This web interface is a crucial component of the larger Oil Price Prediction Project. Initially designed for data exploration, the site offers users a comprehensive platform for visualizing, filtering, and understanding historical trends in oil prices. In future releases, predictive analytics capabilities will be integrated into the platform, providing users with actionable insights into future oil price fluctuations.
If you're interested in a deeper understanding of our project—including challenges faced, solutions implemented, and technical details—we encourage you to visit the comprehensive documentation linked below.
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Data Visualization: Utilize interactive charts to explore historical oil price data, gaining a clearer picture of market trends over time.
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Data Filtering: Customize your data views with dynamic filtering options, currently limited to only feature selection.
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Graph Type Selection: Choose between Scatter Plot and Line Plot visualizations to better suit your analysis needs.
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Upcoming Predictions: In future releases, users will have the ability to select from a range of predictive models and algorithms to forecast oil prices.
Retrieves a list of all features that can be used for data visualization and filtering.
GET /features| Parameter | Type | Description |
|---|---|---|
| N/A | string |
List of all features |
- Git
- Python 3.x
- Pip
- A web browser
- Clone the project
git clone https://github.com/mariamills/Oil-Price-Prediction-ML.git- Go to the project directory
cd Oil-Price-Prediction-ML- Install dependencies
pip install -r requirements.txt- Start the server
python app.pyor in your IDE, run the app.py file.
- Access the Web Interface Open your web browser and navigate to the following address:
http://127.0.0.1:5000
Frontend: TailwindCSS, HTML, CSS, Javascript
Backend: Javascript, Flask(Python)
The Oil Price Prediction Web Interface is designed to be user-friendly and intuitive. This section provides you with some examples and scenarios to help you make the most out of the data exploration functionalities.
To quickly begin, navigate to the website by clicking on the following link: Oil Price Prediction Data Explorer
A quick user guide is available within the website to assist you in navigating the various features and functionalities. Look on the page and please read the Important Notes
Choose Graph Type: Decide whether you want to see the data as a scatter plot or a line plot using the dropdown selection.
In the upcoming releases, you will be able to select various predictive models to get future oil price estimates. This feature along with others such as time range selection and more is currently under development and will be announced when available.
Coming soon
You can access a live demo of our Oil Price Prediction Web Interface by clicking here.
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Initial Load Time: The app will shut down upon inactivity, so it may take a while to initially load.
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Resource Constraints: Performing too many operations, such as selecting a large number of features multiple times for visualization, may result in memory overflow.
If the chart takes longer than 3 minutes to generate, it's likely that the server is struggling with the load. In such cases, refreshing the page and selecting fewer features may resolve the issue.
Feature Selection: Start by selecting just a few features for your initial exploration to avoid any potential memory issues.
User Guide: View the quick & simple user guide on the webpage for a quick overview of the website functionalities.
The data was provided by our 'sponsor,' a professor in the Economics department of our school. The data files include Macroeconomic Data.csv, which contains macroeconomic indicators from January 1986 to June 2023. We also received RWTCm.xls, containing data on the Cushing, OK WTI Spot Price FOB (Dollars per Barrel) from January 1986 to July 2023.
The data spans from January 1986 to June 2023 for macroeconomic indicators and until July 2023 for oil prices. Please note that this data is not regularly updated as it serves the educational purposes of a school project, designed as an introduction to machine learning.
This project is part of our Software Engineering class (CPSC 4175), where we were assigned a machine learning project focusing on oil price prediction. We were also assigned a 'sponsor' to simulate a real-world software engineering environment. Our team, "The Oval Table," comprises four members. The main requirement of this project is to use the provided data to train machine learning models capable of predicting oil prices.
Initially, we built the web interface for data exploration to visually analyze the correlation between the various features and the 'real oil price'—which we calculate by adjusting the nominal price using the CPI. Our ambition extends beyond just completing the class project; we aimed to provide an easy-to-use interface for our 'sponsor,' who is not a computer science major. Rather than just delivering raw code or notebooks, we wanted to offer an intuitive, user-friendly experience.
If you have any feedback, please reach out to us at maria@mariamills.org
Contributions are always welcome!
Just fork the repo and submit a pull request. Please be as descriptive as possible in your pull request.
If you encounter any issues or have questions, please report them using the "Issues" section of the GitHub repository. Your input is valuable to us!