Data Pipeline for Predicting Trading Volume

Description

This release contains a data pipeline that predicts the trading volume of equities using a LightGBM model. The pipeline consists of several components, including data ingestion, data transformation, feature engineering, model training, and a Flask web app for serving predictions.

Key features of the project release include:

• Efficient data ingestion using pandas and thread pools to reduce ingestion time
• Data transformation and feature engineering for preparing the data for modeling
• A LightGBM model trained on the transformed data for predicting trading volume
• A Flask web app for serving predictions to users
• Docker containerization for easy deployment and reproducibility