multi-class-prediction-of-obesity-risk.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/szymnq/multi-class-prediction-of-obesity-risk-ensemble?scriptVersionId=164490682\" 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"},{"cell_type":"code","execution_count":1,"id":"22e0563e","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:42.097804Z","iopub.status.busy":"2024-02-27T06:17:42.097383Z","iopub.status.idle":"2024-02-27T06:17:44.983685Z","shell.execute_reply":"2024-02-27T06:17:44.982584Z"},"papermill":{"duration":2.900583,"end_time":"2024-02-27T06:17:44.986415","exception":false,"start_time":"2024-02-27T06:17:42.085832","status":"completed"},"tags":[]},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import os\n","import seaborn as sns\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.ensemble import AdaBoostClassifier, ExtraTreesClassifier, GradientBoostingClassifier, \\\n","    RandomForestClassifier, VotingClassifier, StackingClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score\n","from xgboost import XGBClassifier"]},{"cell_type":"code","execution_count":2,"id":"e6f25254","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:45.007Z","iopub.status.busy":"2024-02-27T06:17:45.006142Z","iopub.status.idle":"2024-02-27T06:17:45.014019Z","shell.execute_reply":"2024-02-27T06:17:45.012628Z"},"papermill":{"duration":0.020791,"end_time":"2024-02-27T06:17:45.016566","exception":false,"start_time":"2024-02-27T06:17:44.995775","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/playground-series-s4e2/sample_submission.csv\n","/kaggle/input/playground-series-s4e2/train.csv\n","/kaggle/input/playground-series-s4e2/test.csv\n"]}],"source":["for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))"]},{"cell_type":"code","execution_count":3,"id":"dd448d9b","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:45.03605Z","iopub.status.busy":"2024-02-27T06:17:45.03563Z","iopub.status.idle":"2024-02-27T06:17:45.205158Z","shell.execute_reply":"2024-02-27T06:17:45.20401Z"},"papermill":{"duration":0.182069,"end_time":"2024-02-27T06:17:45.207805","exception":false,"start_time":"2024-02-27T06:17:45.025736","status":"completed"},"tags":[]},"outputs":[],"source":["train_df = pd.read_csv('../input/playground-series-s4e2/train.csv')\n","test_df = pd.read_csv('../input/playground-series-s4e2/test.csv')\n","combine = [train_df, test_df]"]},{"cell_type":"code","execution_count":4,"id":"7f3279b5","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:45.228137Z","iopub.status.busy":"2024-02-27T06:17:45.227457Z","iopub.status.idle":"2024-02-27T06:17:45.256207Z","shell.execute_reply":"2024-02-27T06:17:45.255011Z"},"papermill":{"duration":0.041633,"end_time":"2024-02-27T06:17:45.258652","exception":false,"start_time":"2024-02-27T06:17:45.217019","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>id</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>family_history_with_overweight</th>\n","      <th>FAVC</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CAEC</th>\n","      <th>SMOKE</th>\n","      <th>CH2O</th>\n","      <th>SCC</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","      <th>CALC</th>\n","      <th>MTRANS</th>\n","      <th>NObeyesdad</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>Male</td>\n","      <td>24.443011</td>\n","      <td>1.699998</td>\n","      <td>81.669950</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.000000</td>\n","      <td>2.983297</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>2.763573</td>\n","      <td>no</td>\n","      <td>0.000000</td>\n","      <td>0.976473</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","      <td>Overweight_Level_II</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>Female</td>\n","      <td>18.000000</td>\n","      <td>1.560000</td>\n","      <td>57.000000</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.000000</td>\n","      <td>3.000000</td>\n","      <td>Frequently</td>\n","      <td>no</td>\n","      <td>2.000000</td>\n","      <td>no</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>no</td>\n","      <td>Automobile</td>\n","      <td>Normal_Weight</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>Female</td>\n","      <td>18.000000</td>\n","      <td>1.711460</td>\n","      <td>50.165754</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>1.880534</td>\n","      <td>1.411685</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>1.910378</td>\n","      <td>no</td>\n","      <td>0.866045</td>\n","      <td>1.673584</td>\n","      <td>no</td>\n","      <td>Public_Transportation</td>\n","      <td>Insufficient_Weight</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>Female</td>\n","      <td>20.952737</td>\n","      <td>1.710730</td>\n","      <td>131.274851</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>1.674061</td>\n","      <td>no</td>\n","      <td>1.467863</td>\n","      <td>0.780199</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","      <td>Obesity_Type_III</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>Male</td>\n","      <td>31.641081</td>\n","      <td>1.914186</td>\n","      <td>93.798055</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.679664</td>\n","      <td>1.971472</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>1.979848</td>\n","      <td>no</td>\n","      <td>1.967973</td>\n","      <td>0.931721</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","      <td>Overweight_Level_II</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   id  Gender        Age    Height      Weight family_history_with_overweight  \\\n","0   0    Male  24.443011  1.699998   81.669950                            yes   \n","1   1  Female  18.000000  1.560000   57.000000                            yes   \n","2   2  Female  18.000000  1.711460   50.165754                            yes   \n","3   3  Female  20.952737  1.710730  131.274851                            yes   \n","4   4    Male  31.641081  1.914186   93.798055                            yes   \n","\n","  FAVC      FCVC       NCP        CAEC SMOKE      CH2O SCC       FAF  \\\n","0  yes  2.000000  2.983297   Sometimes    no  2.763573  no  0.000000   \n","1  yes  2.000000  3.000000  Frequently    no  2.000000  no  1.000000   \n","2  yes  1.880534  1.411685   Sometimes    no  1.910378  no  0.866045   \n","3  yes  3.000000  3.000000   Sometimes    no  1.674061  no  1.467863   \n","4  yes  2.679664  1.971472   Sometimes    no  1.979848  no  1.967973   \n","\n","        TUE       CALC                 MTRANS           NObeyesdad  \n","0  0.976473  Sometimes  Public_Transportation  Overweight_Level_II  \n","1  1.000000         no             Automobile        Normal_Weight  \n","2  1.673584         no  Public_Transportation  Insufficient_Weight  \n","3  0.780199  Sometimes  Public_Transportation     Obesity_Type_III  \n","4  0.931721  Sometimes  Public_Transportation  Overweight_Level_II  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":5,"id":"1d0973eb","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:45.279337Z","iopub.status.busy":"2024-02-27T06:17:45.278651Z","iopub.status.idle":"2024-02-27T06:17:45.298261Z","shell.execute_reply":"2024-02-27T06:17:45.297079Z"},"papermill":{"duration":0.032756,"end_time":"2024-02-27T06:17:45.30076","exception":false,"start_time":"2024-02-27T06:17:45.268004","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>id</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>family_history_with_overweight</th>\n","      <th>FAVC</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CAEC</th>\n","      <th>SMOKE</th>\n","      <th>CH2O</th>\n","      <th>SCC</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","      <th>CALC</th>\n","      <th>MTRANS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>20758</td>\n","      <td>Male</td>\n","      <td>26.899886</td>\n","      <td>1.848294</td>\n","      <td>120.644178</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.938616</td>\n","      <td>3.000000</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>2.825629</td>\n","      <td>no</td>\n","      <td>0.855400</td>\n","      <td>0.000000</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>20759</td>\n","      <td>Female</td>\n","      <td>21.000000</td>\n","      <td>1.600000</td>\n","      <td>66.000000</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>3.000000</td>\n","      <td>no</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>20760</td>\n","      <td>Female</td>\n","      <td>26.000000</td>\n","      <td>1.643355</td>\n","      <td>111.600553</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>2.621877</td>\n","      <td>no</td>\n","      <td>0.000000</td>\n","      <td>0.250502</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>20761</td>\n","      <td>Male</td>\n","      <td>20.979254</td>\n","      <td>1.553127</td>\n","      <td>103.669116</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.000000</td>\n","      <td>2.977909</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>2.786417</td>\n","      <td>no</td>\n","      <td>0.094851</td>\n","      <td>0.000000</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>20762</td>\n","      <td>Female</td>\n","      <td>26.000000</td>\n","      <td>1.627396</td>\n","      <td>104.835346</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>2.653531</td>\n","      <td>no</td>\n","      <td>0.000000</td>\n","      <td>0.741069</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      id  Gender        Age    Height      Weight  \\\n","0  20758    Male  26.899886  1.848294  120.644178   \n","1  20759  Female  21.000000  1.600000   66.000000   \n","2  20760  Female  26.000000  1.643355  111.600553   \n","3  20761    Male  20.979254  1.553127  103.669116   \n","4  20762  Female  26.000000  1.627396  104.835346   \n","\n","  family_history_with_overweight FAVC      FCVC       NCP       CAEC SMOKE  \\\n","0                            yes  yes  2.938616  3.000000  Sometimes    no   \n","1                            yes  yes  2.000000  1.000000  Sometimes    no   \n","2                            yes  yes  3.000000  3.000000  Sometimes    no   \n","3                            yes  yes  2.000000  2.977909  Sometimes    no   \n","4                            yes  yes  3.000000  3.000000  Sometimes    no   \n","\n","       CH2O SCC       FAF       TUE       CALC                 MTRANS  \n","0  2.825629  no  0.855400  0.000000  Sometimes  Public_Transportation  \n","1  3.000000  no  1.000000  0.000000  Sometimes  Public_Transportation  \n","2  2.621877  no  0.000000  0.250502  Sometimes  Public_Transportation  \n","3  2.786417  no  0.094851  0.000000  Sometimes  Public_Transportation  \n","4  2.653531  no  0.000000  0.741069  Sometimes  Public_Transportation  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["test_df.head()"]},{"cell_type":"code","execution_count":6,"id":"09ca4430","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:45.323437Z","iopub.status.busy":"2024-02-27T06:17:45.323072Z","iopub.status.idle":"2024-02-27T06:17:45.358658Z","shell.execute_reply":"2024-02-27T06:17:45.3573Z"},"papermill":{"duration":0.04964,"end_time":"2024-02-27T06:17:45.361301","exception":false,"start_time":"2024-02-27T06:17:45.311661","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["id                                0\n","Gender                            0\n","Age                               0\n","Height                            0\n","Weight                            0\n","family_history_with_overweight    0\n","FAVC                              0\n","FCVC                              0\n","NCP                               0\n","CAEC                              0\n","SMOKE                             0\n","CH2O                              0\n","SCC                               0\n","FAF                               0\n","TUE                               0\n","CALC                              0\n","MTRANS                            0\n","NObeyesdad                        0\n","dtype: int64\n"]}],"source":["train_df = train_df.fillna(np.nan)\n","\n","missing_value_count = train_df.isnull().sum()\n","print(missing_value_count)"]},{"cell_type":"code","execution_count":7,"id":"10df0f09","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:45.38254Z","iopub.status.busy":"2024-02-27T06:17:45.381915Z","iopub.status.idle":"2024-02-27T06:17:45.429572Z","shell.execute_reply":"2024-02-27T06:17:45.428414Z"},"papermill":{"duration":0.060784,"end_time":"2024-02-27T06:17:45.431808","exception":false,"start_time":"2024-02-27T06:17:45.371024","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>id</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CH2O</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>20758.00000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>10378.50000</td>\n","      <td>23.841804</td>\n","      <td>1.700245</td>\n","      <td>87.887768</td>\n","      <td>2.445908</td>\n","      <td>2.761332</td>\n","      <td>2.029418</td>\n","      <td>0.981747</td>\n","      <td>0.616756</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>5992.46278</td>\n","      <td>5.688072</td>\n","      <td>0.087312</td>\n","      <td>26.379443</td>\n","      <td>0.533218</td>\n","      <td>0.705375</td>\n","      <td>0.608467</td>\n","      <td>0.838302</td>\n","      <td>0.602113</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.00000</td>\n","      <td>14.000000</td>\n","      <td>1.450000</td>\n","      <td>39.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>5189.25000</td>\n","      <td>20.000000</td>\n","      <td>1.631856</td>\n","      <td>66.000000</td>\n","      <td>2.000000</td>\n","      <td>3.000000</td>\n","      <td>1.792022</td>\n","      <td>0.008013</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>10378.50000</td>\n","      <td>22.815416</td>\n","      <td>1.700000</td>\n","      <td>84.064875</td>\n","      <td>2.393837</td>\n","      <td>3.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>0.573887</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>15567.75000</td>\n","      <td>26.000000</td>\n","      <td>1.762887</td>\n","      <td>111.600553</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>2.549617</td>\n","      <td>1.587406</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>20757.00000</td>\n","      <td>61.000000</td>\n","      <td>1.975663</td>\n","      <td>165.057269</td>\n","      <td>3.000000</td>\n","      <td>4.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                id           Age        Height        Weight          FCVC  \\\n","count  20758.00000  20758.000000  20758.000000  20758.000000  20758.000000   \n","mean   10378.50000     23.841804      1.700245     87.887768      2.445908   \n","std     5992.46278      5.688072      0.087312     26.379443      0.533218   \n","min        0.00000     14.000000      1.450000     39.000000      1.000000   \n","25%     5189.25000     20.000000      1.631856     66.000000      2.000000   \n","50%    10378.50000     22.815416      1.700000     84.064875      2.393837   \n","75%    15567.75000     26.000000      1.762887    111.600553      3.000000   \n","max    20757.00000     61.000000      1.975663    165.057269      3.000000   \n","\n","                NCP          CH2O           FAF           TUE  \n","count  20758.000000  20758.000000  20758.000000  20758.000000  \n","mean       2.761332      2.029418      0.981747      0.616756  \n","std        0.705375      0.608467      0.838302      0.602113  \n","min        1.000000      1.000000      0.000000      0.000000  \n","25%        3.000000      1.792022      0.008013      0.000000  \n","50%        3.000000      2.000000      1.000000      0.573887  \n","75%        3.000000      2.549617      1.587406      1.000000  \n","max        4.000000      3.000000      3.000000      2.000000  "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["train_df.describe()"]},{"cell_type":"markdown","id":"64aa21ca","metadata":{"papermill":{"duration":0.009683,"end_time":"2024-02-27T06:17:45.451653","exception":false,"start_time":"2024-02-27T06:17:45.44197","status":"completed"},"tags":[]},"source":["Data exploring"]},{"cell_type":"markdown","id":"29be0fe6","metadata":{"papermill":{"duration":0.009208,"end_time":"2024-02-27T06:17:45.470488","exception":false,"start_time":"2024-02-27T06:17:45.46128","status":"completed"},"tags":[]},"source":["Numerical variables"]},{"cell_type":"code","execution_count":8,"id":"5c765652","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:45.491957Z","iopub.status.busy":"2024-02-27T06:17:45.491148Z","iopub.status.idle":"2024-02-27T06:17:45.979673Z","shell.execute_reply":"2024-02-27T06:17:45.978893Z"},"papermill":{"duration":0.501874,"end_time":"2024-02-27T06:17:45.98221","exception":false,"start_time":"2024-02-27T06:17:45.480336","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.heatmap(train_df[['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE']].corr(), annot=True, fmt=\".2f\", cmap=\"coolwarm\")"]},{"cell_type":"markdown","id":"598d0fac","metadata":{"papermill":{"duration":0.010376,"end_time":"2024-02-27T06:17:46.003411","exception":false,"start_time":"2024-02-27T06:17:45.993035","status":"completed"},"tags":[]},"source":["Categorical variables"]},{"cell_type":"code","execution_count":9,"id":"7045297e","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:46.02742Z","iopub.status.busy":"2024-02-27T06:17:46.026823Z","iopub.status.idle":"2024-02-27T06:17:46.438801Z","shell.execute_reply":"2024-02-27T06:17:46.437622Z"},"papermill":{"duration":0.42702,"end_time":"2024-02-27T06:17:46.441416","exception":false,"start_time":"2024-02-27T06:17:46.014396","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600.25x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.catplot(x=\"MTRANS\", hue=\"Gender\", data=train_df, kind=\"count\")\n","g = g.set_ylabels(\"Count\")\n","g = g.set_xticklabels(rotation=45)"]},{"cell_type":"code","execution_count":10,"id":"8be4bece","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:46.465449Z","iopub.status.busy":"2024-02-27T06:17:46.465054Z","iopub.status.idle":"2024-02-27T06:17:46.848095Z","shell.execute_reply":"2024-02-27T06:17:46.846967Z"},"papermill":{"duration":0.397932,"end_time":"2024-02-27T06:17:46.850566","exception":false,"start_time":"2024-02-27T06:17:46.452634","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600.25x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.catplot(x=\"CAEC\", hue=\"Gender\", data=train_df, kind=\"count\")\n","g = g.set_ylabels(\"Count\")\n","g = g.set_xticklabels(rotation=45)"]},{"cell_type":"code","execution_count":11,"id":"5417184c","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:46.877182Z","iopub.status.busy":"2024-02-27T06:17:46.876767Z","iopub.status.idle":"2024-02-27T06:17:47.284072Z","shell.execute_reply":"2024-02-27T06:17:47.282896Z"},"papermill":{"duration":0.423306,"end_time":"2024-02-27T06:17:47.286606","exception":false,"start_time":"2024-02-27T06:17:46.8633","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600.25x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.catplot(x=\"CALC\", hue=\"Gender\", data=train_df, kind=\"count\")\n","g = g.set_ylabels(\"Count\")\n","g = g.set_xticklabels(rotation=45)"]},{"cell_type":"code","execution_count":12,"id":"0559ad72","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:47.312721Z","iopub.status.busy":"2024-02-27T06:17:47.3123Z","iopub.status.idle":"2024-02-27T06:17:47.796742Z","shell.execute_reply":"2024-02-27T06:17:47.795577Z"},"papermill":{"duration":0.500406,"end_time":"2024-02-27T06:17:47.799196","exception":false,"start_time":"2024-02-27T06:17:47.29879","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600.25x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.catplot(x=\"NObeyesdad\", hue=\"Gender\", data=train_df, kind=\"count\")\n","g = g.set_ylabels(\"Count\")\n","g = g.set_xticklabels(rotation=45)"]},{"cell_type":"markdown","id":"e8f33a5c","metadata":{"papermill":{"duration":0.012325,"end_time":"2024-02-27T06:17:47.824497","exception":false,"start_time":"2024-02-27T06:17:47.812172","status":"completed"},"tags":[]},"source":["preprocessing"]},{"cell_type":"code","execution_count":13,"id":"971cbba4","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:47.853515Z","iopub.status.busy":"2024-02-27T06:17:47.85313Z","iopub.status.idle":"2024-02-27T06:17:47.873678Z","shell.execute_reply":"2024-02-27T06:17:47.872482Z"},"papermill":{"duration":0.037035,"end_time":"2024-02-27T06:17:47.875852","exception":false,"start_time":"2024-02-27T06:17:47.838817","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>id</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>family_history_with_overweight</th>\n","      <th>FAVC</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CAEC</th>\n","      <th>SMOKE</th>\n","      <th>CH2O</th>\n","      <th>SCC</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","      <th>CALC</th>\n","      <th>MTRANS</th>\n","      <th>NObeyesdad</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>Male</td>\n","      <td>24.443011</td>\n","      <td>1.699998</td>\n","      <td>81.669950</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.000000</td>\n","      <td>2.983297</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>2.763573</td>\n","      <td>no</td>\n","      <td>0.000000</td>\n","      <td>0.976473</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","      <td>Overweight_Level_II</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>Female</td>\n","      <td>18.000000</td>\n","      <td>1.560000</td>\n","      <td>57.000000</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.000000</td>\n","      <td>3.000000</td>\n","      <td>Frequently</td>\n","      <td>no</td>\n","      <td>2.000000</td>\n","      <td>no</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>no</td>\n","      <td>Automobile</td>\n","      <td>Normal_Weight</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>Female</td>\n","      <td>18.000000</td>\n","      <td>1.711460</td>\n","      <td>50.165754</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>1.880534</td>\n","      <td>1.411685</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>1.910378</td>\n","      <td>no</td>\n","      <td>0.866045</td>\n","      <td>1.673584</td>\n","      <td>no</td>\n","      <td>Public_Transportation</td>\n","      <td>Insufficient_Weight</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>Female</td>\n","      <td>20.952737</td>\n","      <td>1.710730</td>\n","      <td>131.274851</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>1.674061</td>\n","      <td>no</td>\n","      <td>1.467863</td>\n","      <td>0.780199</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","      <td>Obesity_Type_III</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>Male</td>\n","      <td>31.641081</td>\n","      <td>1.914186</td>\n","      <td>93.798055</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>2.679664</td>\n","      <td>1.971472</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>1.979848</td>\n","      <td>no</td>\n","      <td>1.967973</td>\n","      <td>0.931721</td>\n","      <td>Sometimes</td>\n","      <td>Public_Transportation</td>\n","      <td>Overweight_Level_II</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   id  Gender        Age    Height      Weight family_history_with_overweight  \\\n","0   0    Male  24.443011  1.699998   81.669950                            yes   \n","1   1  Female  18.000000  1.560000   57.000000                            yes   \n","2   2  Female  18.000000  1.711460   50.165754                            yes   \n","3   3  Female  20.952737  1.710730  131.274851                            yes   \n","4   4    Male  31.641081  1.914186   93.798055                            yes   \n","\n","  FAVC      FCVC       NCP        CAEC SMOKE      CH2O SCC       FAF  \\\n","0  yes  2.000000  2.983297   Sometimes    no  2.763573  no  0.000000   \n","1  yes  2.000000  3.000000  Frequently    no  2.000000  no  1.000000   \n","2  yes  1.880534  1.411685   Sometimes    no  1.910378  no  0.866045   \n","3  yes  3.000000  3.000000   Sometimes    no  1.674061  no  1.467863   \n","4  yes  2.679664  1.971472   Sometimes    no  1.979848  no  1.967973   \n","\n","        TUE       CALC                 MTRANS           NObeyesdad  \n","0  0.976473  Sometimes  Public_Transportation  Overweight_Level_II  \n","1  1.000000         no             Automobile        Normal_Weight  \n","2  1.673584         no  Public_Transportation  Insufficient_Weight  \n","3  0.780199  Sometimes  Public_Transportation     Obesity_Type_III  \n","4  0.931721  Sometimes  Public_Transportation  Overweight_Level_II  "]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":14,"id":"1a936011","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:47.904142Z","iopub.status.busy":"2024-02-27T06:17:47.903738Z","iopub.status.idle":"2024-02-27T06:17:47.942248Z","shell.execute_reply":"2024-02-27T06:17:47.941253Z"},"papermill":{"duration":0.055789,"end_time":"2024-02-27T06:17:47.944832","exception":false,"start_time":"2024-02-27T06:17:47.889043","status":"completed"},"tags":[]},"outputs":[],"source":["for dataset in combine:\n","    dataset['Gender'] = dataset['Gender'].map({'Male': 0, 'Female': 1}).astype(int)\n","    dataset['family_history_with_overweight'] = dataset['family_history_with_overweight'].map({'no': 0, 'yes': 1}).astype(int)\n","    dataset['FAVC'] = dataset['FAVC'].map({'no': 0, 'yes': 1}).astype(int)\n","    dataset['CAEC'] = dataset['CAEC'].map({'no': 0, 'Frequently': 1, 'Sometimes': 2, 'Always': 3}).astype(int)\n","    dataset['SMOKE'] = dataset['SMOKE'].map({'no': 0, 'yes': 1}).astype(int)\n","    dataset['SCC'] = dataset['SCC'].map({'no': 0, 'yes': 1}).astype(int)\n","    dataset['CALC'] = dataset['CALC'].map({'no': 0, 'Frequently': 1, 'Sometimes': 2, 'Always': 3}).astype(int)\n","    dataset['MTRANS'] = dataset['MTRANS'].map({'Automobile': 0, 'Motorbike': 1, 'Public_Transportation': 2, 'Bike': 3, 'Walking': 4})"]},{"cell_type":"code","execution_count":15,"id":"215549a8","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:47.973655Z","iopub.status.busy":"2024-02-27T06:17:47.973238Z","iopub.status.idle":"2024-02-27T06:17:47.994834Z","shell.execute_reply":"2024-02-27T06:17:47.993762Z"},"papermill":{"duration":0.039339,"end_time":"2024-02-27T06:17:47.997404","exception":false,"start_time":"2024-02-27T06:17:47.958065","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>id</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>family_history_with_overweight</th>\n","      <th>FAVC</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CAEC</th>\n","      <th>SMOKE</th>\n","      <th>CH2O</th>\n","      <th>SCC</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","      <th>CALC</th>\n","      <th>MTRANS</th>\n","      <th>NObeyesdad</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>24.443011</td>\n","      <td>1.699998</td>\n","      <td>81.669950</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2.000000</td>\n","      <td>2.983297</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2.763573</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>0.976473</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>Overweight_Level_II</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>18.000000</td>\n","      <td>1.560000</td>\n","      <td>57.000000</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2.000000</td>\n","      <td>3.000000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2.000000</td>\n","      <td>0</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>Normal_Weight</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>18.000000</td>\n","      <td>1.711460</td>\n","      <td>50.165754</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1.880534</td>\n","      <td>1.411685</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1.910378</td>\n","      <td>0</td>\n","      <td>0.866045</td>\n","      <td>1.673584</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>Insufficient_Weight</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>20.952737</td>\n","      <td>1.710730</td>\n","      <td>131.274851</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1.674061</td>\n","      <td>0</td>\n","      <td>1.467863</td>\n","      <td>0.780199</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>Obesity_Type_III</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>31.641081</td>\n","      <td>1.914186</td>\n","      <td>93.798055</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2.679664</td>\n","      <td>1.971472</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1.979848</td>\n","      <td>0</td>\n","      <td>1.967973</td>\n","      <td>0.931721</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>Overweight_Level_II</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   id  Gender        Age    Height      Weight  \\\n","0   0       0  24.443011  1.699998   81.669950   \n","1   1       1  18.000000  1.560000   57.000000   \n","2   2       1  18.000000  1.711460   50.165754   \n","3   3       1  20.952737  1.710730  131.274851   \n","4   4       0  31.641081  1.914186   93.798055   \n","\n","   family_history_with_overweight  FAVC      FCVC       NCP  CAEC  SMOKE  \\\n","0                               1     1  2.000000  2.983297     2      0   \n","1                               1     1  2.000000  3.000000     1      0   \n","2                               1     1  1.880534  1.411685     2      0   \n","3                               1     1  3.000000  3.000000     2      0   \n","4                               1     1  2.679664  1.971472     2      0   \n","\n","       CH2O  SCC       FAF       TUE  CALC  MTRANS           NObeyesdad  \n","0  2.763573    0  0.000000  0.976473     2       2  Overweight_Level_II  \n","1  2.000000    0  1.000000  1.000000     0       0        Normal_Weight  \n","2  1.910378    0  0.866045  1.673584     0       2  Insufficient_Weight  \n","3  1.674061    0  1.467863  0.780199     2       2     Obesity_Type_III  \n","4  1.979848    0  1.967973  0.931721     2       2  Overweight_Level_II  "]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["combine[0].head()"]},{"cell_type":"code","execution_count":16,"id":"f578407b","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.026965Z","iopub.status.busy":"2024-02-27T06:17:48.026175Z","iopub.status.idle":"2024-02-27T06:17:48.041676Z","shell.execute_reply":"2024-02-27T06:17:48.040546Z"},"papermill":{"duration":0.032945,"end_time":"2024-02-27T06:17:48.04427","exception":false,"start_time":"2024-02-27T06:17:48.011325","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0    3\n","1    1\n","2    0\n","3    6\n","4    3\n","Name: NObeyesdad, dtype: int64"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["target = combine[0][\"NObeyesdad\"]\n","test_id = combine[1][\"id\"]\n","\n","combine[0] = combine[0].drop([\"id\", \"NObeyesdad\"], axis=1)\n","combine[1] = combine[1].drop(\"id\", axis=1)\n","\n","dict_target = {'Insufficient_Weight':0,\n","                'Normal_Weight':1,\n","                'Overweight_Level_I':2,\n","                'Overweight_Level_II':3,\n","                'Obesity_Type_I':4,\n","                'Obesity_Type_II':5,\n","                'Obesity_Type_III':6}\n","\n","target = target.map(dict_target)\n","target.head()"]},{"cell_type":"code","execution_count":17,"id":"9ed1c7a7","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.073614Z","iopub.status.busy":"2024-02-27T06:17:48.073214Z","iopub.status.idle":"2024-02-27T06:17:48.084692Z","shell.execute_reply":"2024-02-27T06:17:48.08355Z"},"papermill":{"duration":0.029218,"end_time":"2024-02-27T06:17:48.0871","exception":false,"start_time":"2024-02-27T06:17:48.057882","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["NObeyesdad\n","6    4046\n","5    3248\n","1    3082\n","4    2910\n","0    2523\n","3    2522\n","2    2427\n","Name: count, dtype: int64"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["target.value_counts()"]},{"cell_type":"code","execution_count":18,"id":"6385f6d2","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.117076Z","iopub.status.busy":"2024-02-27T06:17:48.116077Z","iopub.status.idle":"2024-02-27T06:17:48.127318Z","shell.execute_reply":"2024-02-27T06:17:48.12633Z"},"papermill":{"duration":0.029266,"end_time":"2024-02-27T06:17:48.129937","exception":false,"start_time":"2024-02-27T06:17:48.100671","status":"completed"},"tags":[]},"outputs":[],"source":["combine[0], combine[1] = combine[0].astype(float), combine[1].astype(float)"]},{"cell_type":"code","execution_count":19,"id":"22e3c196","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.159159Z","iopub.status.busy":"2024-02-27T06:17:48.158761Z","iopub.status.idle":"2024-02-27T06:17:48.192416Z","shell.execute_reply":"2024-02-27T06:17:48.191454Z"},"papermill":{"duration":0.051488,"end_time":"2024-02-27T06:17:48.194957","exception":false,"start_time":"2024-02-27T06:17:48.143469","status":"completed"},"tags":[]},"outputs":[],"source":["scaler = MinMaxScaler()\n","\n","for dataset in combine:\n","    dataset_numeric = dataset.apply(pd.to_numeric)\n","    dataset_scaled = scaler.fit_transform(dataset_numeric)\n","    dataset_scaled = pd.DataFrame(dataset_scaled, columns=dataset.columns)\n","    dataset_scaled = dataset_scaled.astype(float)\n","    dataset[:] = dataset_scaled"]},{"cell_type":"code","execution_count":20,"id":"4434a550","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.224262Z","iopub.status.busy":"2024-02-27T06:17:48.223896Z","iopub.status.idle":"2024-02-27T06:17:48.228674Z","shell.execute_reply":"2024-02-27T06:17:48.227559Z"},"papermill":{"duration":0.022332,"end_time":"2024-02-27T06:17:48.230858","exception":false,"start_time":"2024-02-27T06:17:48.208526","status":"completed"},"tags":[]},"outputs":[],"source":["train_df, test_df = combine[0], combine[1]"]},{"cell_type":"code","execution_count":21,"id":"2e5a7054","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.261067Z","iopub.status.busy":"2024-02-27T06:17:48.260666Z","iopub.status.idle":"2024-02-27T06:17:48.282357Z","shell.execute_reply":"2024-02-27T06:17:48.281353Z"},"papermill":{"duration":0.03951,"end_time":"2024-02-27T06:17:48.284547","exception":false,"start_time":"2024-02-27T06:17:48.245037","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>Gender</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>family_history_with_overweight</th>\n","      <th>FAVC</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CAEC</th>\n","      <th>SMOKE</th>\n","      <th>CH2O</th>\n","      <th>SCC</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","      <th>CALC</th>\n","      <th>MTRANS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>0.222192</td>\n","      <td>0.475586</td>\n","      <td>0.338497</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.500000</td>\n","      <td>0.661099</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>0.881787</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.488237</td>\n","      <td>1.0</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>0.085106</td>\n","      <td>0.209260</td>\n","      <td>0.142792</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.500000</td>\n","      <td>0.666667</td>\n","      <td>0.333333</td>\n","      <td>0.0</td>\n","      <td>0.500000</td>\n","      <td>0.0</td>\n","      <td>0.333333</td>\n","      <td>0.500000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>0.085106</td>\n","      <td>0.497391</td>\n","      <td>0.088577</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.440267</td>\n","      <td>0.137228</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>0.455189</td>\n","      <td>0.0</td>\n","      <td>0.288682</td>\n","      <td>0.836792</td>\n","      <td>0.0</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>0.147931</td>\n","      <td>0.496002</td>\n","      <td>0.732007</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","      <td>0.666667</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>0.337031</td>\n","      <td>0.0</td>\n","      <td>0.489288</td>\n","      <td>0.390099</td>\n","      <td>1.0</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0</td>\n","      <td>0.375342</td>\n","      <td>0.883049</td>\n","      <td>0.434708</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.839832</td>\n","      <td>0.323824</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>0.489924</td>\n","      <td>0.0</td>\n","      <td>0.655991</td>\n","      <td>0.465861</td>\n","      <td>1.0</td>\n","      <td>0.5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Gender       Age    Height    Weight  family_history_with_overweight  FAVC  \\\n","0     0.0  0.222192  0.475586  0.338497                             1.0   1.0   \n","1     1.0  0.085106  0.209260  0.142792                             1.0   1.0   \n","2     1.0  0.085106  0.497391  0.088577                             1.0   1.0   \n","3     1.0  0.147931  0.496002  0.732007                             1.0   1.0   \n","4     0.0  0.375342  0.883049  0.434708                             1.0   1.0   \n","\n","       FCVC       NCP      CAEC  SMOKE      CH2O  SCC       FAF       TUE  \\\n","0  0.500000  0.661099  0.666667    0.0  0.881787  0.0  0.000000  0.488237   \n","1  0.500000  0.666667  0.333333    0.0  0.500000  0.0  0.333333  0.500000   \n","2  0.440267  0.137228  0.666667    0.0  0.455189  0.0  0.288682  0.836792   \n","3  1.000000  0.666667  0.666667    0.0  0.337031  0.0  0.489288  0.390099   \n","4  0.839832  0.323824  0.666667    0.0  0.489924  0.0  0.655991  0.465861   \n","\n","   CALC  MTRANS  \n","0   1.0     0.5  \n","1   0.0     0.0  \n","2   0.0     0.5  \n","3   1.0     0.5  \n","4   1.0     0.5  "]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":22,"id":"efc8ea4c","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.313613Z","iopub.status.busy":"2024-02-27T06:17:48.313237Z","iopub.status.idle":"2024-02-27T06:17:48.334943Z","shell.execute_reply":"2024-02-27T06:17:48.333847Z"},"papermill":{"duration":0.038789,"end_time":"2024-02-27T06:17:48.33713","exception":false,"start_time":"2024-02-27T06:17:48.298341","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>Gender</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>family_history_with_overweight</th>\n","      <th>FAVC</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CAEC</th>\n","      <th>SMOKE</th>\n","      <th>CH2O</th>\n","      <th>SCC</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","      <th>CALC</th>\n","      <th>MTRANS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>0.274466</td>\n","      <td>0.751498</td>\n","      <td>0.647675</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.969308</td>\n","      <td>0.666667</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>0.912815</td>\n","      <td>0.0</td>\n","      <td>0.285133</td>\n","      <td>0.000000</td>\n","      <td>0.666667</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>0.148936</td>\n","      <td>0.283019</td>\n","      <td>0.214188</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.500000</td>\n","      <td>0.000000</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.333333</td>\n","      <td>0.000000</td>\n","      <td>0.666667</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>0.255319</td>\n","      <td>0.364821</td>\n","      <td>0.575933</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","      <td>0.666667</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>0.810939</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.125251</td>\n","      <td>0.666667</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.0</td>\n","      <td>0.148495</td>\n","      <td>0.194579</td>\n","      <td>0.513014</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.500000</td>\n","      <td>0.659303</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>0.893209</td>\n","      <td>0.0</td>\n","      <td>0.031617</td>\n","      <td>0.000000</td>\n","      <td>0.666667</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.0</td>\n","      <td>0.255319</td>\n","      <td>0.334709</td>\n","      <td>0.522265</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","      <td>0.666667</td>\n","      <td>0.666667</td>\n","      <td>0.0</td>\n","      <td>0.826766</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.370534</td>\n","      <td>0.666667</td>\n","      <td>0.5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Gender       Age    Height    Weight  family_history_with_overweight  FAVC  \\\n","0     0.0  0.274466  0.751498  0.647675                             1.0   1.0   \n","1     1.0  0.148936  0.283019  0.214188                             1.0   1.0   \n","2     1.0  0.255319  0.364821  0.575933                             1.0   1.0   \n","3     0.0  0.148495  0.194579  0.513014                             1.0   1.0   \n","4     1.0  0.255319  0.334709  0.522265                             1.0   1.0   \n","\n","       FCVC       NCP      CAEC  SMOKE      CH2O  SCC       FAF       TUE  \\\n","0  0.969308  0.666667  0.666667    0.0  0.912815  0.0  0.285133  0.000000   \n","1  0.500000  0.000000  0.666667    0.0  1.000000  0.0  0.333333  0.000000   \n","2  1.000000  0.666667  0.666667    0.0  0.810939  0.0  0.000000  0.125251   \n","3  0.500000  0.659303  0.666667    0.0  0.893209  0.0  0.031617  0.000000   \n","4  1.000000  0.666667  0.666667    0.0  0.826766  0.0  0.000000  0.370534   \n","\n","       CALC  MTRANS  \n","0  0.666667     0.5  \n","1  0.666667     0.5  \n","2  0.666667     0.5  \n","3  0.666667     0.5  \n","4  0.666667     0.5  "]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["test_df.head()"]},{"cell_type":"markdown","id":"df8fe40e","metadata":{"papermill":{"duration":0.013901,"end_time":"2024-02-27T06:17:48.365578","exception":false,"start_time":"2024-02-27T06:17:48.351677","status":"completed"},"tags":[]},"source":["Train model"]},{"cell_type":"code","execution_count":23,"id":"06affc6b","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.396774Z","iopub.status.busy":"2024-02-27T06:17:48.39635Z","iopub.status.idle":"2024-02-27T06:17:48.409163Z","shell.execute_reply":"2024-02-27T06:17:48.408194Z"},"papermill":{"duration":0.031104,"end_time":"2024-02-27T06:17:48.411423","exception":false,"start_time":"2024-02-27T06:17:48.380319","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["((14530, 16), (6228, 16), (14530,), (6228,))"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["X_train, X_val, y_train, y_val = train_test_split(train_df, target, test_size=0.3, random_state=42)\n","X_train.shape, X_val.shape, y_train.shape, y_val.shape"]},{"cell_type":"code","execution_count":24,"id":"2442c329","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.44467Z","iopub.status.busy":"2024-02-27T06:17:48.443854Z","iopub.status.idle":"2024-02-27T06:17:48.450375Z","shell.execute_reply":"2024-02-27T06:17:48.449188Z"},"papermill":{"duration":0.026462,"end_time":"2024-02-27T06:17:48.453095","exception":false,"start_time":"2024-02-27T06:17:48.426633","status":"completed"},"tags":[]},"outputs":[],"source":["def evaluate(clf):\n","    y_pred_train = clf.predict(X_train)\n","    y_pred_test = clf.predict(X_val)\n","    train_score = np.mean(y_pred_train == y_train)\n","    val_score = np.mean(y_pred_test == y_val)\n","    \n","    print(\"Train score:\", round(train_score, 4))\n","    print(\"Test score:\", round(val_score, 4))\n","    \n","    if train_score - val_score > 0.05:\n","        print(\"Overfitting warning!!!\")"]},{"cell_type":"code","execution_count":25,"id":"aa2142b2","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:17:48.483532Z","iopub.status.busy":"2024-02-27T06:17:48.483111Z","iopub.status.idle":"2024-02-27T06:20:28.024304Z","shell.execute_reply":"2024-02-27T06:20:28.023401Z"},"papermill":{"duration":159.558907,"end_time":"2024-02-27T06:20:28.026634","exception":false,"start_time":"2024-02-27T06:17:48.467727","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["XGBoost\n","Train score: 0.9343\n","Test score: 0.9048\n","Best set of hyperparameters:  {'learning_rate': 0.1, 'max_depth': 5, 'subsample': 0.7}\n","Best score:  0.9067446662078458\n","XGBoost\n","Train score: 0.9343\n","Test score: 0.9048\n"]}],"source":["param_grid = {\n","    'max_depth': [3, 5, 7],\n","    'learning_rate': [0.1, 0.01, 0.001],\n","    'subsample': [0.5, 0.7, 1]\n","    }\n","\n","\n","clf1 = XGBClassifier()\n","\n","clf1 = GridSearchCV(clf1, param_grid, cv=5, scoring='accuracy') \n","clf1.fit(X_train, y_train)\n","print(\"XGBoost\")\n","evaluate(clf1)\n","\n","print(\"Best set of hyperparameters: \", clf1.best_params_)\n","print(\"Best score: \", clf1.best_score_)\n","\n","clf1_ = XGBClassifier(**clf1.best_params_)\n","clf1_.fit(X_train, y_train)\n","print(\"XGBoost\")\n","evaluate(clf1_)"]},{"cell_type":"code","execution_count":26,"id":"62636e19","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:20:28.0578Z","iopub.status.busy":"2024-02-27T06:20:28.057186Z","iopub.status.idle":"2024-02-27T06:20:47.704463Z","shell.execute_reply":"2024-02-27T06:20:47.703612Z"},"papermill":{"duration":19.665567,"end_time":"2024-02-27T06:20:47.706846","exception":false,"start_time":"2024-02-27T06:20:28.041279","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["AdaBoost\n","Train score: 0.4491\n","Test score: 0.4425\n","Best set of hyperparameters:  {'learning_rate': 0.001, 'n_estimators': 50}\n","Best score:  0.44920853406744665\n","AdaBoost\n","Train score: 0.8851\n","Test score: 0.8704\n"]}],"source":["param_grid = {\"n_estimators\": [1, 10, 20, 50],\n","              \"learning_rate\": [0.001, 0.005, .01]\n","             }\n","\n","clf2 = AdaBoostClassifier()\n","\n","clf2 = GridSearchCV(clf2, param_grid, cv=5, scoring='accuracy') \n","clf2.fit(X_train, y_train)\n","print(\"AdaBoost\")\n","evaluate(clf2)\n","\n","print(\"Best set of hyperparameters: \", clf2.best_params_)\n","print(\"Best score: \", clf2.best_score_)\n","\n","clf2_ = XGBClassifier(**clf2.best_params_)\n","clf2_.fit(X_train, y_train)\n","print(\"AdaBoost\")\n","evaluate(clf2_)"]},{"cell_type":"code","execution_count":27,"id":"ab3a9e04","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:20:47.739145Z","iopub.status.busy":"2024-02-27T06:20:47.738277Z","iopub.status.idle":"2024-02-27T06:24:53.872245Z","shell.execute_reply":"2024-02-27T06:24:53.871211Z"},"papermill":{"duration":246.153169,"end_time":"2024-02-27T06:24:53.874945","exception":false,"start_time":"2024-02-27T06:20:47.721776","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Best set of hyperparameters:  {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20}\n","Best score:  0.8911218169304886\n","Gradient Boosting\n","Train score: 0.8955\n","Test score: 0.8813\n"]}],"source":["param_grid = {\n","    \"n_estimators\":[5, 10, 20],\n","    \"max_depth\":[1, 3, 5],\n","    \"learning_rate\":[0.01, 0.1, 1]\n","    }\n","\n","clf3 = GradientBoostingClassifier()\n","\n","clf3 = GridSearchCV(clf3, param_grid, cv=5, scoring='accuracy') \n","clf3.fit(X_train, y_train)\n","\n","print(\"Best set of hyperparameters: \", clf3.best_params_)\n","print(\"Best score: \", clf3.best_score_)\n","\n","clf3_ = XGBClassifier(**clf3.best_params_)\n","clf3_.fit(X_train, y_train)\n","print(\"Gradient Boosting\")\n","evaluate(clf3_)"]},{"cell_type":"code","execution_count":28,"id":"1227dd55","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:24:53.907983Z","iopub.status.busy":"2024-02-27T06:24:53.907164Z","iopub.status.idle":"2024-02-27T06:24:56.772621Z","shell.execute_reply":"2024-02-27T06:24:56.770857Z"},"papermill":{"duration":2.884796,"end_time":"2024-02-27T06:24:56.775245","exception":false,"start_time":"2024-02-27T06:24:53.890449","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Random Forest\n","Train score: 1.0\n","Test score: 0.898\n","Overfitting warning!!!\n"]}],"source":["clf4 = RandomForestClassifier()\n","clf4.fit(X_train, y_train)\n","\n","print(\"Random Forest\")\n","evaluate(clf4)"]},{"cell_type":"code","execution_count":29,"id":"73a4a39f","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:24:56.808797Z","iopub.status.busy":"2024-02-27T06:24:56.808298Z","iopub.status.idle":"2024-02-27T06:24:58.800779Z","shell.execute_reply":"2024-02-27T06:24:58.798492Z"},"papermill":{"duration":2.013371,"end_time":"2024-02-27T06:24:58.80447","exception":false,"start_time":"2024-02-27T06:24:56.791099","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["K Neighbors\n","Train score: 0.8094\n","Test score: 0.7206\n","Overfitting warning!!!\n"]}],"source":["clf5 = KNeighborsClassifier()\n","clf5.fit(X_train, y_train)\n","\n","print(\"K Neighbors\")\n","evaluate(clf5)"]},{"cell_type":"code","execution_count":30,"id":"dc77d29d","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:24:58.845012Z","iopub.status.busy":"2024-02-27T06:24:58.844574Z","iopub.status.idle":"2024-02-27T06:24:58.973528Z","shell.execute_reply":"2024-02-27T06:24:58.972248Z"},"papermill":{"duration":0.15108,"end_time":"2024-02-27T06:24:58.97657","exception":false,"start_time":"2024-02-27T06:24:58.82549","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Decision Tree\n","Train score: 1.0\n","Test score: 0.8459\n","Overfitting warning!!!\n"]}],"source":["clf6 = DecisionTreeClassifier()\n","clf6.fit(X_train, y_train)\n","\n","print(\"Decision Tree\")\n","evaluate(clf6)"]},{"cell_type":"code","execution_count":31,"id":"105a32a3","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:24:59.01184Z","iopub.status.busy":"2024-02-27T06:24:59.011378Z","iopub.status.idle":"2024-02-27T06:25:01.709949Z","shell.execute_reply":"2024-02-27T06:25:01.707713Z"},"papermill":{"duration":2.720531,"end_time":"2024-02-27T06:25:01.712657","exception":false,"start_time":"2024-02-27T06:24:58.992126","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Extra Trees\n","Train score: 1.0\n","Test score: 0.8732\n","Overfitting warning!!!\n"]}],"source":["clf7 = ExtraTreesClassifier()\n","clf7.fit(X_train, y_train)\n","\n","print(\"Extra Trees\")\n","evaluate(clf7)"]},{"cell_type":"markdown","id":"49a7178b","metadata":{"papermill":{"duration":0.015294,"end_time":"2024-02-27T06:25:01.74507","exception":false,"start_time":"2024-02-27T06:25:01.729776","status":"completed"},"tags":[]},"source":["Ensemble"]},{"cell_type":"code","execution_count":32,"id":"fb5a7e6c","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:25:01.780346Z","iopub.status.busy":"2024-02-27T06:25:01.779903Z","iopub.status.idle":"2024-02-27T06:25:54.708647Z","shell.execute_reply":"2024-02-27T06:25:54.707262Z"},"papermill":{"duration":52.949625,"end_time":"2024-02-27T06:25:54.712375","exception":false,"start_time":"2024-02-27T06:25:01.76275","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Train score: 0.9682\n","Test score: 0.9027\n","Overfitting warning!!!\n"]}],"source":["gradient_boosting_clf = GradientBoostingClassifier(learning_rate = 0.1, max_depth = 5, n_estimators = 20)\n","random_forest_clf = RandomForestClassifier()\n","xgb_clf = XGBClassifier(learning_rate = 0.1, max_depth = 5, subsample = 0.7)\n","\n","# voting_clf = VotingClassifier(estimators=[('gb', gradient_boosting_clf),\n","#                                          ('rf', random_forest_clf),\n","#                                          ('xgb', xgb_clf)])\n","# voting_clf.fit(X_train, y_train)\n","# result_predict = pd.Series(voting_clf.predict(test_df), name=\"NObeyesdad\")\n","\n","estimators=[('gb', gradient_boosting_clf),\n","            ('rf', random_forest_clf),\n","            ('xgb', xgb_clf)\n","]\n","\n","stacking_clf = StackingClassifier(\n","    estimators=estimators, final_estimator=LogisticRegression(solver='lbfgs', max_iter=10000)\n",")\n","\n","stacking_clf.fit(X_train, y_train)\n","\n","evaluate(stacking_clf)\n","result_predict = pd.Series(stacking_clf.predict(test_df), name=\"NObeyesdad\")"]},{"cell_type":"code","execution_count":33,"id":"5dfee7e0","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:25:54.773314Z","iopub.status.busy":"2024-02-27T06:25:54.772539Z","iopub.status.idle":"2024-02-27T06:25:54.786369Z","shell.execute_reply":"2024-02-27T06:25:54.784978Z"},"papermill":{"duration":0.048292,"end_time":"2024-02-27T06:25:54.790078","exception":false,"start_time":"2024-02-27T06:25:54.741786","status":"completed"},"tags":[]},"outputs":[],"source":["reverse_dict_target = {v: k for k, v in dict_target.items()}\n","target_decoded = result_predict.map(reverse_dict_target)"]},{"cell_type":"code","execution_count":34,"id":"80cdcda4","metadata":{"execution":{"iopub.execute_input":"2024-02-27T06:25:54.839491Z","iopub.status.busy":"2024-02-27T06:25:54.83901Z","iopub.status.idle":"2024-02-27T06:25:54.867078Z","shell.execute_reply":"2024-02-27T06:25:54.86607Z"},"papermill":{"duration":0.048969,"end_time":"2024-02-27T06:25:54.86974","exception":false,"start_time":"2024-02-27T06:25:54.820771","status":"completed"},"tags":[]},"outputs":[],"source":["result = pd.concat([test_id, target_decoded], axis=1)\n","result.to_csv(\"/kaggle/working/result.csv\", index=False)"]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":7609535,"sourceId":68479,"sourceType":"competition"}],"dockerImageVersionId":30646,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"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":"3.10.13"},"papermill":{"default_parameters":{},"duration":496.289319,"end_time":"2024-02-27T06:25:55.71424","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-02-27T06:17:39.424921","version":"2.5.0"}},"nbformat":4,"nbformat_minor":5}