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1
Show your ID card.
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2
Explain your notebook/project.
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3
Explain your EDA.
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4
Explain your univariate analysis.
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Explain your bivariate analysis.
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6
Explain the graphs you created and the insights from them.
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7
How did you handle missing values?
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8
What is SimpleImputer?
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How does SimpleImputer work?
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10
Explain your preprocessing pipeline.
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Have you used Pipeline?
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Why did you use (or not use) Pipeline?
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Explain your feature engineering.
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Did you create any new features?
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What encoding technique did you use?
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Why did you choose that encoder?
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Replace OrdinalEncoder with OneHotEncoder.
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Difference between Label Encoding and OneHot Encoding.
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What scaler did you use?
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Why did you choose MinMaxScaler over StandardScaler?
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Explain StandardScaler.
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Difference between StandardScaler and MinMaxScaler.
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What models did you try?
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Which model performed the best?
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Why didn't you explore linear models?
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Explain your model comparison.
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Explain the hyperparameters you tuned.
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What is the learning rate?
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Is a higher or lower learning rate better? Why?
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30
What is the Bias-Variance Tradeoff?
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31
What is Boosting?
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What is Gradient Boosting?
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What algorithm does Boosting use?
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34
What is Bagging?
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Difference between Bagging and Boosting.
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36
Difference between XGBoost and LightGBM.
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37
What is R² Score?
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Write the formula for R² Score.
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39
From which library do you import r2_score?
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From which library do you import OneHotEncoder?
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From which library do you import XGBoost?
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42
Why is Logistic Regression called "Regression" in a classification task?
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43
What is F1-Score?
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Write the formulas for Accuracy, Precision, and F1-Score.
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45
What are Ensemble methods?
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What resources did you use while building the project?
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Coding: Create a dataframe containing only categorical (object) columns.
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Coding: Apply OneHotEncoder to categorical columns with ≤10 unique values and find the new number of columns.
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Coding: Split the training data into numerical and categorical dataframes.
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Coding: Build separate preprocessing pipelines for numerical and categorical features using ColumnTransformer.
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Coding: Perform a train-test split.
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Coding: Print all numerical and non-numerical columns.
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Coding: Print Recall Score instead of Accuracy Score.
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Coding: Implement RandomForestRegressor (import, initialize, fit, predict).
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Coding: Build a simple preprocessing pipeline.
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Coding: Write a custom preprocessing pipeline.
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Coding: Apply Label Encoding to ["green", "blue", "white", "blue", "green"].
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Coding: Filter rows where RegionCode = "Florida".
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Coding: Filter rows where RegionCode = "Florida" and TargetValue > 50000.
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Coding: Create a subset of the data for RegionCode = "Florida".
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Coding: Drop two specified columns from the training dataset.