title: Predict test data using model based on training data set?
body: <p>Im new to Data Science and Analysis. 
  After going through a lot of kernels on Kaggle, I made a model that predicts the price of a property. Ive tested this model using my training data, but now I want to run it on my test data. Ive got a test.csv file and I want to use it. How do I do that?
  What i previously did with my training dataset:</p>
  
  <pre><code>#loading my train dataset into python
  train = pd.read_csv('/Users/sohaib/Downloads/test.csv')
  
  #factors that will predict the price
  train_pr = ['OverallQual','GrLivArea','GarageCars','TotalBsmtSF','FullBath','YearBuilt']
  
  #set my model to DecisionTree
  model = DecisionTreeRegressor()
  
  #set prediction data to factors that will predict, and set target to SalePrice
  prdata = train[train_pr]
  target = train.SalePrice
  
  #fitting model with prediction data and telling it my target
  model.fit(prdata, target)
  
  model.predict(prdata.head())
  </code></pre>
  
  <p>Now what I tried to do is, copy the whole code, and change the "train" with "test", and "predate" with "testprdata", and I thought it will work, but sadly no. I know I'm doing something wrong with this, idk what it is.</p>