Were there any difficulties in leading features? How did you solve them?

Feature engineering often presents challenges, such as:

• High correlation between features, which can lead to multicollinearity.
• A large number of features, complicating feature selection and potentially causing overfitting.
• The presence of noisy or irrelevant features.

To address these issues, I used the following approaches:

• Correlation analysis and removal of highly correlated features.
• Feature selection methods such as Recursive Feature Elimination (RFE) or using models with regularization (L1/L2).
• Visualization and statistical analysis to identify irrelevant features.
• Dimensionality reduction techniques (PCA, t-SNE) to understand data structure.

This systematic approach helps improve model quality and reduce data redundancy.