NLP End to End Industry Level Project Part 2 LSTM Model Training

Описание: In this video, we continue building the industry-level NLP project and move deeper into data preprocessing, tokenization, vector representation, and LSTM model building for complaint classification. This is Part 2 of the series where we transform raw complaint narratives into numerical sequences and train a deep learning model.

Here is the GitHub repo link:
https://github.com/switch2ai

You can download all the code, scripts, and documents from the above GitHub repository.

We start by revisiting the real-world problem where consumer complaints must be automatically routed to the correct department. The dataset comes from the Consumer Financial Protection Bureau (CFPB) which contains millions of complaint records related to financial services.

The objective of the project is to classify each complaint into one of the departments such as Loan, Card, Credit Report, Services, or Others. From a technical perspective, this becomes a multi-class text classification problem in Natural Language Processing.

After loading the dataset, we perform exploratory data analysis and observe that the dataset contains more than 2.3 million rows and 18 columns. For our use case we only require two columns: Product and Consumer Complaint Narrative. All other columns are removed to simplify the dataset.

We then analyze missing values and observe that around 65 percent of the complaint narrative column contains null values. Instead of performing data augmentation or generating synthetic data using LLMs, we choose to drop null rows because the remaining dataset is still large enough to train a robust model.

Next we explore the target column and notice that there are 18 different department categories. Many of these categories are closely related, so after discussion with domain experts we merge similar categories into broader classes such as Loan, Card, Services, Credit Report, and Others. This helps simplify the classification task and improves model performance.

We then check for class imbalance and discuss possible solutions such as oversampling, undersampling, SMOTE, or using class weights. Since the dataset is imbalanced, accuracy alone may not be a reliable evaluation metric.

After that we perform text preprocessing on the complaint narratives. This includes converting text to lowercase, removing masked personally identifiable information, removing non-alphabet characters, and cleaning text using regular expressions.

Next we perform tokenization using Keras Tokenizer. The tokenizer builds a vocabulary from the dataset and converts each complaint narrative into sequences of integer token IDs. This process allows neural networks to process textual data numerically.

We then prepare the dataset for training by converting product labels into one-hot encoded vectors and padding sequences so that all input sequences have the same length.

Next we split the dataset into training and testing sets using stratified sampling to preserve class distribution.

Finally, we build a deep learning model using an embedding layer, SpatialDropout for regularization, stacked LSTM layers for sequence learning, and a Dense output layer with softmax activation for multi-class classification.

This part of the project demonstrates how real-world NLP systems convert raw text data into vector representations and train deep learning models for large-scale classification tasks.

Channel Name: Switch 2 AI

Hashtags

#NLPProject
#LSTM
#TextClassification
#DeepLearning
#MachineLearning
#ComplaintClassification
#AIProject
#TensorFlow
#NLP
#Switch2AI

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