text: 
  
  #You do not talk about Fight Club
  import os
  os.chdir('....\\data\\input.csv')
  
  #You DO NOT talk about Fight Club
  import pandas as pd
  from sklearn.preprocessing import LabelEncoder
  from fancyimpute import KNN
  from xgboost import XGBClassifier
  from sklearn.metrics import accuracy_score
  
  
  #Only two guys to a fight
  train = pd.read_csv('train.csv')
  test  = pd.read_csv('test.csv')
  
  
  #Someone yells stop, goes limp, taps out, the fight is over
  train.isnull().sum()
  train = KNN(k=3).complete(train)
  test  = KNN(k=3).complete(test)
  
  
  #One fight at a time
  le = LabelEncoder()
  cat = ['genre','certificate', 'distributor']
  for col in cat:
      train[col] = le.fit_transform(train[col])
      test[col] = le.fit_transform(test[col])
  
  
  #no shirts, no shoes
  train_X = train.drop(['year','oscar', 'movie_name', 'actor_name', 'href'], axis=1)  
  test_X = test.drop(['year','oscar', 'movie_name', 'actor_name', 'href'], axis = 1)
  train_Y = train['oscar']
  
  
  #Fights will go on as long as they want to
  model = XGBClassifier()
  model.fit(train_X, train_Y)
  
  #If this is your first night at Fight Club, you have to fight.
  pred_xgb = model.predict_proba(test_X)[:,1]
  xgb_prediction = pd.DataFrame(pred_xgb, test['movie_name'])
  
  