Project: Classify Kaggle Consumer Finance Complaints

Highlights:

• This is a multi-class text classification (sentence classification) problem.
• The purpose of this project is to classify Kaggle Consumer Finance Complaints into 11 classes.
• The model was built with Convolutional Neural Network (CNN) and Word Embeddings on TensorFlow 2 / Keras.

Data: Kaggle Consumer Finance Complaints

• Input: consumer_complaint_narrative

  • Example: "someone in north Carolina has stolen my identity information and has purchased items including XXXX cell phones thru XXXX on XXXX/XXXX/2015. A police report was filed as soon as I found out about it on XXXX/XXXX/2015. A investigation from XXXX is under way thru there fraud department and our local police department.\n"

• Output: product

  • Example: Credit reporting

Setup:

python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt

Train:

• Command: python3 train.py <data_file> <params_file>
• Example: python3 train.py ./data/consumer_complaints.csv.zip ./parameters.json

A directory (trained_model_<timestamp>/) will be created during training:

• best_model.keras — model with best validation accuracy
• train_config.json — training metadata, label mapping, and vocabulary

Predict:

Provide the model directory (created when running train.py) and new data to predict.py.

• Command: python3 predict.py <model_directory> <test_data.json>
• Example: python3 predict.py ./trained_model_1780290823/ ./data/small_samples.json

Predictions are saved to ./data/predictions_output.json.

Reference:

• Implement a CNN for text classification in TensorFlow