title: What is the correct way to prepare dataset for machine learning?
body: <p>First of all, thank you for reading this post.</p>
  
  <p>I am a noob when it comes to machine learning and I am trying to use ML to classify some data. Now I have done some basic reading on supervised and unsupervised learning algorithms such as decision trees, clustering, neural networks ..etc.</p>
  
  <p>What I'm struggling to understand is the correct overall procedure for preparing datasets for a ML problem.</p>
  
  <p>How do I prepare the dataset for ML so that I can measure the accuracy of the algorithms? </p>
  
  <p>My current understanding is that to assess accuracy, the algorithm should be fed with pre-labelled results (from a significant subset of the dataset?) so as to assess the difference between the expected outcome and the algorithm's decision?</p>
  
  <p>If this is correct then how does one go about pre-labelling large datasets? My dataset is quite big and manual labelling is not feasible.</p>
  
  <p>Also, any tips on doing machine learning in Python would be much appreciated!</p>
  
  <p>Thank you very much for your help in advance!</p>
  
  <p>Best regards,</p>
  
  <p>Mike</p>