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See reviews mlp_level1_viva7_MLP_Viva MLP Level 1
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  1. 1
    Explain your notebook/project.
    Times asked 21
  2. 2
    Explain your code and approach.
    Times asked 1
  3. 3
    Have you used Pipeline?
    Times asked 2
  4. 4
    Have you used ColumnTransformer?
    Times asked 1
  5. 5
    Why is Pipeline used?
    Times asked 1
  6. 6
    Explain SimpleImputer.
    Times asked 1
  7. 7
    Explain One-Hot Encoding.
    Times asked 2
  8. 8
    Difference between One-Hot Encoding and Label Encoding.
    Times asked 2
  9. 9
    Which Cross Validation (CV) technique did you use?
    Times asked 1
  10. 10
    What is XGBoost?
    Times asked 2
  11. 11
    How does XGBoost work?
    Times asked 2
  12. 12
    What is overfitting?
    Times asked 5
  13. 13
    What is underfitting?
    Times asked 3
See reviews Level2_81 MLP Level 1
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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
See reviews Level2_103 MLP Level 1
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  1. 1
    Explain your Kaggle/Colab notebook (project).
    Times asked 1
  2. 2
    What steps did you take to improve your model/score?
    Times asked 1
  3. 3
    Why did you use this model?
    Times asked 1
  4. 4
    Why did you use this imputer?
    Times asked 1
  5. 5
    Why did you use this scaler?
    Times asked 1
  6. 6
    Explain the working of the models you used.
    Times asked 1
  7. 7
    Explain the parameters/hyperparameters used in your model.
    Times asked 1
  8. 8
    What is a decision tree?
    Times asked 1
  9. 9
    Compare K-Means Clustering and KNN.
    Times asked 1
  10. 10
    What is a loss function?
    Times asked 1
  11. 11
    Why do we need a loss function?
    Times asked 1
  12. 12
    What is feature selection?
    Times asked 1
  13. 13
    Why do we need feature selection?
    Times asked 1
  14. 14
    What is data preprocessing?
    Times asked 2
  15. 15
    What is EDA (Exploratory Data Analysis)?
    Times asked 1
  16. 16
    What is dimensionality reduction?
    Times asked 1
  17. 17
    Explain PCA.
    Times asked 1
  18. 18
    What is hyperparameter tuning?
    Times asked 2
  19. 19
    Explain Logistic Regression.
    Times asked 5
  20. 20
    What is overfitting?
    Times asked 5
  21. 21
    What is underfitting?
    Times asked 3
  22. 22
    How can you prevent overfitting and underfitting?
    Times asked 1
  23. 23
    What is GridSearchCV?
    Times asked 1
  24. 24
    What is RandomizedSearchCV?
    Times asked 1
  25. 25
    What is Cross Validation (CV)?
    Times asked 2
  26. 26
    What are evaluation metrics?
    Times asked 1
See reviews Level2_72 MLP Level 1
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  1. 1
    What is stratify in train_test_split?
    Times asked 1
  2. 2
    Why do we use stratify?
    Times asked 1
  3. 3
    How can you find correlation between categorical variables?
    Times asked 1
  4. 4
    How can you determine whether a model is biased?
    Times asked 1
  5. 5
    These were already included in your master list: Theory about the models used. Precision, Recall, F1 Score. Suggestions for improving the model. Where else the model can be applied. General conceptual questions from the notebook.
    Times asked 1
  6. 6
    What is stratify?
    Times asked 1
  7. 7
    Why is stratify used?
    Times asked 1
  8. 8
    How do you measure correlation between categorical variables?
    Times asked 1
  9. 9
    How do you identify whether a machine learning model is biased?
    Times asked 1
See reviews mlp_level1_viva3 MLP Level 1
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  1. 1
    Why did you choose this preprocessing strategy instead of another?
    Times asked 1
  2. 2
    Did you consider any alternative preprocessing techniques?
    Times asked 2
  3. 3
    Did you consider any alternative preprocessing techniques?
    Times asked 2
  4. 4
    If you had more time, what would you do to improve your model/project?
    Times asked 1
  5. 5
    Did you refer to any research papers, books, or other external resources while building the project?
    Times asked 1
  6. 6
    Show your Kaggle competition submissions.
    Times asked 2
  7. 7
    Create a preprocessing pipeline using: Mean imputation for numerical columns Mode (most frequent) imputation for categorical columns.
    Times asked 1
  8. 8
    Modify an existing pipeline to replace Median imputation with Mode.
    Times asked 1
  9. 9
    Fill categorical columns using SimpleImputer(strategy="most_frequent").
    Times asked 2
  10. 10
    Fill categorical columns with mode using Pandas (without SimpleImputer).
    Times asked 1
  11. 11
    Import the California Housing dataset.
    Times asked 1
  12. 12
    Perform an 80:20 train-test split.
    Times asked 1
  13. 13
    Load the Iris dataset and train an SGDClassifier.
    Times asked 1
  14. 14
    Load the Iris dataset and train SGDClassifier without train_test_split.
    Times asked 1
  15. 15
    Filter rows where: RegionCode == "Florida" AND TargetValue > 50000
    Times asked 1
  16. 16
    Why did you choose this preprocessing strategy?
    Times asked 1
  17. 17
    Did you consider alternative preprocessing methods?
    Times asked 1
  18. 18
    If you had more time, how would you improve the model/project?
    Times asked 1
  19. 19
    Did you use research papers/books/external resources?
    Times asked 1
  20. 20
    Show your Kaggle competition submissions.
    Times asked 2
  21. 21
    Create preprocessing pipeline with mean (numerical) and mode (categorical) imputation.
    Times asked 1
  22. 22
    Replace median imputation with mode imputation.
    Times asked 1
  23. 23
    Fill categorical columns using SimpleImputer(strategy="most_frequent").
    Times asked 2
  24. 24
    Fill categorical columns using Pandas mode.
    Times asked 1
  25. 25
    Load California Housing dataset and perform an 80:20 train-test split.
    Times asked 1
  26. 26
    Train an SGDClassifier on the Iris dataset (with/without train-test split).
    Times asked 1
  27. 27
    Filter a DataFrame where RegionCode == "Florida" and TargetValue > 50000.
    Times asked 1
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  1. 1
    What are the parameters of Logistic Regression?
    Times asked 1
  2. 2
    Can Logistic Regression be used to predict a continuous value (e.g., rainfall)? Why or why not?
    Times asked 1
  3. 3
    What are the parameters of Random Forest?
    Times asked 1
  4. 4
    What is the loss function of Random Forest?
    Times asked 1
  5. 5
    Difference between parameters and hyperparameters.
    Times asked 2
  6. 6
    What are the important XGBoost parameters?
    Times asked 1
  7. 7
    If XGBoost is overfitting, should you increase or decrease n_estimators? Why?
    Times asked 1
  8. 8
    If XGBoost is overfitting, should you increase or decrease learning_rate? Why?
    Times asked 1
  9. 9
    What does colsample_bytree / colsample_bylevel / colsample_bynode (referred to as col_by_sample) do?
    Times asked 1
  10. 10
    What is n_iter in RandomizedSearchCV?
    Times asked 1
  11. 11
    What does verbose mean?
    Times asked 1
  12. 12
    What does n_jobs do?
    Times asked 1
  13. 13
    What is the mathematical range of R² score?
    Times asked 1
  14. 14
    Why can R² be negative?
    Times asked 1
  15. 15
    What is data leakage?
    Times asked 1
  16. 16
    How did you ensure data leakage was avoided?
    Times asked 1
  17. 17
    Why should you not train the final model on the entire dataset before evaluation?
    Times asked 1
  18. 18
    How do you ensure reproducibility of results?
    Times asked 1
  19. 19
    What is autocorrelation?
    Times asked 1
  20. 20
    Explain the parameters of TF-IDF.
    Times asked 1
  21. 21
    Create a preprocessing Pipeline using: SimpleImputer RobustScaler KNNImputer OneHotEncoder
    Times asked 1
  22. 22
    Add RobustScaler to an existing preprocessing pipeline.
    Times asked 1
  23. 23
    Explain what changes after adding RobustScaler.
    Times asked 1
  24. 24
    Load the Diabetes dataset.
    Times asked 1
  25. 25
    Convert it into a Pandas DataFrame.
    Times asked 1
  26. 26
    Print the number of features.
    Times asked 1
  27. 27
    Fit a Logistic Regression model on it.
    Times asked 1
  28. 28
    Explain EDA overall, not line by line.
    Times asked 1
  29. 29
    Mention one important observation from the dataset.
    Times asked 1
  30. 30
    Show all model score comparisons.
    Times asked 1
  31. 31
    How many models were trained and how were they compared?
    Times asked 1
  32. 32
    Explain the mandatory Level-1 notebook sections one by one.
    Times asked 1
  33. 33
    Were any custom/user-defined functions used?
    Times asked 1
  34. 34
    Were any LLMs used while building the notebook?
    Times asked 1
  35. 35
    Parameters of Logistic Regression
    Times asked 1
  36. 36
    Parameters of Random Forest
    Times asked 1
  37. 37
    Loss function of Random Forest
    Times asked 1
  38. 38
    Difference between Parameters and Hyperparameters
    Times asked 1
  39. 39
    Important XGBoost parameters
    Times asked 1
  40. 40
    Effect of n_estimators on overfitting
    Times asked 1
  41. 41
    Effect of learning_rate on overfitting
    Times asked 1
  42. 42
    colsample_* parameter in XGBoost
    Times asked 1
  43. 43
    verbose
    Times asked 1
  44. 44
    n_jobs
    Times asked 1
  45. 45
    Mathematical range of R²
    Times asked 1
  46. 46
    Why R² can be negative
    Times asked 1
  47. 47
    Data Leakage
    Times asked 1
  48. 48
    How to prevent Data Leakage
    Times asked 1
  49. 49
    Why not train the final model on the full dataset before evaluation
    Times asked 1
  50. 50
    Reproducibility of results
    Times asked 1
  51. 51
    Autocorrelation
    Times asked 1
  52. 52
    TF-IDF parameters
    Times asked 1
  53. 53
    Can Logistic Regression predict rainfall? Why/Why not?
    Times asked 1
  54. 54
    Create/Edit preprocessing pipeline with RobustScaler, KNNImputer, OHE
    Times asked 1
  55. 55
    Load Diabetes dataset → DataFrame → Number of features → Fit Logistic Regression
    Times asked 1
  56. 56
    Explain one key insight from EDA
    Times asked 1
  57. 57
    Compare all trained models
    Times asked 1
  58. 58
    User-defined functions
    Times asked 1
  59. 59
    LLM usage in the notebook
    Times asked 1
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  1. 1
    what is solver
    Times asked 1
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  1. 1
    Why did you use One-Hot Encoding instead of Label Encoding?
    Times asked 1
  2. 2
    For every preprocessing step: Why this technique and not another one?
    Times asked 1
  3. 3
    Why did you choose each hyperparameter in your model?
    Times asked 1
  4. 4
    Why did you use eval_metric='logloss'?
    Times asked 1
  5. 5
    Why did you choose a particular threshold value?
    Times asked 1
  6. 6
    Is Pickup Location ID useful? Why?
    Times asked 1
  7. 7
    Is Drop Location ID useful? Why?
    Times asked 1
  8. 8
    What do you observe about the target variable?
    Times asked 1
  9. 9
    Why do you say the target variable is normally distributed?
    Times asked 1
  10. 10
    Explain the advantages and disadvantages of each model you used compared to the others.
    Times asked 1
  11. 11
    Show graphs during EDA.
    Times asked 1
  12. 12
    Explain every graph and why you plotted it.
    Times asked 1
  13. 13
    Plot a graph comparing y_pred vs y_test (or prediction error) for every model.
    Times asked 1
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  1. 1
    What is Support in a Classification Report?
    Times asked 2
  2. 2
    What is ROC-AUC? (Full form)
    Times asked 1
  3. 3
    How do you interpret the ROC-AUC score?
    Times asked 1
  4. 4
    What is Correlation?
    Times asked 2
  5. 5
    What do you infer from a Correlation Matrix?
    Times asked 1
  6. 6
    Explain Decision Tree working in depth.
    Times asked 1
  7. 7
    What are the assumptions of Linear Regression?
    Times asked 1
  8. 8
    What is the Curse of Dimensionality?
    Times asked 2
  9. 9
    How does Dimensionality Reduction work?
    Times asked 1
  10. 10
    Does dimensionality reduction remove/discard data points?
    Times asked 1
  11. 11
    What is random_state=42?
    Times asked 1
  12. 12
    Why is 42 commonly used?
    Times asked 1
  13. 13
    Why choose a learning rate like 0.01 / 0.1 / 0.2 instead of 1 / 2 / 3?
    Times asked 1
  14. 14
    What is the objective of your project?
    Times asked 1
  15. 15
    What is a Loss Function?
    Times asked 1
  16. 16
    What properties should a good loss function have?
    Times asked 1
  17. 17
    Explain Bias vs Variance.
    Times asked 1
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  1. 1
    Self introduction.
    Times asked 1
  2. 2
    Explain the problem statement.
    Times asked 6
  3. 3
    Explain your notebook to a non-technical person (e.g., your boss).
    Times asked 1
  4. 4
    Abstract questions about the dataset (not technical).
    Times asked 1
  5. 5
    Questions about college life.
    Times asked 1
  6. 6
    How many models did you try?
    Times asked 2
  7. 7
    How many models did you perform hyperparameter tuning on?
    Times asked 1
  8. 8
    Why didn't you submit earlier?
    Times asked 2
  9. 9
    Why F1 Score instead of Accuracy?
    Times asked 1
  10. 10
    How is F1 Score calculated?
    Times asked 1
  11. 11
    Macro vs Micro averaging.
    Times asked 1
  12. 12
    Why only Accuracy was considered?
    Times asked 1
  13. 13
    Did Hyperparameter Tuning improve your score?
    Times asked 1
  14. 14
    Which is your baseline model?
    Times asked 1
  15. 15
    What is imputation?
    Times asked 1
  16. 16
    Why did you choose this imputation strategy?
    Times asked 1
  17. 17
    What is scaling?
    Times asked 5
  18. 18
    What is normalization?
    Times asked 2
  19. 19
    What effect does normalization have on models?
    Times asked 1
  20. 20
    What do you think about outliers in your dataset?
    Times asked 1
  21. 21
    How did you handle class imbalance?
    Times asked 3
  22. 22
    Explain Hyperparameter Tuning.
    Times asked 1
  23. 23
    What did you do during Hyperparameter Tuning?
    Times asked 1
  24. 24
    Why didn't you perform Hyperparameter Tuning? (if missing from notebook)
    Times asked 1
  25. 25
    How much time did Hyperparameter Tuning take?
    Times asked 1
  26. 26
    Loss function of Logistic Regression.
    Times asked 2
  27. 27
    Loss function of Linear Regression.
    Times asked 1
  28. 28
    Overfitting vs Underfitting.
    Times asked 1
  29. 29
    How do you identify overfitting?
    Times asked 1
  30. 30
    How do you overcome overfitting?
    Times asked 1
  31. 31
    What is Cross Validation?
    Times asked 1
  32. 32
    Pipeline.
    Times asked 1
  33. 33
    PCA.
    Times asked 1
  34. 34
    Regularization.
    Times asked 1
  35. 35
    L1 vs L2 Penalty.
    Times asked 1
  36. 36
    Explain how L1/L2 Regularization works.
    Times asked 1
  37. 37
    Other ensemble models.
    Times asked 1
  38. 38
    Boosting vs XGBoost vs LightGBM.
    Times asked 1
  39. 39
    Explain how XGBoost works.
    Times asked 1
  40. 40
    Explain how LightGBM works.
    Times asked 1
  41. 41
    Can Gradient Boosting be converted into LightGBM?
    Times asked 1
  42. 42
    Which was your best model?
    Times asked 1
  43. 43
    Explain your best model in detail.
    Times asked 1
  44. 44
    Advantages of your best model.
    Times asked 1
  45. 45
    Difference between hstack() and vertical stacking (vstack()).
    Times asked 1
  46. 46
    Difference between Seaborn and Matplotlib.
    Times asked 1
  47. 47
    Load the Iris dataset.
    Times asked 1
  48. 48
    Train a Logistic Regression model on it.
    Times asked 1
  49. 49
    Self introduction
    Times asked 1
  50. 50
    Problem statement explanation
    Times asked 1
  51. 51
    Explain notebook to a non-technical person
    Times asked 1
  52. 52
    Abstract questions on dataset
    Times asked 1
  53. 53
    Questions about college life
    Times asked 1
  54. 54
    How many models tried?
    Times asked 1
  55. 55
    How many hyperparameter tuned?
    Times asked 1
  56. 56
    Why didn't you submit earlier?
    Times asked 2
  57. 57
    Why F1 instead of Accuracy?
    Times asked 1
  58. 58
    F1 Score calculation
    Times asked 1
  59. 59
    Macro vs Micro
    Times asked 1
  60. 60
    Imputation
    Times asked 1
  61. 61
    Imputation strategy
    Times asked 1
  62. 62
    Scaling
    Times asked 1
  63. 63
    Normalization and its effect
    Times asked 1
  64. 64
    Outliers
    Times asked 1
  65. 65
    Class imbalance handling
    Times asked 1
  66. 66
    Hyperparameter tuning explanation
    Times asked 1
  67. 67
    HPT improvement in score
    Times asked 1
  68. 68
    HPT execution time
    Times asked 1
  69. 69
    Logistic Regression loss function
    Times asked 1
  70. 70
    Linear Regression loss function
    Times asked 1
  71. 71
    Cross Validation
    Times asked 1
  72. 72
    PCA
    Times asked 1
  73. 73
    Pipeline
    Times asked 1
  74. 74
    Regularization
    Times asked 1
  75. 75
    L1 vs L2
    Times asked 1
  76. 76
    Overfitting vs Underfitting
    Times asked 1
  77. 77
    Detecting overfitting
    Times asked 1
  78. 78
    Preventing overfitting
    Times asked 1
  79. 79
    Boosting vs XGBoost vs LightGBM
    Times asked 1
  80. 80
    LightGBM working
    Times asked 1
  81. 81
    XGBoost working
    Times asked 1
  82. 82
    Other ensemble models
    Times asked 1
  83. 83
    Gradient Boosting → LightGBM conversion
    Times asked 1
  84. 84
    Best model explanation
    Times asked 1
  85. 85
    Advantages of best model
    Times asked 1
  86. 86
    hstack vs vstack
    Times asked 1
  87. 87
    Seaborn vs Matplotlib
    Times asked 1
  88. 88
    Logistic Regression coding on Iris dataset
    Times asked 1
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