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Proctor Level2_124

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  1. 1
    Show your ID card.
    Times asked 23
  2. 2
    Introduction.
    Times asked 9
  3. 3
    Tell me about yourself.
    Times asked 6
  4. 4
    How was your experience with the project/course?
    Times asked 1
  5. 5
    Show your Kaggle leaderboard score and rank.
    Times asked 2
  6. 6
    Explain the problem statement.
    Times asked 6
  7. 7
    Explain your notebook/project.
    Times asked 21
  8. 8
    How can you improve your model accuracy further?
    Times asked 1
  9. 9
    How do you know your model is not overfitting or underfitting?
    Times asked 1
  10. 10
    How did you encode categorical features?
    Times asked 1
  11. 11
    Why did you use Label Encoding instead of One-Hot Encoding?
    Times asked 1
  12. 12
    What is data preprocessing?
    Times asked 2
  13. 13
    Why is preprocessing necessary?
    Times asked 1
  14. 14
    What is Hyperparameter Tuning?
    Times asked 4
  15. 15
    Why is Hyperparameter Tuning important?
    Times asked 1
  16. 16
    Difference between GridSearchCV and RandomizedSearchCV.
    Times asked 5
  17. 17
    Explain Logistic Regression.
    Times asked 5
  18. 18
    Why is Logistic Regression called "Regression" even though it is used for classification?
    Times asked 1
  19. 19
    What is the loss function of Logistic Regression?
    Times asked 3
  20. 20
    What does the C parameter in Logistic Regression represent?
    Times asked 2
  21. 21
    Explain Decision Tree.
    Times asked 3
  22. 22
    Where are Decision Trees used in real life?
    Times asked 1
  23. 23
    What is KNN?
    Times asked 1
  24. 24
    Why is KNN called a lazy learner?
    Times asked 2
  25. 25
    Difference between KNN and K-Means.
    Times asked 1
  26. 26
    Explain the K-Means algorithm.
    Times asked 1
  27. 27
    Difference between XGBoost and LightGBM.
    Times asked 5
  28. 28
    Why did you use CatBoost?
    Times asked 1
  29. 29
    Difference between Bagging and Boosting.
    Times asked 4
  30. 30
    Which should be preferred when the model is overfitting?
    Times asked 1
  31. 31
    Explain RFE.
    Times asked 1
  32. 32
    Explain evaluation metrics such as Accuracy, Precision, Recall, F1-Score, R² Score, and Confusion Matrix.
    Times asked 1
  33. 33
    How did you conclude that your model is not overfitting?
    Times asked 1
  34. 34
    What are the limitations of a Correlation Matrix?
    Times asked 1
  35. 35
    How can you detect non-linear relationships between features?
    Times asked 1
  36. 36
    Coding: Load a dataset.
    Times asked 1
  37. 37
    Coding: Show the first few rows of the dataset.
    Times asked 1
  38. 38
    Coding: Count the number of ? values in specified columns.
    Times asked 1
  39. 39
    Coding: Replace ? with NaN.
    Times asked 1
  40. 40
    Coding: Impute or remove missing values.
    Times asked 1
  41. 41
    Coding: Convert object/string columns to numeric.
    Times asked 1
  42. 42
    Coding: Filter rows based on given conditions.
    Times asked 1
  43. 43
    Coding: Find the correlation between two columns.
    Times asked 1
  44. 44
    Coding: Plot a correlation heatmap.
    Times asked 1
  45. 45
    Coding: Normalize selected columns using MinMaxScaler or StandardScaler.
    Times asked 1
  46. 46
    Coding: Perform RFE and select the top 2 features.
    Times asked 1
  47. 47
    Coding: Perform preprocessing before applying RFE.
    Times asked 1
  48. 48
    Coding: Add two columns (e.g., V1 + V2).
    Times asked 1
  49. 49
    Coding: Perform a train-test split on the Breast Cancer dataset.
    Times asked 1
  50. 50
    Coding: Load the Breast Cancer dataset and print its shape.
    Times asked 1
  51. 51
    Coding: Load the Iris dataset and perform RFE.
    Times asked 1
  52. 52
    Coding: Explain the RFE implementation after writing the code.
    Times asked 1
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