def linearRegressionDemo(conn):
'''
Demonstrate Linear Regression
'''
mdl = LinearRegression(conn)
#Train Model and Score
lreg = LinearRegression(conn)
mdl_dict, mdl_params = lreg.train('public.wine_training_set',['1','alcohol','proline','hue','color_intensity','flavanoids'],'quality')
#Show model params
mdl_params
#Now do prediction
predictions = lreg.predict('public.wine_test_set','quality')
#Show prediction results
predictions.head()
#Show Scatter Matrix of Actual Vs Predicted
smat = scatter_matrix(predictions.get(['quality','prediction']), diagonal='kde')
# 1 b) Linear Regression with categorical variables
# We'll use the auto_mpg dataset from UCI : http://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names
# make, fuel_type, fuel_system are all categorical variables, rest are real.
#Train Linear Regression Model on a mixture of Numeric and Categorical Variables
mdl_dict, mdl_params = lreg.train('public.auto_mpg_train',['1','height','width','length','highway_mpg','engine_size','make','fuel_type','fuel_system'],'price')
predictions = lreg.predict('public.auto_mpg_test','price')
#Show sample predictions
predictions.head()
#Display Scatter Plot of Actual Vs Predicted Values
smat = scatter_matrix(predictions.get(['price','prediction']), diagonal='kde')
def linearRegressionDemo(conn):
'''
Demonstrate Linear Regression
'''
mdl = LinearRegression(conn)
#Train Model and Score
lreg = LinearRegression(conn)
mdl_dict, mdl_params = lreg.train(
'public.wine_training_set',
['1', 'alcohol', 'proline', 'hue', 'color_intensity', 'flavanoids'],
'quality')
#Show model params
mdl_params
#Now do prediction
predictions = lreg.predict('public.wine_test_set', 'quality')
#Show prediction results
predictions.head()
#Show Scatter Matrix of Actual Vs Predicted
smat = scatter_matrix(predictions.get(['quality', 'prediction']),
diagonal='kde')
# 1 b) Linear Regression with categorical variables
# We'll use the auto_mpg dataset from UCI : http://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names
# make, fuel_type, fuel_system are all categorical variables, rest are real.
#Train Linear Regression Model on a mixture of Numeric and Categorical Variables
mdl_dict, mdl_params = lreg.train('public.auto_mpg_train', [
'1', 'height', 'width', 'length', 'highway_mpg', 'engine_size', 'make',
'fuel_type', 'fuel_system'
], 'price')
predictions = lreg.predict('public.auto_mpg_test', 'price')
#Show sample predictions
predictions.head()
#Display Scatter Plot of Actual Vs Predicted Values
smat = scatter_matrix(predictions.get(['price', 'prediction']),
diagonal='kde')
def linearRegressionDemo(conn):
'''
Demonstrate Linear Regression
'''
lreg = LinearRegression(conn)
lreg.train('public.wine_training_set',['1','alcohol','proline','hue','color_intensity','flavanoids'],'quality')
cursor = lreg.predict('public.wine_test_set','quality')
rowset = conn.printTable(cursor,['id','quality','prediction'])
cols = conn.fetchColumns(rowset,['quality','prediction'])
actual = cols['quality']
predicted = cols['prediction']
scatterPlot(actual,predicted, 'wine_test_set')
# 1 b) Linear Regression with categorical variables
# We'll use the auto_mpg dataset from UCI : http://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names
# make, fuel_type, fuel_system are all categorical variables, rest are real.
lreg.train('public.auto_mpg_train',['1','height','width','length','highway_mpg','engine_size','make','fuel_type','fuel_system'],'price')
cursor = lreg.predict('public.auto_mpg_test','price')
rowset = conn.printTable(cursor,['id','price','prediction'])
cols = conn.fetchColumns(rowset,['price','prediction'])
print '\n\n Linear Regression Predictions (with categorical variables) :'
actual = cols['price']
predicted = cols['prediction']
scatterPlot(actual,predicted, 'auto_mpg_test')
