# To do so we can create a python function:
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
lr = LinearRegression()
dr = DecisionTreeRegressor()
rr = RandomForestRegressor()
#creating a function model
def model(X,y,model_1,model_2,model_3):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)
model_1.fit(X_train, y_train)
model_2.fit(X_train, y_train)
model_3.fit(X_train, y_train)
model_1_accuracy = model_1.score(X_train, y_train)
model_2_accuracy = model_2.score(X_train, y_train)
model_3_accuracy = model_3.score(X_train, y_train)
overall = pd.DataFrame({"Model 1 Accuracy" : model_1_accuracy,
"Model 2 Accuracy" : model_2_accuracy,
"Model 3 Accuracy" : model_3_accuracy}, index =[0])
return overall.transpose()
model(X,y,lr,dr,rr)