th>\n",
       "      <th>Intravenous</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ReAdmis</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>1</th>\n",
       "      <td>0.882274</td>\n",
       "      <td>1.01504</td>\n",
       "      <td>0.983601</td>\n",
       "      <td>2.68937</td>\n",
       "      <td>-0.680536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Initial_days  TotalCharge  Emergency_admission  CT_scan  Intravenous\n",
       "ReAdmis                                                                      \n",
       "1            0.882274      1.01504             0.983601  2.68937    -0.680536"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b2cf3573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initial_days</th>\n",
       "      <th>TotalCharge</th>\n",
       "      <th>Emergency_admission</th>\n",
       "      <th>CT_scan</th>\n",
       "      <th>Intravenous</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ReAdmis</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>1</th>\n",
       "      <td>0.959566</td>\n",
       "      <td>0.95741</td>\n",
       "      <td>1.001601</td>\n",
       "      <td>2.638519</td>\n",
       "      <td>-0.658397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Initial_days  TotalCharge  Emergency_admission   CT_scan  Intravenous\n",
       "ReAdmis                                                                       \n",
       "1            0.959566      0.95741             1.001601  2.638519    -0.658397"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7255ad78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(metric='manhattan', n_neighbors=2, weights='distance')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "k = 2\n",
    "knn = KNeighborsClassifier(n_neighbors=k, metric='manhattan', weights='distance')\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "print(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3369dd70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1545   38]\n",
      " [  29  888]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98      1583\n",
      "           1       0.96      0.97      0.96       917\n",
      "\n",
      "    accuracy                           0.97      2500\n",
      "   macro avg       0.97      0.97      0.97      2500\n",
      "weighted avg       0.97      0.97      0.97      2500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Metrics\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(conf_matrix)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "676e7ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score is:  0.9732\n",
      "Precision score is:  0.958963282937365\n",
      "Recall score is:  0.9683751363140676\n",
      "Specificity score is:  0.9759949463044851\n"
     ]
    }
   ],
   "source": [
    "TN = conf_matrix[0,0]\n",
    "TP = conf_matrix[1,1]\n",
    "FN = conf_matrix[1,0]\n",
    "FP = conf_matrix[0,1]\n",
    "\n",
    "# # Accuracy is the proportion of the correct predictions\n",
    "accuracy = (TN + TP)/(TN + TP +FN + FP)\n",
    "print('Accuracy score is: ',accuracy)\n",
    "\n",
    "# # Precision measures the accuracy of a positive outcome\n",
    "precision = TP/(TP + FP)\n",
    "print('Precision score is: ',precision)\n",
    "\n",
    "# # Recall or Sensitiviy measures model's strength to predict positive outcome \n",
    "recall = TP/(TP + FN)\n",
    "print('Recall score is: ',recall)\n",
    "\n",
    "## Specificity measures a mode's ability to predict a negative outcome\n",
    "specificity = TN/(TN + FP)\n",
    "print('Specificity score is: ', specificity)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8f489fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "false_positive_rate, true_positive_rate, threshold = roc_curve(y_test, y_pred) # from model3.predict(x)\n",
    "# roc_df = pd.DataFrame({'recall':false_positive_rate, 'specificity':true_positive_rate})\n",
    "plt.plot(false_positive_rate, true_positive_rate, label='KNN')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('KNN ROC Curve');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7fa461f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.9721850413092764\n"
     ]
    }
   ],
   "source": [
    "# AUC perfect classfier will have an AUC of 1, a random classifier will have an AUC of 0.5.\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "print('AUC: ',auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b1b4d3",
   "metadata": {},
   "source": [
    "## Best Parameter and Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "80b4e1e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum error:- 0.0152 at K = 19\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Error rate calculation lower k has higher error rate\n",
    "\n",
    "error_rate = []\n",
    "for i in range(1,75):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))\n",
    "    \n",
    "    \n",
    "print(\"Minimum error:-\",min(error_rate),\"at K =\",error_rate.index(min(error_rate)))\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,75),error_rate,color='blue', linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "08f25271",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum accuracy:- 0.9848 at K = 19\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a graph of k neighbors and accuracy\n",
    "\n",
    "acc = []\n",
    "\n",
    "from sklearn import metrics\n",
    "for i in range(1,75):\n",
    "    neigh = KNeighborsClassifier(n_neighbors = i).fit(X_train,y_train)\n",
    "    yhat = neigh.predict(X_test)\n",
    "    acc.append(metrics.accuracy_score(y_test, yhat))\n",
    "    \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,75),acc,color = 'blue',linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('accuracy vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Accuracy')\n",
    "print(\"Maximum accuracy:-\",max(acc),\"at K =\",acc.index(max(acc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e74f4777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n",
      "{'metric': 'euclidean', 'n_neighbors': 45, 'weights': 'distance'}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1583\n",
      "           1       0.98      0.98      0.98       917\n",
      "\n",
      "    accuracy                           0.98      2500\n",
      "   macro avg       0.98      0.98      0.98      2500\n",
      "weighted avg       0.98      0.98      0.98      2500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grid Search\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'n_neighbors': np.arange(5, 75, 5), \n",
    "               'metric':('manhattan','euclidean','minkowski'),\n",
    "               'weights':('uniform','distance')}\n",
    "\n",
    "\n",
    "grid = GridSearchCV(KNeighborsClassifier(), param_grid, refit = True, verbose = 1,n_jobs=1) \n",
    "   \n",
    "# fitting the model for grid search \n",
    "grid.fit(X_train, y_train) \n",
    " \n",
    "# print best parameter after tuning \n",
    "print(grid.best_params_) \n",
    "grid_predictions = grid.predict(X_test) \n",
    "   \n",
    "# print classification report \n",
    "print(classification_report(y_test, grid_predictions)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68197adc",
   "metadata": {},
   "source": [
    "### Final Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "708ba5e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(metric='euclidean', n_neighbors=45, weights='distance')\n"
     ]
    }
   ],
   "source": [
    "# Final classification with best parameters\n",
    "knn = KNeighborsClassifier(metric='euclidean', n_neighbors =45, weights = 'distance')\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "print(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86cb2eff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1561   22]\n",
      " [  20  897]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1583\n",
      "           1       0.98      0.98      0.98       917\n",
      "\n",
      "    accuracy                           0.98      2500\n",
      "   macro avg       0.98      0.98      0.98      2500\n",
      "weighted avg       0.98      0.98      0.98      2500\n",
      "\n",
      "AUC:  0.9821460432581455\n"
     ]
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(conf_matrix)\n",
    "print(classification_report(y_test, y_pred))\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "print('AUC: ',auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "715d3a41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score is:  0.9832\n",
      "Precision score is:  0.9760609357997824\n",
      "Recall score is:  0.9781897491821155\n",
      "Specificity score is:  0.9861023373341756\n"
     ]
    }
   ],
   "source": [
    "TN = conf_matrix[0,0]\n",
    "TP = conf_matrix[1,1]\n",
    "FN = conf_matrix[1,0]\n",
    "FP = conf_matrix[0,1]\n",
    "\n",
    "# # Accuracy is the proportion of the correct predictions\n",
    "accuracy = (TN + TP)/(TN + TP +FN + FP)\n",
    "print('Accuracy score is: ',accuracy)\n",
    "\n",
    "# # Precision measures the accuracy of a positive outcome\n",
    "precision = TP/(TP + FP)\n",
    "print('Precision score is: ',precision)\n",
    "\n",
    "# # Recall or Sensitiviy measures model's strength to predict positive outcome \n",
    "recall = TP/(TP + FN)\n",
    "print('Recall score is: ',recall)\n",
    "\n",
    "## Specificity measures a mode's ability to predict a negative outcome\n",
    "specificity = TN/(TN + FP)\n",
    "print('Specificity score is: ', specificity)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c76a4045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_positive_rate, true_positive_rate, threshold = roc_curve(y_test, y_pred) # from model3.predict(x)\n",
    "# roc_df = pd.DataFrame({'recall':false_positive_rate, 'specificity':true_positive_rate})\n",
    "plt.plot(false_positive_rate, true_positive_rate, label='KNN')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('KNN ROC Curve');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9805002b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.9821460432581455\n"
     ]
    }
   ],
   "source": [
    "# AUC perfect classfier will have an AUC of 1, a random classifier will have an AUC of 0.5.\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "print('AUC: ',auc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}