The Stock price prediction is a Machine learning model based project which helps us to predict the future price of stocks.

• Introduction
• Prerequisites
• Installation
• Data
• Model
• Results
• Contributing
• License

This project aims to predict stock prices using machine learning techniques. Predicting stock prices is a challenging task, and this project provides an example of how to approach it using various machine learning algorithms.

Key Features:

• Data preprocessing and feature engineering.
• Building and training stock price prediction models.
• Visualizing and evaluating model performance.
• Making predictions on future stock prices.

Before you begin, ensure you have met the following requirements:

• Python (3.6 or higher)
• Visual Studio Code.

1. Clone this repository:

   git clone https://github.com/Mitesh315/stock-price-prediction.git

2. Navigate to the project directory:

   cd stock-price-prediction

3. Install the required Python packages:

   pip install -r requirements.txt

The data used for this project can be found in the data directory. The dataset contains historical stock price data for training and testing purposes. You can replace it with your own data if needed.

We have implemented various machine learning models for stock price prediction, including:

• Linear Regression
• Decision Trees
• Random Forest
• XGBoost regressor

The model files and code can be found in the models directory.

We have evaluated the models' performance using various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). You can find the results in the results directory.

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

1. Fork the repository on GitHub.
2. Clone the forked repository to your local machine.
3. Create a new branch for your feature/bugfix.
4. Make your changes and commit them.
5. Push the changes to your fork on GitHub.
6. Create a pull request to the original repository.

This project is licensed under the MIT License - see the LICENSE file for details.

Feel free to add more details and documentation as needed for your specific project. A well-documented README.md file not only helps others understand your project but also encourages collaboration and contributions from the community.