Stock Market Price Predictor

Overview

This project is a Stock Market Price Predictor built using Linear Regression. It aims to predict future stock prices based on historical data. The project demonstrates how machine learning can be applied to financial markets and provides insights into the stock price trends.

Features

• Predict future stock prices using historical data.
• Visualize trends and predictions with graphs.
• Simple and easy-to-use implementation with Python.

Technologies Used

• Programming Language: Python
• Libraries:
  • NumPy
  • Pandas
  • Matplotlib
  • scikit-learn

Installation

1. Clone this repository:

   git clone https://github.com/souradeepdutta/Stock-Market-Price-Predictor.git

2. Import Notebook in Kaggle and run notebook

Usage

1. Import a dataset from Kaggle

2. Run the notebook.

3. The program will:

   • Load the dataset.
   • Train the Linear Regression model.
   • Predict future prices.
   • Display a graph comparing actual vs predicted prices.

Output

Contributing

Contributions are welcome! If you have suggestions or improvements, feel free to:

• Fork this repository.
• Create a new branch.
• Submit a pull request.

License

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

---

Disclaimer: This project is for educational purposes only and should not be used for actual financial decisions. Always consult a financial advisor before making investment decisions.