In [1]:

    

import os
import sys 
sys.path.append("..")
sys.path.append("../..")

import pandas as pd 
import yellowbrick as yb
import matplotlib as mpl
import matplotlib.pyplot as plt 

%matplotlib notebook


    

In [2]:

    

DATA = os.path.normpath("../data/")

def load_data(name):
    path = os.path.join(DATA, name, name + ".csv")
    return pd.read_csv(path)


    

In [3]:

    

df = load_data("concrete")
df.head()


    

    
Out[3]:






  

    

      

      
cement
      
slag
      
ash
      
water
      
splast
      
coarse
      
fine
      
age
      
strength
    
  
  

    

      
0
      
540.0
      
0.0
      
0.0
      
162.0
      
2.5
      
1040.0
      
676.0
      
28
      
79.986111
    
    

      
1
      
540.0
      
0.0
      
0.0
      
162.0
      
2.5
      
1055.0
      
676.0
      
28
      
61.887366
    
    

      
2
      
332.5
      
142.5
      
0.0
      
228.0
      
0.0
      
932.0
      
594.0
      
270
      
40.269535
    
    

      
3
      
332.5
      
142.5
      
0.0
      
228.0
      
0.0
      
932.0
      
594.0
      
365
      
41.052780
    
    

      
4
      
198.6
      
132.4
      
0.0
      
192.0
      
0.0
      
978.4
      
825.5
      
360
      
44.296075
    
  

In [4]:

    

from sklearn.model_selection import train_test_split as tts

features = ["cement", "slag", "ash", "water", "splast", "coarse", "fine", "age"]
target = "strength"

X = df[features]
y = df[target]

X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)


    

In [5]:

    

from sklearn.linear_model import LassoCV 
from yellowbrick.regressor import PredictionError

model = PredictionError(LassoCV())
model.fit(X_train, y_train)
model.score(X_test, y_test)
model.show()