Getting Started To get started with the project, you will need Python 3.x installed on your system, along with the following libraries:
Pandas NumPy Matplotlib Scikit-learn You can install these libraries using pip package manager:
Copy code pip install pandas numpy matplotlib scikit-learn Dataset We will be using a dataset that contains historical stock prices of a particular company. The dataset contains the following columns:
Date: Date of the stock price Open: Opening price of the stock High: Highest price of the stock Low: Lowest price of the stock Close: Closing price of the stock Volume: Total number of stocks traded that day Name: Name of the company The dataset can be downloaded from Yahoo Finance.
Project Structure The project contains the following files and directories:
data/: This directory contains the dataset in CSV format. models/: This directory contains trained machine learning models. notebooks/: This directory contains Jupyter notebooks that were used to develop and train the machine learning models. scripts/: This directory contains Python scripts that can be used to train and test the machine learning models. README.md: This file. Running the Code To train and test the machine learning models, run the following command from the project directory:
bash Copy code python scripts/train_test.py This will train and test the machine learning models on the provided dataset. The trained models will be saved in the models/ directory.
To predict stock prices using the trained models, run the following command from the project directory:
bash Copy code python scripts/predict.py This will use the trained models to predict future stock prices. The predicted prices will be displayed on the screen.
Contributing Contributions to this project are welcome. If you find any bugs or issues, feel free to open an issue or submit a pull request.