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1
Explain your Kaggle/Colab notebook (project).
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2
What steps did you take to improve your model/score?
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3
Why did you use this model?
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4
Why did you use this imputer?
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5
Why did you use this scaler?
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6
Explain the working of the models you used.
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7
Explain the parameters/hyperparameters used in your model.
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8
What is a decision tree?
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9
Compare K-Means Clustering and KNN.
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10
What is a loss function?
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11
Why do we need a loss function?
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12
What is feature selection?
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13
Why do we need feature selection?
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14
What is data preprocessing?
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15
What is EDA (Exploratory Data Analysis)?
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16
What is dimensionality reduction?
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17
Explain PCA.
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18
What is hyperparameter tuning?
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19
Explain Logistic Regression.
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20
What is overfitting?
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21
What is underfitting?
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22
How can you prevent overfitting and underfitting?
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23
What is GridSearchCV?
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24
What is RandomizedSearchCV?
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25
What is Cross Validation (CV)?
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26
What are evaluation metrics?