Stock-Market-Prediction-Project

This project aims to leverage the power of machine learning and deep learning techniques to predict stock prices. Specifically, it utilizes a Long Short-Term Memory (LSTM) network to train a robust model using historical data from Google stocks. By harnessing the capabilities of LSTM, which is a type of recurrent neural network (RNN), this project endeavors to accurately forecast future stock prices based on patterns and trends discovered in the historical data used to make predictions about future stock prices. These predictions are typically presented in the form of a graph that compares the predicted price of the stock to the actual price of the stock over a given period of time. The resulting graph provides investors with a deep analysis of the trend of the stock market price based on the training data. This information can be used to make informed decisions about when to buy or sell stocks, based on the predicted future price of the stock

Installation

Clone the repository:

git clone https://github.com/kreutzi/Stock-Market-Prediction-Project.git
cd Stock-Market-Prediction-Project

Install the required dependencies:

pip install numpy pandas matplotlib scikit-learn keras

Usage

Ensure that the training and test datasets are placed in the Data directory. Run the main script:

python Project.py

References:

• Simplilearn. "Stock Price Prediction Using Machine Learning."LINK
• Brownlee, J. (2017). Long Short-Term Memory Networks with Python. Machine Learning Mastery.LINK
• Zhang, Y., & Zheng, Z. (2021). Stock price prediction using long short-term memory (LSTM) neural network. Physica A: Statistical Mechanics and its Applications, 581, 126139. LINK