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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+argile.jpeg filter=lfs diff=lfs merge=lfs -text
+bad.jpeg filter=lfs diff=lfs merge=lfs -text

concrete.csv

@@ -0,0 +1,1031 @@
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logs.log

@@ -0,0 +1,902 @@
+2024-06-20 19:34:24,157:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2024-06-20 19:34:24,157:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2024-06-20 19:34:24,157:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2024-06-20 19:34:24,157:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2024-06-20 19:35:11,420:INFO:PyCaret RegressionExperiment
+2024-06-20 19:35:11,420:INFO:Logging name: reg-default-name
+2024-06-20 19:35:11,420:INFO:ML Usecase: MLUsecase.REGRESSION
+2024-06-20 19:35:11,420:INFO:version 3.3.2
+2024-06-20 19:35:11,420:INFO:Initializing setup()
+2024-06-20 19:35:11,421:INFO:self.USI: 9ad7
+2024-06-20 19:35:11,421:INFO:self._variable_keys: {'html_param', 'idx', '_ml_usecase', 'X', 'target_param', 'USI', 'memory', 'log_plots_param', 'exp_id', 'y_train', 'seed', 'gpu_param', 'fold_generator', 'fold_groups_param', 'X_test', 'transform_target_param', 'y_test', 'n_jobs_param', 'exp_name_log', '_available_plots', 'gpu_n_jobs_param', 'logging_param', 'y', 'X_train', 'fold_shuffle_param', 'data', 'pipeline'}
+2024-06-20 19:35:11,421:INFO:Checking environment
+2024-06-20 19:35:11,421:INFO:python_version: 3.11.9
+2024-06-20 19:35:11,421:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr  2 2024 10:12:12')
+2024-06-20 19:35:11,421:INFO:machine: AMD64
+2024-06-20 19:35:11,421:INFO:platform: Windows-10-10.0.22631-SP0
+2024-06-20 19:35:11,428:INFO:Memory: svmem(total=16948453376, available=2113404928, percent=87.5, used=14835048448, free=2113404928)
+2024-06-20 19:35:11,428:INFO:Physical Core: 6
+2024-06-20 19:35:11,428:INFO:Logical Core: 12
+2024-06-20 19:35:11,428:INFO:Checking libraries
+2024-06-20 19:35:11,428:INFO:System:
+2024-06-20 19:35:11,428:INFO:    python: 3.11.9 (tags/v3.11.9:de54cf5, Apr  2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)]
+2024-06-20 19:35:11,428:INFO:executable: c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Scripts\python.exe
+2024-06-20 19:35:11,428:INFO:   machine: Windows-10-10.0.22631-SP0
+2024-06-20 19:35:11,429:INFO:PyCaret required dependencies:
+2024-06-20 19:35:15,895:INFO:                 pip: 24.0
+2024-06-20 19:35:15,895:INFO:          setuptools: 65.5.0
+2024-06-20 19:35:15,895:INFO:             pycaret: 3.3.2
+2024-06-20 19:35:15,895:INFO:             IPython: 8.25.0
+2024-06-20 19:35:15,895:INFO:          ipywidgets: 8.1.3
+2024-06-20 19:35:15,895:INFO:                tqdm: 4.66.4
+2024-06-20 19:35:15,895:INFO:               numpy: 1.26.4
+2024-06-20 19:35:15,895:INFO:              pandas: 2.1.4
+2024-06-20 19:35:15,896:INFO:              jinja2: 3.1.4
+2024-06-20 19:35:15,896:INFO:               scipy: 1.11.4
+2024-06-20 19:35:15,896:INFO:              joblib: 1.3.2
+2024-06-20 19:35:15,896:INFO:             sklearn: 1.4.2
+2024-06-20 19:35:15,896:INFO:                pyod: 2.0.0
+2024-06-20 19:35:15,896:INFO:            imblearn: 0.12.3
+2024-06-20 19:35:15,896:INFO:   category_encoders: 2.6.3
+2024-06-20 19:35:15,896:INFO:            lightgbm: 4.4.0
+2024-06-20 19:35:15,896:INFO:               numba: 0.60.0
+2024-06-20 19:35:15,896:INFO:            requests: 2.32.3
+2024-06-20 19:35:15,896:INFO:          matplotlib: 3.7.5
+2024-06-20 19:35:15,896:INFO:          scikitplot: 0.3.7
+2024-06-20 19:35:15,896:INFO:         yellowbrick: 1.5
+2024-06-20 19:35:15,896:INFO:              plotly: 5.22.0
+2024-06-20 19:35:15,896:INFO:    plotly-resampler: Not installed
+2024-06-20 19:35:15,896:INFO:             kaleido: 0.2.1
+2024-06-20 19:35:15,896:INFO:           schemdraw: 0.15
+2024-06-20 19:35:15,896:INFO:         statsmodels: 0.14.2
+2024-06-20 19:35:15,896:INFO:              sktime: 0.26.0
+2024-06-20 19:35:15,896:INFO:               tbats: 1.1.3
+2024-06-20 19:35:15,896:INFO:            pmdarima: 2.0.4
+2024-06-20 19:35:15,896:INFO:              psutil: 5.9.8
+2024-06-20 19:35:15,896:INFO:          markupsafe: 2.1.5
+2024-06-20 19:35:15,896:INFO:             pickle5: Not installed
+2024-06-20 19:35:15,896:INFO:         cloudpickle: 3.0.0
+2024-06-20 19:35:15,896:INFO:         deprecation: 2.1.0
+2024-06-20 19:35:15,896:INFO:              xxhash: 3.4.1
+2024-06-20 19:35:15,896:INFO:           wurlitzer: Not installed
+2024-06-20 19:35:15,896:INFO:PyCaret optional dependencies:
+2024-06-20 19:35:24,088:INFO:                shap: 0.44.1
+2024-06-20 19:35:24,088:INFO:           interpret: 0.6.1
+2024-06-20 19:35:24,088:INFO:                umap: 0.5.6
+2024-06-20 19:35:24,088:INFO:     ydata_profiling: 4.8.3
+2024-06-20 19:35:24,088:INFO:  explainerdashboard: 0.4.7
+2024-06-20 19:35:24,088:INFO:             autoviz: Not installed
+2024-06-20 19:35:24,088:INFO:           fairlearn: 0.7.0
+2024-06-20 19:35:24,088:INFO:          deepchecks: Not installed
+2024-06-20 19:35:24,088:INFO:             xgboost: Not installed
+2024-06-20 19:35:24,088:INFO:            catboost: 1.2.5
+2024-06-20 19:35:24,088:INFO:              kmodes: 0.12.2
+2024-06-20 19:35:24,088:INFO:             mlxtend: 0.23.1
+2024-06-20 19:35:24,088:INFO:       statsforecast: 1.5.0
+2024-06-20 19:35:24,088:INFO:        tune_sklearn: Not installed
+2024-06-20 19:35:24,088:INFO:                 ray: Not installed
+2024-06-20 19:35:24,088:INFO:            hyperopt: 0.2.7
+2024-06-20 19:35:24,088:INFO:              optuna: 3.6.1
+2024-06-20 19:35:24,088:INFO:               skopt: 0.10.2
+2024-06-20 19:35:24,088:INFO:              mlflow: 2.14.0
+2024-06-20 19:35:24,088:INFO:              gradio: 4.36.1
+2024-06-20 19:35:24,088:INFO:             fastapi: 0.111.0
+2024-06-20 19:35:24,088:INFO:             uvicorn: 0.30.1
+2024-06-20 19:35:24,088:INFO:              m2cgen: 0.10.0
+2024-06-20 19:35:24,088:INFO:           evidently: 0.4.27
+2024-06-20 19:35:24,088:INFO:               fugue: 0.8.7
+2024-06-20 19:35:24,088:INFO:           streamlit: Not installed
+2024-06-20 19:35:24,088:INFO:             prophet: Not installed
+2024-06-20 19:35:24,088:INFO:None
+2024-06-20 19:35:24,088:INFO:Set up data.
+2024-06-20 19:35:24,099:INFO:Set up folding strategy.
+2024-06-20 19:35:24,099:INFO:Set up train/test split.
+2024-06-20 19:35:24,114:INFO:Set up index.
+2024-06-20 19:35:24,119:INFO:Assigning column types.
+2024-06-20 19:35:24,123:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
+2024-06-20 19:35:24,123:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None.
+2024-06-20 19:35:24,127:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
+2024-06-20 19:35:24,131:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
+2024-06-20 19:35:24,181:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:24,216:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
+2024-06-20 19:35:24,216:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:24,216:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,064:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,069:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,074:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,118:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,153:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,153:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,153:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,154:INFO:Engine successfully changes for model 'lasso' to 'sklearn'.
+2024-06-20 19:35:25,158:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,161:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,205:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,239:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,239:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,239:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,244:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,247:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,310:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,345:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,346:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,346:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,346:INFO:Engine successfully changes for model 'ridge' to 'sklearn'.
+2024-06-20 19:35:25,354:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,398:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,432:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,433:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,433:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,441:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,484:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,518:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,518:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,518:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,519:INFO:Engine successfully changes for model 'en' to 'sklearn'.
+2024-06-20 19:35:25,575:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,608:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,608:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,609:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,661:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,694:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,695:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,695:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,695:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
+2024-06-20 19:35:25,748:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,788:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,789:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,839:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
+2024-06-20 19:35:25,874:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,874:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:25,874:INFO:Engine successfully changes for model 'svm' to 'sklearn'.
+2024-06-20 19:35:25,960:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:25,960:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:26,046:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:26,046:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:26,048:INFO:Preparing preprocessing pipeline...
+2024-06-20 19:35:26,048:INFO:Set up simple imputation.
+2024-06-20 19:35:26,078:INFO:Finished creating preprocessing pipeline.
+2024-06-20 19:35:26,083:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\kowom\AppData\Local\Temp\joblib),
+         steps=[('numerical_imputer',
+                 TransformerWrapper(include=['cement', 'slag', 'ash', 'water',
+                                             'superplastic', 'coarseagg',
+                                             'fineagg', 'age'],
+                                    transformer=SimpleImputer())),
+                ('categorical_imputer',
+                 TransformerWrapper(include=[],
+                                    transformer=SimpleImputer(strategy='most_frequent')))])
+2024-06-20 19:35:26,083:INFO:Creating final display dataframe.
+2024-06-20 19:35:26,124:INFO:Setup _display_container:                     Description             Value
+0                    Session id               123
+1                        Target          strength
+2                   Target type        Regression
+3           Original data shape         (1030, 9)
+4        Transformed data shape         (1030, 9)
+5   Transformed train set shape          (824, 9)
+6    Transformed test set shape          (206, 9)
+7              Numeric features                 8
+8                    Preprocess              True
+9               Imputation type            simple
+10           Numeric imputation              mean
+11       Categorical imputation              mode
+12               Fold Generator             KFold
+13                  Fold Number                10
+14                     CPU Jobs                -1
+15                      Use GPU             False
+16               Log Experiment             False
+17              Experiment Name  reg-default-name
+18                          USI              9ad7
+2024-06-20 19:35:26,236:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:26,236:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:26,331:WARNING:
+'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
+Alternately, you can install this by running `pip install pycaret[models]`
+2024-06-20 19:35:26,331:INFO:Soft dependency imported: catboost: 1.2.5
+2024-06-20 19:35:26,332:INFO:setup() successfully completed in 14.97s...............
+2024-06-20 19:35:53,507:INFO:Initializing compare_models()
+2024-06-20 19:35:53,507:INFO:compare_models(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': <pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': <class 'pycaret.regression.oop.RegressionExperiment'>})
+2024-06-20 19:35:53,507:INFO:Checking exceptions
+2024-06-20 19:35:53,508:INFO:Preparing display monitor
+2024-06-20 19:35:53,531:INFO:Initializing Linear Regression
+2024-06-20 19:35:53,531:INFO:Total runtime is 0.0 minutes
+2024-06-20 19:35:53,535:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:35:53,536:INFO:Initializing create_model()
+2024-06-20 19:35:53,536:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:35:53,536:INFO:Checking exceptions
+2024-06-20 19:35:53,536:INFO:Importing libraries
+2024-06-20 19:35:53,536:INFO:Copying training dataset
+2024-06-20 19:35:53,542:INFO:Defining folds
+2024-06-20 19:35:53,542:INFO:Declaring metric variables
+2024-06-20 19:35:53,544:INFO:Importing untrained model
+2024-06-20 19:35:53,549:INFO:Linear Regression Imported successfully
+2024-06-20 19:35:53,555:INFO:Starting cross validation
+2024-06-20 19:35:53,566:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:04,301:INFO:Calculating mean and std
+2024-06-20 19:36:04,305:INFO:Creating metrics dataframe
+2024-06-20 19:36:04,385:INFO:Uploading results into container
+2024-06-20 19:36:04,387:INFO:Uploading model into container now
+2024-06-20 19:36:04,388:INFO:_master_model_container: 1
+2024-06-20 19:36:04,388:INFO:_display_container: 2
+2024-06-20 19:36:04,389:INFO:LinearRegression(n_jobs=-1)
+2024-06-20 19:36:04,389:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:04,555:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:04,555:INFO:Creating metrics dataframe
+2024-06-20 19:36:04,563:INFO:Initializing Lasso Regression
+2024-06-20 19:36:04,563:INFO:Total runtime is 0.18385961850484211 minutes
+2024-06-20 19:36:04,565:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:04,566:INFO:Initializing create_model()
+2024-06-20 19:36:04,566:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:04,566:INFO:Checking exceptions
+2024-06-20 19:36:04,566:INFO:Importing libraries
+2024-06-20 19:36:04,566:INFO:Copying training dataset
+2024-06-20 19:36:04,571:INFO:Defining folds
+2024-06-20 19:36:04,572:INFO:Declaring metric variables
+2024-06-20 19:36:04,574:INFO:Importing untrained model
+2024-06-20 19:36:04,578:INFO:Lasso Regression Imported successfully
+2024-06-20 19:36:04,588:INFO:Starting cross validation
+2024-06-20 19:36:04,591:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:07,100:INFO:Calculating mean and std
+2024-06-20 19:36:07,101:INFO:Creating metrics dataframe
+2024-06-20 19:36:07,104:INFO:Uploading results into container
+2024-06-20 19:36:07,105:INFO:Uploading model into container now
+2024-06-20 19:36:07,105:INFO:_master_model_container: 2
+2024-06-20 19:36:07,105:INFO:_display_container: 2
+2024-06-20 19:36:07,106:INFO:Lasso(random_state=123)
+2024-06-20 19:36:07,106:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:07,235:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:07,235:INFO:Creating metrics dataframe
+2024-06-20 19:36:07,254:INFO:Initializing Ridge Regression
+2024-06-20 19:36:07,254:INFO:Total runtime is 0.22871678670247395 minutes
+2024-06-20 19:36:07,256:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:07,257:INFO:Initializing create_model()
+2024-06-20 19:36:07,257:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:07,257:INFO:Checking exceptions
+2024-06-20 19:36:07,257:INFO:Importing libraries
+2024-06-20 19:36:07,257:INFO:Copying training dataset
+2024-06-20 19:36:07,260:INFO:Defining folds
+2024-06-20 19:36:07,260:INFO:Declaring metric variables
+2024-06-20 19:36:07,263:INFO:Importing untrained model
+2024-06-20 19:36:07,266:INFO:Ridge Regression Imported successfully
+2024-06-20 19:36:07,271:INFO:Starting cross validation
+2024-06-20 19:36:07,271:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:07,370:INFO:Calculating mean and std
+2024-06-20 19:36:07,370:INFO:Creating metrics dataframe
+2024-06-20 19:36:07,372:INFO:Uploading results into container
+2024-06-20 19:36:07,372:INFO:Uploading model into container now
+2024-06-20 19:36:07,372:INFO:_master_model_container: 3
+2024-06-20 19:36:07,374:INFO:_display_container: 2
+2024-06-20 19:36:07,374:INFO:Ridge(random_state=123)
+2024-06-20 19:36:07,374:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:07,495:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:07,496:INFO:Creating metrics dataframe
+2024-06-20 19:36:07,503:INFO:Initializing Elastic Net
+2024-06-20 19:36:07,503:INFO:Total runtime is 0.23286216656366981 minutes
+2024-06-20 19:36:07,507:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:07,507:INFO:Initializing create_model()
+2024-06-20 19:36:07,507:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:07,507:INFO:Checking exceptions
+2024-06-20 19:36:07,507:INFO:Importing libraries
+2024-06-20 19:36:07,508:INFO:Copying training dataset
+2024-06-20 19:36:07,511:INFO:Defining folds
+2024-06-20 19:36:07,511:INFO:Declaring metric variables
+2024-06-20 19:36:07,515:INFO:Importing untrained model
+2024-06-20 19:36:07,520:INFO:Elastic Net Imported successfully
+2024-06-20 19:36:07,528:INFO:Starting cross validation
+2024-06-20 19:36:07,529:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:07,636:INFO:Calculating mean and std
+2024-06-20 19:36:07,637:INFO:Creating metrics dataframe
+2024-06-20 19:36:07,639:INFO:Uploading results into container
+2024-06-20 19:36:07,640:INFO:Uploading model into container now
+2024-06-20 19:36:07,640:INFO:_master_model_container: 4
+2024-06-20 19:36:07,640:INFO:_display_container: 2
+2024-06-20 19:36:07,640:INFO:ElasticNet(random_state=123)
+2024-06-20 19:36:07,640:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:07,749:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:07,749:INFO:Creating metrics dataframe
+2024-06-20 19:36:07,755:INFO:Initializing Least Angle Regression
+2024-06-20 19:36:07,755:INFO:Total runtime is 0.2370623429616292 minutes
+2024-06-20 19:36:07,757:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:07,757:INFO:Initializing create_model()
+2024-06-20 19:36:07,757:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:07,758:INFO:Checking exceptions
+2024-06-20 19:36:07,758:INFO:Importing libraries
+2024-06-20 19:36:07,758:INFO:Copying training dataset
+2024-06-20 19:36:07,760:INFO:Defining folds
+2024-06-20 19:36:07,760:INFO:Declaring metric variables
+2024-06-20 19:36:07,763:INFO:Importing untrained model
+2024-06-20 19:36:07,766:INFO:Least Angle Regression Imported successfully
+2024-06-20 19:36:07,771:INFO:Starting cross validation
+2024-06-20 19:36:07,772:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:08,278:INFO:Calculating mean and std
+2024-06-20 19:36:08,278:INFO:Creating metrics dataframe
+2024-06-20 19:36:08,280:INFO:Uploading results into container
+2024-06-20 19:36:08,281:INFO:Uploading model into container now
+2024-06-20 19:36:08,281:INFO:_master_model_container: 5
+2024-06-20 19:36:08,282:INFO:_display_container: 2
+2024-06-20 19:36:08,282:INFO:Lars(random_state=123)
+2024-06-20 19:36:08,282:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:08,407:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:08,408:INFO:Creating metrics dataframe
+2024-06-20 19:36:08,414:INFO:Initializing Lasso Least Angle Regression
+2024-06-20 19:36:08,414:INFO:Total runtime is 0.2480380018552144 minutes
+2024-06-20 19:36:08,418:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:08,418:INFO:Initializing create_model()
+2024-06-20 19:36:08,418:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:08,418:INFO:Checking exceptions
+2024-06-20 19:36:08,418:INFO:Importing libraries
+2024-06-20 19:36:08,418:INFO:Copying training dataset
+2024-06-20 19:36:08,422:INFO:Defining folds
+2024-06-20 19:36:08,422:INFO:Declaring metric variables
+2024-06-20 19:36:08,424:INFO:Importing untrained model
+2024-06-20 19:36:08,427:INFO:Lasso Least Angle Regression Imported successfully
+2024-06-20 19:36:08,433:INFO:Starting cross validation
+2024-06-20 19:36:08,434:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:08,512:INFO:Calculating mean and std
+2024-06-20 19:36:08,512:INFO:Creating metrics dataframe
+2024-06-20 19:36:08,514:INFO:Uploading results into container
+2024-06-20 19:36:08,515:INFO:Uploading model into container now
+2024-06-20 19:36:08,515:INFO:_master_model_container: 6
+2024-06-20 19:36:08,515:INFO:_display_container: 2
+2024-06-20 19:36:08,515:INFO:LassoLars(random_state=123)
+2024-06-20 19:36:08,515:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:08,643:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:08,643:INFO:Creating metrics dataframe
+2024-06-20 19:36:08,649:INFO:Initializing Orthogonal Matching Pursuit
+2024-06-20 19:36:08,649:INFO:Total runtime is 0.251967978477478 minutes
+2024-06-20 19:36:08,652:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:08,652:INFO:Initializing create_model()
+2024-06-20 19:36:08,652:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:08,652:INFO:Checking exceptions
+2024-06-20 19:36:08,652:INFO:Importing libraries
+2024-06-20 19:36:08,653:INFO:Copying training dataset
+2024-06-20 19:36:08,656:INFO:Defining folds
+2024-06-20 19:36:08,656:INFO:Declaring metric variables
+2024-06-20 19:36:08,659:INFO:Importing untrained model
+2024-06-20 19:36:08,662:INFO:Orthogonal Matching Pursuit Imported successfully
+2024-06-20 19:36:08,668:INFO:Starting cross validation
+2024-06-20 19:36:08,670:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:08,743:INFO:Calculating mean and std
+2024-06-20 19:36:08,743:INFO:Creating metrics dataframe
+2024-06-20 19:36:08,745:INFO:Uploading results into container
+2024-06-20 19:36:08,745:INFO:Uploading model into container now
+2024-06-20 19:36:08,745:INFO:_master_model_container: 7
+2024-06-20 19:36:08,746:INFO:_display_container: 2
+2024-06-20 19:36:08,746:INFO:OrthogonalMatchingPursuit()
+2024-06-20 19:36:08,746:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:08,868:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:08,868:INFO:Creating metrics dataframe
+2024-06-20 19:36:08,874:INFO:Initializing Bayesian Ridge
+2024-06-20 19:36:08,874:INFO:Total runtime is 0.25572057167689 minutes
+2024-06-20 19:36:08,878:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:08,878:INFO:Initializing create_model()
+2024-06-20 19:36:08,878:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:08,878:INFO:Checking exceptions
+2024-06-20 19:36:08,878:INFO:Importing libraries
+2024-06-20 19:36:08,878:INFO:Copying training dataset
+2024-06-20 19:36:08,882:INFO:Defining folds
+2024-06-20 19:36:08,882:INFO:Declaring metric variables
+2024-06-20 19:36:08,884:INFO:Importing untrained model
+2024-06-20 19:36:08,889:INFO:Bayesian Ridge Imported successfully
+2024-06-20 19:36:08,893:INFO:Starting cross validation
+2024-06-20 19:36:08,895:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:08,973:INFO:Calculating mean and std
+2024-06-20 19:36:08,973:INFO:Creating metrics dataframe
+2024-06-20 19:36:08,975:INFO:Uploading results into container
+2024-06-20 19:36:08,975:INFO:Uploading model into container now
+2024-06-20 19:36:08,975:INFO:_master_model_container: 8
+2024-06-20 19:36:08,975:INFO:_display_container: 2
+2024-06-20 19:36:08,975:INFO:BayesianRidge()
+2024-06-20 19:36:08,976:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:09,087:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:09,087:INFO:Creating metrics dataframe
+2024-06-20 19:36:09,092:INFO:Initializing Passive Aggressive Regressor
+2024-06-20 19:36:09,092:INFO:Total runtime is 0.25934991439183547 minutes
+2024-06-20 19:36:09,094:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:09,094:INFO:Initializing create_model()
+2024-06-20 19:36:09,094:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:09,094:INFO:Checking exceptions
+2024-06-20 19:36:09,095:INFO:Importing libraries
+2024-06-20 19:36:09,095:INFO:Copying training dataset
+2024-06-20 19:36:09,099:INFO:Defining folds
+2024-06-20 19:36:09,099:INFO:Declaring metric variables
+2024-06-20 19:36:09,101:INFO:Importing untrained model
+2024-06-20 19:36:09,105:INFO:Passive Aggressive Regressor Imported successfully
+2024-06-20 19:36:09,110:INFO:Starting cross validation
+2024-06-20 19:36:09,111:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:09,205:INFO:Calculating mean and std
+2024-06-20 19:36:09,206:INFO:Creating metrics dataframe
+2024-06-20 19:36:09,208:INFO:Uploading results into container
+2024-06-20 19:36:09,208:INFO:Uploading model into container now
+2024-06-20 19:36:09,210:INFO:_master_model_container: 9
+2024-06-20 19:36:09,210:INFO:_display_container: 2
+2024-06-20 19:36:09,210:INFO:PassiveAggressiveRegressor(random_state=123)
+2024-06-20 19:36:09,210:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:09,338:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:09,338:INFO:Creating metrics dataframe
+2024-06-20 19:36:09,344:INFO:Initializing Huber Regressor
+2024-06-20 19:36:09,344:INFO:Total runtime is 0.26354595025380445 minutes
+2024-06-20 19:36:09,346:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:09,347:INFO:Initializing create_model()
+2024-06-20 19:36:09,347:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:09,347:INFO:Checking exceptions
+2024-06-20 19:36:09,347:INFO:Importing libraries
+2024-06-20 19:36:09,347:INFO:Copying training dataset
+2024-06-20 19:36:09,351:INFO:Defining folds
+2024-06-20 19:36:09,351:INFO:Declaring metric variables
+2024-06-20 19:36:09,354:INFO:Importing untrained model
+2024-06-20 19:36:09,358:INFO:Huber Regressor Imported successfully
+2024-06-20 19:36:09,364:INFO:Starting cross validation
+2024-06-20 19:36:09,365:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:09,426:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,427:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,437:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,437:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,440:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,443:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,445:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,461:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,467:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+
+2024-06-20 19:36:09,481:INFO:Calculating mean and std
+2024-06-20 19:36:09,481:INFO:Creating metrics dataframe
+2024-06-20 19:36:09,484:INFO:Uploading results into container
+2024-06-20 19:36:09,484:INFO:Uploading model into container now
+2024-06-20 19:36:09,484:INFO:_master_model_container: 10
+2024-06-20 19:36:09,484:INFO:_display_container: 2
+2024-06-20 19:36:09,485:INFO:HuberRegressor()
+2024-06-20 19:36:09,485:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:09,594:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:09,595:INFO:Creating metrics dataframe
+2024-06-20 19:36:09,602:INFO:Initializing K Neighbors Regressor
+2024-06-20 19:36:09,602:INFO:Total runtime is 0.26784221331278474 minutes
+2024-06-20 19:36:09,605:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:09,605:INFO:Initializing create_model()
+2024-06-20 19:36:09,606:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:09,606:INFO:Checking exceptions
+2024-06-20 19:36:09,606:INFO:Importing libraries
+2024-06-20 19:36:09,606:INFO:Copying training dataset
+2024-06-20 19:36:09,608:INFO:Defining folds
+2024-06-20 19:36:09,608:INFO:Declaring metric variables
+2024-06-20 19:36:09,610:INFO:Importing untrained model
+2024-06-20 19:36:09,614:INFO:K Neighbors Regressor Imported successfully
+2024-06-20 19:36:09,619:INFO:Starting cross validation
+2024-06-20 19:36:09,620:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:09,722:INFO:Calculating mean and std
+2024-06-20 19:36:09,723:INFO:Creating metrics dataframe
+2024-06-20 19:36:09,725:INFO:Uploading results into container
+2024-06-20 19:36:09,725:INFO:Uploading model into container now
+2024-06-20 19:36:09,725:INFO:_master_model_container: 11
+2024-06-20 19:36:09,725:INFO:_display_container: 2
+2024-06-20 19:36:09,725:INFO:KNeighborsRegressor(n_jobs=-1)
+2024-06-20 19:36:09,725:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:09,852:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:09,852:INFO:Creating metrics dataframe
+2024-06-20 19:36:09,859:INFO:Initializing Decision Tree Regressor
+2024-06-20 19:36:09,859:INFO:Total runtime is 0.27212407191594434 minutes
+2024-06-20 19:36:09,862:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:09,863:INFO:Initializing create_model()
+2024-06-20 19:36:09,863:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:09,863:INFO:Checking exceptions
+2024-06-20 19:36:09,863:INFO:Importing libraries
+2024-06-20 19:36:09,863:INFO:Copying training dataset
+2024-06-20 19:36:09,867:INFO:Defining folds
+2024-06-20 19:36:09,868:INFO:Declaring metric variables
+2024-06-20 19:36:09,871:INFO:Importing untrained model
+2024-06-20 19:36:09,874:INFO:Decision Tree Regressor Imported successfully
+2024-06-20 19:36:09,879:INFO:Starting cross validation
+2024-06-20 19:36:09,880:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:09,958:INFO:Calculating mean and std
+2024-06-20 19:36:09,958:INFO:Creating metrics dataframe
+2024-06-20 19:36:09,960:INFO:Uploading results into container
+2024-06-20 19:36:09,960:INFO:Uploading model into container now
+2024-06-20 19:36:09,960:INFO:_master_model_container: 12
+2024-06-20 19:36:09,960:INFO:_display_container: 2
+2024-06-20 19:36:09,961:INFO:DecisionTreeRegressor(random_state=123)
+2024-06-20 19:36:09,961:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:10,077:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:10,077:INFO:Creating metrics dataframe
+2024-06-20 19:36:10,084:INFO:Initializing Random Forest Regressor
+2024-06-20 19:36:10,084:INFO:Total runtime is 0.2758734345436095 minutes
+2024-06-20 19:36:10,086:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:10,086:INFO:Initializing create_model()
+2024-06-20 19:36:10,086:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:10,086:INFO:Checking exceptions
+2024-06-20 19:36:10,086:INFO:Importing libraries
+2024-06-20 19:36:10,086:INFO:Copying training dataset
+2024-06-20 19:36:10,089:INFO:Defining folds
+2024-06-20 19:36:10,090:INFO:Declaring metric variables
+2024-06-20 19:36:10,092:INFO:Importing untrained model
+2024-06-20 19:36:10,095:INFO:Random Forest Regressor Imported successfully
+2024-06-20 19:36:10,100:INFO:Starting cross validation
+2024-06-20 19:36:10,101:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:10,714:INFO:Calculating mean and std
+2024-06-20 19:36:10,716:INFO:Creating metrics dataframe
+2024-06-20 19:36:10,717:INFO:Uploading results into container
+2024-06-20 19:36:10,717:INFO:Uploading model into container now
+2024-06-20 19:36:10,718:INFO:_master_model_container: 13
+2024-06-20 19:36:10,718:INFO:_display_container: 2
+2024-06-20 19:36:10,718:INFO:RandomForestRegressor(n_jobs=-1, random_state=123)
+2024-06-20 19:36:10,718:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:10,829:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:10,829:INFO:Creating metrics dataframe
+2024-06-20 19:36:10,836:INFO:Initializing Extra Trees Regressor
+2024-06-20 19:36:10,836:INFO:Total runtime is 0.2884133060773213 minutes
+2024-06-20 19:36:10,838:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:10,838:INFO:Initializing create_model()
+2024-06-20 19:36:10,838:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:10,838:INFO:Checking exceptions
+2024-06-20 19:36:10,840:INFO:Importing libraries
+2024-06-20 19:36:10,840:INFO:Copying training dataset
+2024-06-20 19:36:10,842:INFO:Defining folds
+2024-06-20 19:36:10,842:INFO:Declaring metric variables
+2024-06-20 19:36:10,844:INFO:Importing untrained model
+2024-06-20 19:36:10,848:INFO:Extra Trees Regressor Imported successfully
+2024-06-20 19:36:10,855:INFO:Starting cross validation
+2024-06-20 19:36:10,855:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:11,284:INFO:Calculating mean and std
+2024-06-20 19:36:11,285:INFO:Creating metrics dataframe
+2024-06-20 19:36:11,286:INFO:Uploading results into container
+2024-06-20 19:36:11,286:INFO:Uploading model into container now
+2024-06-20 19:36:11,287:INFO:_master_model_container: 14
+2024-06-20 19:36:11,287:INFO:_display_container: 2
+2024-06-20 19:36:11,287:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123)
+2024-06-20 19:36:11,287:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:11,396:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:11,396:INFO:Creating metrics dataframe
+2024-06-20 19:36:11,402:INFO:Initializing AdaBoost Regressor
+2024-06-20 19:36:11,402:INFO:Total runtime is 0.297850203514099 minutes
+2024-06-20 19:36:11,404:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:11,404:INFO:Initializing create_model()
+2024-06-20 19:36:11,404:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:11,406:INFO:Checking exceptions
+2024-06-20 19:36:11,406:INFO:Importing libraries
+2024-06-20 19:36:11,406:INFO:Copying training dataset
+2024-06-20 19:36:11,408:INFO:Defining folds
+2024-06-20 19:36:11,408:INFO:Declaring metric variables
+2024-06-20 19:36:11,411:INFO:Importing untrained model
+2024-06-20 19:36:11,413:INFO:AdaBoost Regressor Imported successfully
+2024-06-20 19:36:11,418:INFO:Starting cross validation
+2024-06-20 19:36:11,419:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:11,636:INFO:Calculating mean and std
+2024-06-20 19:36:11,637:INFO:Creating metrics dataframe
+2024-06-20 19:36:11,638:INFO:Uploading results into container
+2024-06-20 19:36:11,638:INFO:Uploading model into container now
+2024-06-20 19:36:11,639:INFO:_master_model_container: 15
+2024-06-20 19:36:11,639:INFO:_display_container: 2
+2024-06-20 19:36:11,639:INFO:AdaBoostRegressor(random_state=123)
+2024-06-20 19:36:11,639:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:11,753:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:11,754:INFO:Creating metrics dataframe
+2024-06-20 19:36:11,761:INFO:Initializing Gradient Boosting Regressor
+2024-06-20 19:36:11,761:INFO:Total runtime is 0.30383289655049633 minutes
+2024-06-20 19:36:11,764:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:11,765:INFO:Initializing create_model()
+2024-06-20 19:36:11,765:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:11,765:INFO:Checking exceptions
+2024-06-20 19:36:11,765:INFO:Importing libraries
+2024-06-20 19:36:11,765:INFO:Copying training dataset
+2024-06-20 19:36:11,769:INFO:Defining folds
+2024-06-20 19:36:11,769:INFO:Declaring metric variables
+2024-06-20 19:36:11,771:INFO:Importing untrained model
+2024-06-20 19:36:11,774:INFO:Gradient Boosting Regressor Imported successfully
+2024-06-20 19:36:11,780:INFO:Starting cross validation
+2024-06-20 19:36:11,780:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:12,076:INFO:Calculating mean and std
+2024-06-20 19:36:12,077:INFO:Creating metrics dataframe
+2024-06-20 19:36:12,079:INFO:Uploading results into container
+2024-06-20 19:36:12,079:INFO:Uploading model into container now
+2024-06-20 19:36:12,079:INFO:_master_model_container: 16
+2024-06-20 19:36:12,079:INFO:_display_container: 2
+2024-06-20 19:36:12,080:INFO:GradientBoostingRegressor(random_state=123)
+2024-06-20 19:36:12,080:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:12,197:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:12,197:INFO:Creating metrics dataframe
+2024-06-20 19:36:12,206:INFO:Initializing Light Gradient Boosting Machine
+2024-06-20 19:36:12,206:INFO:Total runtime is 0.31123881737391146 minutes
+2024-06-20 19:36:12,209:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:12,210:INFO:Initializing create_model()
+2024-06-20 19:36:12,210:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:12,210:INFO:Checking exceptions
+2024-06-20 19:36:12,210:INFO:Importing libraries
+2024-06-20 19:36:12,210:INFO:Copying training dataset
+2024-06-20 19:36:12,214:INFO:Defining folds
+2024-06-20 19:36:12,214:INFO:Declaring metric variables
+2024-06-20 19:36:12,217:INFO:Importing untrained model
+2024-06-20 19:36:12,220:INFO:Light Gradient Boosting Machine Imported successfully
+2024-06-20 19:36:12,226:INFO:Starting cross validation
+2024-06-20 19:36:12,227:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:13,201:INFO:Calculating mean and std
+2024-06-20 19:36:13,202:INFO:Creating metrics dataframe
+2024-06-20 19:36:13,204:INFO:Uploading results into container
+2024-06-20 19:36:13,205:INFO:Uploading model into container now
+2024-06-20 19:36:13,205:INFO:_master_model_container: 17
+2024-06-20 19:36:13,205:INFO:_display_container: 2
+2024-06-20 19:36:13,205:INFO:LGBMRegressor(n_jobs=-1, random_state=123)
+2024-06-20 19:36:13,207:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:13,346:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:13,346:INFO:Creating metrics dataframe
+2024-06-20 19:36:13,353:INFO:Initializing CatBoost Regressor
+2024-06-20 19:36:13,353:INFO:Total runtime is 0.3303659796714782 minutes
+2024-06-20 19:36:13,355:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:13,356:INFO:Initializing create_model()
+2024-06-20 19:36:13,356:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=catboost, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:13,356:INFO:Checking exceptions
+2024-06-20 19:36:13,356:INFO:Importing libraries
+2024-06-20 19:36:13,356:INFO:Copying training dataset
+2024-06-20 19:36:13,359:INFO:Defining folds
+2024-06-20 19:36:13,359:INFO:Declaring metric variables
+2024-06-20 19:36:13,361:INFO:Importing untrained model
+2024-06-20 19:36:13,376:INFO:CatBoost Regressor Imported successfully
+2024-06-20 19:36:13,382:INFO:Starting cross validation
+2024-06-20 19:36:13,383:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:16,893:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: 
+9 fits failed out of a total of 10.
+The score on these train-test partitions for these parameters will be set to 0.0.
+If these failures are not expected, you can try to debug them by setting error_score='raise'.
+
+Below are more details about the failures:
+--------------------------------------------------------------------------------
+9 fits failed with the following error:
+Traceback (most recent call last):
+  File "c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
+    estimator.fit(X_train, y_train, **fit_params)
+  File "c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit
+    fitted_estimator = self._memory_fit(
+                       ^^^^^^^^^^^^^^^^^
+  File "c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\joblib\memory.py", line 353, in __call__
+    return self.func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one
+    transformer.fit(*args)
+  File "c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\catboost\core.py", line 5827, in fit
+    return self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline,
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\catboost\core.py", line 2400, in _fit
+    self._train(
+  File "c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\catboost\core.py", line 1780, in _train
+    self._object._train(train_pool, test_pool, params, allow_clear_pool, init_model._object if init_model else None)
+  File "_catboost.pyx", line 4833, in _catboost._CatBoost._train
+  File "_catboost.pyx", line 4882, in _catboost._CatBoost._train
+_catboost.CatBoostError: C:/Go_Agent/pipelines/BuildMaster/catboost.git/catboost/libs/train_lib/dir_helper.cpp:20: Can't create train working dir: catboost_info
+
+  warnings.warn(some_fits_failed_message, FitFailedWarning)
+
+2024-06-20 19:36:16,893:INFO:Calculating mean and std
+2024-06-20 19:36:16,895:INFO:Creating metrics dataframe
+2024-06-20 19:36:16,897:INFO:Uploading results into container
+2024-06-20 19:36:16,897:INFO:Uploading model into container now
+2024-06-20 19:36:16,897:INFO:_master_model_container: 18
+2024-06-20 19:36:16,898:INFO:_display_container: 2
+2024-06-20 19:36:16,898:INFO:<catboost.core.CatBoostRegressor object at 0x000002BBE3872D10>
+2024-06-20 19:36:16,898:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:17,022:WARNING:create_model() for <catboost.core.CatBoostRegressor object at 0x000002BBE3872D10> raised an exception or returned all 0.0, trying without fit_kwargs:
+2024-06-20 19:36:17,046:WARNING:Traceback (most recent call last):
+  File "c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 797, in compare_models
+    np.sum(
+AssertionError
+
+2024-06-20 19:36:17,047:INFO:Initializing create_model()
+2024-06-20 19:36:17,047:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=catboost, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:17,047:INFO:Checking exceptions
+2024-06-20 19:36:17,047:INFO:Importing libraries
+2024-06-20 19:36:17,047:INFO:Copying training dataset
+2024-06-20 19:36:17,050:INFO:Defining folds
+2024-06-20 19:36:17,050:INFO:Declaring metric variables
+2024-06-20 19:36:17,053:INFO:Importing untrained model
+2024-06-20 19:36:17,056:INFO:CatBoost Regressor Imported successfully
+2024-06-20 19:36:17,064:INFO:Starting cross validation
+2024-06-20 19:36:17,064:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:24,419:INFO:Calculating mean and std
+2024-06-20 19:36:24,420:INFO:Creating metrics dataframe
+2024-06-20 19:36:24,421:INFO:Uploading results into container
+2024-06-20 19:36:24,422:INFO:Uploading model into container now
+2024-06-20 19:36:24,423:INFO:_master_model_container: 19
+2024-06-20 19:36:24,423:INFO:_display_container: 2
+2024-06-20 19:36:24,423:INFO:<catboost.core.CatBoostRegressor object at 0x000002BBE48AE910>
+2024-06-20 19:36:24,423:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:24,548:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:24,548:INFO:Creating metrics dataframe
+2024-06-20 19:36:24,565:INFO:Initializing Dummy Regressor
+2024-06-20 19:36:24,565:INFO:Total runtime is 0.5172213117281594 minutes
+2024-06-20 19:36:24,568:INFO:SubProcess create_model() called ==================================
+2024-06-20 19:36:24,570:INFO:Initializing create_model()
+2024-06-20 19:36:24,570:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x000002BBD987F0D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:24,570:INFO:Checking exceptions
+2024-06-20 19:36:24,570:INFO:Importing libraries
+2024-06-20 19:36:24,570:INFO:Copying training dataset
+2024-06-20 19:36:24,573:INFO:Defining folds
+2024-06-20 19:36:24,573:INFO:Declaring metric variables
+2024-06-20 19:36:24,576:INFO:Importing untrained model
+2024-06-20 19:36:24,579:INFO:Dummy Regressor Imported successfully
+2024-06-20 19:36:24,585:INFO:Starting cross validation
+2024-06-20 19:36:24,586:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:24,651:INFO:Calculating mean and std
+2024-06-20 19:36:24,654:INFO:Creating metrics dataframe
+2024-06-20 19:36:24,655:INFO:Uploading results into container
+2024-06-20 19:36:24,655:INFO:Uploading model into container now
+2024-06-20 19:36:24,656:INFO:_master_model_container: 20
+2024-06-20 19:36:24,656:INFO:_display_container: 2
+2024-06-20 19:36:24,656:INFO:DummyRegressor()
+2024-06-20 19:36:24,656:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:24,777:INFO:SubProcess create_model() end ==================================
+2024-06-20 19:36:24,777:INFO:Creating metrics dataframe
+2024-06-20 19:36:24,784:WARNING:c:\Users\kowom\Desktop\SN Keyce\PycaretVenv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead.
+  .applymap(highlight_cols, subset=["TT (Sec)"])
+
+2024-06-20 19:36:24,791:INFO:Initializing create_model()
+2024-06-20 19:36:24,791:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=<catboost.core.CatBoostRegressor object at 0x000002BBE48AE910>, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:24,791:INFO:Checking exceptions
+2024-06-20 19:36:24,793:INFO:Importing libraries
+2024-06-20 19:36:24,793:INFO:Copying training dataset
+2024-06-20 19:36:24,796:INFO:Defining folds
+2024-06-20 19:36:24,796:INFO:Declaring metric variables
+2024-06-20 19:36:24,796:INFO:Importing untrained model
+2024-06-20 19:36:24,796:INFO:Declaring custom model
+2024-06-20 19:36:24,797:INFO:CatBoost Regressor Imported successfully
+2024-06-20 19:36:24,797:INFO:Cross validation set to False
+2024-06-20 19:36:24,798:INFO:Fitting Model
+2024-06-20 19:36:26,176:INFO:<catboost.core.CatBoostRegressor object at 0x000002BBE4008B10>
+2024-06-20 19:36:26,176:INFO:create_model() successfully completed......................................
+2024-06-20 19:36:26,307:INFO:_master_model_container: 20
+2024-06-20 19:36:26,308:INFO:_display_container: 2
+2024-06-20 19:36:26,308:INFO:<catboost.core.CatBoostRegressor object at 0x000002BBE4008B10>
+2024-06-20 19:36:26,308:INFO:compare_models() successfully completed......................................
+2024-06-20 19:36:51,865:INFO:Initializing create_model()
+2024-06-20 19:36:51,865:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=catboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:36:51,865:INFO:Checking exceptions
+2024-06-20 19:36:51,878:INFO:Importing libraries
+2024-06-20 19:36:51,878:INFO:Copying training dataset
+2024-06-20 19:36:51,883:INFO:Defining folds
+2024-06-20 19:36:51,883:INFO:Declaring metric variables
+2024-06-20 19:36:51,887:INFO:Importing untrained model
+2024-06-20 19:36:51,891:INFO:CatBoost Regressor Imported successfully
+2024-06-20 19:36:51,898:INFO:Starting cross validation
+2024-06-20 19:36:51,900:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:36:58,006:INFO:Calculating mean and std
+2024-06-20 19:36:58,007:INFO:Creating metrics dataframe
+2024-06-20 19:36:58,011:INFO:Finalizing model
+2024-06-20 19:36:59,345:INFO:Uploading results into container
+2024-06-20 19:36:59,346:INFO:Uploading model into container now
+2024-06-20 19:36:59,353:INFO:_master_model_container: 21
+2024-06-20 19:36:59,353:INFO:_display_container: 3
+2024-06-20 19:36:59,353:INFO:<catboost.core.CatBoostRegressor object at 0x000002BBE41ED510>
+2024-06-20 19:36:59,353:INFO:create_model() successfully completed......................................
+2024-06-20 19:37:12,970:INFO:Initializing plot_model()
+2024-06-20 19:37:12,970:INFO:plot_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=<catboost.core.CatBoostRegressor object at 0x000002BBE41ED510>, plot=residuals, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None)
+2024-06-20 19:37:12,970:INFO:Checking exceptions
+2024-06-20 19:37:12,973:INFO:Preloading libraries
+2024-06-20 19:37:12,976:INFO:Copying training dataset
+2024-06-20 19:37:12,976:INFO:Plot type: residuals
+2024-06-20 19:37:13,092:INFO:Fitting Model
+2024-06-20 19:37:13,178:INFO:Scoring test/hold-out set
+2024-06-20 19:37:13,598:INFO:Visual Rendered Successfully
+2024-06-20 19:37:13,738:INFO:plot_model() successfully completed......................................
+2024-06-20 19:59:19,422:INFO:Initializing create_model()
+2024-06-20 19:59:19,422:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=lightgbm, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
+2024-06-20 19:59:19,422:INFO:Checking exceptions
+2024-06-20 19:59:19,435:INFO:Importing libraries
+2024-06-20 19:59:19,436:INFO:Copying training dataset
+2024-06-20 19:59:19,438:INFO:Defining folds
+2024-06-20 19:59:19,438:INFO:Declaring metric variables
+2024-06-20 19:59:19,440:INFO:Importing untrained model
+2024-06-20 19:59:19,443:INFO:Light Gradient Boosting Machine Imported successfully
+2024-06-20 19:59:19,451:INFO:Starting cross validation
+2024-06-20 19:59:19,452:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
+2024-06-20 19:59:35,670:INFO:Calculating mean and std
+2024-06-20 19:59:35,674:INFO:Creating metrics dataframe
+2024-06-20 19:59:35,681:INFO:Finalizing model
+2024-06-20 19:59:35,778:INFO:[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
+2024-06-20 19:59:35,778:INFO:You can set `force_col_wise=true` to remove the overhead.
+2024-06-20 19:59:35,778:INFO:[LightGBM] [Info] Total Bins 1054
+2024-06-20 19:59:35,779:INFO:[LightGBM] [Info] Number of data points in the train set: 824, number of used features: 8
+2024-06-20 19:59:35,779:INFO:[LightGBM] [Info] Start training from score 35.658204
+2024-06-20 19:59:35,789:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,794:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,794:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,800:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,806:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,816:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,820:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,826:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,834:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,837:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,842:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+2024-06-20 19:59:35,864:INFO:Uploading results into container
+2024-06-20 19:59:35,865:INFO:Uploading model into container now
+2024-06-20 19:59:35,877:INFO:_master_model_container: 22
+2024-06-20 19:59:35,877:INFO:_display_container: 4
+2024-06-20 19:59:35,878:INFO:LGBMRegressor(n_jobs=-1, random_state=123)
+2024-06-20 19:59:35,878:INFO:create_model() successfully completed......................................
+2024-06-20 19:59:36,071:INFO:Initializing plot_model()
+2024-06-20 19:59:36,071:INFO:plot_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x000002BBD987D6D0>, estimator=LGBMRegressor(n_jobs=-1, random_state=123), plot=residuals, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None)
+2024-06-20 19:59:36,071:INFO:Checking exceptions
+2024-06-20 19:59:36,073:INFO:Preloading libraries
+2024-06-20 19:59:36,080:INFO:Copying training dataset
+2024-06-20 19:59:36,081:INFO:Plot type: residuals
+2024-06-20 19:59:36,250:INFO:Fitting Model
+2024-06-20 19:59:36,291:INFO:Scoring test/hold-out set
+2024-06-20 19:59:36,580:INFO:Visual Rendered Successfully
+2024-06-20 19:59:36,700:INFO:plot_model() successfully completed......................................

modelCiment.pkl

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modelCiment2.pkl

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requirements.txt

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+
+matplotlib==3.7.5
+numpy==1.24.4
+pandas==1.5.3
+plotly==5.20.0
+pybase64==1.3.2
+pycaret==3.3.0
+scikit-learn==1.4.1.post1
+seaborn==0.12.2
+streamlit==1.33.0
+streamlit-camera-input-live==0.2.0
+streamlit-card==1.0.0
+streamlit-embedcode==0.1.2
+streamlit-extras==0.4.2
+streamlit-faker==0.0.3
+streamlit-image-coordinates==0.1.6
+streamlit-keyup==0.2.4
+streamlit-option-menu==0.3.12
+streamlit-player==0.1.5
+streamlit-toggle-switch==1.0.2
+streamlit-vertical-slider==2.5.5

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tmp.py

@@ -0,0 +1,301 @@
+import streamlit as st 
+import pandas as pd 
+import matplotlib.pyplot as plt
+import numpy as np 
+import seaborn as sns 
+import base64
+import time
+import pickle
+from streamlit_extras import let_it_rain
+from sklearn.preprocessing import LabelEncoder
+st.set_page_config(layout="wide", initial_sidebar_state="expanded")
+
+WelcomeText="""
+Les assurances offrent une tranquillité d'esprit en protégeant contre 
+les imprévus et les risques financiers. Elles permettent de se prémunir contre les pertes matérielles, 
+les accidents, les maladies et autres événements inattendus. En souscrivant à une assurance, on se donne la garantie d'être soutenu et indemnisé en cas de sinistre, ce qui contribue à sécuriser son avenir et celui de ses proches. Les assurances jouent donc un rôle essentiel dans la gestion des risques
+et la préservation du patrimoine, offrant ainsi une protection précieuse pour faire face aux aléas de la vie.
+"""
+
+
+@st.cache_data
+def loadData(path):
+    data= pd.read_csv(path)
+    return data 
+
+def filedownload(df): #telecharger un fichier depuis streamlit
+        csv = df.to_csv(index=False)
+        b64 = base64.b64encode(csv.encode()).decode()  # strings <-> bytes conversions
+        href = f'<a href="data:file/csv;base64,{b64}" download="assurance_pred.csv">Telecharger les predictions</a>'
+        return href
+    
+def displayText(texte):
+    for word in texte.split(" "):
+        yield word+ " "
+        time.sleep(0.02)   
+        
+def pluie_billets_d_argent():
+    let_it_rain.rain(
+        emoji="💶💵", 
+        font_size=60, 
+        falling_speed=3, 
+        animation_length="2",
+        # color=["#FFD700", "#C0C0C0", "#FFA500", "#FFFF00"]
+    )
+
+        
+        #fonction pour les simulations
+def input_simulation():
+    # 'age'	'sex'	'bmi'	'children'	'smoker'	'region'	'charges']
+    age= st.slider("Age", 1, 100,step=1)
+    tmp_sex= st.selectbox("Quel Est Votre Sexe? ", ["Masculin", "Feminin"])
+    sex=0 #pour un hommme
+    if tmp_sex== "Masculin":
+        sex=0
+    else:
+        sex=1
+    bmi = st.slider("BMI", 0.0, max_value=1000.0,step=0.1)
+    children = st.slider("Nombre d'enfants: ", 0, step=1)
+    tmp_smoker= st.selectbox("Prenez vous de la cigarette / drogue...? ",["oui", "Non"])
+    smoker=1 #on pars sur la base qu'il ne fume pas
+    if tmp_smoker=="oui":
+        smoker=0
+    else:
+        smoker=1
+    region = 1
+    tmp_region= st.selectbox("Quelle est votre région d'origine? ",['southwest','southeast','northwest','northeast'])
+    # [0,1,2,3]
+    if tmp_region=="southwest":
+            region= 0
+    elif tmp_region=="southeast":
+            region=1
+    elif tmp_region=="northwest":
+            region=2           
+    elif tmp_region=="northeast":
+            region=3   
+    data = {
+        'age':age,
+        'sex':sex,
+        'bmi':bmi,
+        'children':children,	
+        'smoker':smoker,
+        'region':region
+    }
+    feature = pd.DataFrame(data, index=[0])
+    return feature
+
+
+#feature with text input
+def input_simulation2():
+    # 'age'	'sex'	'bmi'	'children'	'smoker'	'region'	'charges']
+    age= st.slider("Age", 1, 100,step=1)
+    tmp_sex= st.selectbox("Quel Est Votre Sexe? ", ["Masculin", "Feminin"])
+    sex=0 #pour un hommme
+    if tmp_sex== "Masculin":
+        sex=0
+    else:
+        sex=1
+    
+    bmi = st.text_input("Entrez votre BMi", placeholder="Entrez votre BMI")
+    children = st.text_input("Nombre d'enfants",placeholder="Entrez votre Nombre d'enfants")
+    tmp_smoker= st.selectbox("Prenez vous de la cigarette / drogue...? ",["oui", "Non"])
+    smoker=1 #on pars sur la base qu'il ne fume pas
+    if tmp_smoker=="oui":
+        smoker=0
+    else:
+        smoker=1
+    region = 1
+    tmp_region= st.selectbox("Quelle est votre région d'origine? ",['southwest','southeast','northwest','northeast'])
+    # [0,1,2,3]
+    if tmp_region=="southwest":
+            region= 0
+    elif tmp_region=="southeast":
+            region=1
+    elif tmp_region=="northwest":
+            region=2           
+    elif tmp_region=="northeast":
+            region=3   
+    data = {
+        'age':[age if age else 10],
+        'sex':sex,
+        'bmi':[bmi if bmi else 10],
+        'children':[children if children else 0],	
+        'smoker':smoker,
+        'region':region
+    }
+    feature = pd.DataFrame(data, index=[0])
+    return feature
+
+
+
+st.sidebar.image("1099.jpg", width=300) #ajout d'une image sur la barre de navigation Gauche
+menuList= ["Accueil", "Visualisation", "Simulation", "Predictions"]
+choosen = st.sidebar.selectbox("Selectionnez une option", menuList)
+data= loadData("insurance.csv")
+
+def displayText1(string):
+    for word in string.split(" "):
+        yield word+ " "
+        time.sleep(0.1)
+
+
+def main():
+    data= loadData("insurance.csv")
+    if choosen== menuList[0]:
+        st.markdown("<h1 style='text-align:center;color: #005580; text-transform: uppercase'>Streamlit Assurance App 💶</h1>",unsafe_allow_html=True)#  affiche des titres html
+        st.markdown("<br/> " , unsafe_allow_html=True)
+        col1,col2,col3= st.columns((2,3,2))
+        with col1:
+            st.write(displayText(WelcomeText))
+        with col3:
+            st.image("ass2.jpg", use_column_width=True)
+        with col2:
+            st.image("ass1.jpg")
+        with st.expander("Voir les  Données"):
+            st.dataframe(data.head())
+    elif choosen == menuList[1]:
+        # st.balloons()
+        with st.expander("Matrice de correlation"):
+            tmp = data 
+            le= LabelEncoder()
+            tmp["sex"]= le.fit_transform(tmp["sex"])
+            le2= LabelEncoder()
+            tmp["region"]=le2.fit_transform(tmp["region"])
+            le3= LabelEncoder()
+            tmp["smoker"]=le3.fit_transform(tmp['smoker'])
+            #matrice de correlation seaborn 
+            mask = np.triu(np.ones_like(tmp.corr(), dtype=bool))
+            f, ax = plt.subplots(figsize=(3,4))
+            cmap = sns.diverging_palette(230, 20, as_cmap=True)
+            sns.heatmap(tmp.corr(), mask=mask, cmap=cmap, vmax=.2, center=0,
+            square=True, linewidths=.3, cbar_kws={"shrink": .4})
+            st.pyplot(f, use_container_width=False)
+        
+        with st.expander("Charges d'assurance Vs Age"):
+            sns.set_style("whitegrid")
+            fig3= plt.figure(figsize=(5, 4))
+            sns.lineplot(x='age', y='charges', data=tmp, marker='o', color='blue', linewidth=2)
+            plt.title('Relation entre les charges d\'assurances  et l\'âge')
+            plt.xlabel('Âge')
+            plt.ylabel('Charge')
+            plt.grid(True)
+            st.pyplot(fig3, use_container_width=False)
+        with st.expander("Nombre enfants Vs charges d'assurance"):
+            sns.set_style("whitegrid")
+            fig3= plt.figure(figsize=(5, 4))
+            sns.lineplot(x='children', y='charges', data=tmp, marker='o', color='blue', linewidth=2)
+            plt.title('Relation entre les charges d\'assurances  et le nombre d\'enfant')
+            plt.xlabel('Nombre d\'enfants')
+            plt.ylabel('Charge')
+            plt.grid(True)
+            st.pyplot(fig3, use_container_width=False)   
+             
+        with st.expander("Nombre enfants, Age Vs charges"):
+            fig4= plt.figure(figsize=(10, 6))
+            sns.scatterplot(x='age', y='charges', hue='children', data=tmp, palette='Set2', s=100)
+            plt.title('Relation entre les charges, l\'âge et le nombre d\'enfants')
+            plt.xlabel('Âge')
+            plt.ylabel('Charges')
+            plt.legend(title='Nombre d\'enfants')
+            plt.grid(True)
+            plt.show()
+            st.pyplot(fig4, use_container_width=True)
+    elif choosen== menuList[2]:
+        st.markdown("<h1 style='text-align:center;color: #005580; text-transform: uppercase'> Simulation de vos frais d'assurances 💶</h1>",unsafe_allow_html=True)#  affiche des titres html
+
+        col1,col2,col3= st.columns(3)
+        df=None
+        with col1:
+            df= input_simulation()
+        
+        with col2:
+            st.image("man.jpg")
+        with col3:
+            pickled_model = pickle.load(open('assurance.pkl', 'rb'))
+            prediction= pickled_model.predict(df)
+            
+            string= "Vos Charges d'assurances s'élèvent à: "
+            x= str(int(prediction[0]))+" CFA"
+            st.write(displayText1(string))
+            st.title(x)
+    elif choosen == menuList[3]:
+        tab1, tab2, tab3 = st.tabs([":clipboard: Data ",":bar_chart: Visualisation", " 💶Prediction"])
+        file= st.sidebar.file_uploader("choisissez un fichier à uploader", type=['csv'])  
+        globalData = []  
+        with tab1:
+            if st.sidebar.checkbox("Pas de fichier? Utiliser notre fichier  test"):
+                file="test.csv"
+            if file:
+                df= loadData(file)
+                st.write(df)
+                 #matrice de correlation seaborn 
+                mask = np.triu(np.ones_like(df.corr(), dtype=bool))
+                f, ax = plt.subplots(figsize=(3,4))
+                cmap = sns.diverging_palette(230, 20, as_cmap=True)
+                sns.heatmap(df.corr(), mask=mask, cmap=cmap, vmax=.2, center=0,
+                square=True, linewidths=.3, cbar_kws={"shrink": .4})
+                st.pyplot(f, use_container_width=False)
+                pickled_model = pickle.load(open('assurance.pkl', 'rb'))
+                prediction= pickled_model.predict(df)
+                df["charges"]=prediction
+                with tab2:
+                    col1,col2, col3= st.columns(3)
+                    possibilities =['age',	'sex',	'bmi',	'children',	'smoker',	'region'	, 'charges']
+                    userchoice=""
+                    userSecondChoice=""
+                    with col1:
+                        userchoice= st.selectbox("Exprimé", possibilities)
+                    with col2:
+                        userSecondChoice= st.selectbox("En fonction de: ", possibilities)
+                    sns.set_style("whitegrid")
+                    fig3= plt.figure(figsize=(5, 4))
+                    sns.lineplot(x=userchoice, y=userSecondChoice, data=df, marker='o', color='blue', linewidth=2)
+                    sf= "Relation entre "+userchoice+" VS "+userSecondChoice
+                    plt.title(sf)
+                    plt.xlabel(userchoice)
+                    plt.ylabel(userSecondChoice)
+                    plt.grid(True)
+                    st.pyplot(fig3, use_container_width=False)   
+                with tab3:
+                    
+                    # time.sleep(20)
+                    progress_text = "Prédictions en cours... veuillez patienter"
+                    my_bar = st.progress(0, text=progress_text)
+                    for percent_complete in range(100):
+                        time.sleep(0.01)
+                        my_bar.progress(percent_complete + 1, text=progress_text)
+                        time.sleep(0.1)
+                        my_bar.empty()
+                    st.write(df)
+                    if st.button("Download"):
+                        st.markdown(filedownload(df), unsafe_allow_html=True)
+            else:
+            
+                data=""
+                
+                col1, col2, col3= st.columns(3)
+                with col2:
+                    data = input_simulation2()
+                with col1:
+                    st.write(data)
+                prediction= []
+                string= "Vos Charges d'assurances s'élèvent à: "
+                pickled_model = pickle.load(open('assurance.pkl', 'rb'))
+                prediction= pickled_model.predict(data)
+                x= str(int(prediction[0]))+" FCFA"
+                data["prediction"]=prediction[0]
+                with col1:
+                    st.write(displayText1(string))
+                    st.success(x)
+                            
+                with tab2:
+                    st.write(data)
+                with tab3:
+                    st.write(displayText1(string))
+                    st.success(x) 
+                    pluie_billets_d_argent()
+  
+main()                  
+    
+
+