Stock Market Prediction

Overview

The Stock Market Prediction project uses machine learning techniques to predict stock prices. This project is developed using Python and leverages various libraries such as pandas, numpy, sklearn, and more. The notebook is designed to process stock market data, train a predictive model, and evaluate its performance.

Features

1. Data Collection
   • Collects historical stock market data.
2. Data Preprocessing
   • Cleans and prepares the data for training.
3. Model Training
   • Trains a machine learning model to predict stock prices.
4. Model Evaluation
   • Evaluates the model's performance using metrics like RMSE.
5. Prediction
   • Predicts future stock prices based on historical data.

Technologies Used

• Programming Language: Python
• Libraries: pandas, numpy, sklearn, matplotlib, seaborn
• Platform: Google Colab

Prerequisites

• Python 3.x
• Google Colab account
• Required Python libraries (pandas, numpy, sklearn, matplotlib, seaborn)

Code Structure

• Stock_Market_Prediction.ipynb: The main notebook containing the implementation of the stock market prediction model.

Usage

1. Run the notebook in Google Colab.
2. The notebook will collect and preprocess historical stock data.
3. It will then train a machine learning model to predict future stock prices.
4. Finally, it will evaluate the model and make predictions.

Contribution

Contributions are welcome! Please fork the repository and create a pull request with your changes.

License

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

Contact

For any questions or inquiries, please contact:

• Name: Limbachiya Chintan Bharatbhai
• Email: [email protected]
• LinkedIn: Chintan Limbachiya