The following are some of the common pitfalls seen in any machine learning project:

• Unrealistic objectives, unclear problem definition with no proper objectives
• Data problems:
  • Insufficient data to establish predictive patterns
  • Incorrect selection of predictor attributes
  • Data preparation problems
  • Data normalization problems—failure to normalize data across datasets
  • Bias in data use to solve the problem
• Inappropriate machine learning method selection:
  • The ML method selected doesn't suit the problem statement defined
  • Not trying alternative algorithms
• Giving up too soon. This happens very often. Engineers tend to lose interest if they don't see initial results and are unable to do the various permutations and combinations of various dependent factors, and also do systematic book keeping for the results. If pursued continuously/methodically with proper record keeping and trying out the various possibilities, machine learning problems can be easily solved.