Oil Price Modeling with Machine Learning Method

This is a return forecasting data science work to predict the future oil price with many different features ranging from marco economical data to industry information.
All external data is provided in the link under External Data.txt but the imtermediate data are ignored, however one should still be able to reproduce the results by following the workflow.
Step1 to Step7 Jupyter notebook provide code and presenation.
Project summary is the report of this research including explaination and visualization on key ideas.

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
.gitignore		.gitignore
External Data Link.txt		External Data Link.txt
Oil Price Modeling with Machine Learning Methods.pdf		Oil Price Modeling with Machine Learning Methods.pdf
README.md		README.md
Step1_RawY_variable_and_processing.csv		Step1_RawY_variable_and_processing.csv
Step1_RawY_variable_and_processing.ipynb		Step1_RawY_variable_and_processing.ipynb
Step2 PET+STEO Data Extract.ipynb		Step2 PET+STEO Data Extract.ipynb
Step2_Y_variable.csv		Step2_Y_variable.csv
Step3 Data Clean.ipynb		Step3 Data Clean.ipynb
Step4 Feature Engineer and NA Handling.ipynb		Step4 Feature Engineer and NA Handling.ipynb
Step5 Generate Y Variables.ipynb		Step5 Generate Y Variables.ipynb
Step6 XY Matching.ipynb		Step6 XY Matching.ipynb
Step7 Prediction.ipynb		Step7 Prediction.ipynb
WTICrudeOilPrice.csv		WTICrudeOilPrice.csv

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