Получение ошибки «Ожидается двухмерный массив, вместо него получен одномерный массив», разделение набора данных (csv) для создания линейного

Question

Я разделил набор данных следующим образом

X = []
y = []
# first, compute the number of samples in the training set:
n_train = int(len(df) * 0.7)

# The training set is the first n_train samples in the dataset
X_train = df[: n_train]
Y_train = df[: n_train] # INSERT YOUR CODE HERE

# The test set is the remaining samples in the dataset
X_test = df[n_train:] 
Y_test = df[n_train:]

# Print the number of samples in the training set
print('The number of samples in the training set:')
# INSERT YOUR CODE HERE
print(len(Y_train))

# Print the number of samples in the test set
print('The number of samples in the test set:')
# INSERT YOUR CODE HERE
print(len(Y_test))

Затем я создал линейную модель, подобную этой

lr = linear_model.LinearRegression()

Но когда я пытаюсь вписать в нее данные своего поезда

lr.fit(X_train, Y_train)

Я получаю эту ошибку

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-65-9d85ca185925> in <module>
      2 
      3 # INSERT YOUR CODE HERE
----> 4 lr.fit(X_train, Y_train)

~\Anaconda3\ana01\lib\site-packages\sklearn\linear_model\base.py in fit(self, X, y, sample_weight)
    456         n_jobs_ = self.n_jobs
    457         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
--> 458                          y_numeric=True, multi_output=True)
    459 
    460         if sample_weight is not None and np.atleast_1d(sample_weight).ndim > 1:

~\Anaconda3\ana01\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
    754                     ensure_min_features=ensure_min_features,
    755                     warn_on_dtype=warn_on_dtype,
--> 756                     estimator=estimator)
    757     if multi_output:
    758         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

~\Anaconda3\ana01\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    550                     "Reshape your data either using array.reshape(-1, 1) if "
    551                     "your data has a single feature or array.reshape(1, -1) "
--> 552                     "if it contains a single sample.".format(array))
    553 
    554         # in the future np.flexible dtypes will be handled like object dtypes

ValueError: Expected 2D array, got 1D array instead:
array=[].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Набор данных

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2938 entries, 0 to 2937
Data columns (total 22 columns):
Country                            2938 non-null object
Year                               2938 non-null int64
Status                             2938 non-null object
Life                               2938 non-null float64
Adult Mortality                    2938 non-null float64
infant deaths                      2938 non-null int64
Alcohol                            2938 non-null float64
percentage expenditure             2938 non-null float64
Hepatitis B                        2938 non-null float64
Measles                            2938 non-null int64
BMI                                2938 non-null float64
under-five deaths                  2938 non-null int64
Polio                              2938 non-null float64
Total expenditure                  2938 non-null float64
Diphtheria                         2938 non-null float64
HIV/AIDS                           2938 non-null float64
GDP                                2938 non-null float64
Population                         2938 non-null float64
thinness  1-19 years               2938 non-null float64
thinness 5-9 years                 2938 non-null float64
Income composition of resources    2938 non-null float64
Schooling                          2938 non-null float64
dtypes: float64(16), int64(4), object(2)
memory usage: 505.0+ KB
None

пример набора данных

Answer 1

Пожалуйста, следуйте инструкциям, приведенным ниже,

import pandas as pd
df = pd.read_csv("example.csv")

X = df.drop('Target_variable' , axis = 1)
Y = df['Target_variable']

n_train = int(len(df) * 0.7)

# The training set is the first n_train samples in the dataset
X_train = X[: n_train]
Y_train = Y[: n_train] # INSERT YOUR CODE HERE

# The test set is the remaining samples in the dataset
X_test = df[n_train:] 
Y_test = df[n_train:]

# Print the number of samples in the training set
print('The number of samples in the training set:')
# INSERT YOUR CODE HERE
print(len(Y_train))

# Print the number of samples in the test set
print('The number of samples in the test set:')
# INSERT YOUR CODE HERE
print(len(Y_test))

from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, Y_train)