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  1. 1
    Explain your notebook/project.
    Times asked 21
  2. 2
    Explain the problem statement.
    Times asked 6
  3. 3
    What approach did you follow to solve the problem?
    Times asked 1
  4. 4
    What are the top insights from your EDA?
    Times asked 1
  5. 5
    Explain every graph used in your EDA.
    Times asked 1
  6. 6
    Why did you choose those plots?
    Times asked 1
  7. 7
    How can you make your graphs cleaner?
    Times asked 1
  8. 8
    What did you understand from the describe() output?
    Times asked 1
  9. 9
    How did you correlate features with the target variable?
    Times asked 1
  10. 10
    Why did you drop specific columns?
    Times asked 1
  11. 11
    How did you identify and remove duplicates?
    Times asked 1
  12. 12
    How did you handle missing values?
    Times asked 4
  13. 13
    Why did you choose that imputation technique?
    Times asked 1
  14. 14
    What preprocessing did you perform?
    Times asked 1
  15. 15
    Why is preprocessing required?
    Times asked 1
  16. 16
    What feature engineering did you perform?
    Times asked 2
  17. 17
    Did you create any feature that improved your score significantly?
    Times asked 2
  18. 18
    Did you create any feature that improved your score significantly?
    Times asked 2
  19. 19
    How do lag features help?
    Times asked 1
  20. 20
    How do rolling features help?
    Times asked 1
  21. 21
    How did you implement lag and rolling features?
    Times asked 1
  22. 22
    What is Backward Fill?
    Times asked 1
  23. 23
    What is Forward Fill?
    Times asked 1
  24. 24
    What encoding techniques did you use?
    Times asked 1
  25. 25
    Explain One-Hot Encoding.
    Times asked 2
  26. 26
    Explain Label Encoding.
    Times asked 1
  27. 27
    Explain Ordinal Encoding.
    Times asked 2
  28. 28
    Difference between One-Hot Encoding and Label Encoding.
    Times asked 2
  29. 29
    When should you use Label Encoding?
    Times asked 1
  30. 30
    What are the drawbacks of One-Hot Encoding?
    Times asked 1
  31. 31
    What scaling techniques did you use?
    Times asked 1
  32. 32
    Why is scaling required?
    Times asked 1
  33. 33
    What happens if scaling is not applied?
    Times asked 1
  34. 34
    How does StandardScaler work?
    Times asked 2
  35. 35
    How does MinMaxScaler work?
    Times asked 1
  36. 36
    Difference between StandardScaler and MinMaxScaler.
    Times asked 2
  37. 37
    What is the range after applying StandardScaler?
    Times asked 1
  38. 38
    What is the range after applying MinMaxScaler?
    Times asked 1
  39. 39
    After applying StandardScaler, what are the new mean and standard deviation?
    Times asked 1
  40. 40
    What percentage of values lie between -3σ and +3σ in a normal distribution?
    Times asked 1
  41. 41
    Explain TF-IDF.
    Times asked 1
  42. 42
    Why did you choose those models?
    Times asked 3
  43. 43
    Explain the working of each model.
    Times asked 1
  44. 44
    Explain Logistic Regression.
    Times asked 5
  45. 45
    Explain the Sigmoid function.
    Times asked 1
  46. 46
    Write the Sigmoid function formula.
    Times asked 2
  47. 47
    How does Logistic Regression learn weights?
    Times asked 1
  48. 48
    What is the loss function of Logistic Regression?
    Times asked 3
  49. 49
    What changes are required for multiclass Logistic Regression?
    Times asked 1
  50. 50
    Explain SVM.
    Times asked 3
  51. 51
    SVM vs Logistic Regression for outliers.
    Times asked 1
  52. 52
    Explain Decision Tree.
    Times asked 3
  53. 53
    Explain Random Forest.
    Times asked 2
  54. 54
    Explain Naive Bayes.
    Times asked 1
  55. 55
    What are the limitations of Naive Bayes?
    Times asked 1
  56. 56
    Explain XGBoost.
    Times asked 2
  57. 57
    Explain LightGBM.
    Times asked 2
  58. 58
    Difference between XGBoost and LightGBM.
    Times asked 5
  59. 59
    Why is LightGBM better than other boosting algorithms?
    Times asked 1
  60. 60
    Explain level-wise vs leaf-wise tree growth.
    Times asked 1
  61. 61
    Explain Bagging.
    Times asked 6
  62. 62
    Explain Boosting.
    Times asked 6
  63. 63
    Which reduces bias and which reduces variance?
    Times asked 1
  64. 64
    What is Gini Index?
    Times asked 1
  65. 65
    What is Information Gain?
    Times asked 3
  66. 66
    What is SMOTE?
    Times asked 1
  67. 67
    Explain ROC Curve.
    Times asked 1
  68. 68
    Explain Confusion Matrix.
    Times asked 1
  69. 69
    What is hyperparameter tuning?
    Times asked 2
  70. 70
    How does GridSearchCV work?
    Times asked 1
  71. 71
    Why did you choose only those parameters/solvers for GridSearchCV?
    Times asked 1
  72. 72
    How does Pipeline work?
    Times asked 1
  73. 73
    What is overfitting?
    Times asked 5
  74. 74
    How can you reduce overfitting?
    Times asked 1
  75. 75
    How did you handle outliers?
    Times asked 3
  76. 76
    How did you improve your model score?
    Times asked 1
  77. 77
    How can you further improve model performance?
    Times asked 1
  78. 78
    Why did you use R² score instead of Accuracy?
    Times asked 1
  79. 79
    Explain ANOVA Test.
    Times asked 1
  80. 80
    Explain Chi-Square Test.
    Times asked 1
  81. 81
    What is PCA?
    Times asked 3
  82. 82
    Explain the working of PCA.
    Times asked 1
  83. 83
    What is multicollinearity?
    Times asked 1
  84. 84
    How does Multiple Linear Regression work?
    Times asked 1
  85. 85
    What is RFE?
    Times asked 1
  86. 86
    How does RFE work?
    Times asked 1
  87. 87
    How do you select important features from a dataset?
    Times asked 1
  88. 88
    What feature extraction techniques have you used and why?
    Times asked 1
  89. 89
    What are soft class predictions and hard class predictions?
    Times asked 1
  90. 90
    Coding: Implement RFE to find the top 2 features.
    Times asked 1
  91. 91
    Coding: Load the Breast Cancer dataset and find the top features using RFE.
    Times asked 1
  92. 92
    Coding: Load the Diabetes dataset and find the top features using RFE.
    Times asked 1
  93. 93
    Coding: Load the Iris dataset and use SelectKBest with Chi-Square to find the top 2 features.
    Times asked 1
  94. 94
    Coding: Load the Iris dataset, train a Logistic Regression model with GridSearchCV, and print the classification report.
    Times asked 1
  95. 95
    Coding: Generate a word cloud for each label/class separately.
    Times asked 1
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