- This is a multi-class text 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.
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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"
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Output: product
- Example: Credit reporting
- Command: python3 train.py training_data.file training_parameters.file
- Example:
python3 train.py ./data/consumer_complaints.csv.zip ./parameters.json
A directory will be generated during training, and it contains model which will be loaded when running predict.py.
Provide the model directory (generated when running train.py) and test file to predict.py.
- Command: python3 predict.py ./trained_model_directory/ test_samples.file
- Example:
python3 predict.py ./trained_model_1479757124/ ./data/small_samples.json