hospital_stay.ipynb

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   "file_extension": ".py",
   "mimetype": "text/x-python",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Using TensorFlow backend.\n"
    }
   ],
   "source": [
    "import tensorflow as tf \n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('dataset/train.csv')\n",
    "test_df = pd.read_csv('dataset/test.csv')\n",
    "#sample_df = pd.read_csv('datatset/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   case_id  Hospital_code Hospital_type_code  City_Code_Hospital  \\\n0        1              8                  c                   3   \n1        2              2                  c                   5   \n2        3             10                  e                   1   \n3        4             26                  b                   2   \n4        5             26                  b                   2   \n\n  Hospital_region_code  Available Extra Rooms in Hospital    Department  \\\n0                    Z                                  3  radiotherapy   \n1                    Z                                  2  radiotherapy   \n2                    X                                  2    anesthesia   \n3                    Y                                  2  radiotherapy   \n4                    Y                                  2  radiotherapy   \n\n  Ward_Type Ward_Facility_Code  Bed Grade  patientid  City_Code_Patient  \\\n0         R                  F        2.0      31397                7.0   \n1         S                  F        2.0      31397                7.0   \n2         S                  E        2.0      31397                7.0   \n3         R                  D        2.0      31397                7.0   \n4         S                  D        2.0      31397                7.0   \n\n  Type of Admission Severity of Illness  Visitors with Patient    Age  \\\n0         Emergency             Extreme                      2  51-60   \n1            Trauma             Extreme                      2  51-60   \n2            Trauma             Extreme                      2  51-60   \n3            Trauma             Extreme                      2  51-60   \n4            Trauma             Extreme                      2  51-60   \n\n   Admission_Deposit   Stay  \n0             4911.0   0-10  \n1             5954.0  41-50  \n2             4745.0  31-40  \n3             7272.0  41-50  \n4             5558.0  41-50  ",
      "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>case_id</th>\n      <th>Hospital_code</th>\n      <th>Hospital_type_code</th>\n      <th>City_Code_Hospital</th>\n      <th>Hospital_region_code</th>\n      <th>Available Extra Rooms in Hospital</th>\n      <th>Department</th>\n      <th>Ward_Type</th>\n      <th>Ward_Facility_Code</th>\n      <th>Bed Grade</th>\n      <th>patientid</th>\n      <th>City_Code_Patient</th>\n      <th>Type of Admission</th>\n      <th>Severity of Illness</th>\n      <th>Visitors with Patient</th>\n      <th>Age</th>\n      <th>Admission_Deposit</th>\n      <th>Stay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>8</td>\n      <td>c</td>\n      <td>3</td>\n      <td>Z</td>\n      <td>3</td>\n      <td>radiotherapy</td>\n      <td>R</td>\n      <td>F</td>\n      <td>2.0</td>\n      <td>31397</td>\n      <td>7.0</td>\n      <td>Emergency</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>4911.0</td>\n      <td>0-10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>c</td>\n      <td>5</td>\n      <td>Z</td>\n      <td>2</td>\n      <td>radiotherapy</td>\n      <td>S</td>\n      <td>F</td>\n      <td>2.0</td>\n      <td>31397</td>\n      <td>7.0</td>\n      <td>Trauma</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>5954.0</td>\n      <td>41-50</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>10</td>\n      <td>e</td>\n      <td>1</td>\n      <td>X</td>\n      <td>2</td>\n      <td>anesthesia</td>\n      <td>S</td>\n      <td>E</td>\n      <td>2.0</td>\n      <td>31397</td>\n      <td>7.0</td>\n      <td>Trauma</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>4745.0</td>\n      <td>31-40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>26</td>\n      <td>b</td>\n      <td>2</td>\n      <td>Y</td>\n      <td>2</td>\n      <td>radiotherapy</td>\n      <td>R</td>\n      <td>D</td>\n      <td>2.0</td>\n      <td>31397</td>\n      <td>7.0</td>\n      <td>Trauma</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>7272.0</td>\n      <td>41-50</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>26</td>\n      <td>b</td>\n      <td>2</td>\n      <td>Y</td>\n      <td>2</td>\n      <td>radiotherapy</td>\n      <td>S</td>\n      <td>D</td>\n      <td>2.0</td>\n      <td>31397</td>\n      <td>7.0</td>\n      <td>Trauma</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>5558.0</td>\n      <td>41-50</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 99
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 318438 entries, 0 to 318437\nData columns (total 18 columns):\n #   Column                             Non-Null Count   Dtype  \n---  ------                             --------------   -----  \n 0   case_id                            318438 non-null  int64  \n 1   Hospital_code                      318438 non-null  int64  \n 2   Hospital_type_code                 318438 non-null  object \n 3   City_Code_Hospital                 318438 non-null  int64  \n 4   Hospital_region_code               318438 non-null  object \n 5   Available Extra Rooms in Hospital  318438 non-null  int64  \n 6   Department                         318438 non-null  object \n 7   Ward_Type                          318438 non-null  object \n 8   Ward_Facility_Code                 318438 non-null  object \n 9   Bed Grade                          318325 non-null  float64\n 10  patientid                          318438 non-null  int64  \n 11  City_Code_Patient                  313906 non-null  float64\n 12  Type of Admission                  318438 non-null  object \n 13  Severity of Illness                318438 non-null  object \n 14  Visitors with Patient              318438 non-null  int64  \n 15  Age                                318438 non-null  object \n 16  Admission_Deposit                  318438 non-null  float64\n 17  Stay                               318438 non-null  object \ndtypes: float64(3), int64(6), object(9)\nmemory usage: 43.7+ MB\n"
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "case_id                                 0\nHospital_code                           0\nHospital_type_code                      0\nCity_Code_Hospital                      0\nHospital_region_code                    0\nAvailable Extra Rooms in Hospital       0\nDepartment                              0\nWard_Type                               0\nWard_Facility_Code                      0\nBed Grade                             113\npatientid                               0\nCity_Code_Patient                    4532\nType of Admission                       0\nSeverity of Illness                     0\nVisitors with Patient                   0\nAge                                     0\nAdmission_Deposit                       0\nStay                                    0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 101
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x223c6ace648>"
     },
     "metadata": {},
     "execution_count": 102
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.heatmap(train_df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x223d0ffb748>"
     },
     "metadata": {},
     "execution_count": 103
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(train_df['Stay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x224a661b708>"
     },
     "metadata": {},
     "execution_count": 104
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "sns.countplot(train_df['Stay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_new = train_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Bed Grade'] = test_df['Bed Grade'].fillna(float(test_df['Bed Grade'].mode()))\n",
    "test_df['City_Code_Patient'] = test_df['City_Code_Patient'].fillna(float(test_df['City_Code_Patient'].mode()))\n",
    "test_new = test_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_new.drop(columns=['case_id','Hospital_code','patientid','City_Code_Hospital','Hospital_region_code'],inplace=True)\n",
    "test_new.drop(columns=['case_id','Hospital_code','patientid','City_Code_Hospital','Hospital_region_code'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  Hospital_type_code  Available Extra Rooms in Hospital    Department  \\\n0                  c                                  3  radiotherapy   \n1                  c                                  2  radiotherapy   \n2                  e                                  2    anesthesia   \n3                  b                                  2  radiotherapy   \n4                  b                                  2  radiotherapy   \n\n  Ward_Type Ward_Facility_Code  Bed Grade  City_Code_Patient  \\\n0         R                  F        2.0                7.0   \n1         S                  F        2.0                7.0   \n2         S                  E        2.0                7.0   \n3         R                  D        2.0                7.0   \n4         S                  D        2.0                7.0   \n\n  Type of Admission Severity of Illness  Visitors with Patient    Age  \\\n0         Emergency             Extreme                      2  51-60   \n1            Trauma             Extreme                      2  51-60   \n2            Trauma             Extreme                      2  51-60   \n3            Trauma             Extreme                      2  51-60   \n4            Trauma             Extreme                      2  51-60   \n\n   Admission_Deposit   Stay  \n0             4911.0   0-10  \n1             5954.0  41-50  \n2             4745.0  31-40  \n3             7272.0  41-50  \n4             5558.0  41-50  ",
      "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>Hospital_type_code</th>\n      <th>Available Extra Rooms in Hospital</th>\n      <th>Department</th>\n      <th>Ward_Type</th>\n      <th>Ward_Facility_Code</th>\n      <th>Bed Grade</th>\n      <th>City_Code_Patient</th>\n      <th>Type of Admission</th>\n      <th>Severity of Illness</th>\n      <th>Visitors with Patient</th>\n      <th>Age</th>\n      <th>Admission_Deposit</th>\n      <th>Stay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>c</td>\n      <td>3</td>\n      <td>radiotherapy</td>\n      <td>R</td>\n      <td>F</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>Emergency</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>4911.0</td>\n      <td>0-10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>c</td>\n      <td>2</td>\n      <td>radiotherapy</td>\n      <td>S</td>\n      <td>F</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>Trauma</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>5954.0</td>\n      <td>41-50</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>e</td>\n      <td>2</td>\n      <td>anesthesia</td>\n      <td>S</td>\n      <td>E</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>Trauma</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>4745.0</td>\n      <td>31-40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>b</td>\n      <td>2</td>\n      <td>radiotherapy</td>\n      <td>R</td>\n      <td>D</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>Trauma</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>7272.0</td>\n      <td>41-50</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>b</td>\n      <td>2</td>\n      <td>radiotherapy</td>\n      <td>S</td>\n      <td>D</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>Trauma</td>\n      <td>Extreme</td>\n      <td>2</td>\n      <td>51-60</td>\n      <td>5558.0</td>\n      <td>41-50</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 109
    }
   ],
   "source": [
    "train_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "le_hos_type = LabelEncoder()\n",
    "le_dpt = LabelEncoder()\n",
    "le_ward_type = LabelEncoder()\n",
    "le_ward_fac = LabelEncoder()\n",
    "le_type_add = LabelEncoder()\n",
    "le_severity = LabelEncoder()\n",
    "le_age = LabelEncoder()\n",
    "le_stay = LabelEncoder() \n",
    "\n",
    "train_new.iloc[:,0] = le_hos_type.fit_transform(train_new.iloc[:,0])\n",
    "train_new['Department'] = le_dpt.fit_transform(train_new['Department'])\n",
    "train_new['Ward_Type'] = le_ward_type.fit_transform(train_new['Ward_Type'])\n",
    "train_new['Ward_Facility_Code'] = le_ward_fac.fit_transform(train_new['Ward_Facility_Code'])\n",
    "train_new['Type of Admission'] = le_type_add.fit_transform(train_new['Type of Admission'])\n",
    "train_new.iloc[:,8] = le_severity.fit_transform(train_new.iloc[:,8])\n",
    "train_new['Age'] = le_age.fit_transform(train_new['Age'])\n",
    "train_new['Stay'] = le_stay.fit_transform(train_new['Stay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_new.iloc[:,0] = le_hos_type.transform(test_new.iloc[:,0])\n",
    "test_new['Department'] = le_dpt.transform(test_new['Department'])\n",
    "test_new['Ward_Type'] = le_ward_type.transform(test_new['Ward_Type'])\n",
    "test_new['Ward_Facility_Code'] = le_ward_fac.transform(test_new['Ward_Facility_Code'])\n",
    "test_new['Type of Admission'] = le_type_add.transform(test_new['Type of Admission'])\n",
    "test_new.iloc[:,8] = le_severity.transform(test_new.iloc[:,8])\n",
    "test_new['Age'] = le_age.transform(test_new['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train_new.drop('Stay',axis=1)\n",
    "y = train_new['Stay'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "y_bin = to_categorical(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm = MinMaxScaler()\n",
    "x = mm.fit_transform(x)\n",
    "x_test = mm.transform(test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_val,y_train,y_val = train_test_split(x,y_bin,test_size=0.2,random_state=RANDOM_SEED,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "DecisionTreeClassifier()"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The Accuracy of Decision Tree Model is 0.29555920266415975\n"
    }
   ],
   "source": [
    "y_pred_dt = dt.predict(x_val)\n",
    "print(f'The Accuracy of Decision Tree Model is {accuracy_score(y_pred_dt,y_val)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "RandomForestClassifier()"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The accuracy of Random Forest Model is 0.3805669306394302\n"
    }
   ],
   "source": [
    "y_pred_rf = rf.predict(x_val)\n",
    "print(f\"The accuracy of Random Forest Model is {accuracy_score(y_pred_rf,y_val)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "KNeighborsClassifier()"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "knn  = KNeighborsClassifier()\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The Accuracy Score of K-Nearest Neighbors is 0.3060118867413439\n"
    }
   ],
   "source": [
    "y_pred_knn = knn.predict(x_val)\n",
    "print(f\"The Accuracy Score of K-Nearest Neighbors is {accuracy_score(y_pred_knn,y_val)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout\n",
    "from livelossplot import PlotLossesKeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(512,activation='relu',input_shape=(x_train.shape[1],)))\n",
    "model.add(Dense(512,activation='relu'))\n",
    "model.add(Dense(11,activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy',metrics=['accuracy'],optimizer='adam')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "accuracy\n\tvalidation       \t (min:    0.399, max:    0.420, cur:    0.418)\n\ttraining         \t (min:    0.380, max:    0.422, cur:    0.422)\nLoss\n\tvalidation       \t (min:    1.515, max:    1.574, cur:    1.517)\n\ttraining         \t (min:    1.495, max:    1.635, cur:    1.495)\n"
    }
   ],
   "source": [
    "h = model.fit(x_train,y_train,\n",
    "                validation_data=(x_val,y_val),\n",
    "                epochs=21,\n",
    "                batch_size=128,\n",
    "                callbacks = [PlotLossesKeras()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_smpl = pd.read_csv(\"dataset/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = model.predict_classes(x_test)\n",
    "#y_test = np.argmax(y_test)\n",
    "#y_test = np.reshape(y_test,(1))\n",
    "y_test = le_stay.inverse_transform(y_test)\n",
    "sub = pd.DataFrame({'case_id':test_smpl.case_id,'Stay':y_test})\n",
    "sub.to_csv('Submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}