Stock Price Prediction Using Machine Learning Stacked LSTM Model Project Version 1.0

Welcome to the Stock Price Prediction project repository! This project utilizes machine learning techniques to predict stock prices based on historical data. Predicting stock prices is a challenging task, and this project aims to explore different machine learning models to forecast future stock prices.

Key Features:

• Data Collection: Gathered historical stock price data from reliable sources to build a comprehensive dataset.
• Exploratory Data Analysis (EDA): Conducted thorough analysis to understand patterns, trends, and relationships within the stock data.
• Feature Engineering: Extracted relevant features and engineered new ones to improve the model's predictive performance.
• Machine Learning Models: Implemented and compared various machine learning models, including but not limited to LSTM (Long Short-Term Memory) networks, to predict stock prices.
• Evaluation Metrics: Utilized appropriate metrics to evaluate and compare the performance of the models.
• Visualization: Created insightful visualizations to better understand the predictions and model performance.

Technologies Used:

• Python
• TensorFlow
• Scikit-Learn
• Pandas
• Matplotlib
• Jupyter Notebooks
• yfinance
• keras-models

Getting Started:

Clone the project

  git clone https://github.com/rohitbhure65/stock-price-prediction-using-machine-learning

Go to the project directory

  cd stock-price-prediction-using-machine-learning

install important python Libraries

  pip3 install -r requirement.txt

Start the server

  streamlit run stock_price_prediction.py

Explore the Jupyter Notebooks in the notebooks directory for detailed analysis and model implementation.

License:

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