USO Stock Price Prediction

Table of Content

• Results
• Synopsis
• Appendix
• Links
• Directory Tree
• Features
• Run Locally
• License
• Technology Used

Results

Method Used	Result
ARIMA MODEL
LSTM

Synopsis

United States Oil ETF Stock is forecasted from the period of 2018 to 2019, by using the previous year's data from the period of 2011 to 2018, I have considered both statistical and as well as machine-learning approaches, the dataset is scraped from an online website and with overall more information about the data, I have only considered the date and the close value of the stock, since with the help of those two data we are going to forecast the values and find out which approach have performed well.

So based on the given data,

Independent variable (X): Date

Dependent variable (Y): Close value for the stock of the particular date

Statistical Model : ARIMA

Machine learning model : LSTM

Data preprocessing : Pandas

Data visualization : Matplotlib

Appendix

The requirement for developing this model is present in requirements.txt file.

The development of the model is present in main.ipynb file.

Links

• Dataset link : https://github.com/Vedakeerthi/USO_Stock_Price_Prediction/tree/main/Datasets

• Github link : https://github.com/Vedakeerthi/USO_Stock_Price_Prediction

Directory Tree

├── Datasets
    ├── FINAL_USO.xls
├── LICENSE
├── README.md
├── Results of Arima Model.png
├── Results of LSTM.png
├── USO Stock Price Prediction.ipynb
├── requirements.txt

Features

• Live prediction analysis.
• Forecasting for the future value with much better accuracy.
• Compatible for training model with other stock data.

Run Locally

Clone the project

  git clone https://github.com/Vedakeerthi/USO_Stock_Price_Prediction.git

Install dependencies

  pip install -r requirements.txt

Start the server and run the file

  python USO Stock Price Prediction.ipynb

License

Technology Used