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1
Tell me about yourself.
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2
What is your Kaggle leaderboard score/rank?
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3
Explain your notebook/project.
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4
Explain your EDA.
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5
How many graphs did you create?
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6
Explain each graph you created.
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7
What observations did you make from your graphs?
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8
How do you hide/show values in a correlation heatmap?
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9
Explain your boxplot.
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10
Explain your density regression plot.
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11
What feature engineering did you perform?
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12
Why did you perform that feature engineering?
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13
How did you handle missing values?
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14
How did you handle numerical missing values?
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15
How did you handle categorical missing values?
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How did you handle disguised missing values (None, NaN, Unknown, Unspecified)?
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17
Why did you preserve the "Unknown" category?
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18
What numerical transformers did you use?
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19
What categorical transformers did you use?
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20
Why did you use those transformers?
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21
What is Data Leakage?
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22
When does Data Leakage occur?
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How did you prevent Data Leakage?
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24
What is an N-gram?
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25
What is Unigram?
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26
What is Bigram?
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27
What is a 6-gram, 8-gram, or 11-gram?
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28
What is TF-IDF?
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29
Difference between word-level and character-level TF-IDF.
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30
What does TF-IDF Vectorizer do?
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31
Explain the parameters of TF-IDF Vectorizer.
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32
Why did you choose 50,000 TF-IDF features?
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33
What models did you use?
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34
Why did you choose those models?
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35
What is your best model?
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36
Why is it your best model?
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37
What is an Ensemble model?
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38
Explain your final modeling approach.
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39
What hyperparameter tuning did you perform?
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40
Which hyperparameters did you tune?
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41
How many model fits will GridSearchCV perform?
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42
How many model fits will RandomizedSearchCV perform?
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43
Difference between GridSearchCV and RandomizedSearchCV.
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44
How do you know how many fits will be performed?
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45
What is n_estimators?
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46
Why did you choose n_estimators = 100?
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47
What happens if n_estimators = 2000?
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48
What is max_depth?
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49
What is learning_rate?
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50
What is n_iter?
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51
What would you do if the model overfits because of max_depth?
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52
How do you prevent overfitting?
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53
How do you improve an underfitting model?
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54
What is IQR?
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55
How does outlier detection help?
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56
How does a boxplot help detect outliers?
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57
How does ColumnTransformer work?
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58
Why didn't you use scaling?
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59
Should SGD be used without scaling?
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60
What is SGD?
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61
Why did you use SGD?
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62
Difference between Accuracy, Precision, Recall, and F1-Score.
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63
Why did you use F1-Score?
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64
What is RMSLE?
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65
What is Blended Score?
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66
Why did your RMSLE score decrease after blending?
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67
What is a Decision Tree?
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68
What algorithm do XGBoost, LightGBM, and CatBoost use?
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69
Difference between XGBoost, LightGBM, and CatBoost.
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70
Are these models linear or non-linear?
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71
Explain your complete preprocessing and modeling pipeline.
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72
How does your complete pipeline work?
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73
What are custom classes?
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74
Why did you create custom classes?
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75
Did you use any LLMs while building the project?
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76
Coding: Impute the most frequent categorical value for each column.
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77
Coding: Perform PCA on numerical features and obtain the top two components.
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78
Coding: Perform K-Means clustering on a dataset.
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79
Coding: Load a dataset, preprocess it, perform train-test split, and build a Linear Regression model.
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Coding: Implement RandomizedSearchCV.
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81
Coding: Create a complete Pipeline with ColumnTransformer, Imputers, Transformers, and a model.