healthcare-data-predicting-length-of-stay.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/swapanroy/predict-length-of-stay-w-gradient-boosting-algo?scriptVersionId=100294550\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"code","execution_count":1,"id":"f6f7b991","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:23.965669Z","iopub.status.busy":"2022-07-07T21:19:23.965101Z","iopub.status.idle":"2022-07-07T21:19:25.985457Z","shell.execute_reply":"2022-07-07T21:19:25.984594Z"},"papermill":{"duration":2.038943,"end_time":"2022-07-07T21:19:25.988173","exception":false,"start_time":"2022-07-07T21:19:23.94923","status":"completed"},"tags":[]},"outputs":[],"source":["# Libraries needed for data Analysis \n","import pandas as pd\n","import numpy as np\n","\n","# Libraries needed for models and visualization \n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Libraries needed for pre-processing \n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.preprocessing import StandardScaler\n","\n","# models\n","from sklearn.ensemble import AdaBoostClassifier\n","from catboost import CatBoostClassifier, Pool\n","from catboost.utils import get_confusion_matrix\n","\n","\n","#Validation \n","from sklearn.metrics import confusion_matrix, classification_report, make_scorer, accuracy_score\n","from sklearn.model_selection import cross_val_score, train_test_split\n","\n","# Library to ignore warnings \n","import warnings; warnings.filterwarnings('ignore')"]},{"cell_type":"markdown","id":"93584db7","metadata":{"papermill":{"duration":0.012078,"end_time":"2022-07-07T21:19:26.013606","exception":false,"start_time":"2022-07-07T21:19:26.001528","status":"completed"},"tags":[]},"source":["\n","### Implementation  \n","Below implementation cover's: \n","\n","**AdaBoost** - Adaptive Boost, is a statistical classification meta-algorithm refers to a particular method of training a boosted classifier.\n","\n","\n","**CATBoost** - Provides a gradient boosting framework which among other features attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm.\n","\n","### Before starting review these links: \n","https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html \n","\n","https://github.com/catboost/catboost\n","\n"]},{"cell_type":"markdown","id":"2468a8dc","metadata":{"papermill":{"duration":0.013894,"end_time":"2022-07-07T21:19:26.040511","exception":false,"start_time":"2022-07-07T21:19:26.026617","status":"completed"},"tags":[]},"source":["# Read Data"]},{"cell_type":"code","execution_count":2,"id":"2d5cf8ee","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:26.069858Z","iopub.status.busy":"2022-07-07T21:19:26.069226Z","iopub.status.idle":"2022-07-07T21:19:27.229937Z","shell.execute_reply":"2022-07-07T21:19:27.22882Z"},"papermill":{"duration":1.179439,"end_time":"2022-07-07T21:19:27.233236","exception":false,"start_time":"2022-07-07T21:19:26.053797","status":"completed"},"tags":[]},"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>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","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>318433</th>\n","      <td>318434</td>\n","      <td>6</td>\n","      <td>a</td>\n","      <td>6</td>\n","      <td>X</td>\n","      <td>3</td>\n","      <td>radiotherapy</td>\n","      <td>Q</td>\n","      <td>F</td>\n","      <td>4.0</td>\n","      <td>86499</td>\n","      <td>23.0</td>\n","      <td>Emergency</td>\n","      <td>Moderate</td>\n","      <td>3</td>\n","      <td>41-50</td>\n","      <td>4144.0</td>\n","      <td>11-20</td>\n","    </tr>\n","    <tr>\n","      <th>318434</th>\n","      <td>318435</td>\n","      <td>24</td>\n","      <td>a</td>\n","      <td>1</td>\n","      <td>X</td>\n","      <td>2</td>\n","      <td>anesthesia</td>\n","      <td>Q</td>\n","      <td>E</td>\n","      <td>4.0</td>\n","      <td>325</td>\n","      <td>8.0</td>\n","      <td>Urgent</td>\n","      <td>Moderate</td>\n","      <td>4</td>\n","      <td>81-90</td>\n","      <td>6699.0</td>\n","      <td>31-40</td>\n","    </tr>\n","    <tr>\n","      <th>318435</th>\n","      <td>318436</td>\n","      <td>7</td>\n","      <td>a</td>\n","      <td>4</td>\n","      <td>X</td>\n","      <td>3</td>\n","      <td>gynecology</td>\n","      <td>R</td>\n","      <td>F</td>\n","      <td>4.0</td>\n","      <td>125235</td>\n","      <td>10.0</td>\n","      <td>Emergency</td>\n","      <td>Minor</td>\n","      <td>3</td>\n","      <td>71-80</td>\n","      <td>4235.0</td>\n","      <td>11-20</td>\n","    </tr>\n","    <tr>\n","      <th>318436</th>\n","      <td>318437</td>\n","      <td>11</td>\n","      <td>b</td>\n","      <td>2</td>\n","      <td>Y</td>\n","      <td>3</td>\n","      <td>anesthesia</td>\n","      <td>Q</td>\n","      <td>D</td>\n","      <td>3.0</td>\n","      <td>91081</td>\n","      <td>8.0</td>\n","      <td>Trauma</td>\n","      <td>Minor</td>\n","      <td>5</td>\n","      <td>11-20</td>\n","      <td>3761.0</td>\n","      <td>11-20</td>\n","    </tr>\n","    <tr>\n","      <th>318437</th>\n","      <td>318438</td>\n","      <td>19</td>\n","      <td>a</td>\n","      <td>7</td>\n","      <td>Y</td>\n","      <td>5</td>\n","      <td>gynecology</td>\n","      <td>Q</td>\n","      <td>C</td>\n","      <td>2.0</td>\n","      <td>21641</td>\n","      <td>8.0</td>\n","      <td>Emergency</td>\n","      <td>Minor</td>\n","      <td>2</td>\n","      <td>11-20</td>\n","      <td>4752.0</td>\n","      <td>0-10</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>318438 rows × 18 columns</p>\n","</div>"],"text/plain":["        case_id  Hospital_code Hospital_type_code  City_Code_Hospital  \\\n","0             1              8                  c                   3   \n","1             2              2                  c                   5   \n","2             3             10                  e                   1   \n","3             4             26                  b                   2   \n","4             5             26                  b                   2   \n","...         ...            ...                ...                 ...   \n","318433   318434              6                  a                   6   \n","318434   318435             24                  a                   1   \n","318435   318436              7                  a                   4   \n","318436   318437             11                  b                   2   \n","318437   318438             19                  a                   7   \n","\n","       Hospital_region_code  Available Extra Rooms in Hospital    Department  \\\n","0                         Z                                  3  radiotherapy   \n","1                         Z                                  2  radiotherapy   \n","2                         X                                  2    anesthesia   \n","3                         Y                                  2  radiotherapy   \n","4                         Y                                  2  radiotherapy   \n","...                     ...                                ...           ...   \n","318433                    X                                  3  radiotherapy   \n","318434                    X                                  2    anesthesia   \n","318435                    X                                  3    gynecology   \n","318436                    Y                                  3    anesthesia   \n","318437                    Y                                  5    gynecology   \n","\n","       Ward_Type Ward_Facility_Code  Bed Grade  patientid  City_Code_Patient  \\\n","0              R                  F        2.0      31397                7.0   \n","1              S                  F        2.0      31397                7.0   \n","2              S                  E        2.0      31397                7.0   \n","3              R                  D        2.0      31397                7.0   \n","4              S                  D        2.0      31397                7.0   \n","...          ...                ...        ...        ...                ...   \n","318433         Q                  F        4.0      86499               23.0   \n","318434         Q                  E        4.0        325                8.0   \n","318435         R                  F        4.0     125235               10.0   \n","318436         Q                  D        3.0      91081                8.0   \n","318437         Q                  C        2.0      21641                8.0   \n","\n","       Type of Admission Severity of Illness  Visitors with Patient    Age  \\\n","0              Emergency             Extreme                      2  51-60   \n","1                 Trauma             Extreme                      2  51-60   \n","2                 Trauma             Extreme                      2  51-60   \n","3                 Trauma             Extreme                      2  51-60   \n","4                 Trauma             Extreme                      2  51-60   \n","...                  ...                 ...                    ...    ...   \n","318433         Emergency            Moderate                      3  41-50   \n","318434            Urgent            Moderate                      4  81-90   \n","318435         Emergency               Minor                      3  71-80   \n","318436            Trauma               Minor                      5  11-20   \n","318437         Emergency               Minor                      2  11-20   \n","\n","        Admission_Deposit   Stay  \n","0                  4911.0   0-10  \n","1                  5954.0  41-50  \n","2                  4745.0  31-40  \n","3                  7272.0  41-50  \n","4                  5558.0  41-50  \n","...                   ...    ...  \n","318433             4144.0  11-20  \n","318434             6699.0  31-40  \n","318435             4235.0  11-20  \n","318436             3761.0  11-20  \n","318437             4752.0   0-10  \n","\n","[318438 rows x 18 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["df_train=pd.read_csv('../input/av-healthcare-analytics-ii/healthcare/train_data.csv',sep=',')\n","df_train"]},{"cell_type":"markdown","id":"fac54f0c","metadata":{"papermill":{"duration":0.012429,"end_time":"2022-07-07T21:19:27.258423","exception":false,"start_time":"2022-07-07T21:19:27.245994","status":"completed"},"tags":[]},"source":["# Review Data"]},{"cell_type":"code","execution_count":3,"id":"ee2b5410","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:27.28554Z","iopub.status.busy":"2022-07-07T21:19:27.285131Z","iopub.status.idle":"2022-07-07T21:19:27.616311Z","shell.execute_reply":"2022-07-07T21:19:27.615108Z"},"papermill":{"duration":0.34822,"end_time":"2022-07-07T21:19:27.619398","exception":false,"start_time":"2022-07-07T21:19:27.271178","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(318438, 18)\n","<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 318438 entries, 0 to 318437\n","Data 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 \n","dtypes: float64(3), int64(6), object(9)\n","memory usage: 43.7+ MB\n"]}],"source":["\n","# Data dimensionality (rows, colums)\n","print(df_train.shape)\n","# Data distributing\n","df_train.info()\n"]},{"cell_type":"code","execution_count":4,"id":"08077091","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:27.647705Z","iopub.status.busy":"2022-07-07T21:19:27.647278Z","iopub.status.idle":"2022-07-07T21:19:27.671529Z","shell.execute_reply":"2022-07-07T21:19:27.670377Z"},"papermill":{"duration":0.04105,"end_time":"2022-07-07T21:19:27.674097","exception":false,"start_time":"2022-07-07T21:19:27.633047","status":"completed"},"tags":[]},"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>Column</th>\n","      <th>Description</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>case_id</td>\n","      <td>Case_ID registered in Hospital</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hospital_code</td>\n","      <td>Unique code for the Hospital</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Hospital_type_code</td>\n","      <td>Unique code for the type of Hospital</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>City_Code_Hospital</td>\n","      <td>City Code of the Hospital</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Hospital_region_code</td>\n","      <td>Region Code of the Hospital</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Available Extra Rooms in Hospital</td>\n","      <td>Number of Extra rooms available in the Hospital</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Department</td>\n","      <td>Department overlooking the case</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Ward_Type</td>\n","      <td>Code for the Ward type</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ward_Facility_Code</td>\n","      <td>Code for the Ward Facility</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Bed Grade</td>\n","      <td>Condition of Bed in the Ward</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>patientid</td>\n","      <td>Unique Patient Id</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>City_Code_Patient</td>\n","      <td>City Code for the patient</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Type of Admission</td>\n","      <td>Admission Type registered by the Hospital</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Severity of Illness</td>\n","      <td>Severity of the illness recorded at the time o...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Visitors with Patient</td>\n","      <td>Number of Visitors with the patient</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Age</td>\n","      <td>Age of the patient</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Admission_Deposit</td>\n","      <td>Deposit at the Admission Time</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Stay</td>\n","      <td>Stay Days by the patient</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                               Column  \\\n","0                             case_id   \n","1                       Hospital_code   \n","2                  Hospital_type_code   \n","3                  City_Code_Hospital   \n","4                Hospital_region_code   \n","5   Available Extra Rooms in Hospital   \n","6                          Department   \n","7                           Ward_Type   \n","8                  Ward_Facility_Code   \n","9                           Bed Grade   \n","10                          patientid   \n","11                  City_Code_Patient   \n","12                  Type of Admission   \n","13                Severity of Illness   \n","14              Visitors with Patient   \n","15                                Age   \n","16                  Admission_Deposit   \n","17                               Stay   \n","\n","                                          Description  \n","0                      Case_ID registered in Hospital  \n","1                        Unique code for the Hospital  \n","2                Unique code for the type of Hospital  \n","3                           City Code of the Hospital  \n","4                         Region Code of the Hospital  \n","5     Number of Extra rooms available in the Hospital  \n","6                     Department overlooking the case  \n","7                              Code for the Ward type  \n","8                          Code for the Ward Facility  \n","9                        Condition of Bed in the Ward  \n","10                                  Unique Patient Id  \n","11                          City Code for the patient  \n","12          Admission Type registered by the Hospital  \n","13  Severity of the illness recorded at the time o...  \n","14                Number of Visitors with the patient  \n","15                                 Age of the patient  \n","16                      Deposit at the Admission Time  \n","17                           Stay Days by the patient  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df_train_dic = pd.read_csv('../input/av-healthcare-analytics-ii/healthcare/train_data_dictionary.csv', sep =',')\n","df_train_dic"]},{"cell_type":"code","execution_count":5,"id":"1fac2b98","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:27.702967Z","iopub.status.busy":"2022-07-07T21:19:27.702147Z","iopub.status.idle":"2022-07-07T21:19:28.094188Z","shell.execute_reply":"2022-07-07T21:19:28.09287Z"},"papermill":{"duration":0.409543,"end_time":"2022-07-07T21:19:28.097002","exception":false,"start_time":"2022-07-07T21:19:27.687459","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Index(['case_id', 'Hospital_code', 'Hospital_type_code', 'City_Code_Hospital',\n","       'Hospital_region_code', 'Available Extra Rooms in Hospital',\n","       'Department', 'Ward_Type', 'Ward_Facility_Code', 'Bed Grade',\n","       'patientid', 'City_Code_Patient', 'Type of Admission',\n","       'Severity of Illness', 'Visitors with Patient', 'Age',\n","       'Admission_Deposit'],\n","      dtype='object')"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df_test = pd.read_csv('../input/av-healthcare-analytics-ii/healthcare/test_data.csv', sep =',')\n","df_test.columns"]},{"cell_type":"code","execution_count":6,"id":"acba5b54","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:28.126825Z","iopub.status.busy":"2022-07-07T21:19:28.126448Z","iopub.status.idle":"2022-07-07T21:19:28.350951Z","shell.execute_reply":"2022-07-07T21:19:28.349759Z"},"papermill":{"duration":0.242705,"end_time":"2022-07-07T21:19:28.353986","exception":false,"start_time":"2022-07-07T21:19:28.111281","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Feature: Hospital_type_code\n","['c', 'e', 'b', 'a', 'f', 'd', 'g']\n","Categories (7, object): ['a', 'b', 'c', 'd', 'e', 'f', 'g']\n","\n","Feature: Hospital_region_code\n","['Z', 'X', 'Y']\n","Categories (3, object): ['X', 'Y', 'Z']\n","\n","Feature: Department\n","['radiotherapy', 'anesthesia', 'gynecology', 'TB & Chest disease', 'surgery']\n","Categories (5, object): ['TB & Chest disease', 'anesthesia', 'gynecology', 'radiotherapy', 'surgery']\n","\n","Feature: Ward_Type\n","['R', 'S', 'Q', 'P', 'T', 'U']\n","Categories (6, object): ['P', 'Q', 'R', 'S', 'T', 'U']\n","\n","Feature: Ward_Facility_Code\n","['F', 'E', 'D', 'B', 'A', 'C']\n","Categories (6, object): ['A', 'B', 'C', 'D', 'E', 'F']\n","\n","Feature: Type of Admission\n","['Emergency', 'Trauma', 'Urgent']\n","Categories (3, object): ['Emergency', 'Trauma', 'Urgent']\n","\n","Feature: Severity of Illness\n","['Extreme', 'Moderate', 'Minor']\n","Categories (3, object): ['Extreme', 'Minor', 'Moderate']\n","\n","Feature: Age\n","['51-60', '71-80', '31-40', '41-50', '81-90', '61-70', '21-30', '11-20', '0-10', '91-100']\n","Categories (10, object): ['0-10', '11-20', '21-30', '31-40', ..., '61-70', '71-80', '81-90', '91-100']\n","\n","Feature: Stay\n","['0-10', '41-50', '31-40', '11-20', '51-60', ..., '71-80', 'More than 100 Days', '81-90', '61-70', '91-100']\n","Length: 11\n","Categories (11, object): ['0-10', '11-20', '21-30', '31-40', ..., '71-80', '81-90', '91-100', 'More than 100 Days']\n"]}],"source":["for feature in df_train.columns: \n","    if df_train[feature].dtype == object:\n","        print('\\nFeature:',feature)\n","        print(pd.Categorical(df_train[feature].unique()))"]},{"cell_type":"code","execution_count":7,"id":"28e5aa18","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:28.383929Z","iopub.status.busy":"2022-07-07T21:19:28.383175Z","iopub.status.idle":"2022-07-07T21:19:28.728719Z","shell.execute_reply":"2022-07-07T21:19:28.72755Z"},"papermill":{"duration":0.362976,"end_time":"2022-07-07T21:19:28.731355","exception":false,"start_time":"2022-07-07T21:19:28.368379","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1440x720 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["x = df_train.groupby(by=['Department'])['patientid'].sum().reset_index().sort_values(['patientid'],\n","                    ascending=False).head(10).plot(x='Department',y='patientid',kind='bar', figsize=(20,10))\n","\n","for i in x.containers:\n"," x.bar_label(i,)\n"]},{"cell_type":"code","execution_count":8,"id":"92eacb57","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:28.762062Z","iopub.status.busy":"2022-07-07T21:19:28.76136Z","iopub.status.idle":"2022-07-07T21:19:29.337493Z","shell.execute_reply":"2022-07-07T21:19:29.336284Z"},"papermill":{"duration":0.594632,"end_time":"2022-07-07T21:19:29.340242","exception":false,"start_time":"2022-07-07T21:19:28.74561","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1440x360 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.figure(figsize=(20,5))\n","x = sns.countplot(df_train['Stay'], order = df_train['Stay'].value_counts().index)\n","for i in x.containers:\n"," x.bar_label(i,)"]},{"cell_type":"code","execution_count":9,"id":"120ef8d4","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:29.373408Z","iopub.status.busy":"2022-07-07T21:19:29.372201Z","iopub.status.idle":"2022-07-07T21:19:29.50496Z","shell.execute_reply":"2022-07-07T21:19:29.503773Z"},"papermill":{"duration":0.153094,"end_time":"2022-07-07T21:19:29.507937","exception":false,"start_time":"2022-07-07T21:19:29.354843","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Department          Stay                Stay              \n","TB & Chest disease  0-10                0-10                    791\n","                    11-20               11-20                  2379\n","                    21-30               21-30                  2618\n","                    31-40               31-40                  1753\n","                    41-50               41-50                   300\n","                    51-60               51-60                  1047\n","                    61-70               61-70                    76\n","                    71-80               71-80                   294\n","                    81-90               81-90                   140\n","                    91-100              91-100                   64\n","                    More than 100 Days  More than 100 Days      124\n","anesthesia          0-10                0-10                   1519\n","                    11-20               11-20                  8823\n","                    21-30               21-30                  8950\n","                    31-40               31-40                  4958\n","                    41-50               41-50                   851\n","                    51-60               51-60                  2527\n","                    61-70               61-70                   173\n","                    71-80               71-80                   703\n","                    81-90               81-90                   384\n","                    91-100              91-100                  156\n","                    More than 100 Days  More than 100 Days      605\n","gynecology          0-10                0-10                  18849\n","                    11-20               11-20                 59690\n","                    21-30               21-30                 68383\n","                    31-40               31-40                 43535\n","                    41-50               41-50                  9671\n","                    51-60               51-60                 27989\n","                    61-70               61-70                  2226\n","                    71-80               71-80                  8165\n","                    81-90               81-90                  3748\n","                    91-100              91-100                 2269\n","                    More than 100 Days  More than 100 Days     4961\n","radiotherapy        0-10                0-10                   2379\n","                    11-20               11-20                  6988\n","                    21-30               21-30                  7263\n","                    31-40               31-40                  4713\n","                    41-50               41-50                   879\n","                    51-60               51-60                  3256\n","                    61-70               61-70                   264\n","                    71-80               71-80                  1043\n","                    81-90               81-90                   528\n","                    91-100              91-100                  256\n","                    More than 100 Days  More than 100 Days      947\n","surgery             0-10                0-10                     66\n","                    11-20               11-20                   259\n","                    21-30               21-30                   277\n","                    31-40               31-40                   200\n","                    41-50               41-50                    42\n","                    51-60               51-60                   199\n","                    61-70               61-70                     5\n","                    71-80               71-80                    49\n","                    81-90               81-90                    38\n","                    91-100              91-100                   20\n","                    More than 100 Days  More than 100 Days       46\n","Name: Stay, dtype: int64"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df_x = df_train.groupby(by=['Department','Stay'])\n","df_x['Stay'].value_counts()"]},{"cell_type":"code","execution_count":10,"id":"073ecdd8","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:29.539079Z","iopub.status.busy":"2022-07-07T21:19:29.538645Z","iopub.status.idle":"2022-07-07T21:19:29.569907Z","shell.execute_reply":"2022-07-07T21:19:29.568315Z"},"papermill":{"duration":0.049859,"end_time":"2022-07-07T21:19:29.572559","exception":false,"start_time":"2022-07-07T21:19:29.5227","status":"completed"},"tags":[]},"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>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","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>23</td>\n","      <td>a</td>\n","      <td>6</td>\n","      <td>X</td>\n","      <td>2</td>\n","      <td>anesthesia</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>4449.0</td>\n","      <td>11-20</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>32</td>\n","      <td>f</td>\n","      <td>9</td>\n","      <td>Y</td>\n","      <td>1</td>\n","      <td>radiotherapy</td>\n","      <td>S</td>\n","      <td>B</td>\n","      <td>3.0</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>6167.0</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>23</td>\n","      <td>a</td>\n","      <td>6</td>\n","      <td>X</td>\n","      <td>4</td>\n","      <td>radiotherapy</td>\n","      <td>Q</td>\n","      <td>F</td>\n","      <td>3.0</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>5571.0</td>\n","      <td>41-50</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>1</td>\n","      <td>d</td>\n","      <td>10</td>\n","      <td>Y</td>\n","      <td>2</td>\n","      <td>gynecology</td>\n","      <td>R</td>\n","      <td>B</td>\n","      <td>4.0</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>7223.0</td>\n","      <td>51-60</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>e</td>\n","      <td>1</td>\n","      <td>X</td>\n","      <td>2</td>\n","      <td>gynecology</td>\n","      <td>S</td>\n","      <td>E</td>\n","      <td>3.0</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>6056.0</td>\n","      <td>31-40</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>11</td>\n","      <td>22</td>\n","      <td>g</td>\n","      <td>9</td>\n","      <td>Y</td>\n","      <td>2</td>\n","      <td>radiotherapy</td>\n","      <td>S</td>\n","      <td>B</td>\n","      <td>2.0</td>\n","      <td>31397</td>\n","      <td>7.0</td>\n","      <td>Urgent</td>\n","      <td>Extreme</td>\n","      <td>2</td>\n","      <td>51-60</td>\n","      <td>5797.0</td>\n","      <td>21-30</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>12</td>\n","      <td>26</td>\n","      <td>b</td>\n","      <td>2</td>\n","      <td>Y</td>\n","      <td>4</td>\n","      <td>radiotherapy</td>\n","      <td>R</td>\n","      <td>D</td>\n","      <td>1.0</td>\n","      <td>31397</td>\n","      <td>7.0</td>\n","      <td>Urgent</td>\n","      <td>Extreme</td>\n","      <td>2</td>\n","      <td>51-60</td>\n","      <td>5993.0</td>\n","      <td>11-20</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>13</td>\n","      <td>16</td>\n","      <td>c</td>\n","      <td>3</td>\n","      <td>Z</td>\n","      <td>2</td>\n","      <td>radiotherapy</td>\n","      <td>R</td>\n","      <td>A</td>\n","      <td>3.0</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>5141.0</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>14</td>\n","      <td>9</td>\n","      <td>d</td>\n","      <td>5</td>\n","      <td>Z</td>\n","      <td>3</td>\n","      <td>radiotherapy</td>\n","      <td>S</td>\n","      <td>F</td>\n","      <td>3.0</td>\n","      <td>31397</td>\n","      <td>7.0</td>\n","      <td>Urgent</td>\n","      <td>Extreme</td>\n","      <td>2</td>\n","      <td>51-60</td>\n","      <td>8477.0</td>\n","      <td>21-30</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    case_id  Hospital_code Hospital_type_code  City_Code_Hospital  \\\n","0         1              8                  c                   3   \n","1         2              2                  c                   5   \n","2         3             10                  e                   1   \n","3         4             26                  b                   2   \n","4         5             26                  b                   2   \n","5         6             23                  a                   6   \n","6         7             32                  f                   9   \n","7         8             23                  a                   6   \n","8         9              1                  d                  10   \n","9        10             10                  e                   1   \n","10       11             22                  g                   9   \n","11       12             26                  b                   2   \n","12       13             16                  c                   3   \n","13       14              9                  d                   5   \n","\n","   Hospital_region_code  Available Extra Rooms in Hospital    Department  \\\n","0                     Z                                  3  radiotherapy   \n","1                     Z                                  2  radiotherapy   \n","2                     X                                  2    anesthesia   \n","3                     Y                                  2  radiotherapy   \n","4                     Y                                  2  radiotherapy   \n","5                     X                                  2    anesthesia   \n","6                     Y                                  1  radiotherapy   \n","7                     X                                  4  radiotherapy   \n","8                     Y                                  2    gynecology   \n","9                     X                                  2    gynecology   \n","10                    Y                                  2  radiotherapy   \n","11                    Y                                  4  radiotherapy   \n","12                    Z                                  2  radiotherapy   \n","13                    Z                                  3  radiotherapy   \n","\n","   Ward_Type Ward_Facility_Code  Bed Grade  patientid  City_Code_Patient  \\\n","0          R                  F        2.0      31397                7.0   \n","1          S                  F        2.0      31397                7.0   \n","2          S                  E        2.0      31397                7.0   \n","3          R                  D        2.0      31397                7.0   \n","4          S                  D        2.0      31397                7.0   \n","5          S                  F        2.0      31397                7.0   \n","6          S                  B        3.0      31397                7.0   \n","7          Q                  F        3.0      31397                7.0   \n","8          R                  B        4.0      31397                7.0   \n","9          S                  E        3.0      31397                7.0   \n","10         S                  B        2.0      31397                7.0   \n","11         R                  D        1.0      31397                7.0   \n","12         R                  A        3.0      31397                7.0   \n","13         S                  F        3.0      31397                7.0   \n","\n","   Type of Admission Severity of Illness  Visitors with Patient    Age  \\\n","0          Emergency             Extreme                      2  51-60   \n","1             Trauma             Extreme                      2  51-60   \n","2             Trauma             Extreme                      2  51-60   \n","3             Trauma             Extreme                      2  51-60   \n","4             Trauma             Extreme                      2  51-60   \n","5             Trauma             Extreme                      2  51-60   \n","6          Emergency             Extreme                      2  51-60   \n","7             Trauma             Extreme                      2  51-60   \n","8             Trauma             Extreme                      2  51-60   \n","9             Trauma             Extreme                      2  51-60   \n","10            Urgent             Extreme                      2  51-60   \n","11            Urgent             Extreme                      2  51-60   \n","12         Emergency             Extreme                      2  51-60   \n","13            Urgent             Extreme                      2  51-60   \n","\n","    Admission_Deposit   Stay  \n","0              4911.0   0-10  \n","1              5954.0  41-50  \n","2              4745.0  31-40  \n","3              7272.0  41-50  \n","4              5558.0  41-50  \n","5              4449.0  11-20  \n","6              6167.0   0-10  \n","7              5571.0  41-50  \n","8              7223.0  51-60  \n","9              6056.0  31-40  \n","10             5797.0  21-30  \n","11             5993.0  11-20  \n","12             5141.0   0-10  \n","13             8477.0  21-30  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["# Sample testing \n","df_patient_31397 = df_train[df_train['patientid'] == 31397]\n","df_patient_31397\n"]},{"cell_type":"markdown","id":"cb15c593","metadata":{"papermill":{"duration":0.015531,"end_time":"2022-07-07T21:19:29.603843","exception":false,"start_time":"2022-07-07T21:19:29.588312","status":"completed"},"tags":[]},"source":["There are multiple entries for a patient ID with different department and length of stay. Date will be a relevant parameter here to understand timeline of events."]},{"cell_type":"markdown","id":"edefa969","metadata":{"papermill":{"duration":0.015286,"end_time":"2022-07-07T21:19:29.634988","exception":false,"start_time":"2022-07-07T21:19:29.619702","status":"completed"},"tags":[]},"source":["# Check for nulls"]},{"cell_type":"code","execution_count":11,"id":"47b2aaab","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:29.667355Z","iopub.status.busy":"2022-07-07T21:19:29.666952Z","iopub.status.idle":"2022-07-07T21:19:29.983595Z","shell.execute_reply":"2022-07-07T21:19:29.982326Z"},"papermill":{"duration":0.335914,"end_time":"2022-07-07T21:19:29.986164","exception":false,"start_time":"2022-07-07T21:19:29.65025","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["case_id                                 0\n","Hospital_code                           0\n","Hospital_type_code                      0\n","City_Code_Hospital                      0\n","Hospital_region_code                    0\n","Available Extra Rooms in Hospital       0\n","Department                              0\n","Ward_Type                               0\n","Ward_Facility_Code                      0\n","Bed Grade                             113\n","patientid                               0\n","City_Code_Patient                    4532\n","Type of Admission                       0\n","Severity of Illness                     0\n","Visitors with Patient                   0\n","Age                                     0\n","Admission_Deposit                       0\n","Stay                                    0\n","dtype: int64"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df_train.isna().sum()"]},{"cell_type":"markdown","id":"3bd56efd","metadata":{"papermill":{"duration":0.015277,"end_time":"2022-07-07T21:19:30.016871","exception":false,"start_time":"2022-07-07T21:19:30.001594","status":"completed"},"tags":[]},"source":["# Check for dupes"]},{"cell_type":"code","execution_count":12,"id":"f7919b8a","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:30.05122Z","iopub.status.busy":"2022-07-07T21:19:30.050018Z","iopub.status.idle":"2022-07-07T21:19:30.411061Z","shell.execute_reply":"2022-07-07T21:19:30.409826Z"},"papermill":{"duration":0.380939,"end_time":"2022-07-07T21:19:30.41375","exception":false,"start_time":"2022-07-07T21:19:30.032811","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Total duplicate values in the dataset :  0\n"]}],"source":["print('Total duplicate values in the dataset : ',df_train.duplicated().sum())"]},{"cell_type":"markdown","id":"24390b08","metadata":{"papermill":{"duration":0.015504,"end_time":"2022-07-07T21:19:30.445225","exception":false,"start_time":"2022-07-07T21:19:30.429721","status":"completed"},"tags":[]},"source":["Insights:\n","\n","Categorical features which need to be converted from object to Int:\n","* Department\n","* Ward_Type\n","* Admission\n","* Illness\n","\n","Bins (range) which need to be encoded:\n","* Age\n","* Stay (Target)\n","\n","Columns that are not needed\n","* Hospital_region_code\n","* Bed Grade\n","* City_Code_Patient\n","* patientid\n","\n","\n","\n","Note: Test does not have Stay column; which needs to be predicted"]},{"cell_type":"code","execution_count":13,"id":"25868ace","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:30.478305Z","iopub.status.busy":"2022-07-07T21:19:30.477704Z","iopub.status.idle":"2022-07-07T21:19:30.517315Z","shell.execute_reply":"2022-07-07T21:19:30.516422Z"},"papermill":{"duration":0.058875,"end_time":"2022-07-07T21:19:30.519671","exception":false,"start_time":"2022-07-07T21:19:30.460796","status":"completed"},"tags":[]},"outputs":[],"source":["df_train = df_train.drop(['Hospital_region_code', 'Bed Grade', 'patientid', 'City_Code_Patient'],axis =1)\n","df_test = df_test.drop(['Hospital_region_code', 'Bed Grade', 'patientid', 'City_Code_Patient'], axis = 1)\n"]},{"cell_type":"markdown","id":"ba46ebeb","metadata":{"papermill":{"duration":0.015678,"end_time":"2022-07-07T21:19:30.550802","exception":false,"start_time":"2022-07-07T21:19:30.535124","status":"completed"},"tags":[]},"source":["Merging Train and Test on multi dimention to pre-process data."]},{"cell_type":"code","execution_count":14,"id":"2430d8d2","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:30.584475Z","iopub.status.busy":"2022-07-07T21:19:30.583891Z","iopub.status.idle":"2022-07-07T21:19:30.609482Z","shell.execute_reply":"2022-07-07T21:19:30.608126Z"},"papermill":{"duration":0.045584,"end_time":"2022-07-07T21:19:30.612133","exception":false,"start_time":"2022-07-07T21:19:30.566549","status":"completed"},"tags":[]},"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>case_id</th>\n","      <th>Hospital_code</th>\n","      <th>Hospital_type_code</th>\n","      <th>City_Code_Hospital</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>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>3</td>\n","      <td>radiotherapy</td>\n","      <td>R</td>\n","      <td>F</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>2</td>\n","      <td>radiotherapy</td>\n","      <td>S</td>\n","      <td>F</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>2</td>\n","      <td>anesthesia</td>\n","      <td>S</td>\n","      <td>E</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>2</td>\n","      <td>radiotherapy</td>\n","      <td>R</td>\n","      <td>D</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>2</td>\n","      <td>radiotherapy</td>\n","      <td>S</td>\n","      <td>D</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","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>318433</th>\n","      <td>318434</td>\n","      <td>6</td>\n","      <td>a</td>\n","      <td>6</td>\n","      <td>3</td>\n","      <td>radiotherapy</td>\n","      <td>Q</td>\n","      <td>F</td>\n","      <td>Emergency</td>\n","      <td>Moderate</td>\n","      <td>3</td>\n","      <td>41-50</td>\n","      <td>4144.0</td>\n","      <td>11-20</td>\n","    </tr>\n","    <tr>\n","      <th>318434</th>\n","      <td>318435</td>\n","      <td>24</td>\n","      <td>a</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>anesthesia</td>\n","      <td>Q</td>\n","      <td>E</td>\n","      <td>Urgent</td>\n","      <td>Moderate</td>\n","      <td>4</td>\n","      <td>81-90</td>\n","      <td>6699.0</td>\n","      <td>31-40</td>\n","    </tr>\n","    <tr>\n","      <th>318435</th>\n","      <td>318436</td>\n","      <td>7</td>\n","      <td>a</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>gynecology</td>\n","      <td>R</td>\n","      <td>F</td>\n","      <td>Emergency</td>\n","      <td>Minor</td>\n","      <td>3</td>\n","      <td>71-80</td>\n","      <td>4235.0</td>\n","      <td>11-20</td>\n","    </tr>\n","    <tr>\n","      <th>318436</th>\n","      <td>318437</td>\n","      <td>11</td>\n","      <td>b</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>anesthesia</td>\n","      <td>Q</td>\n","      <td>D</td>\n","      <td>Trauma</td>\n","      <td>Minor</td>\n","      <td>5</td>\n","      <td>11-20</td>\n","      <td>3761.0</td>\n","      <td>11-20</td>\n","    </tr>\n","    <tr>\n","      <th>318437</th>\n","      <td>318438</td>\n","      <td>19</td>\n","      <td>a</td>\n","      <td>7</td>\n","      <td>5</td>\n","      <td>gynecology</td>\n","      <td>Q</td>\n","      <td>C</td>\n","      <td>Emergency</td>\n","      <td>Minor</td>\n","      <td>2</td>\n","      <td>11-20</td>\n","      <td>4752.0</td>\n","      <td>0-10</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>318438 rows × 14 columns</p>\n","</div>"],"text/plain":["        case_id  Hospital_code Hospital_type_code  City_Code_Hospital  \\\n","0             1              8                  c                   3   \n","1             2              2                  c                   5   \n","2             3             10                  e                   1   \n","3             4             26                  b                   2   \n","4             5             26                  b                   2   \n","...         ...            ...                ...                 ...   \n","318433   318434              6                  a                   6   \n","318434   318435             24                  a                   1   \n","318435   318436              7                  a                   4   \n","318436   318437             11                  b                   2   \n","318437   318438             19                  a                   7   \n","\n","        Available Extra Rooms in Hospital    Department Ward_Type  \\\n","0                                       3  radiotherapy         R   \n","1                                       2  radiotherapy         S   \n","2                                       2    anesthesia         S   \n","3                                       2  radiotherapy         R   \n","4                                       2  radiotherapy         S   \n","...                                   ...           ...       ...   \n","318433                                  3  radiotherapy         Q   \n","318434                                  2    anesthesia         Q   \n","318435                                  3    gynecology         R   \n","318436                                  3    anesthesia         Q   \n","318437                                  5    gynecology         Q   \n","\n","       Ward_Facility_Code Type of Admission Severity of Illness  \\\n","0                       F         Emergency             Extreme   \n","1                       F            Trauma             Extreme   \n","2                       E            Trauma             Extreme   \n","3                       D            Trauma             Extreme   \n","4                       D            Trauma             Extreme   \n","...                   ...               ...                 ...   \n","318433                  F         Emergency            Moderate   \n","318434                  E            Urgent            Moderate   \n","318435                  F         Emergency               Minor   \n","318436                  D            Trauma               Minor   \n","318437                  C         Emergency               Minor   \n","\n","        Visitors with Patient    Age  Admission_Deposit   Stay  \n","0                           2  51-60             4911.0   0-10  \n","1                           2  51-60             5954.0  41-50  \n","2                           2  51-60             4745.0  31-40  \n","3                           2  51-60             7272.0  41-50  \n","4                           2  51-60             5558.0  41-50  \n","...                       ...    ...                ...    ...  \n","318433                      3  41-50             4144.0  11-20  \n","318434                      4  81-90             6699.0  31-40  \n","318435                      3  71-80             4235.0  11-20  \n","318436                      5  11-20             3761.0  11-20  \n","318437                      2  11-20             4752.0   0-10  \n","\n","[318438 rows x 14 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["df_merge = [df_train, df_test]\n","df_merge[0]"]},{"cell_type":"code","execution_count":15,"id":"81bba965","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:30.646164Z","iopub.status.busy":"2022-07-07T21:19:30.645716Z","iopub.status.idle":"2022-07-07T21:19:31.592977Z","shell.execute_reply":"2022-07-07T21:19:31.59177Z"},"papermill":{"duration":0.967491,"end_time":"2022-07-07T21:19:31.595715","exception":false,"start_time":"2022-07-07T21:19:30.628224","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[        case_id  Hospital_code  Hospital_type_code  City_Code_Hospital  \\\n"," 0             1              8                   2                   3   \n"," 1             2              2                   2                   5   \n"," 2             3             10                   4                   1   \n"," 3             4             26                   1                   2   \n"," 4             5             26                   1                   2   \n"," ...         ...            ...                 ...                 ...   \n"," 318433   318434              6                   0                   6   \n"," 318434   318435             24                   0                   1   \n"," 318435   318436              7                   0                   4   \n"," 318436   318437             11                   1                   2   \n"," 318437   318438             19                   0                   7   \n"," \n","         Available Extra Rooms in Hospital  Department  Ward_Type  \\\n"," 0                                       3           3          2   \n"," 1                                       2           3          3   \n"," 2                                       2           1          3   \n"," 3                                       2           3          2   \n"," 4                                       2           3          3   \n"," ...                                   ...         ...        ...   \n"," 318433                                  3           3          1   \n"," 318434                                  2           1          1   \n"," 318435                                  3           2          2   \n"," 318436                                  3           1          1   \n"," 318437                                  5           2          1   \n"," \n","         Ward_Facility_Code  Type of Admission  Severity of Illness  \\\n"," 0                        5                  0                    0   \n"," 1                        5                  1                    0   \n"," 2                        4                  1                    0   \n"," 3                        3                  1                    0   \n"," 4                        3                  1                    0   \n"," ...                    ...                ...                  ...   \n"," 318433                   5                  0                    2   \n"," 318434                   4                  2                    2   \n"," 318435                   5                  0                    1   \n"," 318436                   3                  1                    1   \n"," 318437                   2                  0                    1   \n"," \n","         Visitors with Patient    Age  Admission_Deposit   Stay  \n"," 0                           2  51-60             4911.0   0-10  \n"," 1                           2  51-60             5954.0  41-50  \n"," 2                           2  51-60             4745.0  31-40  \n"," 3                           2  51-60             7272.0  41-50  \n"," 4                           2  51-60             5558.0  41-50  \n"," ...                       ...    ...                ...    ...  \n"," 318433                      3  41-50             4144.0  11-20  \n"," 318434                      4  81-90             6699.0  31-40  \n"," 318435                      3  71-80             4235.0  11-20  \n"," 318436                      5  11-20             3761.0  11-20  \n"," 318437                      2  11-20             4752.0   0-10  \n"," \n"," [318438 rows x 14 columns],\n","         case_id  Hospital_code  Hospital_type_code  City_Code_Hospital  \\\n"," 0        318439             21                   2                   3   \n"," 1        318440             29                   0                   4   \n"," 2        318441             26                   1                   2   \n"," 3        318442              6                   0                   6   \n"," 4        318443             28                   1                  11   \n"," ...         ...            ...                 ...                 ...   \n"," 137052   455491             11                   1                   2   \n"," 137053   455492             25                   4                   1   \n"," 137054   455493             30                   2                   3   \n"," 137055   455494              5                   0                   1   \n"," 137056   455495              6                   0                   6   \n"," \n","         Available Extra Rooms in Hospital  Department  Ward_Type  \\\n"," 0                                       3           2          3   \n"," 1                                       2           2          3   \n"," 2                                       3           2          1   \n"," 3                                       3           2          1   \n"," 4                                       2           2          2   \n"," ...                                   ...         ...        ...   \n"," 137052                                  4           1          1   \n"," 137053                                  2           3          2   \n"," 137054                                  2           1          2   \n"," 137055                                  2           1          2   \n"," 137056                                  3           2          1   \n"," \n","         Ward_Facility_Code  Type of Admission  Severity of Illness  \\\n"," 0                        0                  0                    2   \n"," 1                        5                  1                    2   \n"," 2                        3                  0                    2   \n"," 3                        5                  1                    2   \n"," 4                        5                  1                    2   \n"," ...                    ...                ...                  ...   \n"," 137052                   3                  0                    1   \n"," 137053                   4                  0                    2   \n"," 137054                   0                  2                    1   \n"," 137055                   4                  1                    1   \n"," 137056                   5                  1                    0   \n"," \n","         Visitors with Patient    Age  Admission_Deposit  \n"," 0                           2  71-80             3095.0  \n"," 1                           4  71-80             4018.0  \n"," 2                           3  71-80             4492.0  \n"," 3                           3  71-80             4173.0  \n"," 4                           4  71-80             4161.0  \n"," ...                       ...    ...                ...  \n"," 137052                      4  41-50             6313.0  \n"," 137053                      2   0-10             3510.0  \n"," 137054                      2   0-10             7190.0  \n"," 137055                      2  41-50             5435.0  \n"," 137056                      5  51-60             4702.0  \n"," \n"," [137057 rows x 13 columns]]"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["for df in df_merge:\n","    label = LabelEncoder()\n","    df['Department'] = label.fit_transform(df['Department'])\n","    df['Hospital_type_code'] = label.fit_transform(df['Hospital_type_code'])\n","    df['Ward_Facility_Code'] = label.fit_transform(df['Ward_Facility_Code'])\n","    df['Ward_Type'] = label.fit_transform(df['Ward_Type'])\n","    df['Type of Admission'] = label.fit_transform(df['Type of Admission'])\n","    df['Severity of Illness'] = label.fit_transform(df['Severity of Illness'])\n","df_merge"]},{"cell_type":"markdown","id":"44ae8449","metadata":{"papermill":{"duration":0.016389,"end_time":"2022-07-07T21:19:31.628722","exception":false,"start_time":"2022-07-07T21:19:31.612333","status":"completed"},"tags":[]},"source":["# Binning "]},{"cell_type":"code","execution_count":16,"id":"9f896d78","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:31.663509Z","iopub.status.busy":"2022-07-07T21:19:31.663085Z","iopub.status.idle":"2022-07-07T21:19:31.718448Z","shell.execute_reply":"2022-07-07T21:19:31.717193Z"},"papermill":{"duration":0.076257,"end_time":"2022-07-07T21:19:31.721435","exception":false,"start_time":"2022-07-07T21:19:31.645178","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array(['0-10', '41-50', '31-40', '11-20', '51-60', '21-30', '71-80',\n","       'More than 100 Days', '81-90', '61-70', '91-100'], dtype=object)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["df_merge[0].Age.unique()\n","\n","df_merge[0].Stay.unique()"]},{"cell_type":"code","execution_count":17,"id":"e195e4d4","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:31.756195Z","iopub.status.busy":"2022-07-07T21:19:31.755758Z","iopub.status.idle":"2022-07-07T21:19:31.762525Z","shell.execute_reply":"2022-07-07T21:19:31.761246Z"},"papermill":{"duration":0.027446,"end_time":"2022-07-07T21:19:31.76522","exception":false,"start_time":"2022-07-07T21:19:31.737774","status":"completed"},"tags":[]},"outputs":[],"source":["age_value = {'0-10': 0, '11-20': 1, '21-30': 2, '31-40': 3, '41-50': 4, '51-60': 5, '61-70': 6, '71-80': 7, '81-90': 8, '91-100': 9}\n","stay_value = {'0-10': 0, '11-20': 1, '21-30': 2, '31-40': 3, '41-50': 4, '51-60': 5, '61-70': 6, '71-80': 7, '81-90': 8, '91-100': 9, 'More than 100 Days': 10}"]},{"cell_type":"markdown","id":"834fc68c","metadata":{"papermill":{"duration":0.0163,"end_time":"2022-07-07T21:19:31.798072","exception":false,"start_time":"2022-07-07T21:19:31.781772","status":"completed"},"tags":[]},"source":["Replacing Age and Stay with Int values"]},{"cell_type":"code","execution_count":18,"id":"ef0c129e","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:31.833754Z","iopub.status.busy":"2022-07-07T21:19:31.833117Z","iopub.status.idle":"2022-07-07T21:19:32.678139Z","shell.execute_reply":"2022-07-07T21:19:32.676827Z"},"papermill":{"duration":0.866428,"end_time":"2022-07-07T21:19:32.680941","exception":false,"start_time":"2022-07-07T21:19:31.814513","status":"completed"},"tags":[]},"outputs":[],"source":["\n","df_merge[0]['Age'] = df_merge[0]['Age'].replace(age_value.keys(), age_value.values())\n","df_merge[0]['Stay'] = df_merge[0]['Stay'].replace(stay_value.keys(), stay_value.values())\n","df_merge[1]['Age'] = df_merge[1]['Age'].replace(age_value.keys(), age_value.values())\n"]},{"cell_type":"code","execution_count":19,"id":"9ed8d179","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:32.716441Z","iopub.status.busy":"2022-07-07T21:19:32.716012Z","iopub.status.idle":"2022-07-07T21:19:32.72457Z","shell.execute_reply":"2022-07-07T21:19:32.723101Z"},"papermill":{"duration":0.02998,"end_time":"2022-07-07T21:19:32.727517","exception":false,"start_time":"2022-07-07T21:19:32.697537","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(318438, 14)\n","(137057, 13)\n"]}],"source":["\n","print(df_merge[0].shape)\n","print(df_merge[1].shape)"]},{"cell_type":"code","execution_count":20,"id":"442b1cc4","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:32.762916Z","iopub.status.busy":"2022-07-07T21:19:32.762499Z","iopub.status.idle":"2022-07-07T21:19:32.785041Z","shell.execute_reply":"2022-07-07T21:19:32.783172Z"},"papermill":{"duration":0.043822,"end_time":"2022-07-07T21:19:32.788236","exception":false,"start_time":"2022-07-07T21:19:32.744414","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["[        case_id  Hospital_code  Hospital_type_code  City_Code_Hospital  \\\n","0             1              8                   2                   3   \n","1             2              2                   2                   5   \n","2             3             10                   4                   1   \n","3             4             26                   1                   2   \n","4             5             26                   1                   2   \n","...         ...            ...                 ...                 ...   \n","318433   318434              6                   0                   6   \n","318434   318435             24                   0                   1   \n","318435   318436              7                   0                   4   \n","318436   318437             11                   1                   2   \n","318437   318438             19                   0                   7   \n","\n","        Available Extra Rooms in Hospital  Department  Ward_Type  \\\n","0                                       3           3          2   \n","1                                       2           3          3   \n","2                                       2           1          3   \n","3                                       2           3          2   \n","4                                       2           3          3   \n","...                                   ...         ...        ...   \n","318433                                  3           3          1   \n","318434                                  2           1          1   \n","318435                                  3           2          2   \n","318436                                  3           1          1   \n","318437                                  5           2          1   \n","\n","        Ward_Facility_Code  Type of Admission  Severity of Illness  \\\n","0                        5                  0                    0   \n","1                        5                  1                    0   \n","2                        4                  1                    0   \n","3                        3                  1                    0   \n","4                        3                  1                    0   \n","...                    ...                ...                  ...   \n","318433                   5                  0                    2   \n","318434                   4                  2                    2   \n","318435                   5                  0                    1   \n","318436                   3                  1                    1   \n","318437                   2                  0                    1   \n","\n","        Visitors with Patient  Age  Admission_Deposit  Stay  \n","0                           2    5             4911.0     0  \n","1                           2    5             5954.0     4  \n","2                           2    5             4745.0     3  \n","3                           2    5             7272.0     4  \n","4                           2    5             5558.0     4  \n","...                       ...  ...                ...   ...  \n","318433                      3    4             4144.0     1  \n","318434                      4    8             6699.0     3  \n","318435                      3    7             4235.0     1  \n","318436                      5    1             3761.0     1  \n","318437                      2    1             4752.0     0  \n","\n","[318438 rows x 14 columns],         case_id  Hospital_code  Hospital_type_code  City_Code_Hospital  \\\n","0        318439             21                   2                   3   \n","1        318440             29                   0                   4   \n","2        318441             26                   1                   2   \n","3        318442              6                   0                   6   \n","4        318443             28                   1                  11   \n","...         ...            ...                 ...                 ...   \n","137052   455491             11                   1                   2   \n","137053   455492             25                   4                   1   \n","137054   455493             30                   2                   3   \n","137055   455494              5                   0                   1   \n","137056   455495              6                   0                   6   \n","\n","        Available Extra Rooms in Hospital  Department  Ward_Type  \\\n","0                                       3           2          3   \n","1                                       2           2          3   \n","2                                       3           2          1   \n","3                                       3           2          1   \n","4                                       2           2          2   \n","...                                   ...         ...        ...   \n","137052                                  4           1          1   \n","137053                                  2           3          2   \n","137054                                  2           1          2   \n","137055                                  2           1          2   \n","137056                                  3           2          1   \n","\n","        Ward_Facility_Code  Type of Admission  Severity of Illness  \\\n","0                        0                  0                    2   \n","1                        5                  1                    2   \n","2                        3                  0                    2   \n","3                        5                  1                    2   \n","4                        5                  1                    2   \n","...                    ...                ...                  ...   \n","137052                   3                  0                    1   \n","137053                   4                  0                    2   \n","137054                   0                  2                    1   \n","137055                   4                  1                    1   \n","137056                   5                  1                    0   \n","\n","        Visitors with Patient  Age  Admission_Deposit  \n","0                           2    7             3095.0  \n","1                           4    7             4018.0  \n","2                           3    7             4492.0  \n","3                           3    7             4173.0  \n","4                           4    7             4161.0  \n","...                       ...  ...                ...  \n","137052                      4    4             6313.0  \n","137053                      2    0             3510.0  \n","137054                      2    0             7190.0  \n","137055                      2    4             5435.0  \n","137056                      5    5             4702.0  \n","\n","[137057 rows x 13 columns]]\n"]}],"source":["print(df_merge)"]},{"cell_type":"markdown","id":"e71c79e8","metadata":{"papermill":{"duration":0.016965,"end_time":"2022-07-07T21:19:32.823359","exception":false,"start_time":"2022-07-07T21:19:32.806394","status":"completed"},"tags":[]},"source":["Applying Standard scaling to get optimize results"]},{"cell_type":"code","execution_count":21,"id":"84f9ae3e","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:32.862508Z","iopub.status.busy":"2022-07-07T21:19:32.862122Z","iopub.status.idle":"2022-07-07T21:19:32.869307Z","shell.execute_reply":"2022-07-07T21:19:32.8684Z"},"papermill":{"duration":0.029576,"end_time":"2022-07-07T21:19:32.871526","exception":false,"start_time":"2022-07-07T21:19:32.84195","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['Type of Admission',\n"," 'Available Extra Rooms in Hospital',\n"," 'Severity of Illness',\n"," 'Visitors with Patient']"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["columns_list = ['Type of Admission', 'Available Extra Rooms in Hospital','Severity of Illness', 'Visitors with Patient']\n","columns_list"]},{"cell_type":"code","execution_count":22,"id":"08278702","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:32.91505Z","iopub.status.busy":"2022-07-07T21:19:32.914655Z","iopub.status.idle":"2022-07-07T21:19:33.061214Z","shell.execute_reply":"2022-07-07T21:19:33.059937Z"},"papermill":{"duration":0.171539,"end_time":"2022-07-07T21:19:33.064208","exception":false,"start_time":"2022-07-07T21:19:32.892669","status":"completed"},"tags":[]},"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>case_id</th>\n","      <th>Hospital_code</th>\n","      <th>Hospital_type_code</th>\n","      <th>City_Code_Hospital</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>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>2</td>\n","      <td>3</td>\n","      <td>-0.169177</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>-1.136165</td>\n","      <td>-1.788287</td>\n","      <td>-0.727923</td>\n","      <td>5</td>\n","      <td>4911.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>-1.025217</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>0.315306</td>\n","      <td>-1.788287</td>\n","      <td>-0.727923</td>\n","      <td>5</td>\n","      <td>5954.0</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>10</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>-1.025217</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>0.315306</td>\n","      <td>-1.788287</td>\n","      <td>-0.727923</td>\n","      <td>5</td>\n","      <td>4745.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>26</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-1.025217</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0.315306</td>\n","      <td>-1.788287</td>\n","      <td>-0.727923</td>\n","      <td>5</td>\n","      <td>7272.0</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>26</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-1.025217</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>0.315306</td>\n","      <td>-1.788287</td>\n","      <td>-0.727923</td>\n","      <td>5</td>\n","      <td>5558.0</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>318433</th>\n","      <td>318434</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>-0.169177</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>-1.136165</td>\n","      <td>0.814607</td>\n","      <td>-0.161049</td>\n","      <td>4</td>\n","      <td>4144.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>318434</th>\n","      <td>318435</td>\n","      <td>24</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>-1.025217</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>1.766778</td>\n","      <td>0.814607</td>\n","      <td>0.405826</td>\n","      <td>8</td>\n","      <td>6699.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>318435</th>\n","      <td>318436</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>-0.169177</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>-1.136165</td>\n","      <td>-0.486840</td>\n","      <td>-0.161049</td>\n","      <td>7</td>\n","      <td>4235.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>318436</th>\n","      <td>318437</td>\n","      <td>11</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-0.169177</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0.315306</td>\n","      <td>-0.486840</td>\n","      <td>0.972701</td>\n","      <td>1</td>\n","      <td>3761.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>318437</th>\n","      <td>318438</td>\n","      <td>19</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>1.542903</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-1.136165</td>\n","      <td>-0.486840</td>\n","      <td>-0.727923</td>\n","      <td>1</td>\n","      <td>4752.0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>318438 rows × 14 columns</p>\n","</div>"],"text/plain":["        case_id  Hospital_code  Hospital_type_code  City_Code_Hospital  \\\n","0             1              8                   2                   3   \n","1             2              2                   2                   5   \n","2             3             10                   4                   1   \n","3             4             26                   1                   2   \n","4             5             26                   1                   2   \n","...         ...            ...                 ...                 ...   \n","318433   318434              6                   0                   6   \n","318434   318435             24                   0                   1   \n","318435   318436              7                   0                   4   \n","318436   318437             11                   1                   2   \n","318437   318438             19                   0                   7   \n","\n","        Available Extra Rooms in Hospital  Department  Ward_Type  \\\n","0                               -0.169177           3          2   \n","1                               -1.025217           3          3   \n","2                               -1.025217           1          3   \n","3                               -1.025217           3          2   \n","4                               -1.025217           3          3   \n","...                                   ...         ...        ...   \n","318433                          -0.169177           3          1   \n","318434                          -1.025217           1          1   \n","318435                          -0.169177           2          2   \n","318436                          -0.169177           1          1   \n","318437                           1.542903           2          1   \n","\n","        Ward_Facility_Code  Type of Admission  Severity of Illness  \\\n","0                        5          -1.136165            -1.788287   \n","1                        5           0.315306            -1.788287   \n","2                        4           0.315306            -1.788287   \n","3                        3           0.315306            -1.788287   \n","4                        3           0.315306            -1.788287   \n","...                    ...                ...                  ...   \n","318433                   5          -1.136165             0.814607   \n","318434                   4           1.766778             0.814607   \n","318435                   5          -1.136165            -0.486840   \n","318436                   3           0.315306            -0.486840   \n","318437                   2          -1.136165            -0.486840   \n","\n","        Visitors with Patient  Age  Admission_Deposit  Stay  \n","0                   -0.727923    5             4911.0     0  \n","1                   -0.727923    5             5954.0     4  \n","2                   -0.727923    5             4745.0     3  \n","3                   -0.727923    5             7272.0     4  \n","4                   -0.727923    5             5558.0     4  \n","...                       ...  ...                ...   ...  \n","318433              -0.161049    4             4144.0     1  \n","318434               0.405826    8             6699.0     3  \n","318435              -0.161049    7             4235.0     1  \n","318436               0.972701    1             3761.0     1  \n","318437              -0.727923    1             4752.0     0  \n","\n","[318438 rows x 14 columns]"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["sc= StandardScaler()\n","\n","for df in df_merge:\n","    df[columns_list]= sc.fit_transform(df[columns_list].values)\n","df_merge[0]"]},{"cell_type":"code","execution_count":23,"id":"c427e0e9","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:33.101151Z","iopub.status.busy":"2022-07-07T21:19:33.100645Z","iopub.status.idle":"2022-07-07T21:19:34.401287Z","shell.execute_reply":"2022-07-07T21:19:34.40001Z"},"papermill":{"duration":1.322922,"end_time":"2022-07-07T21:19:34.404674","exception":false,"start_time":"2022-07-07T21:19:33.081752","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 864x864 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["\n","correlations = df_merge[0].corr()\n","# Plot figsize\n","fig, ax = plt.subplots(figsize=(12, 12))\n","# Generate Color Map\n","colormap = sns.diverging_palette(20, 10, as_cmap=True)\n","# Generate Heat Map, allow annotations and place floats in map\n","sns.heatmap(correlations, cmap=colormap, annot=True, fmt=\".1f\")\n","ax.set_xticklabels(\n","    df_merge[0],\n","    rotation=45,\n","    horizontalalignment='right'\n",");\n","ax.set_yticklabels(df_merge[0]);\n","plt.show()"]},{"cell_type":"code","execution_count":24,"id":"589d5fd2","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:34.447254Z","iopub.status.busy":"2022-07-07T21:19:34.446833Z","iopub.status.idle":"2022-07-07T21:19:34.468054Z","shell.execute_reply":"2022-07-07T21:19:34.466952Z"},"papermill":{"duration":0.046153,"end_time":"2022-07-07T21:19:34.470555","exception":false,"start_time":"2022-07-07T21:19:34.424402","status":"completed"},"tags":[]},"outputs":[],"source":["df_train_new = df_merge[0]\n","df_test_new = df_merge[1]\n","\n","y = df_train_new['Stay']\n","X = df_train_new.drop(columns=[\"Stay\"])\n","\n","\n"]},{"cell_type":"code","execution_count":25,"id":"15a2ad58","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:34.511093Z","iopub.status.busy":"2022-07-07T21:19:34.50989Z","iopub.status.idle":"2022-07-07T21:19:34.582534Z","shell.execute_reply":"2022-07-07T21:19:34.581662Z"},"papermill":{"duration":0.095389,"end_time":"2022-07-07T21:19:34.585013","exception":false,"start_time":"2022-07-07T21:19:34.489624","status":"completed"},"tags":[]},"outputs":[],"source":["\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"]},{"cell_type":"markdown","id":"225a7cfb","metadata":{"papermill":{"duration":0.019157,"end_time":"2022-07-07T21:19:34.623347","exception":false,"start_time":"2022-07-07T21:19:34.60419","status":"completed"},"tags":[]},"source":["# AdaBoost Classifier\n","An AdaBoost classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of error classified instances are adjusted."]},{"cell_type":"code","execution_count":26,"id":"5bbff7cf","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:34.663896Z","iopub.status.busy":"2022-07-07T21:19:34.663307Z","iopub.status.idle":"2022-07-07T21:19:40.238879Z","shell.execute_reply":"2022-07-07T21:19:40.237653Z"},"papermill":{"duration":5.598538,"end_time":"2022-07-07T21:19:40.241402","exception":false,"start_time":"2022-07-07T21:19:34.642864","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy score 33.8\n","Confusion Matrix\n"," [[   38  1483  3151     0     0    17     0     0     0     0     0]\n"," [   95  4661  9763     0     0  1042     0     0     0     0     0]\n"," [   83  5250 11859     0     0   411     0     0     0     0     0]\n"," [   24  2530  4543     0     0  3884     0     0     0     0     0]\n"," [   17   680  1357     0     0   303     0     0     0     0     0]\n"," [    4  1085  1095     0     0  4944     0     0     0     0     0]\n"," [    0   121   269     0     0   164     0     0     0     0     0]\n"," [    1   328   207     0     0  1495     0     0     0     0     0]\n"," [    0    33    69     0     0   839     0     0     0     0     0]\n"," [    0    90    50     0     0   412     0     0     0     0     0]\n"," [    0    78   114     0     0  1099     0     0     0     0     0]]\n"]}],"source":["ada_classifier = AdaBoostClassifier(n_estimators=5)\n","ada_classifier.fit(X_train, y_train)\n","pred_ada = ada_classifier.predict(X_test)\n","\n","# Cross-validation\n","scores = cross_val_score(ada_classifier,X_test,y_test, cv=12)\n","print('Accuracy score',round(scores.mean() * 100,2))\n","print('Confusion Matrix\\n',confusion_matrix(y_test, pred_ada))"]},{"cell_type":"markdown","id":"0d5460a5","metadata":{"papermill":{"duration":0.01854,"end_time":"2022-07-07T21:19:40.278877","exception":false,"start_time":"2022-07-07T21:19:40.260337","status":"completed"},"tags":[]},"source":["#### AdaBoost's accuracy score is low."]},{"cell_type":"markdown","id":"b2b1b8d0","metadata":{"papermill":{"duration":0.018647,"end_time":"2022-07-07T21:19:40.316536","exception":false,"start_time":"2022-07-07T21:19:40.297889","status":"completed"},"tags":[]},"source":["# CatBoost - Gradient Algorithm \n","\n","Gradient boosting algorithm works by building simpler (weak) prediction models sequentially where each model tries to predict the error left over by the previous model. \n","It find uses in search, recommendation systems, personal assistant, self-driving cars, weather prediction.\n"]},{"cell_type":"markdown","id":"c8411dd8","metadata":{"papermill":{"duration":0.018612,"end_time":"2022-07-07T21:19:40.354304","exception":false,"start_time":"2022-07-07T21:19:40.335692","status":"completed"},"tags":[]},"source":["### Important Parameters of CatBoost Model \n","* **iterations** - It accepts integer specifying the number of trees to train. The default is 1000.\n","* **learning_rate** - It specifies the learning rate during the training process. The default is 0.03.\n","* **l2_leaf_reg** - It accepts float specifying coefficient of L2 regularization of a loss function. The default value is 3.\n","* **loss_function** - It accepts string specifying metric used during training. The gradient boosting algorithm will try to minimize/maximize loss function output depending on the situation.\n","* **eval_metric** - It accepts string specifying metric to evaluate on evaluation set given during training. It has the same options as that of loss_function."]},{"cell_type":"code","execution_count":27,"id":"9b2e9a39","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:40.394114Z","iopub.status.busy":"2022-07-07T21:19:40.393489Z","iopub.status.idle":"2022-07-07T21:19:40.431567Z","shell.execute_reply":"2022-07-07T21:19:40.430566Z"},"papermill":{"duration":0.060819,"end_time":"2022-07-07T21:19:40.434003","exception":false,"start_time":"2022-07-07T21:19:40.373184","status":"completed"},"tags":[]},"outputs":[],"source":["\n","eval_dataset = Pool(X_test,\n","                    y_test)\n","\n","# Define Gradient Boosting Classifier with hyperparameters\n","\n","model = CatBoostClassifier(iterations=1000,\n","                           learning_rate=0.3,\n","                           depth=10,\n","                           l2_leaf_reg = 3,\n","                           random_strength =0,\n","                           loss_function='MultiClass',\n","                           eval_metric='MultiClass')"]},{"cell_type":"code","execution_count":28,"id":"80f4e601","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:19:40.475515Z","iopub.status.busy":"2022-07-07T21:19:40.474854Z","iopub.status.idle":"2022-07-07T21:30:49.428645Z","shell.execute_reply":"2022-07-07T21:30:49.427018Z"},"papermill":{"duration":668.977755,"end_time":"2022-07-07T21:30:49.431597","exception":false,"start_time":"2022-07-07T21:19:40.453842","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0:\tlearn: 1.9411437\ttest: 1.9430596\tbest: 1.9430596 (0)\ttotal: 895ms\tremaining: 14m 54s\n","1:\tlearn: 1.7916964\ttest: 1.7960389\tbest: 1.7960389 (1)\ttotal: 1.59s\tremaining: 13m 15s\n","2:\tlearn: 1.7047421\ttest: 1.7104753\tbest: 1.7104753 (2)\ttotal: 2.24s\tremaining: 12m 24s\n","3:\tlearn: 1.6539964\ttest: 1.6613540\tbest: 1.6613540 (3)\ttotal: 2.9s\tremaining: 12m\n","4:\tlearn: 1.6182168\ttest: 1.6276910\tbest: 1.6276910 (4)\ttotal: 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26s\tremaining: 41.4s\n","938:\tlearn: 0.6707560\ttest: 1.7684078\tbest: 1.5326902 (68)\ttotal: 10m 27s\tremaining: 40.8s\n","939:\tlearn: 0.6703845\ttest: 1.7686511\tbest: 1.5326902 (68)\ttotal: 10m 28s\tremaining: 40.1s\n","940:\tlearn: 0.6699145\ttest: 1.7689562\tbest: 1.5326902 (68)\ttotal: 10m 28s\tremaining: 39.4s\n","941:\tlearn: 0.6694546\ttest: 1.7692205\tbest: 1.5326902 (68)\ttotal: 10m 29s\tremaining: 38.8s\n","942:\tlearn: 0.6690933\ttest: 1.7694057\tbest: 1.5326902 (68)\ttotal: 10m 30s\tremaining: 38.1s\n","943:\tlearn: 0.6687872\ttest: 1.7696094\tbest: 1.5326902 (68)\ttotal: 10m 30s\tremaining: 37.4s\n","944:\tlearn: 0.6683770\ttest: 1.7698768\tbest: 1.5326902 (68)\ttotal: 10m 31s\tremaining: 36.8s\n","945:\tlearn: 0.6678817\ttest: 1.7701581\tbest: 1.5326902 (68)\ttotal: 10m 32s\tremaining: 36.1s\n","946:\tlearn: 0.6675111\ttest: 1.7703306\tbest: 1.5326902 (68)\ttotal: 10m 32s\tremaining: 35.4s\n","947:\tlearn: 0.6670846\ttest: 1.7705957\tbest: 1.5326902 (68)\ttotal: 10m 33s\tremaining: 34.8s\n","948:\tlearn: 0.6668070\ttest: 1.7707561\tbest: 1.5326902 (68)\ttotal: 10m 34s\tremaining: 34.1s\n","949:\tlearn: 0.6664584\ttest: 1.7709615\tbest: 1.5326902 (68)\ttotal: 10m 34s\tremaining: 33.4s\n","950:\tlearn: 0.6661186\ttest: 1.7711577\tbest: 1.5326902 (68)\ttotal: 10m 35s\tremaining: 32.7s\n","951:\tlearn: 0.6655986\ttest: 1.7714487\tbest: 1.5326902 (68)\ttotal: 10m 36s\tremaining: 32.1s\n","952:\tlearn: 0.6650779\ttest: 1.7717617\tbest: 1.5326902 (68)\ttotal: 10m 36s\tremaining: 31.4s\n","953:\tlearn: 0.6648688\ttest: 1.7718111\tbest: 1.5326902 (68)\ttotal: 10m 37s\tremaining: 30.7s\n","954:\tlearn: 0.6645463\ttest: 1.7719521\tbest: 1.5326902 (68)\ttotal: 10m 38s\tremaining: 30.1s\n","955:\tlearn: 0.6641577\ttest: 1.7722535\tbest: 1.5326902 (68)\ttotal: 10m 38s\tremaining: 29.4s\n","956:\tlearn: 0.6637682\ttest: 1.7724296\tbest: 1.5326902 (68)\ttotal: 10m 39s\tremaining: 28.7s\n","957:\tlearn: 0.6633289\ttest: 1.7726554\tbest: 1.5326902 (68)\ttotal: 10m 40s\tremaining: 28.1s\n","958:\tlearn: 0.6627984\ttest: 1.7729460\tbest: 1.5326902 (68)\ttotal: 10m 40s\tremaining: 27.4s\n","959:\tlearn: 0.6624443\ttest: 1.7731780\tbest: 1.5326902 (68)\ttotal: 10m 41s\tremaining: 26.7s\n","960:\tlearn: 0.6619877\ttest: 1.7735046\tbest: 1.5326902 (68)\ttotal: 10m 42s\tremaining: 26.1s\n","961:\tlearn: 0.6617050\ttest: 1.7737568\tbest: 1.5326902 (68)\ttotal: 10m 42s\tremaining: 25.4s\n","962:\tlearn: 0.6613832\ttest: 1.7739235\tbest: 1.5326902 (68)\ttotal: 10m 43s\tremaining: 24.7s\n","963:\tlearn: 0.6610229\ttest: 1.7741692\tbest: 1.5326902 (68)\ttotal: 10m 44s\tremaining: 24.1s\n","964:\tlearn: 0.6604893\ttest: 1.7745103\tbest: 1.5326902 (68)\ttotal: 10m 44s\tremaining: 23.4s\n","965:\tlearn: 0.6601438\ttest: 1.7747976\tbest: 1.5326902 (68)\ttotal: 10m 45s\tremaining: 22.7s\n","966:\tlearn: 0.6597226\ttest: 1.7750667\tbest: 1.5326902 (68)\ttotal: 10m 46s\tremaining: 22s\n","967:\tlearn: 0.6593549\ttest: 1.7752173\tbest: 1.5326902 (68)\ttotal: 10m 46s\tremaining: 21.4s\n","968:\tlearn: 0.6588069\ttest: 1.7755014\tbest: 1.5326902 (68)\ttotal: 10m 47s\tremaining: 20.7s\n","969:\tlearn: 0.6583443\ttest: 1.7757432\tbest: 1.5326902 (68)\ttotal: 10m 47s\tremaining: 20s\n","970:\tlearn: 0.6580762\ttest: 1.7758071\tbest: 1.5326902 (68)\ttotal: 10m 48s\tremaining: 19.4s\n","971:\tlearn: 0.6575865\ttest: 1.7761656\tbest: 1.5326902 (68)\ttotal: 10m 49s\tremaining: 18.7s\n","972:\tlearn: 0.6570577\ttest: 1.7763923\tbest: 1.5326902 (68)\ttotal: 10m 49s\tremaining: 18s\n","973:\tlearn: 0.6564769\ttest: 1.7767726\tbest: 1.5326902 (68)\ttotal: 10m 50s\tremaining: 17.4s\n","974:\tlearn: 0.6560335\ttest: 1.7770691\tbest: 1.5326902 (68)\ttotal: 10m 51s\tremaining: 16.7s\n","975:\tlearn: 0.6555751\ttest: 1.7772512\tbest: 1.5326902 (68)\ttotal: 10m 51s\tremaining: 16s\n","976:\tlearn: 0.6551011\ttest: 1.7774881\tbest: 1.5326902 (68)\ttotal: 10m 52s\tremaining: 15.4s\n","977:\tlearn: 0.6547079\ttest: 1.7777727\tbest: 1.5326902 (68)\ttotal: 10m 53s\tremaining: 14.7s\n","978:\tlearn: 0.6544698\ttest: 1.7779283\tbest: 1.5326902 (68)\ttotal: 10m 53s\tremaining: 14s\n","979:\tlearn: 0.6539686\ttest: 1.7780968\tbest: 1.5326902 (68)\ttotal: 10m 54s\tremaining: 13.4s\n","980:\tlearn: 0.6536026\ttest: 1.7782834\tbest: 1.5326902 (68)\ttotal: 10m 55s\tremaining: 12.7s\n","981:\tlearn: 0.6531695\ttest: 1.7785062\tbest: 1.5326902 (68)\ttotal: 10m 55s\tremaining: 12s\n","982:\tlearn: 0.6529041\ttest: 1.7786736\tbest: 1.5326902 (68)\ttotal: 10m 56s\tremaining: 11.4s\n","983:\tlearn: 0.6524318\ttest: 1.7789828\tbest: 1.5326902 (68)\ttotal: 10m 57s\tremaining: 10.7s\n","984:\tlearn: 0.6519054\ttest: 1.7794248\tbest: 1.5326902 (68)\ttotal: 10m 57s\tremaining: 10s\n","985:\tlearn: 0.6514590\ttest: 1.7798083\tbest: 1.5326902 (68)\ttotal: 10m 58s\tremaining: 9.35s\n","986:\tlearn: 0.6511402\ttest: 1.7799920\tbest: 1.5326902 (68)\ttotal: 10m 59s\tremaining: 8.68s\n","987:\tlearn: 0.6508716\ttest: 1.7801375\tbest: 1.5326902 (68)\ttotal: 11m\tremaining: 8.02s\n","988:\tlearn: 0.6505542\ttest: 1.7803781\tbest: 1.5326902 (68)\ttotal: 11m\tremaining: 7.35s\n","989:\tlearn: 0.6501945\ttest: 1.7807190\tbest: 1.5326902 (68)\ttotal: 11m 1s\tremaining: 6.68s\n","990:\tlearn: 0.6497680\ttest: 1.7809512\tbest: 1.5326902 (68)\ttotal: 11m 2s\tremaining: 6.01s\n","991:\tlearn: 0.6493154\ttest: 1.7812195\tbest: 1.5326902 (68)\ttotal: 11m 2s\tremaining: 5.34s\n","992:\tlearn: 0.6488883\ttest: 1.7814748\tbest: 1.5326902 (68)\ttotal: 11m 3s\tremaining: 4.68s\n","993:\tlearn: 0.6485745\ttest: 1.7816564\tbest: 1.5326902 (68)\ttotal: 11m 4s\tremaining: 4.01s\n","994:\tlearn: 0.6480994\ttest: 1.7820067\tbest: 1.5326902 (68)\ttotal: 11m 4s\tremaining: 3.34s\n","995:\tlearn: 0.6477077\ttest: 1.7822899\tbest: 1.5326902 (68)\ttotal: 11m 5s\tremaining: 2.67s\n","996:\tlearn: 0.6473380\ttest: 1.7825627\tbest: 1.5326902 (68)\ttotal: 11m 5s\tremaining: 2s\n","997:\tlearn: 0.6470658\ttest: 1.7826680\tbest: 1.5326902 (68)\ttotal: 11m 6s\tremaining: 1.33s\n","998:\tlearn: 0.6467846\ttest: 1.7828703\tbest: 1.5326902 (68)\ttotal: 11m 7s\tremaining: 668ms\n","999:\tlearn: 0.6464159\ttest: 1.7830671\tbest: 1.5326902 (68)\ttotal: 11m 7s\tremaining: 0us\n","\n","bestTest = 1.532690202\n","bestIteration = 68\n","\n","Shrink model to first 69 iterations.\n","{'learn': {'MultiClass': 0.6464158856063034}, 'validation': {'MultiClass': 1.5326902022944984}}\n","[[7.4600e+02 2.1500e+03 1.7330e+03 3.8000e+01 4.0000e+00 1.6000e+01\n","  0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00]\n"," [4.3400e+02 6.5790e+03 7.3100e+03 7.0600e+02 5.0000e+00 5.1800e+02\n","  0.0000e+00 7.0000e+00 1.0000e+00 0.0000e+00 1.0000e+00]\n"," [3.9500e+02 4.7020e+03 1.1911e+04 2.9700e+02 5.0000e+00 2.5300e+02\n","  0.0000e+00 5.0000e+00 1.8000e+01 0.0000e+00 1.7000e+01]\n"," [1.6600e+02 1.7300e+03 4.7010e+03 2.1770e+03 2.0000e+00 2.1280e+03\n","  0.0000e+00 1.8000e+01 1.8000e+01 2.0000e+00 3.9000e+01]\n"," [7.5000e+01 4.2900e+02 1.4770e+03 1.8000e+02 5.0000e+00 1.5200e+02\n","  0.0000e+00 6.0000e+00 9.0000e+00 0.0000e+00 2.4000e+01]\n"," [8.5000e+01 4.4200e+02 1.1030e+03 1.6490e+03 6.0000e+00 3.5460e+03\n","  0.0000e+00 3.7000e+01 1.6200e+02 1.0000e+00 9.7000e+01]\n"," [1.7000e+01 6.9000e+01 2.8000e+02 6.5000e+01 3.0000e+00 8.7000e+01\n","  0.0000e+00 7.0000e+00 1.0000e+01 0.0000e+00 1.6000e+01]\n"," [2.8000e+01 1.0000e+02 2.4400e+02 4.2500e+02 2.0000e+00 9.9900e+02\n","  0.0000e+00 5.6000e+01 4.1000e+01 0.0000e+00 1.3600e+02]\n"," [6.0000e+00 3.0000e+01 5.2000e+01 6.3000e+01 1.0000e+00 5.1300e+02\n","  0.0000e+00 1.3000e+01 1.6500e+02 0.0000e+00 9.8000e+01]\n"," [7.0000e+00 2.2000e+01 6.4000e+01 8.3000e+01 2.0000e+00 2.7700e+02\n","  0.0000e+00 7.0000e+00 7.0000e+00 3.0000e+00 8.0000e+01]\n"," [1.9000e+01 4.6000e+01 8.5000e+01 9.5000e+01 1.0000e+00 3.8700e+02\n","  0.0000e+00 3.9000e+01 9.3000e+01 5.0000e+00 5.2100e+02]]\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":["catboost Acc :  0.7392269653850035\n"]}],"source":["model.fit(X_train,\n","          y_train,\n","          eval_set=eval_dataset,\n","          verbose=True)\n","\n","print(model.get_best_score())\n","cm = get_confusion_matrix(model, eval_dataset)\n","print(cm)\n","predict_accuracy_on_test_set = (cm[0,0] + cm[1,1])/(cm[0,0] + cm[1,1]+cm[1,0] + cm[0,1])\n","ax = sns.heatmap(cm, linewidth=1)\n","plt.show()\n","print(\"catboost Acc : \", predict_accuracy_on_test_set)"]},{"cell_type":"markdown","id":"d4454c6b","metadata":{"papermill":{"duration":0.087023,"end_time":"2022-07-07T21:30:49.605973","exception":false,"start_time":"2022-07-07T21:30:49.51895","status":"completed"},"tags":[]},"source":["#### Accuracy score of CATboost is better than Adaboost"]},{"cell_type":"code","execution_count":29,"id":"b446d8de","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:30:49.783714Z","iopub.status.busy":"2022-07-07T21:30:49.782729Z","iopub.status.idle":"2022-07-07T21:30:49.855649Z","shell.execute_reply":"2022-07-07T21:30:49.854507Z"},"papermill":{"duration":0.164609,"end_time":"2022-07-07T21:30:49.858074","exception":false,"start_time":"2022-07-07T21:30:49.693465","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["[[7.4600e+02 2.1500e+03 1.7330e+03 3.8000e+01 4.0000e+00 1.6000e+01\n","  0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00]\n"," [4.3400e+02 6.5790e+03 7.3100e+03 7.0600e+02 5.0000e+00 5.1800e+02\n","  0.0000e+00 7.0000e+00 1.0000e+00 0.0000e+00 1.0000e+00]\n"," [3.9500e+02 4.7020e+03 1.1911e+04 2.9700e+02 5.0000e+00 2.5300e+02\n","  0.0000e+00 5.0000e+00 1.8000e+01 0.0000e+00 1.7000e+01]\n"," [1.6600e+02 1.7300e+03 4.7010e+03 2.1770e+03 2.0000e+00 2.1280e+03\n","  0.0000e+00 1.8000e+01 1.8000e+01 2.0000e+00 3.9000e+01]\n"," [7.5000e+01 4.2900e+02 1.4770e+03 1.8000e+02 5.0000e+00 1.5200e+02\n","  0.0000e+00 6.0000e+00 9.0000e+00 0.0000e+00 2.4000e+01]\n"," [8.5000e+01 4.4200e+02 1.1030e+03 1.6490e+03 6.0000e+00 3.5460e+03\n","  0.0000e+00 3.7000e+01 1.6200e+02 1.0000e+00 9.7000e+01]\n"," [1.7000e+01 6.9000e+01 2.8000e+02 6.5000e+01 3.0000e+00 8.7000e+01\n","  0.0000e+00 7.0000e+00 1.0000e+01 0.0000e+00 1.6000e+01]\n"," [2.8000e+01 1.0000e+02 2.4400e+02 4.2500e+02 2.0000e+00 9.9900e+02\n","  0.0000e+00 5.6000e+01 4.1000e+01 0.0000e+00 1.3600e+02]\n"," [6.0000e+00 3.0000e+01 5.2000e+01 6.3000e+01 1.0000e+00 5.1300e+02\n","  0.0000e+00 1.3000e+01 1.6500e+02 0.0000e+00 9.8000e+01]\n"," [7.0000e+00 2.2000e+01 6.4000e+01 8.3000e+01 2.0000e+00 2.7700e+02\n","  0.0000e+00 7.0000e+00 7.0000e+00 3.0000e+00 8.0000e+01]\n"," [1.9000e+01 4.6000e+01 8.5000e+01 9.5000e+01 1.0000e+00 3.8700e+02\n","  0.0000e+00 3.9000e+01 9.3000e+01 5.0000e+00 5.2100e+02]]\n"]},{"data":{"text/plain":["0.40367102122848886"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["print(cm)\n","\n","pred_cat = model.predict(\n","                 eval_dataset, \n","                 verbose=True)\n","\n","\n","accuracy_score(y_test, pred_cat)"]},{"cell_type":"code","execution_count":30,"id":"2b38b2a0","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:30:50.109588Z","iopub.status.busy":"2022-07-07T21:30:50.108692Z","iopub.status.idle":"2022-07-07T21:30:50.124794Z","shell.execute_reply":"2022-07-07T21:30:50.123849Z"},"papermill":{"duration":0.181087,"end_time":"2022-07-07T21:30:50.127184","exception":false,"start_time":"2022-07-07T21:30:49.946097","status":"completed"},"tags":[]},"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>case_id</th>\n","      <th>Hospital_code</th>\n","      <th>Hospital_type_code</th>\n","      <th>City_Code_Hospital</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>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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>318439</td>\n","      <td>21</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>-0.165503</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>-1.135402</td>\n","      <td>0.814234</td>\n","      <td>-0.722758</td>\n","      <td>7</td>\n","      <td>3095.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>318440</td>\n","      <td>29</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>-1.024428</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>0.314484</td>\n","      <td>0.814234</td>\n","      <td>0.402568</td>\n","      <td>7</td>\n","      <td>4018.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>318441</td>\n","      <td>26</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-0.165503</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>-1.135402</td>\n","      <td>0.814234</td>\n","      <td>-0.160095</td>\n","      <td>7</td>\n","      <td>4492.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>318442</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>-0.165503</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>0.314484</td>\n","      <td>0.814234</td>\n","      <td>-0.160095</td>\n","      <td>7</td>\n","      <td>4173.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>318443</td>\n","      <td>28</td>\n","      <td>1</td>\n","      <td>11</td>\n","      <td>-1.024428</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>0.314484</td>\n","      <td>0.814234</td>\n","      <td>0.402568</td>\n","      <td>7</td>\n","      <td>4161.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   case_id  Hospital_code  Hospital_type_code  City_Code_Hospital  \\\n","0   318439             21                   2                   3   \n","1   318440             29                   0                   4   \n","2   318441             26                   1                   2   \n","3   318442              6                   0                   6   \n","4   318443             28                   1                  11   \n","\n","   Available Extra Rooms in Hospital  Department  Ward_Type  \\\n","0                          -0.165503           2          3   \n","1                          -1.024428           2          3   \n","2                          -0.165503           2          1   \n","3                          -0.165503           2          1   \n","4                          -1.024428           2          2   \n","\n","   Ward_Facility_Code  Type of Admission  Severity of Illness  \\\n","0                   0          -1.135402             0.814234   \n","1                   5           0.314484             0.814234   \n","2                   3          -1.135402             0.814234   \n","3                   5           0.314484             0.814234   \n","4                   5           0.314484             0.814234   \n","\n","   Visitors with Patient  Age  Admission_Deposit  \n","0              -0.722758    7             3095.0  \n","1               0.402568    7             4018.0  \n","2              -0.160095    7             4492.0  \n","3              -0.160095    7             4173.0  \n","4               0.402568    7             4161.0  "]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["df_test.head()"]},{"cell_type":"code","execution_count":31,"id":"7893e102","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:30:50.306311Z","iopub.status.busy":"2022-07-07T21:30:50.305875Z","iopub.status.idle":"2022-07-07T21:30:50.315476Z","shell.execute_reply":"2022-07-07T21:30:50.314296Z"},"papermill":{"duration":0.101815,"end_time":"2022-07-07T21:30:50.317834","exception":false,"start_time":"2022-07-07T21:30:50.216019","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<catboost.core.Pool at 0x7fe81f97aad0>"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["eval_dt = Pool(df_test)\n","eval_dt"]},{"cell_type":"code","execution_count":32,"id":"3b577de4","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:30:50.496565Z","iopub.status.busy":"2022-07-07T21:30:50.496152Z","iopub.status.idle":"2022-07-07T21:30:50.623594Z","shell.execute_reply":"2022-07-07T21:30:50.622382Z"},"papermill":{"duration":0.21947,"end_time":"2022-07-07T21:30:50.626279","exception":false,"start_time":"2022-07-07T21:30:50.406809","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array([[0],\n","       [5],\n","       [2],\n","       ...,\n","       [2],\n","       [1],\n","       [3]])"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["preds_class = model.predict(eval_dt)\n","preds_class.shape\n","preds_class"]},{"cell_type":"markdown","id":"a014d7f3","metadata":{"papermill":{"duration":0.08809,"end_time":"2022-07-07T21:30:50.803561","exception":false,"start_time":"2022-07-07T21:30:50.715471","status":"completed"},"tags":[]},"source":["# Submit "]},{"cell_type":"code","execution_count":33,"id":"a71e978b","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:30:50.984677Z","iopub.status.busy":"2022-07-07T21:30:50.984265Z","iopub.status.idle":"2022-07-07T21:30:51.08133Z","shell.execute_reply":"2022-07-07T21:30:51.08018Z"},"papermill":{"duration":0.190969,"end_time":"2022-07-07T21:30:51.083856","exception":false,"start_time":"2022-07-07T21:30:50.892887","status":"completed"},"tags":[]},"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>case_id</th>\n","      <th>Stay</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>318439</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>318440</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>318441</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>318442</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>318443</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>137052</th>\n","      <td>455491</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>137053</th>\n","      <td>455492</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>137054</th>\n","      <td>455493</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>137055</th>\n","      <td>455494</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>137056</th>\n","      <td>455495</td>\n","      <td>0-10</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>137057 rows × 2 columns</p>\n","</div>"],"text/plain":["        case_id  Stay\n","0        318439  0-10\n","1        318440  0-10\n","2        318441  0-10\n","3        318442  0-10\n","4        318443  0-10\n","...         ...   ...\n","137052   455491  0-10\n","137053   455492  0-10\n","137054   455493  0-10\n","137055   455494  0-10\n","137056   455495  0-10\n","\n","[137057 rows x 2 columns]"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["\n","df_sample = pd.read_csv('../input/av-healthcare-analytics-ii/healthcare/sample_sub.csv', sep = ',')\n","df_sample"]},{"cell_type":"code","execution_count":34,"id":"c9870cde","metadata":{"execution":{"iopub.execute_input":"2022-07-07T21:30:51.263258Z","iopub.status.busy":"2022-07-07T21:30:51.262861Z","iopub.status.idle":"2022-07-07T21:30:51.286981Z","shell.execute_reply":"2022-07-07T21:30:51.285811Z"},"papermill":{"duration":0.116353,"end_time":"2022-07-07T21:30:51.289463","exception":false,"start_time":"2022-07-07T21:30:51.17311","status":"completed"},"tags":[]},"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>case_id</th>\n","      <th>Stay</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>318439</td>\n","      <td>0-10</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>318440</td>\n","      <td>51-60</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>318441</td>\n","      <td>21-30</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>318442</td>\n","      <td>21-30</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>318443</td>\n","      <td>51-60</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   case_id   Stay\n","0   318439   0-10\n","1   318440  51-60\n","2   318441  21-30\n","3   318442  21-30\n","4   318443  51-60"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["df_test\n","\n","\n","df_sample[\"Stay\"] = preds_class\n","swap_value_Stay = dict([(value, key) for key, value in stay_value.items()])\n","df_sample[\"Stay\"].replace(swap_value_Stay, inplace=True)\n","df_sample.head()\n","\n","\n","\n","\n","\n","\n","\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":699.088329,"end_time":"2022-07-07T21:30:52.304048","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-07-07T21:19:13.215719","version":"2.3.4"}},"nbformat":4,"nbformat_minor":5}