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Show your ID card.
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Introduction.
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Tell me about yourself.
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Tell me about yourself.
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Explain the problem statement.
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Explain your notebook/project.
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Explain your preprocessing.
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Explain your EDA.
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Explain your feature engineering.
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Explain your hyperparameter tuning.
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Explain your model comparison.
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What insights did you gain from your analysis?
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What difficulties did you face during the project?
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How did you overcome those difficulties?
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What did you learn from the project?
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Show an overview of your Kaggle submissions.
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What models did you use?
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Why did you use those models?
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Why did you use only tree/leaf-based models?
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Why do we perform hyperparameter tuning?
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What are hyperparameters?
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Difference between parameters and hyperparameters.
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How is hyperparameter tuning done?
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Ways to perform hyperparameter tuning.
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Difference between Pipeline and ColumnTransformer.
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Difference between Pipeline and Transformer.
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Why did you use StandardScaler?
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Which scaling method is better for handling outliers?
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Why do machine learning models use numerical features?
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What is PCA?
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Difference between XGBoost and LightGBM.
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What is overfitting?
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How do you reduce overfitting?
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What is the Bias-Variance Tradeoff?
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Difference between Bagging and Boosting.
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Which sklearn API contains OneHotEncoder?
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Which sklearn API contains StandardScaler?
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Coding: Implement the preprocessing Pipeline used in your notebook.
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Coding: Write a Decision Tree classifier snippet.
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Coding: Load the California Housing dataset.
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Coding: Perform a train-test split on the California Housing dataset.
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Coding: Load the Iris dataset.
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Coding: Perform a train-test split on the Iris dataset.
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Coding: Train a Decision Tree classifier on the Iris dataset.
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Coding: Print the Accuracy Score.
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Coding: Use the describe() function on the California Housing dataset.