Anthony Carlleston de Lima carlleston

## 5-Result.ipynb

      
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## 4-Models_comparison.ipynb

      
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              July 28, 2020 13:23
            
          
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## 3-Data_Analysis.ipynb

      
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## 3-ln_model.ipynb

      
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              August 23, 2020 12:09
            
              
                pre-processing and linear model in pyspark
              
          
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## 2-ETL_news_Studio.ipynb

      
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              July 28, 2020 12:57
            
              
                Extract, transform, classify the news and load.
              
          
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## 1-ETL_TS.ipynb

      
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              July 28, 2020 12:54
            
          
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## dtreepipe_vis6.py
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
import os

os.environ['PATH'] = os.environ['PATH']+';'+os.environ['CONDA_PREFIX']+r"\Library\bin\graphviz"
dot_data = StringIO()
export_graphviz(pipe.named_steps['regressor'].estimators_[0], out_file=dot_data)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())

## dtreepipe_vis5
conda install pydotplus
conda install graphviz

## dtreepipe_vis4.py
regr = RandomForestRegressor(random_state = 100,bootstrap = True, max_depth=2,max_features=2,min_samples_leaf=3,min_samples_split=5,n_estimators=3)
pipe = Pipeline([
        ('scaler', StandardScaler()),
        ('reduce_dim', PCA()),
        ('regressor', regr)
        ])
pipe.fit(X_train,y_train)
ypipe=pipe.predict(X_test)

## dtreepipe_vis3.py
X = df[['GDP','UnemploymentRate']]
y = df['Revenue']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,  random_state=0)
	from sklearn.externals.six import StringIO
	from IPython.display import Image
	from sklearn.tree import export_graphviz
	import pydotplus
	import os

	os.environ['PATH'] = os.environ['PATH']+';'+os.environ['CONDA_PREFIX']+r"\Library\bin\graphviz"
	dot_data = StringIO()
	export_graphviz(pipe.named_steps['regressor'].estimators_[0], out_file=dot_data)
	graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
	regr = RandomForestRegressor(random_state = 100,bootstrap = True, max_depth=2,max_features=2,min_samples_leaf=3,min_samples_split=5,n_estimators=3)
	pipe = Pipeline([
	('scaler', StandardScaler()),
	('reduce_dim', PCA()),
	('regressor', regr)
	])
	pipe.fit(X_train,y_train)
	ypipe=pipe.predict(X_test)
	X = df[['GDP','UnemploymentRate']]
	y = df['Revenue']
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)