How do I integrate external data sources into a Keras model for better accuracy

Question

With the help of code can you tell me How do I integrate external data sources into a Keras model for better accuracy?

Answer 1

To integrate external data sources into a Keras model for better accuracy, preprocess data using Pandas/NumPy, merge structured/unstructured data, use feature engineering, and incorporate embeddings, transfer learning, or multimodal fusion techniques.

Here is the code snippet given below:

In the above code we are using the following techniques:

• Uses External Structured Data (external_features.csv)

  • Loads tabular data (e.g., user metadata, text embeddings, sensor readings) into Keras.

• Multimodal Fusion (Images + External Data)

  • CNN extracts image features, while Dense layers process structured data.

• Merges Features Using Concatenate() Layer

  • Enables joint learning from multiple sources.

• Supports Various External Data Sources:

  • Tabular Data: Use Pandas to merge CSV datasets.
  • Text Data: Convert sentences to embeddings (e.g., BERT, TF-IDF).
  • Time-Series Data: Process sequential data using LSTMs or Transformers.

Hence, integrating external data sources via multimodal learning improves Keras model accuracy by providing additional contextual information.