Conventional ML Models Have Limitations:

• Traditional machine learning models often fail when applied to real-world data in industry settings.

Classroom Learning vs. Real-World Scenarios:

• What you learn in the classroom may not be practical when dealing with complex, messy real-world data.
• Basic algorithms may show good results on clean, well-prepared data, but real data is much more challenging.

Use a Strategic Approach:

• Start with a realistic dataset, not overly simplified ones.
• Build a complete data flow pipeline to handle data properly.

Choose the Right Algorithms:

• Every algorithm works under specific statistical assumptions; learn these assumptions and pick algorithms accordingly.
• Use non-conventional algorithms based on your data’s characteristics.

Map Outcomes to Business Needs:

• Ensure that your analysis aligns with business expectations and solves real business problems.
• Analytics is only valuable if it meets the business's goals.

Check the Data Size:

• Consider whether your data (e.g., 2000 rows) is sufficient for production models. In most cases, small datasets may not be reliable.

Learn from Industry Experts:

• Learn from professionals who work with real-world data to build confidence.
• Just cracking the interview isn’t enough. You need to be able to apply your skills practically to avoid becoming redundant in the industry.