This project contains two main files: a freq_main_processing.py file and a model_traning_eval.py file. The processing file is used to preprocess raw data for model training, while the model training and evaluation file converts the preprocessed data into a matrix format suitable for training a LightGBM model and evaluates the performance of the trained model.
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freq_main_processing.py: This file contains the code for processing the raw data. It performs tasks such as data cleaning, feature engineering, and data transformation to prepare the data for model training. -
model_traning_eval.py: This file is responsible for training a LightGBM model using the preprocessed data. It converts the data into a matrix format that can be consumed by the LightGBM library. Additionally, it evaluates the performance of the trained model using suitable metrics.
util: this folder contains the processors which are used in 'freq_main_processing.py'
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Run the
freq_main_processing.pyscript to preprocess the raw data. Make sure to provide the necessary input data files or modify the code to fetch the data from the appropriate source. The processed data will be stored in a format suitable for the model training and evaluation step. -
Once the data has been processed, run the
model_traning_eval.pyscript. Ensure that the processed data files are accessible by the script. The script will train a LightGBM model using the processed data and output the evaluation results, such as accuracy, precision, recall, or any other relevant metrics.
- JDDB