Level 1 viva 4 MLP Level 1 Aug 7, 2026 Copy link 1 Explain your notebook/code. 2 Questions based on the selector used. 3 Questions based on the scaler used.
mlp_level1_viva14 MLP Level 1 Aug 7, 2026 Copy link 1 Introduction. 2 Show your ID card. 3 Explain your notebook/project. 4 Explain your notebook as a story. 5 Explain your project flow from start to finish. 6 Explain the machine learning models you used. 7 Explain the preprocessing pipeline you used. 8 Explain the hyperparameter tuning process. 9 What is the cv parameter in GridSearchCV/RandomizedSearchCV? 10 How does Cross Validation work? 11 What is K-Fold Cross Validation? 12 Why is a validation set needed? 13 How many iterations/model fits will be performed based on your hyperparameter tuning? 14 Difference between GridSearchCV and RandomizedSearchCV. 15 How do you identify whether a model is overfitting or underfitting? 16 How do you reduce overfitting? 17 What happens if the correlation between features is high? 18 How many models did you try? 19 Did you try different preprocessing techniques? 20 Did you try different models? 21 What resources did you use? 22 What other approaches did you try? 23 What difficulties did you face while doing the project? 24 Have you used any sklearn Pipelines? 25 Explain your missing value handling. 26 How did you handle numerical missing values? 27 How did you handle class imbalance? 28 Why did you use One-Hot Encoding? 29 Have you used scaling? 30 Why did you use StandardScaler? 31 How does StandardScaler work? 32 Difference between StandardScaler and other scaling techniques. 33 What is scaling? 34 Why didn't you use scaling? 35 What is the learning rate parameter? 36 Which scoring metric did you use to compare models? 37 Why did you use F1-Score, Precision, and Recall instead of RMSE? 38 What happens if RMSE is used for Logistic Regression? 39 Explain the ROC Curve. 40 Explain the Confusion Matrix. 41 Point out True Positive (TP) and True Negative (TN) in the Confusion Matrix. 42 What is log1p? 43 What is the mean and standard deviation after StandardScaler? 44 Coding: Implement RandomizedSearchCV for Logistic Regression. 45 Coding: Train an ElasticNet model and evaluate its performance. 46 Coding: Train a Linear Regression model on the raw (non-preprocessed) dataset. 47 Coding: Load the training dataset and print the first 6/7/10 rows. 48 Coding: Filter rows where ManufactureYear is greater than a given year. 49 Coding: Print unique/value counts of VendorPartnerID. 50 Coding: Find the proportion of each category (cardinality) in a feature. 51 Coding: Load the Iris dataset and train a Ridge Regression model. 52 Coding: Load the California Housing dataset.
level1_viva4 MLP Level 1 Aug 5, 2026 Copy link 1 Tell me about yourself. 2 What is your Kaggle leaderboard score/rank? 3 Explain your notebook/project. 4 Explain your EDA. 5 How many graphs did you create? 6 Explain each graph you created. 7 What observations did you make from your graphs? 8 How do you hide/show values in a correlation heatmap? 9 Explain your boxplot. 10 Explain your density regression plot. 11 What feature engineering did you perform? 12 Why did you perform that feature engineering? 13 How did you handle missing values? 14 How did you handle numerical missing values? 15 How did you handle categorical missing values? 16 How did you handle disguised missing values (None, NaN, Unknown, Unspecified)? 17 Why did you preserve the "Unknown" category? 18 What numerical transformers did you use? 19 What categorical transformers did you use? 20 Why did you use those transformers? 21 What is Data Leakage? 22 When does Data Leakage occur? 23 How did you prevent Data Leakage? 24 What is an N-gram? 25 What is Unigram? 26 What is Bigram? 27 What is a 6-gram, 8-gram, or 11-gram? 28 What is TF-IDF? 29 Difference between word-level and character-level TF-IDF. 30 What does TF-IDF Vectorizer do? 31 Explain the parameters of TF-IDF Vectorizer. 32 Why did you choose 50,000 TF-IDF features? 33 What models did you use? 34 Why did you choose those models? 35 What is your best model? 36 Why is it your best model? 37 What is an Ensemble model? 38 Explain your final modeling approach. 39 What hyperparameter tuning did you perform? 40 Which hyperparameters did you tune? 41 How many model fits will GridSearchCV perform? 42 How many model fits will RandomizedSearchCV perform? 43 Difference between GridSearchCV and RandomizedSearchCV. 44 How do you know how many fits will be performed? 45 What is n_estimators? 46 Why did you choose n_estimators = 100? 47 What happens if n_estimators = 2000? 48 What is max_depth? 49 What is learning_rate? 50 What is n_iter? 51 What would you do if the model overfits because of max_depth? 52 How do you prevent overfitting? 53 How do you improve an underfitting model? 54 What is IQR? 55 How does outlier detection help? 56 How does a boxplot help detect outliers? 57 How does ColumnTransformer work? 58 Why didn't you use scaling? 59 Should SGD be used without scaling? 60 What is SGD? 61 Why did you use SGD? 62 Difference between Accuracy, Precision, Recall, and F1-Score. 63 Why did you use F1-Score? 64 What is RMSLE? 65 What is Blended Score? 66 Why did your RMSLE score decrease after blending? 67 What is a Decision Tree? 68 What algorithm do XGBoost, LightGBM, and CatBoost use? 69 Difference between XGBoost, LightGBM, and CatBoost. 70 Are these models linear or non-linear? 71 Explain your complete preprocessing and modeling pipeline. 72 How does your complete pipeline work? 73 What are custom classes? 74 Why did you create custom classes? 75 Did you use any LLMs while building the project? 76 Coding: Impute the most frequent categorical value for each column. 77 Coding: Perform PCA on numerical features and obtain the top two components. 78 Coding: Perform K-Means clustering on a dataset. 79 Coding: Load a dataset, preprocess it, perform train-test split, and build a Linear Regression model. 80 Coding: Implement RandomizedSearchCV. 81 Coding: Create a complete Pipeline with ColumnTransformer, Imputers, Transformers, and a model.
proctor level2_122 MLP Level 1 Aug 5, 2026 Copy link 1 Why do you want to do this course? 2 What are you doing to improve your skills? 3 Explain your notebook/project. 4 Questions based on your project implementation.
level2_72 MLP Level 1 Aug 5, 2026 Copy link 1 Why did you plot ROC Curve if you are already plotting f1-scores.
level2_107 MLP Level 1 Aug 5, 2026 Copy link 1 What is the use of a correlation matrix? 2 How do you handle imbalanced datasets? 3 What techniques are used for handling null values? 4 What is K-Fold Cross Validation? 5 Which Cross Validation technique did you use? 6 Did you drop any columns/features? Why? 7 What is Precision? 8 What is Recall? 9 What is Support in a Classification Report? 10 What is Macro Average in a Classification Report? 11 What is Weighted Average in a Classification Report? 12 What is the degree parameter in SVM? 13 What is the C parameter in SVM? 14 How does an SVM classifier work? 15 What is an activation function? 16 Why are activation functions used in Neural Networks? 17 Name different activation functions. 18 Explain the Tanh activation function. 19 What is zero-centering? 20 What are Neural Networks? 21 Why did you try multiple models? 22 What is Information Gain? 23 How is Information Gain related to Entropy? 24 Explain Entropy. 25 How does a Decision Tree work? 26 Explain the Decision Tree building process. 27 Why did you choose a particular learning rate for LightGBM? 28 Why did your score change after private evaluation? 29 What EDA did you perform? 30 What are the types of Machine Learning? 31 Difference between Supervised and Unsupervised Learning. 32 Difference between K-Means and K-Means++. 33 How does clustering work? 34 How would you use clustering on purchase/customer data? 35 Are more features always better for training? 36 What is the Curse of Dimensionality? 37 How does PCA work? 38 What is Gradient Descent? 39 What is the Sigmoid function? 40 Write the Sigmoid function formula. 41 What threshold is used in Logistic Regression? 42 Explain Logistic Regression. 43 Can Logistic Regression handle outliers? 44 What are the assumptions/conditions for Logistic Regression? 45 What is Log Loss? 46 What is the loss function of Linear Regression? 47 Difference between RMSE and RMSLE. 48 Why did you use RMSLE instead of RMSE? 49 What is Naive Bayes? 50 What is Machine Learning? 51 Give examples of different Machine Learning algorithms. 52 What models can be used for Sentiment Analysis? 53 What is R² Score? 54 How do you calculate mean and variance? 55 How can you make a feature follow a normal distribution? 56 Explain the T-Test. 57 How do you compare the results obtained using RFE? 58 Explain Bagging. 59 Explain Boosting. 60 What is the Bias-Variance Tradeoff? 61 How does a Confusion Matrix work? 62 How do you interpret a Confusion Matrix? 63 What are feature selection techniques? 64 Have you applied feature selection? 65 How do you evaluate the performance of a feature selection technique? 66 What is undersampling? 67 What is oversampling? 68 How do you identify whether a dataset is imbalanced? 69 How do you choose the best model when multiple models have similar accuracy? 70 How do you choose a model if some models take a very long time to train? 71 How do you choose the learning rate (η) in Gradient Descent? 72 What happens if the learning rate is too high or too low? 73 Should the learning rate change during training? 74 How are Precision, Recall, and F1-Score calculated? 75 What are evaluation metrics for Classification? 76 What are evaluation metrics for Regression? 77 Coding: Build an SVM Pipeline (StandardScaler + SVM) on the MNIST/Digits dataset and generate the Classification Report. 78 Coding: Compare SVM performance before and after applying PCA. 79 Coding: Apply TF-IDF manually and calculate TF and IDF values for given documents. 80 Coding: Calculate the mean and variance of given data points manually.
mlp_level1_viva7_MLP_Viva MLP Level 1 Aug 5, 2026 Copy link 1 Explain your notebook/project. 2 Explain your code and approach. 3 Have you used Pipeline? 4 Have you used ColumnTransformer? 5 Why is Pipeline used? 6 Explain SimpleImputer. 7 Explain One-Hot Encoding. 8 Difference between One-Hot Encoding and Label Encoding. 9 Which Cross Validation (CV) technique did you use? 10 What is XGBoost? 11 How does XGBoost work? 12 What is overfitting? 13 What is underfitting?
level2_81 MLP Level 1 Aug 5, 2026 Copy link 1 Explain your notebook/project. 2 Explain the problem statement. 3 What approach did you follow to solve the problem? 4 What are the top insights from your EDA? 5 Explain every graph used in your EDA. 6 Why did you choose those plots? 7 How can you make your graphs cleaner? 8 What did you understand from the describe() output? 9 How did you correlate features with the target variable? 10 Why did you drop specific columns? 11 How did you identify and remove duplicates? 12 How did you handle missing values? 13 Why did you choose that imputation technique? 14 What preprocessing did you perform? 15 Why is preprocessing required? 16 What feature engineering did you perform? 17 Did you create any feature that improved your score significantly? 18 Did you create any feature that improved your score significantly? 19 How do lag features help? 20 How do rolling features help? 21 How did you implement lag and rolling features? 22 What is Backward Fill? 23 What is Forward Fill? 24 What encoding techniques did you use? 25 Explain One-Hot Encoding. 26 Explain Label Encoding. 27 Explain Ordinal Encoding. 28 Difference between One-Hot Encoding and Label Encoding. 29 When should you use Label Encoding? 30 What are the drawbacks of One-Hot Encoding? 31 What scaling techniques did you use? 32 Why is scaling required? 33 What happens if scaling is not applied? 34 How does StandardScaler work? 35 How does MinMaxScaler work? 36 Difference between StandardScaler and MinMaxScaler. 37 What is the range after applying StandardScaler? 38 What is the range after applying MinMaxScaler? 39 After applying StandardScaler, what are the new mean and standard deviation? 40 What percentage of values lie between -3σ and +3σ in a normal distribution? 41 Explain TF-IDF. 42 Why did you choose those models? 43 Explain the working of each model. 44 Explain Logistic Regression. 45 Explain the Sigmoid function. 46 Write the Sigmoid function formula. 47 How does Logistic Regression learn weights? 48 What is the loss function of Logistic Regression? 49 What changes are required for multiclass Logistic Regression? 50 Explain SVM. 51 SVM vs Logistic Regression for outliers. 52 Explain Decision Tree. 53 Explain Random Forest. 54 Explain Naive Bayes. 55 What are the limitations of Naive Bayes? 56 Explain XGBoost. 57 Explain LightGBM. 58 Difference between XGBoost and LightGBM. 59 Why is LightGBM better than other boosting algorithms? 60 Explain level-wise vs leaf-wise tree growth. 61 Explain Bagging. 62 Explain Boosting. 63 Which reduces bias and which reduces variance? 64 What is Gini Index? 65 What is Information Gain? 66 What is SMOTE? 67 Explain ROC Curve. 68 Explain Confusion Matrix. 69 What is hyperparameter tuning? 70 How does GridSearchCV work? 71 Why did you choose only those parameters/solvers for GridSearchCV? 72 How does Pipeline work? 73 What is overfitting? 74 How can you reduce overfitting? 75 How did you handle outliers? 76 How did you improve your model score? 77 How can you further improve model performance? 78 Why did you use R² score instead of Accuracy? 79 Explain ANOVA Test. 80 Explain Chi-Square Test. 81 What is PCA? 82 Explain the working of PCA. 83 What is multicollinearity? 84 How does Multiple Linear Regression work? 85 What is RFE? 86 How does RFE work? 87 How do you select important features from a dataset? 88 What feature extraction techniques have you used and why? 89 What are soft class predictions and hard class predictions? 90 Coding: Implement RFE to find the top 2 features. 91 Coding: Load the Breast Cancer dataset and find the top features using RFE. 92 Coding: Load the Diabetes dataset and find the top features using RFE. 93 Coding: Load the Iris dataset and use SelectKBest with Chi-Square to find the top 2 features. 94 Coding: Load the Iris dataset, train a Logistic Regression model with GridSearchCV, and print the classification report. 95 Coding: Generate a word cloud for each label/class separately.