text: import pandas as pd
  import numpy
  from sklearn import svm
  from sklearn.metrics import classification_report
  from sklearn.model_selection import train_test_split,GridSearchCV
  # using pandas get the occuring no and the count for the data set
  
  
  df = pd.read_csv('/Users/ravinduperera/Desktop/IIT/Research/Development/Dev/csvfile.csv',header=None)
  # print(df)
  # df.groupby([0]).size().reset_index(name="count").to_csv('/Users/ravinduperera/Desktop/IIT/Research/Development/Dev/csvfile.csv')
  cc = df.groupby([0]).size().reset_index(name="count")
  
  # print(cc.iloc[:,1])
  
  data = numpy.array(cc.iloc[:,0]).astype(float)
  target = numpy.array(cc.iloc[:,-1]).astype(float)
  # print(data.shape)
  # print(target.shape)
  # #
  X = data[:1000]
  y = target[:1000]
  
  # print(X)
  # print(y)
  
  
  
  X_train, X_test, y_train, y_test = train_test_split(
      X.reshape(-1,1), y, test_size=0.25, random_state=0)
  
  parameters = [{'kernel': ['rbf'],
                 'gamma': [1e-4, 1e-3, 0.01, 0.1, 0.2, 0.5],
                  'C': [1, 10, 100, 1000]}]
  
  
  clf = GridSearchCV(svm.SVC(decision_function_shape='ovr'), parameters, cv=5)
  clf.fit(X_train, y_train)
  
  print(clf.best_params_)
  print()
  print("Grid scores on training set:")
  print()
  means = clf.cv_results_['mean_test_score']
  stds = clf.cv_results_['std_test_score']
  for mean, std, params in zip(means, stds, clf.cv_results_['params']):
      print("%0.3f (+/-%0.03f) for %r"
            % (mean, std * 2, params))
  
  # clf = svm.SVC()
  # print("work1")
  #
  # print("work2")
  # C = 1.0 # SVM regularization parameter
  # svc = svm.SVC(kernel='rbf', C=C, decision_function_shape='ovr').fit(X.reshape(-1,1), y)
  # print("working 3")
  # Z = svc.predict(1.51344489e+09)
  # # clf.predict([[]])
  #
  # print(Z)
  y_true, y_pred = y_test, clf.predict(X_test)
  print(classification_report(y_true, y_pred))
  # Do the parameter tuning and see the best parmeter with multi-classification
  # check the rate of accuray with the exisiting methods of scklearn 