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import sklearn.datasets as datasets
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.decomposition import PCA
df = pd.read_csv('df.csv', sep = ',')
df.head()
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)
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)
conda install pydotplus
conda install graphviz
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())
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@carlleston
carlleston / 2-ETL_news_Studio.ipynb
Created July 28, 2020 12:57
Extract, transform, classify the news and load.
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@carlleston
carlleston / 3-ln_model.ipynb
Last active August 23, 2020 12:09
pre-processing and linear model in pyspark
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