mtank76 / stock-price-prediction

This repository houses a learning project focused on predicting stock prices utilizing Long Short-Term Memory (LSTM) networks. The project was developed while exploring Python for data analysis and machine learning within Jupyter Notebooks.

Stock Price Prediction using LSTM

This repository houses a learning-hobby project focused on predicting stock prices utilizing Long Short-Term Memory (LSTM) networks. The project was developed while exploring Python for data analysis and machine learning within Jupyter Notebooks.

Project Overview

The project encompasses the following steps:

1. Data Collection: Fetching historical stock data using Yahoo Finance API (yfinance) for a specified period.
2. Data Visualization: Visualizing the stock data with candlestick charts to illustrate price trends and fluctuations.
3. Correlation Analysis: Examining the correlation between stock features and their impact on the closing price.
4. LSTM Model Building: Implementing an LSTM neural network architecture for predicting stock prices.

Project Details

Data Collection and Visualization

• Using Yahoo Finance API, historical data for the Apple stock (AAPL) was fetched for analysis.
• A candlestick chart was plotted to visualize the stock's open, high, low, and close prices over time.

LSTM Model Training

• Features such as open, high, low, and volume were selected for training the LSTM model.
• Data was split into training and test sets using train_test_split.
• A sequential LSTM neural network was constructed using Keras.
• The model was compiled and trained over multiple epochs to minimize mean squared error.

Testing the Trained Model

• The trained LSTM model was tested by providing input values based on historical features.
• Predictions were made to estimate the future closing price of the stock.

Accessing the Project

1. Clone or download this repository to your local machine.

   https://github.com/MTank76/Stock-Price-Prediction.git
   

2. Open the Jupyter Notebook (Stock Price Prediction with LSTM.ipynb) in Jupyter Notebook or JupyterLab to explore the project and its predictions.

Contributing

Contributions are welcome! If you'd like to contribute to this project, feel free to open issues for suggestions or submit pull requests with proposed enhancements.