Stock Price Prediction Using LSTM Networks

Project Overview

This project involves developing a predictive model to forecast stock prices using historical data. The implementation leverages advanced data science techniques and deep learning algorithms to achieve accurate predictions.

Key Features

• Data Handling: Utilized Python with libraries such as pandas for data loading and preprocessing and numpy for numerical operations.
• Visualization: Employed matplotlib to visualize stock price trends and assess model performance.
• Machine Learning Pipeline:
  • Preprocessed data with scaling and normalization to enhance model training.
  • Developed a robust LSTM neural network model using TensorFlow and Keras to predict future stock prices based on past data.
• Model Evaluation: Conducted extensive testing to minimize prediction errors, achieving a low mean squared error, which indicates high predictive accuracy.

Tools & Technologies

• Programming Language: Python
• Libraries: Pandas, NumPy, Matplotlib, Scikit-Learn, TensorFlow, Keras
• Development Tools: Jupyter Notebook

Usage

To replicate this project, clone the repository and install the required packages:

git clone https://github.com/yourusername/your-repository-name.git
cd your-repository-name
pip install -r requirements.txt