hyperparameter tunning

catboost_info/catboost_training.json

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catboost_info/learn_error.tsv

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catboost_info/time_left.tsv

@@ -0,0 +1,101 @@
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logs/09_11_2023_02_15_59.log/09_11_2023_02_15_59.log

@@ -0,0 +1,12 @@
+[ 2023-09-11 02:16:03,696 ] 26 root - INFO - Entered the data ingestion method or component 
+[ 2023-09-11 02:16:03,702 ] 29 root - INFO - read the dataset as dataframe 
+[ 2023-09-11 02:16:03,705 ] 38 root - INFO - Train test split initiated 
+[ 2023-09-11 02:16:03,710 ] 45 root - INFO - ingestion of data completed 
+[ 2023-09-11 02:16:03,712 ] 68 root - INFO - read train and test data completed 
+[ 2023-09-11 02:16:03,712 ] 70 root - INFO - obtaining preprocessing object 
+[ 2023-09-11 02:16:03,712 ] 44 root - INFO - numerical columns: ['writing_score', 'reading_score'] 
+[ 2023-09-11 02:16:03,712 ] 51 root - INFO - categorical columns: ['gender', 'race_ethnicity', 'parental_level_of_education', 'lunch', 'test_preparation_course'] 
+[ 2023-09-11 02:16:03,713 ] 81 root - INFO - applying preprocessing object on training and testing dataframe 
+[ 2023-09-11 02:16:03,724 ] 100 root - INFO - saved preprocessing object. 
+[ 2023-09-11 02:16:03,724 ] 29 root - INFO - spliting training and test input data 
+[ 2023-09-11 02:16:03,724 ] 49 root - INFO - training models 

logs/09_11_2023_02_21_08.log/09_11_2023_02_21_08.log

@@ -0,0 +1,11 @@
+[ 2023-09-11 02:21:09,550 ] 26 root - INFO - Entered the data ingestion method or component 
+[ 2023-09-11 02:21:09,553 ] 29 root - INFO - read the dataset as dataframe 
+[ 2023-09-11 02:21:09,555 ] 38 root - INFO - Train test split initiated 
+[ 2023-09-11 02:21:09,559 ] 45 root - INFO - ingestion of data completed 
+[ 2023-09-11 02:21:09,561 ] 68 root - INFO - read train and test data completed 
+[ 2023-09-11 02:21:09,561 ] 70 root - INFO - obtaining preprocessing object 
+[ 2023-09-11 02:21:09,561 ] 44 root - INFO - numerical columns: ['writing_score', 'reading_score'] 
+[ 2023-09-11 02:21:09,561 ] 51 root - INFO - categorical columns: ['gender', 'race_ethnicity', 'parental_level_of_education', 'lunch', 'test_preparation_course'] 
+[ 2023-09-11 02:21:09,561 ] 81 root - INFO - applying preprocessing object on training and testing dataframe 
+[ 2023-09-11 02:21:09,569 ] 100 root - INFO - saved preprocessing object. 
+[ 2023-09-11 02:21:09,569 ] 29 root - INFO - spliting training and test input data 

logs/09_11_2023_02_21_31.log/09_11_2023_02_21_31.log

@@ -0,0 +1,21 @@
+[ 2023-09-11 02:21:32,314 ] 26 root - INFO - Entered the data ingestion method or component 
+[ 2023-09-11 02:21:32,317 ] 29 root - INFO - read the dataset as dataframe 
+[ 2023-09-11 02:21:32,319 ] 38 root - INFO - Train test split initiated 
+[ 2023-09-11 02:21:32,323 ] 45 root - INFO - ingestion of data completed 
+[ 2023-09-11 02:21:32,324 ] 68 root - INFO - read train and test data completed 
+[ 2023-09-11 02:21:32,324 ] 70 root - INFO - obtaining preprocessing object 
+[ 2023-09-11 02:21:32,324 ] 44 root - INFO - numerical columns: ['writing_score', 'reading_score'] 
+[ 2023-09-11 02:21:32,324 ] 51 root - INFO - categorical columns: ['gender', 'race_ethnicity', 'parental_level_of_education', 'lunch', 'test_preparation_course'] 
+[ 2023-09-11 02:21:32,325 ] 81 root - INFO - applying preprocessing object on training and testing dataframe 
+[ 2023-09-11 02:21:32,332 ] 100 root - INFO - saved preprocessing object. 
+[ 2023-09-11 02:21:32,332 ] 30 root - INFO - spliting training and test input data 
+[ 2023-09-11 02:21:32,333 ] 87 root - INFO - training models 
+[ 2023-09-11 02:21:32,333 ] 33 root - INFO - training started 
+[ 2023-09-11 02:21:35,327 ] 33 root - INFO - training started 
+[ 2023-09-11 02:21:35,455 ] 33 root - INFO - training started 
+[ 2023-09-11 02:21:57,385 ] 33 root - INFO - training started 
+[ 2023-09-11 02:21:57,453 ] 33 root - INFO - training started 
+[ 2023-09-11 02:22:02,593 ] 33 root - INFO - training started 
+[ 2023-09-11 02:22:06,525 ] 33 root - INFO - training started 
+[ 2023-09-11 02:22:13,794 ] 92 root - INFO - model trained 
+[ 2023-09-11 02:22:13,794 ] 105 root - INFO - best model found 

src/components/model_trainer.py

@@ -8,6 +8,7 @@ from sklearn.tree import DecisionTreeRegressor
 from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor,AdaBoostRegressor
 from xgboost import XGBRegressor
 from sklearn.neighbors import KNeighborsRegressor
+from catboost import CatBoostRegressor
 
 
 
@@ -36,19 +37,57 @@ class ModelTrainer:
 
                 )
             models = {
-                "LinearRegression":LinearRegression(),
-                "Ridge":Ridge(),
-                "Lasso":Lasso(),
-                "KNeighborsRegressor":KNeighborsRegressor(),
-                "DecisionTreeRegressor":DecisionTreeRegressor(),
-                "AdaBoostRegressor":AdaBoostRegressor(),
-                "RandomForestRegressor":RandomForestRegressor()
-                # "CatBoostRegressor":CatBoostRegressor(verbose=False),
+                "Random Forest": RandomForestRegressor(),
+                "Decision Tree": DecisionTreeRegressor(),
+                "Gradient Boosting": GradientBoostingRegressor(),
+                "Linear Regression": LinearRegression(),
+                "XGBRegressor": XGBRegressor(),
+                "CatBoosting Regressor": CatBoostRegressor(verbose=False),
+                "AdaBoost Regressor": AdaBoostRegressor(),
             }
+            params={
+                "Decision Tree": {
+                    'criterion':['squared_error', 'friedman_mse', 'absolute_error', 'poisson'],
+                    # 'splitter':['best','random'],
+                    # 'max_features':['sqrt','log2'],
+                },
+                "Random Forest":{
+                    # 'criterion':['squared_error', 'friedman_mse', 'absolute_error', 'poisson'],
+                 
+                    # 'max_features':['sqrt','log2',None],
+                    'n_estimators': [8,16,32,64,128,256]
+                },
+                "Gradient Boosting":{
+                    # 'loss':['squared_error', 'huber', 'absolute_error', 'quantile'],
+                    'learning_rate':[.1,.01,.05,.001],
+                    'subsample':[0.6,0.7,0.75,0.8,0.85,0.9],
+                    # 'criterion':['squared_error', 'friedman_mse'],
+                    # 'max_features':['auto','sqrt','log2'],
+                    'n_estimators': [8,16,32,64,128,256]
+                },
+                "Linear Regression":{},
+                "XGBRegressor":{
+                    'learning_rate':[.1,.01,.05,.001],
+                    'n_estimators': [8,16,32,64,128,256]
+                },
+                "CatBoosting Regressor":{
+                    'depth': [6,8,10],
+                    'learning_rate': [0.01, 0.05, 0.1],
+                    'iterations': [30, 50, 100]
+                },
+                "AdaBoost Regressor":{
+                    'learning_rate':[.1,.01,0.5,.001],
+                    # 'loss':['linear','square','exponential'],
+                    'n_estimators': [8,16,32,64,128,256]
+                }
+                
+            }
+
 
             logging.info('training models')
 
-            model_report:dict=evaluate_models(X_train=X_train,y_train=y_train,X_test=X_test,y_test=y_test,models=models)
+            model_report:dict=evaluate_models(X_train=X_train,y_train=y_train,X_test=X_test,y_test=y_test,
+                                              models=models,params=params)
 
             logging.info("model trained") 
 

src/utils.py

@@ -7,7 +7,7 @@ import pandas as pd
 import pickle
 
 from sklearn.metrics import r2_score
-
+from sklearn.model_selection import GridSearchCV
 
 from src.logger import logging
 from src.exception import CustomException
@@ -22,14 +22,20 @@ def save_object(file_path, obj):
     except Exception as e:
         raise CustomException(e,sys)
     
-def evaluate_models(X_train, y_train, X_test, y_test, models):
+def evaluate_models(X_train, y_train, X_test, y_test, models,params):
     try:
         report = {}
 
         for i in range(len(list(models))):
             model = list(models.values())[i]
+            param = params[list(models.keys())[i]]
             
             logging.info('training started')
+
+            gs = GridSearchCV(model,param_grid=param,cv=5,verbose=False)
+            gs.fit(X_train,y_train)
+
+            model.set_params(**gs.best_params_)
             model.fit(X_train,y_train)
 
             y_train_pred = model.predict(X_train)