crop-yield-estimation-bayesian-optimization.ipynb

{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":7028025,"sourceType":"datasetVersion","datasetId":4042152}],"dockerImageVersionId":30587,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<h1 align=\"center\" style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">Digital Green Crop Yield Estimate Challenge</h1>","metadata":{}},{"cell_type":"markdown","source":"<a id='table_of_contents'></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">Table of Contents</h2>\n\n1. <a href=\"#import\" style=\"text-decoration: None\">Import Libraries and Dataset</a>\n2. <a href=\"#data_preview\" style=\"text-decoration: None\">Dataset Preview</a>\n3. <a href=\"#data_wrangling\" style=\"text-decoration: None\">Data Wrangling</a>\n4. <a href=\"#eda\" style=\"text-decoration: None\">Exploratory Data Analysis</a>\n    - <a href=\"#univariate\" style=\"text-decoration: None\">Univariate Analysis</a>\n    - <a href=\"#bivariate\" style=\"text-decoration: None\">Bivariate Analysis</a>\n5. <a href=\"#data_preprocessing\" style=\"text-decoration: None\">Data Preparation and Preprocessing</a>\n6. <a href=\"#baseline\" style=\"text-decoration: None\">Baseline Models</a>\n7. <a href=\"#optimization\" style=\"text-decoration: None\">Optimization: Hyperparameter Tuning</a>\n    - <a href=\"#grid_search_cv\" style=\"text-decoration: None\">Grid Search CV</a>\n    - <a href=\"#randomized_search_cv\" style=\"text-decoration: None\">Randomized Search CV</a>\n    - <a href=\"#bayes_optimization\" style=\"text-decoration: None\">Bayesian Optimization with Gaussian Process</a>\n8. <a href=\"#pred_test_data\" style=\"text-decoration: None\">Make Prediction on Test Data</a>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"import\"></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">1. Import Libraries and Load Dataset</h2>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\npd.set_option(\"display.max_columns\", 120)\npd.set_option(\"display.max_rows\", 120)\npd.set_option('display.float_format', '{:.2f}'.format)\n\nimport warnings\nimport time\nwarnings.filterwarnings(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:03.487769Z","iopub.execute_input":"2023-11-27T20:57:03.488571Z","iopub.status.idle":"2023-11-27T20:57:03.839205Z","shell.execute_reply.started":"2023-11-27T20:57:03.488523Z","shell.execute_reply":"2023-11-27T20:57:03.838144Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"try:\n    train_df = pd.read_csv('/kaggle/input/digital-crop-yield-estimate-challenge-zindi-zip/Train.csv')\n    test_df = pd.read_csv('/kaggle/input/digital-crop-yield-estimate-challenge-zindi-zip/Test.csv')\n    submission_df = pd.read_csv('/kaggle/input/digital-crop-yield-estimate-challenge-zindi-zip/SampleSubmission.csv')\nexcept:\n    train_df = pd.read_csv('digital-crop-yield-estimate-challenge-zindi-zip/Train.csv')\n    test_df = pd.read_csv('digital-crop-yield-estimate-challenge-zindi-zip/Test.csv')\n    submission_df = pd.read_csv('digital-crop-yield-estimate-challenge-zindi-zip/SampleSubmission.csv')","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:03.840791Z","iopub.execute_input":"2023-11-27T20:57:03.841231Z","iopub.status.idle":"2023-11-27T20:57:03.918510Z","shell.execute_reply.started":"2023-11-27T20:57:03.841200Z","shell.execute_reply":"2023-11-27T20:57:03.917737Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"data_preview\"></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">2. Dataset Preview</h2>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:03.919912Z","iopub.execute_input":"2023-11-27T20:57:03.920382Z","iopub.status.idle":"2023-11-27T20:57:03.954184Z","shell.execute_reply.started":"2023-11-27T20:57:03.920343Z","shell.execute_reply":"2023-11-27T20:57:03.953272Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"                ID District      Block  CultLand  CropCultLand  \\\n0  ID_GTFAC7PEVWQ9  Nalanda  Noorsarai        45            40   \n1  ID_TK40ARLSPOKS  Nalanda     Rajgir        26            26   \n2  ID_1FJY2CRIMLZZ     Gaya      Gurua        10            10   \n3  ID_I3IPXS4DB7NE     Gaya      Gurua        15            15   \n4  ID_4T8YQWXWHB4A  Nalanda  Noorsarai        60            60   \n\n                               LandPreparationMethod CropTillageDate  \\\n0               TractorPlough FourWheelTracRotavator      2022-07-20   \n1  WetTillagePuddling TractorPlough FourWheelTrac...      2022-07-18   \n2               TractorPlough FourWheelTracRotavator      2022-06-30   \n3               TractorPlough FourWheelTracRotavator      2022-06-16   \n4                   TractorPlough WetTillagePuddling      2022-07-19   \n\n   CropTillageDepth         CropEstMethod RcNursEstDate  \\\n0                 5  Manual_PuddledRandom    2022-06-27   \n1                 5  Manual_PuddledRandom    2022-06-20   \n2                 6  Manual_PuddledRandom    2022-06-20   \n3                 6  Manual_PuddledRandom    2022-06-17   \n4                 4  Manual_PuddledRandom    2022-06-21   \n\n  SeedingSowingTransplanting  SeedlingsPerPit  \\\n0                 2022-07-21             2.00   \n1                 2022-07-20             2.00   \n2                 2022-08-13             2.00   \n3                 2022-07-17             2.00   \n4                 2022-07-20             2.00   \n\n                                       NursDetFactor  \\\n0  CalendarDate IrrigWaterAvailability SeedAvaila...   \n1  CalendarDate PreMonsoonShowers IrrigWaterAvail...   \n2  PreMonsoonShowers IrrigWaterAvailability Labou...   \n3  CalendarDate PreMonsoonShowers IrrigWaterAvail...   \n4  CalendarDate IrrigWaterAvailability SeedAvaila...   \n\n                                      TransDetFactor  \\\n0  CalendarDate SeedlingAge RainArrival IrrigWate...   \n1  CalendarDate SeedlingAge RainArrival IrrigWate...   \n2  SeedlingAge IrrigWaterAvailability LaborAvaila...   \n3  CalendarDate SeedlingAge RainArrival IrrigWate...   \n4  SeedlingAge RainArrival IrrigWaterAvailability...   \n\n   TransplantingIrrigationHours TransplantingIrrigationSource  \\\n0                          5.00                        Boring   \n1                          5.00                        Boring   \n2                          4.00                        Boring   \n3                           NaN                           NaN   \n4                          9.00                        Boring   \n\n  TransplantingIrrigationPowerSource  TransIrriCost  StandingWater  \\\n0                           Electric         200.00           2.00   \n1                           Electric         125.00           3.00   \n2                           Electric          80.00           2.00   \n3                                NaN            NaN            NaN   \n4                           Electric         300.00           2.00   \n\n  OrgFertilizers  Ganaura  CropOrgFYM PCropSolidOrgFertAppMethod  \\\n0            NaN      NaN         NaN                        NaN   \n1            NaN      NaN         NaN                        NaN   \n2    Ganaura FYM     1.00        1.00                SoilApplied   \n3        Ganaura     1.00         NaN                SoilApplied   \n4            NaN      NaN         NaN                        NaN   \n\n   NoFertilizerAppln CropbasalFerts  BasalDAP  BasalUrea MineralFertAppMethod  \\\n0                  2           Urea       NaN      20.00         Broadcasting   \n1                  2       DAP Urea     15.00      10.00         Broadcasting   \n2                  2            DAP      4.00        NaN          SoilApplied   \n3                  2       DAP Urea      6.00       3.00         Broadcasting   \n4                  2       Urea DAP     15.00      30.00         Broadcasting   \n\n  FirstTopDressFert  1tdUrea  1appDaysUrea  2tdUrea  2appDaysUrea  \\\n0              Urea    15.00         18.00      NaN           NaN   \n1              Urea    20.00         39.00      NaN           NaN   \n2              Urea     5.00         65.00      NaN           NaN   \n3              Urea     5.00          5.00      NaN           NaN   \n4              Urea    30.00         26.00      NaN           NaN   \n\n  MineralFertAppMethod.1 Harv_method   Harv_date  Harv_hand_rent  \\\n0           Broadcasting     machine  2022-11-16             NaN   \n1           Broadcasting        hand  2022-11-25            3.00   \n2        RootApplication        hand  2022-12-12          480.00   \n3           Broadcasting        hand  2022-12-02          240.00   \n4           Broadcasting     machine  2022-11-30             NaN   \n\n  Threshing_date Threshing_method  Residue_length  Residue_perc  \\\n0     2022-11-16          machine              30            40   \n1     2022-12-24          machine              24            10   \n2     2023-01-11          machine              30            10   \n3     2022-12-29             hand              26            10   \n4     2022-12-02          machine              24            40   \n\n      Stubble_use  Acre  Yield  \n0  plowed_in_soil  0.31    600  \n1  plowed_in_soil  0.31    600  \n2  plowed_in_soil  0.15    225  \n3  plowed_in_soil  0.22    468  \n4  plowed_in_soil  0.47    550  ","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>District</th>\n      <th>Block</th>\n      <th>CultLand</th>\n      <th>CropCultLand</th>\n      <th>LandPreparationMethod</th>\n      <th>CropTillageDate</th>\n      <th>CropTillageDepth</th>\n      <th>CropEstMethod</th>\n      <th>RcNursEstDate</th>\n      <th>SeedingSowingTransplanting</th>\n      <th>SeedlingsPerPit</th>\n      <th>NursDetFactor</th>\n      <th>TransDetFactor</th>\n      <th>TransplantingIrrigationHours</th>\n      <th>TransplantingIrrigationSource</th>\n      <th>TransplantingIrrigationPowerSource</th>\n      <th>TransIrriCost</th>\n      <th>StandingWater</th>\n      <th>OrgFertilizers</th>\n      <th>Ganaura</th>\n      <th>CropOrgFYM</th>\n      <th>PCropSolidOrgFertAppMethod</th>\n      <th>NoFertilizerAppln</th>\n      <th>CropbasalFerts</th>\n      <th>BasalDAP</th>\n      <th>BasalUrea</th>\n      <th>MineralFertAppMethod</th>\n      <th>FirstTopDressFert</th>\n      <th>1tdUrea</th>\n      <th>1appDaysUrea</th>\n      <th>2tdUrea</th>\n      <th>2appDaysUrea</th>\n      <th>MineralFertAppMethod.1</th>\n      <th>Harv_method</th>\n      <th>Harv_date</th>\n      <th>Harv_hand_rent</th>\n      <th>Threshing_date</th>\n      <th>Threshing_method</th>\n      <th>Residue_length</th>\n      <th>Residue_perc</th>\n      <th>Stubble_use</th>\n      <th>Acre</th>\n      <th>Yield</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ID_GTFAC7PEVWQ9</td>\n      <td>Nalanda</td>\n      <td>Noorsarai</td>\n      <td>45</td>\n      <td>40</td>\n      <td>TractorPlough FourWheelTracRotavator</td>\n      <td>2022-07-20</td>\n      <td>5</td>\n      <td>Manual_PuddledRandom</td>\n      <td>2022-06-27</td>\n      <td>2022-07-21</td>\n      <td>2.00</td>\n      <td>CalendarDate IrrigWaterAvailability SeedAvaila...</td>\n      <td>CalendarDate SeedlingAge RainArrival IrrigWate...</td>\n      <td>5.00</td>\n      <td>Boring</td>\n      <td>Electric</td>\n      <td>200.00</td>\n      <td>2.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>Urea</td>\n      <td>NaN</td>\n      <td>20.00</td>\n      <td>Broadcasting</td>\n      <td>Urea</td>\n      <td>15.00</td>\n      <td>18.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Broadcasting</td>\n      <td>machine</td>\n      <td>2022-11-16</td>\n      <td>NaN</td>\n      <td>2022-11-16</td>\n      <td>machine</td>\n      <td>30</td>\n      <td>40</td>\n      <td>plowed_in_soil</td>\n      <td>0.31</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ID_TK40ARLSPOKS</td>\n      <td>Nalanda</td>\n      <td>Rajgir</td>\n      <td>26</td>\n      <td>26</td>\n      <td>WetTillagePuddling TractorPlough FourWheelTrac...</td>\n      <td>2022-07-18</td>\n      <td>5</td>\n      <td>Manual_PuddledRandom</td>\n      <td>2022-06-20</td>\n      <td>2022-07-20</td>\n      <td>2.00</td>\n      <td>CalendarDate PreMonsoonShowers IrrigWaterAvail...</td>\n      <td>CalendarDate SeedlingAge RainArrival IrrigWate...</td>\n      <td>5.00</td>\n      <td>Boring</td>\n      <td>Electric</td>\n      <td>125.00</td>\n      <td>3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>DAP Urea</td>\n      <td>15.00</td>\n      <td>10.00</td>\n      <td>Broadcasting</td>\n      <td>Urea</td>\n      <td>20.00</td>\n      <td>39.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Broadcasting</td>\n      <td>hand</td>\n      <td>2022-11-25</td>\n      <td>3.00</td>\n      <td>2022-12-24</td>\n      <td>machine</td>\n      <td>24</td>\n      <td>10</td>\n      <td>plowed_in_soil</td>\n      <td>0.31</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ID_1FJY2CRIMLZZ</td>\n      <td>Gaya</td>\n      <td>Gurua</td>\n      <td>10</td>\n      <td>10</td>\n      <td>TractorPlough FourWheelTracRotavator</td>\n      <td>2022-06-30</td>\n      <td>6</td>\n      <td>Manual_PuddledRandom</td>\n      <td>2022-06-20</td>\n      <td>2022-08-13</td>\n      <td>2.00</td>\n      <td>PreMonsoonShowers IrrigWaterAvailability Labou...</td>\n      <td>SeedlingAge IrrigWaterAvailability LaborAvaila...</td>\n      <td>4.00</td>\n      <td>Boring</td>\n      <td>Electric</td>\n      <td>80.00</td>\n      <td>2.00</td>\n      <td>Ganaura FYM</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>SoilApplied</td>\n      <td>2</td>\n      <td>DAP</td>\n      <td>4.00</td>\n      <td>NaN</td>\n      <td>SoilApplied</td>\n      <td>Urea</td>\n      <td>5.00</td>\n      <td>65.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>RootApplication</td>\n      <td>hand</td>\n      <td>2022-12-12</td>\n      <td>480.00</td>\n      <td>2023-01-11</td>\n      <td>machine</td>\n      <td>30</td>\n      <td>10</td>\n      <td>plowed_in_soil</td>\n      <td>0.15</td>\n      <td>225</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ID_I3IPXS4DB7NE</td>\n      <td>Gaya</td>\n      <td>Gurua</td>\n      <td>15</td>\n      <td>15</td>\n      <td>TractorPlough FourWheelTracRotavator</td>\n      <td>2022-06-16</td>\n      <td>6</td>\n      <td>Manual_PuddledRandom</td>\n      <td>2022-06-17</td>\n      <td>2022-07-17</td>\n      <td>2.00</td>\n      <td>CalendarDate PreMonsoonShowers IrrigWaterAvail...</td>\n      <td>CalendarDate SeedlingAge RainArrival IrrigWate...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Ganaura</td>\n      <td>1.00</td>\n      <td>NaN</td>\n      <td>SoilApplied</td>\n      <td>2</td>\n      <td>DAP Urea</td>\n      <td>6.00</td>\n      <td>3.00</td>\n      <td>Broadcasting</td>\n      <td>Urea</td>\n      <td>5.00</td>\n      <td>5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Broadcasting</td>\n      <td>hand</td>\n      <td>2022-12-02</td>\n      <td>240.00</td>\n      <td>2022-12-29</td>\n      <td>hand</td>\n      <td>26</td>\n      <td>10</td>\n      <td>plowed_in_soil</td>\n      <td>0.22</td>\n      <td>468</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ID_4T8YQWXWHB4A</td>\n      <td>Nalanda</td>\n      <td>Noorsarai</td>\n      <td>60</td>\n      <td>60</td>\n      <td>TractorPlough WetTillagePuddling</td>\n      <td>2022-07-19</td>\n      <td>4</td>\n      <td>Manual_PuddledRandom</td>\n      <td>2022-06-21</td>\n      <td>2022-07-20</td>\n      <td>2.00</td>\n      <td>CalendarDate IrrigWaterAvailability SeedAvaila...</td>\n      <td>SeedlingAge RainArrival IrrigWaterAvailability...</td>\n      <td>9.00</td>\n      <td>Boring</td>\n      <td>Electric</td>\n      <td>300.00</td>\n      <td>2.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>Urea DAP</td>\n      <td>15.00</td>\n      <td>30.00</td>\n      <td>Broadcasting</td>\n      <td>Urea</td>\n      <td>30.00</td>\n      <td>26.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Broadcasting</td>\n      <td>machine</td>\n      <td>2022-11-30</td>\n      <td>NaN</td>\n      <td>2022-12-02</td>\n      <td>machine</td>\n      <td>24</td>\n      <td>40</td>\n      <td>plowed_in_soil</td>\n      <td>0.47</td>\n      <td>550</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:04.023412Z","iopub.execute_input":"2023-11-27T20:57:04.023749Z","iopub.status.idle":"2023-11-27T20:57:04.046623Z","shell.execute_reply.started":"2023-11-27T20:57:04.023720Z","shell.execute_reply":"2023-11-27T20:57:04.045653Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3870 entries, 0 to 3869\nData columns (total 44 columns):\n #   Column                              Non-Null Count  Dtype  \n---  ------                              --------------  -----  \n 0   ID                                  3870 non-null   object \n 1   District                            3870 non-null   object \n 2   Block                               3870 non-null   object \n 3   CultLand                            3870 non-null   int64  \n 4   CropCultLand                        3870 non-null   int64  \n 5   LandPreparationMethod               3870 non-null   object \n 6   CropTillageDate                     3870 non-null   object \n 7   CropTillageDepth                    3870 non-null   int64  \n 8   CropEstMethod                       3870 non-null   object \n 9   RcNursEstDate                       3787 non-null   object \n 10  SeedingSowingTransplanting          3870 non-null   object \n 11  SeedlingsPerPit                     3581 non-null   float64\n 12  NursDetFactor                       3581 non-null   object \n 13  TransDetFactor                      3581 non-null   object \n 14  TransplantingIrrigationHours        3677 non-null   float64\n 15  TransplantingIrrigationSource       3755 non-null   object \n 16  TransplantingIrrigationPowerSource  3367 non-null   object \n 17  TransIrriCost                       2988 non-null   float64\n 18  StandingWater                       3632 non-null   float64\n 19  OrgFertilizers                      2535 non-null   object \n 20  Ganaura                             1453 non-null   float64\n 21  CropOrgFYM                          1196 non-null   float64\n 22  PCropSolidOrgFertAppMethod          2533 non-null   object \n 23  NoFertilizerAppln                   3870 non-null   int64  \n 24  CropbasalFerts                      3682 non-null   object \n 25  BasalDAP                            3327 non-null   float64\n 26  BasalUrea                           2166 non-null   float64\n 27  MineralFertAppMethod                3870 non-null   object \n 28  FirstTopDressFert                   3385 non-null   object \n 29  1tdUrea                             3314 non-null   float64\n 30  1appDaysUrea                        3314 non-null   float64\n 31  2tdUrea                             1176 non-null   float64\n 32  2appDaysUrea                        1170 non-null   float64\n 33  MineralFertAppMethod.1              3389 non-null   object \n 34  Harv_method                         3870 non-null   object \n 35  Harv_date                           3870 non-null   object \n 36  Harv_hand_rent                      3618 non-null   float64\n 37  Threshing_date                      3870 non-null   object \n 38  Threshing_method                    3870 non-null   object \n 39  Residue_length                      3870 non-null   int64  \n 40  Residue_perc                        3870 non-null   int64  \n 41  Stubble_use                         3870 non-null   object \n 42  Acre                                3870 non-null   float64\n 43  Yield                               3870 non-null   int64  \ndtypes: float64(14), int64(7), object(23)\nmemory usage: 1.3+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"train_df.describe()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:04.232490Z","iopub.execute_input":"2023-11-27T20:57:04.232851Z","iopub.status.idle":"2023-11-27T20:57:04.292083Z","shell.execute_reply.started":"2023-11-27T20:57:04.232820Z","shell.execute_reply":"2023-11-27T20:57:04.291115Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"       CultLand  CropCultLand  CropTillageDepth  SeedlingsPerPit  \\\ncount   3870.00       3870.00           3870.00          3581.00   \nmean      28.53         24.73              4.49             2.71   \nstd       30.45         27.99              1.13             7.62   \nmin        1.00          1.00              1.00             1.00   \n25%       12.00         10.00              4.00             2.00   \n50%       20.00         20.00              4.00             2.00   \n75%       35.00         30.00              5.00             3.00   \nmax      800.00        800.00              8.00           442.00   \n\n       TransplantingIrrigationHours  TransIrriCost  StandingWater  Ganaura  \\\ncount                       3677.00        2988.00        3632.00  1453.00   \nmean                           8.02         379.73           3.25    29.73   \nstd                           42.61         419.72           2.21   122.68   \nmin                            1.00           1.00           1.00     1.00   \n25%                            2.00         150.00           2.00     1.00   \n50%                            4.00         250.00           3.00     3.00   \n75%                            6.00         450.00           4.00     4.00   \nmax                         2000.00        6000.00          15.00  1400.00   \n\n       CropOrgFYM  NoFertilizerAppln  BasalDAP  BasalUrea  1tdUrea  \\\ncount     1196.00            3870.00   3327.00    2166.00  3314.00   \nmean        57.45               2.18     11.45      13.35    11.51   \nstd        328.25               0.63      8.42       9.70     8.72   \nmin          1.00               1.00      1.00       1.00     1.00   \n25%          1.00               2.00      6.00       7.00     6.00   \n50%          2.00               2.00     10.00      10.00    10.00   \n75%          5.00               3.00     15.00      16.00    15.00   \nmax       4000.00               4.00    100.00      90.00    90.00   \n\n       1appDaysUrea  2tdUrea  2appDaysUrea  Harv_hand_rent  Residue_length  \\\ncount       3314.00  1176.00       1170.00         3618.00         3870.00   \nmean          29.20     7.38         58.76          536.62           26.52   \nstd           12.14     5.93         11.36         1138.61            3.19   \nmin            1.00     1.00          1.00            1.00            2.00   \n25%           23.00     4.00         58.00          150.00           25.00   \n50%           28.00     6.00         60.00          400.00           26.00   \n75%           36.00    10.00         65.00          700.00           30.00   \nmax          332.00    67.00         97.00        60000.00           30.00   \n\n       Residue_perc    Acre    Yield  \ncount       3870.00 3870.00  3870.00  \nmean          11.77    0.29   594.27  \nstd            7.06    0.21   651.92  \nmin           10.00    0.05     4.00  \n25%           10.00    0.16   300.00  \n50%           10.00    0.23   425.00  \n75%           10.00    0.37   740.00  \nmax           40.00    2.19 16800.00  ","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>CultLand</th>\n      <th>CropCultLand</th>\n      <th>CropTillageDepth</th>\n      <th>SeedlingsPerPit</th>\n      <th>TransplantingIrrigationHours</th>\n      <th>TransIrriCost</th>\n      <th>StandingWater</th>\n      <th>Ganaura</th>\n      <th>CropOrgFYM</th>\n      <th>NoFertilizerAppln</th>\n      <th>BasalDAP</th>\n      <th>BasalUrea</th>\n      <th>1tdUrea</th>\n      <th>1appDaysUrea</th>\n      <th>2tdUrea</th>\n      <th>2appDaysUrea</th>\n      <th>Harv_hand_rent</th>\n      <th>Residue_length</th>\n      <th>Residue_perc</th>\n      <th>Acre</th>\n      <th>Yield</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3870.00</td>\n      <td>3870.00</td>\n      <td>3870.00</td>\n      <td>3581.00</td>\n      <td>3677.00</td>\n      <td>2988.00</td>\n      <td>3632.00</td>\n      <td>1453.00</td>\n      <td>1196.00</td>\n      <td>3870.00</td>\n      <td>3327.00</td>\n      <td>2166.00</td>\n      <td>3314.00</td>\n      <td>3314.00</td>\n      <td>1176.00</td>\n      <td>1170.00</td>\n      <td>3618.00</td>\n      <td>3870.00</td>\n      <td>3870.00</td>\n      <td>3870.00</td>\n      <td>3870.00</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>28.53</td>\n      <td>24.73</td>\n      <td>4.49</td>\n      <td>2.71</td>\n      <td>8.02</td>\n      <td>379.73</td>\n      <td>3.25</td>\n      <td>29.73</td>\n      <td>57.45</td>\n      <td>2.18</td>\n      <td>11.45</td>\n      <td>13.35</td>\n      <td>11.51</td>\n      <td>29.20</td>\n      <td>7.38</td>\n      <td>58.76</td>\n      <td>536.62</td>\n      <td>26.52</td>\n      <td>11.77</td>\n      <td>0.29</td>\n      <td>594.27</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>30.45</td>\n      <td>27.99</td>\n      <td>1.13</td>\n      <td>7.62</td>\n      <td>42.61</td>\n      <td>419.72</td>\n      <td>2.21</td>\n      <td>122.68</td>\n      <td>328.25</td>\n      <td>0.63</td>\n      <td>8.42</td>\n      <td>9.70</td>\n      <td>8.72</td>\n      <td>12.14</td>\n      <td>5.93</td>\n      <td>11.36</td>\n      <td>1138.61</td>\n      <td>3.19</td>\n      <td>7.06</td>\n      <td>0.21</td>\n      <td>651.92</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>2.00</td>\n      <td>10.00</td>\n      <td>0.05</td>\n      <td>4.00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>12.00</td>\n      <td>10.00</td>\n      <td>4.00</td>\n      <td>2.00</td>\n      <td>2.00</td>\n      <td>150.00</td>\n      <td>2.00</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>2.00</td>\n      <td>6.00</td>\n      <td>7.00</td>\n      <td>6.00</td>\n      <td>23.00</td>\n      <td>4.00</td>\n      <td>58.00</td>\n      <td>150.00</td>\n      <td>25.00</td>\n      <td>10.00</td>\n      <td>0.16</td>\n      <td>300.00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>20.00</td>\n      <td>20.00</td>\n      <td>4.00</td>\n      <td>2.00</td>\n      <td>4.00</td>\n      <td>250.00</td>\n      <td>3.00</td>\n      <td>3.00</td>\n      <td>2.00</td>\n      <td>2.00</td>\n      <td>10.00</td>\n      <td>10.00</td>\n      <td>10.00</td>\n      <td>28.00</td>\n      <td>6.00</td>\n      <td>60.00</td>\n      <td>400.00</td>\n      <td>26.00</td>\n      <td>10.00</td>\n      <td>0.23</td>\n      <td>425.00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>35.00</td>\n      <td>30.00</td>\n      <td>5.00</td>\n      <td>3.00</td>\n      <td>6.00</td>\n      <td>450.00</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>5.00</td>\n      <td>3.00</td>\n      <td>15.00</td>\n      <td>16.00</td>\n      <td>15.00</td>\n      <td>36.00</td>\n      <td>10.00</td>\n      <td>65.00</td>\n      <td>700.00</td>\n      <td>30.00</td>\n      <td>10.00</td>\n      <td>0.37</td>\n      <td>740.00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>800.00</td>\n      <td>800.00</td>\n      <td>8.00</td>\n      <td>442.00</td>\n      <td>2000.00</td>\n      <td>6000.00</td>\n      <td>15.00</td>\n      <td>1400.00</td>\n      <td>4000.00</td>\n      <td>4.00</td>\n      <td>100.00</td>\n      <td>90.00</td>\n      <td>90.00</td>\n      <td>332.00</td>\n      <td>67.00</td>\n      <td>97.00</td>\n      <td>60000.00</td>\n      <td>30.00</td>\n      <td>40.00</td>\n      <td>2.19</td>\n      <td>16800.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"data_wrangling\"></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">3. Data Wrangling</h2>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"raw_df = train_df.copy()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:04.423133Z","iopub.execute_input":"2023-11-27T20:57:04.423855Z","iopub.status.idle":"2023-11-27T20:57:04.428993Z","shell.execute_reply.started":"2023-11-27T20:57:04.423821Z","shell.execute_reply":"2023-11-27T20:57:04.427946Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"\"></a>\n<h4 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">3.1. Drop id column and columns with too much NaNs</h4>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{"execution":{"iopub.status.busy":"2023-11-18T15:51:34.194633Z","iopub.execute_input":"2023-11-18T15:51:34.195178Z","iopub.status.idle":"2023-11-18T15:51:34.206547Z","shell.execute_reply.started":"2023-11-18T15:51:34.195139Z","shell.execute_reply":"2023-11-18T15:51:34.204483Z"}}},{"cell_type":"code","source":"nan_cols = ['Ganaura', 'CropOrgFYM', '2tdUrea', '2appDaysUrea', 'BasalUrea', 'TransIrriCost']","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:04.623439Z","iopub.execute_input":"2023-11-27T20:57:04.624097Z","iopub.status.idle":"2023-11-27T20:57:04.628339Z","shell.execute_reply.started":"2023-11-27T20:57:04.624060Z","shell.execute_reply":"2023-11-27T20:57:04.627314Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"Show the correlation of nan_vols with target","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_style(\"darkgrid\")","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:05.663641Z","iopub.execute_input":"2023-11-27T20:57:05.664024Z","iopub.status.idle":"2023-11-27T20:57:06.092662Z","shell.execute_reply.started":"2023-11-27T20:57:05.663991Z","shell.execute_reply":"2023-11-27T20:57:06.091784Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"num_rows = (len(nan_cols) + 2) // 3\nnum_cols = min(len(nan_cols), 3)\n\nfig, axs = plt.subplots(num_rows, num_cols, figsize=(15, 3 * num_rows))\n\nfor i, col in enumerate(nan_cols):\n    row_index = i // num_cols\n    col_index = i % num_cols\n    axs[row_index, col_index].scatter(train_df[col], train_df['Yield'])\n    axs[row_index, col_index].set_title(f'{col} vs Yield')\n\nfor i in range(len(nan_cols), num_rows * num_cols):\n    axs.flatten()[i].axis('off')\n\n# Adjust layout for better appearance\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:06.094556Z","iopub.execute_input":"2023-11-27T20:57:06.094836Z","iopub.status.idle":"2023-11-27T20:57:08.120254Z","shell.execute_reply.started":"2023-11-27T20:57:06.094810Z","shell.execute_reply":"2023-11-27T20:57:08.119207Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x600 with 6 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Exclude 'BasalUrea' and '2tdUrea' from columns to be dropped\nid_nan_cols = ['ID', 'Ganaura', 'CropOrgFYM', '2appDaysUrea', 'TransIrriCost']","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:08.121798Z","iopub.execute_input":"2023-11-27T20:57:08.122680Z","iopub.status.idle":"2023-11-27T20:57:08.127145Z","shell.execute_reply.started":"2023-11-27T20:57:08.122637Z","shell.execute_reply":"2023-11-27T20:57:08.126273Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"train_df.drop(columns=id_nan_cols, inplace=True)\ntest_df.drop(columns=id_nan_cols, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:08.129374Z","iopub.execute_input":"2023-11-27T20:57:08.129649Z","iopub.status.idle":"2023-11-27T20:57:08.152932Z","shell.execute_reply.started":"2023-11-27T20:57:08.129624Z","shell.execute_reply":"2023-11-27T20:57:08.152031Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"eda\"></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">4. Exploratory Data Analysis</h2>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"import plotly.express as px\nimport plotly.graph_objects as go\nsns.set_style(\"darkgrid\")","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:08.154115Z","iopub.execute_input":"2023-11-27T20:57:08.154362Z","iopub.status.idle":"2023-11-27T20:57:08.529901Z","shell.execute_reply.started":"2023-11-27T20:57:08.154339Z","shell.execute_reply":"2023-11-27T20:57:08.529022Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"!pip install kaleido","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:08.531114Z","iopub.execute_input":"2023-11-27T20:57:08.531606Z","iopub.status.idle":"2023-11-27T20:57:20.017980Z","shell.execute_reply.started":"2023-11-27T20:57:08.531577Z","shell.execute_reply":"2023-11-27T20:57:20.016927Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Requirement already satisfied: kaleido in /opt/conda/lib/python3.10/site-packages (0.2.1)\n","output_type":"stream"}]},{"cell_type":"code","source":"plot_color = ['#008080', 'black']\nsns.set_palette(['#008080', 'black'])","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:20.019436Z","iopub.execute_input":"2023-11-27T20:57:20.019751Z","iopub.status.idle":"2023-11-27T20:57:20.024898Z","shell.execute_reply.started":"2023-11-27T20:57:20.019723Z","shell.execute_reply":"2023-11-27T20:57:20.024033Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"num_cols = train_df.select_dtypes(include=np.number).columns.to_list()\ncat_cols = train_df.select_dtypes(exclude=np.number).columns.to_list()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:20.025921Z","iopub.execute_input":"2023-11-27T20:57:20.026270Z","iopub.status.idle":"2023-11-27T20:57:20.039815Z","shell.execute_reply.started":"2023-11-27T20:57:20.026238Z","shell.execute_reply":"2023-11-27T20:57:20.039070Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"univariate\"></a>\n<h4 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">4.1. Univariate Analysis</h4>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{"execution":{"iopub.status.busy":"2023-11-27T05:49:11.612017Z","iopub.execute_input":"2023-11-27T05:49:11.612423Z","iopub.status.idle":"2023-11-27T05:49:11.620307Z","shell.execute_reply.started":"2023-11-27T05:49:11.612393Z","shell.execute_reply":"2023-11-27T05:49:11.618813Z"}}},{"cell_type":"code","source":"sns.kdeplot(data=train_df, x=\"Yield\", fill=True, alpha=0.5)\nplt.xlabel(\"Yield\")\nplt.title(\"Histogram of Crop Yield\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:20.044081Z","iopub.execute_input":"2023-11-27T20:57:20.044332Z","iopub.status.idle":"2023-11-27T20:57:20.450567Z","shell.execute_reply.started":"2023-11-27T20:57:20.044310Z","shell.execute_reply":"2023-11-27T20:57:20.449605Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Boxplots to visualize outliers\noutlier_columns = ['Harv_hand_rent', 'Yield']\n\ndef plot_outliers(outlier_columns):\n    plt.figure(figsize=(10,8))\n    \n    for idx, column in enumerate(outlier_columns):\n        plt.subplot(2, 2, idx+1)\n        fig = train_df.boxplot(column=column)\n        fig.set_ylabel(column)\n        \n\nplot_outliers(outlier_columns)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:20.451761Z","iopub.execute_input":"2023-11-27T20:57:20.452065Z","iopub.status.idle":"2023-11-27T20:57:20.858662Z","shell.execute_reply.started":"2023-11-27T20:57:20.452038Z","shell.execute_reply":"2023-11-27T20:57:20.857705Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"bivariate\"></a>\n<h4 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">4.2. Bivariate Analysis</h4>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"corrmat = raw_df[num_cols].corr()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:20.860126Z","iopub.execute_input":"2023-11-27T20:57:20.860531Z","iopub.status.idle":"2023-11-27T20:57:20.870263Z","shell.execute_reply.started":"2023-11-27T20:57:20.860492Z","shell.execute_reply":"2023-11-27T20:57:20.869330Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"corr_matrix = train_df[num_cols].corr()\n\nfig = px.imshow(corr_matrix, color_continuous_scale='Viridis', title='Correlation Matrix')\nfig.update_layout(width=800, height=800, title_x=0.6, title_y=0.9)\n\nfig.show('png')","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:20.871315Z","iopub.execute_input":"2023-11-27T20:57:20.871608Z","iopub.status.idle":"2023-11-27T20:57:22.253765Z","shell.execute_reply.started":"2023-11-27T20:57:20.871573Z","shell.execute_reply":"2023-11-27T20:57:22.252792Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"data_preprocessing\"></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">5. Data Preparation and Preprocessing</h2>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"from sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import OneHotEncoder, MinMaxScaler\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import ColumnTransformer\n\nfrom sklearn import set_config\nset_config(display='diagram')","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.255066Z","iopub.execute_input":"2023-11-27T20:57:22.255506Z","iopub.status.idle":"2023-11-27T20:57:22.356173Z","shell.execute_reply.started":"2023-11-27T20:57:22.255463Z","shell.execute_reply":"2023-11-27T20:57:22.355349Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"bivariate\"></a>\n<h4 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">5.1. Fix Outliers</h4>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"def fix_outlier(column, df):\n    IQR = df[column].quantile(0.75) - df[column].quantile(0.25)\n    Lower_fence = df[column].quantile(0.25) - (IQR * 3)\n    Upper_fence = df[column].quantile(0.75) + (IQR * 3)\n    print('{column_name} outliers are values < {lowerboundary} or > {upperboundary}'.format(column_name=column, lowerboundary=Lower_fence, upperboundary=Upper_fence))\n    return df[df[column] <= Upper_fence]","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.357259Z","iopub.execute_input":"2023-11-27T20:57:22.357547Z","iopub.status.idle":"2023-11-27T20:57:22.363949Z","shell.execute_reply.started":"2023-11-27T20:57:22.357521Z","shell.execute_reply":"2023-11-27T20:57:22.363044Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"# for column in outlier_columns:\n#     train_df = fix_outlier(column, train_df)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.365082Z","iopub.execute_input":"2023-11-27T20:57:22.365344Z","iopub.status.idle":"2023-11-27T20:57:22.374798Z","shell.execute_reply.started":"2023-11-27T20:57:22.365321Z","shell.execute_reply":"2023-11-27T20:57:22.374084Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"bivariate\"></a>\n<h4 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">5.2. Data Preprocessing Pipeline</h4>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"X_train = train_df.drop(columns=['Yield'], axis=1)\ny_train = train_df['Yield']","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.375832Z","iopub.execute_input":"2023-11-27T20:57:22.376122Z","iopub.status.idle":"2023-11-27T20:57:22.392429Z","shell.execute_reply.started":"2023-11-27T20:57:22.376089Z","shell.execute_reply":"2023-11-27T20:57:22.391551Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"num_cols = X_train.select_dtypes(include=np.number).columns.to_list()\ncat_cols = X_train.select_dtypes(exclude=np.number).columns.to_list()\n\nprint(num_cols, end='\\n\\n')\nprint(cat_cols)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.393511Z","iopub.execute_input":"2023-11-27T20:57:22.393783Z","iopub.status.idle":"2023-11-27T20:57:22.404846Z","shell.execute_reply.started":"2023-11-27T20:57:22.393759Z","shell.execute_reply":"2023-11-27T20:57:22.404014Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"['CultLand', 'CropCultLand', 'CropTillageDepth', 'SeedlingsPerPit', 'TransplantingIrrigationHours', 'StandingWater', 'NoFertilizerAppln', 'BasalDAP', 'BasalUrea', '1tdUrea', '1appDaysUrea', '2tdUrea', 'Harv_hand_rent', 'Residue_length', 'Residue_perc', 'Acre']\n\n['District', 'Block', 'LandPreparationMethod', 'CropTillageDate', 'CropEstMethod', 'RcNursEstDate', 'SeedingSowingTransplanting', 'NursDetFactor', 'TransDetFactor', 'TransplantingIrrigationSource', 'TransplantingIrrigationPowerSource', 'OrgFertilizers', 'PCropSolidOrgFertAppMethod', 'CropbasalFerts', 'MineralFertAppMethod', 'FirstTopDressFert', 'MineralFertAppMethod.1', 'Harv_method', 'Harv_date', 'Threshing_date', 'Threshing_method', 'Stubble_use']\n","output_type":"stream"}]},{"cell_type":"code","source":"num_pipeline = Pipeline(steps=[\n    ('num_imputer', SimpleImputer(strategy='mean')),\n    ('scaler', MinMaxScaler())\n])\n\ncat_pipeline = Pipeline(steps=[\n    ('cat_imputer', SimpleImputer(strategy='most_frequent')),\n    ('one_hot_enc', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n])","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.405896Z","iopub.execute_input":"2023-11-27T20:57:22.406197Z","iopub.status.idle":"2023-11-27T20:57:22.412950Z","shell.execute_reply.started":"2023-11-27T20:57:22.406173Z","shell.execute_reply":"2023-11-27T20:57:22.412258Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"col_trans = ColumnTransformer(transformers=[\n    ('num_pipeline', num_pipeline, num_cols),\n    ('cat_pipeline', cat_pipeline, cat_cols),\n    ],\n    remainder='drop',\n    n_jobs=-1\n)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.413922Z","iopub.execute_input":"2023-11-27T20:57:22.414260Z","iopub.status.idle":"2023-11-27T20:57:22.424133Z","shell.execute_reply.started":"2023-11-27T20:57:22.414227Z","shell.execute_reply":"2023-11-27T20:57:22.423364Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"set_config(display='diagram')\ndisplay(col_trans)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.425049Z","iopub.execute_input":"2023-11-27T20:57:22.427112Z","iopub.status.idle":"2023-11-27T20:57:22.461775Z","shell.execute_reply.started":"2023-11-27T20:57:22.427086Z","shell.execute_reply":"2023-11-27T20:57:22.460818Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"ColumnTransformer(n_jobs=-1,\n                  transformers=[('num_pipeline',\n                                 Pipeline(steps=[('num_imputer',\n                                                  SimpleImputer()),\n                                                 ('scaler', MinMaxScaler())]),\n                                 ['CultLand', 'CropCultLand',\n                                  'CropTillageDepth', 'SeedlingsPerPit',\n                                  'TransplantingIrrigationHours',\n                                  'StandingWater', 'NoFertilizerAppln',\n                                  'BasalDAP', 'BasalUrea', '1tdUrea',\n                                  '1appDaysUrea', '2tdUrea', 'Harv_hand_rent',\n                                  'Residue_l...\n                                  'RcNursEstDate', 'SeedingSowingTransplanting',\n                                  'NursDetFactor', 'TransDetFactor',\n                                  'TransplantingIrrigationSource',\n                                  'TransplantingIrrigationPowerSource',\n                                  'OrgFertilizers',\n                                  'PCropSolidOrgFertAppMethod',\n                                  'CropbasalFerts', 'MineralFertAppMethod',\n                                  'FirstTopDressFert', 'MineralFertAppMethod.1',\n                                  'Harv_method', 'Harv_date', 'Threshing_date',\n                                  'Threshing_method', 'Stubble_use'])])","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(n_jobs=-1,\n                  transformers=[(&#x27;num_pipeline&#x27;,\n                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n                                                  SimpleImputer()),\n                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n                                 [&#x27;CultLand&#x27;, &#x27;CropCultLand&#x27;,\n                                  &#x27;CropTillageDepth&#x27;, &#x27;SeedlingsPerPit&#x27;,\n                                  &#x27;TransplantingIrrigationHours&#x27;,\n                                  &#x27;StandingWater&#x27;, &#x27;NoFertilizerAppln&#x27;,\n                                  &#x27;BasalDAP&#x27;, &#x27;BasalUrea&#x27;, &#x27;1tdUrea&#x27;,\n                                  &#x27;1appDaysUrea&#x27;, &#x27;2tdUrea&#x27;, &#x27;Harv_hand_rent&#x27;,\n                                  &#x27;Residue_l...\n                                  &#x27;RcNursEstDate&#x27;, &#x27;SeedingSowingTransplanting&#x27;,\n                                  &#x27;NursDetFactor&#x27;, &#x27;TransDetFactor&#x27;,\n                                  &#x27;TransplantingIrrigationSource&#x27;,\n                                  &#x27;TransplantingIrrigationPowerSource&#x27;,\n                                  &#x27;OrgFertilizers&#x27;,\n                                  &#x27;PCropSolidOrgFertAppMethod&#x27;,\n                                  &#x27;CropbasalFerts&#x27;, &#x27;MineralFertAppMethod&#x27;,\n                                  &#x27;FirstTopDressFert&#x27;, &#x27;MineralFertAppMethod.1&#x27;,\n                                  &#x27;Harv_method&#x27;, &#x27;Harv_date&#x27;, &#x27;Threshing_date&#x27;,\n                                  &#x27;Threshing_method&#x27;, &#x27;Stubble_use&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1,\n                  transformers=[(&#x27;num_pipeline&#x27;,\n                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n                                                  SimpleImputer()),\n                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n                                 [&#x27;CultLand&#x27;, &#x27;CropCultLand&#x27;,\n                                  &#x27;CropTillageDepth&#x27;, &#x27;SeedlingsPerPit&#x27;,\n                                  &#x27;TransplantingIrrigationHours&#x27;,\n                                  &#x27;StandingWater&#x27;, &#x27;NoFertilizerAppln&#x27;,\n                                  &#x27;BasalDAP&#x27;, &#x27;BasalUrea&#x27;, &#x27;1tdUrea&#x27;,\n                                  &#x27;1appDaysUrea&#x27;, &#x27;2tdUrea&#x27;, &#x27;Harv_hand_rent&#x27;,\n                                  &#x27;Residue_l...\n                                  &#x27;RcNursEstDate&#x27;, &#x27;SeedingSowingTransplanting&#x27;,\n                                  &#x27;NursDetFactor&#x27;, &#x27;TransDetFactor&#x27;,\n                                  &#x27;TransplantingIrrigationSource&#x27;,\n                                  &#x27;TransplantingIrrigationPowerSource&#x27;,\n                                  &#x27;OrgFertilizers&#x27;,\n                                  &#x27;PCropSolidOrgFertAppMethod&#x27;,\n                                  &#x27;CropbasalFerts&#x27;, &#x27;MineralFertAppMethod&#x27;,\n                                  &#x27;FirstTopDressFert&#x27;, &#x27;MineralFertAppMethod.1&#x27;,\n                                  &#x27;Harv_method&#x27;, &#x27;Harv_date&#x27;, &#x27;Threshing_date&#x27;,\n                                  &#x27;Threshing_method&#x27;, &#x27;Stubble_use&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipeline</label><div class=\"sk-toggleable__content\"><pre>[&#x27;CultLand&#x27;, &#x27;CropCultLand&#x27;, &#x27;CropTillageDepth&#x27;, &#x27;SeedlingsPerPit&#x27;, &#x27;TransplantingIrrigationHours&#x27;, &#x27;StandingWater&#x27;, &#x27;NoFertilizerAppln&#x27;, &#x27;BasalDAP&#x27;, &#x27;BasalUrea&#x27;, &#x27;1tdUrea&#x27;, &#x27;1appDaysUrea&#x27;, &#x27;2tdUrea&#x27;, &#x27;Harv_hand_rent&#x27;, &#x27;Residue_length&#x27;, &#x27;Residue_perc&#x27;, &#x27;Acre&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_pipeline</label><div class=\"sk-toggleable__content\"><pre>[&#x27;District&#x27;, &#x27;Block&#x27;, &#x27;LandPreparationMethod&#x27;, &#x27;CropTillageDate&#x27;, &#x27;CropEstMethod&#x27;, &#x27;RcNursEstDate&#x27;, &#x27;SeedingSowingTransplanting&#x27;, &#x27;NursDetFactor&#x27;, &#x27;TransDetFactor&#x27;, &#x27;TransplantingIrrigationSource&#x27;, &#x27;TransplantingIrrigationPowerSource&#x27;, &#x27;OrgFertilizers&#x27;, &#x27;PCropSolidOrgFertAppMethod&#x27;, &#x27;CropbasalFerts&#x27;, &#x27;MineralFertAppMethod&#x27;, &#x27;FirstTopDressFert&#x27;, &#x27;MineralFertAppMethod.1&#x27;, &#x27;Harv_method&#x27;, &#x27;Harv_date&#x27;, &#x27;Threshing_date&#x27;, &#x27;Threshing_method&#x27;, &#x27;Stubble_use&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"col_trans.fit(X_train)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:22.462887Z","iopub.execute_input":"2023-11-27T20:57:22.463246Z","iopub.status.idle":"2023-11-27T20:57:23.983634Z","shell.execute_reply.started":"2023-11-27T20:57:22.463215Z","shell.execute_reply":"2023-11-27T20:57:23.982593Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"},{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"ColumnTransformer(n_jobs=-1,\n                  transformers=[('num_pipeline',\n                                 Pipeline(steps=[('num_imputer',\n                                                  SimpleImputer()),\n                                                 ('scaler', MinMaxScaler())]),\n                                 ['CultLand', 'CropCultLand',\n                                  'CropTillageDepth', 'SeedlingsPerPit',\n                                  'TransplantingIrrigationHours',\n                                  'StandingWater', 'NoFertilizerAppln',\n                                  'BasalDAP', 'BasalUrea', '1tdUrea',\n                                  '1appDaysUrea', '2tdUrea', 'Harv_hand_rent',\n                                  'Residue_l...\n                                  'RcNursEstDate', 'SeedingSowingTransplanting',\n                                  'NursDetFactor', 'TransDetFactor',\n                                  'TransplantingIrrigationSource',\n                                  'TransplantingIrrigationPowerSource',\n                                  'OrgFertilizers',\n                                  'PCropSolidOrgFertAppMethod',\n                                  'CropbasalFerts', 'MineralFertAppMethod',\n                                  'FirstTopDressFert', 'MineralFertAppMethod.1',\n                                  'Harv_method', 'Harv_date', 'Threshing_date',\n                                  'Threshing_method', 'Stubble_use'])])","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(n_jobs=-1,\n                  transformers=[(&#x27;num_pipeline&#x27;,\n                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n                                                  SimpleImputer()),\n                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n                                 [&#x27;CultLand&#x27;, &#x27;CropCultLand&#x27;,\n                                  &#x27;CropTillageDepth&#x27;, &#x27;SeedlingsPerPit&#x27;,\n                                  &#x27;TransplantingIrrigationHours&#x27;,\n                                  &#x27;StandingWater&#x27;, &#x27;NoFertilizerAppln&#x27;,\n                                  &#x27;BasalDAP&#x27;, &#x27;BasalUrea&#x27;, &#x27;1tdUrea&#x27;,\n                                  &#x27;1appDaysUrea&#x27;, &#x27;2tdUrea&#x27;, &#x27;Harv_hand_rent&#x27;,\n                                  &#x27;Residue_l...\n                                  &#x27;RcNursEstDate&#x27;, &#x27;SeedingSowingTransplanting&#x27;,\n                                  &#x27;NursDetFactor&#x27;, &#x27;TransDetFactor&#x27;,\n                                  &#x27;TransplantingIrrigationSource&#x27;,\n                                  &#x27;TransplantingIrrigationPowerSource&#x27;,\n                                  &#x27;OrgFertilizers&#x27;,\n                                  &#x27;PCropSolidOrgFertAppMethod&#x27;,\n                                  &#x27;CropbasalFerts&#x27;, &#x27;MineralFertAppMethod&#x27;,\n                                  &#x27;FirstTopDressFert&#x27;, &#x27;MineralFertAppMethod.1&#x27;,\n                                  &#x27;Harv_method&#x27;, &#x27;Harv_date&#x27;, &#x27;Threshing_date&#x27;,\n                                  &#x27;Threshing_method&#x27;, &#x27;Stubble_use&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1,\n                  transformers=[(&#x27;num_pipeline&#x27;,\n                                 Pipeline(steps=[(&#x27;num_imputer&#x27;,\n                                                  SimpleImputer()),\n                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n                                 [&#x27;CultLand&#x27;, &#x27;CropCultLand&#x27;,\n                                  &#x27;CropTillageDepth&#x27;, &#x27;SeedlingsPerPit&#x27;,\n                                  &#x27;TransplantingIrrigationHours&#x27;,\n                                  &#x27;StandingWater&#x27;, &#x27;NoFertilizerAppln&#x27;,\n                                  &#x27;BasalDAP&#x27;, &#x27;BasalUrea&#x27;, &#x27;1tdUrea&#x27;,\n                                  &#x27;1appDaysUrea&#x27;, &#x27;2tdUrea&#x27;, &#x27;Harv_hand_rent&#x27;,\n                                  &#x27;Residue_l...\n                                  &#x27;RcNursEstDate&#x27;, &#x27;SeedingSowingTransplanting&#x27;,\n                                  &#x27;NursDetFactor&#x27;, &#x27;TransDetFactor&#x27;,\n                                  &#x27;TransplantingIrrigationSource&#x27;,\n                                  &#x27;TransplantingIrrigationPowerSource&#x27;,\n                                  &#x27;OrgFertilizers&#x27;,\n                                  &#x27;PCropSolidOrgFertAppMethod&#x27;,\n                                  &#x27;CropbasalFerts&#x27;, &#x27;MineralFertAppMethod&#x27;,\n                                  &#x27;FirstTopDressFert&#x27;, &#x27;MineralFertAppMethod.1&#x27;,\n                                  &#x27;Harv_method&#x27;, &#x27;Harv_date&#x27;, &#x27;Threshing_date&#x27;,\n                                  &#x27;Threshing_method&#x27;, &#x27;Stubble_use&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipeline</label><div class=\"sk-toggleable__content\"><pre>[&#x27;CultLand&#x27;, &#x27;CropCultLand&#x27;, &#x27;CropTillageDepth&#x27;, &#x27;SeedlingsPerPit&#x27;, &#x27;TransplantingIrrigationHours&#x27;, &#x27;StandingWater&#x27;, &#x27;NoFertilizerAppln&#x27;, &#x27;BasalDAP&#x27;, &#x27;BasalUrea&#x27;, &#x27;1tdUrea&#x27;, &#x27;1appDaysUrea&#x27;, &#x27;2tdUrea&#x27;, &#x27;Harv_hand_rent&#x27;, &#x27;Residue_length&#x27;, &#x27;Residue_perc&#x27;, &#x27;Acre&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_pipeline</label><div class=\"sk-toggleable__content\"><pre>[&#x27;District&#x27;, &#x27;Block&#x27;, &#x27;LandPreparationMethod&#x27;, &#x27;CropTillageDate&#x27;, &#x27;CropEstMethod&#x27;, &#x27;RcNursEstDate&#x27;, &#x27;SeedingSowingTransplanting&#x27;, &#x27;NursDetFactor&#x27;, &#x27;TransDetFactor&#x27;, &#x27;TransplantingIrrigationSource&#x27;, &#x27;TransplantingIrrigationPowerSource&#x27;, &#x27;OrgFertilizers&#x27;, &#x27;PCropSolidOrgFertAppMethod&#x27;, &#x27;CropbasalFerts&#x27;, &#x27;MineralFertAppMethod&#x27;, &#x27;FirstTopDressFert&#x27;, &#x27;MineralFertAppMethod.1&#x27;, &#x27;Harv_method&#x27;, &#x27;Harv_date&#x27;, &#x27;Threshing_date&#x27;, &#x27;Threshing_method&#x27;, &#x27;Stubble_use&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"# col_trans.get_feature_names_out()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:23.985330Z","iopub.execute_input":"2023-11-27T20:57:23.986234Z","iopub.status.idle":"2023-11-27T20:57:23.990484Z","shell.execute_reply.started":"2023-11-27T20:57:23.986191Z","shell.execute_reply":"2023-11-27T20:57:23.989549Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"X_train = col_trans.transform(X_train)\nX_test = col_trans.transform(test_df)\n\nassert X_train.shape[1] == X_test.shape[1]","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:23.991693Z","iopub.execute_input":"2023-11-27T20:57:23.991988Z","iopub.status.idle":"2023-11-27T20:57:25.026132Z","shell.execute_reply.started":"2023-11-27T20:57:23.991942Z","shell.execute_reply":"2023-11-27T20:57:25.025125Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"}]},{"cell_type":"code","source":"X_train.shape, y_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:25.031758Z","iopub.execute_input":"2023-11-27T20:57:25.032079Z","iopub.status.idle":"2023-11-27T20:57:25.038440Z","shell.execute_reply.started":"2023-11-27T20:57:25.032050Z","shell.execute_reply":"2023-11-27T20:57:25.037496Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"((3870, 930), (3870,))"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"baseline\"></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">6. Baseline Models</h2>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold, StratifiedKFold\nfrom sklearn import metrics\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom sklearn.ensemble import RandomForestRegressor","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:25.039758Z","iopub.execute_input":"2023-11-27T20:57:25.040092Z","iopub.status.idle":"2023-11-27T20:57:26.550634Z","shell.execute_reply.started":"2023-11-27T20:57:25.040064Z","shell.execute_reply":"2023-11-27T20:57:26.549617Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"from catboost import CatBoostRegressor","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:26.551787Z","iopub.execute_input":"2023-11-27T20:57:26.552121Z","iopub.status.idle":"2023-11-27T20:57:26.584435Z","shell.execute_reply.started":"2023-11-27T20:57:26.552093Z","shell.execute_reply":"2023-11-27T20:57:26.583377Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"lgbm_reg = LGBMRegressor(n_jobs=-1, verbose=-1, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:26.585621Z","iopub.execute_input":"2023-11-27T20:57:26.585905Z","iopub.status.idle":"2023-11-27T20:57:26.590659Z","shell.execute_reply.started":"2023-11-27T20:57:26.585879Z","shell.execute_reply":"2023-11-27T20:57:26.589678Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"cv = KFold(n_splits=10, shuffle=True, random_state=42)\nscores = cross_val_score(lgbm_reg, X_train, y_train, scoring='neg_root_mean_squared_error', cv=cv)\n\n# Print the RMSE across folds\nprint('Root Mean Squared Error:', scores.mean()) ","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:26.591938Z","iopub.execute_input":"2023-11-27T20:57:26.592302Z","iopub.status.idle":"2023-11-27T20:57:28.476700Z","shell.execute_reply.started":"2023-11-27T20:57:26.592269Z","shell.execute_reply":"2023-11-27T20:57:28.476001Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"Root Mean Squared Error: -430.70711232707237\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"optimization\"></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">7. Optimization: Hyperparameter Tuning</h2>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"grid_search_cv\"></a>\n<h4 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">7.1. Grid Search CV</h4>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"from sklearn import model_selection ","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:28.478018Z","iopub.execute_input":"2023-11-27T20:57:28.478545Z","iopub.status.idle":"2023-11-27T20:57:28.483194Z","shell.execute_reply.started":"2023-11-27T20:57:28.478515Z","shell.execute_reply":"2023-11-27T20:57:28.482427Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"param_grid = {\n    'learning_rate': [0.01, 0.05, 0.1],\n    'max_depth': [3, 5, 12, 17, 25],\n    'colsample_bytree': [0.6, 0.7, 0.9, 1.0],\n    \n}","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:28.486730Z","iopub.execute_input":"2023-11-27T20:57:28.487272Z","iopub.status.idle":"2023-11-27T20:57:28.492591Z","shell.execute_reply.started":"2023-11-27T20:57:28.487245Z","shell.execute_reply":"2023-11-27T20:57:28.491929Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"model = model_selection.GridSearchCV(\n    estimator = lgbm_reg,\n    param_grid = param_grid,\n    scoring = 'neg_root_mean_squared_error',\n    verbose = 1,\n    cv = cv\n)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:57:28.493414Z","iopub.execute_input":"2023-11-27T20:57:28.494155Z","iopub.status.idle":"2023-11-27T20:57:28.503182Z","shell.execute_reply.started":"2023-11-27T20:57:28.494130Z","shell.execute_reply":"2023-11-27T20:57:28.502275Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"# fit the model and extract best score\nmodel.fit(X_train, y_train)\n\nprint(f\"Best score: {model.best_score_}\")\n\nprint(\"Best parameters set:\")\nbest_parameters = model.best_estimator_.get_params()\nfor param_name in sorted(param_grid.keys()):\n    print(f\"\\t{param_name}: {best_parameters[param_name]}\")","metadata":{"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-11-27T20:57:28.504300Z","iopub.execute_input":"2023-11-27T20:57:28.504572Z","iopub.status.idle":"2023-11-27T20:59:06.900045Z","shell.execute_reply.started":"2023-11-27T20:57:28.504547Z","shell.execute_reply":"2023-11-27T20:59:06.899044Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"Fitting 10 folds for each of 60 candidates, totalling 600 fits\nBest score: -424.5005624536496\nBest parameters set:\n\tcolsample_bytree: 0.9\n\tlearning_rate: 0.05\n\tmax_depth: 17\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"randomized_search_cv\"></a>\n<h4 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">7.2. Randomized Search CV</h4>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"param_grid = {\n    'learning_rate': [0.01, 0.05, 0.1],\n    'max_depth': [3, 5, 12, 15, 17, 25],\n    'colsample_bytree': [0.6, 0.7, 0.9, 1.0],\n    'num_leaves': [20, 31, 63, 127],\n    'subsample': [0.5, 0.8, 0.9, 1.0],\n}","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:59:06.901557Z","iopub.execute_input":"2023-11-27T20:59:06.902154Z","iopub.status.idle":"2023-11-27T20:59:06.908506Z","shell.execute_reply.started":"2023-11-27T20:59:06.902116Z","shell.execute_reply":"2023-11-27T20:59:06.907784Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"model = model_selection.RandomizedSearchCV(\n    estimator = lgbm_reg, \n    param_distributions = param_grid,\n    n_iter = 10,\n    scoring = 'neg_root_mean_squared_error',\n    verbose = 1, \n    cv = cv\n)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:59:06.909648Z","iopub.execute_input":"2023-11-27T20:59:06.910199Z","iopub.status.idle":"2023-11-27T20:59:06.919318Z","shell.execute_reply.started":"2023-11-27T20:59:06.910171Z","shell.execute_reply":"2023-11-27T20:59:06.918696Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"# fit the model and extract best score\nmodel.fit(X_train, y_train)\n\nprint(f\"Best score: {model.best_score_}\")\n\nprint(\"Best parameters set:\")\nbest_parameters = model.best_estimator_.get_params()\nfor param_name in sorted(param_grid.keys()):\n    print(f\"\\t{param_name}: {best_parameters[param_name]}\")","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:59:06.920593Z","iopub.execute_input":"2023-11-27T20:59:06.920853Z","iopub.status.idle":"2023-11-27T20:59:30.801507Z","shell.execute_reply.started":"2023-11-27T20:59:06.920829Z","shell.execute_reply":"2023-11-27T20:59:30.800750Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"Fitting 10 folds for each of 10 candidates, totalling 100 fits\nBest score: -427.42154050191004\nBest parameters set:\n\tcolsample_bytree: 0.9\n\tlearning_rate: 0.05\n\tmax_depth: 12\n\tnum_leaves: 20\n\tsubsample: 0.9\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"bayes_optimization\"></a>\n<h4 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">7.3. Bayesian Optimization with Gaussian Process</h4>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"import warnings\nimport time\nwarnings.filterwarnings(\"ignore\")\nimport lightgbm as lgb\nfrom bayes_opt import BayesianOptimization\nfrom sklearn.metrics import roc_auc_score","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:59:30.805407Z","iopub.execute_input":"2023-11-27T20:59:30.807875Z","iopub.status.idle":"2023-11-27T20:59:30.821022Z","shell.execute_reply.started":"2023-11-27T20:59:30.807841Z","shell.execute_reply":"2023-11-27T20:59:30.820011Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"# Define the objective function to be optimized\ndef lgb_eval(num_leaves, feature_fraction, bagging_fraction, max_depth, lambda_l1, lambda_l2, min_split_gain, min_child_weight):\n    params = {\n        'objective': 'regression',\n        'metric': 'rmse',\n        'num_leaves': int(num_leaves),\n        'feature_fraction': max(min(feature_fraction, 1), 0),\n        'bagging_fraction': max(min(bagging_fraction, 1), 0),\n        'max_depth': int(max_depth),\n        'lambda_l1': max(lambda_l1, 0),\n        'lambda_l2': max(lambda_l2, 0),\n        'min_split_gain': min_split_gain,\n        'min_child_weight': min_child_weight,\n        'verbose': -1,\n        'nthread': -1\n    }\n\n    # LightGBM dataset\n    lgb_train = lgb.Dataset(X_train, y_train)\n\n    # Cross-validate with early stopping\n    cv_result = lgb.cv(params, lgb_train, num_boost_round=1000, nfold=3, metrics=['rmse'], early_stopping_rounds=50, seed=42)\n\n    # Return the negative RMSE (Bayesian optimization minimizes the objective function)\n    return -cv_result['rmse-mean'][-1]\n\n# Define the parameter space to be optimized\npbounds = {\n    'num_leaves': (24, 64),\n    'feature_fraction': (0.1, 0.9),\n    'bagging_fraction': (0.8, 1),\n    'max_depth': (5, 20),\n    'lambda_l1': (0, 5),\n    'lambda_l2': (0, 5),\n    'min_split_gain': (0.001, 0.1),\n    'min_child_weight': (5, 50)\n}\n\n# Bayesian optimization\noptimizer = BayesianOptimization(\n    f=lgb_eval,\n    pbounds=pbounds,\n    random_state=42,\n)\n\n# Maximize the negative objective function\noptimizer.maximize(init_points=30, n_iter=70)\n\n# Get the optimal hyperparameters\noptimal_params = optimizer.max['params']\nprint(\"Optimal Hyperparameters:\")\nprint(optimal_params)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T20:59:30.822361Z","iopub.execute_input":"2023-11-27T20:59:30.823102Z","iopub.status.idle":"2023-11-27T21:01:14.807478Z","shell.execute_reply.started":"2023-11-27T20:59:30.823066Z","shell.execute_reply":"2023-11-27T21:01:14.806367Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"|   iter    |  target   | baggin... | featur... | lambda_l1 | lambda_l2 | max_depth | min_ch... | min_sp... | num_le... 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 \u001b[0m | \u001b[0m1.0      \u001b[0m | \u001b[0m0.9      \u001b[0m | \u001b[0m5.0      \u001b[0m | \u001b[0m2.976    \u001b[0m | \u001b[0m12.93    \u001b[0m | \u001b[0m40.58    \u001b[0m | \u001b[0m0.001    \u001b[0m | \u001b[0m47.31    \u001b[0m |\n| \u001b[0m100      \u001b[0m | \u001b[0m-460.7   \u001b[0m | \u001b[0m0.8      \u001b[0m | \u001b[0m0.9      \u001b[0m | \u001b[0m0.0      \u001b[0m | \u001b[0m0.0      \u001b[0m | \u001b[0m10.96    \u001b[0m | \u001b[0m50.0     \u001b[0m | \u001b[0m0.1      \u001b[0m | \u001b[0m36.36    \u001b[0m |\n=========================================================================================================================\nOptimal Hyperparameters:\n{'bagging_fraction': 0.9539974104852039, 'feature_fraction': 0.5842657262390364, 'lambda_l1': 0.22123377150068504, 'lambda_l2': 4.023521692990339, 'max_depth': 13.241767034739418, 'min_child_weight': 43.263786745437656, 'min_split_gain': 0.0736315896348968, 'num_leaves': 52.78550143581833}\n","output_type":"stream"}]},{"cell_type":"code","source":"optimal_params['num_leaves'] = int(optimal_params['num_leaves'])\noptimal_params['max_depth'] = int(optimal_params['max_depth'])","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:14.809326Z","iopub.execute_input":"2023-11-27T21:01:14.810134Z","iopub.status.idle":"2023-11-27T21:01:14.815190Z","shell.execute_reply.started":"2023-11-27T21:01:14.810102Z","shell.execute_reply":"2023-11-27T21:01:14.814129Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"lgbm_reg = LGBMRegressor(**optimal_params, n_jobs=-1, verbose=-1, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:14.816561Z","iopub.execute_input":"2023-11-27T21:01:14.817779Z","iopub.status.idle":"2023-11-27T21:01:14.828679Z","shell.execute_reply.started":"2023-11-27T21:01:14.817739Z","shell.execute_reply":"2023-11-27T21:01:14.827324Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"cv = KFold(n_splits=10, shuffle=True, random_state=42)\nscores = cross_val_score(lgbm_reg, X_train, y_train, scoring='neg_root_mean_squared_error', cv=cv)\n\n# Print the RMSE across folds\nprint('Root Mean Squared Error:', scores.mean()) ","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:14.830099Z","iopub.execute_input":"2023-11-27T21:01:14.830484Z","iopub.status.idle":"2023-11-27T21:01:16.763268Z","shell.execute_reply.started":"2023-11-27T21:01:14.830448Z","shell.execute_reply":"2023-11-27T21:01:16.762353Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\nRoot Mean Squared Error: -424.6835345260761\n","output_type":"stream"}]},{"cell_type":"code","source":"# Root Mean Squared Error: -106.8470377797091\n# Root Mean Squared Error: -426.36150260357135\n# Root Mean Squared Error: -424.45963055160047","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:16.767218Z","iopub.execute_input":"2023-11-27T21:01:16.767624Z","iopub.status.idle":"2023-11-27T21:01:16.774990Z","shell.execute_reply.started":"2023-11-27T21:01:16.767593Z","shell.execute_reply":"2023-11-27T21:01:16.774166Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"pred_test_data\"></a>\n<h2 style=\"background-color:#0b0504;color:white;border-radius: 8px; padding:12px\">8. Make Prediction on Test Data</h2>\n\n<a href=\"#table_of_contents\" style=\"text-decoration: None\">Table of Contents</a>","metadata":{}},{"cell_type":"code","source":"lgbm_reg.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:16.776569Z","iopub.execute_input":"2023-11-27T21:01:16.776850Z","iopub.status.idle":"2023-11-27T21:01:16.972819Z","shell.execute_reply.started":"2023-11-27T21:01:16.776824Z","shell.execute_reply":"2023-11-27T21:01:16.972036Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stdout","text":"[LightGBM] [Warning] bagging_fraction is set=0.9539974104852039, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9539974104852039\n[LightGBM] [Warning] lambda_l1 is set=0.22123377150068504, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.22123377150068504\n[LightGBM] [Warning] feature_fraction is set=0.5842657262390364, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5842657262390364\n[LightGBM] [Warning] lambda_l2 is set=4.023521692990339, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.023521692990339\n","output_type":"stream"},{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"LGBMRegressor(bagging_fraction=0.9539974104852039,\n              feature_fraction=0.5842657262390364,\n              lambda_l1=0.22123377150068504, lambda_l2=4.023521692990339,\n              max_depth=13, min_child_weight=43.263786745437656,\n              min_split_gain=0.0736315896348968, num_leaves=52, random_state=42,\n              verbose=-1)","text/html":"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(bagging_fraction=0.9539974104852039,\n              feature_fraction=0.5842657262390364,\n              lambda_l1=0.22123377150068504, lambda_l2=4.023521692990339,\n              max_depth=13, min_child_weight=43.263786745437656,\n              min_split_gain=0.0736315896348968, num_leaves=52, random_state=42,\n              verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(bagging_fraction=0.9539974104852039,\n              feature_fraction=0.5842657262390364,\n              lambda_l1=0.22123377150068504, lambda_l2=4.023521692990339,\n              max_depth=13, min_child_weight=43.263786745437656,\n              min_split_gain=0.0736315896348968, num_leaves=52, random_state=42,\n              verbose=-1)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"preds = lgbm_reg.predict(X_test)\npreds","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:16.976584Z","iopub.execute_input":"2023-11-27T21:01:16.977159Z","iopub.status.idle":"2023-11-27T21:01:16.992226Z","shell.execute_reply.started":"2023-11-27T21:01:16.977128Z","shell.execute_reply":"2023-11-27T21:01:16.991429Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"array([535.74097817, 350.85714199, 512.32009226, ..., 238.74740177,\n       333.22495477, 573.5827729 ])"},"metadata":{}}]},{"cell_type":"code","source":"submission_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:16.993609Z","iopub.execute_input":"2023-11-27T21:01:16.994210Z","iopub.status.idle":"2023-11-27T21:01:17.003074Z","shell.execute_reply.started":"2023-11-27T21:01:16.994181Z","shell.execute_reply":"2023-11-27T21:01:17.002165Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"                ID  Yield\n0  ID_F9XXEXN2ADR2      0\n1  ID_SO3VW2X4QO93      0\n2  ID_UKUQ7JM8E894      0\n3  ID_QUISMWEZR2H4      0\n4  ID_25JGI455VKCZ      0","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>Yield</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ID_F9XXEXN2ADR2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ID_SO3VW2X4QO93</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ID_UKUQ7JM8E894</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ID_QUISMWEZR2H4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ID_25JGI455VKCZ</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"submission_df['Yield'] = preds\n\nsubmission_df.to_csv('submission.csv', index=None)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:17.004439Z","iopub.execute_input":"2023-11-27T21:01:17.005259Z","iopub.status.idle":"2023-11-27T21:01:17.021302Z","shell.execute_reply.started":"2023-11-27T21:01:17.005223Z","shell.execute_reply":"2023-11-27T21:01:17.020316Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"submission_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T21:01:17.022705Z","iopub.execute_input":"2023-11-27T21:01:17.023049Z","iopub.status.idle":"2023-11-27T21:01:17.032464Z","shell.execute_reply.started":"2023-11-27T21:01:17.023019Z","shell.execute_reply":"2023-11-27T21:01:17.031504Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"                ID  Yield\n0  ID_F9XXEXN2ADR2 535.74\n1  ID_SO3VW2X4QO93 350.86\n2  ID_UKUQ7JM8E894 512.32\n3  ID_QUISMWEZR2H4 341.26\n4  ID_25JGI455VKCZ 538.85","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>Yield</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ID_F9XXEXN2ADR2</td>\n      <td>535.74</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ID_SO3VW2X4QO93</td>\n      <td>350.86</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ID_UKUQ7JM8E894</td>\n      <td>512.32</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ID_QUISMWEZR2H4</td>\n      <td>341.26</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ID_25JGI455VKCZ</td>\n      <td>538.85</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}