Saaransh Menon saaranshM

## elastic_net.py
from sklearn.linear_model import ElasticNet
elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5, random_state=42)
elastic_net.fit(X, y)

## ridge_regression.py
from sklearn.linear_model import Ridge
ridge_reg = Ridge(alpha=1, solver="cholesky", random_state=42)
ridge_reg.fit(X, y)

# 'cholesky' is a matrix factorixation techinque used in the closed form equation

## lasso_regression.py
from sklearn.linear_model import Lasso
lasso_reg = Lasso(alpha=0.1)
lasso_reg.fit(X, y)
	from sklearn.linear_model import ElasticNet
	elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5, random_state=42)
	elastic_net.fit(X, y)
	from sklearn.linear_model import Ridge
	ridge_reg = Ridge(alpha=1, solver="cholesky", random_state=42)
	ridge_reg.fit(X, y)

	# 'cholesky' is a matrix factorixation techinque used in the closed form equation
	from sklearn.linear_model import Lasso
	lasso_reg = Lasso(alpha=0.1)
	lasso_reg.fit(X, y)