text: import pandas as pd
  import numpy as np
  from sklearn.svm import SVC
  from sklearn.metrics import accuracy_score
  from statistics import mode
  from create_result_df import CreateResult
  
  df = pd.read_csv("../input/train_cleaned_folds.csv")
  test = pd.read_csv("../input/test_cleaned.csv")
  ss = pd.read_csv("../input/gender_submission.csv")
  
  #clf = SVC(C=0.05, gamma=0.0001, kernel='linear', random_state=42)
  
  clf = SVC(C=0.07259632547748618, gamma=0.06186828707791769, kernel='linear', random_state=42)
  
  accuracy =[]
  preds = []
  for f in range(5):
      train = df[df.kfold!= f].reset_index(drop=True)
      valid = df[df.kfold== f].reset_index(drop=True)
  
      X_train = train.drop(["Survived", "kfold"], axis=1)
      y_train = train["Survived"]
      X_valid = valid.drop(["Survived", "kfold"], axis=1)
      y_valid = valid["Survived"]
      X_test = test.drop("Survived", axis=1)
  
      clf.fit(X_train,y_train)
      valid_preds = clf.predict(X_valid)
      preds.append(clf.predict(X_test))
  
      #print(clf.score(X_valid,y_valid))
  
      accuracy.append(accuracy_score(y_valid,valid_preds))
  
  print(accuracy)
  print(sum(accuracy)/5)
  
  svc_pred = np.array([])
  for i in range(0,len(X_test)):
      svc_pred = np.append(svc_pred, mode([preds[0][i],preds[1][i],preds[2][i],preds[3][i],preds[4][i]]))
  
  #Add these data to result dataframe
  c = CreateResult(accuracy,"SVC")
  c.create()
  
  ss["Survived"] = svc_pred
  ss["Survived"] = ss["Survived"].astype(int)
  ss.to_csv("../predictions/svc_pred.csv",index=False)