Steps in Feature Engineering Understand the Data:

Explore the dataset using descriptive statistics and visualization. Identify the target variable and relationships between features. Data Science Classes in Pune. https://www.sevenmentor.com/data-science-course-in-pune.php Data Cleaning:

Handle missing values using imputation techniques or by removing rows/columns. Remove duplicate or irrelevant features. Feature Transformation:

Normalize or scale data to ensure uniformity (e.g., Min-Max Scaling, Standardization). Apply log transformations to handle skewed distributions. Feature Creation:

Domain-Specific Features: Use domain knowledge to create new features. Polynomial Features: Combine existing features to capture non-linear relationships. Date/Time Features: Extract useful components like day, month, hour, or season. Encoding Categorical Variables:

One-Hot Encoding: For nominal variables with no inherent order. Ordinal Encoding: For variables with a meaningful order. Target Encoding: Replace categories with the mean target value for each category. Feature Selection:

Remove irrelevant or redundant features using correlation analysis or feature importance techniques. Techniques for Feature Engineering

1. Handling Missing Values Imputation: Replace missing values with the mean, median, or mode. Predictive Imputation: Use models to predict missing values. Flagging: Create a binary feature to indicate missingness.
2. Scaling and Normalization Standardization: Rescale features to have a mean of 0 and a standard deviation of 1. Normalization: Scale values to a range (e.g., 0 to 1).
3. Encoding Techniques Convert categorical variables into numerical formats using methods like one-hot encoding or label encoding. Data Science Course in Pune.
4. Interaction Features Create new features by combining two or more existing features. For example: Interaction = Feature 1 × Feature 2 Interaction=Feature 1×Feature 2
5. Binning and Bucketing Divide continuous variables into discrete intervals or categories. Example: Age groups (e.g., 0–18, 19–35, 36–50).
6. Dimensionality Reduction Apply PCA (Principal Component Analysis) or t-SNE to reduce feature count while preserving important information.
7. Feature Extraction For textual data: Use NLP techniques like TF-IDF or Word2Vec. For image data: Extract pixel intensities or use pre-trained models for embeddings.