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Kaggle Vs Real-world Projects


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In this deck, I am going to cover the difference between Kaggle & Real-world projects.

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Published in: Data & Analytics
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Kaggle Vs Real-world Projects

  1. 1. # Kaggle Platform # Real-world Projects # Head to Head # General Differences # Concluding Thoughts Agenda Kaggle Vs Real-world Projects
  2. 2. Kaggle Platform Kaggle Vs Real-world Projects Compete Discuss Datasets Courses Notebooks
  3. 3. Kaggle Competitions Kaggle Vs Real-world Projects Build Baseline Review Kernels Engage in Discussions Iterate till Deadline Get Overview
  4. 4. Real-world Projects Kaggle Vs Real-world Projects 1 Identify & evaluate the opportunity 2 Develop business understanding 3 Fetch, qualify & analyze available data 4 Build a prototype/POC 5 Follow CRISP-DM methodology 6 Deploy, host & monitor
  5. 5. Head-to-head Comparison Kaggle Vs Real-world Projects Problem Statement is well defined Data-sets are available Train-Test-Real data are already segregated You may or may not use additional data Evaluation criteria is available You need to submit the results in specific format You get a deadline to submit Need to identify & formulate Problem Statement Need to identify & fetch relevant data Need to segregate Train-Test-Real data You can always identify & use relevant additional data You need to define evaluation criteria You need to deploy & host the model for business You can carry on as long as project funds permit
  6. 6. General Differences Kaggle Vs Real-world Projects You have the leader-board to know where you are Expectation is to move higher the leader-board You can use all the resources you can Competition timeline is important You can collaborate with other competitors to form a team Models can be as complex as they can You are the best as long as you are not challenged You can manage expectations of the stakeholders You take decisions in terms of business value Time to market is an important aspect You need T-shaped professionals to deliver Practical deployment aspects are considered while increasing the complexity
  7. 7. # Kaggle is useful # Real-world projects are different # Efforts Vs value-add # Depends on individual Concluding Thoughts Kaggle Vs Real-world Projects
  8. 8. Connect/Follow