Receiving ValueError("bad input shape {0}".format(shape)) error

[SuryaCitizen]

Hi,

I'm trying to run the below code but receiving "ValueError("bad input shape {0}".format(shape))" error. Can someone please help me resolve this issue? Note: The dataset has 5 rows.https://github.com/SuryaCitizen/Data

#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing the dataset
dataset = pd.read_csv('Lingard.csv')
X = dataset.iloc[:, :-2].values
y = dataset.iloc[:, 7:9].values
print(dataset.head())

#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)

print(X_train)
print(X_test)
print(y_train)
print(y_test)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X_train = sc_X.fit_transform(X_train)
y_train = sc_y.fit_transform(y_train)

#Training the SVR model on the Training set
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X_train, y_train)

#Predicting the Test set results
y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(X_test)))
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),2), y_test.reshape(len(y_test),2)),1))

#Evaluating the Model Performance
from sklearn.metrics import r2_score
r2_score(y_test, y_pred)

[SuryaCitizen]

Please find below complete traceback.


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing the dataset
dataset = pd.read_csv('Lingard.csv')
X = dataset.iloc[:, :-2].values
y = dataset.iloc[:, 7:9].values
print(dataset.head())

#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)

print(X_train)
print(X_test)
print(y_train)
print(y_test)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X_train = sc_X.fit_transform(X_train)
y_train = sc_y.fit_transform(y_train)

#Training the SVR model on the Training set
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X_train, y_train)

#Predicting the Test set results
y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(X_test)))
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),2), y_test.reshape(len(y_test),2)),1))

#Evaluating the Model Performance
from sklearn.metrics import r2_score
r2_score(y_test, y_pred)
Year Matches Shots On target ... Passes passes completed Goals Assists
0 2019 19 24 14 ... 29 86 4 3
1 2018 20 41 23 ... 29 88 8 5
2 2017 18 25 13 ... 30 88 1 2
3 2016 19 23 13 ... 30 85 4 1
4 2015 7 14 8 ... 44 85 2 1

[5 rows x 9 columns]
[[2018 20 41 23 18 29 88]
[2016 19 23 13 10 30 85]
[2015 7 14 8 6 44 85]]
[[2017 18 25 13 12 30 88]
[2019 19 24 14 10 29 86]]
[[8 5]
[4 1]
[2 1]]
[[1 2]
[4 3]]
Traceback (most recent call last):

File "<ipython-input-2-54cb4f9ef960>", line 30, in <module>
regressor.fit(X_train, y_train)

File "C:\Users\Surya\Anaconda3\lib\site-packages\sklearn\svm\base.py", line 146, in fit
accept_large_sparse=False)

File "C:\Users\Surya\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 724, in check_X_y
y = column_or_1d(y, warn=True)

File "C:\Users\Surya\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 760, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))

ValueError: bad input shape (3, 2)

[pyzyx3qwerty]

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