- 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.
-
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
- Command: python3 train.py training_data.file parameters.file
- Example:
python3 train.py ./data/consumer_complaints.csv.zip ./parameters.json
A directory will be created during training, and the best model will be saved in this directory.
Provide the model directory (created when running train.py
) and new data to predict.py
.
- Command: python3 predict.py ./trained_model_directory/ new_data.file
- Example:
python3 predict.py ./trained_model_1479757124/ ./data/small_samples.json