This talk will be around building better models and covering the topics like:

• Build Better Models
• Steps in a Data science problem solving
• Why is domain knowledge required to do well in Data science problem-solving?
• Be Paranoid about Generalization
• Focus on Last Mile Optimization (Debug & Never give up)
• Case Study of Winning Data Science Competition: ACM KDD
• Problem Background
• We should also talk about learnings as part of the talk – what worked and what did not work
• My Winning Solution Components