unable to pass X_train and y_train in my regressor variable. i got a ValueError
import pandas as pd import numpy as np import matplotlib.pyplot as plt data = pd.read_csv('housing.csv')
data.drop('ocean_proximity', axis=1, inplace = True)
data.head()
longitude latitude housing_median_age total_rooms total_bedrooms population households median_income median_house_value 0 -122.23 37.88 41.0 880.0 129.0 322.0 126.0 8.<PHONE_NUMBER>.0 1 -122.22 37.86 21.0 7099.0 1106.0 2401.0 1138.0 8.<PHONE_NUMBER>.0 2 -122.24 37.85 52.0 1467.0 190.0 496.0 177.0 7.<PHONE_NUMBER>.0 3 -122.25 37.85 52.0 1274.0 235.0 558.0 219.0 5.<PHONE_NUMBER>.0 4 -122.25 37.85 52.0 1627.0 280.0 565.0 259.0 3.<PHONE_NUMBER>.0
X = data.iloc[:, 6:-1].values y= data.iloc[:, -1].values
from sklearn.model_selection import train_test_split X_train, y_train, X_test, y_test = train_test_split (X,y, test_size = 0.2, random_state = 0)
print(X_train) [[ 65. 4.2386] [447. 4.3898] [368. 3.9333] ... [393. 3.1977] [468. 5.6315] [298. 856
1.3882]]
print(y_train) [[371. 4.1518] [429. 5.7796] [534. 4.3487] ... [326. 3.2027] [374. 6.1436] [406. 3.3326]]
from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(np.array(X_train).reshape(-1, 1), y_train)
ValueError Traceback (most recent call last) in ----> 1 regressor.fit(np.array(X_train).reshape(-1, 1), y_train)
~\anaconda3\lib\site-packages\sklearn\linear_model_base.py in fit(self, X, y, sample_weight) 516 accept_sparse = False if self.positive else ['csr', 'csc', 'coo'] 517 --> 518 X, y = self._validate_data(X, y, accept_sparse=accept_sparse, 519 y_numeric=True, multi_output=True) 520
~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, check_params) 431 y = check_array(y, check_y_params) 432 else: --> 433 X, y = check_X_y(X, y, **check_params) 434 out = X, y 435
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(args, kwargs) 61 extra_args = len(args) - len(all_args) 62 if extra_args <= 0: ---> 63 return f(args, **kwargs) 64 65 # extra_args > 0
~\anaconda3\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, estimator) 829 y = y.astype(np.float64) 830 --> 831 check_consistent_length(X, y) 832 833 return X, y
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays) 260 uniques = np. 422