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Proctor Level3_31

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Level3_31 MLP Level 1 Aug 7, 2026
  1. 1
    Introduction.
  2. 2
    Tell me about yourself.
  3. 3
    Explain the problem statement.
  4. 4
    Explain your notebook/project.
  5. 5
    Explain why you performed each feature engineering step.
  6. 6
    Explain your EDA.
  7. 7
    Explain any graph you created.
  8. 8
    Which charts did you use and why?
  9. 9
    When should each type of chart be used?
  10. 10
    Explain your Pipeline.
  11. 11
    Explain your best model.
  12. 12
    Explain any model.
  13. 13
    How does the model work?
  14. 14
    What is Hyperparameter Tuning?
  15. 15
    Why is Hyperparameter Tuning required?
  16. 16
    Difference between GridSearchCV and RandomizedSearchCV.
  17. 17
    Explain the hyperparameters you tuned.
  18. 18
    What is the validation size in train_test_split?
  19. 19
    What is Mean, Median, and Mode?
  20. 20
    When should Mean, Median, and Mode be used for imputation?
  21. 21
    What is SimpleImputer?
  22. 22
    How do encoders work?
  23. 23
    Explain the encoding technique you used.
  24. 24
    What is Tokenization?
  25. 25
    Why do we remove stop words?
  26. 26
    Can custom stop words be used?
  27. 27
    What is a Word Cloud?
  28. 28
    Where is a Word Cloud useful?
  29. 29
    What is a Confusion Matrix?
  30. 30
    Explain F1-Score.
  31. 31
    What is the Loss Function?
  32. 32
    What is RMSE?
  33. 33
    Explain Bagging.
  34. 34
    Explain Boosting.
  35. 35
    Explain KNN.
  36. 36
    Explain K-Means.
  37. 37
    Difference between Supervised and Unsupervised Learning.
  38. 38
    What is overfitting?
  39. 39
    What is underfitting?
  40. 40
    Did you face overfitting or underfitting in your project?
  41. 41
    How did you handle it?
  42. 42
    What are outliers?
  43. 43
    How do you detect outliers?
  44. 44
    How do you handle outliers?
  45. 45
    Which visualization library did you use?
  46. 46
    Difference between Matplotlib and Seaborn.
  47. 47
    How many Kaggle submissions did you make?
  48. 48
    What changes improved your leaderboard score?
  49. 49
    How did you improve your project over different versions?
  50. 50
    Did you use AI tools during the project?
  51. 51
    How did you use AI?
  52. 52
    If you get stuck while building a model or debugging code, how would you solve it?
  53. 53
    Scenario: You are given a bank dataset to predict whether a customer will repay a credit bill. Explain your complete approach.
  54. 54
    Which model would you choose for that problem and why?
  55. 55
    Are you a student or a working professional?
  56. 56
    What are your future plans?
  57. 57
    Are you planning for higher studies?
  58. 58
    Are you participating in hackathons?
  59. 59
    Discuss your internships and projects.
Created for educational purposes only. Questions are based on students' personal experiences and may not reflect actual exam content.