2022-2023 Stocks Prediction is an ongoing project developed by the Artificial Intelligence club of the University of Central Florida.
Please follow the instructions below to install the components required for this project:
Opening your terminal and naviagte to your preffered file path. Type the followng command:
git clone https://github.com/kylekaracadag/Stocks-Prediction.git
Using the same file path install the libraries by typing the following command:
pip install -r requirements.txt
- Datasets: Includes all the datasets that were used to train the model.
- Introduction_to_ML: A basic introduction to machine learning with Python for members in our team new to artificial intelligence.
- Models: Downloaded models that can be reused without having to re-train the model for making stocks predictions.
- Predictions: csv files with datapoints that include all the stock price predictions that were made.
- Candle_Sticks_LSTM.ipynb: Generate candle sticks based on the real stock prices.
- Candle_Sticks_Predictions.ipynb: Generate candle sticks based on the predicted stock prices.
- Fix Dataset.ipynb: Program to improve the accuracy of the prediction csv file.
- LSTM_Model.ipynb: The main notebook for our project. The model that was used to make predictions.
- dataset.ipynb: Program that is used to extract datasets from yfinance given a specific time frame.
- requirements.txt: Text file that contains all the required libraries for this project.