In real life actual projects, usually no raw features are selected. Instead, we need to use derived features. Is this true.

 Yes, it's true that in many real-life machine learning projects, the raw features themselves are not used directly for modeling. Instead, feature engineering is often a critical step in which new features, known as derived or engineered features, are created from the raw data to improve the model's performance. Feature engineering involves transforming, combining, or selecting features to make them more informative and suitable for the specific machine learning task.

Here are some reasons why feature engineering with derived features is important:

1. Increased Predictive Power: Raw features may not capture the underlying patterns or relationships in the data effectively. By creating derived features, you can potentially uncover more meaningful information and improve the model's ability to make accurate predictions.

2. 
   

   Dimensionality Reduction: In high-dimensional datasets, it's common to create derived features that capture essential information while reducing the dimensionality of the data. This can help prevent overfitting and reduce computational complexity.

3. 
   

   Handling Non-Linearity: Machine learning models, such as decision trees or linear regression, may struggle to capture non-linear relationships in the raw data. Derived features can be designed to encode non-linearities or interactions between variables.

4. 
   

   Domain-Specific Knowledge: Domain experts often have insights into which features or transformations are likely to be relevant for a specific problem. Incorporating domain knowledge through feature engineering can lead to better models.

5. 
   

   Dealing with Missing Data: Derived features can be designed to handle missing data more effectively, reducing the impact of missing values on model performance.

6. 
   

   Normalization and Scaling: Feature engineering can include standardizing or scaling features to ensure they have similar scales, which is important for algorithms like gradient descent-based optimization.

7. 
   

   Reducing Noise: Some raw features may contain noisy or irrelevant information. Feature engineering can involve filtering out noisy features or creating more robust features that are less sensitive to noise.

8. 
   

   Encoding Categorical Data: Categorical variables need to be encoded numerically for most machine learning algorithms. Feature engineering includes techniques like one-hot encoding, label encoding, or feature hashing to convert categorical data into a suitable format.

9. 
   

   Creating Time-Based Features: For time-series data, derived features can include lag features, rolling statistics, or time-based aggregations to capture temporal patterns.

In summary, feature engineering is a crucial step in the machine learning pipeline, and it often involves creating derived features to improve model performance, handle data-specific challenges, and extract relevant information from raw data. It requires a combination of domain knowledge, creativity, and experimentation to determine which features and transformations are most beneficial for a given task.