I have loaded a dataset and converted into data frame while I am using linear regression I am receiving the following value error as shown in my code below. I am at the moment doing a tutorial and could not figure out how come the arrays are 1-D as the error shows.
from sklearn.datasets import load_boston
boston_dataset=load_boston()
#create a pandas dataframe and store the data
df_boston=pd.DataFrame(boston_dataset.data)
df_boston.columns=boston_dataset.feature_names
#append price, target as a new column in the dataset
df_boston['Price']=boston_dataset.target
#print first five observations
df_boston.head()
CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT Price
0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 15.3 396.90 4.98 24.0
1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 17.8 396.90 9.14 21.6
2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 17.8 392.83 4.03 34.7
3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 18.7 394.63 2.94 33.4
4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 18.7 396.90 5.33 36.2
#assign features on x-axis
X_features=boston_dataset.data
#assign target on y-axis
Y_target=boston_dataset.target
#import linear model-the estimator
from sklearn.linear_model import LinearRegression
lineReg=LinearRegression()
#fit data into the estimator
lineReg.fit(X_features,Y_target)
ValueError Traceback (most recent call last)
<ipython-input-16-4b5b068e587b> in <module>()
1 #fit data into the estimator
----> 2 lineReg.fit(X_features,Y_target)
/usr/local/lib/python3.4/dist-packages/sklearn/linear_model/base.py in fit(self, X, y, sample_weight)
480 n_jobs_ = self.n_jobs
481 X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
--> 482 y_numeric=True, multi_output=True)
483
484 if sample_weight is not None and np.atleast_1d(sample_weight).ndim > 1: