trying to use y_pred = sc_y.inverse_transform(y_pred) but it says Reshape your data either using array.reshape(-1, 1) if your data has a single

Question

#Dataset

Position          Level Salary  
Business Analyst    1   45000   
Junior Consultant   2   50000   
Senior Consultant   3   60000   
Manager             4   80000   
Country Manager     5   110000  
Region Manager      6   150000  
Partner             7   200000  
Senior Partner      8   300000  
C-level             9   500000  
CEO                 10  1000000

SVR

Importing the libraries

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

Importing the dataset

dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

    # Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler(a
X = sc_X.fit_transform(X)
y = np.squeeze(sc_y.fit_transform(y.reshape(-1, 1)))

Fitting SVR to the dataset

from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)

# Predicting a new result

right here i'm getting error from y_pre = y_pred = sc_y.inverse_transform(y_pred) y_pred = regressor.predict([[6.5]]) y_pred = sc_y.inverse_transform(y_pred)

# Visualising the SVR results
plt.scatter(X, y, color = 'red')
plt.plot(X, regressor.predict(X), color = 'blue')
plt.title('Truth or Bluff (SVR)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

Visualising the SVR results (for higher resolution and smoother curve)

X_grid = np.arange(min(X), max(X), 0.01) # choice of 0.01 instead of 0.1 step because the data is feature scaled
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (SVR)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-242-136822b4df3c> in <module>()
      1 y_pred = regressor.predict([[6.5]])
----> 2 y_pred = sc_y.inverse_transform(y_pred)

1 frames
/usr/local/lib/python3.7/dist-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, estimator)
    771                     "Reshape your data either using array.reshape(-1, 1) if "
    772                     "your data has a single feature or array.reshape(1, -1) "
--> 773                     "if it contains a single sample.".format(array)
    774                 )
    775 

ValueError: Expected 2D array, got 1D array instead:
array=[0.01150915].
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.