Deep Neural Network as SQL+Python

Short Outline: DNN with SQL + Python using Built-in SQL Functions

1. Data Preprocessing in SQL:
   • Use SQL's built-in functions (SUM(), AVG(), ROUND(), etc.) to perform calculations directly in the database.
2. Fetch Processed Data in Python:
   • Execute SQL queries with processed results using libraries like pandas and sqlalchemy.
3. Feature Preparation in Python:
   • Split the SQL output into features (X) and targets (y), normalize data, and handle missing values.
4. Build and Train DNN Model:
   • Create a neural network with libraries like TensorFlow/PyTorch.
   • Train using preprocessed features as input.
5. Store Results in SQL:
   • Save predictions or results back into the database for further analysis.