Time Series Data Cleaning by Predicting Missing Data Values

The objective of this project is to predict data values that are missing from a stock dataset using various machine learning techniques.

Data Courtesy of borismarjanovic on Kaggle: https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs

Project Asset Structure

The files in this project are organized in the following structure:

src/preparation

This directory contains the data import code that is used to pull the dataset from disk into memory.

src/processing

This directory contains the data processing code that prepares the data for use with machine learning models. This code will likely be used after the code in src/preparation is used to pull the data from disk.

data/raw

This diretory contains the raw data imported from the source.

data/processed

This directory contains intermediary pre-processed data.

in progress