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1
Tell me about yourself.Times asked 6
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What is your Kaggle leaderboard score/rank?Times asked 1
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Explain your notebook/project.Times asked 21
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Explain your EDA.Times asked 8
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How many graphs did you create?Times asked 1
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Explain each graph you created.Times asked 1
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What observations did you make from your graphs?Times asked 1
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How do you hide/show values in a correlation heatmap?Times asked 1
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Explain your boxplot.Times asked 1
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Explain your density regression plot.Times asked 1
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What feature engineering did you perform?Times asked 2
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Why did you perform that feature engineering?Times asked 1
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How did you handle missing values?Times asked 4
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How did you handle numerical missing values?Times asked 2
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How did you handle categorical missing values?Times asked 1
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How did you handle disguised missing values (None, NaN, Unknown, Unspecified)?Times asked 1
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Why did you preserve the "Unknown" category?Times asked 1
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What numerical transformers did you use?Times asked 1
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What categorical transformers did you use?Times asked 1
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Why did you use those transformers?Times asked 1
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What is Data Leakage?Times asked 1
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When does Data Leakage occur?Times asked 1
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How did you prevent Data Leakage?Times asked 1
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What is an N-gram?Times asked 1
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What is Unigram?Times asked 1
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What is Bigram?Times asked 1
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What is a 6-gram, 8-gram, or 11-gram?Times asked 1
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28
What is TF-IDF?Times asked 2
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29
Difference between word-level and character-level TF-IDF.Times asked 1
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30
What does TF-IDF Vectorizer do?Times asked 1
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31
Explain the parameters of TF-IDF Vectorizer.Times asked 1
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32
Why did you choose 50,000 TF-IDF features?Times asked 1
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33
What models did you use?Times asked 3
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34
Why did you choose those models?Times asked 3
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35
What is your best model?Times asked 1
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36
Why is it your best model?Times asked 2
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What is an Ensemble model?Times asked 1
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Explain your final modeling approach.Times asked 1
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39
What hyperparameter tuning did you perform?Times asked 1
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Which hyperparameters did you tune?Times asked 2
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41
How many model fits will GridSearchCV perform?Times asked 1
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42
How many model fits will RandomizedSearchCV perform?Times asked 1
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43
Difference between GridSearchCV and RandomizedSearchCV.Times asked 5
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44
How do you know how many fits will be performed?Times asked 1
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45
What is n_estimators?Times asked 2
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46
Why did you choose n_estimators = 100?Times asked 1
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47
What happens if n_estimators = 2000?Times asked 1
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48
What is max_depth?Times asked 1
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49
What is learning_rate?Times asked 1
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50
What is n_iter?Times asked 1
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51
What would you do if the model overfits because of max_depth?Times asked 1
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52
How do you prevent overfitting?Times asked 1
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53
How do you improve an underfitting model?Times asked 1
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54
What is IQR?Times asked 1
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55
How does outlier detection help?Times asked 1
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56
How does a boxplot help detect outliers?Times asked 1
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57
How does ColumnTransformer work?Times asked 1
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58
Why didn't you use scaling?Times asked 2
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59
Should SGD be used without scaling?Times asked 1
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60
What is SGD?Times asked 1
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61
Why did you use SGD?Times asked 1
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62
Difference between Accuracy, Precision, Recall, and F1-Score.Times asked 1
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63
Why did you use F1-Score?Times asked 1
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64
What is RMSLE?Times asked 1
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65
What is Blended Score?Times asked 1
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66
Why did your RMSLE score decrease after blending?Times asked 1
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67
What is a Decision Tree?Times asked 1
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68
What algorithm do XGBoost, LightGBM, and CatBoost use?Times asked 1
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69
Difference between XGBoost, LightGBM, and CatBoost.Times asked 1
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70
Are these models linear or non-linear?Times asked 1
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71
Explain your complete preprocessing and modeling pipeline.Times asked 1
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72
How does your complete pipeline work?Times asked 1
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73
What are custom classes?Times asked 1
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74
Why did you create custom classes?Times asked 1
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75
Did you use any LLMs while building the project?Times asked 1
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76
Coding: Impute the most frequent categorical value for each column.Times asked 1
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77
Coding: Perform PCA on numerical features and obtain the top two components.Times asked 1
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78
Coding: Perform K-Means clustering on a dataset.Times asked 1
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79
Coding: Load a dataset, preprocess it, perform train-test split, and build a Linear Regression model.Times asked 1
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80
Coding: Implement RandomizedSearchCV.Times asked 1
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81
Coding: Create a complete Pipeline with ColumnTransformer, Imputers, Transformers, and a model.Times asked 1
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