Industry NLP | DL Project Part 3 Handling Imbalanced Data LSTM Training and Model Saving

In this video, we continue the industry-level NLP project and focus on training the deep learning model, handling class imbalance, and saving the trained model for production use. This is Part 3 of the series where we build the final LSTM architecture and train it on a large-scale real-world complaint dataset. Here is the GitHub repo link: https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. The project goal is to automatically route customer complaints to the correct department using Natural Language Processing. The dataset contains millions of financial complaints provided by the Consumer Financial Protection Bureau (CFPB). Each complaint narrative must be classified into a department such as Loan, Card, Credit Report, Services, or Others. We start by preparing the dataset that was processed in earlier stages. The dataset contains complaint narratives and their corresponding product categories. After preprocessing, tokenization, and padding, the text data is converted into sequences of numerical tokens which can be processed by deep learning models. Next we build the LSTM-based neural network architecture. The model uses an embedding layer to convert tokens into dense vector representations. This embedding layer helps capture semantic meaning between words. After the embedding layer, we apply SpatialDropout1D which helps prevent overfitting in sequential models by randomly dropping embedding features. The model uses stacked LSTM layers to capture long-term dependencies within complaint narratives. The first LSTM layer learns complex sequence patterns and passes the output sequence to the second LSTM layer which extracts higher-level features. Finally, a dense output layer with softmax activation is used to perform multi-class classification across the department categories. During training we observe that the dataset is imbalanced, meaning some classes appear more frequently than others. If imb