title: error : X.shape[1] = 1 should be equal to 9, the number of features at training time
body: <p>I am trying different ml algorithms on a dataset to predict football player's market price. I'm stuck while using SVR. The dataset's dimensions are : 461 rows * 9 columns . Here is the code :</p>
  <pre><code>import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('1603428153-5e748a2d5fc288e9f69c5f86.csv')
  dataset.columns
  X = dataset.drop(['name','club','nationality','position','age_cat','new_signing','new_foreign','big_club'],axis=1)
  
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
  
  # Feature Scaling
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  sc_y = StandardScaler()
  X1 = sc_X.fit_transform(X1)
  y = y.reshape(-1,1)
  y = sc_y.fit_transform(y)
  
  # Fitting SVR to the dataset
  from sklearn.svm import SVR
  regressor = SVR(kernel = 'rbf',epsilon=1.0)
  regressor.fit(X1, y)
  
  pred=regressor.predict(X_test)
  print(regressor.score(X_test,y_test))
  print(r2_score(y_test,pred))
  </code></pre>
  <p>Here in the line &quot;pred = regressor.predict(X_test), it throws the error : X.shape[1] = 9 should be equal to 1, the number of features at training time. Please help</p>