Lakshay lakshay-arora

## plot_3.py
# Dimension Learning
classification.plot_model(classification_dt, plot = 'feature')

# Confusion Matrix
classification.plot_model(classification_dt, plot = 'confusion_matrix')

## plot_2.py
# Precision Recall Curve
classification.plot_model(classification_dt, plot = 'pr')

# Validation Curve
classification.plot_model(classification_dt, plot = 'vc')

## plot_1.py
# AUC-ROC plot
classification.plot_model(classification_dt, plot = 'auc')

# Decision Boundary
classification.plot_model(classification_dt, plot = 'boundary')

## comapre_models.py
# compare performance of different classification models
classification.compare_models()

## pycaret_blender.py
# Ensemble: blending
blender = classification.blend_models(estimator_list=[classification_dt, classification_xgb])

## pycaret_boosting.py
# ensemble boosting
boosting = classification.ensemble_model(classification_dt, method= 'Boosting')

## pycaret_5.py
# build and tune the catboost model
tune_catboost = classification.tune_model('catboost')

## pycaret_4.py
# build the xgboost model
classification_xgb = classification.create_model('xgboost')

## pycaret_3.py
# build the decision tree model
classification_dt = classification.create_model('dt')

## pycaret_2.py
# import the classification module
from pycaret import classification
# setup the environment
classification_setup = classification.setup(data= data_classification, target='Personal Loan')
	# Dimension Learning
	classification.plot_model(classification_dt, plot = 'feature')

	# Confusion Matrix
	classification.plot_model(classification_dt, plot = 'confusion_matrix')
	# Precision Recall Curve
	classification.plot_model(classification_dt, plot = 'pr')

	# Validation Curve
	classification.plot_model(classification_dt, plot = 'vc')
	# AUC-ROC plot
	classification.plot_model(classification_dt, plot = 'auc')

	# Decision Boundary
	classification.plot_model(classification_dt, plot = 'boundary')
	# compare performance of different classification models
	classification.compare_models()
	# Ensemble: blending
	blender = classification.blend_models(estimator_list=[classification_dt, classification_xgb])
	# ensemble boosting
	boosting = classification.ensemble_model(classification_dt, method= 'Boosting')
	# build and tune the catboost model
	tune_catboost = classification.tune_model('catboost')
	# build the xgboost model
	classification_xgb = classification.create_model('xgboost')
	# build the decision tree model
	classification_dt = classification.create_model('dt')
	# import the classification module
	from pycaret import classification
	# setup the environment
	classification_setup = classification.setup(data= data_classification, target='Personal Loan')