-
1
Show your ID card.
-
2
Introduction.
-
3
Tell me about yourself.
-
4
How was your experience with the project/course?
-
5
Show your Kaggle leaderboard score and rank.
-
6
Explain the problem statement.
-
7
Explain your notebook/project.
-
8
How can you improve your model accuracy further?
-
9
How do you know your model is not overfitting or underfitting?
-
10
How did you encode categorical features?
-
11
Why did you use Label Encoding instead of One-Hot Encoding?
-
12
What is data preprocessing?
-
13
Why is preprocessing necessary?
-
14
What is Hyperparameter Tuning?
-
15
Why is Hyperparameter Tuning important?
-
16
Difference between GridSearchCV and RandomizedSearchCV.
-
17
Explain Logistic Regression.
-
18
Why is Logistic Regression called "Regression" even though it is used for classification?
-
19
What is the loss function of Logistic Regression?
-
20
What does the C parameter in Logistic Regression represent?
-
21
Explain Decision Tree.
-
22
Where are Decision Trees used in real life?
-
23
What is KNN?
-
24
Why is KNN called a lazy learner?
-
25
Difference between KNN and K-Means.
-
26
Explain the K-Means algorithm.
-
27
Difference between XGBoost and LightGBM.
-
28
Why did you use CatBoost?
-
29
Difference between Bagging and Boosting.
-
30
Which should be preferred when the model is overfitting?
-
31
Explain RFE.
-
32
Explain evaluation metrics such as Accuracy, Precision, Recall, F1-Score, R² Score, and Confusion Matrix.
-
33
How did you conclude that your model is not overfitting?
-
34
What are the limitations of a Correlation Matrix?
-
35
How can you detect non-linear relationships between features?
-
36
Coding: Load a dataset.
-
37
Coding: Show the first few rows of the dataset.
-
38
Coding: Count the number of ? values in specified columns.
-
39
Coding: Replace ? with NaN.
-
40
Coding: Impute or remove missing values.
-
41
Coding: Convert object/string columns to numeric.
-
42
Coding: Filter rows based on given conditions.
-
43
Coding: Find the correlation between two columns.
-
44
Coding: Plot a correlation heatmap.
-
45
Coding: Normalize selected columns using MinMaxScaler or StandardScaler.
-
46
Coding: Perform RFE and select the top 2 features.
-
47
Coding: Perform preprocessing before applying RFE.
-
48
Coding: Add two columns (e.g., V1 + V2).
-
49
Coding: Perform a train-test split on the Breast Cancer dataset.
-
50
Coding: Load the Breast Cancer dataset and print its shape.
-
51
Coding: Load the Iris dataset and perform RFE.
-
52
Coding: Explain the RFE implementation after writing the code.