Pass images through a pre-trained model (like ResNet) to get high-level feature vectors.
If you provide the column names or a summary, I can generate specific Python code for you.
Use a library like TextBlob or VADER to generate a numerical "mood" for the text. 4. If it contains Image Data Color Histograms: Quantify the distribution of colors. 75bdb.7z
Create new features by multiplying or dividing existing numerical columns (e.g., Price * Quantity ). Polynomial Features: Generate x2x squared for non-linear relationships.
If you can describe the contents or provide a few rows of data, I can give you a specific feature engineering plan. In the meantime, here are common feature generation strategies based on the likely type of data: 1. If it contains Tabular Data (CSV/Excel) Pass images through a pre-trained model (like ResNet)
Replace categorical levels with the mean of the target variable.
Extract the hour, day of the week, month, or "Is Weekend" flag. 3. If it contains Text Data day of the week
Convert text into numerical importance scores.